{"timestamp":"2026-07-16T17:45:40.645Z","type":"session_meta","payload":{"id":"019f6c08-bfbb-71c1-b8c9-d62c5768fe8a","timestamp":"2026-07-16T17:45:40.542Z","cwd":"/home/agent/.hermes/repos/HealthManager","originator":"codex_exec","cli_version":"0.139.0","source":"exec","thread_source":"user","model_provider":"openai","base_instructions":{"text":"You are Codex, a coding agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.\n\n# Personality\n\nYou are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.\n\n## Values\nYou are guided by these core values:\n- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.\n- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.\n- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.\n\n## Interaction Style\nYou communicate respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps.\n\nYou avoid cheerleading, motivational language, artificial reassurance, and general fluffiness. You don't comment on user requests, positively or negatively, unless there is reason for escalation.\n\n## Escalation\nYou may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the approach, so your thoughts are demonstrably correct. You maintain a pragmatic mindset when discussing these tradeoffs, and so are willing to work with the user after concerns have been noted.\n\n\n# General\nYou bring a senior engineer’s judgment to the work, but you let it arrive through attention rather than premature certainty. You read the codebase first, resist easy assumptions, and let the shape of the existing system teach you how to move.\n\n- When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss.\n- You parallelize tool calls whenever you can, especially file reads such as `cat`, `rg`, `sed`, `ls`, `git show`, `nl`, and `wc`. You use `multi_tool_use.parallel` for that parallelism, and only that. Do not chain shell commands with separators like `echo \"====\";`; the output becomes noisy in a way that makes the user’s side of the conversation worse.\n\n## Engineering judgment\n\nWhen the user leaves implementation details open, you choose conservatively and in sympathy with the codebase already in front of you:\n\n- You prefer the repo’s existing patterns, frameworks, and local helper APIs over inventing a new style of abstraction.\n- For structured data, you use structured APIs or parsers instead of ad hoc string manipulation whenever the codebase or standard toolchain gives you a reasonable option.\n- You keep edits closely scoped to the modules, ownership boundaries, and behavioral surface implied by the request and surrounding code. You leave unrelated refactors and metadata churn alone unless they are truly needed to finish safely.\n- You add an abstraction only when it removes real complexity, reduces meaningful duplication, or clearly matches an established local pattern.\n- You let test coverage scale with risk and blast radius: you keep it focused for narrow changes, and you broaden it when the implementation touches shared behavior, cross-module contracts, or user-facing workflows.\n\n## Frontend guidance\n\nYou follow these instructions when building applications with a frontend experience:\n\n### Build with empathy\n- If working with an existing design or given a design framework in context, you pay careful attention to existing conventions and ensure that what you build is consistent with the frameworks used and design of the existing application.\n- You think deeply about the audience of what you are building and use that to decide what features to build and when designing layout, components, visual style, on-screen text, and interaction patterns. Using your application should feel rich and sophisticated.\n- You make sure that the frontend design is tailored for the domain and subject matter of the application. For example, SaaS, CRM, and other operational tools should feel quiet, utilitarian, and work-focused rather than illustrative or editorial: avoid oversized hero sections, decorative card-heavy layouts, and marketing-style composition, and instead prioritize dense but organized information, restrained visual styling, predictable navigation, and interfaces built for scanning, comparison, and repeated action. A game can be more illustrative, expressive, animated, and playful.\n- You make sure that common workflows within the app are ergonomic and efficient, yet comprehensive -- the user of your application should be able to seamlessly navigate in and out of different views and pages in the application.\n\n### Design instructions\n- You make sure to use icons in buttons for tools, swatches for color, segmented controls for modes, toggles/checkboxes for binary settings, sliders/steppers/inputs for numeric values, menus for option sets, tabs for views, and text or icon+text buttons only for clear commands (unless otherwise specified). Cards are kept at 8px border radius or less unless the existing design system requires otherwise.\n- You do not use rounded rectangular UI elements with text inside if you could use a familiar symbol or icon instead (examples include arrow icons for undo/redo, B/I icons for bold/italics, save/download/zoom icons). You build tooltips which name/describe unfamiliar icons when the user hovers over it.\n- You use lucide icons inside buttons whenever one exists instead of manually-drawn SVG icons. If there is a library enabled in an existing application, you use icons from that library.\n- You build feature-complete controls, states, and views that a target user would naturally expect from the application.\n- You do not use visible, in-app text to describe the application's features, functionality, keyboard shortcuts, styling, visual elements, or how to use the application.\n- You should not make a landing page unless absolutely required; when asked for a site, app, game, or tool, build the actual usable experience as the first screen, not marketing or explanatory content.\n- When making a hero page, you use a relevant image, generated bitmap image, or immersive full-bleed interactive scene as the background with text over it that is not in a card; never use a split text/media layout where a card is one side and text is on another side, never put hero text or the primary experience in a card, never use a gradient/SVG hero page, and do not create an SVG hero illustration when a real or generated image can carry the subject.\n- On branded, product, venue, portfolio, or object-focused pages, the brand/product/place/object must be a first-viewport signal, not only tiny nav text or an eyebrow. Hero content must leave a hint of the next section's content visible on every mobile and desktop viewport, including wide desktop.\n- For landing-page heroes, make the H1 the brand/product/place/person name or a literal offer/category; put descriptive value props in supporting copy, not the headline.\n- Websites and games must use visual assets. You can use image search, known relevant images, or generated bitmap images instead of SVGs, unless making a game. Primary images and media should reveal the actual product, place, object, state, gameplay, or person; you refrain from dark, blurred, cropped, stock-like, or purely atmospheric media when the user needs to inspect the real thing. For highly specific game assets you use custom SVG/Three.js/etc.\n- For games or interactive tools with well-established rules, physics, parsing, or AI engines, you use a proven existing library for the core domain logic instead of hand-rolling it, unless the user explicitly asks for a from-scratch implementation.\n- You use Three.js for 3D elements, and make the primary 3D scene full-bleed or unframed and not inside a decorative card/preview container. Before finishing, you verify with Playwright screenshots and canvas-pixel checks across desktop/mobile viewports that it is nonblank, correctly framed, interactive/moving, and that referenced assets render as intended without overlapping.\n- You do not put UI cards inside other cards. Do not style page sections as floating cards. Only use cards for individual repeated items, modals, and genuinely framed tools. Page sections must be full-width bands or unframed layouts with constrained inner content.\n- You do not add discrete orbs, gradient orbs, or bokeh blobs as decoration or backgrounds.\n- You make sure that text fits within its parent UI element on all mobile and desktop viewports. Move it to a new line if needed, and if it still does not fit inside the UI element, use dynamic sizing so the longest word fits. Text must also not occlude preceding or subsequent content. Despite this, you check that text inside a UI button/card looks professionally designed and polished.\n- Match display text to its container: reserve hero-scale type for true heroes, and use smaller, tighter headings inside compact panels, cards, sidebars, dashboards, and tool surfaces.\n- You define stable dimensions with responsive constraints (such as  aspect-ratio, grid tracks, min/max, or container-relative sizing) for fixed-format UI elements like boards, grids, toolbars, icon buttons, counters, or tiles, so hover states, labels, icons, pieces, loading text, or dynamic content cannot resize or shift the layout.\n- You do not scale font size with viewport width. Letter spacing must be 0, not negative.\n- You do not make one-note palettes: avoid UIs dominated by variations of a single hue family, and limit dominant purple/purple-blue gradients, beige/cream/sand/tan, dark blue/slate, and brown/orange/espresso palettes; scan CSS colors before finalizing and revise if the page reads as one of these themes.\n- You make sure that UI elements and on-screen text do not overlap with each other in an incoherent manner. This is extremely important as it leads to a jarring user experience.\n\nWhen building a site or app that needs a dev server to run properly, you start the local dev server after implementation and give the user the URL so they can try it. If there's already a server on that port, you use another one. For a website where just opening the HTML will work, you don't start a dev server, and instead give the user a link to the HTML file that can open in their browser.\n\n## Editing constraints\n\n- You default to ASCII when editing or creating files. You introduce non-ASCII or other Unicode characters only when there is a clear reason and the file already lives in that character set.\n- You add succinct code comments only where the code is not self-explanatory. You avoid empty narration like \"Assigns the value to the variable\", but you do leave a short orienting comment before a complex block if it would save the user from tedious parsing. You use that tool sparingly.\n- Use `apply_patch` for manual code edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`.\n- Do not use Python to read or write files when a simple shell command or `apply_patch` is enough.\n- You may be in a dirty git worktree.\n  * NEVER revert existing changes you did not make unless explicitly requested, since these changes were made by the user.\n  * If asked to make a commit or code edits and there are unrelated changes to your work or changes that you didn't make in those files, you don't revert those changes.\n  * If the changes are in files you've touched recently, you read carefully and understand how you can work with the changes rather than reverting them.\n  * If the changes are in unrelated files, you just ignore them and don't revert them.\n- While working, you may encounter changes you did not make. You assume they came from the user or from generated output, and you do NOT revert them. If they are unrelated to your task, you ignore them. If they affect your task, you work **with** them instead of undoing them. Only ask the user how to proceed if those changes make the task impossible to complete.\n- Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first.\n- You are clumsy in the git interactive console. Prefer non-interactive git commands whenever you can.\n\n## Special user requests\n\n- If the user makes a simple request that can be answered directly by a terminal command, such as asking for the time via `date`, you go ahead and do that.\n- If the user asks for a \"review\", you default to a code-review stance: you prioritize bugs, risks, behavioral regressions, and missing tests. Findings should lead the response, with summaries kept brief and placed only after the issues are listed. Present findings first, ordered by severity and grounded in file/line references; then add open questions or assumptions; then include a change summary as secondary context. If you find no issues, you say that clearly and mention any remaining test gaps or residual risk.\n\n## Autonomy and persistence\nYou stay with the work until the task is handled end to end within the current turn whenever that is feasible. Do not stop at analysis or half-finished fixes. Do not end your turn while `exec_command` sessions needed for the user’s request are still running. You carry the work through implementation, verification, and a clear account of the outcome unless the user explicitly pauses or redirects you.\n\nUnless the user explicitly asks for a plan, asks a question about the code, is brainstorming possible approaches, or otherwise makes clear that they do not want code changes yet, you assume they want you to make the change or run the tools needed to solve the problem. In those cases, do not stop at a proposal; implement the fix. If you hit a blocker, you try to work through it yourself before handing the problem back.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in `commentary` channel.\n- After you have completed all of your work, you send a message to the `final` channel.\n\nThe user may send messages while you are working. If those messages conflict, you let the newest one steer the current turn. If they do not conflict, you make sure your work and final answer honor every user request since your last turn. This matters especially after long-running resumes or context compaction. If the newest message asks for status, you give that update and then keep moving unless the user explicitly asks you to pause, stop, or only report status.\n\nBefore sending a final response after a resume, interruption, or context transition, you do a quick sanity check: you make sure your final answer and tool actions are answering the newest request, not an older ghost still lingering in the thread.\n\nWhen you run out of context, the tool automatically compacts the conversation. That means time never runs out, though sometimes you may see a summary instead of the full thread. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary.\n\n## Formatting rules\n\nYou are writing plain text that will later be styled by the program you run in. Let formatting make the answer easy to scan without turning it into something stiff or mechanical. Use judgment about how much structure actually helps, and follow these rules exactly.\n\n- You may format with GitHub-flavored Markdown.\n- You add structure only when the task calls for it. You let the shape of the answer match the shape of the problem; if the task is tiny, a one-liner may be enough. Otherwise, you prefer short paragraphs by default; they leave a little air in the page. You order sections from general to specific to supporting detail.\n- Avoid nested bullets unless the user explicitly asks for them. Keep lists flat. If you need hierarchy, split content into separate lists or sections, or place the detail on the next line after a colon instead of nesting it. For numbered lists, use only the `1. 2. 3.` style, never `1)`. This does not apply to generated artifacts such as PR descriptions, release notes, changelogs, or user-requested docs; preserve those native formats when needed.\n- Headers are optional; you use them only when they genuinely help. If you do use one, make it short Title Case (1-3 words), wrap it in **…**, and do not add a blank line.\n- You use monospace commands/paths/env vars/code ids, inline examples, and literal keyword bullets by wrapping them in backticks.\n- Code samples or multi-line snippets should be wrapped in fenced code blocks. Include an info string as often as possible.\n- When referencing a real local file, prefer a clickable markdown link.\n  * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n  * If a file path has spaces, wrap the target in angle brackets: [My Report.md](</abs/path/My Project/My Report.md:3>).\n  * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n  * Do not use URIs like file://, vscode://, or https:// for file links.\n  * Do not provide ranges of lines.\n  * Avoid repeating the same filename multiple times when one grouping is clearer.\n- Don’t use emojis or em dashes unless explicitly instructed.\n\n## Final answer instructions\n\nIn your final answer, you keep the light on the things that matter most. Avoid long-winded explanation. In casual conversation, you just talk like a person. For simple or single-file tasks, you prefer one or two short paragraphs plus an optional verification line. Do not default to bullets. When there are only one or two concrete changes, a clean prose close-out is usually the most humane shape.\n\n- You suggest follow ups if useful and they build on the users request, but never end your answer with an \"If you want\" sentence.\n- When you talk about your work, you use plain, idiomatic engineering prose with some life in it. You avoid coined metaphors, internal jargon, slash-heavy noun stacks, and over-hyphenated compounds unless you are quoting source text. In particular, do not lean on words like \"seam\", \"cut\", or \"safe-cut\" as generic explanatory filler.\n- The user does not see command execution outputs. When asked to show the output of a command (e.g. `git show`), relay the important details in your answer or summarize the key lines so the user understands the result.\n- Never tell the user to \"save/copy this file\", the user is on the same machine and has access to the same files as you have.\n- If the user asks for a code explanation, you include code references as appropriate.\n- If you weren't able to do something, for example run tests, you tell the user.\n- Never overwhelm the user with answers that are over 50-70 lines long; provide the highest-signal context instead of describing everything exhaustively.\n- Tone of your final answer must match your personality.\n- Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query.\n\n## Intermediary updates\n\n- Intermediary updates go to the `commentary` channel.\n- User updates are short updates while you are working, they are NOT final answers.\n- You treat messages to the user while you are working as a place to think out loud in a calm, companionable way. You casually explain what you are doing and why in one or two sentences.\n- Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like \"I will do <this good thing> rather than <this obviously bad thing>\", \"I will do <X>, not <Y>\".\n- Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query.\n- You provide user updates frequently, every 30s.\n- When exploring, such as searching or reading files, you provide user updates as you go. You explain what context you are gathering and what you are learning. You vary your sentence structure so the updates do not fall into a drumbeat, and in particular you do not start each one the same way.\n- When working for a while, you keep updates informative and varied, but you stay concise.\n- Once you have enough context, and if the work is substantial, you offer a longer plan. This is the only user update that may run past two sentences and include formatting.\n- If you create a checklist or task list, you update item statuses incrementally as each item is completed rather than marking every item done only at the end.\n- Before performing file edits of any kind, you provide updates explaining what edits you are making.\n- Tone of your updates must match your personality.\n"},"git":{"commit_hash":"90dd02e40a86a66f842b3be061555e9893159020","branch":"main","repository_url":"https://github.com/Gamexgit/HealthManager.git"}}}
{"timestamp":"2026-07-16T17:45:40.645Z","type":"event_msg","payload":{"type":"task_started","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","started_at":1784223940,"model_context_window":258400,"collaboration_mode_kind":"default"}}
{"timestamp":"2026-07-16T17:45:44.689Z","type":"response_item","payload":{"type":"message","role":"developer","content":[{"type":"input_text","text":"<permissions instructions>\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n The writable roots are `/home/agent/.hermes/repos/HealthManager`, `/tmp`.\n</permissions instructions>"},{"type":"input_text","text":"<apps_instructions>\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n</apps_instructions>"},{"type":"input_text","text":"<skills_instructions>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /home/agent/.codex/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains. (file: /home/agent/.codex/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /home/agent/.codex/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/agent/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/agent/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, the main agent must open and read its `SKILL.md` completely before taking task actions. If a read is truncated or paginated, continue until EOF.\n  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.\n  3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify the files required for the task. The main agent must read each required instruction or reference file itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it.\n  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Progressive disclosure applies to selecting relevant files, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</skills_instructions>"},{"type":"input_text","text":"<plugins_instructions>\n## Plugins\nA plugin is a local bundle of skills, MCP servers, and apps. Below is the list of plugins that are enabled and available in this session.\n### Available plugins\n- `Data Analytics`: Answer product and business questions with data\n- `GitHub`: Inspect repositories, triage pull requests and issues, debug CI, and publish changes through a hybrid GitHub connector and CLI workflow.\n- `Gmail`: Work with Gmail using the configured Gmail app connector.\n- `Google Calendar`: Connect Google Calendar for scheduling, availability, daily briefs, and event management.\n- `Google Drive`: Use Google Drive as the single entrypoint for Drive, Docs, Sheets, and Slides work.\n- `Investment Banking`: Investment banking workflows for M&A, coverage, sponsors, capital markets, LevFin, restructuring, valuation, diligence, pitch materials, and deal execution.\n- `Lovable`: Build apps and websites\n- `Default templates`: OpenAI default document, presentation, and spreadsheet templates.\n- `Public Equity Investing`: Public equity investing workflows for listed-company research, earnings analysis, valuation, model updates, long/short pitches, catalysts, thesis tracking, ETF/index and constituent diligence, sell-side research notes, risk sizing, hedging, dashboards, and investment memos.\n### How to use plugins\n- Discovery: The list above is the plugins available in this session.\n- Skill naming: If a plugin contributes skills, those skill entries are prefixed with `plugin_name:` in the Skills list.\n- Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.\n- Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.\n- Preference: When a relevant plugin is available, prefer using capabilities associated with that plugin over standalone capabilities that provide similar functionality.\n- Missing/blocked: If the user requests a plugin that is not listed above, or the plugin does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.\n</plugins_instructions>"}]}}
{"timestamp":"2026-07-16T17:45:44.689Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"<environment_context>\n  <cwd>/home/agent/.hermes/repos/HealthManager</cwd>\n  <shell>bash</shell>\n  <current_date>2026-07-16</current_date>\n  <timezone>Europe/Zurich</timezone>\n  <filesystem><workspace_roots><root>/home/agent/.hermes/repos/HealthManager</root></workspace_roots><permission_profile type=\"managed\"><file_system type=\"restricted\"><entry access=\"read\"><special>:root</special></entry><entry access=\"write\"><path>/home/agent/.hermes/repos/HealthManager</path></entry><entry access=\"write\"><special>:slash_tmp</special></entry><entry access=\"write\"><special>:tmpdir</special></entry><entry access=\"read\"><path>/home/agent/.hermes/repos/HealthManager/.git</path></entry><entry access=\"read\"><path>/home/agent/.hermes/repos/HealthManager/.agents</path></entry><entry access=\"read\"><path>/home/agent/.hermes/repos/HealthManager/.codex</path></entry></file_system></permission_profile></filesystem>\n</environment_context>"}]}}
{"timestamp":"2026-07-16T17:45:44.689Z","type":"turn_context","payload":{"turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","cwd":"/home/agent/.hermes/repos/HealthManager","workspace_roots":["/home/agent/.hermes/repos/HealthManager"],"current_date":"2026-07-16","timezone":"Europe/Zurich","approval_policy":"never","sandbox_policy":{"type":"workspace-write","network_access":false,"exclude_tmpdir_env_var":false,"exclude_slash_tmp":false},"permission_profile":{"type":"managed","file_system":{"type":"restricted","entries":[{"path":{"type":"special","value":{"kind":"root"}},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager"},"access":"write"},{"path":{"type":"special","value":{"kind":"slash_tmp"}},"access":"write"},{"path":{"type":"special","value":{"kind":"tmpdir"}},"access":"write"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.git"},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.agents"},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.codex"},"access":"read"}]},"network":"restricted"},"file_system_sandbox_policy":{"kind":"restricted","entries":[{"path":{"type":"special","value":{"kind":"root"}},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager"},"access":"write"},{"path":{"type":"special","value":{"kind":"slash_tmp"}},"access":"write"},{"path":{"type":"special","value":{"kind":"tmpdir"}},"access":"write"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.git"},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.agents"},"access":"read"},{"path":{"type":"path","path":"/home/agent/.hermes/repos/HealthManager/.codex"},"access":"read"}]},"model":"gpt-5.5","personality":"pragmatic","collaboration_mode":{"mode":"default","settings":{"model":"gpt-5.5","reasoning_effort":null,"developer_instructions":null}},"multi_agent_version":"v1","realtime_active":false,"summary":"auto"}}
{"timestamp":"2026-07-16T17:45:44.698Z","type":"response_item","payload":{"type":"message","role":"user","content":[{"type":"input_text","text":"Repair the current uncommitted Sprint 6G-A implementation against this frozen review. Do not commit/push/deploy or access production. Keep existing API paths and ECharts helper. Critical required fixes: (1) Mapping action contract and dialog must include explicit decision assign|composite|ignore, alias, canonical food, SIGHi 0-3 or unknown, confidence low|medium|high, mapping method allowlist, source label+version, note bounded, ingredient_review_required boolean, and personal tolerance as a separate optional neutral documentation field. No traffic-light medical semantics and no 'safe' label. Composite decision MUST always remain unclassified/needs ingredient review and cannot assign a SIGHi score; do not infer composite solely from name punctuation. Ignore must not alter mappings. (2) Worker validation exact-shape, transactional/idempotent, audit all decision/provenance/version fields, map every item whose normalized product slug equals queue_key, recompute only their affected days. (3) Recompute must preserve missing macros as unknown rather than zero and rebuild allowlisted nutrient_json from nutrition_item_nutrients without exposing unknown-unit micronutrients. Histamine index is sum of mapped SIGHi classifications, max, mapping coverage, unknown count, definition version; complete only when all items mapped. UI wording neutral, no Ampel, no causality. (4) Add idempotent explicit migration script with backup/integrity/repeat/restore tests or, if project convention uses schema+worker bootstrap, add focused migration tests proving those gates. Never mutate raw item tables. (5) Worker V5 regeneration must preserve full health-record-6e flags including explorer-6c and calendar-day-6d. (6) Read APIs must never expose source IDs/raw JSON/internal numeric IDs; mapping queue may expose documented product label as required but queue_key opaque/slug only. Nutrients only allowlisted known units; unknown unit omitted. (7) Mapping POST must use existing same-origin + one-time CSRF and exact single-value bounded form. Add tests for CSRF, queued-before-worker, composite rejection, ignore, idempotency, affected-day-only, missing nutrients. (8) Expand ADR 0003 with actual technical inventory counts abstracted/no product names, required DII methodology from Shivappa et al. 2014, input coverage/unit findings, licensing/reference-data limitation, missing-parameter normalization/versioning, and explicit conclusion 'Entzündungsindex derzeit nicht belastbar berechenbar'; show only fiber/sugar/saturated fat/sodium where known. (9) UI mapping workspace must be functional and nutrition day groups must cover breakfast/lunch/dinner/snack/unassigned. Keep V4 untouched. Run focused tests/static only."}]}}
{"timestamp":"2026-07-16T17:45:44.698Z","type":"event_msg","payload":{"type":"user_message","message":"Repair the current uncommitted Sprint 6G-A implementation against this frozen review. Do not commit/push/deploy or access production. Keep existing API paths and ECharts helper. Critical required fixes: (1) Mapping action contract and dialog must include explicit decision assign|composite|ignore, alias, canonical food, SIGHi 0-3 or unknown, confidence low|medium|high, mapping method allowlist, source label+version, note bounded, ingredient_review_required boolean, and personal tolerance as a separate optional neutral documentation field. No traffic-light medical semantics and no 'safe' label. Composite decision MUST always remain unclassified/needs ingredient review and cannot assign a SIGHi score; do not infer composite solely from name punctuation. Ignore must not alter mappings. (2) Worker validation exact-shape, transactional/idempotent, audit all decision/provenance/version fields, map every item whose normalized product slug equals queue_key, recompute only their affected days. (3) Recompute must preserve missing macros as unknown rather than zero and rebuild allowlisted nutrient_json from nutrition_item_nutrients without exposing unknown-unit micronutrients. Histamine index is sum of mapped SIGHi classifications, max, mapping coverage, unknown count, definition version; complete only when all items mapped. UI wording neutral, no Ampel, no causality. (4) Add idempotent explicit migration script with backup/integrity/repeat/restore tests or, if project convention uses schema+worker bootstrap, add focused migration tests proving those gates. Never mutate raw item tables. (5) Worker V5 regeneration must preserve full health-record-6e flags including explorer-6c and calendar-day-6d. (6) Read APIs must never expose source IDs/raw JSON/internal numeric IDs; mapping queue may expose documented product label as required but queue_key opaque/slug only. Nutrients only allowlisted known units; unknown unit omitted. (7) Mapping POST must use existing same-origin + one-time CSRF and exact single-value bounded form. Add tests for CSRF, queued-before-worker, composite rejection, ignore, idempotency, affected-day-only, missing nutrients. (8) Expand ADR 0003 with actual technical inventory counts abstracted/no product names, required DII methodology from Shivappa et al. 2014, input coverage/unit findings, licensing/reference-data limitation, missing-parameter normalization/versioning, and explicit conclusion 'Entzündungsindex derzeit nicht belastbar berechenbar'; show only fiber/sugar/saturated fat/sodium where known. (9) UI mapping workspace must be functional and nutrition day groups must cover breakfast/lunch/dinner/snack/unassigned. Keep V4 untouched. Run focused tests/static only.","images":[],"local_images":[],"text_elements":[]}}
{"timestamp":"2026-07-16T17:45:51.329Z","type":"event_msg","payload":{"type":"agent_message","message":"I’ll inspect the current diff and test layout first, then patch the Sprint 6G-A files in place. I’ll keep the scope to the uncommitted implementation and avoid any production-facing actions.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:45:51.335Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"I’ll inspect the current diff and test layout first, then patch the Sprint 6G-A files in place. I’ll keep the scope to the uncommitted implementation and avoid any production-facing actions."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:45:51.339Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git status --short\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_XkV3nZKmYTgIaoXadasOIUix"}}
{"timestamp":"2026-07-16T17:45:51.339Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"6G|histamine|SIGHi|mapping|ECharts|Entz|Ampel|safe|queue_key|health-record-6e|explorer-6c|calendar-day-6d|nutrition_item_nutrients|nutrient_json|csrf|CSRF|ADR 0003|0003\\\" -S .\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_XU2aagycenpWm1pYeSgPyZGA"}}
{"timestamp":"2026-07-16T17:45:51.480Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg --files\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_MuxM3BI6oQwJz9M2xMrHxTnF"}}
{"timestamp":"2026-07-16T17:45:51.515Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_XkV3nZKmYTgIaoXadasOIUix","output":"Chunk ID: eb50f8\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 183\nOutput:\n M database/schema.sql\n M scripts/health/assets/health-assets/dashboard-v5-api-explorer.js\n M scripts/health/assets/health-assets/dashboard-v5.css\n M scripts/health/assets/health-assets/dashboard-v5.js\n M scripts/health/dashboard_v5/contracts.py\n M scripts/health/dashboard_v5/data_provider.py\n M scripts/health/dashboard_v5/read_api.py\n M scripts/health/dashboard_v5/render.py\n M scripts/health/health_dashboard_action_worker.py\n M scripts/health/health_dashboard_server.py\n M tests/fixtures/dashboard_v5_fixture.py\n?? docs/adr/0003-dietary-inflammation-model.md\n?? scripts/health/assets/health-assets/dashboard-v5-echarts.js\n?? tests/browser/dashboard_v5_sprint6g_echarts.spec.js\n?? tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:45:51.517Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_XU2aagycenpWm1pYeSgPyZGA","output":"Chunk ID: d393cb\nWall time: 0.0005 seconds\nProcess exited with code 0\nOriginal token count: 262144\nOutput:\nTotal output lines: 759\n\n./tests/test_dashboard_v5_capture_sprint5c.py:36:        \"csrf_token\": [\"synthetic-csrf\"],\n./tests/test_dashboard_v5_capture_sprint5c.py:53:        \"<input name='csrf_token' value='__CSRF_TOKEN__'>\"\n./tests/test_dashboard_v5_capture_sprint5c.py:75:        match = re.search(r\"name='csrf_token' value='([^']+)'\", body)\n./tests/test_dashboard_v5_capture_sprint5c.py:83:            \"csrf_token\": token,\n./tests/test_dashboard_v5_capture_sprint5c.py:120:        fields[\"csrf_token\"] = token\n./tests/test_dashboard_v5_capture_sprint5c.py:140:        fields[\"csrf_token\"] = token\n./tests/test_dashboard_v5_capture_sprint5c.py:334:    monkeypatch.setenv(\"HEALTH_DASHBOARD_TEST_INSTANCE_ID\", \"sprint5c-safe\")\n./tests/browser/dashboard_v5_explorer_6c.spec.js:35:test('preview has exactly one visible ECharts explorer and one session bootstrap', async ({ page }) => {\n./tests/browser/dashboard_v5_explorer_6c.spec.js:49:  expect(state.url + state.local + state.session).not.toMatch(/token|authorization|csrf|\\/home\\//i);\n./tests/browser/dashboard_v5_explorer_6c.spec.js:53:test('30 days uses authoritative today minus 29 in URL, requests and ECharts axis', async ({ page }) => {\n./tests/browser/dashboard_v5_api_session.spec.js:7:test('synthetic V5 uses an HttpOnly same-origin CSRF session without exposed credentials', async ({ page }) => {\n./tests/browser/dashboard_v5_record_6e.spec.js:187:test('record failure is isolated to one tab and exposes retry with safe code', async ({ page }) => {\n./tests/browser/dashboard_v5_calendar_day_6d.spec.js:79:test('ECharts point opens the same central day endpoint and view', async ({ page }) => {\n./tests/browser/dashboard_v5_calendar_day_6d.spec.js:144:test('day view renders documented metrics as 7-day ECharts context and keeps missing areas compact', async ({ page }) => {\n./tests/browser/dashboard_v5_sprint6g_echarts.spec.js:3:test('health-record V5 is ECharts-only for cockpit, metric cards, and nutrition', async ({ page }) => {\n./tests/browser/dashboard_v5.spec.js:236:  await expect(page.locator('#metric-picker input[value=\"nutrition.histamine\"]')).toBeDisabled();\n./tests/browser/dashboard_v5.spec.js:558:    bundle.nutrition = { daily: [], mapping_review: [], mapping_open_count: 0 };\n./tests/browser/run_v5_record_6e1_focused.sh:16:  if [[ \"$mode\" == \"record\" ]]; then flag=\"--health-record-6e\"; else flag=\"\"; fi\n./tests/browser/run_v5_record_6e1_focused.sh:19:  python3 -c 'import secrets;print(secrets.token_urlsafe(32))' > \"$dir/token\"; chmod 600 \"$dir/token\" \"$dir/fixture.db\"\n./tests/browser/dashboard_v5_echarts_prototype.spec.js:28:test('ECharts prototype renders 365 days and eight synthetic series with required controls', async ({ page }) => {\n./tests/browser/dashboard_v5_echarts_prototype.spec.js:171:test('prototype keeps CSP, reduced-motion and print-safe local rendering', async ({ page }) => {\n./tests/browser/dashboard_v5_echarts_prototype.spec.js:177:  expect(csp).not.toContain(\"'unsafe-inline'\");\n./tests/test_dashboard_v5_explorer_sprint5b.py:28:    (\"histamine_documentation\", \"Dokumentierter Histamin-Score\", \"raw\", [\"nutrition.histamine\"]),\n./tests/browser/run_v5_isolated_matrix.sh:18:    python3 scripts/health/health_dashboard_v5.py --db \"$dir/fixture.db\" --output \"$dir/v5.html\" --today 2026-06-15 --generated-at 2026-06-15T12:00:00+00:00 --health-record-6e >/dev/null\n./tests/browser/run_v5_isolated_matrix.sh:20:    python3 scripts/health/health_dashboard_v5.py --db \"$dir/fixture.db\" --output \"$dir/v5.html\" --today 2026-06-15 --generated-at 2026-06-15T12:00:00+00:00 --calendar-day-6d >/dev/null\n./tests/browser/run_v5_isolated_matrix.sh:22:    python3 scripts/health/health_dashboard_v5.py --db \"$dir/fixture.db\" --output \"$dir/v5.html\" --today 2026-06-15 --generated-at 2026-06-15T12:00:00+00:00 --explorer-6c >/dev/null\n./tests/browser/run_v5_isolated_matrix.sh:26:  printf '<html>synthetic v4</html>' > \"$dir/v4.html\"; python3 -c 'import secrets;print(secrets.token_urlsafe(32))' > \"$dir/token\"; chmod 600 \"$dir/token\"; mkdir -m 700 \"$dir/actions\"\n./tests/test_dashboard_v5_sprint6b1_integration.py:39:        \"<html><head><meta name='health-browser-session-csrf' content='__BROWSER_SESSION_CSRF__'></head><body>synthetic v5</body></html>\",\n./tests/test_dashboard_v5_sprint6b1_integration.py:59:        match = re.search(r\"name='health-browser-session-csrf' content='([^']+)'\", html)\n./tests/test_dashboard_v5_sprint6b1_integration.py:61:        csrf_token = match.group(1)\n./tests/test_dashboard_v5_sprint6b1_integration.py:62:        assert csrf_token not in {\"\", \"__BROWSER_SESSION_CSRF__\"}\n./tests/test_dashboard_v5_sprint6b1_integration.py:68:        missing_csrf = urllib.request.Request(\n./tests/test_dashboard_v5_sprint6b1_integration.py:75:            browser.open(missing_csrf, timeout=5)\n./tests/test_dashboard_v5_sprint6b1_integration.py:85:                \"X-Health-Browser-CSRF\": csrf_token,\n./tests/test_dashboard_v5_sprint6b_catalog.py:192:                   datum TEXT,histamine_score REAL,item_count INTEGER,\n./tests/test_dashboard_v5_sprint6b_catalog.py:193:                   histamine_unknown_count INTEGER,histamine_label TEXT\n./tests/test_dashboard_v5_sprint6b_catalog.py:208:        histamine = catalog_search(connection, \"Histamin\")[0]\n./tests/test_dashboard_v5_sprint6b_catalog.py:214:    assert histamine[\"availability\"] == {\"status\": \"available\", \"observations\": 1}\n./tests/test_health_system_migrate_sprint2.py:88:            ('nutrition:histamine_score','symptom_total',0,'unknown',0,1,\n./tests/test_health_system_migrate_sprint2.py:89:             1,'insufficient_n','old-safe-method','[]','old aggregate row');\n./tests/test_health_system_migrate_sprint2.py:109:        assert row == (\"nutrition:histamine_score\", \"symptom_total\", 0, 0, 0.0)\n./tests/fixtures/dashboard_v5_fixture.py:33:SPRINT4B_PREDICTORS: Final = (\"apple:heart_rate_variability\", \"nutrition:histamine_score\")\n./tests/fixtures/dashboard_v5_fixture.py:235:            \"\"\"INSERT INTO nutrition_histamine_scores\n./tests/fixtures/dashboard_v5_fixture.py:239:                (1, \"synthetic cucumber\", 0, \"green\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n./tests/fixtures/dashboard_v5_fixture.py:245:               (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n./tests/fixtures/dashboard_v5_fixture.py:246:                histamine_max,histamine_unknown_count,histamine_label,nutrient_json,updated_at)\n./tests/fixtures/dashboard_v5_fixture.py:252:               (datum,meal,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_label,updated_at)\n./tests/fixtures/dashboard_v5_fixture.py:260:            (\"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\", \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\", 1, _iso_day(current), _iso_day(current), \"synthetic reviewed food\", 1, \"synthetic missing mapping\", \"open\", fixture_timestamp),\n./tests/fixtures/dashboard_v5_fixture.py:308:                \"nutrition_items\", \"nutrition_histamine_scores\", \"symptom_log\",\n./tests/fixtures/dashboard_v5_fixture.py:325:            \"food_mapping_states\": connection.execute(\n./tests/fixtures/dashboard_v5_fixture.py:328:                   FROM nutrition_histamine_scores\"\"\"\n./tests/test_dashboard_v5_sprint6g_nutrition.py:69:    assert days[\"days\"][-1][\"histamine\"][\"score\"] is None\n./tests/test_dashboard_v5_sprint6g_nutrition.py:70:    assert days[\"days\"][-1][\"histamine\"][\"status\"] == \"incomplete\"\n./tests/test_dashboard_v5_sprint6g_nutrition.py:80:    queue = dispatch_api(fixture_db, \"/api/v1/nutrition/mapping-queue\", \"status=open\")\n./tests/test_dashboard_v5_sprint6g_nutrition.py:81:    assert queue[\"items\"][0][\"queue_key\"] == \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\"\n./tests/test_dashboard_v5_sprint6g_nutrition.py:85:def test_mapping_worker_is_transactional_idempotent_and_recomputes_only_affected_days(\n./tests/test_dashboard_v5_sprint6g_nutrition.py:94:        \"action\": \"nutrition_mapping\",\n./tests/test_dashboard_v5_sprint6g_nutrition.py:95:        \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n./tests/test_dashboard_v5_sprint6g_nutrition.py:99:        \"mapping_scope\": \"canonical\",\n./tests/test_dashboard_v5_sprint6g_nutrition.py:103:    worker.apply_mapping_action(payload, \"a\" * 32)\n./tests/test_dashboard_v5_sprint6g_nutrition.py:104:    worker.apply_mapping_action(payload, \"a\" * 32)\n./tests/test_dashboard_v5_sprint6g_nutrition.py:109:        day = connection.execute(\"SELECT histamine_unknown_count,histamine_score,histamine_label FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n./tests/test_dashboard_v5_sprint6g_nutrition.py:110:        assert dict(day) == {\"histamine_unknown_count\": 0, \"histamine_score\": 1.0, \"histamine_label\": \"yellow\"}\n./tests/test_dashboard_v5_sprint6g_nutrition.py:111:        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 1\n./tests/test_dashboard_v5_sprint6g_nutrition.py:112:        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n./tests/test_dashboard_v5_sprint6e4a_recovery.py:79:    monkeypatch.setattr(sys, \"argv\", [\"health_dashboard_v5.py\", \"--db\", str(database), \"--output\", str(preview), \"--health-record-6e\"])\n./tests/test_dashboard_v5_sprint6e4a_recovery.py:95:    assert \"Nur Anzeige – Bearbeitung folgt im Ernährungs-Sprint 6G\" in preview_html\n./tests/test_dashboard_v5_sprint6e3_profile.py:50:        command.append(\"--health-record-6e\")\n./tests/test_dashboard_v5_sprint6e3_profile.py:69:    monkeypatch.setenv(\"HEALTH_DASHBOARD_V5_PROFILE\", \"health-record-6e\")\n./tests/test_dashboard_v5_sprint6e3_profile.py:79:    monkeypatch.setattr(module, \"DASHBOARD_V5_PROFILE\", \"health-record-6e\")\n./tests/test_dashboard_v5_sprint6e3_profile.py:115:    assert \"--health-record-6e\" not in commands[-1]\n./tests/test_dashboard_v5_sprint6e3_profile.py:122:    assert \"Environment=HEALTH_DASHBOARD_V5_PROFILE=health-record-6e\" in unit\n./tests/test_dashboard_v5_sprint6e3_profile.py:126:    for invalid in (\"unknown\", \"default,health-record-6e\", \" health-record-6e\"):\n./tests/test_multimodal_correlations_v1.py:31:            nutrient_json TEXT, histamine_score REAL,\n./tests/test_multimodal_correlations_v1.py:33:            histamine_unknown_count INTEGER DEFAULT 0\n./tests/test_multimodal_correlations_v1.py:116:            \"INSERT INTO nutrition_daily_summary_v2(datum,kcal,protein_g,nutrient_json,histamine_score) VALUES(?,?,?,?,?)\",\n./tests/test_multimodal_correlations_v1.py:124:    row = next(r for r in results if r.predictor == \"nutrition:histamine_score\" and r.lag_days == 0)\n./tests/test_multimodal_correlations_v1.py:158:            \"INSERT INTO nutrition_daily_summary_v2(datum,kcal,protein_g,nutrient_json,histamine_score) VALUES(?,?,?,?,?)\",\n./tests/test_multimodal_correlations_v1.py:165:    row = next(r for r in results if r.predictor == \"nutrition:histamine_score\" and r.lag_days == 0)\n./tests/test_multimodal_correlations_v1.py:184:            \"INSERT INTO nutrition_daily_summary_v2(datum,kcal,protein_g,nutrient_json,histamine_score) VALUES(?,?,?,?,?)\",\n./tests/test_multimodal_correlations_v1.py:191:    phases = {result.medication_phase for result in results if result.predictor == \"nutrition:histamine_score\"}\n./tests/test_multimodal_correlations_v1.py:196:        if result.predictor == \"nutrition:histamine_score\" and result.lag_days == 2\n./tests/test_multimodal_correlations_v1.py:274:        \"INSERT INTO nutrition_daily_summary_v2(datum,kcal,protein_g,nutrient_json,histamine_score) VALUES(?,?,?,?,?)\",\n./tests/test_multimodal_correlations_v1.py:280:        \"nutrition:histamine_score\": {\"2026-01-01\": 2.0}\n./tests/test_multimodal_correlations_v1.py:319:def test_unknown_histamine_items_are_missing_not_zero(engine):\n./tests/test_multimodal_correlations_v1.py:324:           (datum,kcal,protein_g,nutrient_json,histamine_score,item_count,histamine_unknown_count)\n./tests/test_multimodal_correlations_v1.py:335:        \"nutrition:histamine_score\": {\"2026-01-01\": 0.0}\n./tests/test_multimodal_correlations_v1.py:520:           (datum,histamine_score,item_count,histamine_unknown_count) VALUES(?,?,?,?)\"\"\",\n./tests/test_multimodal_correlations_v1.py:532:        if result.predictor == \"nutrition:histamine_score\" and result.lag_days == 0\n./tests/test_multimodal_correlations_v1.py:548:            \"INSERT INTO nutrition_daily_summary_v2(datum,histamine_score,item_count,histamine_unknown_count) VALUES(?,?,?,?)\",\n./tests/test_multimodal_correlations_v1.py:558:        if result.predictor == \"nutrition:histamine_score\" and result.lag_days == 0\n./tests/test_multimodal_correlations_v1.py:574:               (datum,histamine_score,item_count,histamine_unknown_count) VALUES(?,?,?,?)\"\"\",\n./tests/test_multimodal_correlations_v1.py:593:        predictor=\"nutrition:histamine_score\",\n./tests/test_multimodal_correlations_v1.py:621:def test_storage_boundary_rejects_non_allowlisted_predictor_without_deleting_safe_rows(engine):\n./tests/test_multimodal_correlations_v1.py:624:    safe = mod.CorrelationResult(\n./tests/test_multimodal_correlations_v1.py:625:        predictor=\"nutrition:histamine_score\",\n./tests/test_multimodal_correlations_v1.py:642:    mod.store_results(c, [safe])\n./tests/test_multimodal_correlations_v1.py:645:    malicious = replace(safe, predictor=\"lab:free text / Source Hospital\")\n./tests/test_multimodal_correlations_v1.py:649:    assert c.execute(\"SELECT predictor FROM multimodal_correlation_results\").fetchone()[0] == safe.predictor\n./tests/test_multimodal_correlations_v1.py:669:    safe = mod.CorrelationResult(\n./tests/test_multimodal_correlations_v1.py:670:        predictor=\"nutrition:histamine_score\",\n./tests/test_multimodal_correlations_v1.py:687:    mod.store_results(c, [safe])\n./tests/test_multimodal_correlations_v1.py:691:        mod.store_results(c, [replace(safe, **changes)])\n./tests/test_multimodal_correlations_v1.py:695:    ).fetchone() == (safe.predictor, safe.n, safe.target_coverage)\n./tests/test_sprint0_safety.py:251:    unsafe = tmp_path / \"gog_keyring.env\"\n./tests/test_sprint0_safety.py:253:    unsafe.write_text(\"GOG_KEYRING_PASSWORD='\" + fixture_value + \"'\\n\", encoding=\"utf-8\")\n./tests/test_sprint0_safety.py:254:    unsafe.chmod(0o644)\n./tests/test_sprint0_safety.py:265:        monkeypatch.setattr(mod, \"GOG_SECRET_ENV\", unsafe)\n./tests/test_sprint0_safety.py:266:        with pytest.raises(RuntimeError, match=\"Unsafe permissions\"):\n./tests/test_sprint0_safety.py:280:    assert \"keine automatische medizinische Risiko-, Ampel- oder Entwarnungsbewertung\" in source\n./tests/test_health_dashboard_v4.py:67:           (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,histamine_unknown_count,histamine_label)\n./tests/test_health_dashboard_v4.py:78:        \"\"\"INSERT INTO nutrition_histamine_scores\n./tests/test_health_dashboard_v4.py:167:    assert \"🟢 safe\" not in html\n./tests/test_health_dashboard_v4.py:173:def test_dashboard_v4_baseline_and_source_freshness_are_missingness_safe():\n./tests/test_health_dashboard_v4.py:235:    assert module.consume_csrf_token(\n./tests/test_health_dashboard_v4.py:236:        module.issue_csrf_token(now=100.0),\n./tests/test_health_dashboard_v4.py:237:        now=100.0 + module.CSRF_TTL_SECONDS,\n./tests/test_health_dashboard_v4.py:239:    replayed = module.issue_csrf_token(now=200.0)\n./tests/test_health_dashboard_v4.py:240:    assert module.consume_csrf_token(replayed, now=201.0)\n./tests/test_health_dashboard_v4.py:241:    assert not module.consume_csrf_token(replayed, now=202.0)\n./tests/test_health_dashboard_v4.py:242:    expired = module.issue_csrf_token(now=300.0)\n./tests/test_health_dashboard_v4.py:243:    assert not module.consume_csrf_token(\n./tests/test_health_dashboard_v4.py:244:        expired, now=301.0 + module.CSRF_TTL_SECONDS\n./tests/test_health_dashboard_v4.py:257:        assert \"'unsafe-inline'\" not in csp\n./tests/test_health_dashboard_v4.py:261:        assert \"health_csrf=\" in response.headers[\"Set-Cookie\"]\n./tests/test_health_dashboard_v4.py:306:    assert module.safe_report_path(\"escape.pdf\") is None\n./tests/test_health_dashboard_v4.py:309:    assert module.safe_report_path(\"valid.pdf\") == valid.resolve()\n./tests/test_health_dashboard_v4.py:317:def test_touch_checkin_requires_same_origin_one_time_csrf_and_complete_scores(\n./tests/test_health_dashboard_v4.py:322:        \"<html><body><form><input value='__CSRF_TOKEN__'></form></body></html>\",\n./tests/test_health_dashboard_v4.py:350:            \"csrf_token\": token,\n./tests/test_sprint2_integration_safety.py:20:    assert \"int(row[\\\"histamine_unknown_count\\\"] or 0) == 0\" in source\n./tests/test_dashboard_v5_sprint6a.py:1:\"\"\"Sprint-6A foundation, parity and synthetic ECharts prototype contracts.\"\"\"\n./tests/test_dashboard_v5_sprint6a.py:106:    for option in (\"Apache ECharts 6.1.0\", \"Chart.js 4.5.1\", \"Plotly.js\", \"Grafana OSS\"):\n./tests/test_dashboard_v5_sprint6a.py:109:        \"Entscheidung: ECharts\",\n./tests/test_dashboard_v5_sprint6d_day_calendar.py:58:            (\"2026-06-20\", \"Synthetic Clinician\", \"Synthetic Clinic\", \"Synthetic follow-up\", \"safe synthetic note\"),\n./tests/test_sprint4b_correlation_contract.py:111:        predictor=\"nutrition:histamine_score\", target=\"symptom_total\", lag_days=0,\n./tests/test_sprint4b_correlation_contract.py:124:        (\"unsafe:predictor\", \"symptom_total\", 1, \"unknown\", 14, 14, 14, 1.0, 0,\n./skills/health-data-management/SKILL.md:141:- Arztbericht/PDF: `scripts/generate_doctor_report.py` erzeugt `reports/arztbericht_aktuell.pdf` im Querformat mit automatischem Tabellen-Zeilenumbruch, KPI-Ampelsystem nach Gesundheitsbereich, Gesamtgesundheitsindikator-Liniendiagramm und Apple-Health-Grafiken. Es lädt das PDF in Google Drive Ordner `Gesundheitsberichte` (`1oVpcbpelbt2IwHL9oGw9QTi2haQL63-v`) hoch und schreibt `reports/arztbericht_drive_link.json`. Wichtig: Bericht verwendet nur validierte Laborwerte mit `abnahme_datum`/`befund_datum`; `ermittlung_datum` ist Import-/OCR-Zeitstempel und darf nicht als medizinisches Datum angezeigt werden. Dashboard v3.1 zeigt lokale PDF- und Drive-Links sowie Apple-Health-Module für Schritte, Distanz, Ruhepuls, HRV, Schlaf, SpO₂, Atemfrequenz, Aktivität, Physical Effort und Gewicht.\n./README.md:39:- [`docs/sprint0-safety.md`](docs/sprint0-safety.md) – Credential-, Import- und Laborwarnungs-Härtung\n./tests/test_health_dashboard_v5.py:20:from dashboard_v5.contracts import safe_json, validate_bundle  # noqa: E402\n./tests/test_health_dashboard_v5.py:97:def test_next_medication_is_earliest_future_and_status_safe() -> None:\n./tests/test_health_dashboard_v5.py:190:def test_incomplete_histamine_is_null_not_zero() -> None:\n./tests/test_health_dashboard_v5.py:194:           (datum,kcal,item_count,histamine_score,histamine_unknown_count,histamine_label)\n./tests/test_health_dashboard_v5.py:203:    assert daily[0][\"histamine_score\"] is None and daily[0][\"quality\"] == \"incomplete\"\n./tests/test_health_dashboard_v5.py:204:    assert daily[1][\"histamine_score\"] == 0 and daily[1][\"kcal\"] == 0\n./tests/test_health_dashboard_v5.py:207:def test_render_has_real_hidden_views_and_safe_json() -> None:\n./tests/test_health_dashboard_v5.py:218:    json.loads(safe_json(bundle))\n./tests/test_health_dashboard_v5.py:226:    del missing[\"nutrition\"][\"mapping_open_count\"]\n./tests/test_health_dashboard_v5.py:242:    unsafe_lab = copy.deepcopy(bundle)\n./tests/test_health_dashboard_v5.py:243:    unsafe_lab[\"labs\"] = [{\n./tests/test_health_dashboard_v5.py:249:        validate_bundle(unsafe_lab)\n./tests/test_health_dashboard_v5.py:260:    for unsafe_value in (\"NOT A NUMBER\", \"١٢\", \"１２\", \"<5\", \"1e3\", \"NaN\"):\n./tests/test_health_dashboard_v5.py:263:            \"parameter\": \"CRP\", \"value\": unsafe_value, \"unit\": \"mg/L\",\n./tests/test_health_dashboard_v5.py:270:    unsafe_reference = copy.deepcopy(bundle)\n./tests/test_health_dash…252144 tokens truncated…ransform:lK,parseAxisBreakOptionInwardTransform:uK,makeAxisLabelFormatterParamBreak:hK},Hp||(Hp=t)}var pK=Pa();function fK(t,e,n,i,r){var o=n.axis;if(!o.scale.isBlank()&&Wp()){var a=Wp().retrieveAxisBreakPairs(o.scale.getTicks({breakTicks:\"only_break\"}),function(t){return t.break},!1);if(a.length){var s=n.getModel(\"breakArea\"),l=s.get(\"zigzagAmplitude\"),u=s.get(\"zigzagMinSpan\"),c=s.get(\"zigzagMaxSpan\");u=Math.max(2,u||0),c=Math.max(u,c||0);var h=s.get(\"expandOnClick\"),d=s.get(\"zigzagZ\"),p=s.getModel(\"itemStyle\").getItemStyle(),f=p.stroke,g=p.lineWidth,v=p.lineDash,y=p.fill,m=new vo({ignoreModelZ:!0}),x=o.isHorizontal(),_=pK(e).visualList||(pK(e).visualList=[]);B(_,function(t){return t.shouldRemove=!0});for(var b=function(t){var e=a[t][0].break.parsedBreak,s=[];s[0]=o.toGlobalCoord(o.dataToCoord(e.vmin,!0)),s[1]=o.toGlobalCoord(o.dataToCoord(e.vmax,!0)),s[1]<s[0]&&s.reverse();var b=function(t,e){var n=H(t,function(t){return Wp().identifyAxisBreak(t.parsedBreak.breakOption,e.breakOption)});return n||t.push(n={zigzagRandomList:[],parsedBreak:e,shouldRemove:!1}),n}(_,e);b.shouldRemove=!1;var w=new vo;!function(t,e,n,r,o,a){var s={stroke:f,lineWidth:g,lineDash:v,fill:\"none\"},h=o?0:1,m=1-h,x=i[dd[m]]+i[pd[m]];function _(t){var e=[],n=[];e[h]=n[h]=t,e[m]=i[dd[m]],n[m]=x;var r={x1:e[0],y1:e[1],x2:n[0],y2:n[1]};return iu(r,r,{lineWidth:1}),e[0]=r.x1,e[1]=r.y1,e[h]}n=_(n),r=_(r);for(var b=[],w=[],S=!0,M=i[dd[m]],I=0;;I++){var T=M===i[dd[m]],C=M>=x;C&&(M=x);var D=[],A=[];D[h]=n,A[h]=r,T||C||(D[h]+=S?-l:l,A[h]-=S?l:-l),D[m]=M,A[m]=M,b.push(D),w.push(A);var k=void 0;if(I<t.length?k=t[I]:(k=Math.random(),t.push(k)),M+=k*(c-u)+u,S=!S,C)break}var L=Wp().serializeAxisBreakIdentifier(a.breakOption);if(e.add(new kh({anid:\"break_a_\"+L,shape:{points:b},style:s,z:d})),0!==a.gapReal){e.add(new kh({anid:\"break_b_\"+L,shape:{points:w},style:s,z:d}));var P=w.slice();P.reverse();var O=b.concat(P);e.add(new Dh({anid:\"break_c_\"+L,shape:{points:O},style:{fill:y,opacity:p.opacity},z:d}))}}(b.zigzagRandomList,w,s[0],s[1],x,e),h&&w.on(\"click\",function(){var t={type:ZC,breaks:[{start:e.breakOption.start,end:e.breakOption.end}]};t[o.dim+\"AxisIndex\"]=n.componentIndex,r.dispatchAction(t)}),w.silent=!h,m.add(w)},w=0;w<a.length;w++)b(w);t.add(m),function(t){for(var e=t.length-1;e>=0;e--)t[e].shouldRemove&&t.splice(e,1)}(_)}}}function gK(t,e,n,i){var r=t.axis,o=n.transform;ct(i.style);var a=r.getExtent();r.inverse&&(a=a.slice()).reverse();var s=V(Wp().retrieveAxisBreakPairs(r.scale.getTicks({breakTicks:\"only_break\"}),function(t){return t.break},!1),function(t){var e=t[0].break.parsedBreak,n=[r.dataToCoord(e.vmin,!0),r.dataToCoord(e.vmax,!0)];return n[0]>n[1]&&n.reverse(),{coordPair:n,brkId:Wp().serializeAxisBreakIdentifier(e.breakOption)}});s.sort(function(t,e){return t.coordPair[0]-e.coordPair[0]});for(var l=a[0],u=null,c=0;c<s.length;c++){var h=s[c],d=Math.max(h.coordPair[0],a[0]),p=Math.min(h.coordPair[1],a[1]);l<=d&&f(l,d,u,h),l=p,u=h}function f(t,n,r,a){function s(t,e){o&&(Yt(t,t,o),Yt(e,e,o))}function l(t,e){var n={x1:t[0],y1:t[1],x2:e[0],y2:e[1]};iu(n,n,i.style),t[0]=n.x1,t[1]=n.y1,e[0]=n.x2,e[1]=n.y2}var u=[t,0],c=[n,0],h=[t,5],d=[n,5];s(u,h),l(u,h),s(c,d),l(c,d),l(u,c);var p=new Oh(k({shape:{x1:u[0],y1:u[1],x2:c[0],y2:c[1]}},i));e.add(p),p.anid=\"breakLine_\"+(r?r.brkId:\"\\0\")+\"_\\0_\"+(a?a.brkId:\"\\0\")}l<=a[1]&&f(l,a[1],u,null)}function vK(t,e,n){if(!H(n,function(t){return!t})){var i=new Pe;if(aI(n[0],n[1],i,{direction:-(t?e+Math.PI:e),touchThreshold:0,bidirectional:!1})){var r=[1,0,0,1,0,0];Ce(r,r,-e);var o=V(n,function(t){return t.transform?Ie([1,0,0,1,0,0],r,t.transform):r}),a=.5;if(g(0)||g(1)){var s=V(n,function(t,e){var n=t.localRect.clone();return n.applyTransform(o[e]),n}),l=new Pe;l.copy(n[0].label).add(n[1].label).scale(.5),l.transform(r);var u=i.clone().transform(r),c=(s[0].x+s[1].x+(u.x>=0?s[0].width:s[1].width)+u.x)/2-l.x,h=Math.min(c,c-u.x),d=Math.max(c,c-u.x);a=(c-(d<0?d:h>0?h:0))/u.x}var p=new Pe,f=new Pe;Pe.scale(p,i,-a),Pe.scale(f,i,1-a),eI(n[0],p),eI(n[1],f)}}function g(t){var e=n[0].localRect,i=new Pe(e[pd[t]]*o[0][0],e[pd[t]]*o[0][1]);return Math.abs(i.y)<1e-5}}function yK(t,e){var n={breaks:[]};return B(e.breaks,function(i){if(i){var r=H(t.get(\"breaks\",!0),function(t){return Wp().identifyAxisBreak(t,i)});if(r){var o=e.type,a={isExpanded:!!r.isExpanded};r.isExpanded=o===ZC||o!==jC&&(o===qC?!r.isExpanded:r.isExpanded),n.breaks.push({start:r.start,end:r.end,isExpanded:!!r.isExpanded,old:a})}}}),n}function mK(){var t;t={adjustBreakLabelPair:vK,buildAxisBreakLine:gK,rectCoordBuildBreakAxis:fK,updateModelAxisBreak:yK},YC||(YC=t)}function xK(t,e){B(t,function(t){if(!t.model.get([\"axisLabel\",\"inside\"])){var n=function(t){var e,n,i=t.model,r=t.scale;if(!i.get([\"axisLabel\",\"show\"])||r.isBlank())return;var o=r.getExtent();n=r instanceof Mw?r.count():(e=r.getTicks()).length;var a,s=t.getLabelModel(),l=Zw(t),u=1;n>40&&(u=Math.ceil(n/40));for(var c=0;c<n;c+=u){var h=l(e?e[c]:{value:o[0]+c},c),d=p(s.getTextRect(h),s.get(\"rotate\")||0);a?a.union(d):a=d}return a;function p(t,e){var n=e*Math.PI/180,i=t.width,r=t.height,o=i*Math.abs(Math.cos(n))+Math.abs(r*Math.sin(n)),a=i*Math.abs(Math.sin(n))+Math.abs(r*Math.cos(n));return new Xe(t.x,t.y,o,a)}}(t);if(n){var i=t.isHorizontal()?\"height\":\"width\",r=t.model.get([\"axisLabel\",\"margin\"]);e[i]-=n[i]+r,\"top\"===t.position?e.y+=n.height+r:\"left\"===t.position&&(e.x+=n.width+r)}}})}gb([function(t){t.registerPainter(\"canvas\",XT)}]),gb([function(t){t.registerPainter(\"svg\",DT)}]),gb([function(t){t.registerChartView(VC),t.registerSeriesModel(ZT),t.registerLayout(GC(\"line\",!0)),t.registerVisual({seriesType:\"line\",reset:function(t){var e=t.getData(),n=t.getModel(\"lineStyle\").getLineStyle();n&&!n.stroke&&(n.stroke=e.getVisual(\"style\").fill),e.setVisual(\"legendLineStyle\",n)}}),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,WC(\"line\"))},function(t){t.registerChartView(rA),t.registerSeriesModel(KD),t.registerLayout(t.PRIORITY.VISUAL.LAYOUT,YD(PD)),t.registerLayout(t.PRIORITY.VISUAL.PROGRESSIVE_LAYOUT,XD(PD)),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,WC(PD)),t.registerAction({type:\"changeAxisOrder\",event:\"changeAxisOrder\",update:\"update\"},function(t,e){var n=t.componentType||\"series\";e.eachComponent({mainType:n,query:t},function(e){t.sortInfo&&e.axis.setCategorySortInfo(t.sortInfo)})}),jD(t)},function(t){t.registerChartView(GA),t.registerSeriesModel(TA),$m(MA,t.registerAction),t.registerLayout(zA),t.registerProcessor(_A(MA)),t.registerProcessor(function(t){return{seriesType:t,reset:function(t,e){var n=t.getData();n.filterSelf(function(t){var e=n.mapDimension(\"value\"),i=n.get(e,t);return!(K(i)&&!isNaN(i)&&i<0)})}}}(MA))},function(t){gb(Ok),t.registerSeriesModel(FA),t.registerChartView(YA),t.registerLayout(GC(\"scatter\"))},function(t){gb(eL),t.registerChartView(Yk),t.registerSeriesModel(Uk),t.registerLayout(nL),t.registerProcessor(_A(\"radar\")),t.registerPreprocessor(Hk)},function(t){gb(TO),t.registerChartView(gO),t.registerSeriesModel(cO),t.registerLayout(CO),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,DO),$m(\"map\",t.registerAction)},function(t){t.registerChartView(iR),t.registerSeriesModel(tR),t.registerLayout(hR),t.registerVisual(dR),function(t){t.registerAction({type:\"treeExpandAndCollapse\",event:\"treeExpandAndCollapse\",update:\"update\"},function(t,e){e.eachComponent({mainType:Du,subType:QO,query:t},function(e){var n=t.dataIndex,i=e.getData().tree.getNodeByDataIndex(n);i.isExpand=!i.isExpand})}),UP(t,Du,QO)}(t)},function(t){t.registerSeriesModel(gR),t.registerChartView(WR),t.registerVisual(aN),t.registerLayout(DR),function(t){for(var e=0;e<pR.length;e++)t.registerAction({type:pR[e],update:\"updateView\"},St);t.registerAction({type:\"treemapRootToNode\",update:\"updateView\"},function(t,e){e.eachComponent({mainType:\"series\",subType:\"treemap\",query:t},function(e,n){var i=qO(t,[\"treemapZoomToNode\",\"treemapRootToNode\"],e);if(i){var r=e.getViewRoot();r&&(t.direction=$O(r,i.node)?\"rollUp\":\"drillDown\"),e.resetViewRoot(i.node)}})})}(t)},function(t){t.registerChartView(pE),t.registerSeriesModel(IN),t.registerProcessor(fE),t.registerVisual(gE),t.registerVisual(CN),t.registerLayout(kN),t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,BN),t.registerLayout(GN),t.registerCoordinateSystem(\"graphView\",{dimensions:nP.dimensions,create:FN}),t.registerAction({type:\"focusNodeAdjacency\",event:\"focusNodeAdjacency\",update:\"series:focusNodeAdjacency\"},St),t.registerAction({type:\"unfocusNodeAdjacency\",event:\"unfocusNodeAdjacency\",update:\"series:unfocusNodeAdjacency\"},St),UP(t,Du,MN)},function(t){t.registerChartView(wE),t.registerSeriesModel(yE),t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,ME),t.registerProcessor(_A(\"chord\"))},function(t){t.registerChartView(DE),t.registerSeriesModel(AE)},function(t){t.registerChartView(RE),t.registerSeriesModel(LE),t.registerLayout(NE),t.registerProcessor(_A(kE))},function(t){gb(iB),t.registerChartView(zE),t.registerSeriesModel(WE),t.registerVisual(t.PRIORITY.VISUAL.BRUSH,XE)},function(t){t.registerChartView(lB),t.registerSeriesModel(oB),t.registerLayout(cB),t.registerVisual(wB),t.registerAction({type:\"dragNode\",event:\"dragnode\",update:\"update\"},function(t,e){e.eachComponent({mainType:Du,subType:rB,query:t},function(e){e.setNodePosition(t.dataIndex,[t.localX,t.localY])})}),UP(t,Du,rB)},function(t){t.registerSeriesModel(TB),t.registerChartView(CB),t.registerLayout(RB),t.registerTransform(NB),function(t){OB(t,function(){var e=AD(IB);xS(t,{key:e,seriesType:IB,getMetrics:LD}),AS(e,DD(e))})}(t)},function(t){t.registerChartView(ZB),t.registerSeriesModel(zB),t.registerPreprocessor(rV),t.registerVisual(YB),t.registerLayout(aV),function(t){oV(t,function(){var e=AD(EB);xS(t,{key:e,seriesType:EB,getMetrics:LD}),AS(e,DD(e))})}(t)},function(t){t.registerChartView(cV),t.registerSeriesModel(dV),t.registerLayout(GC(\"effectScatter\"))},function(t){t.registerChartView(_V),t.registerSeriesModel(MV),t.registerLayout(xV),t.registerVisual(TV)},function(t){t.registerChartView(DV),t.registerSeriesModel(AV)},function(t){t.registerChartView(OV),t.registerSeriesModel($V),t.registerLayout(t.PRIORITY.VISUAL.LAYOUT,YD(OD)),t.registerLayout(t.PRIORITY.VISUAL.PROGRESSIVE_LAYOUT,XD(OD)),jD(t)},function(t){t.registerChartView(tG),t.registerSeriesModel(QV),t.registerLayout(eG),t.registerProcessor(_A(JV))},function(t){t.registerChartView(uG),t.registerSeriesModel(oG),t.registerLayout(dG),t.registerVisual(cG),function(t){t.registerAction({type:sG,update:\"updateView\"},function(t,e){e.eachComponent({mainType:\"series\",subType:\"sunburst\",query:t},function(e,n){var i=qO(t,[sG],e);if(i){var r=e.getViewRoot();r&&(t.direction=$O(r,i.node)?\"rollUp\":\"drillDown\"),e.resetViewRoot(i.node)}})}),t.registerAction({type:lG,update:\"none\"},function(t,e,n){t=k({},t),e.eachComponent({mainType:\"series\",subType:\"sunburst\",query:t},function(e){var n=qO(t,[lG],e);n&&(t.dataIndex=n.node.dataIndex)}),n.dispatchAction(k(t,{type:\"highlight\"}))}),t.registerAction({type:\"sunburstUnhighlight\",update:\"updateView\"},function(t,e,n){t=k({},t),n.dispatchAction(k(t,{type:\"downplay\"}))})}(t)},function(t){t.registerChartView(QG),t.registerSeriesModel(yG)}]),gb(function(t){gb(Ok),gb(nH)}),gb(function(t){gb(nH),wk.registerAxisPointerClass(\"PolarAxisPointer\",iH),t.registerCoordinateSystem(\"polar\",xH),t.registerComponentModel(sH),t.registerComponentView(RH),JA(t,\"angle\",uH,PH),JA(t,\"radius\",cH,OH),t.registerComponentView(MH),t.registerComponentView(CH),t.registerLayout(LH),function(t,e){AH(t,function(){var n=kD(e,oH);RD(t,n,e,oH),AS(n,DD(n))})}(t,PD)}),gb(TO),gb(function(t){gb(nH),wk.registerAxisPointerClass(\"SingleAxisPointer\",XH),t.registerComponentView(KH),t.registerComponentView(zH),t.registerComponentModel(Ek),JA(t,\"single\",Ek,Ek.defaultOption),t.registerCoordinateSystem(\"single\",WH)}),gb(iB),gb(function(t){t.registerComponentModel($H),t.registerComponentView(QH),t.registerCoordinateSystem(\"calendar\",eW)}),gb(function(t){t.registerComponentModel(DW),t.registerComponentView(OW),t.registerCoordinateSystem(\"matrix\",FW)}),gb(function(t){t.registerComponentModel(lU),t.registerComponentView(hU),t.registerPreprocessor(function(t){var e=t.graphic;X(e)?e[0]&&e[0].elements?t.graphic=[t.graphic[0]]:t.graphic=[{elements:e}]:e&&!e.elements&&(t.graphic=[{elements:[e]}])})}),gb(function(t){t.registerComponentModel(GU),t.registerComponentView(HU),BU(\"saveAsImage\",UU),BU(\"magicType\",ZU),BU(\"dataView\",tY),BU(\"dataZoom\",SY),BU(\"restore\",oY),gb(NU)}),gb(function(t){gb(nH),t.registerComponentModel(TY),t.registerComponentView(WY),t.registerAction({type:\"showTip\",event:\"showTip\",update:\"tooltip:manuallyShowTip\"},St),t.registerAction({type:\"hideTip\",event:\"hideTip\",update:\"tooltip:manuallyHideTip\"},St)}),gb(nH),gb(function(t){t.registerComponentView(hX),t.registerComponentModel(dX),t.registerPreprocessor(jY),t.registerVisual(t.PRIORITY.VISUAL.BRUSH,aX),t.registerAction({type:\"brush\",event:\"brush\",update:\"updateVisual\"},function(t,e){e.eachComponent({mainType:\"brush\",query:t},function(e){e.setAreas(t.areas)})}),t.registerAction({type:\"brushSelect\",event:\"brushSelected\",update:\"none\"},St),t.registerAction({type:\"brushEnd\",event:\"brushEnd\",update:\"none\"},St),BU(\"brush\",gX)}),gb(function(t){t.registerComponentModel(vX),t.registerComponentView(yX)}),gb(function(t){t.registerComponentModel(xX),t.registerComponentView(MX),t.registerSubTypeDefaulter(\"timeline\",function(){return\"slider\"}),function(t){t.registerAction({type:\"timelineChange\",event:\"timelineChanged\",update:\"prepareAndUpdate\"},function(t,e,n){var i=e.getComponent(\"timeline\");return i&&null!=t.currentIndex&&(i.setCurrentIndex(t.currentIndex),!i.get(\"loop\",!0)&&i.isIndexMax()&&i.getPlayState()&&(i.setPlayState(!1),n.dispatchAction({type:\"timelinePlayChange\",playState:!1,from:t.from}))),e.resetOption(\"timeline\",{replaceMerge:i.get(\"replaceMerge\",!0)}),P({currentIndex:i.option.currentIndex},t)}),t.registerAction({type:\"timelinePlayChange\",event:\"timelinePlayChanged\",update:\"update\"},function(t,e){var n=e.getComponent(\"timeline\");n&&null!=t.playState&&n.setPlayState(t.playState)})}(t),t.registerPreprocessor(CX)}),gb(function(t){t.registerComponentModel(RX),t.registerComponentView(XX),t.registerPreprocessor(function(t){kX(t.series,\"markPoint\")&&(t.markPoint=t.markPoint||{})})}),gb(function(t){t.registerComponentModel(ZX),t.registerComponentView(tZ),t.registerPreprocessor(function(t){kX(t.series,\"markLine\")&&(t.markLine=t.markLine||{})})}),gb(function(t){t.registerComponentModel(eZ),t.registerComponentView(uZ),t.registerPreprocessor(function(t){kX(t.series,\"markArea\")&&(t.markArea=t.markArea||{})})}),gb(function(t){gb(bZ),gb(DZ)}),gb(function(t){gb(VZ),gb(jZ)}),gb(VZ),gb(jZ),gb(function(t){gb(Dj),gb(Oj)}),gb(Dj),gb(Oj),gb(function(t){t.registerComponentModel(Nj),t.registerComponentView(Ej)}),gb(function(t){t.registerPreprocessor(Wj),t.registerVisual(t.PRIORITY.VISUAL.ARIA,Hj)}),gb(function(t){t.registerTransform(eq),t.registerTransform(nq)}),gb(function(t){t.registerComponentModel(iq),t.registerComponentView(rq)}),gb(function(t){t.registerUpdateLifecycle(\"series:beforeupdate\",function(t,e,n){B(_a(n.seriesTransition),function(t){B(_a(t.to),function(t){for(var e=n.updatedSeries,i=0;i<e.length;i++)(null!=t.seriesIndex&&t.seriesIndex===e[i].seriesIndex||null!=t.seriesId&&t.seriesId===e[i].id)&&(e[i][Fy]=!0)})})}),t.registerUpdateLifecycle(\"series:transition\",function(t,e,n){var i=Vq(e);if(i.oldSeries&&n.updatedSeries&&n.optionChanged){var r=n.seriesTransition;if(r)B(_a(r),function(t){!function(t,e,n,i){var r=[],o=[];B(_a(t.from),function(t){var n=Kq(e.oldSeries,t);n>=0&&r.push({dataGroupId:e.oldDataGroupIds[n],data:e.oldData[n],divide:qq(e.oldData[n]),groupIdDim:t.dimension})}),B(_a(t.to),function(t){var i=Kq(n.updatedSeries,t);if(i>=0){var r=n.updatedSeries[i].getData();o.push({dataGroupId:e.oldDataGroupIds[i],data:r,divide:qq(r),groupIdDim:t.dimension})}}),r.length>0&&o.length>0&&Xq(r,o,i)}(t,i,n,e)});else{var o=function(t,e){var n=mt(),i=mt(),r=mt();return B(t.oldSeries,function(e,n){var o=t.oldDataGroupIds[n],a=t.oldData[n],s=Zq(e),l=jq(s);i.set(l,{dataGroupId:o,data:a}),X(s)&&B(s,function(t){r.set(t,{key:l,dataGroupId:o,data:a})})}),B(e.updatedSeries,function(t){if(t.isUniversalTransitionEnabled()&&t.isAnimationEnabled()){var e=t.get(\"dataGroupId\"),o=t.getData(),a=Zq(t),s=jq(a),l=i.get(s);if(l)n.set(s,{oldSeries:[{dataGroupId:l.dataGroupId,divide:qq(l.data),data:l.data}],newSeries:[{dataGroupId:e,divide:qq(o),data:o}]});else if(X(a)){var u=[];B(a,function(t){var e=i.get(t);e.data&&u.push({dataGroupId:e.dataGroupId,divide:qq(e.data),data:e.data})}),u.length&&n.set(s,{oldSeries:u,newSeries:[{dataGroupId:e,data:o,divide:qq(o)}]})}else{var c=r.get(a);if(c){var h=n.get(c.key);h||(h={oldSeries:[{dataGroupId:c.dataGroupId,data:c.data,divide:qq(c.data)}],newSeries:[]},n.set(c.key,h)),h.newSeries.push({dataGroupId:e,data:o,divide:qq(o)})}}}}),n}(i,n);B(o.keys(),function(t){var n=o.get(t);Xq(n.oldSeries,n.newSeries,e)})}B(n.updatedSeries,function(t){t[Fy]&&(t[Fy]=!1)})}for(var a=t.getSeries(),s=i.oldSeries=[],l=i.oldDataGroupIds=[],u=i.oldData=[],c=0;c<a.length;c++){var h=a[c].getData();h.count()<1e4&&(s.push(a[c]),l.push(a[c].get(\"dataGroupId\")),u.push(h))}})}),gb(function(t){t.registerUpdateLifecycle(\"series:beforeupdate\",function(t,e,n){var i=vI(e).labelManager;i||(i=vI(e).labelManager=new gI),i.clearLabels()}),t.registerUpdateLifecycle(\"series:layoutlabels\",function(t,e,n){var i=vI(e).labelManager;B(n.updatedSeries,function(t){i.addLabelsOfSeries(e.getViewOfSeriesModel(t))}),i.updateLayoutConfig(e),i.layout(e),i.processLabelsOverall()})}),gb(function(t){!function(t){function e(t,e){var n=[],i=Ra(e,t);function r(e,r){B(i[e],function(e){B(e.updateAxisBreaks(t).breaks,function(t){var i;n.push(P(((i={})[r]=e.componentIndex,i),t))})})}return r(\"xAxisModels\",\"xAxisIndex\"),r(\"yAxisModels\",\"yAxisIndex\"),r(\"singleAxisModels\",\"singleAxisIndex\"),{eventBreaks:n}}t.registerAction($C,e),t.registerAction(JC,e),t.registerAction(QC,e)}(t),dK(),mK()}),gb(function(){ok=xK}),gb(function(t){t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,Fk())}),t.Axis=SM,t.ChartView=tm,t.ComponentModel=lg,t.ComponentView=Ky,t.List=Hb,t.Model=kp,t.PRIORITY=t_,t.SeriesModel=Hy,t.color=Di,t.connect=function(t){if(X(t)){var e=t;t=null,B(e,function(e){null!=e.group&&(t=e.group)}),t=t||\"g_\"+X_++,B(e,function(e){e.group=t})}return U_[t]=!0,t},t.dataTool={},t.dependencies={zrender:\"6.1.0\"},t.disConnect=q_,t.disconnect=j_,t.dispose=function(t){j(t)?t=W_[t]:t instanceof L_||(t=K_(t)),t instanceof L_&&!t.isDisposed()&&t.dispose()},t.env=r,t.extendChartView=function(t){var e=tm.extend(t);return tm.registerClass(e),e},t.extendComponentModel=function(t){var e=lg.extend(t);return lg.registerClass(e),e},t.extendComponentView=function(t){var e=Ky.extend(t);return Ky.registerClass(e),e},t.extendSeriesModel=function(t){var e=Hy.extend(t);return Hy.registerClass(e),e},t.format=nM,t.getCoordinateSystemDimensions=function(t){var e=Vf.get(t);if(e)return e.getDimensionsInfo?e.getDimensionsInfo():e.dimensions.slice()},t.getInstanceByDom=K_,t.getInstanceById=function(t){return W_[t]},t.getMap=function(t){var e=nx(\"getMap\");return e&&e(t)},t.graphic=eM,t.helper=VS,t.init=function(t,e,n){var i=!(n&&n.ssr);if(i){0;var r=K_(t);if(r)return r;0}var o=new L_(t,e,n);return o.id=\"ec_\"+Y_++,W_[o.id]=o,i&&Ga(t,Z_,o.id),I_(o),tx.trigger(\"afterinit\",o),o},t.innerDrawElementOnCanvas=Fx,t.matrix=Le,t.number=QS,t.parseGeoJSON=JS,t.parseGeoJson=JS,t.registerAction=ib,t.registerCoordinateSystem=rb,t.registerCustomSeries=function(t,e){rx(t,e)},t.registerLayout=ob,t.registerLoading=ub,t.registerLocale=Gp,t.registerMap=cb,t.registerPostInit=tb,t.registerPostUpdate=eb,t.registerPreprocessor=J_,t.registerProcessor=Q_,t.registerTheme=$_,t.registerTransform=hb,t.registerUpdateLifecycle=nb,t.registerVisual=ab,t.setCanvasCreator=function(t){h({createCanvas:t})},t.setPlatformAPI=h,t.throttle=um,t.time=tM,t.use=gb,t.util=iM,t.vector=jt,t.version=\"6.1.0\",t.zrUtil=Tt,t.zrender=Io,Object.defineProperty(t,\"__esModule\",{value:!0})});\n"}}
{"timestamp":"2026-07-16T17:45:51.531Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_MuxM3BI6oQwJz9M2xMrHxTnF","output":"Chunk ID: 7998bd\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1660\nOutput:\ntests/test_apple_health_analytics_v2.py\ntests/test_dashboard_v5_capture_sprint5c.py\ntests/browser/dashboard_v5_record_standard_6e1.spec.js\ntests/browser/dashboard_v5_explorer_6c.spec.js\ntests/browser/dashboard_v5_api_session.spec.js\ntests/browser/dashboard_v5_record_6e.spec.js\ntests/browser/dashboard_v5_calendar_day_6d.spec.js\ntests/browser/dashboard_v5_record_original_6e4b.spec.js\ntests/browser/dashboard_v5_sprint6g_echarts.spec.js\ntests/browser/dashboard_v5.spec.js\ntests/browser/run_v5_record_6e1_focused.sh\ntests/browser/dashboard_v5_echarts_prototype.spec.js\ntests/browser/run_v5_isolated_matrix.sh\ntests/fixtures/dashboard_v5_fixture.py\ntests/fixtures/dashboard_v5_original_fixture.py\ntests/test_multimodal_correlations_v1.py\ntests/test_sprint2_integration_safety.py\ntests/test_health_dashboard_v5.py\ntests/test_dashboard_v5_sprint6d_day_calendar.py\ntests/test_dashboard_v5_sprint6b_api.py\ntests/test_dashboard_v5_sprint6a.py\ntests/test_health_system_migrate_sprint2.py\ntests/test_dashboard_v5_sprint6e_record.py\ntests/test_dashboard_v5_sprint6e4a_recovery.py\ntests/test_dashboard_v5_sprint6e3_profile.py\ntests/test_dashboard_v5_sprint6e4b.py\ntests/test_sprint0_safety.py\ntests/test_health_symptom_quick_add.py\ntests/test_dashboard_v5_explorer_sprint5b.py\ntests/test_dashboard_v5_sprint6b1_integration.py\ntests/test_dashboard_v5_sprint6b_catalog.py\ntests/test_dashboard_v5_sprint6c_api_contracts.py\ntests/test_dashboard_v5_sprint6g_nutrition.py\ntests/test_health_dashboard_v4.py\ntests/test_dashboard_v5_sprint6e1_original.py\ntests/test_sprint4b_correlation_contract.py\nplaywright.config.js\npackage.json\ndeploy/systemd/health-dashboard-action-worker.path\ndeploy/systemd/health-dashboard-action-worker.service\ndeploy/systemd/health-dashboard.service\nREADME.md\nskills/health-data-management/SKILL.md\nskills/health-data-management/references/drive-workflow.md\nskills/health-data-management/references/labor-report-generation.md\nskills/health-data-management/references/friday-db-schema-compat.md\nskills/health-data-management/references/gog-email-attachments.md\nskills/health-data-management/references/apple-health-auto-export.md\nskills/health-data-management/references/document-classification.md\nskills/health-data-management/references/docling-reprocessing-workflow.md\nskills/health-data-management/references/health-manager-pitfalls.md\ndatabase/integrity_check.txt\ndatabase/schema.sql\ndatabase/schema_manifest.json\nscripts/health/apple_health_analytics.py\nscripts/health/health_dashboard_action_worker.py\nscripts/health/health_add_event_period.py\nscripts/health/health_dashboard_v3_migrate.py\nscripts/health/apple_health_drive_sync.py\nscripts/health/generate_doctor_report.py\nscripts/health/document_fts_migrate.py\nscripts/health/init_db.py\nscripts/health/health_watchdog.sh\nscripts/health/import_existing_docs.py\nscripts/health/health_dashboard_v4.py\nscripts/health/health_dashboard_server.py\nscripts/health/health_watchdog.service\nscripts/health/apple_health_import.py\nscripts/health/health_watchdog.py\nscripts/health/process_all_health_documents.py\nscripts/health/health_manager.py\nscripts/health/assets/health-assets/dashboard-v5-echarts-prototype.js\nscripts/health/assets/health-assets/dashboard-v5-day-controller.js\nscripts/health/assets/health-assets/dashboard-v5-api-explorer.js\nscripts/health/assets/health-assets/echarts-6.1.0.min.js\nscripts/health/assets/health-assets/dashboard-v5.js\nscripts/health/assets/health-assets/dashboard-v5-record.js\nscripts/health/assets/health-assets/FULLCALENDAR-6.1.21-LICENSE.md\nscripts/health/assets/health-assets/dashboard-v5.css\nscripts/health/assets/health-assets/fullcalendar-6.1.21.metadata.json\nscripts/health/assets/health-assets/ECHARTS-6.1.0-LICENSE-D3.txt\nscripts/health/assets/health-assets/fullcalendar-6.1.21.min.js\nscripts/health/assets/health-assets/chart.umd.min.js\nscripts/health/assets/health-assets/dashboard-v5-global-search.js\nscripts/health/assets/health-assets/dashboard-v5-range.js\nscripts/health/assets/health-assets/dashboard-v5-echarts.js\nscripts/health/assets/health-assets/ECHARTS-6.1.0-LICENSE.txt\nscripts/health/assets/health-assets/ECHARTS-6.1.0-NOTICE.txt\nscripts/health/assets/health-assets/echarts-6.1.0.metadata.json\nscripts/health/generate_health_data_quality_report.py\nscripts/health/generate_labor_report.py\nscripts/health/health_system_migrate.py\nscripts/health/analyze_document.py\nscripts/health/multimodal_correlations.py\nscripts/health/link_lab_values_to_documents.py\nscripts/health/health_pipeline.py\nscripts/health/health_dashboard_v5.py\nscripts/health/verify_system.py\nscripts/health/test_pdf.py\nscripts/health/dashboard_v5/__init__.py\nscripts/health/dashboard_v5/data_provider.py\nscripts/health/dashboard_v5/document_originals.py\nscripts/health/dashboard_v5/contracts.py\nscripts/health/dashboard_v5/read_api.py\nscripts/health/dashboard_v5/document_chunks.py\nscripts/health/dashboard_v5/render.py\nscripts/health/dashboard_v5/metric_registry.py\nscripts/health/dashboard_v5/lab_registry.py\nscripts/health/dashboard_v5/metric_catalog_v2.py\nscripts/health/dashboard_v5/apple_identifier_report.py\nscripts/health/yazio_nutrition_sync.py\nscripts/health/health_dashboard_v3.py\nscripts/health/health_symptom_quick_add.py\nscripts/cron/apple_health_daily_sync.py\nscripts/cron/yazio_sync.py\nscripts/cron/health_weekly_report.py\nscripts/cron/health_daily_sync.py\nscripts/cron/weekly_health_drive_backup.py\nscripts/cron/morning_briefing.py\nTHIRD_PARTY_NOTICES.md\ndocs/sprint5b-dashboard-v5-explorer.md\ndocs/sprint5c-dashboard-v5-mobile-capture.md\ndocs/sprint6e3-preview-deployment.md\ndocs/adr/0002-document-search.md\ndocs/adr/0003-dietary-inflammation-model.md\ndocs/adr/0001-dashboard-v5-chart-engine.md\ndocs/dashboard_v5_architecture_target.md\ndocs/generated/apple-health-identifier-inventory-v1.json\ndocs/sprint6a-dashboard-v5-foundation-echarts.md\ndocs/dashboard-v5-acceptance-matrix.md\ndocs/sprint6e4a-preview-recovery.md\ndocs/sprint1-apple-health-analytics-v2.md\ndocs/architecture.md\ndocs/sprint6e4b-record-calendar-day-ui.md\ndocs/sprint0-safety.md\ndocs/sprint6e-health-record-document-search.md\ndocs/health_intelligence_workflow.md\ndocs/sprint3-dashboard-v4.md\ndocs/sprint6e2-record-correctness.md\ndocs/dashboard-v5-preview-profile.md\ndocs/sprint6e1-health-record-hardening.md\ndocs/sprint4-dashboard-v5.md\ndocs/sprint2-multimodal-correlations-v1.md\ndocs/sprint6b-metric-catalog-read-api.md\ndocs/sprint6d-calendar-day-drilldown.md\ndocs/sprint5-dashboard-v5-desktop-first.md\ndocs/source/healthmanager-v5-repo-audit-zielbild-sprint6.docx\ndocs/source/healthmanager-v5-repo-audit-zielbild-sprint6.md\ndocs/dashboard_v4_v5_parity.md\npackage-lock.json\n"}}
{"timestamp":"2026-07-16T17:45:51.531Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":13013,"cached_input_tokens":2432,"output_tokens":274,"reasoning_output_tokens":0,"total_tokens":13287},"last_token_usage":{"input_tokens":13013,"cached_input_tokens":2432,"output_tokens":274,"reasoning_output_tokens":0,"total_tokens":13287},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:45:53.487Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRjRJKyO3jLTrgXyXZenIEbsI1GAxpflykNxPJXOyenpfiOc0KGcttphGkcaGM6KWOCER0dRw5GYuuFqHFSfjkpR5YRtSUp2J9-_sNUj9TRArFUxBJnKyjDF1FMRsovAzEChV-hAKmbA95CbMfaqUOO2n1CuxUh49be4Vnf1QxY3vcrN1Run2CiJgqYiy9AAMv8cMlfK6caAWaQn3ww50RIwkNvxrDBgDZuLgblJKw7OCqFR7yozESHDENX6ovYSxALR2zuauY1pzBEgoBL_9zT5oMGdWOAaUXJW9FUN6rIvRnQbMo5p_kZN5wyKYxVPIid0-Nn2Z2bnRW-CjRNxHWPAZXL8T-J4MnpZqNM5I-XIQAuUfqiVzci4lXnBZw41U3IaIscr3Uay3twRbtTatE2QQjoaloFAmYEHSLYpuVp5bP9eDl4GmqTwtYKLRNOOXeBMPttrlh6govW_5sCtgaFokRbScUnAHY4L9YgoUAc1kqKttIcFSGryI0JCrwR-W6gJSx4J_ho8wk3u7h_yZgkz_U0e1e4ePvz21hHfvx30im2pbNyEZXdbp3CxwWfTk0WUb-LMsD9AzU3VW9KfYXshDnCyt8xFKBycq6PUbYSchaLXA2PfFDgpLno4CWNd76NoNp1ZcMbEmudhn8hpY0nqmoN53SSNrpClYIcPto9pCJXxp7oqhwBo4dZjpplRm--wxelK-14qbgCSoB8AlAu9ZSnIkWPu0iSsqfr3d9rs3eyq8aBUJg1ATXcOnejRfZeb3mSyv3TyJLSEyiOZwHJuInRBJdoK45wFPfo0z2LI3ZCxgBsltDScI-L7Lw-ZWm0HtGxAYmHcyynft7iO0VwKkG_2JRWv23ITdC-rpzjjeahbiIb5uvph-HpfCN-K4I8x3fZ-ZKkJjsNGb5j2yyphrh_Gmx6WEklhu4le0HHQ394MJzQo8u0qvS7O-Q-SqTHAVDtfjy6IAUc9z6-8t-CqWRYZqrtaf8FqdcsucecfOtw="}}
{"timestamp":"2026-07-16T17:45:59.275Z","type":"event_msg","payload":{"type":"agent_message","message":"The current change set is concentrated in V5 dashboard code, the worker, schema, fixture, and new Sprint 6G tests/docs. I’m reading the exact implementation now so the fixes line up with existing contracts instead of adding a parallel path.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:45:59.277Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The current change set is concentrated in V5 dashboard code, the worker, schema, fixture, and new Sprint 6G tests/docs. I’m reading the exact implementation now so the fixes line up with existing contracts instead of adding a parallel path."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:45:59.277Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,260p' tests/test_dashboard_v5_sprint6g_nutrition.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_lqcUs0Q1QeYlqdnfDmdFNX2v"}}
{"timestamp":"2026-07-16T17:45:59.277Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,320p' scripts/health/health_dashboard_action_worker.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_SnGgbopdVTTBRP0SXWFJn7oH"}}
{"timestamp":"2026-07-16T17:45:59.277Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,320p' scripts/health/dashboard_v5/data_provider.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_hKzD1c8q90uplykoIGvxUCSI"}}
{"timestamp":"2026-07-16T17:45:59.288Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,300p' scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_EACs7yk2Qx4YpZGHRgtT2QwG"}}
{"timestamp":"2026-07-16T17:45:59.361Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_lqcUs0Q1QeYlqdnfDmdFNX2v","output":"Chunk ID: 840956\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1176\nOutput:\nfrom __future__ import annotations\n\nimport json\nimport sqlite3\nimport importlib.util\nfrom datetime import date\nfrom pathlib import Path\n\nimport pytest\n\nROOT = Path(__file__).resolve().parents[1]\n\nimport sys\n\nsys.path.insert(0, str(ROOT / \"scripts\" / \"health\"))\n\nfrom dashboard_v5 import read_api  # noqa: E402\nfrom dashboard_v5.read_api import dispatch_api  # noqa: E402\nfrom dashboard_v5.data_provider import build_bundle, connect  # noqa: E402\nfrom dashboard_v5.render import render  # noqa: E402\nfixture_spec = importlib.util.spec_from_file_location(\n    \"dashboard_v5_fixture\", ROOT / \"tests\" / \"fixtures\" / \"dashboard_v5_fixture.py\"\n)\nassert fixture_spec and fixture_spec.loader\nfixture_module = importlib.util.module_from_spec(fixture_spec)\nfixture_spec.loader.exec_module(fixture_module)\nbuild_dashboard_v5_fixture = fixture_module.build_dashboard_v5_fixture\n\n\nTODAY = date(2026, 6, 15)\n\n\n@pytest.fixture()\ndef fixture_db(tmp_path: Path) -> Path:\n    db = tmp_path / \"fixture.db\"\n    build_dashboard_v5_fixture(db, anchor_date=TODAY)\n    return db\n\n\ndef test_health_record_v5_uses_one_local_echarts_and_no_chartjs_or_svg_sparklines(fixture_db: Path) -> None:\n    connection = connect(fixture_db)\n    try:\n        bundle = build_bundle(connection, today=TODAY.isoformat())\n    finally:\n        connection.close()\n    html = render(bundle, health_record_6e=True)\n\n    assert html.count(\"/health-assets/echarts-6.1.0.min.js\") == 1\n    assert \"/health-assets/dashboard-v5-echarts.js\" in html\n    assert \"chart.umd.min.js\" not in html\n    assert \"<canvas id='cockpit-chart'\" not in html\n    assert \"<canvas id='nutrition-chart'\" not in html\n    assert \"<svg\" not in html\n    assert \"cockpit-chart-table\" in html\n    assert \"nutrition-chart-table\" in html\n\n\ndef test_nutrition_read_apis_are_bounded_allowlisted_and_incomplete_not_zero(\n    fixture_db: Path, monkeypatch: pytest.MonkeyPatch\n) -> None:\n    monkeypatch.setattr(read_api, \"local_today\", lambda: TODAY)\n\n    days = dispatch_api(\n        fixture_db,\n        \"/api/v1/nutrition/days\",\n        \"from=2026-06-01&to=2026-06-15\",\n    )\n    assert days[\"definition\"][\"insufficient_inputs\"] == \"no_index\"\n    assert days[\"days\"][-1][\"histamine\"][\"score\"] is None\n    assert days[\"days\"][-1][\"histamine\"][\"status\"] == \"incomplete\"\n\n    detail = dispatch_api(fixture_db, \"/api/v1/nutrition/day/2026-06-14\", \"\")\n    assert detail[\"coverage\"][\"status\"] == \"incomplete\"\n    assert detail[\"coverage\"][\"insufficient_inputs\"] == \"no_index\"\n    assert set(detail[\"nutrients\"]) <= set(read_api.NUTRIENT_ALLOWLIST)\n    assert all(\"id\" not in item and \"raw\" not in json.dumps(item).casefold() for item in detail[\"items\"])\n    serialized = json.dumps(detail, ensure_ascii=False).casefold()\n    assert not any(forbidden in serialized for forbidden in (\"raw_json\", \"source_product_id\", \"http://\", \"https://\", \"/home/\", \"/tmp/\", \".hermes\"))\n\n    queue = dispatch_api(fixture_db, \"/api/v1/nutrition/mapping-queue\", \"status=open\")\n    assert queue[\"items\"][0][\"queue_key\"] == \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\"\n    assert \"source_product_id\" not in json.dumps(queue)\n\n\ndef test_mapping_worker_is_transactional_idempotent_and_recomputes_only_affected_days(\n    fixture_db: Path, monkeypatch: pytest.MonkeyPatch\n) -> None:\n    import health_dashboard_action_worker as worker\n\n    monkeypatch.setattr(worker, \"DASHBOARD_DB\", fixture_db)\n    monkeypatch.setattr(worker, \"DASHBOARD_V5_FILE\", None)\n    payload = {\n        \"version\": 1,\n        \"action\": \"nutrition_mapping\",\n        \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n        \"canonical_food\": \"synthetic reviewed food\",\n        \"sighi_score\": 1,\n        \"traffic_light\": \"yellow\",\n        \"mapping_scope\": \"canonical\",\n        \"personal_tolerance\": \"unknown\",\n    }\n\n    worker.apply_mapping_action(payload, \"a\" * 32)\n    worker.apply_mapping_action(payload, \"a\" * 32)\n\n    connection = sqlite3.connect(fixture_db)\n    connection.row_factory = sqlite3.Row\n    try:\n        day = connection.execute(\"SELECT histamine_unknown_count,histamine_score,histamine_label FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n        assert dict(day) == {\"histamine_unknown_count\": 0, \"histamine_score\": 1.0, \"histamine_label\": \"yellow\"}\n        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 1\n        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n        assert connection.execute(\"SELECT COUNT(*) FROM personal_food_tolerance\").fetchone()[0] == 0\n    finally:\n        connection.close()\n"}}
{"timestamp":"2026-07-16T17:45:59.361Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_SnGgbopdVTTBRP0SXWFJn7oH","output":"Chunk ID: 2e66c7\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3005\nOutput:\n#!/usr/bin/env python3\n\"\"\"Consume locally queued Dashboard actions outside the network-facing service.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport os\nimport re\nimport stat\nimport subprocess\nimport sys\nimport sqlite3\nimport hashlib\nfrom datetime import date, datetime\nfrom pathlib import Path\nfrom typing import Any\nfrom zoneinfo import ZoneInfo\n\nBASE = Path.home() / \".hermes\" / \"assets\" / \"Gesundheit\"\nDEFAULT_DASHBOARD_DB = (BASE / \"health_data.db\").resolve()\nDASHBOARD_DB = (\n    Path(os.environ[\"HEALTH_DASHBOARD_DB\"]).resolve()\n    if os.environ.get(\"HEALTH_DASHBOARD_DB\")\n    else None\n)\nACTION_INBOX = Path(\n    os.environ.get(\n        \"HEALTH_DASHBOARD_ACTION_INBOX\",\n        str(BASE / \"runtime\" / \"dashboard-actions\"),\n    )\n).resolve()\nDASHBOARD_FILE = Path(\n    os.environ.get(\n        \"HEALTH_DASHBOARD_FILE\",\n        str(BASE / \"reports\" / \"health_dashboard_v4.html\"),\n    )\n).resolve()\nDASHBOARD_V5_FILE = (\n    Path(os.environ[\"HEALTH_DASHBOARD_V5_FILE\"]).resolve()\n    if os.environ.get(\"HEALTH_DASHBOARD_V5_FILE\")\n    else None\n)\nSCRIPT_DIR = Path(__file__).resolve().parent\nQUICK_ADD = SCRIPT_DIR / \"health_symptom_quick_add.py\"\nDASHBOARD_V4 = SCRIPT_DIR / \"health_dashboard_v4.py\"\nDASHBOARD_V5 = SCRIPT_DIR / \"health_dashboard_v5.py\"\nFIELDS = (\"aphthen\", \"gi\", \"fatigue\", \"skin\", \"eyes\", \"joints\", \"vascular\")\nNAME_RE = re.compile(r\"[0-9a-f]{32}\\.json\")\nLOCAL_TIMEZONE = ZoneInfo(\"Europe/Zurich\")\nV5_PROFILE_DEFAULT = \"default\"\nV5_PROFILE_HEALTH_RECORD_6E = \"health-record-6e\"\nALLOWED_V5_PROFILES = frozenset({V5_PROFILE_DEFAULT, V5_PROFILE_HEALTH_RECORD_6E})\n\n\ndef parse_v5_profile(raw: str | None) -> str:\n    \"\"\"Return the one allowed regeneration profile or fail before any mutation.\"\"\"\n    value = V5_PROFILE_DEFAULT if raw is None or raw == \"\" else raw\n    if value not in ALLOWED_V5_PROFILES:\n        raise RuntimeError(\"invalid HEALTH_DASHBOARD_V5_PROFILE\")\n    return value\n\n\nDASHBOARD_V5_PROFILE = parse_v5_profile(os.environ.get(\"HEALTH_DASHBOARD_V5_PROFILE\"))\n\n\ndef local_today() -> date:\n    return datetime.now(LOCAL_TIMEZONE).date()\n\n\ndef validate_symptom_payload(payload: dict[str, Any]) -> dict[str, Any]:\n    if set(payload) != {\n        \"version\",\n        \"action\",\n        \"date\",\n        \"scores\",\n        \"notes\",\n    }:\n        raise ValueError(\"invalid payload shape\")\n    if payload[\"version\"] != 1 or payload[\"action\"] != \"symptom_checkin\":\n        raise ValueError(\"unsupported action\")\n    try:\n        day = date.fromisoformat(payload[\"date\"])\n    except (TypeError, ValueError) as exc:\n        raise ValueError(\"invalid date\") from exc\n    if day.year < 2000 or day > local_today():\n        raise ValueError(\"date outside allowed range\")\n    scores = payload[\"scores\"]\n    if not isinstance(scores, dict) or set(scores) != set(FIELDS):\n        raise ValueError(\"incomplete scores\")\n    if any(\n        type(scores[field]) is not int or scores[field] not in range(4)\n        for field in FIELDS\n    ):\n        raise ValueError(\"invalid score\")\n    notes = payload[\"notes\"]\n    if not isinstance(notes, str) or len(notes) > 300:\n        raise ValueError(\"invalid notes\")\n    return {\n        \"version\": 1,\n        \"action\": \"symptom_checkin\",\n        \"date\": day.isoformat(),\n        \"scores\": {field: scores[field] for field in FIELDS},\n        \"notes\": notes.strip(),\n    }\n\n\ndef validate_mapping_payload(payload: dict[str, Any]) -> dict[str, Any]:\n    if set(payload) != {\n        \"version\",\n        \"action\",\n        \"queue_key\",\n        \"canonical_food\",\n        \"sighi_score\",\n        \"traffic_light\",\n        \"mapping_scope\",\n        \"personal_tolerance\",\n    }:\n        raise ValueError(\"invalid mapping payload shape\")\n    if payload[\"version\"] != 1 or payload[\"action\"] != \"nutrition_mapping\":\n        raise ValueError(\"unsupported action\")\n    queue_key = payload[\"queue_key\"]\n    canonical = \" \".join(str(payload[\"canonical_food\"] or \"\").split())\n    score = payload[\"sighi_score\"]\n    light = payload[\"traffic_light\"]\n    scope = payload[\"mapping_scope\"]\n    tolerance = payload[\"personal_tolerance\"]\n    if not isinstance(queue_key, str) or not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n        raise ValueError(\"invalid queue key\")\n    if not isinstance(canonical, str) or len(canonical) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical, re.I):\n        raise ValueError(\"invalid canonical food\")\n    if score is not None and (type(score) is not int or score not in range(4)):\n        raise ValueError(\"invalid score\")\n    if light not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"} or scope != \"canonical\" or tolerance not in {\"unknown\", \"safe\", \"problematic\", \"unclear\"}:\n        raise ValueError(\"invalid mapping value\")\n    if score is None and light != \"unknown\":\n        raise ValueError(\"unknown score must keep unknown light\")\n    if score is not None and not canonical:\n        raise ValueError(\"canonical food required\")\n    return {\n        \"version\": 1,\n        \"action\": \"nutrition_mapping\",\n        \"queue_key\": queue_key,\n        \"canonical_food\": canonical,\n        \"sighi_score\": score,\n        \"traffic_light\": light,\n        \"mapping_scope\": scope,\n        \"personal_tolerance\": tolerance,\n    }\n\n\ndef validate_payload(payload: Any) -> dict[str, Any]:\n    if not isinstance(payload, dict):\n        raise ValueError(\"invalid payload shape\")\n    action = payload.get(\"action\")\n    if action == \"symptom_checkin\":\n        return validate_symptom_payload(payload)\n    if action == \"nutrition_mapping\":\n        return validate_mapping_payload(payload)\n    raise ValueError(\"unsupported action\")\n\n\ndef reject_duplicate_object_pairs(pairs: list[tuple[str, object]]) -> dict[str, object]:\n    result: dict[str, object] = {}\n    for key, value in pairs:\n        if key in result:\n            raise ValueError(\"duplicate JSON key\")\n        result[key] = value\n    return result\n\n\ndef ensure_private_inbox() -> None:\n    ACTION_INBOX.mkdir(mode=0o700, parents=True, exist_ok=True)\n    metadata = ACTION_INBOX.lstat()\n    if not stat.S_ISDIR(metadata.st_mode) or metadata.st_uid != os.getuid():\n        raise OSError(\"action inbox is not a private owned directory\")\n    os.chmod(ACTION_INBOX, 0o700)\n\n\ndef load_action(path: Path) -> dict[str, Any]:\n    if not NAME_RE.fullmatch(path.name):\n        raise ValueError(\"invalid filename\")\n    descriptor = os.open(path, os.O_RDONLY | os.O_NOFOLLOW)\n    try:\n        metadata = os.fstat(descriptor)\n        if (\n            not stat.S_ISREG(metadata.st_mode)\n            or metadata.st_uid != os.getuid()\n            or stat.S_IMODE(metadata.st_mode) != 0o600\n        ):\n            raise ValueError(\"invalid action file permissions\")\n        if metadata.st_size > 4096:\n            raise ValueError(\"invalid action file\")\n        raw = os.read(descriptor, 4097)\n    finally:\n        os.close(descriptor)\n    if len(raw) > 4096:\n        raise ValueError(\"action too large\")\n    return validate_payload(\n        json.loads(raw.decode(\"utf-8\"), object_pairs_hook=reject_duplicate_object_pairs)\n    )\n\n\ndef apply_symptom_action(day: str, scores: dict[str, int], notes: str) -> None:\n    if DASHBOARD_DB is None:\n        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\n    command = [\n        sys.executable,\n        str(QUICK_ADD),\n        \"--db\",\n        str(DASHBOARD_DB),\n        \"--date\",\n        day,\n        \"--notes\",\n        notes,\n    ]\n    for field in FIELDS:\n        command.extend((f\"--{field}\", str(scores[field])))\n    subprocess.run(command, check=True, timeout=120, capture_output=True, text=True)\n    if DASHBOARD_DB == DEFAULT_DASHBOARD_DB:\n        subprocess.run(\n            [sys.executable, str(DASHBOARD_V4), \"--output\", str(DASHBOARD_FILE)],\n            check=True,\n            timeout=120,\n            capture_output=True,\n            text=True,\n        )\n\n\ndef _slug(value: str) -> str:\n    normalized = value.casefold().translate(str.maketrans({\"ä\": \"ae\", \"ö\": \"oe\", \"ü\": \"ue\", \"ß\": \"ss\"}))\n    return re.sub(r\"[^a-z0-9]+\", \"_\", normalized).strip(\"_\")\n\n\ndef _score_label(max_score: int, unknown: int, load: float) -> str:\n    if max_score >= 3:\n        return \"red\"\n    if max_score >= 2 or load >= 4:\n        return \"orange\"\n    if max_score == 1 or load >= 1:\n        return \"yellow\"\n    if unknown:\n        return \"unknown\"\n    return \"green\"\n\n\ndef ensure_nutrition_mapping_schema(connection: sqlite3.Connection) -> None:\n    connection.executescript(\n        \"\"\"\n        CREATE TABLE IF NOT EXISTS nutrition_mapping_action_log (\n            action_id TEXT PRIMARY KEY,\n            action_hash TEXT NOT NULL,\n            queue_key TEXT NOT NULL,\n            applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n        );\n        CREATE TABLE IF NOT EXISTS nutrition_mapping_provenance (\n            id INTEGER PRIMARY KEY AUTOINCREMENT,\n            action_id TEXT NOT NULL,\n            normalized_name TEXT NOT NULL,\n            canonical_food TEXT,\n            sighi_score INTEGER,\n            traffic_light TEXT NOT NULL,\n            mapping_scope TEXT NOT NULL,\n            affected_day TEXT NOT NULL,\n            applied_at TEXT DEFAULT CURRENT_TIMESTAMP,\n            UNIQUE(action_id, affected_day)\n        );\n        CREATE TABLE IF NOT EXISTS nutrition_composite_product_review (\n            normalized_name TEXT PRIMARY KEY,\n            canonical_food TEXT,\n            status TEXT NOT NULL DEFAULT 'needs_ingredient_review',\n            updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n        );\n        \"\"\"\n    )\n\n\ndef recompute_nutrition_day(connection: sqlite3.Connection, day: str) -> None:\n    rows = list(\n        connection.execute(\n            \"\"\"SELECT i.meal,i.kcal,i.protein_g,i.carb_g,i.fat_g,h.sighi_score\n               FROM nutrition_items i LEFT JOIN nutrition_histamine_scores h ON h.item_id=i.id\n               WHERE i.datum=?\"\"\",\n            (day,),\n        )\n    )\n    connection.execute(\"DELETE FROM nutrition_meal_summary WHERE datum=?\", (day,))\n    if not rows:\n        connection.execute(\"DELETE FROM nutrition_daily_summary_v2 WHERE datum=?\", (day,))\n        return\n    totals = {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []}\n    by_meal: dict[str, dict[str, Any]] = {}\n    for row in rows:\n        meal = row[\"meal\"] or \"unknown\"\n        bucket = by_meal.setdefault(meal, {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []})\n        for target in (bucket, totals):\n            target[\"items\"] += 1\n            target[\"kcal\"] += float(row[\"kcal\"] or 0)\n            target[\"protein\"] += float(row[\"protein_g\"] or 0)\n            target[\"carb\"] += float(row[\"carb_g\"] or 0)\n            target[\"fat\"] += float(row[\"fat_g\"] or 0)\n            if row[\"sighi_score\"] is None:\n                target[\"unknown\"] += 1\n            else:\n                score = int(row[\"sighi_score\"])\n                target[\"scores\"].append(score)\n                target[\"load\"] += score\n    for meal, bucket in by_meal.items():\n        max_score = max(bucket[\"scores\"] or [0])\n        label = _score_label(max_score, int(bucket[\"unknown\"]), float(bucket[\"load\"]))\n        connection.execute(\n            \"\"\"INSERT OR REPLACE INTO nutrition_meal_summary\n               (datum,meal,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_label,updated_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n            (day, meal, round(bucket[\"kcal\"], 1), round(bucket[\"protein\"], 1), round(bucket[\"carb\"], 1), round(bucket[\"fat\"], 1), int(bucket[\"items\"]), \"{}\", round(bucket[\"load\"], 1), label),\n        )\n    max_score = max(totals[\"scores\"] or [0])\n    label = _score_label(max_score, int(totals[\"unknown\"]), float(totals[\"load\"]))\n    connection.execute(\n        \"\"\"INSERT OR REPLACE INTO nutrition_daily_summary_v2\n           (datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_max,histamine_unknown_count,histamine_label,updated_at)\n"}}
{"timestamp":"2026-07-16T17:45:59.361Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_hKzD1c8q90uplykoIGvxUCSI","output":"Chunk ID: 3dbda0\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3675\nOutput:\n\"\"\"Read-only, allowlisted data provider for Dashboard v5.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport re\nimport sqlite3\nfrom collections import defaultdict\nfrom datetime import date, datetime, timedelta, timezone\nfrom pathlib import Path\nfrom statistics import median\nfrom typing import Any\n\nfrom apple_health_analytics import daily_series as apple_daily\nfrom dashboard_v5.contracts import SCHEMA_VERSION\nfrom dashboard_v5.lab_registry import EXACT_LAB_NUMBER, LAB_ALLOWLIST\nfrom dashboard_v5.metric_registry import BY_ID, public_explorer, public_registry\n\nTZ = \"Europe/Zurich\"\nSYMPTOM_LABELS = {\n    \"Aphthen/Mundulzera\", \"GI/Darm\", \"Müdigkeit/Fatigue\", \"Haut\", \"Augen\",\n    \"Gelenke\", \"Vaskulär/Thrombose-Warnzeichen\",\n}\nAPPLE_SPECS = {\n    \"apple.sleep\": (\"sleep_analysis\", \"sum\"),\n    \"apple.resting_heart_rate\": (\"resting_heart_rate\", \"avg\"),\n    \"apple.hrv\": (\"heart_rate_variability\", \"avg\"),\n    \"apple.steps\": (\"step_count\", \"sum\"),\n}\nADMINISTERED = {\n    \"administered\", \"verabreicht\",\n    \"erste injektion / therapiebeginn\", \"planmäßige injektion\", \"planmässige injektion\", \"planmaessige injektion\",\n}\nEXPLICIT_PLANS = {\"planned\", \"scheduled\", \"geplant\"}\nEXCLUDED_PLANS = {\"cancelled\", \"canceled\", \"abgesagt\", \"missed\", \"verpasst\"}\nPUBLIC_TEXT = re.compile(r\"^[^\\x00-\\x1f\\x7f]{1,160}$\")\nIDENTIFIER = re.compile(r\"^[a-z0-9_:.-]+$\")\n\n\ndef public_text(value: Any, maximum: int = 120, *, allow_empty: bool = False) -> str | None:\n    text = \" \".join(str(value or \"\").split())\n    if (not text and not allow_empty) or len(text) > maximum or not PUBLIC_TEXT.fullmatch(text or \" \"):\n        return None\n    if re.search(r\"^(?:/|~/|[A-Za-z]:[\\\\/]|file:|https?://)|(?:^|/)\\.hermes(?:/|$)\", text, re.I):\n        return None\n    return text\n\n\ndef connect(path: Path) -> sqlite3.Connection:\n    connection = sqlite3.connect(f\"file:{path}?mode=ro\", uri=True)\n    connection.row_factory = sqlite3.Row\n    return connection\n\n\ndef table_exists(connection: sqlite3.Connection, name: str) -> bool:\n    return connection.execute(\n        \"SELECT 1 FROM sqlite_master WHERE type='table' AND name=?\", (name,)\n    ).fetchone() is not None\n\n\ndef slug(value: Any) -> str:\n    normalized = str(value or \"\").casefold().translate(\n        str.maketrans({\"ä\": \"ae\", \"ö\": \"oe\", \"ü\": \"ue\", \"ß\": \"ss\"})\n    )\n    return re.sub(r\"[^a-z0-9]+\", \"_\", normalized).strip(\"_\")\n\n\ndef unit_slug(value: Any) -> str:\n    return slug(str(value or \"\").replace(\"µ\", \"u\").replace(\"μ\", \"u\").replace(\"%\", \" percent \"))\n\n\ndef parse_day(value: Any) -> str | None:\n    text = str(value or \"\").strip()\n    if not re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", text):\n        return None\n    try:\n        return date.fromisoformat(text).isoformat()\n    except ValueError:\n        return None\n\n\ndef parse_score(value: Any) -> int | None:\n    match = re.search(r\"(?:^|\\()([0-3])\\)?\\s*$\", str(value or \"\").strip())\n    return int(match.group(1)) if match else None\n\n\ndef rolling_baseline(points: list[dict[str, Any]], days: int, minimum: int = 7) -> list[dict[str, Any]]:\n    observed = {date.fromisoformat(p[\"date\"]): float(p[\"value\"]) for p in points if p[\"value\"] is not None}\n    result: list[dict[str, Any]] = []\n    for point in points:\n        current = date.fromisoformat(point[\"date\"])\n        start = current - timedelta(days=days)\n        values = [value for day, value in observed.items() if start <= day < current]\n        result.append({\"date\": point[\"date\"], \"value\": round(median(values), 3) if len(values) >= minimum else None})\n    return result\n\n\ndef symptom_series(connection: sqlite3.Connection) -> tuple[list[dict[str, Any]], set[str]]:\n    if not table_exists(connection, \"symptom_log\"):\n        return [], set()\n    grouped: dict[str, dict[str, list[int | None]]] = defaultdict(lambda: defaultdict(list))\n    for row in connection.execute(\n        \"SELECT datum,symptom,schwergrad FROM symptom_log WHERE kontext='daily_quick_score' ORDER BY datum,id\"\n    ):\n        day = parse_day(row[\"datum\"])\n        score = parse_score(row[\"schwergrad\"])\n        label = str(row[\"symptom\"] or \"\")\n        if day and label in SYMPTOM_LABELS:\n            grouped[day][label].append(score)\n    complete = {\n        day\n        for day, values in grouped.items()\n        if set(values) == SYMPTOM_LABELS\n        and all(len(scores) == 1 and scores[0] is not None for scores in values.values())\n    }\n    return [\n        {\"date\": day, \"value\": sum(score for scores in grouped[day].values() for score in scores if score is not None), \"quality\": \"complete\"}\n        for day in sorted(complete)\n    ], complete\n\n\ndef apple_series(connection: sqlite3.Connection, metric: str, mode: str) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"apple_health_records\"):\n        return []\n    rows = list(connection.execute(\n        \"\"\"SELECT id,metric,value,unit,start_date,end_date,source_name,file_name,file_hash\n           FROM apple_health_records WHERE metric=? AND value IS NOT NULL AND start_date IS NOT NULL\n           ORDER BY start_date,end_date,source_name,id\"\"\", (metric,)\n    ))\n    values = apple_daily(metric, mode, _rows=rows)\n    return [{\"date\": day, \"value\": value, \"quality\": \"observed\"} for day, value in sorted(values.items())]\n\n\ndef medication_data(connection: sqlite3.Connection, today: str) -> tuple[dict[str, Any], list[dict[str, Any]]]:\n    if not table_exists(connection, \"medication_administrations\"):\n        return {\"last_administered\": None, \"next_planned\": None}, []\n    rows = list(connection.execute(\n        \"SELECT datum,medication_name,dose,event_type,scheduled_next_date FROM medication_administrations ORDER BY datum\"\n    ))\n    actual = [\n        row for row in rows\n        if str(row[\"event_type\"] or \"\").strip().casefold() in ADMINISTERED\n        and (administered_day := parse_day(row[\"datum\"])) is not None\n        and administered_day <= today\n    ]\n    last = actual[-1] if actual else None\n    cancelled_plans = {\n        (str(row[\"medication_name\"] or \"\").strip().casefold(), day)\n        for row in rows\n        if str(row[\"event_type\"] or \"\").strip().casefold() in EXCLUDED_PLANS\n        for day in (parse_day(row[\"datum\"]), parse_day(row[\"scheduled_next_date\"]))\n        if day\n    }\n    candidates = []\n    for row in rows:\n        event_type = str(row[\"event_type\"] or \"\").strip().casefold()\n        medication_key = str(row[\"medication_name\"] or \"\").strip().casefold()\n        scheduled = parse_day(row[\"scheduled_next_date\"])\n        explicit_day = parse_day(row[\"datum\"])\n        planned = None\n        if (\n            scheduled\n            and event_type in ADMINISTERED\n            and explicit_day is not None\n            and explicit_day <= today\n        ):\n            planned = scheduled\n        elif event_type in EXPLICIT_PLANS:\n            planned = explicit_day\n        if planned and planned > today and (medication_key, planned) not in cancelled_plans:\n            candidates.append((planned, row))\n    next_item = min(candidates, key=lambda item: item[0]) if candidates else None\n    def public_medication(row: sqlite3.Row | None, day: str | None, *, planned: bool) -> dict[str, Any] | None:\n        if row is None or day is None:\n            return None\n        name = public_text(row[\"medication_name\"], 120)\n        dose = public_text(row[\"dose\"], 40, allow_empty=True)\n        if name is None or dose is None:\n            return None\n        item: dict[str, Any] = {\"date\": day, \"name\": name, \"dose\": dose}\n        if planned:\n            item[\"status\"] = \"planned\"\n        return item\n\n    medication = {\n        \"last_administered\": public_medication(last, parse_day(last[\"datum\"]) if last else None, planned=False),\n        \"next_planned\": public_medication(next_item[1], next_item[0], planned=True) if next_item else None,\n    }\n    events = []\n    for row in actual:\n        label = public_text(row[\"medication_name\"], 120)\n        day = parse_day(row[\"datum\"])\n        if label and day:\n            events.append({\"date\": day, \"type\": \"medication_administered\", \"label\": label})\n    return medication, events\n\n\ndef verified_labs(\n    connection: sqlite3.Connection,\n    limit: int = 12,\n    *,\n    through_day: str | None = None,\n) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"laborwerte\"):\n        return []\n    rows = list(connection.execute(\n        \"\"\"SELECT parameter_name,wert,einheit,reference_min,reference_max,abnahme_datum,befund_datum,\n                  reference_range_source,source_type\n           FROM laborwerte\n           WHERE lower(trim(COALESCE(validierungsstatus,'')))='validiert'\n             AND verified_against_original=1\n             AND reference_range_source='scanned_original'\n             AND trim(COALESCE(einheit,''))<>''\n           ORDER BY id\"\"\"\n    ))\n    candidates: dict[tuple[str, str, str], list[sqlite3.Row]] = defaultdict(list)\n    for row in rows:\n        raw_key = (slug(row[\"parameter_name\"]), unit_slug(row[\"einheit\"]))\n        canonical = LAB_ALLOWLIST.get(raw_key)\n        day = parse_day(row[\"abnahme_datum\"]) or parse_day(row[\"befund_datum\"])\n        if canonical and day and (through_day is None or day <= through_day):\n            canonical_parameter, canonical_unit = canonical\n            candidates[(canonical_parameter, canonical_unit, day)].append(row)\n    result = []\n    for (canonical_parameter, canonical_unit, day), same_day in candidates.items():\n        if len(same_day) != 1:\n            continue\n        row = same_day[0]\n        if not EXACT_LAB_NUMBER.fullmatch(str(row[\"wert\"] or \"\").strip()):\n            continue\n        result.append({\n            \"parameter\": canonical_parameter, \"value\": str(row[\"wert\"]).strip(),\n            \"unit\": canonical_unit, \"date\": day,\n            \"reference_min\": row[\"reference_min\"], \"reference_max\": row[\"reference_max\"],\n            \"quality\": \"verified_original\", \"source_type\": \"scanned_original\",\n        })\n    result.sort(key=lambda item: item[\"date\"], reverse=True)\n    return result[:limit]\n\n\ndef nutrition_data(connection: sqlite3.Connection) -> dict[str, Any]:\n    daily: list[dict[str, Any]] = []\n    if table_exists(connection, \"nutrition_daily_summary_v2\"):\n        for row in connection.execute(\"SELECT * FROM nutrition_daily_summary_v2 ORDER BY datum DESC LIMIT 180\"):\n            day = parse_day(row[\"datum\"])\n            complete = (\n                day is not None and row[\"item_count\"] is not None and int(row[\"item_count\"]) > 0\n                and row[\"histamine_unknown_count\"] is not None and int(row[\"histamine_unknown_count\"]) == 0\n                and row[\"histamine_score\"] is not None\n                and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n            )\n            if day:\n                daily.append({\n                    \"date\": day, \"kcal\": row[\"kcal\"], \"item_count\": row[\"item_count\"],\n                    \"histamine_score\": row[\"histamine_score\"] if complete else None,\n                    \"histamine_status\": str(row[\"histamine_label\"]) if complete else \"unknown\",\n                    \"unknown_items\": row[\"histamine_unknown_count\"], \"quality\": \"complete\" if complete else \"incomplete\",\n                })\n    review = []\n    if table_exists(connection, \"nutrition_review_queue\"):\n        for row in connection.execute(\"SELECT normalized_name,example_name,occurrence_count,suggested_canonical_food,suggested_score FROM nutrition_review_queue WHERE status='open' ORDER BY occurrence_count DESC,last_seen DESC LIMIT 25\"):\n            name = public_text(row[\"example_name\"], 160)\n            if name:\n                queue_key = slug(row[\"normalized_name\"])\n                suggested = public_text(row[\"suggested_canonical_food\"], 120, allow_empty=True)\n                score = row[\"suggested_score\"] if row[\"suggested_score\"] in (0, 1, 2, 3) else None\n                review.append({\n                    \"queue_key\": queue_key,\n                    \"name\": name,\n                    \"occurrences\": int(row[\"occurrence_count\"] or 0),\n                    \"reason\": \"Zuordnung fehlt\",\n                    \"suggested_canonical_food\": suggested or \"\",\n                    \"suggested_score\": score if score is not None else \"unknown\",\n                    \"suggested_traffic_light\": \"unknown\",\n                })\n    return {\"daily\": list(reversed(daily)), \"mapping_review\": review, \"mapping_open_count\": len(review)}\n\n\ndef correlations(connection: sqlite3.Connection) -> dict[str, Any]:\n    dimensions = {\n        \"lag_days\": [0, 1, 2, 3],\n        \"medication_phases\": [\"baseline_pre_treatment\", \"early_treatment\", \"stable_treatment\", \"unknown\"],\n        \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\n    }\n    if not table_exists(connection, \"multimodal_correlation_results\"):\n        return {\"dimensions\": dimensions, \"results\": []}\n\n    import multimodal_correlations as engine\n\n    results = []\n    rows = connection.execute(\n        \"\"\"SELECT predictor,target,lag_days,medication_phase,n,eligible_target_days,\n                  expected_target_days,target_coverage,missing_pairs,rho,p_value,q_value,\n                  status,method,quality_flags,interpretation\n           FROM multimodal_correlation_results\n           ORDER BY predictor,target,lag_days,medication_phase LIMIT 1000\"\"\"\n    )\n    for row in rows:\n        try:\n            flags_raw = json.loads(str(row[\"quality_flags\"]))\n            if not isinstance(flags_raw, list) or not all(isinstance(flag, str) for flag in flags_raw):\n                continue\n            result = engine.CorrelationResult(\n                predictor=str(row[\"predictor\"]), target=str(row[\"target\"]),\n                lag_days=int(row[\"lag_days\"]), medication_phase=str(row[\"medication_phase\"]),\n                n=int(row[\"n\"]), eligible_target_days=int(row[\"eligible_target_days\"]),\n                expected_target_days=int(row[\"expected_target_days\"]),\n                target_coverage=float(row[\"target_coverage\"]), missing_pairs=int(row[\"missing_pairs\"]),\n                rho=row[\"rho\"], p_value=row[\"p_value\"], q_value=row[\"q_value\"],\n                status=str(row[\"status\"]), method=str(row[\"method\"]),\n                quality_flags=tuple(flags_raw), interpretation=str(row[\"interpretation\"]),\n            )\n            engine._validate_result_for_storage(result)\n        except (TypeError, ValueError, json.JSONDecodeError):\n            continue\n        results.append({\n            \"id\": {\"predictor\": result.predictor, \"target\": result.target},\n            \"dimensions\": {\"lag_days\": result.lag_days, \"medication_phase\": result.medication_phase},\n            \"sample\": {\n                \"eligible_target_days\": result.eligible_target_days,\n                \"expected_target_days\": result.expected_target_days,\n"}}
{"timestamp":"2026-07-16T17:45:59.362Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_EACs7yk2Qx4YpZGHRgtT2QwG","output":"Chunk ID: 1f4da0\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 2442\nOutput:\n\"\"\"Bounded read-only data API provider for Dashboard v5 Sprint 6B.\"\"\"\n\nfrom __future__ import annotations\n\nimport base64\nimport hashlib\nimport hmac\nimport json\nimport math\nimport re\nimport secrets\nimport sqlite3\nimport time\nfrom statistics import median\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom datetime import date, datetime, timedelta\nfrom pathlib import Path\nfrom typing import Any\nfrom urllib.parse import parse_qs\nfrom zoneinfo import ZoneInfo\n\nfrom apple_health_analytics import daily_points\nfrom dashboard_v5.data_provider import (\n    ADMINISTERED,\n    SYMPTOM_LABELS,\n    parse_day,\n    parse_score,\n    public_text,\n)\nfrom dashboard_v5.document_chunks import (\n    decode_chunk_cursor,\n    document_chunks,\n    encode_chunk_cursor,\n)\nfrom dashboard_v5.document_originals import probe_original\nfrom dashboard_v5.lab_registry import EXACT_LAB_NUMBER, LAB_ALLOWLIST\nfrom dashboard_v5.metric_catalog_v2 import (\n    APPLE_ANALYTICS_UNITS,\n    APPLE_SOURCE_SPECS,\n    BY_ID_V2,\n    LAB_CATALOG_SPECS,\n    MetricV2,\n    SourceInventoryLimitError,\n    catalog_search,\n    inventory_sources,\n    public_catalog,\n)\n\nTZ_NAME = \"Europe/Zurich\"\nLOCAL_TZ = ZoneInfo(TZ_NAME)\nMAX_RANGE_DAYS = 3660\nMAX_SERIES_ROWS = 3660\nMAX_RAW_APPLE_ROWS = 100000\nMAX_RAW_LAB_ROWS = 10000\nMAX_EVENT_SOURCE_ROWS = 10000\nMAX_DAILY_SOURCE_ROWS = 30000\nMAX_EVENT_ROWS = 1000\nMAX_LAB_ROWS = 100\nMAX_SEARCH_ROWS_PER_GROUP = 20\nMAX_CALENDAR_DAYS = 62\nMAX_NUTRITION_DAYS = 180\nMAX_NUTRITION_ITEMS = 200\nMAX_NUTRITION_QUEUE = 50\nMAX_FUTURE_DAYS = 366\nMAX_QUERY_BYTES = 512\nQUERY_TIMEOUT_SECONDS = 1.0\nALLOWED_RESOLUTIONS = frozenset({\"day\", \"week\"})\nALLOWED_EVENT_TYPES = frozenset(\n    {\"medication_administered\", \"symptom_day\", \"health_event\", \"health_period\"}\n)\nNUTRIENT_ALLOWLIST = {\n    \"energy.energy\": (\"Energie\", \"kcal\"),\n    \"nutrient.protein\": (\"Protein\", \"g\"),\n    \"nutrient.carb\": (\"Kohlenhydrate\", \"g\"),\n    \"nutrient.fat\": (\"Fett\", \"g\"),\n    \"nutrient.fiber\": (\"Ballaststoffe\", \"g\"),\n    \"nutrient.sugar\": (\"Zucker\", \"g\"),\n    \"nutrient.salt\": (\"Salz\", \"g\"),\n    \"nutrient.sodium\": (\"Natrium\", \"mg\"),\n    \"nutrient.vitamin_c\": (\"Vitamin C\", \"mg\"),\n    \"nutrient.calcium\": (\"Calcium\", \"mg\"),\n    \"nutrient.iron\": (\"Eisen\", \"mg\"),\n    \"nutrient.magnesium\": (\"Magnesium\", \"mg\"),\n}\nMEAL_LABELS = {\n    \"breakfast\": \"Frühstück\",\n    \"lunch\": \"Mittagessen\",\n    \"dinner\": \"Abendessen\",\n    \"snack\": \"Snack\",\n    \"unknown\": \"Unbekannt\",\n}\nFORBIDDEN_METADATA_TEXT = re.compile(\n    r\"(?:/home/|/tmp/|/Users/|/(?:var|etc|usr|opt|srv|root|run|mnt|media|private)/|\"\n    r\"~/|file:|https?://|[A-Za-z]:[\\\\/]|\"\n    r\"(?:^|[\\s:|])/(?:[^/\\s]+/)+[^/\\s]+|\\.\\.[\\\\/]|\\\\\\\\[^\\\\/\\s]+[\\\\/]|\"\n    r\"\\.hermes(?:/|$))\",\n    re.IGNORECASE,\n)\nDRIVE_ID_TOKEN = re.compile(r\"(?<![A-Za-z0-9_-])[A-Za-z0-9_-]{25,}(?![A-Za-z0-9_-])\")\nAPI_PATHS = frozenset(\n    {\n        \"/api/v1/metric-catalog\",\n        \"/api/v1/series\",\n        \"/api/v1/events\",\n        \"/api/v1/labs\",\n        \"/api/v1/calendar\",\n        \"/api/v1/search\",\n        \"/api/v1/nutrition/days\",\n        \"/api/v1/nutrition/mapping-queue\",\n    }\n)\nEXPECTED_ANALYTICS_UNITS = APPLE_ANALYTICS_UNITS\n\n\n@dataclass\nclass APIError(Exception):\n    status: int\n    code: str\n\n    def __str__(self) -> str:\n        return self.code\n\n\ndef safe_metadata_text(\n    value: Any, maximum: int, *, allow_empty: bool = False\n) -> str | None:\n    text = public_text(value, maximum, allow_empty=allow_empty)\n    drive_id_like = bool(\n        text\n        and any(\n            any(char.isalpha() for char in token)\n            and any(char.isdigit() for char in token)\n            for token in DRIVE_ID_TOKEN.findall(text)\n        )\n    )\n    if text is None or FORBIDDEN_METADATA_TEXT.search(text) or drive_id_like:\n        return None\n    return text\n\n\ndef api_document_id(\n    raw_id: Any, document_day: str | None, category: str, institution: str\n) -> str:\n    material = json.dumps(\n        [int(raw_id), document_day, category, institution],\n        ensure_ascii=False,\n        separators=(\",\", \":\"),\n    ).encode(\"utf-8\")\n    return f\"api-document-{hashlib.sha256(b'health-api-document-v1:' + material).hexdigest()[:24]}\"\n\n\ndef connect_read_only(database: Path) -> sqlite3.Connection:\n    connection = sqlite3.connect(f\"{database.resolve().as_uri()}?mode=ro\", uri=True)\n    connection.row_factory = sqlite3.Row\n    connection.execute(\"PRAGMA query_only=ON\")\n    connection.execute(\"PRAGMA trusted_schema=OFF\")\n    deadline = time.monotonic() + QUERY_TIMEOUT_SECONDS\n    connection.set_progress_handler(lambda: int(time.monotonic() > deadline), 1000)\n    return connection\n\n\ndef table_exists(connection: sqlite3.Connection, table: str) -> bool:\n    return (\n        connection.execute(\n            \"SELECT 1 FROM sqlite_master WHERE type='table' AND name=?\", (table,)\n        ).fetchone()\n        is not None\n    )\n\n\ndef parse_query(query: str, allowed: set[str]) -> dict[str, str]:\n    if len(query.encode(\"utf-8\")) > MAX_QUERY_BYTES:\n        raise APIError(414, \"query_too_large\")\n    try:\n        parsed = (\n            parse_qs(\n                query,\n                keep_blank_values=True,\n                strict_parsing=True,\n                max_num_fields=max(1, len(allowed) + 1),\n            )\n            if query\n            else {}\n        )\n    except ValueError as error:\n        raise APIError(400, \"invalid_query\") from error\n    if not set(parsed) <= allowed:\n        raise APIError(400, \"unknown_parameter\")\n    if any(len(values) != 1 for values in parsed.values()):\n        raise APIError(400, \"duplicate_parameter\")\n    return {key: values[0] for key, values in parsed.items()}\n\n\ndef parse_iso_day(raw: str, *, code: str = \"invalid_date\") -> date:\n    if not re.fullmatch(r\"\\d{4}-\\d{2}-\\d{2}\", raw):\n        raise APIError(400, code)\n    try:\n        return date.fromisoformat(raw)\n    except ValueError as error:\n        raise APIError(400, code) from error\n\n\ndef local_today() -> date:\n    return datetime.now(LOCAL_TZ).date()\n\n\ndef parse_range(\n    params: dict[str, str], *, required: bool = False\n) -> tuple[date | None, date | None]:\n    raw_start = params.get(\"from\")\n    raw_end = params.get(\"to\")\n    if bool(raw_start) != bool(raw_end):\n        raise APIError(400, \"incomplete_range\")\n    if required and not raw_start:\n        raise APIError(400, \"range_required\")\n    if not raw_start:\n        return None, None\n    start = parse_iso_day(raw_start)\n    end = parse_iso_day(raw_end or \"\")\n    if end < start:\n        raise APIError(400, \"range_reversed\")\n    if start > local_today() or end > local_today():\n        raise APIError(422, \"future_range_not_allowed\")\n    if (end - start).days + 1 > MAX_RANGE_DAYS:\n        raise APIError(422, \"range_too_large\")\n    return start, end\n\n\ndef _apple_rows(\n    connection: sqlite3.Connection,\n    identifier: str,\n    start: date | None,\n    end: date | None,\n    *,\n    include_raw_json: bool = False,\n) -> list[sqlite3.Row]:\n    if include_raw_json:\n        sql = \"\"\"SELECT id,metric,value,unit,start_date,end_date,source_name,\n                        file_name,file_hash,raw_json\n                 FROM apple_health_records WHERE metric=? AND start_date IS NOT NULL\"\"\"\n    else:\n        sql = \"\"\"SELECT id,metric,value,unit,start_date,end_date,source_name,file_name,file_hash\n                 FROM apple_health_records WHERE metric=? AND start_date IS NOT NULL\"\"\"\n    parameters: list[Any] = [identifier]\n    # Keep the (metric,start_date) index usable with a deliberately coarse raw\n    # candidate interval.  It includes one whole calendar day on either side;\n    # the upper edge is exclusive so all lexical ISO values on end + 1 are kept.\n    # daily_points remains the sole authority for Europe/Zurich assignment and\n    # exact start/end filtering—this SQL clause never assigns a health day.\n    if start is not None and start > date.min:\n        sql += \" AND start_date>=?\"\n        parameters.append((start - timedelta(days=1)).isoformat())\n    # date.max cannot represent the exclusive edge after end + 1.  Omitting\n    # that impossible upper bound preserves the exact canonical filter while\n    # avoiding an arithmetic overflow at the ISO boundary.\n    if end is not None and end < date.max - timedelta(days=1):\n        sql += \" AND start_date<?\"\n        parameters.append((end + timedelta(days=2)).isoformat())\n    sql += \" ORDER BY start_date,end_date,source_name,id LIMIT ?\"\n    parameters.append(MAX_RAW_APPLE_ROWS + 1)\n    rows = list(connection.execute(sql, parameters))\n    if len(rows) > MAX_RAW_APPLE_ROWS:\n        raise APIError(422, \"source_row_limit_exceeded\")\n    return rows\n\n\ndef _numeric(value: float, precision: int, value_type: str) -> int | float:\n    rounded = round(float(value), precision)\n    if value_type == \"integer\" or (precision == 0 and rounded.is_integer()):\n        return int(rounded)\n    return rounded\n\n\ndef _apple_points(\n    connection: sqlite3.Connection,\n    metric: MetricV2,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    rows = _apple_rows(connection, metric.source_identifier, start, end)\n    points = daily_points(\n        metric.source_identifier,\n        mode=metric.aggregation,\n        stable_only=False,\n        start_date=start.isoformat() if start else None,\n        end_date=end.isoformat() if end else None,\n        _rows=rows,\n    )\n    result = []\n    for day, point in sorted(points.items()):\n        if point.unit != EXPECTED_ANALYTICS_UNITS.get(metric.source_identifier):\n            continue\n        result.append(\n            {\n                \"date\": day,\n                \"value\": _numeric(point.value, metric.precision, metric.value_type),\n                \"quality\": point.quality,\n                \"source_class\": point.source_class,\n            }\n"}}
{"timestamp":"2026-07-16T17:45:59.362Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":40374,"cached_input_tokens":15104,"output_tokens":615,"reasoning_output_tokens":18,"total_tokens":40989},"last_token_usage":{"input_tokens":27361,"cached_input_tokens":12672,"output_tokens":341,"reasoning_output_tokens":18,"total_tokens":27702},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:46:05.706Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '320,700p' scripts/health/health_dashboard_action_worker.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}","call_id":"call_teZgShpuNOmZIwcsNwvP4HbW"}}
{"timestamp":"2026-07-16T17:46:05.715Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '300,760p' scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}","call_id":"call_mMGTxKm2uBErDsUEXLLpE7mm"}}
{"timestamp":"2026-07-16T17:46:05.715Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,300p' scripts/health/health_dashboard_server.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}","call_id":"call_vWYQPGxQM0VrJLmUUy6C7Kej"}}
{"timestamp":"2026-07-16T17:46:05.745Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,260p' scripts/health/dashboard_v5/contracts.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":25000}","call_id":"call_r6irc44sf5uEtrqJu4VbjAKv"}}
{"timestamp":"2026-07-16T17:46:05.772Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_teZgShpuNOmZIwcsNwvP4HbW","output":"Chunk ID: 54e684\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1720\nOutput:\n           (datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_max,histamine_unknown_count,histamine_label,updated_at)\n           VALUES(?,?,?,?,?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n        (day, round(totals[\"kcal\"], 1), round(totals[\"protein\"], 1), round(totals[\"carb\"], 1), round(totals[\"fat\"], 1), int(totals[\"items\"]), \"{}\", round(totals[\"load\"], 1), max_score, int(totals[\"unknown\"]), label),\n    )\n\n\ndef apply_mapping_action(payload: dict[str, Any], action_id: str) -> None:\n    if DASHBOARD_DB is None:\n        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\n    action_hash = hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(\",\", \":\")).encode(\"utf-8\")).hexdigest()\n    connection = sqlite3.connect(DASHBOARD_DB)\n    connection.row_factory = sqlite3.Row\n    try:\n        with connection:\n            ensure_nutrition_mapping_schema(connection)\n            existing = connection.execute(\"SELECT action_hash FROM nutrition_mapping_action_log WHERE action_id=?\", (action_id,)).fetchone()\n            if existing is not None:\n                if existing[\"action_hash\"] != action_hash:\n                    raise RuntimeError(\"conflicting idempotency key\")\n                return\n            queue = connection.execute(\"SELECT normalized_name,example_name FROM nutrition_review_queue WHERE status='open'\").fetchall()\n            matched = [row for row in queue if _slug(str(row[\"normalized_name\"])) == payload[\"queue_key\"]]\n            if len(matched) != 1:\n                raise RuntimeError(\"mapping queue item not found\")\n            normalized = str(matched[0][\"normalized_name\"])\n            canonical = payload[\"canonical_food\"]\n            score = payload[\"sighi_score\"]\n            light = payload[\"traffic_light\"]\n            affected = sorted({row[\"datum\"] for row in connection.execute(\"SELECT datum FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)) if row[\"datum\"]})\n            if len(affected) > 366:\n                raise RuntimeError(\"affected day limit exceeded\")\n            if re.search(r\"\\+|,|;|\\bmit\\b|\\bund\\b\", str(matched[0][\"example_name\"]), re.I):\n                connection.execute(\n                    \"\"\"INSERT OR REPLACE INTO nutrition_composite_product_review\n                       (normalized_name,canonical_food,status,updated_at)\n                       VALUES(?,?, 'needs_ingredient_review', CURRENT_TIMESTAMP)\"\"\",\n                    (normalized, canonical),\n                )\n                score = None\n                light = \"unknown\"\n            for row in connection.execute(\"SELECT id FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)):\n                connection.execute(\n                    \"\"\"INSERT OR REPLACE INTO nutrition_histamine_scores\n                       (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n                       VALUES(?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n                    (row[\"id\"], canonical or None, score, light, \"worker_mapping_v1\", \"medium\", \"local validated mapping action\"),\n                )\n            connection.execute(\"UPDATE nutrition_review_queue SET status='mapped',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n            if payload[\"personal_tolerance\"] != \"unknown\" and canonical:\n                connection.execute(\n                    \"\"\"INSERT INTO personal_food_tolerance(canonical_food,personal_status,evidence_level,notes,updated_at)\n                       VALUES(?,?, 'low', 'separate personal tolerance label from mapping action', CURRENT_TIMESTAMP)\n                       ON CONFLICT(canonical_food) DO UPDATE SET personal_status=excluded.personal_status,updated_at=CURRENT_TIMESTAMP\"\"\",\n                    (canonical, payload[\"personal_tolerance\"]),\n                )\n            for day in affected:\n                recompute_nutrition_day(connection, str(day))\n                connection.execute(\n                    \"\"\"INSERT OR IGNORE INTO nutrition_mapping_provenance\n                       (action_id,normalized_name,canonical_food,sighi_score,traffic_light,mapping_scope,affected_day)\n                       VALUES(?,?,?,?,?,?,?)\"\"\",\n                    (action_id, normalized, canonical or None, score, light, payload[\"mapping_scope\"], str(day)),\n                )\n            connection.execute(\n                \"INSERT INTO nutrition_mapping_action_log(action_id,action_hash,queue_key) VALUES(?,?,?)\",\n                (action_id, action_hash, payload[\"queue_key\"]),\n            )\n    finally:\n        connection.close()\n\n\ndef apply_action(payload: dict[str, Any] | str, action_id: str | dict[str, int], notes: str | None = None) -> None:\n    if isinstance(payload, str):\n        if not isinstance(action_id, dict) or notes is None:\n            raise ValueError(\"invalid legacy symptom action\")\n        normalized_payload: dict[str, Any] = {\n            \"action\": \"symptom_checkin\",\n            \"date\": payload,\n            \"scores\": action_id,\n            \"notes\": notes,\n        }\n        normalized_action_id = \"legacy-symptom-action\"\n    else:\n        normalized_payload = payload\n        if not isinstance(action_id, str):\n            raise ValueError(\"invalid action id\")\n        normalized_action_id = action_id\n    if normalized_payload[\"action\"] == \"symptom_checkin\":\n        apply_symptom_action(normalized_payload[\"date\"], normalized_payload[\"scores\"], normalized_payload[\"notes\"])\n    elif normalized_payload[\"action\"] == \"nutrition_mapping\":\n        apply_mapping_action(normalized_payload, normalized_action_id)\n    else:\n        raise ValueError(\"unsupported action\")\n    if DASHBOARD_V5_FILE is not None:\n        v5_command = [\n            sys.executable,\n            str(DASHBOARD_V5),\n            \"--db\",\n            str(DASHBOARD_DB),\n            \"--output\",\n            str(DASHBOARD_V5_FILE),\n            \"--today\",\n            local_today().isoformat(),\n        ]\n        if DASHBOARD_V5_PROFILE == V5_PROFILE_HEALTH_RECORD_6E:\n            v5_command.append(\"--health-record-6e\")\n        subprocess.run(\n            v5_command,\n            check=True,\n            timeout=120,\n            capture_output=True,\n            text=True,\n        )\n\n\ndef main() -> int:\n    if DASHBOARD_DB is None:\n        print(\"HEALTH_DASHBOARD_DB is required\", file=sys.stderr)\n        return 2\n    ensure_private_inbox()\n    failures = 0\n    for path in sorted(ACTION_INBOX.glob(\"*.json\")):\n        try:\n            payload = load_action(path)\n            apply_action(payload, path.stem)\n            path.unlink()\n        except Exception:\n            failures += 1\n            try:\n                path.replace(path.with_suffix(\".failed\"))\n            except OSError:\n                pass\n    return 1 if failures else 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"}}
{"timestamp":"2026-07-16T17:46:05.772Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_mMGTxKm2uBErDsUEXLLpE7mm","output":"Chunk ID: 893c69\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 4416\nOutput:\n            }\n        )\n    return result\n\n\ndef _blood_pressure_points(\n    connection: sqlite3.Connection,\n    metric: MetricV2,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    component = \"systolic\" if metric.id.endswith(\".systolic\") else \"diastolic\"\n    by_day: dict[str, tuple[datetime, float]] = {}\n    for row in _apple_rows(\n        connection, \"blood_pressure\", start, end, include_raw_json=True\n    ):\n        try:\n            timestamp = datetime.fromisoformat(\n                str(row[\"start_date\"]).replace(\"Z\", \"+00:00\")\n            )\n            if timestamp.tzinfo is None:\n                timestamp = timestamp.replace(tzinfo=LOCAL_TZ)\n            day = timestamp.astimezone(LOCAL_TZ).date()\n            payload = json.loads(str(row[\"raw_json\"] or \"\"))\n            unit = re.sub(r\"[^a-z]\", \"\", str(row[\"unit\"] or \"\").casefold())\n            systolic = float(payload[\"systolic\"])\n            diastolic = float(payload[\"diastolic\"])\n            if unit != \"mmhg\" or not all(\n                math.isfinite(item) for item in (systolic, diastolic)\n            ):\n                continue\n            value = systolic if component == \"systolic\" else diastolic\n        except (ValueError, TypeError, KeyError, json.JSONDecodeError):\n            continue\n        if not math.isfinite(value):\n            continue\n        if start is not None and day < start:\n            continue\n        if end is not None and day > end:\n            continue\n        existing = by_day.get(day.isoformat())\n        if existing is None or timestamp > existing[0]:\n            by_day[day.isoformat()] = (timestamp, value)\n    return [\n        {\n            \"date\": day,\n            \"value\": _numeric(item[1], metric.precision, metric.value_type),\n            \"quality\": \"paired_observation\",\n            \"source_class\": \"apple_health\",\n        }\n        for day, item in sorted(by_day.items())\n    ]\n\n\ndef _symptom_dimension_points(\n    connection: sqlite3.Connection,\n    metric: MetricV2,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    sql = \"SELECT datum,schwergrad FROM symptom_log WHERE kontext='daily_quick_score' AND symptom=?\"\n    parameters: list[Any] = [metric.source_identifier]\n    if start:\n        sql += \" AND datum>=?\"\n        parameters.append(start.isoformat())\n    if end:\n        sql += \" AND datum<=?\"\n        parameters.append(end.isoformat())\n    sql += \" ORDER BY datum,id LIMIT ?\"\n    parameters.append(MAX_DAILY_SOURCE_ROWS + 1)\n    rows = list(connection.execute(sql, parameters))\n    if len(rows) > MAX_DAILY_SOURCE_ROWS:\n        raise APIError(422, \"source_row_limit_exceeded\")\n    grouped: dict[str, list[int | None]] = defaultdict(list)\n    for row in rows:\n        day = parse_day(row[\"datum\"])\n        if day:\n            grouped[day].append(parse_score(row[\"schwergrad\"]))\n    return [\n        {\"date\": day, \"value\": scores[0], \"quality\": \"observed\"}\n        for day, scores in sorted(grouped.items())\n        if len(scores) == 1 and scores[0] is not None\n    ]\n\n\ndef _symptom_total_points(\n    connection: sqlite3.Connection,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    sql = \"SELECT datum,symptom,schwergrad FROM symptom_log WHERE kontext='daily_quick_score'\"\n    parameters: list[Any] = []\n    if start:\n        sql += \" AND datum>=?\"\n        parameters.append(start.isoformat())\n    if end:\n        sql += \" AND datum<=?\"\n        parameters.append(end.isoformat())\n    sql += \" ORDER BY datum,id LIMIT ?\"\n    parameters.append(MAX_DAILY_SOURCE_ROWS + 1)\n    rows = list(connection.execute(sql, parameters))\n    if len(rows) > MAX_DAILY_SOURCE_ROWS:\n        raise APIError(422, \"source_row_limit_exceeded\")\n    grouped: dict[str, dict[str, list[int | None]]] = defaultdict(\n        lambda: defaultdict(list)\n    )\n    for row in rows:\n        day = parse_day(row[\"datum\"])\n        label = str(row[\"symptom\"] or \"\")\n        if day and label in SYMPTOM_LABELS:\n            grouped[day][label].append(parse_score(row[\"schwergrad\"]))\n    result = []\n    for day, dimensions in sorted(grouped.items()):\n        if set(dimensions) != SYMPTOM_LABELS:\n            continue\n        if not all(\n            len(scores) == 1 and scores[0] is not None for scores in dimensions.values()\n        ):\n            continue\n        result.append(\n            {\n                \"date\": day,\n                \"value\": sum(int(scores[0]) for scores in dimensions.values()),\n                \"quality\": \"complete\",\n            }\n        )\n    return result\n\n\ndef _nutrition_points(\n    connection: sqlite3.Connection,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    sql = \"\"\"SELECT datum,histamine_score,item_count,histamine_unknown_count,histamine_label\n             FROM nutrition_daily_summary_v2 WHERE item_count>0\n               AND histamine_unknown_count=0 AND histamine_score IS NOT NULL\n               AND histamine_label IN ('green','yellow','orange','red')\"\"\"\n    parameters: list[Any] = []\n    if start:\n        sql += \" AND datum>=?\"\n        parameters.append(start.isoformat())\n    if end:\n        sql += \" AND datum<=?\"\n        parameters.append(end.isoformat())\n    sql += \" ORDER BY datum LIMIT ?\"\n    parameters.append(MAX_DAILY_SOURCE_ROWS + 1)\n    rows = list(connection.execute(sql, parameters))\n    if len(rows) > MAX_DAILY_SOURCE_ROWS:\n        raise APIError(422, \"source_row_limit_exceeded\")\n    result = []\n    for row in rows:\n        day = parse_day(row[\"datum\"])\n        try:\n            value = float(row[\"histamine_score\"])\n        except (TypeError, ValueError):\n            continue\n        if day and math.isfinite(value):\n            result.append({\"date\": day, \"value\": value, \"quality\": \"complete\"})\n    return result\n\n\ndef _lab_key(parameter: Any, unit: Any) -> tuple[str, str]:\n    def slug(value: Any) -> str:\n        normalized = (\n            str(value or \"\")\n            .casefold()\n            .replace(\"µ\", \"u\")\n            .replace(\"μ\", \"u\")\n            .replace(\"%\", \" percent \")\n        )\n        return re.sub(r\"[^a-z0-9]+\", \"_\", normalized).strip(\"_\")\n\n    return slug(parameter), slug(unit)\n\n\ndef _verified_lab_rows(\n    connection: sqlite3.Connection,\n    *,\n    include_missing_reference: bool = False,\n    start: date | None = None,\n    end: date | None = None,\n) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"laborwerte\"):\n        return []\n    date_expression = \"COALESCE(NULLIF(l.abnahme_datum,''),l.befund_datum)\"\n    filters = [\n        \"lower(trim(COALESCE(l.validierungsstatus,'')))='validiert'\",\n        \"l.verified_against_original=1\",\n        \"l.reference_range_source='scanned_original'\",\n        \"trim(COALESCE(l.einheit,''))<>''\",\n    ]\n    values: list[Any] = []\n    if start:\n        filters.append(f\"{date_expression}>=?\")\n        values.append(start.isoformat())\n    if end:\n        filters.append(f\"{date_expression}<=?\")\n        values.append(end.isoformat())\n    rows = list(\n        connection.execute(\n            \"\"\"SELECT l.id,l.parameter_name,l.wert,l.einheit,l.reference_min,l.reference_max,\n                  l.abnahme_datum,l.befund_datum,l.reference_range_source,l.source_type,\n                  l.canonical_document_id,d.document_date,d.kategorie,d.institution,d.review_status\n               FROM laborwerte l LEFT JOIN dokumente d ON d.id=l.canonical_document_id\n               WHERE \"\"\"\n            + \" AND \".join(filters)\n            + f\" ORDER BY {date_expression},l.id LIMIT ?\",\n            (*values, MAX_RAW_LAB_ROWS + 1),\n        )\n    )\n    if len(rows) > MAX_RAW_LAB_ROWS:\n        raise APIError(422, \"source_row_limit_exceeded\")\n    grouped: dict[tuple[str, str, str], list[sqlite3.Row]] = defaultdict(list)\n    for row in rows:\n        canonical = LAB_ALLOWLIST.get(_lab_key(row[\"parameter_name\"], row[\"einheit\"]))\n        day = parse_day(row[\"abnahme_datum\"]) or parse_day(row[\"befund_datum\"])\n        if canonical and day and date.fromisoformat(day) <= local_today():\n            grouped[(canonical[0], canonical[1], day)].append(row)\n    result: list[dict[str, Any]] = []\n    for (parameter, unit, day), same_day in sorted(grouped.items()):\n        if len(same_day) != 1:\n            continue\n        row = same_day[0]\n        raw_value = (\"\" if row[\"wert\"] is None else str(row[\"wert\"])).strip()\n        if not EXACT_LAB_NUMBER.fullmatch(raw_value):\n            continue\n        numeric = float(raw_value.replace(\",\", \".\"))\n        if not math.isfinite(numeric):\n            continue\n        value: int | float = int(numeric) if numeric.is_integer() else numeric\n        reference_min = str(row[\"reference_min\"] or \"\").strip()\n        reference_max = str(row[\"reference_max\"] or \"\").strip()\n        reference_min = (\n            reference_min\n            if EXACT_LAB_NUMBER.fullmatch(reference_min)\n            and math.isfinite(float(reference_min.replace(\",\", \".\")))\n            else None\n        )\n        reference_max = (\n            reference_max\n            if EXACT_LAB_NUMBER.fullmatch(reference_max)\n            and math.isfinite(float(reference_max.replace(\",\", \".\")))\n            else None\n        )\n        item: dict[str, Any] = {\n            \"id\": f\"lab-observation-{int(row['id'])}\",\n            \"metric_id\": LAB_CATALOG_SPECS[parameter].metric_id,\n            \"parameter\": parameter,\n            \"value\": value,\n            \"unit\": unit,\n            \"date\": day,\n            \"quality\": \"verified_original\",\n        }\n        if reference_min is None and reference_max is None:\n            if not include_missing_reference:\n                continue\n            item[\"reference_status\"] = \"missing_reference\"\n        else:\n            item[\"reference\"] = {\n                \"min\": reference_min,\n                \"max\": reference_max,\n                \"source\": \"scanned_original\",\n            }\n            item[\"reference_status\"] = \"verified_observation_specific\"\n        if (\n            row[\"canonical_document_id\"] is not None\n            and row[\"review_status\"] == \"geprueft\"\n        ):\n            category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\n            institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\n            document_day = parse_day(row[\"document_date\"])\n            if category is not None and institution is not None:\n                item[\"document\"] = {\n                    \"id\": api_document_id(\n                        row[\"canonical_document_id\"],\n                        document_day,\n                        category,\n                        institution,\n                    ),\n                    \"date\": document_day,\n                    \"category\": category,\n                    \"institution\": institution,\n                }\n        result.append(item)\n    return result\n\n\ndef _metric_points(\n    connection: sqlite3.Connection,\n    metric: MetricV2,\n    start: date | None,\n    end: date | None,\n) -> list[dict[str, Any]]:\n    if metric.value_type == \"panel\":\n        raise APIError(400, \"metric_not_series\")\n    if metric.source == \"apple_health\":\n        if metric.source_identifier == \"blood_pressure\":\n            return _blood_pressure_points(connection, metric, start, end)\n        return _apple_points(connection, metric, start, end)\n    if metric.id == \"symptom.total\":\n        return _symptom_total_points(connection, start, end)\n    if metric.id == \"symptom.aphthae\":\n        return _symptom_dimension_points(connection, metric, start, end)\n    if metric.id == \"nutrition.histamine\":\n        return _nutrition_points(connection, start, end)\n    if metric.source == \"laborwerte\":\n        return [\n            {\n                key: row[key]\n                for key in (\"date\", \"value\", \"quality\", \"reference\", \"reference_status\")\n                if key in row\n            }\n            for row in _verified_lab_rows(connection, include_missing_reference=True)\n            if row[\"metric_id\"] == metric.id\n            and (start is None or row[\"date\"] >= start.isoformat())\n            and (end is None or row[\"date\"] <= end.isoformat())\n        ]\n    return []\n\n\ndef _weekly_rule(metric: MetricV2) -> dict[str, Any]:\n    if metric.aggregation == \"observation\" or metric.value_type == \"panel\":\n        return {\n            \"id\": \"unsupported_for_metric\",\n            \"minimum_observations\": None,\n            \"requires_complete_calendar_week\": False,\n        }\n    if metric.expected_frequency == \"daily\":\n        return {\n            \"id\": \"complete_calendar_week_daily_metric\",\n            \"minimum_observations\": 7,\n            \"requires_complete_calendar_week\": True,\n        }\n    if metric.expected_frequency == \"intermittent\":\n        return {\n            \"id\": \"observed_measurements_within_iso_week\",\n            \"minimum_observations\": 1,\n            \"requires_complete_calendar_week\": False,\n        }\n    return {\n        \"id\": \"unsupported_for_metric\",\n        \"minimum_observations\": None,\n        \"requires_complete_calendar_week\": False,\n    }\n\n\ndef _add_past_only_baselines(points: list[dict[str, Any]], metric: MetricV2) -> None:\n    rule = metric.baseline_rule\n    if not rule:\n        return\n    window = int(rule[\"window_days\"])\n    minimum = int(rule[\"minimum_observations\"])\n    for point in points:\n        day = date.fromisoformat(point[\"date\"])\n        prior = [\n            float(candidate[\"value\"])\n            for candidate in points\n            if date.fromisoformat(candidate[\"date\"]) < day\n            and (day - date.fromisoformat(candidate[\"date\"])).days <= window\n        ]\n        point[\"baseline\"] = (\n            _numeric(float(median(prior)), metric.precision, metric.value_type)\n            if len(prior) >= minimum\n            else None\n        )\n\n\ndef _weekly(\n    points: list[dict[str, Any]], metric: MetricV2, rule: dict[str, Any]\n) -> list[dict[str, Any]]:\n    if rule[\"id\"] == \"unsupported_for_metric\":\n        return []\n    grouped: dict[tuple[int, int], list[dict[str, Any]]] = defaultdict(list)\n    for point in points:\n        day = date.fromisoformat(point[\"date\"])\n        iso = day.isocalendar()\n        grouped[(iso.year, iso.week)].append(point)\n    result = []\n    for (year, week), rows in sorted(grouped.items()):\n        distinct = {row[\"date\"] for row in rows}\n        if rule[\"requires_complete_calendar_week\"] and len(distinct) != 7:\n            continue\n        values = [float(row[\"value\"]) for row in rows]\n        if metric.aggregation == \"sum\":\n            value = sum(values)\n        elif metric.aggregation == \"last\":\n            value = float(max(rows, key=lambda row: row[\"date\"])[\"value\"])\n        else:\n            value = sum(values) / len(values)\n        week_start = date.fromisocalendar(year, week, 1)\n        week_end = week_start + timedelta(days=6)\n        item = {\n            \"week\": f\"{year}-W{week:02d}\",\n            \"date\": min(distinct),\n            \"week_start\": week_start.isoformat(),\n            \"week_end\": week_end.isoformat(),\n            \"drilldown_date\": week_start.isoformat(),\n            \"value\": _numeric(value, metric.precision, metric.value_type),\n            \"observation_count\": len(rows),\n            \"observed_days\": len(distinct),\n            \"quality\": (\n                \"complete_calendar_week\"\n                if rule[\"requires_complete_calendar_week\"]\n                else \"observed_measurements\"\n            ),\n        }\n        result.append(item)\n    return result\n\n\ndef _coverage(\n    points: list[dict[str, Any]], start: date | None, end: date | None\n) -> dict[str, Any]:\n    observed = sorted({date.fromisoformat(point[\"date\"]) for point in points})\n    effective_start = start or (observed[0] if observed else None)\n    effective_end = end or (observed[-1] if observed else None)\n    if effective_start is not None and effective_end is not None:\n        span = (effective_end - effective_start).days + 1\n        if span > MAX_RANGE_DAYS:\n            raise APIError(422, \"range_too_large\")\n    else:\n        span = 0\n    return {\n        \"from\": effective_start.isoformat() if effective_start else None,\n        \"to\": effective_end.isoformat() if effective_end else None,\n        \"expected_days\": span,\n        \"observed_days\": len(observed),\n        \"observation_count\": len(points),\n        \"missing_days\": max(0, span - len(observed)),\n        \"gaps\": \"omitted_not_interpolated\",\n    }\n\n\ndef _series(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n    metric_id = params.get(\"metric\", \"\")\n    metric = BY_ID_V2.get(metric_id)\n    if metric is None:\n        raise APIError(404, \"metric_not_allowed\")\n    resolution = params.get(\"resolution\", \"day\")\n    if resolution not in ALLOWED_RESOLUTIONS:\n        raise APIError(400, \"resolution_not_allowed\")\n    start, end = parse_range(params)\n    points = _metric_points(connection, metric, start, end)\n    today = local_today().isoformat()\n    points = [point for point in points if point[\"date\"] <= today]\n    _add_past_only_baselines(points, metric)\n    if start is None and points:\n        observed_start = date.fromisoformat(points[0][\"date\"])\n        observed_end = date.fromisoformat(points[-1][\"date\"])\n        if (observed_end - observed_start).days + 1 > MAX_RANGE_DAYS:\n            raise APIError(422, \"range_too_large\")\n    coverage = _coverage(points, start, end)\n    if end is None:\n        coverage[\"to\"] = today\n        if coverage[\"from\"] is not None:\n            coverage[\"expected_days\"] = (\n                date.fromisoformat(today) - date.fromisoformat(coverage[\"from\"])\n            ).days + 1\n            coverage[\"missing_days\"] = max(\n                0, coverage[\"expected_days\"] - coverage[\"observed_days\"]\n"}}
{"timestamp":"2026-07-16T17:46:05.772Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_vWYQPGxQM0VrJLmUUy6C7Kej","output":"Chunk ID: 734855\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 2542\nOutput:\n#!/usr/bin/env python3\n\"\"\"Serve the local Health Dashboard with strict routes and privacy headers.\"\"\"\n\nfrom __future__ import annotations\n\nimport mimetypes\nimport json\nimport os\nimport re\nimport secrets\nimport sqlite3\nimport stat\nimport threading\nimport time\nfrom datetime import date, datetime\nfrom http.cookies import CookieError, SimpleCookie\nfrom http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, quote, unquote, urlparse\nfrom zoneinfo import ZoneInfo\n\nfrom dashboard_v5.read_api import (\n    APIError,\n    connect_read_only,\n    dispatch_api,\n    _resolve_record_document,\n)\nfrom dashboard_v5.document_originals import configured_original_roots, probe_original\n\nBASE = Path.home() / \".hermes\" / \"assets\" / \"Gesundheit\"\nAPI_DB = (\n    Path(os.environ[\"HEALTH_DASHBOARD_DB\"]).resolve()\n    if os.environ.get(\"HEALTH_DASHBOARD_DB\")\n    else None\n)\nDB = BASE / \"health_data.db\"\nREPORTS = BASE / \"reports\"\nDASHBOARD_FILE = Path(os.environ[\"HEALTH_DASHBOARD_FILE\"]).resolve()\nDASHBOARD_V5_FILE = (\n    Path(os.environ[\"HEALTH_DASHBOARD_V5_FILE\"]).resolve()\n    if os.environ.get(\"HEALTH_DASHBOARD_V5_FILE\")\n    else None\n)\nASSET_DIR = Path(\n    os.environ.get(\n        \"HEALTH_DASHBOARD_ASSET_DIR\",\n        str(BASE / \"scripts\" / \"assets\" / \"health-assets\"),\n    )\n).resolve()\nBIND_HOST = os.environ.get(\"HEALTH_DASHBOARD_HOST\", \"127.0.0.1\")\nBIND_PORT = int(os.environ.get(\"HEALTH_DASHBOARD_PORT\", \"8014\"))\nALLOWED_HOSTS = {\n    value.strip().lower().rstrip(\".\")\n    for value in os.environ.get(\n        \"HEALTH_DASHBOARD_ALLOWED_HOSTS\", \"127.0.0.1,localhost\"\n    ).split(\",\")\n    if value.strip()\n}\nROUTE = \"/health-dashboard\"\nV5_ROUTE = \"/health-dashboard-v5\"\nASSET_ROUTES = {\n    \"/health-assets/chart.umd.min.js\": (\n        \"chart.umd.min.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5.js\": (\n        \"dashboard-v5.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-range.js\": (\n        \"dashboard-v5-range.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-global-search.js\": (\n        \"dashboard-v5-global-search.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-api-explorer.js\": (\n        \"dashboard-v5-api-explorer.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-echarts.js\": (\n        \"dashboard-v5-echarts.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-day-controller.js\": (\n        \"dashboard-v5-day-controller.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-record.js\": (\n        \"dashboard-v5-record.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5.css\": (\"dashboard-v5.css\", \"text/css; charset=utf-8\"),\n    \"/health-assets/echarts-6.1.0.min.js\": (\n        \"echarts-6.1.0.min.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/fullcalendar-6.1.21.min.js\": (\n        \"fullcalendar-6.1.21.min.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n    \"/health-assets/dashboard-v5-echarts-prototype.js\": (\n        \"dashboard-v5-echarts-prototype.js\",\n        \"text/javascript; charset=utf-8\",\n    ),\n}\nCHECKIN_ROUTE = \"/health-actions/symptom-checkin\"\nNUTRITION_MAPPING_ROUTE = \"/health-actions/nutrition-mapping\"\nACTION_INBOX = Path(\n    os.environ.get(\n        \"HEALTH_DASHBOARD_ACTION_INBOX\",\n        str(BASE / \"runtime\" / \"dashboard-actions\"),\n    )\n).resolve()\nTEST_INSTANCE_ID = os.environ.get(\"HEALTH_DASHBOARD_TEST_INSTANCE_ID\", \"\").strip()\nAPI_TOKEN_FILE = (\n    Path(os.environ[\"HEALTH_DASHBOARD_API_TOKEN_FILE\"]).expanduser()\n    if os.environ.get(\"HEALTH_DASHBOARD_API_TOKEN_FILE\")\n    else None\n)\nBROWSER_SESSION_TTL_SECONDS = max(\n    60, min(int(os.environ.get(\"HEALTH_DASHBOARD_BROWSER_SESSION_TTL\", \"900\")), 3600)\n)\nBROWSER_SESSION_COOKIE_SECURE = (\n    os.environ.get(\"HEALTH_DASHBOARD_BROWSER_SESSION_COOKIE_SECURE\") == \"1\"\n)\nif TEST_INSTANCE_ID:\n    if not re.fullmatch(r\"[A-Za-z0-9_-]{8,64}\", TEST_INSTANCE_ID):\n        raise RuntimeError(\"invalid HEALTH_DASHBOARD_TEST_INSTANCE_ID\")\n    if not ACTION_INBOX.is_relative_to(Path(\"/tmp\").resolve()):\n        raise RuntimeError(\n            \"synthetic test instance requires an action inbox below /tmp\"\n        )\nSYMPTOM_FIELDS = (\"aphthen\", \"gi\", \"fatigue\", \"skin\", \"eyes\", \"joints\", \"vascular\")\nMAPPING_SCORES = {\"0\", \"1\", \"2\", \"3\", \"unknown\"}\nMAPPING_LIGHTS = {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}\nMAPPING_SCOPES = {\"canonical\"}\nPERSONAL_TOLERANCE = {\"unknown\", \"safe\", \"problematic\", \"unclear\"}\nCSRF_TTL_SECONDS = max(\n    60, min(int(os.environ.get(\"HEALTH_DASHBOARD_CSRF_TTL\", \"900\")), 3600)\n)\nQUEUE_RECEIPT_TTL_SECONDS = 120\nMAX_API_RESPONSE_BYTES = 512_000\nCAPTURE_TIMEZONE = ZoneInfo(\"Europe/Zurich\")\nMAX_PENDING_ACTIONS = max(\n    1, min(int(os.environ.get(\"HEALTH_DASHBOARD_MAX_PENDING_ACTIONS\", \"64\")), 256)\n)\nMAX_ORIGINAL_BYTES = 20 * 1024 * 1024\nORIGINAL_MIME_MAGIC = (\n    (b\"%PDF-\", \"application/pdf\", \".pdf\"),\n    (b\"\\x89PNG\\r\\n\\x1a\\n\", \"image/png\", \".png\"),\n    (b\"\\xff\\xd8\\xff\", \"image/jpeg\", \".jpg\"),\n)\nORIGINAL_ROOTS = configured_original_roots()\n_csrf_tokens: dict[str, float] = {}\n_csrf_lock = threading.Lock()\n_queue_receipts: dict[str, float] = {}\n_queue_receipt_lock = threading.Lock()\n_browser_sessions: dict[str, float] = {}\n_browser_session_lock = threading.Lock()\n_action_lock = threading.Lock()\n\n\nclass QueueFullError(OSError):\n    \"\"\"Raised when the bounded private action queue cannot accept more work.\"\"\"\n\n\ndef load_api_token() -> str:\n    if API_TOKEN_FILE is None:\n        raise OSError(\"read-only API authentication is not configured\")\n    descriptor = os.open(\n        API_TOKEN_FILE,\n        os.O_RDONLY | os.O_NOFOLLOW | os.O_NONBLOCK,\n    )\n    try:\n        metadata = os.fstat(descriptor)\n        if (\n            not stat.S_ISREG(metadata.st_mode)\n            or metadata.st_uid != os.getuid()\n            or stat.S_IMODE(metadata.st_mode) != 0o600\n            or metadata.st_size > 256\n        ):\n            raise OSError(\"read-only API token file is not private\")\n        raw = os.read(descriptor, 257)\n    finally:\n        os.close(descriptor)\n    token = raw.decode(\"ascii\").strip()\n    if not re.fullmatch(r\"[A-Za-z0-9_-]{32,128}\", token):\n        raise OSError(\"read-only API token is invalid\")\n    return token\n\n\ndef api_request_is_authenticated(header: str, token: str) -> bool:\n    if not header.startswith(\"Bearer \") or header.count(\" \") != 1:\n        return False\n    supplied = header.removeprefix(\"Bearer \")\n    return len(supplied) == len(token) and secrets.compare_digest(supplied, token)\n\n\ndef issue_browser_session() -> str:\n    now = time.monotonic()\n    with _browser_session_lock:\n        expired = [key for key, expiry in _browser_sessions.items() if expiry <= now]\n        for key in expired:\n            del _browser_sessions[key]\n        session_id = secrets.token_urlsafe(32)\n        _browser_sessions[session_id] = now + BROWSER_SESSION_TTL_SECONDS\n    return session_id\n\n\ndef browser_session_is_authenticated(cookie_header: str) -> bool:\n    try:\n        cookie = SimpleCookie(cookie_header)\n    except (CookieError, ValueError):\n        return False\n    morsel = cookie.get(\"health_api_session\")\n    if morsel is None:\n        return False\n    session_id = morsel.value\n    if not re.fullmatch(r\"[A-Za-z0-9_-]{32,128}\", session_id):\n        return False\n    now = time.monotonic()\n    with _browser_session_lock:\n        expiry = _browser_sessions.get(session_id)\n        if expiry is None or expiry <= now:\n            _browser_sessions.pop(session_id, None)\n            return False\n        return True\n\n\ndef resolve_doc_path(*values: object) -> Path | None:\n    for value in values:\n        if not value:\n            continue\n        raw = str(value)\n        candidates = [\n            Path(os.path.expanduser(raw)),\n            BASE / raw,\n            Path.home() / raw,\n            Path(raw.replace(str(Path.home() / \"Gesundheit\"), str(BASE))),\n        ]\n        for candidate in candidates:\n            try:\n                resolved = candidate.resolve()\n                if resolved.is_file() and (\n                    resolved.is_relative_to(BASE.resolve())\n                    or resolved.is_relative_to(REPORTS.resolve())\n                ):\n                    return resolved\n            except OSError:\n                continue\n    return None\n\n\ndef document_path(doc_id: int) -> tuple[Path, str] | None:\n    connection = sqlite3.connect(f\"{DB.resolve().as_uri()}?mode=ro\", uri=True)\n    connection.execute(\"PRAGMA query_only=ON\")\n    connection.row_factory = sqlite3.Row\n    try:\n        row = connection.execute(\n            \"SELECT datei_name,dateipfad,local_original_path FROM dokumente WHERE id=?\",\n            (doc_id,),\n        ).fetchone()\n        if not row:\n            return None\n        path = resolve_doc_path(row[\"local_original_path\"], row[\"dateipfad\"])\n        if not path:\n            return None\n        return path, str(row[\"datei_name\"] or path.name)\n    finally:\n        connection.close()\n\n\ndef _open_regular_beneath(root: Path, candidate: Path) -> int:\n    \"\"\"Open each path component without following symlinks.\"\"\"\n    root = root.absolute()\n    candidate = candidate.absolute()\n    try:\n        relative = candidate.relative_to(root)\n    except ValueError as error:\n        raise OSError(\"outside_root\") from error\n    if not relative.parts or any(part in {\"\", \".\", \"..\"} for part in relative.parts):\n        raise OSError(\"invalid_path\")\n    directory = os.open(root, os.O_RDONLY | os.O_DIRECTORY | os.O_NOFOLLOW)\n    try:\n        for part in relative.parts[:-1]:\n            child = os.open(\n                part,\n                os.O_RDONLY | os.O_DIRECTORY | os.O_NOFOLLOW,\n                dir_fd=directory,\n            )\n            os.close(directory)\n            directory = child\n        return os.open(\n            relative.parts[-1],\n            os.O_RDONLY | os.O_NOFOLLOW | getattr(os, \"O_NONBLOCK\", 0),\n            dir_fd=directory,\n        )\n    finally:\n"}}
{"timestamp":"2026-07-16T17:46:05.793Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_r6irc44sf5uEtrqJu4VbjAKv","output":"Chunk ID: 014123\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3567\nOutput:\n\"\"\"Versioned, privacy-minimised Dashboard v5 bundle contract.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport math\nimport re\nfrom datetime import date, datetime, timedelta\nfrom statistics import median\nfrom typing import Any\n\nfrom dashboard_v5.lab_registry import EXACT_LAB_NUMBER, PUBLIC_LAB_PAIRS\nfrom dashboard_v5.metric_registry import public_explorer, public_registry\n\nSCHEMA_VERSION = \"health_dashboard.bundle.v2\"\nALLOWED_TOP_LEVEL = {\n    \"schema_version\", \"generated_at\", \"timezone\", \"today\", \"metrics\", \"explorer\", \"series\",\n    \"events\", \"medication\", \"labs\", \"nutrition\", \"tasks\", \"notices\", \"freshness\",\n    \"correlations\",\n}\nMAX_BUNDLE_BYTES = 512_000\nMAX_METRICS = 32\nMAX_SERIES_POINTS = 3_660\nMAX_EVENTS = 1_000\nMAX_LABS = 100\nMAX_NUTRITION_DAYS = 1_000\nMAX_REVIEW_ITEMS = 100\nMAX_TASKS = 20\nMAX_PRESETS = 16\nMAX_CORRELATIONS = 4_096\nPATH_LIKE = re.compile(r\"^(?:/|~/|[A-Za-z]:[\\\\/]|file:|https?://)|(?:^|/)\\.hermes(?:/|$)\", re.I)\n\n\ndef _bounded_list(value: Any, path: str, maximum: int) -> list[Any]:\n    if not isinstance(value, list):\n        raise TypeError(f\"{path} must be a list\")\n    if len(value) > maximum:\n        raise ValueError(f\"too many items at {path}\")\n    return value\n\n\ndef _exact(mapping: Any, keys: set[str], path: str) -> dict[str, Any]:\n    if not isinstance(mapping, dict) or set(mapping) != keys:\n        raise ValueError(f\"invalid fields at {path}\")\n    return mapping\n\n\ndef _bounded_text(value: Any, path: str, maximum: int = 200, *, allow_empty: bool = False) -> str:\n    if not isinstance(value, str):\n        raise TypeError(f\"non-string at {path}\")\n    if value != value.strip() or any(ord(char) < 32 for char in value):\n        raise ValueError(f\"unsafe whitespace at {path}\")\n    if (not value and not allow_empty) or len(value) > maximum or PATH_LIKE.search(value):\n        raise ValueError(f\"unsafe text at {path}\")\n    return value\n\n\ndef _iso_day(value: Any, path: str) -> str:\n    text = _bounded_text(value, path, 10)\n    if date.fromisoformat(text).isoformat() != text:\n        raise ValueError(f\"invalid day at {path}\")\n    return text\n\n\ndef _finite_optional(value: Any, path: str) -> None:\n    if value is None:\n        return\n    if type(value) not in {int, float} or not math.isfinite(value):\n        raise ValueError(f\"non-finite/non-numeric value at {path}\")\n\n\ndef _validate_metric(entry: Any, index: int) -> str:\n    path = f\"metrics[{index}]\"\n    metric = _exact(entry, {\n        \"id\", \"label\", \"unit\", \"unit_family\", \"raw_overlay_group\", \"group\", \"aggregation\",\n        \"baseline_days\", \"baseline_min_observations\", \"index_mode\", \"correlation_id\", \"decimals\",\n    }, path)\n    metric_id = _bounded_text(metric[\"id\"], f\"{path}.id\", 80)\n    if not re.fullmatch(r\"[a-z][a-z0-9_.]*\", metric_id):\n        raise ValueError(f\"invalid metric id at {path}\")\n    for field, maximum in ((\"label\", 80), (\"unit\", 24), (\"unit_family\", 40), (\"raw_overlay_group\", 40), (\"correlation_id\", 80), (\"group\", 40), (\"aggregation\", 16)):\n        _bounded_text(metric[field], f\"{path}.{field}\", maximum)\n    if not re.fullmatch(r\"[a-z][a-z0-9_:]*\", metric[\"correlation_id\"]):\n        raise ValueError(f\"invalid correlation id at {path}\")\n    if metric[\"index_mode\"] not in {\"ratio_100\", \"unsupported\"}:\n        raise ValueError(f\"invalid index mode at {path}\")\n    if type(metric[\"baseline_days\"]) is not int or not 1 <= metric[\"baseline_days\"] <= 365:\n        raise ValueError(f\"invalid baseline at {path}\")\n    if type(metric[\"baseline_min_observations\"]) is not int or not 1 <= metric[\"baseline_min_observations\"] <= metric[\"baseline_days\"]:\n        raise ValueError(f\"invalid baseline minimum at {path}\")\n    if type(metric[\"decimals\"]) is not int or not 0 <= metric[\"decimals\"] <= 4:\n        raise ValueError(f\"invalid decimals at {path}\")\n    return metric_id\n\n\ndef validate_bundle(bundle: dict[str, Any]) -> None:\n    _exact(bundle, ALLOWED_TOP_LEVEL, \"bundle\")\n    if bundle[\"schema_version\"] != SCHEMA_VERSION:\n        raise ValueError(\"unsupported schema version\")\n    datetime.fromisoformat(_bounded_text(bundle[\"generated_at\"], \"generated_at\", 40).replace(\"Z\", \"+00:00\"))\n    if bundle[\"timezone\"] != \"Europe/Zurich\":\n        raise ValueError(\"unexpected timezone\")\n    today = _iso_day(bundle[\"today\"], \"today\")\n\n    metrics = _bounded_list(bundle[\"metrics\"], \"metrics\", MAX_METRICS)\n    metric_ids = {_validate_metric(entry, index) for index, entry in enumerate(metrics)}\n    if len(metric_ids) != len(metrics):\n        raise ValueError(\"duplicate metric id\")\n    if metrics != public_registry():\n        raise ValueError(\"metric registry differs from the canonical server configuration\")\n\n    explorer = _exact(bundle[\"explorer\"], {\"modes\", \"presets\"}, \"explorer\")\n    modes = _exact(explorer[\"modes\"], {\"raw\", \"baseline_index\"}, \"explorer.modes\")\n    if modes[\"raw\"] != {\"max_series\": 2, \"compatibility\": \"same_raw_overlay_group\"}:\n        raise ValueError(\"invalid raw Explorer contract\")\n    if modes[\"baseline_index\"] != {\"max_series\": 4, \"reference\": 100}:\n        raise ValueError(\"invalid baseline Explorer contract\")\n    metric_by_id = {entry[\"id\"]: entry for entry in metrics}\n    preset_ids: set[str] = set()\n    presets = _bounded_list(explorer[\"presets\"], \"explorer.presets\", MAX_PRESETS)\n    for index, entry in enumerate(presets):\n        path = f\"explorer.presets[{index}]\"\n        preset = _exact(entry, {\"id\", \"label\", \"mode\", \"metric_ids\"}, path)\n        preset_id = _bounded_text(preset[\"id\"], f\"{path}.id\", 60)\n        _bounded_text(preset[\"label\"], f\"{path}.label\", 80)\n        if preset_id in preset_ids or preset[\"mode\"] not in modes:\n            raise ValueError(f\"invalid preset at {path}\")\n        preset_ids.add(preset_id)\n        selected = preset[\"metric_ids\"]\n        if not isinstance(selected, list) or not selected or len(selected) != len(set(selected)):\n            raise ValueError(f\"invalid preset metrics at {path}\")\n        maximum = modes[preset[\"mode\"]][\"max_series\"]\n        if len(selected) > maximum or any(metric_id not in metric_ids for metric_id in selected):\n            raise ValueError(f\"preset exceeds allowlist or limit at {path}\")\n        if preset[\"mode\"] == \"raw\":\n            groups = {metric_by_id[metric_id][\"raw_overlay_group\"] for metric_id in selected}\n            if len(groups) != 1:\n                raise ValueError(f\"incompatible raw preset at {path}\")\n        elif any(metric_by_id[metric_id][\"index_mode\"] != \"ratio_100\" for metric_id in selected):\n            raise ValueError(f\"unsupported index preset at {path}\")\n    if explorer != public_explorer():\n        raise ValueError(\"Explorer registry differs from the canonical server configuration\")\n\n    if not isinstance(bundle[\"series\"], dict) or set(bundle[\"series\"]) != metric_ids:\n        raise ValueError(\"series must exactly match the metric registry\")\n    for metric_id, points_value in bundle[\"series\"].items():\n        points = _bounded_list(points_value, f\"series.{metric_id}\", MAX_SERIES_POINTS)\n        metric = metric_by_id[metric_id]\n        previous_day: str | None = None\n        observed: list[tuple[date, float]] = []\n        for index, entry in enumerate(points):\n            path = f\"series.{metric_id}[{index}]\"\n            point = _exact(entry, {\"date\", \"value\", \"quality\", \"baseline\"}, path)\n            day = _iso_day(point[\"date\"], f\"{path}.date\")\n            if day > today:\n                raise ValueError(f\"future measurement day at {path}\")\n            if previous_day is not None and day <= previous_day:\n                raise ValueError(f\"series dates must be strictly ascending at {path}\")\n            previous_day = day\n            _finite_optional(point[\"value\"], f\"{path}.value\")\n            _finite_optional(point[\"baseline\"], f\"{path}.baseline\")\n            quality = point[\"quality\"]\n            if quality not in {\"complete\", \"observed\", \"incomplete\"}:\n                raise ValueError(f\"invalid quality at {path}\")\n            if (quality == \"incomplete\") != (point[\"value\"] is None):\n                raise ValueError(f\"inconsistent value/quality at {path}\")\n            current_day = date.fromisoformat(day)\n            start = current_day - timedelta(days=metric[\"baseline_days\"])\n            prior = [value for prior_day, value in observed if start <= prior_day < current_day]\n            expected_baseline = round(median(prior), 3) if len(prior) >= metric[\"baseline_min_observations\"] else None\n            if expected_baseline is None:\n                if point[\"baseline\"] is not None:\n                    raise ValueError(f\"premature baseline at {path}\")\n            elif point[\"baseline\"] is None or not math.isclose(float(point[\"baseline\"]), expected_baseline, rel_tol=0, abs_tol=1e-9):\n                raise ValueError(f\"incorrect baseline at {path}\")\n            if point[\"value\"] is not None:\n                observed.append((current_day, float(point[\"value\"])))\n\n    events = _bounded_list(bundle[\"events\"], \"events\", MAX_EVENTS)\n    for index, entry in enumerate(events):\n        event = _exact(entry, {\"date\", \"type\", \"label\"}, f\"events[{index}]\")\n        event_day = _iso_day(event[\"date\"], f\"events[{index}].date\")\n        if event_day > today or event[\"type\"] != \"medication_administered\":\n            raise ValueError(f\"invalid event day or type at events[{index}]\")\n        _bounded_text(event[\"type\"], f\"events[{index}].type\", 40)\n        _bounded_text(event[\"label\"], f\"events[{index}].label\", 120)\n\n    medication = _exact(bundle[\"medication\"], {\"last_administered\", \"next_planned\"}, \"medication\")\n    for field, keys in (\n        (\"last_administered\", {\"date\", \"name\", \"dose\"}),\n        (\"next_planned\", {\"date\", \"name\", \"dose\", \"status\"}),\n    ):\n        item = medication[field]\n        if item is None:\n            continue\n        item = _exact(item, keys, f\"medication.{field}\")\n        medication_day = _iso_day(item[\"date\"], f\"medication.{field}.date\")\n        if (field == \"last_administered\" and medication_day > today) or (field == \"next_planned\" and medication_day <= today):\n            raise ValueError(f\"invalid medication date at medication.{field}\")\n        _bounded_text(item[\"name\"], f\"medication.{field}.name\", 120)\n        _bounded_text(item[\"dose\"], f\"medication.{field}.dose\", 40, allow_empty=True)\n        if field == \"next_planned\" and item[\"status\"] != \"planned\":\n            raise ValueError(\"invalid planned medication status\")\n\n    labs = _bounded_list(bundle[\"labs\"], \"labs\", MAX_LABS)\n    for index, entry in enumerate(labs):\n        path = f\"labs[{index}]\"\n        lab = _exact(entry, {\"parameter\", \"value\", \"unit\", \"date\", \"reference_min\", \"reference_max\", \"quality\", \"source_type\"}, path)\n        for field, maximum in ((\"parameter\", 100), (\"value\", 32), (\"unit\", 24), (\"source_type\", 32)):\n            _bounded_text(lab[field], f\"{path}.{field}\", maximum)\n        for field in (\"reference_min\", \"reference_max\"):\n            if lab[field] is not None:\n                reference_value = str(lab[field]).strip()\n                _bounded_text(reference_value, f\"{path}.{field}\", 32)\n                if not EXACT_LAB_NUMBER.fullmatch(reference_value):\n                    raise ValueError(f\"non-numeric lab reference at {path}.{field}\")\n        if _iso_day(lab[\"date\"], f\"{path}.date\") > today:\n            raise ValueError(f\"future lab day at {path}\")\n        if not EXACT_LAB_NUMBER.fullmatch(lab[\"value\"]):\n            raise ValueError(f\"non-numeric verified lab value at {path}\")\n        if lab[\"quality\"] != \"verified_original\" or lab[\"source_type\"] != \"scanned_original\":\n            raise ValueError(f\"invalid lab provenance at {path}\")\n        if (lab[\"parameter\"], lab[\"unit\"]) not in PUBLIC_LAB_PAIRS:\n            raise ValueError(f\"non-allowlisted lab parameter/unit at {path}\")\n\n    nutrition = _exact(bundle[\"nutrition\"], {\"daily\", \"mapping_review\", \"mapping_open_count\"}, \"nutrition\")\n    if type(nutrition[\"mapping_open_count\"]) is not int or nutrition[\"mapping_open_count\"] < 0:\n        raise ValueError(\"invalid mapping count\")\n    daily_rows = _bounded_list(nutrition[\"daily\"], \"nutrition.daily\", MAX_NUTRITION_DAYS)\n    previous_nutrition_day: str | None = None\n    for index, entry in enumerate(daily_rows):\n        path = f\"nutrition.daily[{index}]\"\n        row = _exact(entry, {\"date\", \"kcal\", \"item_count\", \"histamine_score\", \"histamine_status\", \"unknown_items\", \"quality\"}, path)\n        nutrition_day = _iso_day(row[\"date\"], f\"{path}.date\")\n        if nutrition_day > today or (previous_nutrition_day is not None and nutrition_day <= previous_nutrition_day):\n            raise ValueError(f\"invalid nutrition date order at {path}\")\n        previous_nutrition_day = nutrition_day\n        for field in (\"kcal\", \"histamine_score\"):\n            _finite_optional(row[field], f\"{path}.{field}\")\n        for field in (\"item_count\", \"unknown_items\"):\n            if row[field] is not None and (type(row[field]) is not int or row[field] < 0):\n                raise ValueError(f\"invalid count at {path}.{field}\")\n        if row[\"histamine_status\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n            raise ValueError(f\"invalid histamine status at {path}\")\n        if row[\"quality\"] not in {\"complete\", \"incomplete\"}:\n            raise ValueError(f\"invalid nutrition quality at {path}\")\n        if row[\"quality\"] == \"complete\":\n            if row[\"histamine_score\"] is None or row[\"histamine_status\"] == \"unknown\" or row[\"unknown_items\"] != 0:\n                raise ValueError(f\"inconsistent complete nutrition row at {path}\")\n        elif row[\"histamine_score\"] is not None or row[\"histamine_status\"] != \"unknown\":\n            raise ValueError(f\"inconsistent incomplete nutrition row at {path}\")\n    review_rows = _bounded_list(nutrition[\"mapping_review\"], \"nutrition.mapping_review\", MAX_REVIEW_ITEMS)\n    for index, entry in enumerate(review_rows):\n        path = f\"nutrition.mapping_review[{index}]\"\n        allowed = {\n            \"name\", \"occurrences\", \"reason\", \"queue_key\", \"suggested_canonical_food\",\n            \"suggested_score\", \"suggested_traffic_light\",\n        }\n        if not isinstance(entry, dict) or not {\"name\", \"occurrences\", \"reason\"} <= set(entry) <= allowed:\n            raise ValueError(f\"invalid fields at {path}\")\n        row = entry\n"}}
{"timestamp":"2026-07-16T17:46:05.793Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":78467,"cached_input_tokens":42112,"output_tokens":873,"reasoning_output_tokens":18,"total_tokens":79340},"last_token_usage":{"input_tokens":38093,"cached_input_tokens":27008,"output_tokens":258,"reasoning_output_tokens":0,"total_tokens":38351},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:46:17.302Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '260,620p' scripts/health/dashboard_v5/contracts.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":35000}","call_id":"call_nrAg0MGu7naQ5kM5d2ZtFw22"}}
{"timestamp":"2026-07-16T17:46:17.309Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '300,700p' scripts/health/health_dashboard_server.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":45000}","call_id":"call_GPWuP2bU3u3T3Us9ilILmWQe"}}
{"timestamp":"2026-07-16T17:46:17.309Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '760,1180p' scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}","call_id":"call_TJA6GPBsDQkVmlgoaWIBY3B4"}}
{"timestamp":"2026-07-16T17:46:17.310Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,360p' scripts/health/assets/health-assets/dashboard-v5.js\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}","call_id":"call_d4IssQvTh0jxqsNDefZAnvKt"}}
{"timestamp":"2026-07-16T17:46:17.365Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_nrAg0MGu7naQ5kM5d2ZtFw22","output":"Chunk ID: 76d091\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1610\nOutput:\n        row = entry\n        _bounded_text(row[\"name\"], f\"{path}.name\", 160)\n        _bounded_text(row[\"reason\"], f\"{path}.reason\", 120)\n        if type(row[\"occurrences\"]) is not int or row[\"occurrences\"] < 0:\n            raise ValueError(f\"invalid occurrences at {path}\")\n        if \"queue_key\" in row:\n            if not isinstance(row[\"queue_key\"], str) or not re.fullmatch(r\"[a-z0-9_]{0,80}\", row[\"queue_key\"]):\n                raise ValueError(f\"invalid queue key at {path}\")\n        if \"suggested_canonical_food\" in row:\n            _bounded_text(row[\"suggested_canonical_food\"], f\"{path}.suggested_canonical_food\", 120, allow_empty=True)\n        if \"suggested_score\" in row and row[\"suggested_score\"] not in {0, 1, 2, 3, \"unknown\"}:\n            raise ValueError(f\"invalid suggested score at {path}\")\n        if \"suggested_traffic_light\" in row and row[\"suggested_traffic_light\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n            raise ValueError(f\"invalid suggested traffic light at {path}\")\n\n    tasks = _bounded_list(bundle[\"tasks\"], \"tasks\", min(MAX_TASKS, 3))\n    for index, entry in enumerate(tasks):\n        allowed = {\"id\", \"label\", \"action\", \"count\"}\n        if not isinstance(entry, dict) or not {\"id\", \"label\", \"action\"} <= set(entry) <= allowed:\n            raise ValueError(f\"invalid task at {index}\")\n        for field in (\"id\", \"label\", \"action\"):\n            _bounded_text(entry[field], f\"tasks[{index}].{field}\", 120)\n        if \"count\" in entry and (type(entry[\"count\"]) is not int or entry[\"count\"] < 0):\n            raise ValueError(f\"invalid task count at {index}\")\n\n    if bundle[\"notices\"] != [] or bundle[\"freshness\"] != []:\n        raise ValueError(\"notices/freshness are reserved in bundle v2\")\n\n    import multimodal_correlations as engine\n\n    correlations = _exact(bundle[\"correlations\"], {\"dimensions\", \"results\"}, \"correlations\")\n    dimensions = _exact(correlations[\"dimensions\"], {\"lag_days\", \"medication_phases\", \"lag_direction\"}, \"correlations.dimensions\")\n    if dimensions != {\n        \"lag_days\": list(engine.LAGS),\n        \"medication_phases\": sorted(engine.ALLOWED_PHASES),\n        \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\n    }:\n        raise ValueError(\"invalid correlation dimensions\")\n    result_rows = _bounded_list(correlations[\"results\"], \"correlations.results\", MAX_CORRELATIONS)\n    previous_order: tuple[str, str, int, str] | None = None\n    base_flags = [\"complete_case\", \"phase_stratified\", \"7d_block_permutation\", \"no_causality\"]\n    for index, entry in enumerate(result_rows):\n        path = f\"correlations.results[{index}]\"\n        row = _exact(entry, {\"id\", \"dimensions\", \"sample\", \"statistics\", \"status\", \"method\", \"quality_flags\"}, path)\n        identity = _exact(row[\"id\"], {\"predictor\", \"target\"}, f\"{path}.id\")\n        dims = _exact(row[\"dimensions\"], {\"lag_days\", \"medication_phase\"}, f\"{path}.dimensions\")\n        sample = _exact(row[\"sample\"], {\"eligible_target_days\", \"expected_target_days\", \"observed_pairs\", \"missing_pairs\", \"target_coverage\"}, f\"{path}.sample\")\n        statistics = _exact(row[\"statistics\"], {\"rho\", \"p_value\", \"q_value\"}, f\"{path}.statistics\")\n        if identity[\"predictor\"] not in engine.ALLOWED_PREDICTORS or identity[\"target\"] not in engine.ALLOWED_TARGETS:\n            raise ValueError(f\"unallowlisted correlation identity at {path}\")\n        if dims[\"lag_days\"] not in engine.LAGS or dims[\"medication_phase\"] not in engine.ALLOWED_PHASES:\n            raise ValueError(f\"invalid correlation dimensions at {path}\")\n        if row[\"status\"] not in engine.ALLOWED_STATUSES or row[\"method\"] != engine.METHOD:\n            raise ValueError(f\"invalid correlation status or method at {path}\")\n        order = (identity[\"predictor\"], identity[\"target\"], dims[\"lag_days\"], dims[\"medication_phase\"])\n        if previous_order is not None and order <= previous_order:\n            raise ValueError(\"correlation results are not uniquely and deterministically ordered\")\n        previous_order = order\n        counts = [sample[key] for key in (\"observed_pairs\", \"eligible_target_days\", \"expected_target_days\", \"missing_pairs\")]\n        if any(type(value) is not int or value < 0 for value in counts):\n            raise ValueError(f\"invalid correlation counts at {path}\")\n        n, eligible, expected, missing = counts\n        if expected < 1 or not n <= eligible <= expected or missing != eligible - n:\n            raise ValueError(f\"inconsistent correlation counts at {path}\")\n        coverage = sample[\"target_coverage\"]\n        _finite_optional(coverage, f\"{path}.sample.target_coverage\")\n        if not 0 <= coverage <= 1 or not math.isclose(coverage, eligible / expected, rel_tol=0, abs_tol=5e-7):\n            raise ValueError(f\"inconsistent target coverage at {path}\")\n        for field, lower, upper in ((\"rho\", -1, 1), (\"p_value\", 0, 1), (\"q_value\", 0, 1)):\n            value = statistics[field]\n            _finite_optional(value, f\"{path}.statistics.{field}\")\n            if value is not None and not lower <= value <= upper:\n                raise ValueError(f\"out-of-range statistic at {path}.{field}\")\n        if row[\"status\"] == \"computed\":\n            if any(statistics[field] is None for field in statistics) or coverage < engine.MIN_TARGET_COVERAGE or n < engine.MIN_PAIRS:\n                raise ValueError(f\"inconsistent computed result at {path}\")\n            expected_flags = base_flags\n        elif row[\"status\"] == \"insufficient_coverage\":\n            if any(statistics[field] is not None for field in statistics) or coverage >= engine.MIN_TARGET_COVERAGE:\n                raise ValueError(f\"inconsistent low-coverage result at {path}\")\n            expected_flags = base_flags + [\"low_target_coverage\"]\n        else:\n            if any(statistics[field] is not None for field in statistics) or coverage < engine.MIN_TARGET_COVERAGE:\n                raise ValueError(f\"inconsistent low-n result at {path}\")\n            expected_flags = base_flags + [\"low_n_or_variance\"]\n        if row[\"quality_flags\"] != expected_flags:\n            raise ValueError(f\"invalid quality flags at {path}\")\n\n\ndef safe_json(bundle: dict[str, Any]) -> str:\n    validate_bundle(bundle)\n    payload = json.dumps(bundle, ensure_ascii=False, separators=(\",\", \":\")).replace(\"<\", \"\\\\u003c\")\n    if len(payload.encode(\"utf-8\")) > MAX_BUNDLE_BYTES:\n        raise ValueError(\"bundle exceeds serialized size budget\")\n    return payload\n"}}
{"timestamp":"2026-07-16T17:46:17.365Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_GPWuP2bU3u3T3Us9ilILmWQe","output":"Chunk ID: e1f3b5\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3708\nOutput:\n    finally:\n        os.close(directory)\n\n\ndef open_verified_original(\n    database: Path, opaque: str\n) -> tuple[int, int, str, str] | None:\n    \"\"\"Resolve a reviewed document and pin the same descriptor used by HEAD/GET.\"\"\"\n    connection = connect_read_only(database)\n    try:\n        row = _resolve_record_document(connection, opaque, reviewed=True)\n    except APIError:\n        return None\n    finally:\n        connection.close()\n    roots = (\n        ORIGINAL_ROOTS\n        if os.environ.get(\"HEALTH_DASHBOARD_ORIGINAL_ROOTS\")\n        else (BASE.absolute(), REPORTS.absolute())\n    )\n    probe = probe_original(\n        row[\"local_original_path\"] or row[\"dateipfad\"],\n        reviewed=True,\n        roots=roots,\n        max_bytes=MAX_ORIGINAL_BYTES,\n        opener=_open_regular_beneath,\n    )\n    if probe.status != \"available\" or probe.descriptor is None:\n        probe.close()\n        return None\n    return probe.descriptor, probe.size, probe.mime, probe.download_name\n\n\ndef safe_report_path(name: str) -> Path | None:\n    clean = Path(unquote(name)).name\n    path = (REPORTS / clean).resolve()\n    try:\n        if path.is_file() and path.is_relative_to(REPORTS.resolve()):\n            return path\n    except OSError:\n        return None\n    return None\n\n\ndef canonical_request_host(raw_host: str) -> str | None:\n    if not raw_host or any(char in raw_host for char in (\"/\", \"@\", \"\\\\\")):\n        return None\n    try:\n        parsed = urlparse(\"//\" + raw_host)\n        host = parsed.hostname\n        _ = parsed.port\n    except ValueError:\n        return None\n    return host.lower().rstrip(\".\") if host else None\n\n\ndef host_is_allowed(raw_host: str) -> bool:\n    host = canonical_request_host(raw_host)\n    return host is not None and host in ALLOWED_HOSTS\n\n\ndef origin_matches_request(origin: str, raw_host: str) -> bool:\n    try:\n        request_authority = urlparse(\"//\" + raw_host)\n        supplied = urlparse(origin)\n        request_port = request_authority.port\n        supplied_port = supplied.port\n    except ValueError:\n        return False\n    if (\n        supplied.scheme != \"http\"\n        or not supplied.hostname\n        or supplied.username is not None\n        or supplied.password is not None\n        or supplied.path\n        or supplied.params\n        or supplied.query\n        or supplied.fragment\n    ):\n        return False\n    request_host = request_authority.hostname\n    return (\n        request_host is not None\n        and supplied.hostname.lower().rstrip(\".\") == request_host.lower().rstrip(\".\")\n        and supplied_port == request_port\n    )\n\n\ndef issue_csrf_token(now: float | None = None) -> str:\n    issued_at = time.monotonic() if now is None else now\n    token = secrets.token_urlsafe(24)\n    with _csrf_lock:\n        expired = [\n            existing\n            for existing, timestamp in _csrf_tokens.items()\n            if issued_at - timestamp > CSRF_TTL_SECONDS\n        ]\n        for existing in expired:\n            _csrf_tokens.pop(existing, None)\n        _csrf_tokens[token] = issued_at\n        while len(_csrf_tokens) > 256:\n            oldest = min(_csrf_tokens, key=_csrf_tokens.get)  # type: ignore[arg-type]\n            _csrf_tokens.pop(oldest, None)\n    return token\n\n\ndef consume_csrf_token(token: str, now: float | None = None) -> bool:\n    checked_at = time.monotonic() if now is None else now\n    with _csrf_lock:\n        issued_at = _csrf_tokens.pop(token, None)\n    return issued_at is not None and 0 <= checked_at - issued_at <= CSRF_TTL_SECONDS\n\n\ndef issue_queue_receipt(now: float | None = None) -> str:\n    issued_at = time.monotonic() if now is None else now\n    token = secrets.token_urlsafe(18)\n    with _queue_receipt_lock:\n        _queue_receipts[token] = issued_at\n        expired = [\n            key\n            for key, timestamp in _queue_receipts.items()\n            if issued_at - timestamp > QUEUE_RECEIPT_TTL_SECONDS\n        ]\n        for key in expired:\n            _queue_receipts.pop(key, None)\n        while len(_queue_receipts) > 256:\n            oldest = min(_queue_receipts, key=_queue_receipts.get)  # type: ignore[arg-type]\n            _queue_receipts.pop(oldest, None)\n    return token\n\n\ndef consume_queue_receipt(token: str, now: float | None = None) -> bool:\n    checked_at = time.monotonic() if now is None else now\n    with _queue_receipt_lock:\n        issued_at = _queue_receipts.pop(token, None)\n    return (\n        issued_at is not None\n        and 0 <= checked_at - issued_at <= QUEUE_RECEIPT_TTL_SECONDS\n    )\n\n\ndef dashboard_html_with_nonce(\n    path: Path,\n    *,\n    capture_queued: bool = False,\n) -> tuple[bytes, str, str, str]:\n    nonce = secrets.token_urlsafe(18)\n    csrf_token = issue_csrf_token()\n    browser_session_csrf_token = issue_csrf_token()\n    text = path.read_text(encoding=\"utf-8\")\n    capture_day = local_today()\n    text = text.replace(\"__CSRF_TOKEN__\", csrf_token)\n    text = text.replace(\"__BROWSER_SESSION_CSRF__\", browser_session_csrf_token)\n    text = text.replace(\"__CAPTURE_QUEUED__\", \"true\" if capture_queued else \"false\")\n    text = text.replace(\"__CAPTURE_DATE__\", capture_day.isoformat())\n    text = text.replace(\"__CAPTURE_DATE_LABEL__\", capture_day.strftime(\"%d.%m.%Y\"))\n    text = text.replace(\"<script\", f\"<script nonce='{nonce}'\")\n    text = text.replace(\"<style\", f\"<style nonce='{nonce}'\")\n    return text.encode(\"utf-8\"), nonce, csrf_token, browser_session_csrf_token\n\n\ndef local_today() -> date:\n    return datetime.now(CAPTURE_TIMEZONE).date()\n\n\ndef validate_symptom_form(\n    form: dict[str, list[str]],\n) -> tuple[str, dict[str, int], str]:\n    day = (form.get(\"date\") or [\"\"])[0]\n    try:\n        parsed_day = date.fromisoformat(day)\n    except ValueError as exc:\n        raise ValueError(\"invalid date\") from exc\n    if parsed_day.year < 2000 or parsed_day > local_today():\n        raise ValueError(\"date outside allowed range\")\n    scores: dict[str, int] = {}\n    for field in SYMPTOM_FIELDS:\n        raw = (form.get(field) or [\"\"])[0]\n        if raw not in {\"0\", \"1\", \"2\", \"3\"}:\n            raise ValueError(f\"invalid or missing score: {field}\")\n        scores[field] = int(raw)\n    notes = (form.get(\"notes\") or [\"\"])[0].strip()\n    if len(notes) > 300:\n        raise ValueError(\"notes too long\")\n    return parsed_day.isoformat(), scores, notes\n\n\ndef validate_symptom_submission(\n    form: dict[str, list[str]],\n) -> tuple[str, dict[str, int], str, str]:\n    required = {\"csrf_token\", \"date\", \"notes\", *SYMPTOM_FIELDS}\n    allowed = required | {\"return_to\"}\n    keys = frozenset(form)\n    if keys not in {frozenset(required), frozenset(allowed)}:\n        raise ValueError(\"invalid submission shape\")\n    if any(len(values) != 1 for values in form.values()):\n        raise ValueError(\"every field must have a single value\")\n    return_to = form.get(\"return_to\", [\"v4\"])[0]\n    if return_to not in {\"v4\", \"v5\"}:\n        raise ValueError(\"invalid return target\")\n    day, scores, notes = validate_symptom_form(form)\n    return day, scores, notes, return_to\n\n\ndef validate_mapping_submission(form: dict[str, list[str]]) -> tuple[dict[str, object], str]:\n    required = {\n        \"csrf_token\",\n        \"queue_key\",\n        \"canonical_food\",\n        \"sighi_score\",\n        \"traffic_light\",\n        \"mapping_scope\",\n        \"personal_tolerance\",\n        \"return_to\",\n    }\n    if frozenset(form) != frozenset(required) or any(len(values) != 1 for values in form.values()):\n        raise ValueError(\"invalid mapping shape\")\n    return_to = form[\"return_to\"][0]\n    if return_to != \"v5\":\n        raise ValueError(\"invalid return target\")\n    queue_key = form[\"queue_key\"][0].strip()\n    canonical_food = \" \".join(form[\"canonical_food\"][0].split())\n    score = form[\"sighi_score\"][0]\n    traffic_light = form[\"traffic_light\"][0]\n    scope = form[\"mapping_scope\"][0]\n    tolerance = form[\"personal_tolerance\"][0]\n    if not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n        raise ValueError(\"invalid queue key\")\n    if len(canonical_food) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical_food, re.I):\n        raise ValueError(\"invalid canonical food\")\n    if score not in MAPPING_SCORES or traffic_light not in MAPPING_LIGHTS or scope not in MAPPING_SCOPES or tolerance not in PERSONAL_TOLERANCE:\n        raise ValueError(\"invalid mapping value\")\n    if score == \"unknown\" and traffic_light != \"unknown\":\n        raise ValueError(\"unknown score must keep unknown light\")\n    if score != \"unknown\" and not canonical_food:\n        raise ValueError(\"canonical food required\")\n    return {\n        \"version\": 1,\n        \"action\": \"nutrition_mapping\",\n        \"queue_key\": queue_key,\n        \"canonical_food\": canonical_food,\n        \"sighi_score\": None if score == \"unknown\" else int(score),\n        \"traffic_light\": traffic_light,\n        \"mapping_scope\": scope,\n        \"personal_tolerance\": tolerance,\n    }, return_to\n\n\ndef ensure_private_inbox() -> None:\n    ACTION_INBOX.mkdir(mode=0o700, parents=True, exist_ok=True)\n    metadata = ACTION_INBOX.lstat()\n    if not stat.S_ISDIR(metadata.st_mode) or metadata.st_uid != os.getuid():\n        raise OSError(\"action inbox is not a private owned directory\")\n    os.chmod(ACTION_INBOX, 0o700)\n\n\ndef write_action_payload(payload: dict[str, object]) -> None:\n    \"\"\"Atomically enqueue a validated action; the network server never writes the DB.\"\"\"\n    with _action_lock:\n        ensure_private_inbox()\n        if sum(1 for _ in ACTION_INBOX.glob(\"*.json\")) >= MAX_PENDING_ACTIONS:\n            raise QueueFullError(\"private action queue is full\")\n        token = secrets.token_hex(16)\n        temporary = ACTION_INBOX / f\".{token}.tmp\"\n        destination = ACTION_INBOX / f\"{token}.json\"\n        packed = json.dumps(\n            payload,\n            ensure_ascii=False,\n            separators=(\",\", \":\"),\n        ).encode(\"utf-8\")\n        descriptor = os.open(temporary, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600)\n        try:\n            with os.fdopen(descriptor, \"wb\") as handle:\n                handle.write(packed)\n                handle.flush()\n                os.fsync(handle.fileno())\n            temporary.replace(destination)\n            directory = os.open(ACTION_INBOX, os.O_RDONLY | os.O_DIRECTORY)\n            try:\n                os.fsync(directory)\n            finally:\n                os.close(directory)\n        except Exception:\n            temporary.unlink(missing_ok=True)\n            raise\n\n\ndef write_symptom_checkin(day: str, scores: dict[str, int], notes: str) -> None:\n    write_action_payload(\n        {\n            \"version\": 1,\n            \"action\": \"symptom_checkin\",\n            \"date\": day,\n            \"scores\": scores,\n            \"notes\": notes,\n        }\n    )\n\n\nclass Handler(BaseHTTPRequestHandler):\n    def send_error(\n        self,\n        code: int,\n        message: str | None = None,\n        explain: str | None = None,\n    ) -> None:\n        request_path = urlparse(getattr(self, \"path\", \"\")).path\n        if request_path.startswith(\"/api/v1/\"):\n            send_body = getattr(self, \"command\", \"\") != \"HEAD\"\n            if not host_is_allowed(self.headers.get(\"Host\", \"\")):\n                self._send_api_error(421, \"invalid_host\", send_body)\n                return\n            status = 405 if code == 501 else code\n            error_code = \"read_only_endpoint\" if code == 501 else \"request_rejected\"\n            self._send_api_error(status, error_code, send_body)\n            return\n        super().send_error(code, message, explain)\n\n    def _reject_unsupported(self) -> None:\n        if not host_is_allowed(self.headers.get(\"Host\", \"\")):\n            if urlparse(self.path).path.startswith(\"/api/v1/\"):\n                self._send_api_error(421, \"host_not_allowed\", self.command != \"HEAD\")\n            else:\n                self.send_error(421)\n            return\n        if urlparse(self.path).path.startswith(\"/api/v1/\"):\n            self._send_api_error(405, \"read_only_endpoint\", self.command != \"HEAD\")\n            return\n        self.send_error(405)\n\n    do_OPTIONS = _reject_unsupported  # noqa: N815\n    do_PUT = _reject_unsupported  # noqa: N815\n    do_DELETE = _reject_unsupported  # noqa: N815\n    do_PATCH = _reject_unsupported  # noqa: N815\n    do_TRACE = _reject_unsupported  # noqa: N815\n    do_CONNECT = _reject_unsupported  # noqa: N815\n\n    def do_HEAD(self) -> None:  # noqa: N802\n        if not host_is_allowed(self.headers.get(\"Host\", \"\")):\n            if urlparse(self.path).path.startswith(\"/api/v1/\"):\n                self._send_api_error(421, \"host_not_allowed\", False)\n            else:\n                self.send_error(421)\n            return\n        self._handle(send_body=False)\n\n    def do_GET(self) -> None:  # noqa: N802\n        if not host_is_allowed(self.headers.get(\"Host\", \"\")):\n            if urlparse(self.path).path.startswith(\"/api/v1/\"):\n                self._send_api_error(421, \"host_not_allowed\", True)\n            else:\n                self.send_error(421)\n            return\n        self._handle(send_body=True)\n\n    def do_POST(self) -> None:  # noqa: N802\n        if not host_is_allowed(self.headers.get(\"Host\", \"\")):\n            if urlparse(self.path).path.startswith(\"/api/v1/\"):\n                self._send_api_error(421, \"host_not_allowed\", True)\n            else:\n                self.send_error(421)\n            return\n        api_path = urlparse(self.path).path\n        if api_path.startswith(\"/api/v1/\"):\n            if api_path == \"/api/v1/browser-session\":\n                self._handle_browser_session(True)\n            else:\n                self._send_api_error(405, \"read_only_endpoint\", True)\n            return\n        action_path = urlparse(self.path).path\n        if action_path not in {CHECKIN_ROUTE, NUTRITION_MAPPING_ROUTE}:\n            self.send_error(404)\n            return\n        origin = self.headers.get(\"Origin\", \"\")\n        host = self.headers.get(\"Host\", \"\")\n        if not origin_matches_request(origin, host):\n            self.send_error(403)\n            return\n        if self.headers.get_content_type() != \"application/x-www-form-urlencoded\":\n            self.send_error(415)\n            return\n        try:\n            length = int(self.headers.get(\"Content-Length\", \"0\"))\n        except ValueError:\n            self.send_error(400)\n            return\n        if length < 1 or length > 4096:\n            self.send_error(413)\n            return\n        try:\n            form = parse_qs(\n                self.rfile.read(length).decode(\"utf-8\"),\n                keep_blank_values=True,\n                strict_parsing=True,\n            )\n        except (UnicodeError, ValueError):\n            self.send_error(400)\n            return\n        token = (form.get(\"csrf_token\") or [\"\"])[0]\n        cookie = SimpleCookie(self.headers.get(\"Cookie\", \"\"))\n        cookie_token = cookie.get(\"health_csrf\")\n"}}
{"timestamp":"2026-07-16T17:46:17.365Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_TJA6GPBsDQkVmlgoaWIBY3B4","output":"Chunk ID: 0308be\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 4157\nOutput:\n                0, coverage[\"expected_days\"] - coverage[\"observed_days\"]\n            )\n    aggregation_rule = {\n        \"id\": \"daily_metric_points\",\n        \"minimum_observations\": 1,\n        \"requires_complete_calendar_week\": False,\n    }\n    if resolution == \"week\":\n        aggregation_rule = _weekly_rule(metric)\n        points = _weekly(points, metric, aggregation_rule)\n    if len(points) > MAX_SERIES_ROWS:\n        raise APIError(422, \"row_limit_exceeded\")\n    return {\n        \"metric\": metric.id,\n        \"label\": metric.label,\n        \"unit\": metric.unit,\n        \"aggregation\": metric.aggregation,\n        \"aggregation_rule\": aggregation_rule,\n        \"resolution\": resolution,\n        \"source\": {\n            \"type\": metric.source,\n            \"identifier\": metric.source_identifier,\n            \"parser\": metric.parser_contract,\n        },\n        \"coverage\": coverage,\n        \"timezone\": TZ_NAME,\n        \"today\": today,\n        \"points\": points,\n    }\n\n\ndef _events(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n    start, end = parse_range(params, required=True)\n    assert start is not None and end is not None\n    raw_types = params.get(\"types\", \"\")\n    if not raw_types:\n        selected = set(ALLOWED_EVENT_TYPES)\n    else:\n        selected = set(raw_types.split(\",\"))\n        if \"\" in selected or not selected <= ALLOWED_EVENT_TYPES:\n            raise APIError(400, \"event_type_not_allowed\")\n    result: list[dict[str, Any]] = []\n    if \"medication_administered\" in selected and table_exists(\n        connection, \"medication_administrations\"\n    ):\n        administered_values = tuple(sorted(ADMINISTERED))\n        if len(administered_values) != 6:\n            raise APIError(503, \"api_contract_invalid\")\n        rows = list(\n            connection.execute(\n                \"\"\"SELECT datum,medication_name,event_type FROM medication_administrations\n               WHERE datum>=? AND datum<=?\n                 AND lower(trim(COALESCE(event_type,''))) IN (?,?,?,?,?,?)\n               ORDER BY datum,id LIMIT ?\"\"\",\n                (\n                    start.isoformat(),\n                    end.isoformat(),\n                    *administered_values,\n                    MAX_EVENT_SOURCE_ROWS + 1,\n                ),\n            )\n        )\n        if len(rows) > MAX_EVENT_SOURCE_ROWS:\n            raise APIError(422, \"source_row_limit_exceeded\")\n        for row in rows:\n            label = safe_metadata_text(row[\"medication_name\"], 120)\n            day = parse_day(row[\"datum\"])\n            if day and label:\n                result.append(\n                    {\"date\": day, \"type\": \"medication_administered\", \"label\": label}\n                )\n    if \"symptom_day\" in selected and table_exists(connection, \"symptom_log\"):\n        for point in _symptom_total_points(connection, start, end):\n            result.append(\n                {\n                    \"date\": point[\"date\"],\n                    \"type\": \"symptom_day\",\n                    \"label\": \"Symptome vollständig dokumentiert\",\n                }\n            )\n    if \"health_event\" in selected and table_exists(connection, \"health_events\"):\n        rows = list(\n            connection.execute(\n                \"SELECT date,category,parameter FROM health_events WHERE date>=? AND date<=? ORDER BY date,id LIMIT ?\",\n                (start.isoformat(), end.isoformat(), MAX_EVENT_SOURCE_ROWS + 1),\n            )\n        )\n        if len(rows) > MAX_EVENT_SOURCE_ROWS:\n            raise APIError(422, \"source_row_limit_exceeded\")\n        for row in rows:\n            category = safe_metadata_text(row[\"category\"], 80, allow_empty=True)\n            parameter = safe_metadata_text(row[\"parameter\"], 120, allow_empty=True)\n            day = parse_day(row[\"date\"])\n            if day and category is not None and parameter is not None:\n                result.append(\n                    {\n                        \"date\": day,\n                        \"type\": \"health_event\",\n                        \"category\": category,\n                        \"label\": parameter,\n                    }\n                )\n    if \"health_period\" in selected and table_exists(connection, \"health_event_periods\"):\n        rows = list(\n            connection.execute(\n                \"\"\"SELECT start_date,end_date,event_type,label FROM health_event_periods\n               WHERE start_date<=? AND COALESCE(end_date,start_date)>=?\n               ORDER BY start_date,id LIMIT ?\"\"\",\n                (end.isoformat(), start.isoformat(), MAX_EVENT_SOURCE_ROWS + 1),\n            )\n        )\n        if len(rows) > MAX_EVENT_SOURCE_ROWS:\n            raise APIError(422, \"source_row_limit_exceeded\")\n        for row in rows:\n            event_type = safe_metadata_text(row[\"event_type\"], 80)\n            label = safe_metadata_text(row[\"label\"], 120, allow_empty=True)\n            start_day = parse_day(row[\"start_date\"])\n            end_day = parse_day(row[\"end_date\"]) if row[\"end_date\"] else None\n            if (\n                start_day is None\n                or (end_day is not None and end_day < start_day)\n                or (end_day is not None and date.fromisoformat(end_day) > local_today())\n            ):\n                continue\n            if event_type and label is not None:\n                result.append(\n                    {\n                        \"date\": start_day,\n                        \"end_date\": end_day,\n                        \"type\": \"health_period\",\n                        \"category\": event_type,\n                        \"label\": label,\n                    }\n                )\n    result.sort(key=lambda item: (item[\"date\"], item[\"type\"], item.get(\"label\", \"\")))\n    if len(result) > MAX_EVENT_ROWS:\n        raise APIError(422, \"row_limit_exceeded\")\n    return {\n        \"from\": start.isoformat(),\n        \"to\": end.isoformat(),\n        \"types\": sorted(selected),\n        \"timezone\": TZ_NAME,\n        \"events\": result,\n    }\n\n\ndef _safe_documents(\n    connection: sqlite3.Connection, needle: str\n) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"dokumente\"):\n        return []\n    result = []\n    for row in connection.execute(\n        \"\"\"SELECT id,document_date,kategorie,institution FROM dokumente\n           WHERE review_status='geprueft' ORDER BY document_date DESC,id DESC LIMIT 200\"\"\"\n    ):\n        category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\n        institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\n        document_day = parse_day(row[\"document_date\"])\n        if category is None or institution is None:\n            continue\n        haystack = f\"{category} {institution} {document_day or ''}\".casefold()\n        if needle.casefold() not in haystack:\n            continue\n        result.append(\n            {\n                \"id\": api_document_id(row[\"id\"], document_day, category, institution),\n                \"date\": document_day,\n                \"category\": category,\n                \"institution\": institution,\n                \"drill_down_target\": \"document_metadata\",\n            }\n        )\n        if len(result) >= MAX_SEARCH_ROWS_PER_GROUP:\n            break\n    return result\n\n\ndef _fts_search_documents(\n    connection: sqlite3.Connection, query: str\n) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"health_document_fts\"):\n        return []\n    page = _record_document_rows(\n        connection,\n        {\"q\": query, \"limit\": str(MAX_SEARCH_ROWS_PER_GROUP)},\n    )\n    return [\n        {\n            \"id\": row[\"id\"],\n            \"date\": row[\"document_date\"],\n            \"category\": row[\"category\"],\n            \"institution\": row[\"institution\"],\n            \"snippet\": row[\"snippet\"],\n            \"drill_down_target\": \"document\",\n        }\n        for row in page[\"documents\"]\n    ]\n\n\ndef _search(connection: sqlite3.Connection, query: str) -> dict[str, Any]:\n    clean = \" \".join(query.split())\n    if len(clean) < 2 or len(clean) > 80:\n        raise APIError(400, \"query_length_invalid\")\n    matches = catalog_search(connection, clean)\n    groups: dict[str, list[dict[str, Any]]] = {\n        \"metrics\": [],\n        \"laboratory\": [],\n        \"symptoms_events\": [],\n        \"documents\": [],\n        \"days\": [],\n    }\n    for item in matches:\n        target = (\n            \"laboratory\"\n            if item[\"category\"] == \"laboratory\"\n            else (\"symptoms_events\" if item[\"category\"] == \"symptoms\" else \"metrics\")\n        )\n        if len(groups[target]) < MAX_SEARCH_ROWS_PER_GROUP:\n            groups[target].append(item)\n    normalized = clean.casefold()\n    if normalized in {\"medikamente\", \"medikament\", \"medikation\", \"arznei\"}:\n        groups[\"symptoms_events\"].append(\n            {\n                \"id\": \"event.medication\",\n                \"label\": \"Medikamente\",\n                \"category\": \"events\",\n                \"drill_down_target\": \"day\",\n                \"availability\": {\"status\": \"supported\"},\n            }\n        )\n    groups[\"documents\"] = _fts_search_documents(connection, clean)\n    if not groups[\"documents\"]:\n        groups[\"documents\"] = _safe_documents(connection, clean)\n    try:\n        parsed = parse_iso_day(clean)\n    except APIError:\n        parsed = None\n    if parsed is not None and parsed <= local_today():\n        groups[\"days\"] = [{\"date\": parsed.isoformat(), \"drill_down_target\": \"day\"}]\n    return {\"query\": clean, \"timezone\": TZ_NAME, \"groups\": groups}\n\n\ndef _safe_day_text(\n    value: Any, maximum: int = 160, *, allow_empty: bool = True\n) -> str | None:\n    return safe_metadata_text(value, maximum, allow_empty=allow_empty)\n\n\ndef _day_symptoms(connection: sqlite3.Connection, day: str) -> dict[str, Any]:\n    dimensions = {\n        label: {\"value\": None, \"note\": None, \"status\": \"not_documented\"}\n        for label in sorted(SYMPTOM_LABELS)\n    }\n    if table_exists(connection, \"symptom_log\"):\n        rows = list(\n            connection.execute(\n                \"SELECT symptom,schwergrad,notizen FROM symptom_log WHERE datum=? AND kontext='daily_quick_score' ORDER BY id LIMIT 100\",\n                (day,),\n            )\n        )\n        for row in rows:\n            label = str(row[\"symptom\"] or \"\")\n            if label not in dimensions:\n                continue\n            score = parse_score(row[\"schwergrad\"])\n            note = _safe_day_text(row[\"notizen\"], 300)\n            dimensions[label] = {\n                \"value\": score,\n                \"note\": note,\n                \"status\": \"observed\" if score is not None else \"not_documented\",\n            }\n    values = [entry[\"value\"] for entry in dimensions.values()]\n    complete = all(value is not None for value in values)\n    notes = sorted({entry[\"note\"] for entry in dimensions.values() if entry[\"note\"]})\n    return {\n        \"dimensions\": [{\"name\": name, **entry} for name, entry in dimensions.items()],\n        \"total\": sum(values) if complete else None,\n        \"complete\": complete,\n        \"documented_dimensions\": sum(value is not None for value in values),\n        \"expected_dimensions\": len(dimensions),\n        \"notes\": notes,\n    }\n\n\ndef _day_medications(\n    connection: sqlite3.Connection, requested: str\n) -> dict[str, list[dict[str, Any]]]:\n    result = {\"planned\": [], \"administered\": [], \"missed\": [], \"corrected\": []}\n    if not table_exists(connection, \"medication_administrations\"):\n        return result\n    rows = list(\n        connection.execute(\n            \"\"\"SELECT datum,medication_name,dose,route,event_type,scheduled_next_date,notes,source\n           FROM medication_administrations\n           WHERE datum=? OR scheduled_next_date=? ORDER BY id LIMIT 200\"\"\",\n            (requested, requested),\n        )\n    )\n    missed = {\"missed\", \"verpasst\", \"ausgelassen\"}\n    corrected = {\"corrected\", \"korrigiert\", \"correction\"}\n    for row in rows:\n        event_type = str(row[\"event_type\"] or \"\").strip().casefold()\n        name = _safe_day_text(row[\"medication_name\"], 120, allow_empty=False)\n        dose = _safe_day_text(row[\"dose\"], 40)\n        route = _safe_day_text(row[\"route\"], 40)\n        note = _safe_day_text(row[\"notes\"], 300)\n        source = _safe_day_text(row[\"source\"], 80)\n        if not name or dose is None or route is None or note is None or source is None:\n            continue\n        item = {\n            \"name\": name,\n            \"dose\": dose,\n            \"route\": route,\n            \"note\": note,\n            \"source\": source,\n        }\n        datum = parse_day(row[\"datum\"])\n        scheduled = parse_day(row[\"scheduled_next_date\"])\n        if datum == requested and event_type in ADMINISTERED:\n            result[\"administered\"].append(item)\n        elif datum == requested and event_type in missed:\n            result[\"missed\"].append(item)\n        elif datum == requested and event_type in corrected:\n            result[\"corrected\"].append(item)\n        elif (\n            datum == requested and event_type in {\"planned\", \"scheduled\", \"geplant\"}\n        ) or scheduled == requested:\n            result[\"planned\"].append(item)\n    return result\n\n\ndef _day_events(\n    connection: sqlite3.Connection, requested: str\n) -> dict[str, list[dict[str, Any]]]:\n    events: list[dict[str, Any]] = []\n    periods: list[dict[str, Any]] = []\n    if table_exists(connection, \"health_events\"):\n        for row in connection.execute(\n            \"SELECT category,parameter,value,unit,source,notes FROM health_events WHERE date=? ORDER BY id LIMIT 200\",\n            (requested,),\n        ):\n            fields = {\n                \"category\": _safe_day_text(row[\"category\"], 80),\n                \"label\": _safe_day_text(row[\"parameter\"], 120),\n                \"value\": _safe_day_text(row[\"value\"], 80),\n                \"unit\": _safe_day_text(row[\"unit\"], 40),\n                \"source\": _safe_day_text(row[\"source\"], 80),\n                \"note\": _safe_day_text(row[\"notes\"], 300),\n            }\n            if all(value is not None for value in fields.values()):\n                events.append(fields)\n    if table_exists(connection, \"health_event_periods\"):\n        for row in connection.execute(\n            \"\"\"SELECT start_date,end_date,event_type,label,source,notes\n               FROM health_event_periods WHERE start_date<=? AND COALESCE(end_date,start_date)>=?\n               ORDER BY start_date,id LIMIT 200\"\"\",\n            (requested, requested),\n        ):\n            start = parse_day(row[\"start_date\"])\n            end = parse_day(row[\"end_date\"]) if row[\"end_date\"] else start\n            event_type = _safe_day_text(row[\"event_type\"], 80, allow_empty=False)\n            label = _safe_day_text(row[\"label\"], 120)\n            source = _safe_day_text(row[\"source\"], 80)\n            note = _safe_day_text(row[\"notes\"], 300)\n            if (\n                start\n                and end\n                and start <= requested <= end\n                and event_type\n                and label is not None\n                and source is not None\n                and note is not None\n            ):\n                periods.append(\n                    {\n                        \"start_date\": start,\n                        \"end_date\": end,\n                        \"type\": event_type,\n                        \"label\": label,\n                        \"source\": source,\n                        \"note\": note,\n                    }\n                )\n    return {\"events\": events, \"periods\": periods}\n\n\ndef _day_documents(\n    connection: sqlite3.Connection, requested: str\n) -> list[dict[str, Any]]:\n    if not table_exists(connection, \"dokumente\"):\n        return []\n    result = []\n    rows = connection.execute(\n        \"\"\"SELECT id,document_date,kategorie,institution FROM dokumente\n           WHERE document_date=? AND review_status='geprueft' ORDER BY id LIMIT 100\"\"\",\n        (requested,),\n    )\n    for row in rows:\n        category = _safe_day_text(row[\"kategorie\"], 80)\n        institution = _safe_day_text(row[\"institution\"], 120)\n        if category is None or institution is None:\n            continue\n        result.append(\n            {\n                \"id\": api_document_id(row[\"id\"], requested, category, institution),\n                \"date\": requested,\n                \"category\": category,\n                \"institution\": institution,\n            }\n        )\n    return result\n\n\ndef _day_appointments(connection: sqlite3.Connection, requested: str) -> dict[str, Any]:\n    if not table_exists(connection, \"arztbesuche\"):\n        return {\"status\": \"supported_no_data\", \"items\": []}\n    items = []\n    for row in connection.execute(\n        \"SELECT arzt,klinik,grund,notizen FROM arztbesuche WHERE datum=? ORDER BY id LIMIT 100\",\n        (requested,),\n"}}
{"timestamp":"2026-07-16T17:46:17.365Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_d4IssQvTh0jxqsNDefZAnvKt","output":"Chunk ID: f3c8cd\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3850\nOutput:\n(() => {\n  \"use strict\";\n\n  const VIEW_IDS = [\"today\", \"cockpit\", \"explorer\", \"nutrition\", \"doctor\", \"more\"];\n  if (document.querySelector(\"[data-dashboard-panel='calendar']\")) VIEW_IDS.push(\"calendar\");\n  if (document.querySelector(\"[data-dashboard-panel='day']\")) VIEW_IDS.push(\"day\");\n  const PERIODS = [\"7\", \"30\", \"90\", \"180\", \"all\"];\n  const charts = new Map();\n  let bundle = {};\n  let activeView = \"today\";\n  let activePeriod = \"30\";\n  let explorerMode = \"raw\";\n  let explorerCadence = \"daily\";\n\n  const q = (selector, root = document) => root.querySelector(selector);\n  const qa = (selector, root = document) => Array.from(root.querySelectorAll(selector));\n\n  function requestBrowserSession() {\n    if (window.healthApiSessionReady) return window.healthApiSessionReady;\n    const meta = document.querySelector(\"meta[name='health-browser-session-csrf']\");\n    window.healthApiSessionReady = meta?.content ? fetch(\"/api/v1/browser-session\", {\n      method: \"POST\",\n      credentials: \"same-origin\",\n      headers: { \"X-Health-Browser-CSRF\": meta.content },\n    }).then((response) => {\n      if (!response.ok) throw new Error(`session_${response.status}`);\n    }) : Promise.reject(new Error(\"missing_csrf\"));\n    return window.healthApiSessionReady;\n  }\n\n  async function apiGet(path) {\n    await requestBrowserSession();\n    const response = await fetch(path, { credentials: \"same-origin\" });\n    let data = null;\n    try { data = await response.json(); } catch (_error) { data = null; }\n    if (!response.ok) throw new Error(data?.error || `api_${response.status}`);\n    return data;\n  }\n\n  function readBundle() {\n    const node = document.getElementById(\"health-dashboard-bundle\");\n    if (!node) return {};\n    try {\n      const value = JSON.parse(node.textContent || \"{}\");\n      return value && typeof value === \"object\" && !Array.isArray(value) ? value : {};\n    } catch (_error) {\n      const status = q(\"[data-dashboard-status]\");\n      if (status) status.textContent = \"Die Dashboard-Daten konnten nicht gelesen werden.\";\n      return {};\n    }\n  }\n\n  function getDate(item) {\n    if (!item || typeof item !== \"object\") return null;\n    const raw = item.date || item.datetime || item.timestamp || item.measured_at || item.recorded_at;\n    if (!raw) return null;\n    const value = new Date(raw);\n    return Number.isNaN(value.getTime()) ? null : value;\n  }\n\n  function periodData(value) {\n    if (!Array.isArray(value)) return value;\n    const range = window.healthRange?.get?.();\n    if (range?.from && range?.to) {\n      return value.filter((item) => {\n        const date = getDate(item);\n        if (!date) return true;\n        const day = date.toISOString().slice(0, 10);\n        return day >= range.from && day <= range.to;\n      });\n    }\n    if (activePeriod === \"all\") return value;\n    const cutoff = new Date(`${bundle.today || new Date().toISOString().slice(0, 10)}T00:00:00`);\n    cutoff.setDate(cutoff.getDate() - Number(activePeriod) + 1);\n    return value.filter((item) => {\n      const date = getDate(item);\n      return !date || date >= cutoff;\n    });\n  }\n\n  function lookup(path) {\n    if (!path) return null;\n    const value = path.split(\".\").reduce((current, key) => {\n      return current && Object.prototype.hasOwnProperty.call(current, key) ? current[key] : undefined;\n    }, bundle);\n    return periodData(value);\n  }\n\n  function formatValue(value, format) {\n    if (value === null || value === undefined || value === \"\") return \"–\";\n    if (format === \"date\") {\n      const date = new Date(value);\n      return Number.isNaN(date.getTime()) ? \"–\" : new Intl.DateTimeFormat(\"de-CH\").format(date);\n    }\n    if (typeof value === \"number\") return new Intl.NumberFormat(\"de-CH\", { maximumFractionDigits: 1 }).format(value);\n    return String(value);\n  }\n\n  function updateBindings(panel) {\n    qa(\"[data-bundle-value]\", panel).forEach((node) => {\n      const value = lookup(node.dataset.bundleValue);\n      node.textContent = formatValue(value, node.dataset.format);\n    });\n    qa(\"[data-bundle-count]\", panel).forEach((node) => {\n      const value = lookup(node.dataset.bundleCount);\n      node.textContent = Array.isArray(value) ? String(value.length) : \"0\";\n    });\n    qa(\"[data-empty-for]\", panel).forEach((node) => {\n      const value = lookup(node.dataset.emptyFor);\n      node.hidden = Array.isArray(value) ? value.length > 0 : Boolean(value);\n    });\n  }\n\n  function chartConfig(canvas) {\n    const rows = lookup(canvas.dataset.chartSource);\n    if (!Array.isArray(rows) || !rows.length) return null;\n    const xKey = canvas.dataset.chartX || \"date\";\n    const yKeys = (canvas.dataset.chartY || \"value\").split(\",\").map((key) => key.trim()).filter(Boolean);\n    const labels = rows.map((row) => formatValue(row[xKey], xKey.includes(\"date\") ? \"date\" : \"\"));\n    const palette = [\"#315b70\", \"#826a3d\", \"#66567a\", \"#536b58\"];\n    return {\n      type: canvas.dataset.chartType || \"line\",\n      data: {\n        labels,\n        datasets: yKeys.map((key, index) => ({\n          label: canvas.dataset[`label${index + 1}`] || key,\n          data: rows.map((row) => Number.isFinite(Number(row[key])) ? Number(row[key]) : null),\n          borderColor: palette[index % palette.length],\n          backgroundColor: `${palette[index % palette.length]}26`,\n          pointRadius: rows.length > 45 ? 0 : 2,\n          borderWidth: 2,\n          tension: 0.2,\n          spanGaps: false\n        }))\n      },\n      options: {\n        responsive: true,\n        maintainAspectRatio: false,\n        animation: window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches ? false : undefined,\n        interaction: { intersect: false, mode: \"index\" },\n        plugins: { legend: { position: \"bottom\", labels: { usePointStyle: true } } },\n        scales: {\n          x: { grid: { display: false }, ticks: { maxTicksLimit: 7 } },\n          y: { beginAtZero: canvas.dataset.beginZero === \"true\", ticks: { maxTicksLimit: 6 } }\n        }\n      }\n    };\n  }\n\n  function initializeCharts(panel) {\n    if (!window.Chart) return;\n    qa(\"canvas[data-chart-source]\", panel).forEach((canvas) => {\n      const previous = charts.get(canvas);\n      const config = chartConfig(canvas);\n      if (!config) {\n        if (previous) previous.destroy();\n        charts.delete(canvas);\n        const empty = canvas.closest(\"[data-chart-frame]\")?.querySelector(\"[data-chart-empty]\");\n        if (empty) empty.hidden = false;\n        canvas.hidden = true;\n        return;\n      }\n      canvas.hidden = false;\n      const empty = canvas.closest(\"[data-chart-frame]\")?.querySelector(\"[data-chart-empty]\");\n      if (empty) empty.hidden = true;\n      if (previous) {\n        previous.data = config.data;\n        previous.options = config.options;\n        previous.update();\n        previous.resize();\n      } else {\n        charts.set(canvas, new window.Chart(canvas.getContext(\"2d\"), config));\n      }\n    });\n  }\n\n  function metricSeries(metricId) {\n    return periodData(bundle.series && Array.isArray(bundle.series[metricId]) ? bundle.series[metricId] : []);\n  }\n\n  function isoWeekMonday(day) {\n    const value = new Date(`${day}T00:00:00Z`);\n    if (Number.isNaN(value.getTime())) return null;\n    const offset = (value.getUTCDay() + 6) % 7;\n    value.setUTCDate(value.getUTCDate() - offset);\n    return value.toISOString().slice(0, 10);\n  }\n\n  function completeWeeklySeries(rows, valueFor, aggregation = \"avg\") {\n    const buckets = new Map();\n    rows.forEach((row) => {\n      const monday = isoWeekMonday(row.date);\n      if (!monday) return;\n      if (!buckets.has(monday)) buckets.set(monday, new Map());\n      buckets.get(monday).set(row.date, valueFor(row));\n    });\n    return [...buckets].sort(([left], [right]) => left.localeCompare(right)).map(([date, byDay]) => {\n      const values = [...byDay.values()];\n      const numericDays = values.filter((value) => typeof value === \"number\" && Number.isFinite(value)).length;\n      const complete = byDay.size === 7 && numericDays === 7;\n      const value = !complete ? null : aggregation === \"sum\"\n        ? values.reduce((total, item) => total + item, 0)\n        : values.reduce((total, item) => total + item, 0) / values.length;\n      return { date, value: value === null ? null : Number(value.toFixed(3)), complete_days: numericDays };\n    });\n  }\n\n  function explorerSeries(metricId, metric, mode = explorerMode) {\n    const rows = metricSeries(metricId);\n    if (explorerCadence === \"daily\") {\n      return rows.map((point) => ({ date: point.date, value: explorerPointValue(point, metric, mode) }));\n    }\n    const aggregation = mode === \"baseline\" ? \"avg\" : metric?.aggregation || \"avg\";\n    return completeWeeklySeries(rows, (point) => explorerPointValue(point, metric, mode), aggregation);\n  }\n\n  function weeklyAxisDays(observedDays) {\n    if (!observedDays.length) return [];\n    const days = [];\n    const cursor = new Date(`${observedDays[0]}T00:00:00Z`);\n    const end = new Date(`${observedDays[observedDays.length - 1]}T00:00:00Z`);\n    while (cursor <= end) {\n      days.push(cursor.toISOString().slice(0, 10));\n      cursor.setUTCDate(cursor.getUTCDate() + 7);\n    }\n    return days;\n  }\n\n  function node(tag, className, text) {\n    const element = document.createElement(tag);\n    if (className) element.className = className;\n    if (text !== undefined) element.textContent = text;\n    return element;\n  }\n\n  function latestPoint(metricId) {\n    const rows = metricSeries(metricId).filter((row) => row.value !== null && row.value !== undefined);\n    return rows.length ? rows[rows.length - 1] : null;\n  }\n\n  function daysBetween(start, end) {\n    const left = new Date(`${start}T00:00:00Z`);\n    const right = new Date(`${end}T00:00:00Z`);\n    if (Number.isNaN(left.getTime()) || Number.isNaN(right.getTime())) return null;\n    return Math.round((right.getTime() - left.getTime()) / 86400000);\n  }\n\n  function measurementAge(day) {\n    const age = daysBetween(day, bundle.today);\n    if (age === null || age < 0) return \"Messalter unbekannt\";\n    if (age === 0) return \"heute dokumentiert\";\n    if (age === 1) return \"vor 1 Tag dokumentiert\";\n    return `vor ${age} Tagen dokumentiert`;\n  }\n\n  function documentationCoverage(metricId) {\n    const rows = metricSeries(metricId).filter((row) => row.value !== null && row.value !== undefined);\n    let expected = activePeriod === \"all\" ? 0 : Number(activePeriod);\n    if (activePeriod === \"all\" && rows.length) expected = Math.max(1, (daysBetween(rows[0].date, bundle.today) || 0) + 1);\n    return { observed: new Set(rows.map((row) => row.date)).size, expected };\n  }\n\n  function sparkline(metricId, label) {\n    const rows = metricSeries(metricId).filter((row) => row.value !== null && row.value !== undefined).slice(-45);\n    if (!rows.length) return node(\"div\", \"v5-empty\", \"Keine Trendpunkte im Zeitraum\");\n    const chart = node(\"div\", \"metric-sparkline\");\n    chart.setAttribute(\"role\", \"img\");\n    chart.setAttribute(\"aria-label\", `${label}: ${rows.length} dokumentierte Trendpunkte; Lücken werden nicht verbunden.`);\n    chart.dataset.sparklineMetric = metricId;\n    return chart;\n  }\n\n  function renderSparkline(element) {\n    if (!window.HealthV5ECharts?.hasECharts?.()) return;\n    const metricId = element.dataset.sparklineMetric;\n    const metric = (bundle.metrics || []).find((item) => item.id === metricId);\n    const rows = metricSeries(metricId).filter((row) => row.value !== null && row.value !== undefined).slice(-45);\n    if (!metric || !rows.length) return;\n    const days = rows.map((row) => row.date);\n    window.HealthV5ECharts.renderLine(element, {\n      metrics: [metric],\n      rowsByMetric: new Map([[metric.id, rows]]),\n      days,\n      source: \"metric_card\",\n      aria: `${metric.label}: kompakter dokumentierter Verlauf; fehlende Tage bleiben Lücken.`,\n    });\n    const instance = window.echarts?.getInstanceByDom?.(element);\n    instance?.setOption({\n      tooltip: { show: false },\n      legend: { show: false },\n      grid: { left: 4, right: 4, top: 4, bottom: 4 },\n      xAxis: { show: false },\n      yAxis: { show: false, scale: true },\n      series: [{ symbolSize: 0, lineStyle: { width: 2 } }],\n    });\n  }\n\n  function renderMetricCards() {\n    const target = q(\"#metric-cards\");\n    if (!target || target.dataset.renderedPeriod === activePeriod) return;\n    target.replaceChildren();\n    (bundle.metrics || []).forEach((metric) => {\n      const point = latestPoint(metric.id);\n      const coverage = documentationCoverage(metric.id);\n      const card = node(\"article\", \"v5-card metric-card\");\n      const displayedValue = point\n        ? metric.id === \"symptom.total\" ? `${formatValue(point.value)} / 21` : `${formatValue(point.value)} ${metric.unit}`\n        : \"unbekannt\";\n      const deltaUnit = metric.id === \"symptom.total\" ? \"Punkte\" : metric.unit;\n      card.dataset.private = \"true\";\n      card.append(node(\"h3\", \"v5-label\", metric.label));\n      card.append(node(\"div\", \"v5-kpi\", displayedValue));\n      card.append(node(\"div\", \"v5-meta\", point ? `${formatValue(point.date, \"date\")} · ${measurementAge(point.date)}` : \"Keine dokumentierte Beobachtung im Zeitraum\"));\n      const comparison = point && point.baseline !== null && point.baseline !== undefined\n        ? `Abweichung zur persönlichen ${metric.baseline_days}-Tage-Basis: ${formatValue(point.value - point.baseline)} ${deltaUnit}`\n        : \"Persönliche Vergleichsbasis noch nicht ausreichend\";\n      card.append(node(\"div\", \"v5-meta\", comparison));\n      card.append(sparkline(metric.id, metric.label));\n      const footer = node(\"div\", \"metric-card-footer\");\n      footer.append(node(\"span\", \"v5-badge\", point?.quality === \"complete\" ? \"vollständig\" : point ? \"dokumentiert\" : \"unbekannt\"));\n      footer.append(node(\"span\", \"v5-meta\", coverage.expected ? `${coverage.observed}/${coverage.expected} Tage im Zeitraum` : `${coverage.observed} dokumentierte Tage`));\n      card.append(footer);\n      card.tabIndex = 0;\n      card.setAttribute(\"role\", \"button\");\n      card.setAttribute(\"aria-label\", `${metric.label} im Explorer öffnen`);\n      card.addEventListener(\"click\", () => openMetricInExplorer(metric.id));\n      card.addEventListener(\"keydown\", (event) => {\n        if (event.key === \"Enter\" || event.key === \" \") {\n          event.preventDefault();\n          openMetricInExplorer(metric.id);\n        }\n      });\n      target.append(card);\n      const spark = card.querySelector(\"[data-sparkline-metric]\");\n      if (spark) renderSparkline(spark);\n    });\n    target.dataset.renderedPeriod = activePeriod;\n  }\n\n  function openMetricInExplorer(metricId) {\n    selectView(\"explorer\");\n    window.healthExplorerAddMetric?.(metricId);\n  }\n\n  function renderDataRecency() {\n    const target = q(\"#header-data-status\");\n    if (!target) return;\n    const days = Object.values(bundle.series || {}).flat()\n      .filter((point) => point.value !== null && point.value !== undefined)\n      .map((point) => point.date)\n      .concat((bundle.labs || []).map((item) => item.date))\n      .concat(bundle.medication?.last_administered?.date || [])\n      .filter((day) => typeof day === \"string\" && day <= bundle.today)\n      .sort();\n    if (!days.length) {\n      target.textContent = \"Keine aktuellen Daten dokumentiert\";\n      return;\n    }\n    const latest = days[days.length - 1];\n    target.textContent = `Neuester Datenpunkt: ${measurementAge(latest)}`;\n"}}
{"timestamp":"2026-07-16T17:46:17.365Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":128523,"cached_input_tokens":79872,"output_tokens":1134,"reasoning_output_tokens":18,"total_tokens":129657},"last_token_usage":{"input_tokens":50056,"cached_input_tokens":37760,"output_tokens":261,"reasoning_output_tokens":0,"total_tokens":50317},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:46:26.659Z","type":"event_msg","payload":{"type":"agent_message","message":"Two review gaps are already clear: the mapping contract still uses traffic-light fields and personal tolerance values like `safe`, and the worker infers composites from punctuation/name tokens. I’m replacing that with an explicit decision contract and neutral index fields, then I’ll harden the read API and tests around it.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:46:26.661Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Two review gaps are already clear: the mapping contract still uses traffic-light fields and personal tolerance values like `safe`, and the worker infers composites from punctuation/name tokens. I’m replacing that with an explicit decision contract and neutral index fields, then I’ll harden the read API and tests around it."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:46:26.665Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"nutrition|mapping|traffic|safe|Ampel|sighi|histamine|meal|Früh|Snack|csrf|NUTRITION\\\" scripts/health/assets/health-assets/dashboard-v5.js scripts/health/assets/health-assets/dashboard-v5.css scripts/health/dashboard_v5/render.py tests/fixtures/dashboard_v5_fixture.py database/schema.sql docs/adr/0003-dietary-inflammation-model.md -S\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}","call_id":"call_ZcWUUmmEDblI1CiR0zq9ILSy"}}
{"timestamp":"2026-07-16T17:46:26.667Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1180,1540p' scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":45000}","call_id":"call_6yrgeOqCVTymznjki2u7ohUN"}}
{"timestamp":"2026-07-16T17:46:26.671Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,420p' tests/fixtures/dashboard_v5_fixture.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}","call_id":"call_bEcdlMAdFdl17pUe5XHj7pgr"}}
{"timestamp":"2026-07-16T17:46:26.729Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '1,260p' docs/adr/0003-dietary-inflammation-model.md\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":25000}","call_id":"call_iEvZvVyxnsWrh53m7mW2XAkZ"}}
{"timestamp":"2026-07-16T17:46:26.738Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ZcWUUmmEDblI1CiR0zq9ILSy","output":"Chunk ID: 8a2e40\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3681\nOutput:\ndocs/adr/0003-dietary-inflammation-model.md:11:Nutrition data combines imported meal records, nutrient totals, SIGHi-style histamine mappings, symptom dimensions, and optional context such as medication phases or documented events. These inputs are incomplete on many days and may contain unknown food mappings.\ndocs/adr/0003-dietary-inflammation-model.md:18:- versioned histamine mapping coverage,\ndocs/adr/0003-dietary-inflammation-model.md:21:- side-by-side ECharts views for nutrition and Aphthae when both are documented.\ndocs/adr/0003-dietary-inflammation-model.md:23:The dashboard does not compute a dietary inflammation index when required inputs are insufficient. Unknown or unmapped foods make the histamine component incomplete rather than zero. Composite products require ingredient review before a definitive mapping is applied.\ndocs/adr/0003-dietary-inflammation-model.md:25:Personal tolerance labels remain separate from shared food mapping definitions.\ntests/fixtures/dashboard_v5_fixture.py:33:SPRINT4B_PREDICTORS: Final = (\"apple:heart_rate_variability\", \"nutrition:histamine_score\")\ntests/fixtures/dashboard_v5_fixture.py:225:            \"\"\"INSERT INTO nutrition_items\ntests/fixtures/dashboard_v5_fixture.py:226:               (id,source,source_item_id,datum,meal,name,brand,amount,amount_unit,\ntests/fixtures/dashboard_v5_fixture.py:235:            \"\"\"INSERT INTO nutrition_histamine_scores\ntests/fixtures/dashboard_v5_fixture.py:236:               (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\ntests/fixtures/dashboard_v5_fixture.py:239:                (1, \"synthetic cucumber\", 0, \"green\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\ntests/fixtures/dashboard_v5_fixture.py:244:            \"\"\"INSERT INTO nutrition_daily_summary_v2\ntests/fixtures/dashboard_v5_fixture.py:245:               (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\ntests/fixtures/dashboard_v5_fixture.py:246:                histamine_max,histamine_unknown_count,histamine_label,nutrient_json,updated_at)\ntests/fixtures/dashboard_v5_fixture.py:251:            \"\"\"INSERT INTO nutrition_meal_summary\ntests/fixtures/dashboard_v5_fixture.py:252:               (datum,meal,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_label,updated_at)\ntests/fixtures/dashboard_v5_fixture.py:257:            \"\"\"INSERT INTO nutrition_review_queue\ntests/fixtures/dashboard_v5_fixture.py:260:            (\"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\", \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\", 1, _iso_day(current), _iso_day(current), \"synthetic reviewed food\", 1, \"synthetic missing mapping\", \"open\", fixture_timestamp),\ntests/fixtures/dashboard_v5_fixture.py:308:                \"nutrition_items\", \"nutrition_histamine_scores\", \"symptom_log\",\ntests/fixtures/dashboard_v5_fixture.py:325:            \"food_mapping_states\": connection.execute(\ntests/fixtures/dashboard_v5_fixture.py:326:                \"\"\"SELECT SUM(canonical_food IS NOT NULL AND sighi_score IS NOT NULL),\ntests/fixtures/dashboard_v5_fixture.py:327:                          SUM(canonical_food IS NULL OR sighi_score IS NULL)\ntests/fixtures/dashboard_v5_fixture.py:328:                   FROM nutrition_histamine_scores\"\"\"\ndatabase/schema.sql:97:            nutrition_json TEXT,\ndatabase/schema.sql:158:CREATE TABLE histamine_food_aliases (\ndatabase/schema.sql:161:    FOREIGN KEY (canonical_food) REFERENCES histamine_food_rules(canonical_food)\ndatabase/schema.sql:164:CREATE TABLE histamine_food_rules (\ndatabase/schema.sql:167:    sighi_score INTEGER NOT NULL CHECK(sighi_score BETWEEN 0 AND 3),\ndatabase/schema.sql:172:    source TEXT DEFAULT 'local_seed_sighi_inspired',\ndatabase/schema.sql:234:CREATE TABLE low_histamine_experiments (\ndatabase/schema.sql:262:CREATE TABLE nutrition_action_recommendations (\ndatabase/schema.sql:274:CREATE TABLE nutrition_correlation_results (\ndatabase/schema.sql:287:CREATE TABLE nutrition_daily_features (\ndatabase/schema.sql:290:            histamine_score REAL,\ndatabase/schema.sql:306:CREATE TABLE nutrition_daily_summary_v2 (\ndatabase/schema.sql:314:    histamine_score REAL DEFAULT 0,\ndatabase/schema.sql:315:    histamine_max INTEGER DEFAULT 0,\ndatabase/schema.sql:316:    histamine_unknown_count INTEGER DEFAULT 0,\ndatabase/schema.sql:317:    histamine_label TEXT DEFAULT 'unknown',\ndatabase/schema.sql:321:CREATE TABLE nutrition_food_insights (\ndatabase/schema.sql:324:    insight_type TEXT NOT NULL CHECK(insight_type IN ('trigger_candidate','safe_candidate','insufficient_data')),\ndatabase/schema.sql:329:    histamine_avg REAL,\ndatabase/schema.sql:336:CREATE TABLE nutrition_histamine_scores (\ndatabase/schema.sql:339:    sighi_score INTEGER,\ndatabase/schema.sql:340:    traffic_light TEXT NOT NULL,\ndatabase/schema.sql:345:    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\ndatabase/schema.sql:348:CREATE TABLE nutrition_item_nutrients (\ndatabase/schema.sql:355:    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\ndatabase/schema.sql:358:CREATE TABLE nutrition_items (\ndatabase/schema.sql:364:    meal TEXT NOT NULL,\ndatabase/schema.sql:382:CREATE TABLE nutrition_meal_summary (\ndatabase/schema.sql:384:    meal TEXT NOT NULL,\ndatabase/schema.sql:391:    histamine_score REAL DEFAULT 0,\ndatabase/schema.sql:392:    histamine_label TEXT DEFAULT 'unknown',\ndatabase/schema.sql:394:    PRIMARY KEY (datum, meal)\ndatabase/schema.sql:397:CREATE TABLE nutrition_review_queue (\ndatabase/schema.sql:411:CREATE TABLE nutrition_treatment_phase_summary (\ndatabase/schema.sql:415:        nutrition_days INTEGER DEFAULT 0,\ndatabase/schema.sql:416:        avg_histamine_score REAL,\ndatabase/schema.sql:426:    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\ndatabase/schema.sql:432:CREATE TABLE nutrition_mapping_action_log (\ndatabase/schema.sql:439:CREATE TABLE nutrition_mapping_provenance (\ndatabase/schema.sql:444:    sighi_score INTEGER,\ndatabase/schema.sql:445:    traffic_light TEXT NOT NULL,\ndatabase/schema.sql:446:    mapping_scope TEXT NOT NULL,\ndatabase/schema.sql:452:CREATE TABLE nutrition_composite_product_review (\ndatabase/schema.sql:564:CREATE INDEX idx_nutrition_items_day_meal ON nutrition_items(datum, meal);\ndatabase/schema.sql:566:CREATE INDEX idx_nutrition_items_product ON nutrition_items(source_product_id);\ndatabase/schema.sql:568:CREATE INDEX idx_nutrition_nutrients_key ON nutrition_item_nutrients(nutrient_key);\nscripts/health/assets/health-assets/dashboard-v5.css:197:  .bottom-nav { position: fixed; inset: auto 8px max(8px, env(safe-area-inset-bottom)) 8px; top: auto; display: flex; gap: 4px; margin: 0; overflow-x: auto; overflow-y: hidden; overscroll-behavior-x: contain; scrollbar-width: thin; }\nscripts/health/assets/health-assets/dashboard-v5.css:199:  .app-view { padding-bottom: calc(112px + env(safe-area-inset-bottom)); scroll-padding-bottom: calc(112px + env(safe-area-inset-bottom)); }\nscripts/health/assets/health-assets/dashboard-v5.css:204:  [data-dashboard-v5] { padding: 10px; padding-bottom: max(16px, env(safe-area-inset-bottom)); }\nscripts/health/assets/health-assets/dashboard-v5.css:381:  .range-custom { position: fixed; inset: auto 8px calc(84px + env(safe-area-inset-bottom)) 8px; min-width: 0; max-height: 62vh; overflow: auto; }\nscripts/health/dashboard_v5/render.py:7:from dashboard_v5.contracts import safe_json\nscripts/health/dashboard_v5/render.py:11:    (\"nutrition\", \"Ernährung\", \"Essen\"), (\"doctor\", \"Arzt\", \"Arzt\"),\nscripts/health/dashboard_v5/render.py:20:    payload = safe_json(bundle)\nscripts/health/dashboard_v5/render.py:91:    <section class='v5-surface' data-day-section='nutrition'><h3>Ernährung</h3><div data-day-nutrition></div></section>\nscripts/health/dashboard_v5/render.py:120:<meta name='health-browser-session-csrf' content='__BROWSER_SESSION_CSRF__'>\nscripts/health/dashboard_v5/render.py:135:<dialog id='checkin-dialog' aria-labelledby='checkin-title'><form method='post' action='/health-actions/symptom-checkin'><input type='hidden' name='csrf_token' value='__CSRF_TOKEN__'><input type='hidden' name='return_to' value='v5'><input type='hidden' name='date' value='__CAPTURE_DATE__'><h2 id='checkin-title'>Beschwerden erfassen</h2><p class='v5-meta'>Erfassung für <time id='capture-date' datetime='__CAPTURE_DATE__'>__CAPTURE_DATE_LABEL__</time></p><div class='score-grid'>{symptom_fields}</div><label>Notiz, optional<textarea name='notes' maxlength='300'></textarea></label><div class='dialog-actions'><button type='button' id='close-checkin'>Abbrechen</button><button type='submit'>Sicher vormerken</button></div></form></dialog>\nscripts/health/dashboard_v5/render.py:157:<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen und Unsicherheit; sie sind keine allgemeine Lebensmittelsicherheitsbewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Score'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Hypothesenansicht: gemeinsam dokumentierte Werte werden transparent gegenübergestellt; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'><span class='sr-only'>Nur Anzeige – Bearbeitung folgt im Ernährungs-Sprint 6G. </span>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\nscripts/health/assets/health-assets/dashboard-v5.js:4:  const VIEW_IDS = [\"today\", \"cockpit\", \"explorer\", \"nutrition\", \"doctor\", \"more\"];\nscripts/health/assets/health-assets/dashboard-v5.js:20:    const meta = document.querySelector(\"meta[name='health-browser-session-csrf']\");\nscripts/health/assets/health-assets/dashboard-v5.js:27:    }) : Promise.reject(new Error(\"missing_csrf\"));\nscripts/health/assets/health-assets/dashboard-v5.js:410:    const summary = q(\"#nutrition-summary\");\nscripts/health/assets/health-assets/dashboard-v5.js:413:      const point = latestPoint(\"nutrition.histamine\");\nscripts/health/assets/health-assets/dashboard-v5.js:420:    const review = q(\"#mapping-review\");\nscripts/health/assets/health-assets/dashboard-v5.js:423:      const rows = (bundle.nutrition && bundle.nutrition.mapping_review) || [];\nscripts/health/assets/health-assets/dashboard-v5.js:429:        const form = node(\"form\", \"mapping-action-form\");\nscripts/health/assets/health-assets/dashboard-v5.js:431:        form.action = \"/health-actions/nutrition-mapping\";\nscripts/health/assets/health-assets/dashboard-v5.js:432:        [[\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"], [\"return_to\", \"v5\"], [\"queue_key\", item.queue_key || \"\"], [\"canonical_food\", item.suggested_canonical_food || \"\"], [\"sighi_score\", item.suggested_score ?? \"unknown\"], [\"traffic_light\", item.suggested_traffic_light || \"unknown\"], [\"mapping_scope\", \"canonical\"], [\"personal_tolerance\", \"unknown\"]].forEach(([name, value]) => {\nscripts/health/assets/health-assets/dashboard-v5.js:447:    const dayField = q(\"[data-nutrition-day-date]\");\nscripts/health/assets/health-assets/dashboard-v5.js:449:    const openDay = q(\"[data-nutrition-day-open]\");\nscripts/health/assets/health-assets/dashboard-v5.js:456:  function nutritionGroup(title, rows) {\nscripts/health/assets/health-assets/dashboard-v5.js:457:    const section = node(\"section\", \"nutrition-group\");\nscripts/health/assets/health-assets/dashboard-v5.js:460:    rows.forEach(([label, value, unit]) => section.append(node(\"div\", \"nutrition-row\", `${label}: ${value === null || value === undefined ? \"unbekannt\" : `${formatValue(value)} ${unit || \"\"}`.trim()}`)));\nscripts/health/assets/health-assets/dashboard-v5.js:465:    const status = q(\"[data-nutrition-day-status]\");\nscripts/health/assets/health-assets/dashboard-v5.js:466:    const target = q(\"[data-nutrition-day]\");\nscripts/health/assets/health-assets/dashboard-v5.js:467:    const day = q(\"[data-nutrition-day-date]\")?.value || bundle.today;\nscripts/health/assets/health-assets/dashboard-v5.js:471:      const data = await apiGet(`/api/v1/nutrition/day/${encodeURIComponent(day)}`);\nscripts/health/assets/health-assets/dashboard-v5.js:473:        nutritionGroup(\"Makros\", [[\"Energie\", data.macros?.kcal, \"kcal\"], [\"Protein\", data.macros?.protein_g, \"g\"], [\"Kohlenhydrate\", data.macros?.carb_g, \"g\"], [\"Fett\", data.macros?.fat_g, \"g\"]]),\nscripts/health/assets/health-assets/dashboard-v5.js:474:        nutritionGroup(\"Nährstoffe\", Object.entries(data.nutrients || {}).slice(0, 12).map(([key, item]) => [item.label || key, item.value, item.unit])),\nscripts/health/assets/health-assets/dashboard-v5.js:475:        nutritionGroup(\"Mahlzeiten\", (data.meals || []).map((meal) => [meal.label || meal.meal, meal.item_count, \"Einträge\"])),\nscripts/health/assets/health-assets/dashboard-v5.js:552:    const priorities = [\"symptom.total\", \"apple.sleep\", \"apple.hrv\", \"apple.resting_heart_rate\", \"apple.steps\", \"nutrition.histamine\"];\nscripts/health/assets/health-assets/dashboard-v5.js:768:      early_treatment: \"Frühe Behandlungsphase\",\nscripts/health/assets/health-assets/dashboard-v5.js:930:    if (activeView === \"nutrition\") {\nscripts/health/assets/health-assets/dashboard-v5.js:932:      chartFor(\"nutrition-chart\", [\"nutrition.histamine\"]);\nscripts/health/assets/health-assets/dashboard-v5.js:933:      chartFor(\"nutrition-aphthae-chart\", [\"nutrition.histamine\", \"symptom.aphthae\"]);\n"}}
{"timestamp":"2026-07-16T17:46:26.738Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_6yrgeOqCVTymznjki2u7ohUN","output":"Chunk ID: 603629\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3982\nOutput:\n        (requested,),\n    ):\n        practitioner = _safe_day_text(row[\"arzt\"], 120)\n        institution = _safe_day_text(row[\"klinik\"], 120)\n        reason = _safe_day_text(row[\"grund\"], 160)\n        note = _safe_day_text(row[\"notizen\"], 300)\n        if all(\n            value is not None for value in (practitioner, institution, reason, note)\n        ):\n            items.append(\n                {\n                    \"date\": requested,\n                    \"practitioner\": practitioner,\n                    \"institution\": institution,\n                    \"specialty_or_reason\": reason,\n                    \"note\": note,\n                    # arztbesuche currently has no explicit status column.  Its date\n                    # documents that an entry exists, but it never proves attendance,\n                    # cancellation, or completion; retain that distinction explicitly.\n                    \"status\": \"documented_visit\",\n                }\n            )\n    return {\"status\": \"supported\" if items else \"supported_no_data\", \"items\": items}\n\n\ndef _day_nutrition(connection: sqlite3.Connection, requested: str) -> dict[str, Any]:\n    if not table_exists(connection, \"nutrition_daily_summary_v2\"):\n        return {\"status\": \"supported_no_data\", \"date\": requested}\n    row = connection.execute(\n        \"\"\"SELECT kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n                  histamine_unknown_count,histamine_label\n           FROM nutrition_daily_summary_v2 WHERE datum=? LIMIT 1\"\"\",\n        (requested,),\n    ).fetchone()\n    if row is None:\n        return {\"status\": \"supported_no_data\", \"date\": requested}\n    complete = (\n        int(row[\"item_count\"] or 0) > 0\n        and int(row[\"histamine_unknown_count\"] or 0) == 0\n        and row[\"histamine_score\"] is not None\n    )\n    return {\n        \"status\": \"documented\",\n        \"date\": requested,\n        \"kcal\": row[\"kcal\"],\n        \"protein_g\": row[\"protein_g\"],\n        \"carb_g\": row[\"carb_g\"],\n        \"fat_g\": row[\"fat_g\"],\n        \"item_count\": row[\"item_count\"],\n        \"histamine_score\": row[\"histamine_score\"] if complete else None,\n        \"histamine_status\": row[\"histamine_label\"] if complete else \"not_documented\",\n        \"unknown_items\": row[\"histamine_unknown_count\"],\n        \"complete\": complete,\n    }\n\n\ndef _nutrition_complete(row: sqlite3.Row) -> bool:\n    return (\n        int(row[\"item_count\"] or 0) > 0\n        and int(row[\"histamine_unknown_count\"] or 0) == 0\n        and row[\"histamine_score\"] is not None\n        and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n    )\n\n\ndef _public_number(value: Any) -> int | float | None:\n    try:\n        numeric = float(value)\n    except (TypeError, ValueError):\n        return None\n    if not math.isfinite(numeric):\n        return None\n    rounded = round(numeric, 3)\n    return int(rounded) if rounded.is_integer() else rounded\n\n\ndef _nutrition_days(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n    start, end = parse_range(params)\n    if not table_exists(connection, \"nutrition_daily_summary_v2\"):\n        return {\"version\": 1, \"timezone\": TZ_NAME, \"definition\": _histamine_definition(), \"days\": []}\n    sql = \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n                    histamine_unknown_count,histamine_label\n             FROM nutrition_daily_summary_v2 WHERE datum<=?\"\"\"\n    values: list[Any] = [local_today().isoformat()]\n    if start:\n        sql += \" AND datum>=?\"\n        values.append(start.isoformat())\n    if end:\n        sql += \" AND datum<=?\"\n        values.append(end.isoformat())\n    sql += \" ORDER BY datum DESC LIMIT ?\"\n    values.append(MAX_NUTRITION_DAYS + 1)\n    rows = list(connection.execute(sql, values))\n    if len(rows) > MAX_NUTRITION_DAYS:\n        raise APIError(422, \"row_limit_exceeded\")\n    days = []\n    for row in reversed(rows):\n        day = parse_day(row[\"datum\"])\n        if not day:\n            continue\n        complete = _nutrition_complete(row)\n        days.append({\n            \"date\": day,\n            \"macros\": {\n                \"kcal\": _public_number(row[\"kcal\"]),\n                \"protein_g\": _public_number(row[\"protein_g\"]),\n                \"carb_g\": _public_number(row[\"carb_g\"]),\n                \"fat_g\": _public_number(row[\"fat_g\"]),\n            },\n            \"item_count\": int(row[\"item_count\"] or 0),\n            \"histamine\": {\n                \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n                \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n                \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n                \"status\": \"complete\" if complete else \"incomplete\",\n            },\n        })\n    return {\"version\": 1, \"timezone\": TZ_NAME, \"definition\": _histamine_definition(), \"days\": days}\n\n\ndef _histamine_definition() -> dict[str, Any]:\n    return {\n        \"id\": \"sighi_mapping_load_v1\",\n        \"version\": 1,\n        \"components\": [\"mapped_item_scores\", \"unknown_item_count\", \"traffic_light\"],\n        \"coverage_semantics\": \"unknown_or_unmapped_items_make_day_incomplete\",\n        \"insufficient_inputs\": \"no_index\",\n    }\n\n\ndef _nutrition_day_detail(connection: sqlite3.Connection, requested: date) -> dict[str, Any]:\n    day = requested.isoformat()\n    if not table_exists(connection, \"nutrition_daily_summary_v2\"):\n        return {\"version\": 1, \"date\": day, \"timezone\": TZ_NAME, \"status\": \"supported_no_data\"}\n    row = connection.execute(\n        \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,\n                  histamine_score,histamine_unknown_count,histamine_label\n           FROM nutrition_daily_summary_v2 WHERE datum=? LIMIT 1\"\"\",\n        (day,),\n    ).fetchone()\n    if row is None:\n        return {\"version\": 1, \"date\": day, \"timezone\": TZ_NAME, \"status\": \"supported_no_data\"}\n    complete = _nutrition_complete(row)\n    nutrients: dict[str, dict[str, Any]] = {}\n    try:\n        raw_nutrients = json.loads(row[\"nutrient_json\"] or \"{}\")\n    except json.JSONDecodeError:\n        raw_nutrients = {}\n    if isinstance(raw_nutrients, dict):\n        for key, (label, unit) in NUTRIENT_ALLOWLIST.items():\n            nutrients[key] = {\"label\": label, \"value\": _public_number(raw_nutrients.get(key)), \"unit\": unit}\n    meals = []\n    if table_exists(connection, \"nutrition_meal_summary\"):\n        for meal in connection.execute(\n            \"\"\"SELECT meal,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,histamine_label\n               FROM nutrition_meal_summary WHERE datum=?\n               ORDER BY CASE meal WHEN 'breakfast' THEN 1 WHEN 'lunch' THEN 2 WHEN 'dinner' THEN 3 WHEN 'snack' THEN 4 ELSE 9 END LIMIT 12\"\"\",\n            (day,),\n        ):\n            meal_id = str(meal[\"meal\"] or \"unknown\")\n            meals.append({\n                \"meal\": meal_id if meal_id in MEAL_LABELS else \"unknown\",\n                \"label\": MEAL_LABELS.get(meal_id, \"Unbekannt\"),\n                \"item_count\": int(meal[\"item_count\"] or 0),\n                \"macros\": {\n                    \"kcal\": _public_number(meal[\"kcal\"]),\n                    \"protein_g\": _public_number(meal[\"protein_g\"]),\n                    \"carb_g\": _public_number(meal[\"carb_g\"]),\n                    \"fat_g\": _public_number(meal[\"fat_g\"]),\n                },\n                \"histamine\": {\n                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else None,\n                    \"traffic_light\": meal[\"histamine_label\"] if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n                },\n            })\n    items = []\n    if table_exists(connection, \"nutrition_items\"):\n        for item in connection.execute(\n            \"\"\"SELECT i.meal,i.name,i.brand,i.amount,i.amount_unit,i.kcal,i.protein_g,i.carb_g,i.fat_g,\n                      h.canonical_food,h.sighi_score,h.traffic_light,h.confidence\n               FROM nutrition_items i LEFT JOIN nutrition_histamine_scores h ON h.item_id=i.id\n               WHERE i.datum=? ORDER BY i.meal,i.id LIMIT ?\"\"\",\n            (day, MAX_NUTRITION_ITEMS + 1),\n        ):\n            if len(items) >= MAX_NUTRITION_ITEMS:\n                raise APIError(422, \"row_limit_exceeded\")\n            name = safe_metadata_text(item[\"name\"], 120, allow_empty=False)\n            brand = safe_metadata_text(item[\"brand\"], 80, allow_empty=True)\n            canonical = safe_metadata_text(item[\"canonical_food\"], 120, allow_empty=True)\n            if name is None or brand is None or canonical is None:\n                continue\n            items.append({\n                \"meal\": item[\"meal\"] if item[\"meal\"] in MEAL_LABELS else \"unknown\",\n                \"name\": name,\n                \"brand\": brand,\n                \"amount\": _public_number(item[\"amount\"]),\n                \"amount_unit\": item[\"amount_unit\"] if item[\"amount_unit\"] in {\"g\", \"ml\", \"piece\", \"portion\"} else \"unknown\",\n                \"macros\": {\n                    \"kcal\": _public_number(item[\"kcal\"]),\n                    \"protein_g\": _public_number(item[\"protein_g\"]),\n                    \"carb_g\": _public_number(item[\"carb_g\"]),\n                    \"fat_g\": _public_number(item[\"fat_g\"]),\n                },\n                \"histamine\": {\n                    \"canonical_food\": canonical or None,\n                    \"score\": int(item[\"sighi_score\"]) if item[\"sighi_score\"] in (0, 1, 2, 3) else None,\n                    \"traffic_light\": item[\"traffic_light\"] if item[\"traffic_light\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n                    \"confidence\": item[\"confidence\"] if item[\"confidence\"] in {\"low\", \"medium\", \"high\"} else \"low\",\n                },\n            })\n    return {\n        \"version\": 1,\n        \"date\": day,\n        \"timezone\": TZ_NAME,\n        \"status\": \"documented\",\n        \"definition\": _histamine_definition(),\n        \"coverage\": {\n            \"status\": \"complete\" if complete else \"incomplete\",\n            \"item_count\": int(row[\"item_count\"] or 0),\n            \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n            \"insufficient_inputs\": None if complete else \"no_index\",\n        },\n        \"macros\": {\n            \"kcal\": _public_number(row[\"kcal\"]),\n            \"protein_g\": _public_number(row[\"protein_g\"]),\n            \"carb_g\": _public_number(row[\"carb_g\"]),\n            \"fat_g\": _public_number(row[\"fat_g\"]),\n        },\n        \"nutrients\": nutrients,\n        \"histamine\": {\n            \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n            \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n        },\n        \"meals\": meals,\n        \"items\": items,\n    }\n\n\ndef _nutrition_mapping_queue(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n    if not table_exists(connection, \"nutrition_review_queue\"):\n        return {\"version\": 1, \"timezone\": TZ_NAME, \"items\": []}\n    status = params.get(\"status\", \"open\")\n    if status not in {\"open\"}:\n        raise APIError(400, \"status_not_allowed\")\n    items = []\n    for row in connection.execute(\n        \"\"\"SELECT normalized_name,example_name,occurrence_count,first_seen,last_seen,\n                  suggested_canonical_food,suggested_score,reason,status\n           FROM nutrition_review_queue WHERE status=?\n           ORDER BY occurrence_count DESC,last_seen DESC LIMIT ?\"\"\",\n        (status, MAX_NUTRITION_QUEUE + 1),\n    ):\n        if len(items) >= MAX_NUTRITION_QUEUE:\n            raise APIError(422, \"row_limit_exceeded\")\n        example = safe_metadata_text(row[\"example_name\"], 160, allow_empty=False)\n        suggested = safe_metadata_text(row[\"suggested_canonical_food\"], 120, allow_empty=True)\n        reason = safe_metadata_text(row[\"reason\"], 160, allow_empty=True)\n        if example is None or suggested is None or reason is None:\n            continue\n        items.append({\n            \"queue_key\": re.sub(r\"[^a-z0-9_]+\", \"_\", str(row[\"normalized_name\"] or \"\").casefold()).strip(\"_\")[:80],\n            \"example_name\": example,\n            \"occurrence_count\": int(row[\"occurrence_count\"] or 0),\n            \"first_seen\": parse_day(row[\"first_seen\"]),\n            \"last_seen\": parse_day(row[\"last_seen\"]),\n            \"suggested_canonical_food\": suggested,\n            \"suggested_score\": int(row[\"suggested_score\"]) if row[\"suggested_score\"] in (0, 1, 2, 3) else None,\n            \"reason\": reason,\n            \"status\": \"open\",\n            \"action_contract\": \"nutrition_mapping_action_v1\",\n        })\n    return {\"version\": 1, \"timezone\": TZ_NAME, \"definition\": _histamine_definition(), \"items\": items}\n\n\ndef _day(connection: sqlite3.Connection, day: date) -> dict[str, Any]:\n    requested = day.isoformat()\n    today = local_today().isoformat()\n    measurements = []\n    for metric in BY_ID_V2.values():\n        if metric.value_type == \"panel\" or metric.source in {\n            \"laborwerte\",\n            \"symptom_log\",\n            \"nutrition_daily_summary_v2\",\n        }:\n            continue\n        points = _metric_points(connection, metric, day, day)\n        if not points:\n            continue\n        point = points[-1]\n        measurements.append(\n            {\n                \"metric\": metric.id,\n                \"label\": metric.label,\n                \"value\": point[\"value\"],\n                \"unit\": metric.unit,\n                \"source\": metric.source,\n                \"aggregation\": metric.aggregation,\n                \"quality\": point[\"quality\"],\n            }\n        )\n    symptoms = _day_symptoms(connection, requested)\n    medications = _day_medications(connection, requested)\n    day_events = _day_events(connection, requested)\n    labs = [\n        row\n        for row in _verified_lab_rows(connection, include_missing_reference=True)\n        if row[\"date\"] == requested\n    ]\n    nutrition = _day_nutrition(connection, requested)\n    documents = _day_documents(connection, requested)\n    appointments = _day_appointments(connection, requested)\n    counts = {\n        \"measurements\": len(measurements),\n        \"symptoms\": symptoms[\"documented_dimensions\"],\n        \"medications\": sum(len(items) for items in medications.values()),\n        \"labs\": len(labs),\n        \"events\": len(day_events[\"events\"]) + len(day_events[\"periods\"]),\n        \"documents\": len(documents),\n        \"nutrition\": 1 if nutrition[\"status\"] == \"documented\" else 0,\n        \"appointments\": len(appointments[\"items\"]),\n    }\n    categories = [name for name, count in counts.items() if count]\n    metrics = {\n        item[\"metric\"]: {\n            \"value\": item[\"value\"],\n            \"unit\": item[\"unit\"],\n            \"aggregation\": item[\"aggregation\"],\n            \"quality\": item[\"quality\"],\n        }\n        for item in measurements\n    }\n    notices = []\n    if not categories:\n        notices.append(\"Für diesen Tag ist nichts dokumentiert.\")\n    if symptoms[\"documented_dimensions\"] and not symptoms[\"complete\"]:\n        notices.append(\"Die Symptomdokumentation dieses Tages ist unvollständig.\")\n    if any(item.get(\"reference_status\") == \"missing_reference\" for item in labs):\n        notices.append(\n            \"Für mindestens eine Laborbeobachtung fehlt ein verifizierter Referenzbereich.\"\n        )\n    return {\n        \"version\": 2,\n        \"date\": requested,\n        \"today\": today,\n        \"timezone\": TZ_NAME,\n        \"is_today\": requested == today,\n        \"is_future\": requested > today,\n        \"coverage\": {\n            \"status\": \"documented\" if categories else \"not_documented\",\n            \"categories\": categories,\n            \"counts\": counts,\n            \"symptoms_complete\": symptoms[\"complete\"],\n        },\n        \"measurements\": measurements,\n        \"metrics\": metrics,\n        \"symptoms\": symptoms,\n        \"medications\": medications,\n        \"events\": day_events,\n        \"labs\": labs,\n        \"nutrition\": nutrition,\n        \"documents\": documents,\n"}}
{"timestamp":"2026-07-16T17:46:26.738Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_bEcdlMAdFdl17pUe5XHj7pgr","output":"Chunk ID: b2039a\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 4684\nOutput:\n\"\"\"Deterministic, synthetic-only SQLite fixture for Dashboard v5 acceptance work.\n\nThis module deliberately knows nothing about a Dashboard v5 Python/UI API.  It only\nmaterializes the canonical schema and representative rows at a caller-provided\npath.  Dates are derived from an explicit anchor so freshness tests do not depend\non wall-clock time.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sqlite3\nimport tempfile\nfrom datetime import date, datetime, time, timedelta, timezone\nfrom pathlib import Path\nfrom typing import Final\n\nROOT: Final = Path(__file__).resolve().parents[2]\nDEFAULT_SCHEMA: Final = ROOT / \"database\" / \"schema.sql\"\nSYMPTOM_DIMENSIONS: Final = (\n    \"Aphthen/Mundulzera\",\n    \"GI/Darm\",\n    \"Müdigkeit/Fatigue\",\n    \"Haut\",\n    \"Augen\",\n    \"Gelenke\",\n    \"Vaskulär/Thrombose-Warnzeichen\",\n)\nSPRINT4B_LAGS: Final = (0, 1, 2, 3)\nSPRINT4B_PHASES: Final = (\n    \"baseline_pre_treatment\", \"early_treatment\", \"stable_treatment\", \"unknown\",\n)\nSPRINT4B_PREDICTORS: Final = (\"apple:heart_rate_variability\", \"nutrition:histamine_score\")\nSPRINT4B_METHOD: Final = \"spearman_7d_block_permutation_complete_case_phase_stratified_bh_v1\"\nSPRINT4B_BASE_FLAGS: Final = [\n    \"complete_case\", \"phase_stratified\", \"7d_block_permutation\", \"no_causality\",\n]\nSYNTHETIC_FIXTURE_MARKER: Final = \"dashboard-v5-synthetic-fixture-v1\"\n\n\ndef sprint4b_correlation_fixture() -> dict[str, object]:\n    \"\"\"Return a deterministic engine-compatible, aggregate-only Sprint-4B grid.\"\"\"\n    results: list[dict[str, object]] = []\n    for predictor in SPRINT4B_PREDICTORS:\n        for lag in SPRINT4B_LAGS:\n            for phase in SPRINT4B_PHASES:\n                status = \"computed\"\n                expected, eligible, observed = 24, 24 - lag, 18 - lag\n                rho: float | None = round(0.42 - lag * 0.08, 2)\n                p_value: float | None = round(0.01 + lag * 0.02, 2)\n                q_value: float | None = round(0.03 + lag * 0.03, 2)\n                flags = list(SPRINT4B_BASE_FLAGS)\n                if predictor == SPRINT4B_PREDICTORS[0] and lag == 0 and phase == SPRINT4B_PHASES[0]:\n                    rho, p_value, q_value = 0.0, 0.0, 0.0\n                elif predictor == SPRINT4B_PREDICTORS[0] and lag == 3 and phase == \"early_treatment\":\n                    status, observed = \"insufficient_n\", 3\n                    rho = p_value = q_value = None\n                    flags.append(\"low_n_or_variance\")\n                elif predictor == SPRINT4B_PREDICTORS[1] and lag == 2 and phase == \"stable_treatment\":\n                    status, expected, eligible, observed = \"insufficient_coverage\", 24, 10, 0\n                    rho = p_value = q_value = None\n                    flags.append(\"low_target_coverage\")\n                results.append({\n                    \"id\": {\"predictor\": predictor, \"target\": \"symptom_total\"},\n                    \"dimensions\": {\"lag_days\": lag, \"medication_phase\": phase},\n                    \"sample\": {\n                        \"eligible_target_days\": eligible,\n                        \"expected_target_days\": expected,\n                        \"observed_pairs\": observed,\n                        \"missing_pairs\": eligible - observed,\n                        \"target_coverage\": round(eligible / expected, 6),\n                    },\n                    \"statistics\": {\"rho\": rho, \"p_value\": p_value, \"q_value\": q_value},\n                    \"status\": status,\n                    \"method\": SPRINT4B_METHOD,\n                    \"quality_flags\": flags,\n                })\n    return {\n        \"dimensions\": {\n            \"lag_days\": list(SPRINT4B_LAGS),\n            \"medication_phases\": list(SPRINT4B_PHASES),\n            \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\n        },\n        \"results\": results,\n    }\n\n\ndef _iso_day(day: date) -> str:\n    return day.isoformat()\n\n\ndef _iso_noon(day: date) -> str:\n    return datetime.combine(day, time(12), tzinfo=timezone.utc).isoformat()\n\n\ndef build_dashboard_v5_fixture(\n    db_path: str | Path,\n    *,\n    anchor_date: date = date(2026, 6, 15),\n    schema_path: str | Path = DEFAULT_SCHEMA,\n) -> dict[str, object]:\n    \"\"\"Create a new synthetic database and return its deterministic case manifest.\n\n    ``db_path`` must not already exist.  This fail-closed behavior prevents this\n    helper from ever replacing a real database accidentally.\n    \"\"\"\n    target = Path(db_path)\n    schema = Path(schema_path)\n    if target.exists():\n        raise FileExistsError(f\"refusing to replace existing database: {target}\")\n    if not schema.is_file():\n        raise FileNotFoundError(f\"schema not found: {schema}\")\n    target.parent.mkdir(parents=True, exist_ok=True)\n\n    current = anchor_date - timedelta(days=1)\n    stale = anchor_date - timedelta(days=45)\n    future = anchor_date + timedelta(days=2)\n    complete_symptom_days = [anchor_date - timedelta(days=offset) for offset in range(1, 9)]\n    incomplete_symptom_day = anchor_date - timedelta(days=9)\n    no_data_day = anchor_date - timedelta(days=10)\n\n    connection = sqlite3.connect(target)\n    try:\n        connection.executescript(schema.read_text(encoding=\"utf-8\"))\n        connection.execute(\n            \"CREATE TABLE dashboard_v5_synthetic_fixture (marker TEXT PRIMARY KEY)\"\n        )\n        connection.execute(\n            \"INSERT INTO dashboard_v5_synthetic_fixture(marker) VALUES (?)\",\n            (SYNTHETIC_FIXTURE_MARKER,),\n        )\n        connection.executemany(\n            \"\"\"INSERT INTO apple_health_records\n               (record_type,metric,start_date,end_date,value,value_text,unit,\n                source_name,source_version,device,file_name,file_hash,record_hash,\n                raw_json,imported_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            [\n                (\"HKQuantityType\", \"heart_rate\", _iso_noon(current), _iso_noon(current), 64.0, None, \"count/min\", \"Synthetic Current Source\", \"1\", \"Synthetic Device\", \"synthetic-current.json\", \"fixture-current-file\", \"fixture-current-record\", \"{}\", _iso_noon(current)),\n                (\"HKQuantityType\", \"step_count\", _iso_noon(stale), _iso_noon(stale), 1234.0, None, \"count\", \"Synthetic Stale Source\", \"1\", \"Synthetic Device\", \"synthetic-stale.json\", \"fixture-stale-file\", \"fixture-stale-record\", \"{}\", _iso_noon(stale)),\n                (\"HKQuantityType\", \"resting_heart_rate\", _iso_noon(future), _iso_noon(future), 61.0, None, \"count/min\", \"Synthetic Future Source\", \"1\", \"Synthetic Device\", \"synthetic-future.json\", \"fixture-future-file\", \"fixture-future-record\", \"{}\", _iso_noon(future)),\n                (\"HKQuantityType\", \"oxygen_saturation\", _iso_noon(current), _iso_noon(current), None, \"not measured\", \"%\", \"Synthetic Missing Value Source\", \"1\", None, \"synthetic-missing.json\", \"fixture-missing-file\", \"fixture-missing-record\", \"{}\", _iso_noon(current)),\n            ],\n        )\n        explorer_rows = []\n        for offset in range(1, 23):\n            day = anchor_date - timedelta(days=offset)\n            for metric, value, unit in (\n                (\"heart_rate_variability\", 42.0 + offset, \"ms\"),\n                (\"resting_heart_rate\", 60.0 + offset, \"count/min\"),\n                (\"sleep_analysis\", 6.5 + offset / 10, \"hr\"),\n                (\"step_count\", 5000.0 + offset * 100, \"count\"),\n            ):\n                if metric == \"sleep_analysis\" and offset == 4:\n                    continue\n                key = f\"fixture-explorer-{metric}-{offset}\"\n                explorer_rows.append((\n                    \"HKQuantityType\", metric, _iso_noon(day), _iso_noon(day), value, None, unit,\n                    \"Synthetic Explorer Source\", \"1\", \"Synthetic Device\", f\"{key}.json\", key,\n                    f\"{key}-record\", \"{}\", _iso_noon(day),\n                ))\n        connection.executemany(\n            \"\"\"INSERT INTO apple_health_records\n               (record_type,metric,start_date,end_date,value,value_text,unit,\n                source_name,source_version,device,file_name,file_hash,record_hash,\n                raw_json,imported_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            explorer_rows,\n        )\n        correlation_results = sprint4b_correlation_fixture()[\"results\"]\n        fixture_timestamp = _iso_noon(anchor_date)\n        assert isinstance(correlation_results, list)\n        for result in correlation_results:\n            assert isinstance(result, dict)\n            connection.execute(\n                \"\"\"INSERT INTO multimodal_correlation_results\n                   (predictor,target,lag_days,medication_phase,n,eligible_target_days,\n                    expected_target_days,target_coverage,missing_pairs,rho,p_value,q_value,status,\n                    method,quality_flags,interpretation,computed_at)\n                   VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n                (\n                    result[\"id\"][\"predictor\"], result[\"id\"][\"target\"],\n                    result[\"dimensions\"][\"lag_days\"], result[\"dimensions\"][\"medication_phase\"],\n                    result[\"sample\"][\"observed_pairs\"], result[\"sample\"][\"eligible_target_days\"],\n                    result[\"sample\"][\"expected_target_days\"], result[\"sample\"][\"target_coverage\"],\n                    result[\"sample\"][\"missing_pairs\"], result[\"statistics\"][\"rho\"],\n                    result[\"statistics\"][\"p_value\"], result[\"statistics\"][\"q_value\"],\n                    result[\"status\"], result[\"method\"], json.dumps(result[\"quality_flags\"]),\n                    \"Kein belastbares korrigiertes Signal; explorativ und keine Kausalität.\"\n                    if result[\"status\"] == \"computed\"\n                    else (\n                        \"Unzureichende Zielabdeckung; keine Korrelation berechnet und keine Kausalität.\"\n                        if result[\"status\"] == \"insufficient_coverage\"\n                        else \"Unzureichende vollständige Datenpaare oder Varianz; keine Kausalität.\"\n                    ),\n                    fixture_timestamp,\n                ),\n            )\n        connection.execute(\n            \"\"\"INSERT INTO laborwerte\n               (parameter_name,wert,einheit,reference_min,reference_max,\n                abnahme_datum,befund_datum,wert_original,quelle,\n                validierungsstatus,source_type,reference_range_source,\n                verified_against_original,provenance_note,ermittlung_datum)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            (\n                \"C-Reaktives Protein (CRP)\",\n                \"12.5\", \"mg/L\", \"0\", \"5\", _iso_day(stale), _iso_day(stale), \"12.5\",\n                \"synthetic laboratory fixture\", \"validiert\", \"synthetic\",\n                \"scanned_original\", 1, \"Synthetic test row; no person or source document.\", fixture_timestamp,\n            ),\n        )\n        connection.executemany(\n            \"\"\"INSERT INTO medication_administrations\n               (datum,medication_name,dose,route,event_type,scheduled_next_date,notes,source,created_at)\n               VALUES(?,?,?,?,?,?,?,?,?)\"\"\",\n            [\n                (_iso_day(anchor_date - timedelta(days=4)), \"SYNTHETIC_EVENT_ON_MEASUREMENT_GAP\", \"10 mg\", \"synthetic\", \"administered\", None, \"timeline gap fixture\", \"fixture\", fixture_timestamp),\n                (_iso_day(current), \"SYNTHETIC_ADMINISTERED_MEDICATION\", \"10 mg\", \"synthetic\", \"administered\", None, \"display as administered only\", \"fixture\", fixture_timestamp),\n                (_iso_day(anchor_date + timedelta(days=1)), \"SYNTHETIC_PLANNED_MEDICATION_WITH_A_VERY_LONG_LABEL_FOR_RESPONSIVE_LAYOUT\", \"10 mg\", \"synthetic\", \"planned\", _iso_day(anchor_date + timedelta(days=1)), \"must not be represented as taken\", \"fixture\", fixture_timestamp),\n                (_iso_day(anchor_date + timedelta(days=1)), \"SYNTHETIC_CANCELLED_MEDICATION\", \"10 mg\", \"synthetic\", \"cancelled\", None, \"must not be represented as taken\", \"fixture\", fixture_timestamp),\n            ],\n        )\n        connection.executemany(\n            \"\"\"INSERT INTO nutrition_items\n               (id,source,source_item_id,datum,meal,name,brand,amount,amount_unit,\n                kcal,protein_g,carb_g,fat_g,item_hash,imported_at,updated_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            [\n                (1, \"fixture\", \"complete\", _iso_day(current), \"lunch\", \"SYNTHETIC_COMPLETE_FOOD_MAPPING\", \"Synthetic Brand\", 100, \"g\", 0, 0, 0, 0, \"fixture-food-complete\", fixture_timestamp, fixture_timestamp),\n                (2, \"fixture\", \"incomplete\", _iso_day(current), \"dinner\", \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\", None, None, \"g\", None, None, None, None, \"fixture-food-incomplete\", fixture_timestamp, fixture_timestamp),\n            ],\n        )\n        connection.executemany(\n            \"\"\"INSERT INTO nutrition_histamine_scores\n               (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n               VALUES(?,?,?,?,?,?,?,?)\"\"\",\n            [\n                (1, \"synthetic cucumber\", 0, \"green\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n                (2, None, None, \"unknown\", None, \"low\", \"intentionally unmapped fixture row\", fixture_timestamp),\n            ],\n        )\n        connection.execute(\n            \"\"\"INSERT INTO nutrition_daily_summary_v2\n               (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n                histamine_max,histamine_unknown_count,histamine_label,nutrient_json,updated_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            (_iso_day(current), None, None, None, None, 2, None, 0, 1, \"unknown\", json.dumps({\"nutrient.protein\": None, \"nutrient.fiber\": 4.2}), _iso_noon(current)),\n        )\n        connection.execute(\n            \"\"\"INSERT INTO nutrition_meal_summary\n               (datum,meal,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_label,updated_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n            (_iso_day(current), \"lunch\", 0, 0, 0, 0, 1, \"{}\", 0, \"green\", _iso_noon(current)),\n        )\n        connection.execute(\n            \"\"\"INSERT INTO nutrition_review_queue\n               (normalized_name,example_name,occurrence_count,first_seen,last_seen,suggested_canonical_food,suggested_score,reason,status,updated_at)\n               VALUES(?,?,?,?,?,?,?,?,?,?)\"\"\",\n            (\"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\", \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\", 1, _iso_day(current), _iso_day(current), \"synthetic reviewed food\", 1, \"synthetic missing mapping\", \"open\", fixture_timestamp),\n        )\n        symptom_rows = []\n        for day_index, day in enumerate(complete_symptom_days):\n            for dimension_index, dimension in enumerate(SYMPTOM_DIMENSIONS):\n                symptom_rows.append((_iso_day(day), dimension, str((day_index + dimension_index) % 4), \"daily_quick_score\", \"synthetic complete day\", fixture_timestamp))\n        for dimension in SYMPTOM_DIMENSIONS[:-1]:\n            symptom_rows.append((_iso_day(incomplete_symptom_day), dimension, \"1\", \"daily_quick_score\", \"synthetic incomplete day\", fixture_timestamp))\n        connection.executemany(\n            \"INSERT INTO symptom_log(datum,symptom,schwergrad,kontext,notizen,created_at) VALUES(?,?,?,?,?,?)\",\n            symptom_rows,\n        )\n        connection.commit()\n        fk_errors = connection.execute(\"PRAGMA foreign_key_check\").fetchall()\n        if fk_errors:\n            raise RuntimeError(f\"fixture violates foreign keys: {fk_errors}\")\n    except Exception:\n        connection.close()\n        target.unlink(missing_ok=True)\n        raise\n    finally:\n        try:\n            connection.close()\n        except Exception:\n            pass\n    target.chmod(0o600)\n\n    return {\n        \"anchor_date\": _iso_day(anchor_date),\n        \"current_source_date\": _iso_day(current),\n        \"stale_source_date\": _iso_day(stale),\n        \"future_source_date\": _iso_day(future),\n        \"complete_symptom_days\": [_iso_day(day) for day in complete_symptom_days],\n        \"incomplete_symptom_day\": _iso_day(incomplete_symptom_day),\n        \"no_data_day\": _iso_day(no_data_day),\n        \"synthetic_only\": True,\n    }\n\n\ndef validate_fixture(db_path: str | Path) -> dict[str, object]:\n    \"\"\"Return structural acceptance checks without importing application code.\"\"\"\n    db = Path(db_path)\n    connection = sqlite3.connect(f\"file:{db}?mode=ro\", uri=True)\n    try:\n        counts = {\n            table: connection.execute(f\"SELECT COUNT(*) FROM {table}\").fetchone()[0]\n            for table in (\n                \"apple_health_records\", \"laborwerte\", \"medication_administrations\",\n                \"nutrition_items\", \"nutrition_histamine_scores\", \"symptom_log\",\n            )\n        }\n        checks = {\n            \"integrity\": connection.execute(\"PRAGMA integrity_check\").fetchone()[0],\n            \"foreign_key_errors\": connection.execute(\"PRAGMA foreign_key_check\").fetchall(),\n            \"verified_lab_outliers\": connection.execute(\n                \"\"\"SELECT COUNT(*) FROM laborwerte\n                   WHERE verified_against_original=1 AND CAST(wert AS REAL)>CAST(reference_max AS REAL)\"\"\"\n            ).fetchone()[0],\n            \"complete_symptom_days\": connection.execute(\n                \"\"\"SELECT COUNT(*) FROM (SELECT datum FROM symptom_log\n                   WHERE kontext='daily_quick_score' GROUP BY datum HAVING COUNT(DISTINCT symptom)=7)\"\"\"\n            ).fetchone()[0],\n            \"medication_states\": sorted(row[0] for row in connection.execute(\n                \"SELECT DISTINCT event_type FROM medication_administrations\"\n            )),\n            \"food_mapping_states\": connection.execute(\n                \"\"\"SELECT SUM(canonical_food IS NOT NULL AND sighi_score IS NOT NULL),\n                          SUM(canonical_food IS NULL OR sighi_score IS NULL)\n                   FROM nutrition_histamine_scores\"\"\"\n            ).fetchone(),\n        }\n    finally:\n        connection.close()\n    return {\"counts\": counts, \"checks\": checks}\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\"output\", nargs=\"?\", help=\"new SQLite path; omit for a temporary fixture\")\n    parser.add_argument(\"--anchor-date\", type=date.fromisoformat, default=date(2026, 6, 15))\n    args = parser.parse_args()\n    if args.output:\n        output = Path(args.output)\n    else:\n        output = Path(tempfile.mkdtemp(prefix=\"dashboard-v5-fixture-\")) / \"health.db\"\n    manifest = build_dashboard_v5_fixture(output, anchor_date=args.anchor_date)\n    print(json.dumps({\"path\": str(output), \"manifest\": manifest, \"validation\": validate_fixture(output)}, indent=2, default=list))\n\n\nif __name__ == \"__main__\":\n    main()\n"}}
{"timestamp":"2026-07-16T17:46:26.780Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_iEvZvVyxnsWrh53m7mW2XAkZ","output":"Chunk ID: f4502f\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 318\nOutput:\n# ADR 0003: Dietary Inflammation Model\n\nDate: 2026-07-16\n\n## Status\n\nAccepted for Dashboard v5 Sprint 6G-A.\n\n## Context\n\nNutrition data combines imported meal records, nutrient totals, SIGHi-style histamine mappings, symptom dimensions, and optional context such as medication phases or documented events. These inputs are incomplete on many days and may contain unknown food mappings.\n\n## Decision\n\nDashboard v5 exposes transparent components only:\n\n- documented macros and allowlisted nutrients,\n- versioned histamine mapping coverage,\n- unknown item counts,\n- explicitly complete or incomplete day semantics,\n- side-by-side ECharts views for nutrition and Aphthae when both are documented.\n\nThe dashboard does not compute a dietary inflammation index when required inputs are insufficient. Unknown or unmapped foods make the histamine component incomplete rather than zero. Composite products require ingredient review before a definitive mapping is applied.\n\nPersonal tolerance labels remain separate from shared food mapping definitions.\n\n## Consequences\n\nThe UI may show fewer derived values, but every displayed component has visible coverage semantics. Charts use neutral hypothesis wording and make no diagnosis, treatment, warning, reassurance, or causal claim.\n"}}
{"timestamp":"2026-07-16T17:46:26.780Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":191531,"cached_input_tokens":129920,"output_tokens":1540,"reasoning_output_tokens":18,"total_tokens":193071},"last_token_usage":{"input_tokens":63008,"cached_input_tokens":50048,"output_tokens":406,"reasoning_output_tokens":0,"total_tokens":63414},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:46:28.642Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRj0e8Nv9zAG3tsyhSjIYvs60L8AQohMXGY8g9gBjI_bvZ__NaUCdXuZEtlMPJNnTXeoPycGSInL7cl0TEw62KhM9ilbKCTFkuR4orlFNVH8seTxEYpMyyttEGVrblXUbE980wA-7G_yFVj6ksO--tWSkiQpcKKZqdvkFT3fYxHvvnqqXLDR4xIWjwd85TELALQrxLhWi8G9cvP3ybTpRtnprJzqSx2f9VR11zAO143zML_0rPGNIpxlMyLuk4fW1IiUgac4LlKV0hpwM6cJMIPG2KuspTrxOkW2meaVsAmovVymlAslxPP3oh98QlHpl8Mw_5UlzRHWDCqoceVjrwmkaycBmvkdmyMlM5yJ0TL5LP-lCPb71vmCW7cTOQF3yvq3NIm6hSvAF853gWFAc4eDqE6-KEOO3uRGcVadwSviFwjU5VcMnDRZQcyej7Igt1gJnlZmtLzJb-tfl7JNyW7RjlUDl9S5EtELcL7ZJlTelEJh4KTvFu6lXFGmg6_GRLMfI0dYO-QVZbeCMtm6K4oqLUtTcKMjQv3ugqtKyaOooaIrRb-v09KCp6-N0-jUts2aE7mdxbdQ_TnHjmEgXSBx2Ahpg8i-ZaW61h8gkArm31GLhC0DSSeqh3FD60RMg-GgZZABmKY-WbDUaT-gKTSFvgaHOFNQPMuZjNENOQAKbQ394FpYcgvhMXimvtuMpzH6ATKdj6MtUcFbF_nQ_PNRen5EnF4Ww_Hkw88gojzz3ZAfiebez0Iv0p-swQ619DfrgrVA6HNsK8a4Sc8WgxdaoU_jX0ubIGHo6DAsnGbTEaBZUnjwlvroHohEByEJh5uaRMLX1rogHu2KHYa7GSqvY8wP49weZHd_ZTk2RSJZemMbGzeXIQtlxTJhrBUSZ7s1w5iDpbQ-ybKVEj6Y0KOA50_lVxczGFAZwEfBNnhtF3doDWcgmbAPqel0JSePlHv_mAcyzvm1R54zJjo9fB8FCO4xluV6hNiZyDVAS2h0W-Y="}}
{"timestamp":"2026-07-16T17:46:35.553Z","type":"event_msg","payload":{"type":"agent_message","message":"The read endpoint also returns `traffic_light` and omits several definition fields the review requires. I’m updating the public JSON shape while preserving the endpoint paths, so callers still use the same URLs but get neutral classification/coverage metadata.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:46:35.560Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The read endpoint also returns `traffic_light` and omits several definition fields the review requires. I’m updating the public JSON shape while preserving the endpoint paths, so callers still use the same URLs but get neutral classification/coverage metadata."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:46:35.561Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '700,980p' scripts/health/health_dashboard_server.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":35000}","call_id":"call_b7SrFDhEkN0gZgtete36LxlQ"}}
{"timestamp":"2026-07-16T17:46:35.561Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '400,520p' scripts/health/assets/health-assets/dashboard-v5.js\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_chTqC5hwcHNC9wjXWJiTUTgB"}}
{"timestamp":"2026-07-16T17:46:35.561Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '130,175p' scripts/health/dashboard_v5/render.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":16000}","call_id":"call_vi9L68CBQeS9diBmk9wBHFAj"}}
{"timestamp":"2026-07-16T17:46:35.628Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '300,470p' database/schema.sql\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":25000}","call_id":"call_fx0yDyZQWrAwQgLVSoLgyaqO"}}
{"timestamp":"2026-07-16T17:46:35.643Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_b7SrFDhEkN0gZgtete36LxlQ","output":"Chunk ID: 4aa7bd\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 2844\nOutput:\n        cookie_token = cookie.get(\"health_csrf\")\n        if (\n            not token\n            or not cookie_token\n            or not secrets.compare_digest(token, cookie_token.value)\n        ):\n            self.send_error(403)\n            return\n        if not consume_csrf_token(token):\n            self.send_error(403)\n            return\n        try:\n            if action_path == CHECKIN_ROUTE:\n                day, scores, notes, return_to = validate_symptom_submission(form)\n                write_symptom_checkin(day, scores, notes)\n            else:\n                payload, return_to = validate_mapping_submission(form)\n                write_action_payload(payload)\n        except ValueError:\n            self.send_error(400)\n            return\n        except QueueFullError:\n            self.send_error(503)\n            return\n        except OSError:\n            self.send_error(500)\n            return\n        if return_to == \"v5\":\n            receipt = quote(issue_queue_receipt(), safe=\"\")\n            target = V5_ROUTE + f\"?queued={receipt}#capture-status\"\n        else:\n            target = ROUTE + \"?queued=1#symptome\"\n        self.send_response(303)\n        self.send_header(\"Location\", target)\n        self.send_header(\"Cache-Control\", \"no-store\")\n        self.end_headers()\n\n    def _handle(self, send_body: bool) -> None:\n        request = urlparse(self.path)\n        path = request.path\n        if path.startswith(\"/api/v1/\"):\n            self._handle_api(path, request.query, send_body)\n            return\n        if path == \"/\":\n            self.send_response(302)\n            self.send_header(\"Location\", ROUTE)\n            self.end_headers()\n            return\n        if path in {ROUTE, V5_ROUTE}:\n            selected = DASHBOARD_FILE if path == ROUTE else DASHBOARD_V5_FILE\n            if selected is None:\n                self.send_error(404)\n                return\n            try:\n                queued_values = parse_qs(request.query, keep_blank_values=True).get(\n                    \"queued\", []\n                )\n                capture_queued = (\n                    send_body\n                    and path == V5_ROUTE\n                    and len(queued_values) == 1\n                    and consume_queue_receipt(queued_values[0])\n                )\n                data, nonce, csrf_token, browser_session_csrf_token = (\n                    dashboard_html_with_nonce(\n                        selected,\n                        capture_queued=capture_queued,\n                    )\n                )\n            except (OSError, UnicodeError):\n                self.send_error(404)\n                return\n            self._send_bytes(\n                data,\n                selected.name,\n                \"text/html; charset=utf-8\",\n                send_body,\n                nonce=nonce,\n                csrf_token=csrf_token,\n                browser_session_csrf_token=browser_session_csrf_token,\n                connect_self=path == V5_ROUTE,\n            )\n            return\n        if path in ASSET_ROUTES:\n            filename, content_type = ASSET_ROUTES[path]\n            self._serve_file(\n                ASSET_DIR / filename,\n                filename,\n                content_type,\n                send_body,\n            )\n            return\n        if path.startswith(\"/health-doc/\"):\n            try:\n                doc_id = int(path.rsplit(\"/\", 1)[-1])\n            except ValueError:\n                self.send_error(404)\n                return\n            found = document_path(doc_id)\n            if not found:\n                self.send_error(404)\n                return\n            self._serve_file(found[0], found[1], None, send_body)\n            return\n        if path.startswith(\"/health-report/\"):\n            report = safe_report_path(path.rsplit(\"/\", 1)[-1])\n            if not report:\n                self.send_error(404)\n                return\n            self._serve_file(report, report.name, None, send_body)\n            return\n        self.send_error(404)\n\n    def _handle_browser_session(self, send_body: bool) -> None:\n        request = urlparse(self.path)\n        if request.query or API_DB is None:\n            self._send_api_error(\n                503 if API_DB is None else 400,\n                \"api_unavailable\" if API_DB is None else \"unknown_parameter\",\n                send_body,\n            )\n            return\n        origin = self.headers.get(\"Origin\", \"\")\n        fetch_site = self.headers.get(\"Sec-Fetch-Site\", \"\")\n        if (\n            origin and not origin_matches_request(origin, self.headers.get(\"Host\", \"\"))\n        ) or (fetch_site and fetch_site not in {\"same-origin\", \"none\"}):\n            self._send_api_error(403, \"origin_not_allowed\", send_body)\n            return\n        if self.headers.get(\"Content-Length\", \"0\") not in {\"\", \"0\"}:\n            self._send_api_error(400, \"request_rejected\", send_body)\n            return\n        try:\n            cookie = SimpleCookie(self.headers.get(\"Cookie\", \"\"))\n        except (CookieError, ValueError):\n            self._send_api_error(403, \"csrf_rejected\", send_body)\n            return\n        csrf_header = self.headers.get(\"X-Health-Browser-CSRF\", \"\")\n        csrf_cookie = cookie.get(\"health_browser_csrf\")\n        if (\n            not csrf_header\n            or csrf_cookie is None\n            or len(csrf_header) != len(csrf_cookie.value)\n            or not secrets.compare_digest(csrf_header, csrf_cookie.value)\n            or not consume_csrf_token(csrf_header)\n        ):\n            self._send_api_error(403, \"csrf_rejected\", send_body)\n            return\n        session_id = issue_browser_session()\n        cookie = (\n            f\"health_api_session={session_id}; Path=/api/v1; HttpOnly; SameSite=Strict; \"\n            f\"Max-Age={BROWSER_SESSION_TTL_SECONDS}\"\n        )\n        if BROWSER_SESSION_COOKIE_SECURE:\n            cookie += \"; Secure\"\n        self.send_response(204)\n        self.send_header(\"Set-Cookie\", cookie)\n        self.send_header(\"Cache-Control\", \"no-store\")\n        self.send_header(\"Pragma\", \"no-cache\")\n        self.send_header(\"X-Content-Type-Options\", \"nosniff\")\n        self.send_header(\"X-Frame-Options\", \"DENY\")\n        self.send_header(\"Referrer-Policy\", \"no-referrer\")\n        self.send_header(\n            \"Content-Security-Policy\", \"default-src 'none'; frame-ancestors 'none'\"\n        )\n        self.send_header(\"Content-Length\", \"0\")\n        self.end_headers()\n\n    def _handle_api(self, path: str, query: str, send_body: bool) -> None:\n        if API_DB is None:\n            self._send_api_error(503, \"api_unavailable\", send_body)\n            return\n        try:\n            token = load_api_token()\n        except (OSError, UnicodeError):\n            token = None\n        bearer_authenticated = token is not None and api_request_is_authenticated(\n            self.headers.get(\"Authorization\", \"\"), token\n        )\n        session_authenticated = browser_session_is_authenticated(\n            self.headers.get(\"Cookie\", \"\")\n        )\n        if token is None:\n            self._send_api_error(503, \"api_unavailable\", send_body)\n            return\n        if not bearer_authenticated and not session_authenticated:\n            self._send_api_error(401, \"authentication_required\", send_body)\n            return\n        origin = self.headers.get(\"Origin\", \"\")\n        fetch_site = self.headers.get(\"Sec-Fetch-Site\", \"\")\n        if (\n            origin and not origin_matches_request(origin, self.headers.get(\"Host\", \"\"))\n        ) or (fetch_site and fetch_site not in {\"same-origin\", \"none\"}):\n            self._send_api_error(403, \"origin_not_allowed\", send_body)\n            return\n        original = re.fullmatch(\n            r\"/api/v1/documents/(api-document-[a-f0-9]{24})/original\", path\n        )\n        if original:\n            if query:\n                self._send_api_error(400, \"unknown_parameter\", send_body)\n                return\n            opened = open_verified_original(API_DB, original.group(1))\n            if not opened:\n                self._send_api_error(404, \"original_not_available\", send_body)\n                return\n            descriptor, size, mime, filename = opened\n            try:\n                self.send_response(200)\n                self.send_header(\"Content-Type\", mime)\n                self.send_header(\"Content-Length\", str(size))\n                self.send_header(\"Content-Disposition\", f\"inline; filename={filename}\")\n                self.send_header(\"Cache-Control\", \"no-store\")\n                self.send_header(\"Pragma\", \"no-cache\")\n                self.send_header(\"X-Content-Type-Options\", \"nosniff\")\n                self.send_header(\"X-Frame-Options\", \"DENY\")\n                self.send_header(\"Referrer-Policy\", \"no-referrer\")\n                self.send_header(\n                    \"Content-Security-Policy\",\n                    \"default-src 'none'; frame-ancestors 'none'\",\n                )\n                self.end_headers()\n                if send_body:\n                    with os.fdopen(descriptor, \"rb\", closefd=False) as handle:\n                        while data := handle.read(64 * 1024):\n                            self.wfile.write(data)\n            except OSError:\n                return\n            finally:\n                os.close(descriptor)\n            return\n        try:\n            payload = dispatch_api(API_DB, path, query)\n        except APIError as error:\n            self._send_api_error(error.status, error.code, send_body)\n            return\n        except (OSError, UnicodeError):\n            self._send_api_error(503, \"api_unavailable\", send_body)\n            return\n        except Exception:\n            self._send_api_error(500, \"internal_error\", send_body)\n            return\n        self._send_api_json(200, payload, send_body)\n\n    def _send_api_error(self, status: int, code: str, send_body: bool) -> None:\n        self._send_api_json(status, {\"error\": {\"code\": code}}, send_body)\n\n    def _send_api_json(\n        self, status: int, payload: dict[str, object], send_body: bool\n    ) -> None:\n        try:\n            data = json.dumps(\n                payload,\n                ensure_ascii=False,\n                allow_nan=False,\n                sort_keys=True,\n                separators=(\",\", \":\"),\n            ).encode(\"utf-8\")\n        except (TypeError, ValueError, OverflowError):\n            status = 500\n            data = b'{\"error\":{\"code\":\"internal_error\"}}'\n        if len(data) > MAX_API_RESPONSE_BYTES:\n            status = 422\n            data = b'{\"error\":{\"code\":\"response_too_large\"}}'\n        self.send_response(status)\n        self.send_header(\"Content-Type\", \"application/json; charset=utf-8\")\n        self.send_header(\"Content-Length\", str(len(data)))\n        self.send_header(\"Cache-Control\", \"no-store\")\n        self.send_header(\"Pragma\", \"no-cache\")\n        self.send_header(\"X-Content-Type-Options\", \"nosniff\")\n        self.send_header(\"X-Frame-Options\", \"DENY\")\n        self.send_header(\"Referrer-Policy\", \"no-referrer\")\n        self.send_header(\n            \"Permissions-Policy\", \"camera=(), microphone=(), geolocation=()\"\n        )\n        self.send_header(\n            \"Content-Security-Policy\", \"default-src 'none'; frame-ancestors 'none'\"\n        )\n        if status == 401:\n            self.send_header(\"WWW-Authenticate\", 'Bearer realm=\"health-dashboard-api\"')\n        if TEST_INSTANCE_ID:\n"}}
{"timestamp":"2026-07-16T17:46:35.643Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_chTqC5hwcHNC9wjXWJiTUTgB","output":"Chunk ID: 6707c1\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1723\nOutput:\n    const last = medication.last_administered;\n    const next = medication.next_planned;\n    target.append(node(\"p\", \"v5-label\", \"Letzte tatsächlich verabreichte Medikation\"));\n    target.append(node(\"p\", \"\", last ? `${last.name} · ${last.dose || \"Dosis unbekannt\"} · ${formatValue(last.date, \"date\")}` : \"unbekannt\"));\n    target.append(node(\"p\", \"v5-label\", \"Nächste geplante Medikation\"));\n    target.append(node(\"p\", \"\", next ? `${next.name} · ${next.dose || \"Dosis unbekannt\"} · ${formatValue(next.date, \"date\")} · geplant` : \"unbekannt\"));\n    target.dataset.private = \"true\";\n  }\n\n  function renderNutrition() {\n    const summary = q(\"#nutrition-summary\");\n    if (summary) {\n      summary.replaceChildren();\n      const point = latestPoint(\"nutrition.histamine\");\n      const card = node(\"article\", \"v5-card\");\n      card.append(node(\"h3\", \"v5-label\", \"Letzter vollständig klassifizierter Histamin-Score\"));\n      card.append(node(\"div\", \"v5-kpi\", point ? formatValue(point.value) : \"unbekannt\"));\n      card.append(node(\"div\", \"v5-meta\", point ? formatValue(point.date, \"date\") : \"Unvollständige Zuordnung bleibt unbekannt\"));\n      summary.append(card);\n    }\n    const review = q(\"#mapping-review\");\n    if (review) {\n      review.replaceChildren();\n      const rows = (bundle.nutrition && bundle.nutrition.mapping_review) || [];\n      if (!rows.length) review.append(node(\"p\", \"v5-empty\", \"Keine offenen Mapping-Prüfungen\"));\n      rows.forEach((item) => {\n        const row = node(\"article\", \"v5-card v5-card--quiet\");\n        row.append(node(\"strong\", \"\", item.name));\n        row.append(node(\"div\", \"v5-meta\", `${item.occurrences} Dokumentation(en) · Status: nicht zugeordnet`));\n        const form = node(\"form\", \"mapping-action-form\");\n        form.method = \"post\";\n        form.action = \"/health-actions/nutrition-mapping\";\n        [[\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"], [\"return_to\", \"v5\"], [\"queue_key\", item.queue_key || \"\"], [\"canonical_food\", item.suggested_canonical_food || \"\"], [\"sighi_score\", item.suggested_score ?? \"unknown\"], [\"traffic_light\", item.suggested_traffic_light || \"unknown\"], [\"mapping_scope\", \"canonical\"], [\"personal_tolerance\", \"unknown\"]].forEach(([name, value]) => {\n          const input = node(\"input\");\n          input.type = \"hidden\";\n          input.name = name;\n          input.value = String(value);\n          form.append(input);\n        });\n        const submit = node(\"button\", \"\", \"Zuordnung vormerken\");\n        submit.type = \"submit\";\n        submit.disabled = !item.queue_key;\n        form.append(submit);\n        row.append(form);\n        review.append(row);\n      });\n    }\n    const dayField = q(\"[data-nutrition-day-date]\");\n    if (dayField && !dayField.value) dayField.value = bundle.today || new Date().toISOString().slice(0, 10);\n    const openDay = q(\"[data-nutrition-day-open]\");\n    if (openDay && openDay.dataset.bound !== \"true\") {\n      openDay.dataset.bound = \"true\";\n      openDay.addEventListener(\"click\", loadNutritionDay);\n    }\n  }\n\n  function nutritionGroup(title, rows) {\n    const section = node(\"section\", \"nutrition-group\");\n    section.append(node(\"h4\", \"\", title));\n    if (!rows.length) section.append(node(\"p\", \"v5-empty\", \"unbekannt\"));\n    rows.forEach(([label, value, unit]) => section.append(node(\"div\", \"nutrition-row\", `${label}: ${value === null || value === undefined ? \"unbekannt\" : `${formatValue(value)} ${unit || \"\"}`.trim()}`)));\n    return section;\n  }\n\n  async function loadNutritionDay() {\n    const status = q(\"[data-nutrition-day-status]\");\n    const target = q(\"[data-nutrition-day]\");\n    const day = q(\"[data-nutrition-day-date]\")?.value || bundle.today;\n    if (!target || !/^\\d{4}-\\d{2}-\\d{2}$/.test(day || \"\")) return;\n    if (status) status.textContent = \"Ernährungstag wird geladen …\";\n    try {\n      const data = await apiGet(`/api/v1/nutrition/day/${encodeURIComponent(day)}`);\n      target.replaceChildren(\n        nutritionGroup(\"Makros\", [[\"Energie\", data.macros?.kcal, \"kcal\"], [\"Protein\", data.macros?.protein_g, \"g\"], [\"Kohlenhydrate\", data.macros?.carb_g, \"g\"], [\"Fett\", data.macros?.fat_g, \"g\"]]),\n        nutritionGroup(\"Nährstoffe\", Object.entries(data.nutrients || {}).slice(0, 12).map(([key, item]) => [item.label || key, item.value, item.unit])),\n        nutritionGroup(\"Mahlzeiten\", (data.meals || []).map((meal) => [meal.label || meal.meal, meal.item_count, \"Einträge\"])),\n      );\n      if (status) status.textContent = data.coverage?.status === \"complete\" ? \"Vollständig klassifizierter Ernährungstag.\" : \"Unvollständige Werte bleiben unbekannt.\";\n    } catch (error) {\n      if (status) status.textContent = `Ernährungstag nicht verfügbar (${error.message || \"api_error\"}).`;\n    }\n  }\n\n  function renderDoctor() {\n    renderMedication(q(\"#doctor-medication\"));\n    const target = q(\"#verified-labs\");\n    if (!target) return;\n    target.replaceChildren();\n    const labs = bundle.labs || [];\n    if (!labs.length) target.append(node(\"p\", \"v5-empty\", \"Keine gegen das Original verifizierten Laborwerte verfügbar\"));\n    labs.forEach((lab) => {\n      const row = node(\"article\", \"v5-card\");\n      row.dataset.private = \"true\";\n      row.append(node(\"h3\", \"v5-label\", lab.parameter));\n      row.append(node(\"div\", \"v5-kpi\", `${lab.value} ${lab.unit || \"\"}`.trim()));\n      row.append(node(\"div\", \"v5-meta\", `${formatValue(lab.date, \"date\")} · Referenz ${lab.reference_min ?? \"–\"} bis ${lab.reference_max ?? \"–\"} · gegen Original verifiziert`));\n      target.append(row);\n    });\n  }\n\n  function renderEventTrack(targetId, domainDays = [], options = {}) {\n    const target = q(`#${targetId}`);\n    if (!target) return;\n    const positionDay = typeof options.positionDay === \"function\" ? options.positionDay : (event) => event.date;\n    target.replaceChildren(node(\"strong\", \"\", targetId === \"explorer-event-track\" ? \"Explorer-Ereignisspur\" : \"Ereignisspur\"));\n    const events = periodData(bundle.events || []);\n    const fallbackDays = Object.values(bundle.series || {}).flat().map((point) => point.date);\n    const sortedDomain = (options.requireDomain ? domainDays : (domainDays.length ? domainDays : fallbackDays)).filter(Boolean).sort();\n    if (!events.length || !sortedDomain.length) {\n      target.append(node(\"p\", \"v5-meta\", \"Keine dokumentierten Ereignisse auf der sichtbaren Zeitachse.\"));\n      return;\n    }\n    const start = sortedDomain[0];\n    const end = sortedDomain[sortedDomain.length - 1];\n    const rawSpan = daysBetween(start, end) || 0;\n    const span = Math.max(1, rawSpan);\n    const visibleEvents = events.filter((event) => {\n      const positioned = positionDay(event);\n      return positioned >= start && positioned <= end;\n    });\n    if (!visibleEvents.length) return target.append(node(\"p\", \"v5-empty\", \"Keine dokumentierten Ereignisse in der sichtbaren Zeitdomäne.\"));\n"}}
{"timestamp":"2026-07-16T17:46:35.643Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_vi9L68CBQeS9diBmk9wBHFAj","output":"Chunk ID: d51dbf\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 2121\nOutput:\n<section id='view-today' class='app-view' data-dashboard-panel='today' aria-labelledby='title-today'><div class='view-heading'><div><span class='eyebrow'>Tagesübersicht</span><h2 id='title-today'>Heute</h2></div><p class='context-note'>Das Wichtigste aus den zuletzt dokumentierten Daten. Fehlende Werte bleiben unbekannt.</p></div>\n<div class='today-grid'><article class='hero-card today-snapshot-card'><h3>Momentaufnahme</h3><div id='today-snapshot'></div></article><article><h3>Aufgaben</h3><div id='today-tasks'></div></article><article><h3>Medikation</h3><div id='today-medication'></div></article></div>\n<p id='capture-status' class='capture-status v5-meta' role='status' aria-live='polite' data-queued='__CAPTURE_QUEUED__' hidden></p>\n<button type='button' class='primary-action mobile-capture-action' id='open-checkin' aria-describedby='capture-helper'><span aria-hidden='true'>＋</span> Beschwerden erfassen</button>\n<p id='capture-helper' class='v5-meta'>Alle sieben Werte sind für einen vollständigen Tag erforderlich. Fehlende Angaben werden nicht als 0 gespeichert.</p>\n<dialog id='checkin-dialog' aria-labelledby='checkin-title'><form method='post' action='/health-actions/symptom-checkin'><input type='hidden' name='csrf_token' value='__CSRF_TOKEN__'><input type='hidden' name='return_to' value='v5'><input type='hidden' name='date' value='__CAPTURE_DATE__'><h2 id='checkin-title'>Beschwerden erfassen</h2><p class='v5-meta'>Erfassung für <time id='capture-date' datetime='__CAPTURE_DATE__'>__CAPTURE_DATE_LABEL__</time></p><div class='score-grid'>{symptom_fields}</div><label>Notiz, optional<textarea name='notes' maxlength='300'></textarea></label><div class='dialog-actions'><button type='button' id='close-checkin'>Abbrechen</button><button type='submit'>Sicher vormerken</button></div></form></dialog>\n</section>\n<section id='view-cockpit' class='app-view' data-dashboard-panel='cockpit' aria-labelledby='title-cockpit' hidden><div class='view-heading'><div><span class='eyebrow'>Persönliche Trends</span><h2 id='title-cockpit'>Cockpit</h2></div><p class='context-note'>Dokumentierte Werte im persönlichen Verlauf; keine generische medizinische Bewertung.</p></div><div id='metric-cards' class='metric-grid'></div><article class='chart-panel cockpit-chart-panel'><div class='panel-heading'><div><span class='v5-badge'>Verlauf</span><h3 id='cockpit-chart-title'>Gesundheitsverlauf</h3></div><p id='cockpit-chart-context' class='v5-meta'></p></div><div id='cockpit-chart' class='v5-echart' role='img' aria-label='Gesundheitsverlauf'></div><p id='cockpit-chart-empty' class='v5-empty' hidden>Keine dokumentierten Beobachtungen im gewählten Zeitraum.</p><div id='cockpit-chart-summary' class='sr-summary'></div><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='cockpit-chart-table'></table></div></details></article><div id='event-track' class='event-track' aria-label='Ereignisspur'></div></section>\n<section id='view-explorer' class='app-view' data-explorer-shell='unified-echarts' data-dashboard-panel='explorer' aria-labelledby='title-explorer' hidden>\n  <div class='explorer-heading'><div><span class='eyebrow'>Explorer</span><h2 id='title-explorer'>Gesundheitsverläufe</h2></div><p class='context-note'>Einheitliche ECharts-Verlaufsansicht. Dokumentierte Werte ohne Diagnose-, Kausalitäts- oder Therapieaussage.</p></div>\n  <div data-api-explorer data-api-explorer-enabled='{int(explorer_6c)}' hidden>\n    <div class='explorer-toolbar v5-card' aria-label='Explorer steuern'>\n      <div class='mode-selector' role='group' aria-label='Darstellung'><button type='button' data-api-explorer-mode='raw' aria-pressed='true'>Rohwerte</button><button type='button' data-api-explorer-mode='baseline' aria-pressed='false'>Baseline · Index 100</button></div>\n      <div class='mode-selector' role='group' aria-label='Auflösung'><button type='button' data-api-explorer-resolution='day' aria-pressed='true'>Täglich</button><button type='button' data-api-explorer-resolution='week' aria-pressed='false'>Wöchentlich</button></div>\n      <button type='button' data-api-explorer-clear>Auswahl leeren</button>\n    </div>\n    <div class='explorer-shortcuts' aria-label='Vorauswahl'><span class='v5-label'>Abkürzungen</span><button type='button' data-api-explorer-shortcut='Herz'>Herz/Kreislauf</button><button type='button' data-api-explorer-shortcut='Schlaf'>Schlaf/Erholung</button><button type='button' data-api-explorer-shortcut='Schritte'>Aktivität</button><button type='button' data-api-explorer-shortcut='Symptome'>Symptome</button></div>\n    <div class='metric-add v5-card'><label for='explorer-metric-search'>Gesundheitswert hinzufügen</label><div class='metric-add-row'><input id='explorer-metric-search' type='search' data-api-explorer-search autocomplete='off'><button type='button' data-api-explorer-search-button>Suchen</button></div><div data-api-explorer-results class='api-explorer-result-list'></div></div>\n    <div data-api-explorer-selected class='api-explorer-selected' aria-live='polite'></div>\n    <div class='explorer-status-row'><p data-api-explorer-status role='status' aria-live='polite'></p><button type='button' data-api-explorer-retry>Erneut versuchen</button></div>\n    <div data-api-explorer-events class='api-explorer-events' aria-live='polite'></div>\n    <div data-api-explorer-chart class='api-explorer-chart' role='img' aria-label='API-basierter ECharts-Gesundheitsverlauf'></div>\n    <details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table data-api-explorer-table></table></div></details>\n  </div>\n  {prototype_markup}\n</section>\n{calendar_markup}\n<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen und Unsicherheit; sie sind keine allgemeine Lebensmittelsicherheitsbewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Score'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Hypothesenansicht: gemeinsam dokumentierte Werte werden transparent gegenübergestellt; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'><span class='sr-only'>Nur Anzeige – Bearbeitung folgt im Ernährungs-Sprint 6G. </span>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\n{record_markup}\n<section id='view-more' class='app-view' data-dashboard-panel='more' aria-labelledby='title-more' hidden><h2 id='title-more'>Daten & System</h2><div id='data-status'></div><p>Technische Datenqualität und sekundäre Arbeitsansichten sind bewusst von den primären Gesundheitsansichten getrennt.</p></section>\n</main>\n<script type='application/json' id='health-dashboard-bundle'>{payload}</script>\n{'' if explorer_6c else \"<script src='/health-assets/chart.umd.min.js'></script>\"}{echarts_scripts}{range_scripts}<script src='/health-assets/dashboard-v5.js'></script>{explorer_scripts}{calendar_scripts}{record_scripts}{global_search_scripts}{prototype_scripts}\n<noscript>Dashboard v5 benötigt lokales JavaScript. Dashboard v4 bleibt als Fallback verfügbar.</noscript>\n</div></body></html>\"\"\"\n"}}
{"timestamp":"2026-07-16T17:46:35.675Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_fx0yDyZQWrAwQgLVSoLgyaqO","output":"Chunk ID: 75450e\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1304\nOutput:\n            positive_hits TEXT,\n            negative_hits TEXT,\n            source TEXT,\n            computed_at TEXT DEFAULT CURRENT_TIMESTAMP\n        );\n\nCREATE TABLE nutrition_daily_summary_v2 (\n    datum TEXT PRIMARY KEY,\n    kcal REAL DEFAULT 0,\n    protein_g REAL DEFAULT 0,\n    carb_g REAL DEFAULT 0,\n    fat_g REAL DEFAULT 0,\n    item_count INTEGER DEFAULT 0,\n    nutrient_json TEXT,\n    histamine_score REAL DEFAULT 0,\n    histamine_max INTEGER DEFAULT 0,\n    histamine_unknown_count INTEGER DEFAULT 0,\n    histamine_label TEXT DEFAULT 'unknown',\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_food_insights (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    canonical_food TEXT NOT NULL,\n    insight_type TEXT NOT NULL CHECK(insight_type IN ('trigger_candidate','safe_candidate','insufficient_data')),\n    days_seen INTEGER DEFAULT 0,\n    avg_same_day_symptom REAL,\n    avg_next_day_symptom REAL,\n    avg_followup_symptom REAL,\n    histamine_avg REAL,\n    confidence TEXT DEFAULT 'low',\n    notes TEXT,\n    computed_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    UNIQUE(canonical_food, insight_type)\n);\n\nCREATE TABLE nutrition_histamine_scores (\n    item_id INTEGER PRIMARY KEY,\n    canonical_food TEXT,\n    sighi_score INTEGER,\n    traffic_light TEXT NOT NULL,\n    tags TEXT,\n    confidence TEXT DEFAULT 'low',\n    reason TEXT,\n    scored_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\n);\n\nCREATE TABLE nutrition_item_nutrients (\n    item_id INTEGER NOT NULL,\n    nutrient_key TEXT NOT NULL,\n    value REAL,\n    unit TEXT,\n    raw_value TEXT,\n    PRIMARY KEY (item_id, nutrient_key),\n    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\n);\n\nCREATE TABLE nutrition_items (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    source TEXT NOT NULL DEFAULT 'yazio_api',\n    source_item_id TEXT,\n    source_product_id TEXT,\n    datum TEXT NOT NULL,\n    meal TEXT NOT NULL,\n    name TEXT NOT NULL,\n    brand TEXT,\n    amount REAL,\n    amount_unit TEXT DEFAULT 'g',\n    serving TEXT,\n    serving_quantity REAL,\n    kcal REAL,\n    protein_g REAL,\n    carb_g REAL,\n    fat_g REAL,\n    raw_item_json TEXT,\n    raw_product_json TEXT,\n    item_hash TEXT NOT NULL UNIQUE,\n    imported_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_meal_summary (\n    datum TEXT NOT NULL,\n    meal TEXT NOT NULL,\n    kcal REAL DEFAULT 0,\n    protein_g REAL DEFAULT 0,\n    carb_g REAL DEFAULT 0,\n    fat_g REAL DEFAULT 0,\n    item_count INTEGER DEFAULT 0,\n    nutrient_json TEXT,\n    histamine_score REAL DEFAULT 0,\n    histamine_label TEXT DEFAULT 'unknown',\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    PRIMARY KEY (datum, meal)\n);\n\nCREATE TABLE nutrition_review_queue (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    normalized_name TEXT NOT NULL UNIQUE,\n    example_name TEXT NOT NULL,\n    occurrence_count INTEGER DEFAULT 0,\n    first_seen TEXT,\n    last_seen TEXT,\n    suggested_canonical_food TEXT,\n    suggested_score INTEGER,\n    reason TEXT,\n    status TEXT DEFAULT 'open' CHECK(status IN ('open','mapped','ignored')),\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_treatment_phase_summary (\n        phase TEXT PRIMARY KEY,\n        start_date TEXT,\n        end_date TEXT,\n        nutrition_days INTEGER DEFAULT 0,\n        avg_histamine_score REAL,\n        avg_symptom_score REAL,\n        avg_kcal REAL,\n        avg_protein_g REAL,\n        notes TEXT,\n        computed_at TEXT DEFAULT CURRENT_TIMESTAMP\n    );\n\nCREATE TABLE personal_food_tolerance (\n    canonical_food TEXT PRIMARY KEY,\n    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n    evidence_level TEXT DEFAULT 'low' CHECK(evidence_level IN ('low','medium','high')),\n    notes TEXT,\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_action_log (\n    action_id TEXT PRIMARY KEY,\n    action_hash TEXT NOT NULL,\n    queue_key TEXT NOT NULL,\n    applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_provenance (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    action_id TEXT NOT NULL,\n    normalized_name TEXT NOT NULL,\n    canonical_food TEXT,\n    sighi_score INTEGER,\n    traffic_light TEXT NOT NULL,\n    mapping_scope TEXT NOT NULL,\n    affected_day TEXT NOT NULL,\n    applied_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    UNIQUE(action_id, affected_day)\n);\n\nCREATE TABLE nutrition_composite_product_review (\n    normalized_name TEXT PRIMARY KEY,\n    canonical_food TEXT,\n    status TEXT NOT NULL DEFAULT 'needs_ingredient_review' CHECK(status IN ('needs_ingredient_review','reviewed')),\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE multimodal_correlation_results (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    predictor TEXT NOT NULL,\n    target TEXT NOT NULL,\n    lag_days INTEGER NOT NULL,\n    medication_phase TEXT NOT NULL,\n    n INTEGER NOT NULL,\n    eligible_target_days INTEGER NOT NULL,\n    expected_target_days INTEGER NOT NULL DEFAULT 0,\n    target_coverage REAL NOT NULL DEFAULT 0,\n    missing_pairs INTEGER NOT NULL,\n    rho REAL,\n"}}
{"timestamp":"2026-07-16T17:46:35.676Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":266981,"cached_input_tokens":192768,"output_tokens":1866,"reasoning_output_tokens":34,"total_tokens":268847},"last_token_usage":{"input_tokens":75450,"cached_input_tokens":62848,"output_tokens":326,"reasoning_output_tokens":16,"total_tokens":75776},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:46:54.235Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRkOOVcjXZDoUeNfOnLK3nwRVxpzj4wv29xE_3nvCYvfzWyGYZOlhA6aURKB01ASdSmiLuZeeZLIijsKZELkIsZDqkFstvRn9CiLUP7an-aWBIp04yfNbCia3li5WyWqm7QyFILYWNIZ8YHJlX9oTNGKJClg1X2bmtnkKtHWn23Jrr1uugK4oYXA75ne44L33SwHArydrIYe6LZGn1uf1I_-7QPGFV_ONaHRhAQU0OQBZ9R6HO415H6J8CSw8kDH9LeZhvaIfiyjfjCEVJ9h4HxadxyE7JxCK0RnKZbQ45_o9QkUPLaz_HjrLuB0fg69rn5S6C4SWJjVTL4oF1N1NhcWLONoQTpcP6Vbe50z9WmdOOWhZfIJuVPGBfRgLWQzKh3OTmPK7IAFQLN7rjEZgnyK0a4NRlWIxPOLrUh8ujRuUriXl6nt58Pg1nuXCP3AxTliI5f9UcN_3V2rLqJ4W61K9W_dxJYP261anOCLXJnSc9AVY-EZFPMmzXt_SmHzzOdyWbW4ypaiSy1hoawJbiy4ZUbwIReeq8kcpxju0t6vHUTBQpeZabFCnT_vfvz_MttPC_gW2JXaitJRKcq2sR2Qw864SIIEf1i4WkfnxtU8cU_fiWcRbzDKiawe2CfOEcHeFjVagLIMxFKTzO52GWmd2w0-zjU__NQBCAl6Nd9VqdVeZhg-g2SioQdWN6yShS3De6cD1ZwxzcEAemt5N-2jI9ETVN_0WMJzxhzSJApOTq9IQMV89mYjgY7TZWQH3rnh0WNYKtKO4EYqU5t8qi8--l06l9SjnFTCtUtu5RSMxiNhvX6M2q_rJpJni1h5xBc0qK9GhQ8hxEXjA6RU7_vzrWzAxSsrpJFt3iVpqybbz-vglCl41eDQHO4g__SgOJHo2aIY1tG9DvtRX19KbQ2tvw0mofiwP9kNZE_rUZayfaF822M3pJyktqOlTCslz00ZKXyw9hWVOev-sJR3_hcio8tue_3-WdtDgxJpqq1786dVPwp41nGnLxXVtkGtD_1-9mrl441b9V4QGcOWsEWc3cTYlVZ8-mXh_QXym3Dk2VlSZykptqfbwkZYTJ160M82R0QnlPYrJyX7IYy2OyntsE8AlTR60DoU991ct5q3vG6AMFESYR7uTAaYJjP77UJlNsAzwCzEy_ui15mOeQGIXF2eV1uk5coGd7QcoPGedoS3bGmJf5ButwteTktSZ7pYuGRlfRkmYTPPDR309r4p6-CD3AVu_H3mU6LCYq0udc2YugD5apAzOVAk4jBP5PwvxikNsqXa1CLoGMRyVc048CaUbns6BtO17eZiA9EMK8dN722XA8c37eGCsM4NR1gTlCU45liWJNjDx2o2DEkz4LRVRGXSg8vWl7mcDzT4f-MfIjpovwm8vMZi_K3p-pu6DikKmfqQ2lreXLaqJSji80lgPtWwiDxRG3fdQ7QkhAvTXFiN2EKxU5JIUgE4bHOHlEy79pxPUTQ7098alNNYoihp66zzdkC0Tz2DTxjyY9yAISTMhIyVFe5SFwv4vcDGApbXkRcy4JPbj-NIRu5UJCkM0tPRXwku3--ujBaMXb8Wt98ElfxIB9I8IWkp9RWqAkqrisLzf8R6YcfN-qo8Prrrr41byQjU03iabu576WteTASCvvbcZt8Ks7ZDwmntZm2fKZCvGWZmsVGumQUfhjOdePlf_YBc2Fc8mJG_gc7zAW4YU-Z-4c3SuRY5eKDilRigQCuTm--n--WHo82BEDhW5hykzZFKQvfzxEy7IC7Vby-ubCooirclEADNMebIrkTqTUHBWIQgTPFu4t-au53IBlZMaV7PhCZUyML8frK4gWcsSuRr1NM6CEf0ps4ZwPaliDRxLSoZ1__9Ocpv1w3Nb7gf1s3bi5N_J7pzPqrI7gDy_KbrRQ6hxrz-PkfPtQbvrZFOkB5hEbdyneHIiFUHRoXL5XODcyH-5ZeCwCw1V9UpOc7y-UlPxxxxf9dJKxLiZTDv_WT0vsy_0VpdIFdLfDJxftkdKrygfUVi1EWrUzPHzdV8GG6tmWXNpqxRt1MZYlGZirZjnZHtq0-jWgTntBD99aok5CcFVOHjdmj0Bkm4SRrQmY6wtwYUEGPPLkgGRG9BBJyQ0XYaDJ7a_z70CAOF5aPLG-_PhYYVtMehZ-0QS-aWiOYSUk5UwsLcuEz5Pp0NXNvZGgyciA6fuUoxw8yrtk2gKMveZxYwtOV7Rlklg1-MNkMczD1nymIcTE3r39nypuIo4etwUSjS0UkZZN5sbQHOE4qDFWthEj3lHYOZ0EMpc1-wMSJjSLvWTG8J8SO2pVs8kZjw6WtV8CmUQDwxBKbXhk-57T27WdxtGmhWfUiY9yzgOAZ87RA3suRektFKeCkzLz_noZ9EELuNC4jSX9gfBQ9V58OuHo-uRO4ttjodfqkVUJsmgp8ViX3xFNF98x7g2iHGiCAywxiL0HqX2ss8aVM4287NI5aq1WWvbEYvMtT48pCTNkQ6smdpoAhxHH7slA6VGLK6X8-puKT7Bp6DDmUW3kMkj55UzuJ4OZzfOkpJHaB8PPHHdYGEEnLqtE4Su38F2CjMavGXYvrS2db9IIo9BtGPq9aVDAVu_iu7aA3RFzrbdWjK7jwQMxNuCUMokmfENSZ5tmUn0u7MIq43Y61UOzcRWEeBmd813FyfMxcGdmx_DJ7CWtgxFLI-Iu3BO-EVbqXTt-R-ZKc0Wa-45uC-InlIfED0pIrhEr5f2El-qD8o0dECSAdAHcJbSmXwwTVr5mk_X1D8_l-hCUTiMFnIGkXUToHe3h2eSR1EYBXZZRMPW8LReEt5a9gbzxJyRE1VG9RY2TeyxxH8FVGaKOiO77AW9avdaobqFtLQLNKgw2BV9auoh8RGjKC1tq-3I6hNEj0oPt79VqkBwEke8rECKUwoDHOoyRD6vgG0fEaecAlK3fgAiDsMUh97vmt4pMppSqrFxT6T0aohkS1Uw9qOMK9_H730wN00c-TMZMViElZs0k-tm6fkkAMhUGwYRabG0jiAqM85quQAY3MwxNz03KcEiwVh1iFHijGnfaqvSC-cRyJviiANhf8owy4OFSQKe1SNyikZa2l9IuSf8kvJ12VcrQ5RCUaJ9T0Jk3d9mq7rqsOqDLVVtI-dLhctOG-NXWrE4WfO_BWcSrCit4yAIW2iIG8omVM4bGaXVleD5NRveiMQtJRitifyiwLQmAqNeVBuFiKcfbEfx2jj7MSYrt76ihpddPty7EvAMcxbEz8esh5x6NWY0a783zSL-jvPVePTFTARzpmFy9v_rSfgpIvr_YXbIeIbO4mC4lqEG9PE6SfZvoyFxXIy-XsklQ0AwPqzW4AwDMmKr346ECkaBFFB0UTVjOrDzNc0gOWcyWDRU04L4Iwyyp_NuNd8Zf4r1T6MRFJkrVqNJ6uH1d9p40T9chz4AP5gzHe2B3VgBQq-uESaXXAzzufZVnvXdTiWSoBgQrMrnvLxLPjwno1gmcLnuHwCWOPPI-1rCcITFUg3ZuCAHQYu-YfIp5JADwGZEO3ZejLzqt3AhV0mF7M01ol41f71HtRpBG2ivkIToXlq4UIGCa3QGQX57qh18yRYXrAasUELA07TAEnWgkkE3GtabxrRJHnB_vSQcB-iktOrjWPJNfRrmLxOVckgDWMX22Vgw3tkTvotEEqP-yFQEuR_ri-Sagz42Cva73_l7zs0N4VnIxWTFYHaCBXt4uNvM65Zgub7laneo8icNxK0RCm9tUpKOY3UkGOcBH18T-wIsYz-_ZOLZFnPLpUD9Dx1WmQet1txvKbK_ffievBfhBTvFibRIfoJ1G_fuN3HBT5WOOImzpCs3A7L1_5BSflfYSF-b2vBYkq3VoND_l4OPeSzjXt0vcS8JNDGZPyu3qZ5sCcj68npWva00ooOpDcei6io-qW1wol4Ho63sFWPy-7Qd4VG9AEk57BmBdQ4yUvMo2j8Y9sYbNkyKZXRP-JOydejZeTd65sQye2bdx_FOy89_Q4PWPfU55c9_QjgmLpKbLpOK5SLmk6UAe_32fluYiEMTaUmHLIKb9RGJQQjDGni-FTYXftRgYg0W2VgZtSC1scjtpu-Kq9WmApAFaawdJHzL0BqhWHsEiqG58zSViov8TO1tz4Jfh5sFeQSRCUChB__5wRxn8pdkViAKXKTHdJdXGiw8gXEYKfrBkDYaFqFT4XDZPJi2It6EDWJlDPiOVLe7VcNEu16D5PC2-u2E2hFYGGlt2c4C1xkR-RQWLqFzqpoOWu5sL0VaLVLRojmTdJwRmWTyLoL_MDcRfbEzWoAcvp0yErDEKTyCM516K2HR2wH3g7nJrnOLSZ_TjJbtqFzIVESAqtbNN4HdJPfH3QRmO61C9avfTU5KrG8eC6BI8owj2cPkcHQeKOzhp5EvRX1UuVknsaAU6bgMUu1sA6oO9wGtCoB58yC-8sWdWVlPnAVpI0lUG8eAYu79lBLAAtlI5wD73UFbYjxbu9tq1nnEzZ9yjKR5fA2ilYsVaKTfTiTgL4caU5Md_APq5kqhc4Jq1jKkhJf0KAx59go6bdwhlWhU9rA0NB5Qw5Z4XLkZG9LrDkDmFJBAulo81eyH69thxPohTolrC2c6_EON3nVOzhPuV-VMWXHliAMXRmGsq-5piTyZauf_OA2oxmObfEbxbFof6GDB7q2drrBxBGkQy5iPaLOKv6WghfizuIPHlUJAUrr26Gcf6JHs4Mr-ocNkQmii0-MgjPtMpPf_PO6ut2aCPcTmE47OszuAWN3iK1PWwzpGU3UOeFp3D1HhG6pnOYZnOi87DmGextF3Lz_JB-jp-GKtzszCiROIoTig8fQPOqH9QKT0vEIUQ3E-Y7X4042ttQMladoadPf-OqfNGs2yEP0MiTxPAoEZu2iZ7lAypWiuwiFpkcPmHPJUy0x3NcUf8GO5gYV2xmvQVPMt4gMcFoEQaUIvQUONneJJSunNFNVdw9a3MD_X3P2GdFXzcHcvkzXLddt_RJpnz_TCne4punZ53-rN6pbywZK_kj7tCVtrNmAb9c0pWRDGk2efN4xz_cioB6XNSGfDVxFU5jrg_OaVxfF_DM5RmJrlTM-E1CNqfkh6vvecDEjRJjp8Y4gH_t8lXGrHoIKn2Myno5xcz2-bk_nYjxtxSeWKYKvMniLnBGGe228uomUEyJ3DtdGA17eqUdOLysFtjlMIh8raDvxW4eiGrUNzZkR9lu4y20UW2Nu4V4Wnp4iVkkpxYLOen--aRt6mir6t7alqsAt9oH-z_5WaMj9xhBQs874uRkIM_pIFEdQZuKu33xceuc6D4mLxUQEVQkLRQP6V23Ifp89L3j0VQIkW7u89-NKDEdZBk14GmQTtjaergv9IL0-FJtDTEA0OarJBZo7punffnQSqyOggAH0QvIrlTaMtvUmEM4hvpLoFwVbKd9WcJFR1nrhkIxEGWo4HX8IjvHL_wBWFra5aGwxQc_R76VS2zyqb-1dHYHOowCnfZBU3Da50FTIVf85wV1rQecK6jaOGpOz3I6FC9giMBrkofpGiX0LaF_wISGvxJ81Lcin2cvcMRdz7YcugiVDpudsu1OB9IuLgzld5Kq6PLie_iiGkWRDQYOIg7lE2lYihw6Y_snhPvlFmArmvP48GI5DXGKF_ffNncT4jRimqfwr_W3N3z0lvpqOHrMerI8QUqhkHVLtKE-1r1bH9lTiolsPYrJw0gKV1qPHrz7Vo0pScXk4tjlvmNcGPQ0RcudKMwXrCkowuFc9rh8Zmnv3alHGBHodUg5JjQShLZeP73F-yzTZjy5AgCpzXvyZM7eM-PDhEiKCSn01k1pg6Rr6TA7Trk82yzH4siAwobrmVkuDLE3Wn0kwxDZshXBG8h9jAqxVQRXsidqXaKDz6Kspij2UyPQ44s0RBYANkWLpTHpjaTDv3ThvbKCzVmvABJPhFXGsVeRMMbigczuSeJuUT7GbVFsMKCJQCIiO7Ky5JY7rLnpx3W1zL1u3aApVAWK1H4GpdC3dpkAAFSYuWjD62-rTaFHX_EWa1irBQdNtJAb6DoaEKUhbNXRRrSSiz0ndYsWO6gC9wmMQpMnQ_iprtqMk_1YIo5HWViHcKQR5jwLyeRzFlXbqCEeAwJvlL5MUdzsaGqWiVCEKweX8MZ-4F1dT3pOswv2Wgz56a6F4PN3Cn1QfKAXsLFDbd_YS8StEFWZBsADWYoKfNZ-qebWDtC_Zp-FxPnLXy52yio9TfOkZBSKztry98P--0PlJNtWVOSomYcUm0C9m9WoH9jTMMV_3Z6tPWvZ2jpt-F_RPanYdCmmRbd_7O34Xy_pOn0Hbx7uNGkCrGek7aAkdsgfzTBs-Kr_Oug9VtLlRgd5V8ig9l3sZtv7IYG7rVwoeC5RwgnnQrWyhtn1wGBPnVyZ6YNY6Nt-80EsQtPQr2paGMtXcJwI7AHBn6qvb1kb7mJCnOuDYduwltMTPDMG53-6PfYQWr-dCve6j-HxBV0CP9LzQUZHBh5SuFSSuZj1K85bsYwrY7401CogqmEi21ltdnd0UE52O7CGuepw2w7zYh5uyR4FaL26pDmJzRNRnfCr3tYyD169F2zW7QuoDbHdqPAcMI-mOh2P3MFtPGDtr5ijj-DepW5B2is4k-Wb2qFbt04sdoZ6xklz9HZh5dri_XBzojIO_5TJj7paviakJKHZnHjA="}}
{"timestamp":"2026-07-16T17:46:55.585Z","type":"event_msg","payload":{"type":"agent_message","message":"I’m about to edit the worker, server form validation, V5 contracts/read API, fixture, schema, UI, tests, and ADR. The main behavioral change is that “classification” becomes a neutral mapping score with explicit provenance, and composite/ignore decisions become first-class outcomes instead of implicit side effects.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:46:55.586Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"I’m about to edit the worker, server form validation, V5 contracts/read API, fixture, schema, UI, tests, and ADR. The main behavioral change is that “classification” becomes a neutral mapping score with explicit provenance, and composite/ignore decisions become first-class outcomes instead of implicit side effects."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:48:25.202Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_GnP3HIjom1EJ5UfkYzl15MJg","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/health_dashboard_action_worker.py\n@@\n ALLOWED_V5_PROFILES = frozenset({V5_PROFILE_DEFAULT, V5_PROFILE_HEALTH_RECORD_6E})\n+MAPPING_DECISIONS = frozenset({\"assign\", \"composite\", \"ignore\"})\n+MAPPING_CONFIDENCE = frozenset({\"low\", \"medium\", \"high\"})\n+MAPPING_METHODS = frozenset({\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"})\n+HISTAMINE_DEFINITION_VERSION = \"sighi_mapping_load_v2\"\n@@\n def validate_mapping_payload(payload: dict[str, Any]) -> dict[str, Any]:\n-    if set(payload) != {\n+    required = {\n         \"version\",\n         \"action\",\n+        \"decision\",\n         \"queue_key\",\n+        \"alias\",\n         \"canonical_food\",\n         \"sighi_score\",\n-        \"traffic_light\",\n-        \"mapping_scope\",\n-        \"personal_tolerance\",\n-    }:\n+        \"confidence\",\n+        \"mapping_method\",\n+        \"source_label\",\n+        \"source_version\",\n+        \"note\",\n+        \"ingredient_review_required\",\n+    }\n+    optional = {\"personal_tolerance\"}\n+    if not required <= set(payload) <= required | optional:\n         raise ValueError(\"invalid mapping payload shape\")\n     if payload[\"version\"] != 1 or payload[\"action\"] != \"nutrition_mapping\":\n         raise ValueError(\"unsupported action\")\n+    decision = payload[\"decision\"]\n     queue_key = payload[\"queue_key\"]\n+    alias = \" \".join(str(payload[\"alias\"] or \"\").split())\n     canonical = \" \".join(str(payload[\"canonical_food\"] or \"\").split())\n-    score = payload[\"sighi_score\"]\n-    light = payload[\"traffic_light\"]\n-    scope = payload[\"mapping_scope\"]\n-    tolerance = payload[\"personal_tolerance\"]\n+    score = payload[\"sighi_score\"]\n+    confidence = payload[\"confidence\"]\n+    method = payload[\"mapping_method\"]\n+    source_label = \" \".join(str(payload[\"source_label\"] or \"\").split())\n+    source_version = \" \".join(str(payload[\"source_version\"] or \"\").split())\n+    note = \" \".join(str(payload[\"note\"] or \"\").split())\n+    ingredient_review = payload[\"ingredient_review_required\"]\n+    tolerance = payload.get(\"personal_tolerance\")\n     if not isinstance(queue_key, str) or not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n         raise ValueError(\"invalid queue key\")\n-    if not isinstance(canonical, str) or len(canonical) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical, re.I):\n-        raise ValueError(\"invalid canonical food\")\n-    if score is not None and (type(score) is not int or score not in range(4)):\n-        raise ValueError(\"invalid score\")\n-    if light not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"} or scope != \"canonical\" or tolerance not in {\"unknown\", \"safe\", \"problematic\", \"unclear\"}:\n+    if decision not in MAPPING_DECISIONS or confidence not in MAPPING_CONFIDENCE or method not in MAPPING_METHODS:\n         raise ValueError(\"invalid mapping value\")\n-    if score is None and light != \"unknown\":\n-        raise ValueError(\"unknown score must keep unknown light\")\n-    if score is not None and not canonical:\n-        raise ValueError(\"canonical food required\")\n+    for field_name, text, maximum in (\n+        (\"alias\", alias, 160),\n+        (\"canonical food\", canonical, 120),\n+        (\"source label\", source_label, 80),\n+        (\"source version\", source_version, 40),\n+        (\"note\", note, 300),\n+    ):\n+        if len(text) > maximum or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", text, re.I):\n+            raise ValueError(f\"invalid {field_name}\")\n+    if not alias or not source_label or not source_version:\n+        raise ValueError(\"required mapping documentation missing\")\n+    if score != \"unknown\" and (type(score) is not int or score not in range(4)):\n+        raise ValueError(\"invalid score\")\n+    if type(ingredient_review) is not bool:\n+        raise ValueError(\"invalid ingredient review flag\")\n+    if tolerance is not None and (not isinstance(tolerance, str) or len(tolerance) > 160 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", tolerance, re.I)):\n+        raise ValueError(\"invalid personal tolerance\")\n+    if decision == \"assign\":\n+        if not canonical or score == \"unknown\" or ingredient_review:\n+            raise ValueError(\"assign requires canonical score without ingredient review\")\n+    elif decision == \"composite\":\n+        if score != \"unknown\" or not ingredient_review:\n+            raise ValueError(\"composite must stay unclassified for ingredient review\")\n+    elif decision == \"ignore\":\n+        if score != \"unknown\" or canonical or ingredient_review:\n+            raise ValueError(\"ignore must not classify\")\n     return {\n         \"version\": 1,\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": decision,\n         \"queue_key\": queue_key,\n+        \"alias\": alias,\n         \"canonical_food\": canonical,\n-        \"sighi_score\": score,\n-        \"traffic_light\": light,\n-        \"mapping_scope\": scope,\n-        \"personal_tolerance\": tolerance,\n+        \"sighi_score\": score,\n+        \"confidence\": confidence,\n+        \"mapping_method\": method,\n+        \"source_label\": source_label,\n+        \"source_version\": source_version,\n+        \"note\": note,\n+        \"ingredient_review_required\": ingredient_review,\n+        \"personal_tolerance\": tolerance,\n     }\n@@\n def _score_label(max_score: int, unknown: int, load: float) -> str:\n-    if max_score >= 3:\n-        return \"red\"\n-    if max_score >= 2 or load >= 4:\n-        return \"orange\"\n-    if max_score == 1 or load >= 1:\n-        return \"yellow\"\n     if unknown:\n         return \"unknown\"\n-    return \"green\"\n+    return \"classified\"\n+\n+\n+def _public_numeric(value: Any) -> float | None:\n+    try:\n+        numeric = float(value)\n+    except (TypeError, ValueError):\n+        return None\n+    return numeric if numeric == numeric and numeric not in {float(\"inf\"), float(\"-inf\")} else None\n+\n+\n+NUTRIENT_ALLOWLIST = {\n+    \"energy.energy\": \"kcal\",\n+    \"nutrient.protein\": \"g\",\n+    \"nutrient.carb\": \"g\",\n+    \"nutrient.fat\": \"g\",\n+    \"nutrient.fiber\": \"g\",\n+    \"nutrient.sugar\": \"g\",\n+    \"nutrient.saturated_fat\": \"g\",\n+    \"nutrient.salt\": \"g\",\n+    \"nutrient.sodium\": \"mg\",\n+}\n+\n+\n+def _nutrient_totals(connection: sqlite3.Connection, item_ids: list[int]) -> str:\n+    if not item_ids:\n+        return \"{}\"\n+    placeholders = \",\".join(\"?\" for _ in item_ids)\n+    totals: dict[str, float] = {}\n+    for row in connection.execute(\n+        f\"\"\"SELECT nutrient_key,value,unit FROM nutrition_item_nutrients\n+            WHERE item_id IN ({placeholders})\"\"\",\n+        item_ids,\n+    ):\n+        key = str(row[\"nutrient_key\"] or \"\")\n+        unit = str(row[\"unit\"] or \"\")\n+        value = _public_numeric(row[\"value\"])\n+        if key not in NUTRIENT_ALLOWLIST or unit != NUTRIENT_ALLOWLIST[key] or value is None:\n+            continue\n+        totals[key] = totals.get(key, 0.0) + value\n+    return json.dumps({key: round(value, 3) for key, value in sorted(totals.items())}, separators=(\",\", \":\"))\n@@\n         CREATE TABLE IF NOT EXISTS nutrition_mapping_action_log (\n             action_id TEXT PRIMARY KEY,\n             action_hash TEXT NOT NULL,\n             queue_key TEXT NOT NULL,\n+            decision TEXT,\n+            alias TEXT,\n+            canonical_food TEXT,\n+            sighi_score INTEGER,\n+            confidence TEXT,\n+            mapping_method TEXT,\n+            source_label TEXT,\n+            source_version TEXT,\n+            note TEXT,\n+            ingredient_review_required INTEGER,\n+            personal_tolerance TEXT,\n+            definition_version TEXT,\n             applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n         );\n         CREATE TABLE IF NOT EXISTS nutrition_mapping_provenance (\n             id INTEGER PRIMARY KEY AUTOINCREMENT,\n             action_id TEXT NOT NULL,\n             normalized_name TEXT NOT NULL,\n+            decision TEXT,\n+            alias TEXT,\n             canonical_food TEXT,\n             sighi_score INTEGER,\n-            traffic_light TEXT NOT NULL,\n-            mapping_scope TEXT NOT NULL,\n+            confidence TEXT,\n+            mapping_method TEXT,\n+            source_label TEXT,\n+            source_version TEXT,\n+            note TEXT,\n+            ingredient_review_required INTEGER,\n+            personal_tolerance TEXT,\n+            definition_version TEXT,\n             affected_day TEXT NOT NULL,\n             applied_at TEXT DEFAULT CURRENT_TIMESTAMP,\n             UNIQUE(action_id, affected_day)\n@@\n         );\n         \"\"\"\n     )\n+    for table, columns in {\n+        \"nutrition_mapping_action_log\": {\n+            \"decision\": \"TEXT\", \"alias\": \"TEXT\", \"canonical_food\": \"TEXT\", \"sighi_score\": \"INTEGER\",\n+            \"confidence\": \"TEXT\", \"mapping_method\": \"TEXT\", \"source_label\": \"TEXT\", \"source_version\": \"TEXT\",\n+            \"note\": \"TEXT\", \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\n+            \"definition_version\": \"TEXT\",\n+        },\n+        \"nutrition_mapping_provenance\": {\n+            \"decision\": \"TEXT\", \"alias\": \"TEXT\", \"confidence\": \"TEXT\", \"mapping_method\": \"TEXT\",\n+            \"source_label\": \"TEXT\", \"source_version\": \"TEXT\", \"note\": \"TEXT\",\n+            \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\n+            \"definition_version\": \"TEXT\",\n+        },\n+    }.items():\n+        existing = {row[\"name\"] for row in connection.execute(f\"PRAGMA table_info({table})\")}\n+        for name, declaration in columns.items():\n+            if name not in existing:\n+                connection.execute(f\"ALTER TABLE {table} ADD COLUMN {name} {declaration}\")\n@@\n-            \"\"\"SELECT i.meal,i.kcal,i.protein_g,i.carb_g,i.fat_g,h.sighi_score\n+            \"\"\"SELECT i.id,i.meal,i.kcal,i.protein_g,i.carb_g,i.fat_g,h.sighi_score\n                FROM nutrition_items i LEFT JOIN nutrition_histamine_scores h ON h.item_id=i.id\n                WHERE i.datum=?\"\"\",\n@@\n-    totals = {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []}\n+    totals = {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": [], \"missing\": set(), \"ids\": []}\n     by_meal: dict[str, dict[str, Any]] = {}\n     for row in rows:\n-        meal = row[\"meal\"] or \"unknown\"\n-        bucket = by_meal.setdefault(meal, {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []})\n+        meal = row[\"meal\"] if row[\"meal\"] in {\"breakfast\", \"lunch\", \"dinner\", \"snack\"} else \"unassigned\"\n+        bucket = by_meal.setdefault(meal, {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": [], \"missing\": set(), \"ids\": []})\n         for target in (bucket, totals):\n             target[\"items\"] += 1\n-            target[\"kcal\"] += float(row[\"kcal\"] or 0)\n-            target[\"protein\"] += float(row[\"protein_g\"] or 0)\n-            target[\"carb\"] += float(row[\"carb_g\"] or 0)\n-            target[\"fat\"] += float(row[\"fat_g\"] or 0)\n+            target[\"ids\"].append(int(row[\"id\"]))\n+            for source, dest in ((\"kcal\", \"kcal\"), (\"protein_g\", \"protein\"), (\"carb_g\", \"carb\"), (\"fat_g\", \"fat\")):\n+                if row[source] is None:\n+                    target[\"missing\"].add(dest)\n+                else:\n+                    target[dest] += float(row[source])\n             if row[\"sighi_score\"] is None:\n                 target[\"unknown\"] += 1\n             else:\n@@\n-        connection.execute(\n+        values = {key: None if key in bucket[\"missing\"] else round(float(bucket[key]), 1) for key in (\"kcal\", \"protein\", \"carb\", \"fat\")}\n+        connection.execute(\n             \"\"\"INSERT OR REPLACE INTO nutrition_meal_summary\n                (datum,meal,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,histamine_score,histamine_label,updated_at)\n                VALUES(?,?,?,?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n-            (day, meal, round(bucket[\"kcal\"], 1), round(bucket[\"protein\"], 1), round(bucket[\"carb\"], 1), round(bucket[\"fat\"], 1), int(bucket[\"items\"]), \"{}\", round(bucket[\"load\"], 1), label),\n+            (day, meal, values[\"kcal\"], values[\"protein\"], values[\"carb\"], values[\"fat\"], int(bucket[\"items\"]), _nutrient_totals(connection, bucket[\"ids\"]), round(bucket[\"load\"], 1) if not bucket[\"unknown\"] else None, label),\n         )\n@@\n-        (day, round(totals[\"kcal\"], 1), round(totals[\"protein\"], 1), round(totals[\"carb\"], 1), round(totals[\"fat\"], 1), int(totals[\"items\"]), \"{}\", round(totals[\"load\"], 1), max_score, int(totals[\"unknown\"]), label),\n+        (\n+            day,\n+            None if \"kcal\" in totals[\"missing\"] else round(float(totals[\"kcal\"]), 1),\n+            None if \"protein\" in totals[\"missing\"] else round(float(totals[\"protein\"]), 1),\n+            None if \"carb\" in totals[\"missing\"] else round(float(totals[\"carb\"]), 1),\n+            None if \"fat\" in totals[\"missing\"] else round(float(totals[\"fat\"]), 1),\n+            int(totals[\"items\"]),\n+            _nutrient_totals(connection, totals[\"ids\"]),\n+            round(totals[\"load\"], 1) if not totals[\"unknown\"] else None,\n+            max_score,\n+            int(totals[\"unknown\"]),\n+            label,\n+        ),\n     )\n@@\n-            matched = [row for row in queue if _slug(str(row[\"normalized_name\"])) == payload[\"queue_key\"]]\n+            matched = [row for row in queue if _slug(str(row[\"normalized_name\"])) == payload[\"queue_key\"]]\n             if len(matched) != 1:\n                 raise RuntimeError(\"mapping queue item not found\")\n             normalized = str(matched[0][\"normalized_name\"])\n             canonical = payload[\"canonical_food\"]\n-            score = payload[\"sighi_score\"]\n-            light = payload[\"traffic_light\"]\n-            affected = sorted({row[\"datum\"] for row in connection.execute(\"SELECT datum FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)) if row[\"datum\"]})\n+            score = None if payload[\"sighi_score\"] == \"unknown\" else payload[\"sighi_score\"]\n+            item_rows = [\n+                row for row in connection.execute(\"SELECT id,datum,name,source_product_id FROM nutrition_items\")\n+                if _slug(str(row[\"name\"] or row[\"source_product_id\"] or \"\")) == payload[\"queue_key\"]\n+                or _slug(str(row[\"source_product_id\"] or row[\"name\"] or \"\")) == payload[\"queue_key\"]\n+            ]\n+            affected = sorted({row[\"datum\"] for row in item_rows if row[\"datum\"]})\n             if len(affected) > 366:\n                 raise RuntimeError(\"affected day limit exceeded\")\n-            if re.search(r\"\\+|,|;|\\bmit\\b|\\bund\\b\", str(matched[0][\"example_name\"]), re.I):\n+            if payload[\"decision\"] == \"composite\":\n                 connection.execute(\n                     \"\"\"INSERT OR REPLACE INTO nutrition_composite_product_review\n                        (normalized_name,canonical_food,status,updated_at)\n@@\n                     (normalized, canonical),\n                 )\n-                score = None\n-                light = \"unknown\"\n-            for row in connection.execute(\"SELECT id FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)):\n-                connection.execute(\n-                    \"\"\"INSERT OR REPLACE INTO nutrition_histamine_scores\n-                       (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n-                       VALUES(?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n-                    (row[\"id\"], canonical or None, score, light, \"worker_mapping_v1\", \"medium\", \"local validated mapping action\"),\n-                )\n-            connection.execute(\"UPDATE nutrition_review_queue SET status='mapped',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n-            if payload[\"personal_tolerance\"] != \"unknown\" and canonical:\n-                connection.execute(\n-                    \"\"\"INSERT INTO personal_food_tolerance(canonical_food,personal_status,evidence_level,notes,updated_at)\n-                       VALUES(?,?, 'low', 'separate personal tolerance label from mapping action', CURRENT_TIMESTAMP)\n-                       ON CONFLICT(canonical_food) DO UPDATE SET personal_status=excluded.personal_status,updated_at=CURRENT_TIMESTAMP\"\"\",\n-                    (canonical, payload[\"personal_tolerance\"]),\n-                )\n+            elif payload[\"decision\"] == \"assign\":\n+                for row in item_rows:\n+                    connection.execute(\n+                        \"\"\"INSERT OR REPLACE INTO nutrition_histamine_scores\n+                           (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n+                           VALUES(?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n+                        (row[\"id\"], canonical, score, \"classified\", payload[\"mapping_method\"], payload[\"confidence\"], payload[\"note\"] or \"local validated mapping action\"),\n+                    )\n+                connection.execute(\"UPDATE nutrition_review_queue SET status='mapped',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n+            else:\n+                connection.execute(\"UPDATE nutrition_review_queue SET status='ignored',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n             for day in affected:\n-                recompute_nutrition_day(connection, str(day))\n+                if payload[\"decision\"] == \"assign\":\n+                    recompute_nutrition_day(connection, str(day))\n                 connection.execute(\n                     \"\"\"INSERT OR IGNORE INTO nutrition_mapping_provenance\n-                       (action_id,normalized_name,canonical_food,sighi_score,traffic_light,mapping_scope,affected_day)\n-                       VALUES(?,?,?,?,?,?,?)\"\"\",\n-                    (action_id, normalized, canonical or None, score, light, payload[\"mapping_scope\"], str(day)),\n+                       (action_id,normalized_name,decision,alias,canonical_food,sighi_score,confidence,\n+                        mapping_method,source_label,source_version,note,ingredient_review_required,\n+                        personal_tolerance,definition_version,affected_day)\n+                       VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n+                    (\n+                        action_id, normalized, payload[\"decision\"], payload[\"alias\"], canonical or None,\n+                        score, payload[\"confidence\"], payload[\"mapping_method\"], payload[\"source_label\"],\n+                        payload[\"source_version\"], payload[\"note\"], int(payload[\"ingredient_review_required\"]),\n+                        payload.get(\"personal_tolerance\"), HISTAMINE_DEFINITION_VERSION, str(day),\n+                    ),\n                 )\n             connection.execute(\n-                \"INSERT INTO nutrition_mapping_action_log(action_id,action_hash,queue_key) VALUES(?,?,?)\",\n-                (action_id, action_hash, payload[\"queue_key\"]),\n+                \"\"\"INSERT INTO nutrition_mapping_action_log\n+                   (action_id,action_hash,queue_key,decision,alias,canonical_food,sighi_score,confidence,\n+                    mapping_method,source_label,source_version,note,ingredient_review_required,\n+                    personal_tolerance,definition_version)\n+                   VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n+                (\n+                    action_id, action_hash, payload[\"queue_key\"], payload[\"decision\"], payload[\"alias\"],\n+                    canonical or None, score, payload[\"confidence\"], payload[\"mapping_method\"],\n+                    payload[\"source_label\"], payload[\"source_version\"], payload[\"note\"],\n+                    int(payload[\"ingredient_review_required\"]), payload.get(\"personal_tolerance\"),\n+                    HISTAMINE_DEFINITION_VERSION,\n+                ),\n             )\n@@\n         if DASHBOARD_V5_PROFILE == V5_PROFILE_HEALTH_RECORD_6E:\n-            v5_command.append(\"--health-record-6e\")\n+            v5_command.extend([\"--health-record-6e\", \"--explorer-6c\", \"--calendar-day-6d\"])\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:48:25.304Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_GnP3HIjom1EJ5UfkYzl15MJg","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/health_dashboard_action_worker.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/health_dashboard_action_worker.py":{"type":"update","unified_diff":"@@ -52,2 +52,6 @@\n ALLOWED_V5_PROFILES = frozenset({V5_PROFILE_DEFAULT, V5_PROFILE_HEALTH_RECORD_6E})\n+MAPPING_DECISIONS = frozenset({\"assign\", \"composite\", \"ignore\"})\n+MAPPING_CONFIDENCE = frozenset({\"low\", \"medium\", \"high\"})\n+MAPPING_METHODS = frozenset({\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"})\n+HISTAMINE_DEFINITION_VERSION = \"sighi_mapping_load_v2\"\n \n@@ -107,12 +111,19 @@\n def validate_mapping_payload(payload: dict[str, Any]) -> dict[str, Any]:\n-    if set(payload) != {\n+    required = {\n         \"version\",\n         \"action\",\n+        \"decision\",\n         \"queue_key\",\n+        \"alias\",\n         \"canonical_food\",\n         \"sighi_score\",\n-        \"traffic_light\",\n-        \"mapping_scope\",\n-        \"personal_tolerance\",\n-    }:\n+        \"confidence\",\n+        \"mapping_method\",\n+        \"source_label\",\n+        \"source_version\",\n+        \"note\",\n+        \"ingredient_review_required\",\n+    }\n+    optional = {\"personal_tolerance\"}\n+    if not required <= set(payload) <= required | optional:\n         raise ValueError(\"invalid mapping payload shape\")\n@@ -120,20 +131,44 @@\n         raise ValueError(\"unsupported action\")\n+    decision = payload[\"decision\"]\n     queue_key = payload[\"queue_key\"]\n+    alias = \" \".join(str(payload[\"alias\"] or \"\").split())\n     canonical = \" \".join(str(payload[\"canonical_food\"] or \"\").split())\n     score = payload[\"sighi_score\"]\n-    light = payload[\"traffic_light\"]\n-    scope = payload[\"mapping_scope\"]\n-    tolerance = payload[\"personal_tolerance\"]\n+    confidence = payload[\"confidence\"]\n+    method = payload[\"mapping_method\"]\n+    source_label = \" \".join(str(payload[\"source_label\"] or \"\").split())\n+    source_version = \" \".join(str(payload[\"source_version\"] or \"\").split())\n+    note = \" \".join(str(payload[\"note\"] or \"\").split())\n+    ingredient_review = payload[\"ingredient_review_required\"]\n+    tolerance = payload.get(\"personal_tolerance\")\n     if not isinstance(queue_key, str) or not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n         raise ValueError(\"invalid queue key\")\n-    if not isinstance(canonical, str) or len(canonical) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical, re.I):\n-        raise ValueError(\"invalid canonical food\")\n-    if score is not None and (type(score) is not int or score not in range(4)):\n+    if decision not in MAPPING_DECISIONS or confidence not in MAPPING_CONFIDENCE or method not in MAPPING_METHODS:\n+        raise ValueError(\"invalid mapping value\")\n+    for field_name, text, maximum in (\n+        (\"alias\", alias, 160),\n+        (\"canonical food\", canonical, 120),\n+        (\"source label\", source_label, 80),\n+        (\"source version\", source_version, 40),\n+        (\"note\", note, 300),\n+    ):\n+        if len(text) > maximum or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", text, re.I):\n+            raise ValueError(f\"invalid {field_name}\")\n+    if not alias or not source_label or not source_version:\n+        raise ValueError(\"required mapping documentation missing\")\n+    if score != \"unknown\" and (type(score) is not int or score not in range(4)):\n         raise ValueError(\"invalid score\")\n-    if light not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"} or scope != \"canonical\" or tolerance not in {\"unknown\", \"safe\", \"problematic\", \"unclear\"}:\n-        raise ValueError(\"invalid mapping value\")\n-    if score is None and light != \"unknown\":\n-        raise ValueError(\"unknown score must keep unknown light\")\n-    if score is not None and not canonical:\n-        raise ValueError(\"canonical food required\")\n+    if type(ingredient_review) is not bool:\n+        raise ValueError(\"invalid ingredient review flag\")\n+    if tolerance is not None and (not isinstance(tolerance, str) or len(tolerance) > 160 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", tolerance, re.I)):\n+        raise ValueError(\"invalid personal tolerance\")\n+    if decision == \"assign\":\n+        if not canonical or score == \"unknown\" or ingredient_review:\n+            raise ValueError(\"assign requires canonical score without ingredient review\")\n+    elif decision == \"composite\":\n+        if score != \"unknown\" or not ingredient_review:\n+            raise ValueError(\"composite must stay unclassified for ingredient review\")\n+    elif decision == \"ignore\":\n+        if score != \"unknown\" or canonical or ingredient_review:\n+            raise ValueError(\"ignore must not classify\")\n     return {\n@@ -141,7 +176,13 @@\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": decision,\n         \"queue_key\": queue_key,\n+        \"alias\": alias,\n         \"canonical_food\": canonical,\n         \"sighi_score\": score,\n-        \"traffic_light\": light,\n-        \"mapping_scope\": scope,\n+        \"confidence\": confidence,\n+        \"mapping_method\": method,\n+        \"source_label\": source_label,\n+        \"source_version\": source_version,\n+        \"note\": note,\n+        \"ingredient_review_required\": ingredient_review,\n         \"personal_tolerance\": tolerance,\n@@ -234,13 +275,47 @@\n def _score_label(max_score: int, unknown: int, load: float) -> str:\n-    if max_score >= 3:\n-        return \"red\"\n-    if max_score >= 2 or load >= 4:\n-        return \"orange\"\n-    if max_score == 1 or load >= 1:\n-        return \"yellow\"\n     if unknown:\n         return \"unknown\"\n-    return \"green\"\n+    return \"classified\"\n \n \n+def _public_numeric(value: Any) -> float | None:\n+    try:\n+        numeric = float(value)\n+    except (TypeError, ValueError):\n+        return None\n+    return numeric if numeric == numeric and numeric not in {float(\"inf\"), float(\"-inf\")} else None\n+\n+\n+NUTRIENT_ALLOWLIST = {\n+    \"energy.energy\": \"kcal\",\n+    \"nutrient.protein\": \"g\",\n+    \"nutrient.carb\": \"g\",\n+    \"nutrient.fat\": \"g\",\n+    \"nutrient.fiber\": \"g\",\n+    \"nutrient.sugar\": \"g\",\n+    \"nutrient.saturated_fat\": \"g\",\n+    \"nutrient.salt\": \"g\",\n+    \"nutrient.sodium\": \"mg\",\n+}\n+\n+\n+def _nutrient_totals(connection: sqlite3.Connection, item_ids: list[int]) -> str:\n+    if not item_ids:\n+        return \"{}\"\n+    placeholders = \",\".join(\"?\" for _ in item_ids)\n+    totals: dict[str, float] = {}\n+    for row in connection.execute(\n+        f\"\"\"SELECT nutrient_key,value,unit FROM nutrition_item_nutrients\n+            WHERE item_id IN ({placeholders})\"\"\",\n+        item_ids,\n+    ):\n+        key = str(row[\"nutrient_key\"] or \"\")\n+        unit = str(row[\"unit\"] or \"\")\n+        value = _public_numeric(row[\"value\"])\n+        if key not in NUTRIENT_ALLOWLIST or unit != NUTRIENT_ALLOWLIST[key] or value is None:\n+            continue\n+        totals[key] = totals.get(key, 0.0) + value\n+    return json.dumps({key: round(value, 3) for key, value in sorted(totals.items())}, separators=(\",\", \":\"))\n+\n+\n def ensure_nutrition_mapping_schema(connection: sqlite3.Connection) -> None:\n@@ -252,2 +327,14 @@\n             queue_key TEXT NOT NULL,\n+            decision TEXT,\n+            alias TEXT,\n+            canonical_food TEXT,\n+            sighi_score INTEGER,\n+            confidence TEXT,\n+            mapping_method TEXT,\n+            source_label TEXT,\n+            source_version TEXT,\n+            note TEXT,\n+            ingredient_review_required INTEGER,\n+            personal_tolerance TEXT,\n+            definition_version TEXT,\n             applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n@@ -258,6 +345,14 @@\n             normalized_name TEXT NOT NULL,\n+            decision TEXT,\n+            alias TEXT,\n             canonical_food TEXT,\n             sighi_score INTEGER,\n-            traffic_light TEXT NOT NULL,\n-            mapping_scope TEXT NOT NULL,\n+            confidence TEXT,\n+            mapping_method TEXT,\n+            source_label TEXT,\n+            source_version TEXT,\n+            note TEXT,\n+            ingredient_review_required INTEGER,\n+            personal_tolerance TEXT,\n+            definition_version TEXT,\n             affected_day TEXT NOT NULL,\n@@ -274,2 +369,20 @@\n     )\n+    for table, columns in {\n+        \"nutrition_mapping_action_log\": {\n+            \"decision\": \"TEXT\", \"alias\": \"TEXT\", \"canonical_food\": \"TEXT\", \"sighi_score\": \"INTEGER\",\n+            \"confidence\": \"TEXT\", \"mapping_method\": \"TEXT\", \"source_label\": \"TEXT\", \"source_version\": \"TEXT\",\n+            \"note\": \"TEXT\", \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\n+            \"definition_version\": \"TEXT\",\n+        },\n+        \"nutrition_mapping_provenance\": {\n+            \"decision\": \"TEXT\", \"alias\": \"TEXT\", \"confidence\": \"TEXT\", \"mapping_method\": \"TEXT\",\n+            \"source_label\": \"TEXT\", \"source_version\": \"TEXT\", \"note\": \"TEXT\",\n+            \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\n+            \"definition_version\": \"TEXT\",\n+        },\n+    }.items():\n+        existing = {row[\"name\"] for row in connection.execute(f\"PRAGMA table_info({table})\")}\n+        for name, declaration in columns.items():\n+            if name not in existing:\n+                connection.execute(f\"ALTER TABLE {table} ADD COLUMN {name} {declaration}\")\n \n@@ -279,3 +392,3 @@\n         connection.execute(\n-            \"\"\"SELECT i.meal,i.kcal,i.protein_g,i.carb_g,i.fat_g,h.sighi_score\n+            \"\"\"SELECT i.id,i.meal,i.kcal,i.protein_g,i.carb_g,i.fat_g,h.sighi_score\n                FROM nutrition_items i LEFT JOIN nutrition_histamine_scores h ON h.item_id=i.id\n@@ -289,13 +402,15 @@\n         return\n-    totals = {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []}\n+    totals = {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": [], \"missing\": set(), \"ids\": []}\n     by_meal: dict[str, dict[str, Any]] = {}\n     for row in rows:\n-        meal = row[\"meal\"] or \"unknown\"\n-        bucket = by_meal.setdefault(meal, {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": []})\n+        meal = row[\"meal\"] if row[\"meal\"] in {\"breakfast\", \"lunch\", \"dinner\", \"snack\"} else \"unassigned\"\n+        bucket = by_meal.setdefault(meal, {\"kcal\": 0.0, \"protein\": 0.0, \"carb\": 0.0, \"fat\": 0.0, \"items\": 0, \"unknown\": 0, \"load\": 0.0, \"scores\": [], \"missing\": set(), \"ids\": []})\n         for target in (bucket, totals):\n             target[\"items\"] += 1\n-            target[\"kcal\"] += float(row[\"kcal\"] or 0)\n-            target[\"protein\"] += float(row[\"protein_g\"] or 0)\n-            target[\"carb\"] += float(row[\"carb_g\"] or 0)\n-            target[\"fat\"] += float(row[\"fat_g\"] or 0)\n+            target[\"ids\"].append(int(row[\"id\"]))\n+            for source, dest in ((\"kcal\", \"kcal\"), (\"protein_g\", \"protein\"), (\"carb_g\", \"carb\"), (\"fat_g\", \"fat\")):\n+                if row[source] is None:\n+                    target[\"missing\"].add(dest)\n+                else:\n+                    target[dest] += float(row[source])\n             if row[\"sighi_score\"] is None:\n@@ -309,2 +424,3 @@\n         label = _score_label(max_score, int(bucket[\"unknown\"]), float(bucket[\"load\"]))\n+        values = {key: None if key in bucket[\"missing\"] else round(float(bucket[key]), 1) for key in (\"kcal\", \"protein\", \"carb\", \"fat\")}\n         connection.execute(\n@@ -313,3 +429,3 @@\n                VALUES(?,?,?,?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n-            (day, meal, round(bucket[\"kcal\"], 1), round(bucket[\"protein\"], 1), round(bucket[\"carb\"], 1), round(bucket[\"fat\"], 1), int(bucket[\"items\"]), \"{}\", round(bucket[\"load\"], 1), label),\n+            (day, meal, values[\"kcal\"], values[\"protein\"], values[\"carb\"], values[\"fat\"], int(bucket[\"items\"]), _nutrient_totals(connection, bucket[\"ids\"]), round(bucket[\"load\"], 1) if not bucket[\"unknown\"] else None, label),\n         )\n@@ -321,3 +437,15 @@\n            VALUES(?,?,?,?,?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n-        (day, round(totals[\"kcal\"], 1), round(totals[\"protein\"], 1), round(totals[\"carb\"], 1), round(totals[\"fat\"], 1), int(totals[\"items\"]), \"{}\", round(totals[\"load\"], 1), max_score, int(totals[\"unknown\"]), label),\n+        (\n+            day,\n+            None if \"kcal\" in totals[\"missing\"] else round(float(totals[\"kcal\"]), 1),\n+            None if \"protein\" in totals[\"missing\"] else round(float(totals[\"protein\"]), 1),\n+            None if \"carb\" in totals[\"missing\"] else round(float(totals[\"carb\"]), 1),\n+            None if \"fat\" in totals[\"missing\"] else round(float(totals[\"fat\"]), 1),\n+            int(totals[\"items\"]),\n+            _nutrient_totals(connection, totals[\"ids\"]),\n+            round(totals[\"load\"], 1) if not totals[\"unknown\"] else None,\n+            max_score,\n+            int(totals[\"unknown\"]),\n+            label,\n+        ),\n     )\n@@ -345,8 +473,12 @@\n             canonical = payload[\"canonical_food\"]\n-            score = payload[\"sighi_score\"]\n-            light = payload[\"traffic_light\"]\n-            affected = sorted({row[\"datum\"] for row in connection.execute(\"SELECT datum FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)) if row[\"datum\"]})\n+            score = None if payload[\"sighi_score\"] == \"unknown\" else payload[\"sighi_score\"]\n+            item_rows = [\n+                row for row in connection.execute(\"SELECT id,datum,name,source_product_id FROM nutrition_items\")\n+                if _slug(str(row[\"name\"] or row[\"source_product_id\"] or \"\")) == payload[\"queue_key\"]\n+                or _slug(str(row[\"source_product_id\"] or row[\"name\"] or \"\")) == payload[\"queue_key\"]\n+            ]\n+            affected = sorted({row[\"datum\"] for row in item_rows if row[\"datum\"]})\n             if len(affected) > 366:\n                 raise RuntimeError(\"affected day limit exceeded\")\n-            if re.search(r\"\\+|,|;|\\bmit\\b|\\bund\\b\", str(matched[0][\"example_name\"]), re.I):\n+            if payload[\"decision\"] == \"composite\":\n                 connection.execute(\n@@ -356,31 +488,43 @@\n                     (normalized, canonical),\n-                )\n-                score = None\n-                light = \"unknown\"\n-            for row in connection.execute(\"SELECT id FROM nutrition_items WHERE lower(name)=lower(?)\", (matched[0][\"example_name\"],)):\n-                connection.execute(\n-                    \"\"\"INSERT OR REPLACE INTO nutrition_histamine_scores\n-                       (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n-                       VALUES(?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n-                    (row[\"id\"], canonical or None, score, light, \"worker_mapping_v1\", \"medium\", \"local validated mapping action\"),\n                 )\n-            connection.execute(\"UPDATE nutrition_review_queue SET status='mapped',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n-            if payload[\"personal_tolerance\"] != \"unknown\" and canonical:\n-                connection.execute(\n-                    \"\"\"INSERT INTO personal_food_tolerance(canonical_food,personal_status,evidence_level,notes,updated_at)\n-                       VALUES(?,?, 'low', 'separate personal tolerance label from mapping action', CURRENT_TIMESTAMP)\n-                       ON CONFLICT(canonical_food) DO UPDATE SET personal_status=excluded.personal_status,updated_at=CURRENT_TIMESTAMP\"\"\",\n-                    (canonical, payload[\"personal_tolerance\"]),\n-                )\n+            elif payload[\"decision\"] == \"assign\":\n+                for row in item_rows:\n+                    connection.execute(\n+                        \"\"\"INSERT OR REPLACE INTO nutrition_histamine_scores\n+                           (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\n+                           VALUES(?,?,?,?,?,?,?,CURRENT_TIMESTAMP)\"\"\",\n+                        (row[\"id\"], canonical, score, \"classified\", payload[\"mapping_method\"], payload[\"confidence\"], payload[\"note\"] or \"local validated mapping action\"),\n+                    )\n+                connection.execute(\"UPDATE nutrition_review_queue SET status='mapped',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n+            else:\n+                connection.execute(\"UPDATE nutrition_review_queue SET status='ignored',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\n             for day in affected:\n-                recompute_nutrition_day(connection, str(day))\n+                if payload[\"decision\"] == \"assign\":\n+                    recompute_nutrition_day(connection, str(day))\n                 connection.execute(\n                     \"\"\"INSERT OR IGNORE INTO nutrition_mapping_provenance\n-                       (action_id,normalized_name,canonical_food,sighi_score,traffic_light,mapping_scope,affected_day)\n-                       VALUES(?,?,?,?,?,?,?)\"\"\",\n-                    (action_id, normalized, canonical or None, score, light, payload[\"mapping_scope\"], str(day)),\n+                       (action_id,normalized_name,decision,alias,canonical_food,sighi_score,confidence,\n+                        mapping_method,source_label,source_version,note,ingredient_review_required,\n+                        personal_tolerance,definition_version,affected_day)\n+                       VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n+                    (\n+                        action_id, normalized, payload[\"decision\"], payload[\"alias\"], canonical or None,\n+                        score, payload[\"confidence\"], payload[\"mapping_method\"], payload[\"source_label\"],\n+                        payload[\"source_version\"], payload[\"note\"], int(payload[\"ingredient_review_required\"]),\n+                        payload.get(\"personal_tolerance\"), HISTAMINE_DEFINITION_VERSION, str(day),\n+                    ),\n                 )\n             connection.execute(\n-                \"INSERT INTO nutrition_mapping_action_log(action_id,action_hash,queue_key) VALUES(?,?,?)\",\n-                (action_id, action_hash, payload[\"queue_key\"]),\n+                \"\"\"INSERT INTO nutrition_mapping_action_log\n+                   (action_id,action_hash,queue_key,decision,alias,canonical_food,sighi_score,confidence,\n+                    mapping_method,source_label,source_version,note,ingredient_review_required,\n+                    personal_tolerance,definition_version)\n+                   VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n+                (\n+                    action_id, action_hash, payload[\"queue_key\"], payload[\"decision\"], payload[\"alias\"],\n+                    canonical or None, score, payload[\"confidence\"], payload[\"mapping_method\"],\n+                    payload[\"source_label\"], payload[\"source_version\"], payload[\"note\"],\n+                    int(payload[\"ingredient_review_required\"]), payload.get(\"personal_tolerance\"),\n+                    HISTAMINE_DEFINITION_VERSION,\n+                ),\n             )\n@@ -424,3 +568,3 @@\n         if DASHBOARD_V5_PROFILE == V5_PROFILE_HEALTH_RECORD_6E:\n-            v5_command.append(\"--health-record-6e\")\n+            v5_command.extend([\"--health-record-6e\", \"--explorer-6c\", \"--calendar-day-6d\"])\n         subprocess.run(\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:48:25.307Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_GnP3HIjom1EJ5UfkYzl15MJg","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/health_dashboard_action_worker.py\n"}}
{"timestamp":"2026-07-16T17:48:25.307Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":350423,"cached_input_tokens":267904,"output_tokens":7551,"reasoning_output_tokens":839,"total_tokens":357974},"last_token_usage":{"input_tokens":83442,"cached_input_tokens":75136,"output_tokens":5685,"reasoning_output_tokens":805,"total_tokens":89127},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:48:27.749Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRlryAGJpEoY_TWv0uv9qHF2WfK7ktn0iIPNJuxaMPUeBaKUDAGBv5XqYMX3Xbn5ryIixL324zyTgz1cLau5r_PtmbJPMGR4BZ3rjZd0bc81M-DQAbcAKCm3d2MqOeXWvYN0_OtYuJGWjKYnCxOOpb0TnA18PE-ao051K5NjfKl-kOBqFY6zFJsjnXVxy1RJCHwQ1vyYDtRHxjB34jQBmQiOc5Jcm2ku6xiO4tohOp_OLpaqI2Y3rLAXoQCiYE0iuY199utzGHMxPc5F2JIo-ZPvRZtm6a6spwH7n-kA1y4A_SDJfWSCzARScuWgzy3J4asGpzuh_-sqY-lRt16DKYZz3Sxx1xOB9su-nadbetbOjKytJQJhEAu7bqzse94s5ONs3X3lCxSGdi4hlzvEkEntRfExNvXwhXAg8kj42E4eD0b3kAyxx1_AakEqWBYo4fSXKfXH2UXz2TXJxbQ27wYOMoYJzs9DD-c0jCh4dv7T7Wcfrlhi3EEEso-ugTLdJ1Z_jpKGxs9gbLYCOIQzDRsmzlQYaJzD3ERg2qHhmmSLYJ7eotCNEXF1CZO5FryB38PznTukZnVXleIWLbRThQgg67EYsoFcEj2eWN7v3w8Co57geIJ7agMZP8Wz6YeqZ305iL-utUHCHCxPqFEa3t9uL5pmNGIKXH_He1WrGYKPDDmq28attFVB0RWtOHBfXkEAfwiVA11G5aVgfTHC9qv88PhElqdBn0Y8wA81fIc82pO2xIE1lBNmr5u0-hZqWnDTt8TzBcUe1hOVfCQ9NZ8KnF7rdjK_H5or7MOu7ezhP8DbVVR9McBGc_qQWPw1S2Qma-NnUxxCvIOcG7GT0cPuaTwbg7BuMZTPH6Ms1CjYMkPCCgFUoIFyYAV78SY7BMLA9R1sivaxySlIAoyCi6a1dEQf55BWkBHXKtIjt6QpegUg7oeuBRvZC9TsoA8M3MLOKMkrTzsNTH8s33MMxi76609cmXf6UQ3wpY7mbpRa_bKs1oNzwsR9M95Nigu8CdZFQ63AFWdiON942nVTM2MerjDSSRRfrxmTKYu3z-EqtaAC1PfaXyVWPrAeF8nWyi6JwuF7n38-artJEHMtzsobSvlrU2MTWyQ2D-5tkL0v-Fw="}}
{"timestamp":"2026-07-16T17:48:28.894Z","type":"event_msg","payload":{"type":"agent_message","message":"The worker now validates the new exact action shape, writes idempotent audit/provenance rows, and no longer classifies composites or changes mappings on ignore. I’m aligning the POST form and public API next so the browser can only enqueue that same contract.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:48:28.894Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The worker now validates the new exact action shape, writes idempotent audit/provenance rows, and no longer classifies composites or changes mappings on ignore. I’m aligning the POST form and public API next so the browser can only enqueue that same contract."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:48:52.355Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_M5TAd0tgLcFEogmzeBBKXu5v","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/health_dashboard_server.py\n@@\n-MAPPING_SCORES = {\"0\", \"1\", \"2\", \"3\", \"unknown\"}\n-MAPPING_LIGHTS = {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}\n-MAPPING_SCOPES = {\"canonical\"}\n-PERSONAL_TOLERANCE = {\"unknown\", \"safe\", \"problematic\", \"unclear\"}\n+MAPPING_DECISIONS = {\"assign\", \"composite\", \"ignore\"}\n+MAPPING_SCORES = {\"0\", \"1\", \"2\", \"3\", \"unknown\"}\n+MAPPING_CONFIDENCE = {\"low\", \"medium\", \"high\"}\n+MAPPING_METHODS = {\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"}\n@@\n def validate_mapping_submission(form: dict[str, list[str]]) -> tuple[dict[str, object], str]:\n     required = {\n         \"csrf_token\",\n+        \"decision\",\n         \"queue_key\",\n+        \"alias\",\n         \"canonical_food\",\n         \"sighi_score\",\n-        \"traffic_light\",\n-        \"mapping_scope\",\n-        \"personal_tolerance\",\n+        \"confidence\",\n+        \"mapping_method\",\n+        \"source_label\",\n+        \"source_version\",\n+        \"note\",\n+        \"ingredient_review_required\",\n         \"return_to\",\n     }\n-    if frozenset(form) != frozenset(required) or any(len(values) != 1 for values in form.values()):\n+    allowed = required | {\"personal_tolerance\"}\n+    if not required <= set(form) <= allowed or any(len(values) != 1 for values in form.values()):\n         raise ValueError(\"invalid mapping shape\")\n     return_to = form[\"return_to\"][0]\n     if return_to != \"v5\":\n         raise ValueError(\"invalid return target\")\n+    decision = form[\"decision\"][0]\n     queue_key = form[\"queue_key\"][0].strip()\n+    alias = \" \".join(form[\"alias\"][0].split())\n     canonical_food = \" \".join(form[\"canonical_food\"][0].split())\n     score = form[\"sighi_score\"][0]\n-    traffic_light = form[\"traffic_light\"][0]\n-    scope = form[\"mapping_scope\"][0]\n-    tolerance = form[\"personal_tolerance\"][0]\n+    confidence = form[\"confidence\"][0]\n+    method = form[\"mapping_method\"][0]\n+    source_label = \" \".join(form[\"source_label\"][0].split())\n+    source_version = \" \".join(form[\"source_version\"][0].split())\n+    note = \" \".join(form[\"note\"][0].split())\n+    review = form[\"ingredient_review_required\"][0]\n+    tolerance = \" \".join(form.get(\"personal_tolerance\", [\"\"])[0].split())\n     if not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n         raise ValueError(\"invalid queue key\")\n-    if len(canonical_food) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical_food, re.I):\n-        raise ValueError(\"invalid canonical food\")\n-    if score not in MAPPING_SCORES or traffic_light not in MAPPING_LIGHTS or scope not in MAPPING_SCOPES or tolerance not in PERSONAL_TOLERANCE:\n+    for value, maximum in ((alias, 160), (canonical_food, 120), (source_label, 80), (source_version, 40), (note, 300), (tolerance, 160)):\n+        if len(value) > maximum or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", value, re.I):\n+            raise ValueError(\"invalid mapping text\")\n+    if not alias or not source_label or not source_version:\n+        raise ValueError(\"mapping documentation required\")\n+    if decision not in MAPPING_DECISIONS or score not in MAPPING_SCORES or confidence not in MAPPING_CONFIDENCE or method not in MAPPING_METHODS or review not in {\"true\", \"false\"}:\n         raise ValueError(\"invalid mapping value\")\n-    if score == \"unknown\" and traffic_light != \"unknown\":\n-        raise ValueError(\"unknown score must keep unknown light\")\n-    if score != \"unknown\" and not canonical_food:\n-        raise ValueError(\"canonical food required\")\n+    ingredient_review_required = review == \"true\"\n+    if decision == \"assign\" and (score == \"unknown\" or not canonical_food or ingredient_review_required):\n+        raise ValueError(\"assign requires canonical score\")\n+    if decision == \"composite\" and (score != \"unknown\" or not ingredient_review_required):\n+        raise ValueError(\"composite remains unclassified\")\n+    if decision == \"ignore\" and (score != \"unknown\" or canonical_food or ingredient_review_required):\n+        raise ValueError(\"ignore must not classify\")\n     return {\n         \"version\": 1,\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": decision,\n         \"queue_key\": queue_key,\n+        \"alias\": alias,\n         \"canonical_food\": canonical_food,\n-        \"sighi_score\": None if score == \"unknown\" else int(score),\n-        \"traffic_light\": traffic_light,\n-        \"mapping_scope\": scope,\n-        \"personal_tolerance\": tolerance,\n+        \"sighi_score\": \"unknown\" if score == \"unknown\" else int(score),\n+        \"confidence\": confidence,\n+        \"mapping_method\": method,\n+        \"source_label\": source_label,\n+        \"source_version\": source_version,\n+        \"note\": note,\n+        \"ingredient_review_required\": ingredient_review_required,\n+        **({\"personal_tolerance\": tolerance} if tolerance else {}),\n     }, return_to\n@@\n         try:\n             form = parse_qs(\n                 self.rfile.read(length).decode(\"utf-8\"),\n                 keep_blank_values=True,\n                 strict_parsing=True,\n+                max_num_fields=20,\n             )\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:48:52.457Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_M5TAd0tgLcFEogmzeBBKXu5v","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/health_dashboard_server.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/health_dashboard_server.py":{"type":"update","unified_diff":"@@ -135,6 +135,6 @@\n SYMPTOM_FIELDS = (\"aphthen\", \"gi\", \"fatigue\", \"skin\", \"eyes\", \"joints\", \"vascular\")\n+MAPPING_DECISIONS = {\"assign\", \"composite\", \"ignore\"}\n MAPPING_SCORES = {\"0\", \"1\", \"2\", \"3\", \"unknown\"}\n-MAPPING_LIGHTS = {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}\n-MAPPING_SCOPES = {\"canonical\"}\n-PERSONAL_TOLERANCE = {\"unknown\", \"safe\", \"problematic\", \"unclear\"}\n+MAPPING_CONFIDENCE = {\"low\", \"medium\", \"high\"}\n+MAPPING_METHODS = {\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"}\n CSRF_TTL_SECONDS = max(\n@@ -506,11 +506,17 @@\n         \"csrf_token\",\n+        \"decision\",\n         \"queue_key\",\n+        \"alias\",\n         \"canonical_food\",\n         \"sighi_score\",\n-        \"traffic_light\",\n-        \"mapping_scope\",\n-        \"personal_tolerance\",\n+        \"confidence\",\n+        \"mapping_method\",\n+        \"source_label\",\n+        \"source_version\",\n+        \"note\",\n+        \"ingredient_review_required\",\n         \"return_to\",\n     }\n-    if frozenset(form) != frozenset(required) or any(len(values) != 1 for values in form.values()):\n+    allowed = required | {\"personal_tolerance\"}\n+    if not required <= set(form) <= allowed or any(len(values) != 1 for values in form.values()):\n         raise ValueError(\"invalid mapping shape\")\n@@ -519,18 +525,30 @@\n         raise ValueError(\"invalid return target\")\n+    decision = form[\"decision\"][0]\n     queue_key = form[\"queue_key\"][0].strip()\n+    alias = \" \".join(form[\"alias\"][0].split())\n     canonical_food = \" \".join(form[\"canonical_food\"][0].split())\n     score = form[\"sighi_score\"][0]\n-    traffic_light = form[\"traffic_light\"][0]\n-    scope = form[\"mapping_scope\"][0]\n-    tolerance = form[\"personal_tolerance\"][0]\n+    confidence = form[\"confidence\"][0]\n+    method = form[\"mapping_method\"][0]\n+    source_label = \" \".join(form[\"source_label\"][0].split())\n+    source_version = \" \".join(form[\"source_version\"][0].split())\n+    note = \" \".join(form[\"note\"][0].split())\n+    review = form[\"ingredient_review_required\"][0]\n+    tolerance = \" \".join(form.get(\"personal_tolerance\", [\"\"])[0].split())\n     if not re.fullmatch(r\"[a-z0-9_]{1,80}\", queue_key):\n         raise ValueError(\"invalid queue key\")\n-    if len(canonical_food) > 120 or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", canonical_food, re.I):\n-        raise ValueError(\"invalid canonical food\")\n-    if score not in MAPPING_SCORES or traffic_light not in MAPPING_LIGHTS or scope not in MAPPING_SCOPES or tolerance not in PERSONAL_TOLERANCE:\n+    for value, maximum in ((alias, 160), (canonical_food, 120), (source_label, 80), (source_version, 40), (note, 300), (tolerance, 160)):\n+        if len(value) > maximum or re.search(r\"[\\x00-\\x1f\\x7f]|https?://|/|\\\\\\\\|\\.hermes\", value, re.I):\n+            raise ValueError(\"invalid mapping text\")\n+    if not alias or not source_label or not source_version:\n+        raise ValueError(\"mapping documentation required\")\n+    if decision not in MAPPING_DECISIONS or score not in MAPPING_SCORES or confidence not in MAPPING_CONFIDENCE or method not in MAPPING_METHODS or review not in {\"true\", \"false\"}:\n         raise ValueError(\"invalid mapping value\")\n-    if score == \"unknown\" and traffic_light != \"unknown\":\n-        raise ValueError(\"unknown score must keep unknown light\")\n-    if score != \"unknown\" and not canonical_food:\n-        raise ValueError(\"canonical food required\")\n+    ingredient_review_required = review == \"true\"\n+    if decision == \"assign\" and (score == \"unknown\" or not canonical_food or ingredient_review_required):\n+        raise ValueError(\"assign requires canonical score\")\n+    if decision == \"composite\" and (score != \"unknown\" or not ingredient_review_required):\n+        raise ValueError(\"composite remains unclassified\")\n+    if decision == \"ignore\" and (score != \"unknown\" or canonical_food or ingredient_review_required):\n+        raise ValueError(\"ignore must not classify\")\n     return {\n@@ -538,8 +556,14 @@\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": decision,\n         \"queue_key\": queue_key,\n+        \"alias\": alias,\n         \"canonical_food\": canonical_food,\n-        \"sighi_score\": None if score == \"unknown\" else int(score),\n-        \"traffic_light\": traffic_light,\n-        \"mapping_scope\": scope,\n-        \"personal_tolerance\": tolerance,\n+        \"sighi_score\": \"unknown\" if score == \"unknown\" else int(score),\n+        \"confidence\": confidence,\n+        \"mapping_method\": method,\n+        \"source_label\": source_label,\n+        \"source_version\": source_version,\n+        \"note\": note,\n+        \"ingredient_review_required\": ingredient_review_required,\n+        **({\"personal_tolerance\": tolerance} if tolerance else {}),\n     }, return_to\n@@ -693,2 +717,3 @@\n                 strict_parsing=True,\n+                max_num_fields=20,\n             )\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:48:52.460Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_M5TAd0tgLcFEogmzeBBKXu5v","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/health_dashboard_server.py\n"}}
{"timestamp":"2026-07-16T17:48:52.461Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":439596,"cached_input_tokens":351232,"output_tokens":8955,"reasoning_output_tokens":874,"total_tokens":448551},"last_token_usage":{"input_tokens":89173,"cached_input_tokens":83328,"output_tokens":1404,"reasoning_output_tokens":35,"total_tokens":90577},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:48:54.376Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRmG9zAy5RJCldEwAuCnkAhLxdP19gJ10pqUYl7YUfDQH2VEgnsos0DDKoZHM-eYApjRn4aGFlqv88go2pDcoV4RxZOU-i4MySKRfb0brB7L3YB_pNQEJjn5O7XMGcxJYID5vXiLSv8kuz-BwsnWcyjnS6lAtrJ5des2XMWDPOcfYD_YIts8l0AckEpHPgHhwuMWY0wU8Ey5lmR6RdrBHc_VKk9ye1ejzo-I5NUVCSrM9bqtNIqNfF7gTytaNGyiKpeWeHgi1TU2-fs-BdyevPpvXkYsFeTLeU_TG2LYuiX4tCsp2NvO_96ORXOZ0eYcYczy9dhqsJhADkhkmPG-6lBfRN05mJH6YusiwsMgL_Iy0Wu5uv0RXXCe7u6azmPf-c4qJqptd0gxqh_GVUolW8qb2E7TH3GUvP20cFTCSp_pI13btYd64942U0PX7i8xqDgn9bMcLwZXZ6zwiP9cKoF8uENHQ5vMCViKU_5h8-iQhyG2Vu-sjtwLNiubPa5O-tdeRDdjvSzxLsGDMmEK49hEeVP6hJIOoDUBCXQHtssP6btgJIBVNW1hJ6wb__A0GfDqnjef4-ooRA3iJyCYCyhikkhiahZYIfc_JCOJOC1aWhV3unXtH_8uViY1aBAEPtBh-0kMchWziGtRIT6utPwDRN4gFDy5VW-j-VdAww-GtzPxgAvMLkVK847P6XMYQByzM3OU6QEKi09gz2pdiy765H4KZc5RwPOas4EGZ6TmvfbwM6axS9FVQ1iGycepzWm2599m9py-s1A6x2K-b5ZsvFYYCmjYFwsUQ1DP5GaSUM5bpZiW1hRQh4BqyxA5ylyE1U25Sc2sDL8nVIQqDbQHAmHEd4Dh6xwr3_oqZzn7bcaqaLKMhLAfNYFNsbIJolQVFaKjZVfVac5U20O9YTLOjPvYrfXuDDzKqH3gS2T3rqH1hgpjPTGnztsKfEHyyLfT"}}
{"timestamp":"2026-07-16T17:49:31.127Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_3zBLKWHdyLTOTTwxp0kGNYlz","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/read_api.py\n@@\n NUTRIENT_ALLOWLIST = {\n     \"energy.energy\": (\"Energie\", \"kcal\"),\n     \"nutrient.protein\": (\"Protein\", \"g\"),\n     \"nutrient.carb\": (\"Kohlenhydrate\", \"g\"),\n     \"nutrient.fat\": (\"Fett\", \"g\"),\n     \"nutrient.fiber\": (\"Ballaststoffe\", \"g\"),\n     \"nutrient.sugar\": (\"Zucker\", \"g\"),\n+    \"nutrient.saturated_fat\": (\"Gesättigte Fettsäuren\", \"g\"),\n     \"nutrient.salt\": (\"Salz\", \"g\"),\n     \"nutrient.sodium\": (\"Natrium\", \"mg\"),\n-    \"nutrient.vitamin_c\": (\"Vitamin C\", \"mg\"),\n-    \"nutrient.calcium\": (\"Calcium\", \"mg\"),\n-    \"nutrient.iron\": (\"Eisen\", \"mg\"),\n-    \"nutrient.magnesium\": (\"Magnesium\", \"mg\"),\n }\n MEAL_LABELS = {\n     \"breakfast\": \"Frühstück\",\n     \"lunch\": \"Mittagessen\",\n     \"dinner\": \"Abendessen\",\n     \"snack\": \"Snack\",\n-    \"unknown\": \"Unbekannt\",\n+    \"unassigned\": \"Nicht zugeordnet\",\n }\n@@\n def _nutrition_complete(row: sqlite3.Row) -> bool:\n     return (\n         int(row[\"item_count\"] or 0) > 0\n         and int(row[\"histamine_unknown_count\"] or 0) == 0\n         and row[\"histamine_score\"] is not None\n-        and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n+        and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n     )\n@@\n def _nutrition_days(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n@@\n         days.append({\n             \"date\": day,\n@@\n             \"histamine\": {\n                 \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-                \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n+                \"max_score\": int(row[\"histamine_score\"]) if complete and row[\"histamine_score\"] is not None else None,\n+                \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n                 \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n                 \"status\": \"complete\" if complete else \"incomplete\",\n+                \"definition_version\": _histamine_definition()[\"version\"],\n             },\n         })\n@@\n def _histamine_definition() -> dict[str, Any]:\n     return {\n         \"id\": \"sighi_mapping_load_v1\",\n-        \"version\": 1,\n-        \"components\": [\"mapped_item_scores\", \"unknown_item_count\", \"traffic_light\"],\n+        \"version\": \"sighi_mapping_load_v2\",\n+        \"components\": [\"mapped_item_score_sum\", \"mapped_item_score_max\", \"mapping_coverage\", \"unknown_item_count\"],\n         \"coverage_semantics\": \"unknown_or_unmapped_items_make_day_incomplete\",\n         \"insufficient_inputs\": \"no_index\",\n+        \"score_scale\": \"SIGHi 0-3 or unknown per mapped item\",\n+        \"complete_rule\": \"complete only when every documented nutrition item has a mapping\",\n     }\n@@\n-    try:\n-        raw_nutrients = json.loads(row[\"nutrient_json\"] or \"{}\")\n-    except json.JSONDecodeError:\n-        raw_nutrients = {}\n-    if isinstance(raw_nutrients, dict):\n-        for key, (label, unit) in NUTRIENT_ALLOWLIST.items():\n-            nutrients[key] = {\"label\": label, \"value\": _public_number(raw_nutrients.get(key)), \"unit\": unit}\n+    nutrient_values: dict[str, int | float | None] = {}\n+    if table_exists(connection, \"nutrition_item_nutrients\"):\n+        for nutrient in connection.execute(\n+            \"\"\"SELECT n.nutrient_key,n.value,n.unit\n+               FROM nutrition_item_nutrients n JOIN nutrition_items i ON i.id=n.item_id\n+               WHERE i.datum=?\"\"\",\n+            (day,),\n+        ):\n+            key = str(nutrient[\"nutrient_key\"] or \"\")\n+            allowed = NUTRIENT_ALLOWLIST.get(key)\n+            if not allowed or str(nutrient[\"unit\"] or \"\") != allowed[1]:\n+                continue\n+            value = _public_number(nutrient[\"value\"])\n+            if value is None:\n+                continue\n+            nutrient_values[key] = _public_number(float(nutrient_values.get(key) or 0) + float(value))\n+    else:\n+        try:\n+            raw_nutrients = json.loads(row[\"nutrient_json\"] or \"{}\")\n+        except json.JSONDecodeError:\n+            raw_nutrients = {}\n+        if isinstance(raw_nutrients, dict):\n+            for key in NUTRIENT_ALLOWLIST:\n+                value = _public_number(raw_nutrients.get(key))\n+                if value is not None:\n+                    nutrient_values[key] = value\n+    for key, (label, unit) in NUTRIENT_ALLOWLIST.items():\n+        if key in nutrient_values:\n+            nutrients[key] = {\"label\": label, \"value\": nutrient_values[key], \"unit\": unit}\n@@\n-                    \"traffic_light\": meal[\"histamine_label\"] if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n+                    \"status\": \"complete\" if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else \"incomplete\",\n                 },\n             })\n+    existing_meals = {meal[\"meal\"] for meal in meals}\n+    for meal_id, label in MEAL_LABELS.items():\n+        if meal_id not in existing_meals:\n+            meals.append({\"meal\": meal_id, \"label\": label, \"item_count\": 0, \"macros\": {\"kcal\": None, \"protein_g\": None, \"carb_g\": None, \"fat_g\": None}, \"histamine\": {\"score\": None, \"status\": \"incomplete\"}})\n+    meals.sort(key=lambda item: [\"breakfast\", \"lunch\", \"dinner\", \"snack\", \"unassigned\"].index(item[\"meal\"]))\n@@\n-                \"meal\": item[\"meal\"] if item[\"meal\"] in MEAL_LABELS else \"unknown\",\n+                \"meal\": item[\"meal\"] if item[\"meal\"] in MEAL_LABELS else \"unassigned\",\n@@\n                     \"canonical_food\": canonical or None,\n                     \"score\": int(item[\"sighi_score\"]) if item[\"sighi_score\"] in (0, 1, 2, 3) else None,\n-                    \"traffic_light\": item[\"traffic_light\"] if item[\"traffic_light\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n+                    \"status\": \"mapped\" if item[\"sighi_score\"] in (0, 1, 2, 3) else \"unmapped\",\n                     \"confidence\": item[\"confidence\"] if item[\"confidence\"] in {\"low\", \"medium\", \"high\"} else \"low\",\n                 },\n             })\n@@\n             \"status\": \"complete\" if complete else \"incomplete\",\n             \"item_count\": int(row[\"item_count\"] or 0),\n             \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n+            \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n             \"insufficient_inputs\": None if complete else \"no_index\",\n         },\n@@\n         \"histamine\": {\n             \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-            \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n+            \"max_score\": _public_number(row[\"histamine_score\"]) if complete else None,\n+            \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n+            \"definition_version\": _histamine_definition()[\"version\"],\n         },\n@@\n             \"suggested_canonical_food\": suggested,\n-            \"suggested_score\": int(row[\"suggested_score\"]) if row[\"suggested_score\"] in (0, 1, 2, 3) else None,\n+            \"suggested_score\": int(row[\"suggested_score\"]) if row[\"suggested_score\"] in (0, 1, 2, 3) else \"unknown\",\n             \"reason\": reason,\n             \"status\": \"open\",\n             \"action_contract\": \"nutrition_mapping_action_v1\",\n+            \"allowed_decisions\": [\"assign\", \"composite\", \"ignore\"],\n+            \"allowed_confidence\": [\"low\", \"medium\", \"high\"],\n+            \"allowed_mapping_methods\": [\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"],\n         })\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:49:31.234Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_3zBLKWHdyLTOTTwxp0kGNYlz","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/read_api.py":{"type":"update","unified_diff":"@@ -78,8 +78,5 @@\n     \"nutrient.sugar\": (\"Zucker\", \"g\"),\n+    \"nutrient.saturated_fat\": (\"Gesättigte Fettsäuren\", \"g\"),\n     \"nutrient.salt\": (\"Salz\", \"g\"),\n     \"nutrient.sodium\": (\"Natrium\", \"mg\"),\n-    \"nutrient.vitamin_c\": (\"Vitamin C\", \"mg\"),\n-    \"nutrient.calcium\": (\"Calcium\", \"mg\"),\n-    \"nutrient.iron\": (\"Eisen\", \"mg\"),\n-    \"nutrient.magnesium\": (\"Magnesium\", \"mg\"),\n }\n@@ -90,3 +87,3 @@\n     \"snack\": \"Snack\",\n-    \"unknown\": \"Unbekannt\",\n+    \"unassigned\": \"Nicht zugeordnet\",\n }\n@@ -1240,3 +1237,3 @@\n         and row[\"histamine_score\"] is not None\n-        and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n+        and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n     )\n@@ -1291,5 +1288,7 @@\n                 \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-                \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n+                \"max_score\": int(row[\"histamine_score\"]) if complete and row[\"histamine_score\"] is not None else None,\n+                \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n                 \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n                 \"status\": \"complete\" if complete else \"incomplete\",\n+                \"definition_version\": _histamine_definition()[\"version\"],\n             },\n@@ -1302,6 +1301,8 @@\n         \"id\": \"sighi_mapping_load_v1\",\n-        \"version\": 1,\n-        \"components\": [\"mapped_item_scores\", \"unknown_item_count\", \"traffic_light\"],\n+        \"version\": \"sighi_mapping_load_v2\",\n+        \"components\": [\"mapped_item_score_sum\", \"mapped_item_score_max\", \"mapping_coverage\", \"unknown_item_count\"],\n         \"coverage_semantics\": \"unknown_or_unmapped_items_make_day_incomplete\",\n         \"insufficient_inputs\": \"no_index\",\n+        \"score_scale\": \"SIGHi 0-3 or unknown per mapped item\",\n+        \"complete_rule\": \"complete only when every documented nutrition item has a mapping\",\n     }\n@@ -1323,9 +1324,31 @@\n     nutrients: dict[str, dict[str, Any]] = {}\n-    try:\n-        raw_nutrients = json.loads(row[\"nutrient_json\"] or \"{}\")\n-    except json.JSONDecodeError:\n-        raw_nutrients = {}\n-    if isinstance(raw_nutrients, dict):\n-        for key, (label, unit) in NUTRIENT_ALLOWLIST.items():\n-            nutrients[key] = {\"label\": label, \"value\": _public_number(raw_nutrients.get(key)), \"unit\": unit}\n+    nutrient_values: dict[str, int | float | None] = {}\n+    if table_exists(connection, \"nutrition_item_nutrients\"):\n+        for nutrient in connection.execute(\n+            \"\"\"SELECT n.nutrient_key,n.value,n.unit\n+               FROM nutrition_item_nutrients n JOIN nutrition_items i ON i.id=n.item_id\n+               WHERE i.datum=?\"\"\",\n+            (day,),\n+        ):\n+            key = str(nutrient[\"nutrient_key\"] or \"\")\n+            allowed = NUTRIENT_ALLOWLIST.get(key)\n+            if not allowed or str(nutrient[\"unit\"] or \"\") != allowed[1]:\n+                continue\n+            value = _public_number(nutrient[\"value\"])\n+            if value is None:\n+                continue\n+            nutrient_values[key] = _public_number(float(nutrient_values.get(key) or 0) + float(value))\n+    else:\n+        try:\n+            raw_nutrients = json.loads(row[\"nutrient_json\"] or \"{}\")\n+        except json.JSONDecodeError:\n+            raw_nutrients = {}\n+        if isinstance(raw_nutrients, dict):\n+            for key in NUTRIENT_ALLOWLIST:\n+                value = _public_number(raw_nutrients.get(key))\n+                if value is not None:\n+                    nutrient_values[key] = value\n+    for key, (label, unit) in NUTRIENT_ALLOWLIST.items():\n+        if key in nutrient_values:\n+            nutrients[key] = {\"label\": label, \"value\": nutrient_values[key], \"unit\": unit}\n     meals = []\n@@ -1351,5 +1374,10 @@\n                     \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else None,\n-                    \"traffic_light\": meal[\"histamine_label\"] if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n+                    \"status\": \"complete\" if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else \"incomplete\",\n                 },\n             })\n+    existing_meals = {meal[\"meal\"] for meal in meals}\n+    for meal_id, label in MEAL_LABELS.items():\n+        if meal_id not in existing_meals:\n+            meals.append({\"meal\": meal_id, \"label\": label, \"item_count\": 0, \"macros\": {\"kcal\": None, \"protein_g\": None, \"carb_g\": None, \"fat_g\": None}, \"histamine\": {\"score\": None, \"status\": \"incomplete\"}})\n+    meals.sort(key=lambda item: [\"breakfast\", \"lunch\", \"dinner\", \"snack\", \"unassigned\"].index(item[\"meal\"]))\n     items = []\n@@ -1371,3 +1399,3 @@\n             items.append({\n-                \"meal\": item[\"meal\"] if item[\"meal\"] in MEAL_LABELS else \"unknown\",\n+                \"meal\": item[\"meal\"] if item[\"meal\"] in MEAL_LABELS else \"unassigned\",\n                 \"name\": name,\n@@ -1385,3 +1413,3 @@\n                     \"score\": int(item[\"sighi_score\"]) if item[\"sighi_score\"] in (0, 1, 2, 3) else None,\n-                    \"traffic_light\": item[\"traffic_light\"] if item[\"traffic_light\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else \"unknown\",\n+                    \"status\": \"mapped\" if item[\"sighi_score\"] in (0, 1, 2, 3) else \"unmapped\",\n                     \"confidence\": item[\"confidence\"] if item[\"confidence\"] in {\"low\", \"medium\", \"high\"} else \"low\",\n@@ -1399,2 +1427,3 @@\n             \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n+            \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n             \"insufficient_inputs\": None if complete else \"no_index\",\n@@ -1410,3 +1439,5 @@\n             \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-            \"traffic_light\": row[\"histamine_label\"] if complete else \"unknown\",\n+            \"max_score\": _public_number(row[\"histamine_score\"]) if complete else None,\n+            \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n+            \"definition_version\": _histamine_definition()[\"version\"],\n         },\n@@ -1445,3 +1476,3 @@\n             \"suggested_canonical_food\": suggested,\n-            \"suggested_score\": int(row[\"suggested_score\"]) if row[\"suggested_score\"] in (0, 1, 2, 3) else None,\n+            \"suggested_score\": int(row[\"suggested_score\"]) if row[\"suggested_score\"] in (0, 1, 2, 3) else \"unknown\",\n             \"reason\": reason,\n@@ -1449,2 +1480,5 @@\n             \"action_contract\": \"nutrition_mapping_action_v1\",\n+            \"allowed_decisions\": [\"assign\", \"composite\", \"ignore\"],\n+            \"allowed_confidence\": [\"low\", \"medium\", \"high\"],\n+            \"allowed_mapping_methods\": [\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"],\n         })\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:49:31.238Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_3zBLKWHdyLTOTTwxp0kGNYlz","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n"}}
{"timestamp":"2026-07-16T17:49:31.238Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":530218,"cached_input_tokens":440192,"output_tokens":11012,"reasoning_output_tokens":883,"total_tokens":541230},"last_token_usage":{"input_tokens":90622,"cached_input_tokens":88960,"output_tokens":2057,"reasoning_output_tokens":9,"total_tokens":92679},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:49:34.431Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRmusm2N-nNCY3eoV22RM54VTOGGZ1kY3tlmKdSbUeNKSycNDSfm3W87PdkoBCKYIKN3fQx33HCGBD5blyYVwoM84SdkTFMbspYsGYspr3M2niodjGVmJSiJhjQcOqevQJMyKSMu8CwivboRx8JbzYcp4CUna9qXCB81-qpC9k5JMPWLQ0sjSgP4Q7kfz-prE_n_sFw1KPAxts48_DsY-KpwCqFGBx_p5Ov7BCdV2D3HdaBsLOFERDOW65E8Dh8dSTasK7eDRiiozYVsimLv8CbzK4-iWYWlQU0n4ffmf9qKAjIDGo9kACfsc4A7lo8KtpZkBc13O8f8rw75Y89UfusVRcSymRXB-ihcj3yeRTZn-Pwk33iI4AX8oLOnhgDxp72G_NMsGWIt4Q2dAXm4GZuinDR-dag16A_qC33BBR2TizAaQclTnlWC2QuCKIzPu767ohNitsnI559c2vr_tEtY9pjrdpNav431RicoJl6W40WifuddBTh0asX0Yd2_VvAOwAqB6EYiQBbT0kyWOY5PwrhS74YgxI3nYifwQMdTJfmjn4QPblq7sq5rPikPiTu_w6Io8MUgHSZ2OvVXUsT56QOCO-2_-NGxS5u48NSkSti5cSysiB6GCO-LiySWHstqIM-t19hXXQr-G2c0hZWv4eFf6U-T-2ShsYYuZFLu4xxM_RT6H7HBDTwUsMmd9OCj4VFqzSGtH6pPZWzkWia1vzb0U0JWCLm5KVmEhieGrxTNY3stQT6qLz_K9pjTa2lmSblJiLuN6ZvedHMmIemWPDCENIsreIMQJZxG5EpIx6oStM_51_bRjg-SGjVu6Up1Oz9ELbCXsmVlJwy3_AO0-Syt9WR4BWzncuQHVAxnQap9c6lfFmgjNnsMdERrK9w5Cr936kB4uX5dHLba1rYXZLS2wclx64n-4usQQExe43uWBdqhvPc1RZ0Ig0nCTIStW7RljEOCSejCmq5TCUxtek6Tx-0CSvS14CSJvXZ_zYnB5H-xBUp1KM6P-8eUp7BtM8ZEkolzF9te0nbEMZp8HxPegLDAZeAkDPG3pr_kA-g="}}
{"timestamp":"2026-07-16T17:49:39.630Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_vfvcmtWxmlzviM5lqlHqXKpy","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/read_api.py\n@@\n-    sql = \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n-                    histamine_unknown_count,histamine_label\n+    sql = \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n+                    histamine_max,histamine_unknown_count,histamine_label\n@@\n-                \"max_score\": int(row[\"histamine_score\"]) if complete and row[\"histamine_score\"] is not None else None,\n+                \"max_score\": int(row[\"histamine_max\"] or 0) if complete else None,\n@@\n-        \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,\n-                  histamine_score,histamine_unknown_count,histamine_label\n+        \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,\n+                  histamine_score,histamine_max,histamine_unknown_count,histamine_label\n@@\n-            \"max_score\": _public_number(row[\"histamine_score\"]) if complete else None,\n+            \"max_score\": int(row[\"histamine_max\"] or 0) if complete else None,\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:49:39.735Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_vfvcmtWxmlzviM5lqlHqXKpy","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/read_api.py":{"type":"update","unified_diff":"@@ -1257,3 +1257,3 @@\n     sql = \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n-                    histamine_unknown_count,histamine_label\n+                    histamine_max,histamine_unknown_count,histamine_label\n              FROM nutrition_daily_summary_v2 WHERE datum<=?\"\"\"\n@@ -1288,3 +1288,3 @@\n                 \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-                \"max_score\": int(row[\"histamine_score\"]) if complete and row[\"histamine_score\"] is not None else None,\n+                \"max_score\": int(row[\"histamine_max\"] or 0) if complete else None,\n                 \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n@@ -1316,3 +1316,3 @@\n         \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,nutrient_json,\n-                  histamine_score,histamine_unknown_count,histamine_label\n+                  histamine_score,histamine_max,histamine_unknown_count,histamine_label\n            FROM nutrition_daily_summary_v2 WHERE datum=? LIMIT 1\"\"\",\n@@ -1439,3 +1439,3 @@\n             \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n-            \"max_score\": _public_number(row[\"histamine_score\"]) if complete else None,\n+            \"max_score\": int(row[\"histamine_max\"] or 0) if complete else None,\n             \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:49:39.740Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_vfvcmtWxmlzviM5lqlHqXKpy","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n"}}
{"timestamp":"2026-07-16T17:49:39.740Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":622943,"cached_input_tokens":530688,"output_tokens":11336,"reasoning_output_tokens":912,"total_tokens":634279},"last_token_usage":{"input_tokens":92725,"cached_input_tokens":90496,"output_tokens":324,"reasoning_output_tokens":29,"total_tokens":93049},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:49:41.985Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRm1kV0GExj6O4xKHC4teX1fRjVbJ_UnKXNg-nCpz4VZf6WZt8M-6pWqKIx8K-wcgldXLJ4SzGtIp87Oqi0Cy3a8jvp_mSkQ-YUYYw6axdkSs0U897EkUH9CkHE9LKwqLWJGMZZYnNE4Oy1ObrSr2Y75N9Jtduj7NQAp-vhCpit6xHvmWhFosJIk563cggXCvaPxzA6n5_35XUbmViB4T7H9wo5IsSjxGqSF3Q6SFGwkxvKGP5YTEjTqulQB9NtjXnIEXYEi-1W3SCzKRfnD4TKA81tER0GvW9Tu3tZuEeHj0vjAVGgGf2CxXKIhdjXAfSDyNZL5RNUx5ljaIeLHVrJ8dwSDqYsOk3MVN4dXoNhCrsb4EdA1p7LZ8UHkAvElaXz4VQdjU_Ea9GdkjrhiWnrSzSH3uEUj8IobO-FIiAVmF03EW4XGoiVJDoGD8yCqqjh0hrgdaF3zNPyoaYGhchdFT3sHuRXVWev3AjoSSqHWeI6xXLV4Zk_lbQtWPzDXNH7oMQ3Yn_5D8nC6Zz5LOZiODAC6WuLt-t59ICS4ytgT4A72qIUxV0LQE6uh8zamDliKzbB9roMg4OOa7zMB87AJClGd--R5YT3EvQtwjR3d_gwWNwA-OCwyJscBfrXGGxP1kJg7H6Qefx6GXiHGEpLvFBg_8Ro5-4IQMZudc0KASHK2quKdPvGj2QiuMLiwtiVyTemz1kiDpqyziMk97UtVCIdz0XIK7dCyfK_y7LaQN5oVI8Do7LndVSirY-jA-ZEpO5A89FMX54xWgWogaHfsbw6cLnrl5BV5HPG5YvdVGYyhoFPzma0TEgk3LGBwwyxpDAZCF9rsCOkQx2Vis9UtBQe6vBokS3chQgDPvcaXfrTTTdH1Os2QL5Hs_DPQg_mLVLjEoDU1K3ORZryEyilswepQ9JrEQkjViCIzkKIO4fPKgKvgUvafnpnz-V3pDz695b5L0aYUSbuNZqecsUp4XQ5hs0I_1-JRWMRFOnWQJgM="}}
{"timestamp":"2026-07-16T17:49:44.933Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_jK6Tb8ak87AoTpPMr3S67Jxs","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/data_provider.py\n@@\n-                and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n+                and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n@@\n-                    \"histamine_status\": str(row[\"histamine_label\"]) if complete else \"unknown\",\n+                    \"histamine_status\": \"classified\" if complete else \"unknown\",\n@@\n-                    \"suggested_score\": score if score is not None else \"unknown\",\n-                    \"suggested_traffic_light\": \"unknown\",\n+                    \"suggested_score\": score if score is not None else \"unknown\",\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:49:45.034Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_jK6Tb8ak87AoTpPMr3S67Jxs","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/data_provider.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/data_provider.py":{"type":"update","unified_diff":"@@ -248,3 +248,3 @@\n                 and row[\"histamine_score\"] is not None\n-                and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n+                and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n             )\n@@ -254,3 +254,3 @@\n                     \"histamine_score\": row[\"histamine_score\"] if complete else None,\n-                    \"histamine_status\": str(row[\"histamine_label\"]) if complete else \"unknown\",\n+                    \"histamine_status\": \"classified\" if complete else \"unknown\",\n                     \"unknown_items\": row[\"histamine_unknown_count\"], \"quality\": \"complete\" if complete else \"incomplete\",\n@@ -272,3 +272,2 @@\n                     \"suggested_score\": score if score is not None else \"unknown\",\n-                    \"suggested_traffic_light\": \"unknown\",\n                 })\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:49:45.039Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_jK6Tb8ak87AoTpPMr3S67Jxs","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/data_provider.py\n"}}
{"timestamp":"2026-07-16T17:49:45.039Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":716038,"cached_input_tokens":623232,"output_tokens":11520,"reasoning_output_tokens":926,"total_tokens":727558},"last_token_usage":{"input_tokens":93095,"cached_input_tokens":92544,"output_tokens":184,"reasoning_output_tokens":14,"total_tokens":93279},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:49:47.219Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRm7xjsWXIKn-7Wz96LZfqRUE-wWpR-9pf7_-iMhzLGZi9XiFJs_yeUBumVVXGegPze2ooyg5MygHqwWFf9l7NmNEAczdk3VKCWMnkjEQcr4XabvOZRKnoQgvg5AWQ5jTrvtLpnz6kuaWnogqVc_w1WEEXscf4yPl3Jd1GqTBA1OPTTXqJN8jBddqSzoa6UYCosIaESs1Q-AK0pl5VhHwbU3hzMg-Y_EnwaU9i5Qgvp-yqRiOKOE9HltdBEbizXnlZ58SHtXwRXtwRiT2kcojkB6zj8YvlE7_J_gUEg0axJ-t0fwiLxBlKA4GG-gQ-7_LxrntXlC6FnR9N7kjWeaVuI04zm5mxVnGkwZswv5CFFCCyPC13D-u9-AZdj8gZa_m-4phDq3YJyoE2T6a4gEthInyCKcfxw_UxcnVI9wImgcmL5IhoxrzMBE-ZrTRZEWJipa3kU4ZR6GHX8V9wDnLVzg7vN_xnpl9Fyw3oSxaFC-vxIPLONSAmw6axwnX-7s3mUQkI6dlG9WW1fZqaYSFr_hpen6UWnYoM_7J6qZ2SUIClFJDClazTNFiPAa8bElvqZD4kKcxLcx6P5O6IPJWAYTW1gt6QirVDFZ-dCepPxB1bpkMjCQDyeaW1QlxERZgl63S3t27-BicIMzrw8Brr_mHCxif9mxTgSMjw2RFO8QC3cMktRsvKl-iZEU7yCJMxOX8FtVtGXhKqTtO6E2CEiiO5BdcC7VgJVBAaABKin3ro1K0vjZOwSgWHIHVw1h7tD9-NnoflHnWnm_nER2mTmKgST_Uy3C9GPmwLEA3lfsfAd6qXvSjJ9sy-vU-UgUXReql8JXo4lxqoSvy2_V7hXVkep0k3gwLouTmhhefXhUunaDKz4_vE9NX6qYithafr2016AFu0YVrX9EKI5TXZTLYDlqriivRIaBm1JoEN0P3Gqr-HACpoMlt3qgZeTZS4H9C69B4KAo6Wbmli0ep3qrq57iz1e4gt7O4bNytRQpJQxCHr4ZjvSYhHaSkZC77nG_WCwU2Mv_O93WQbCQ7FDfmA=="}}
{"timestamp":"2026-07-16T17:49:48.413Z","type":"event_msg","payload":{"type":"agent_message","message":"The API now rebuilds nutrient output from `nutrition_item_nutrients` when available and only includes allowlisted nutrients with expected units. I’m tightening the bundle contract and UI to remove the traffic-light vocabulary and make the mapping workspace actually collect the required provenance fields.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:49:48.413Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The API now rebuilds nutrient output from `nutrition_item_nutrients` when available and only includes allowlisted nutrients with expected units. I’m tightening the bundle contract and UI to remove the traffic-light vocabulary and make the mapping workspace actually collect the required provenance fields."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:49:53.072Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_FG6506AzVVI6FVIG0OLlKEDC","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/contracts.py\n@@\n-        if row[\"histamine_status\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n+        if row[\"histamine_status\"] not in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n@@\n-            if row[\"histamine_score\"] is None or row[\"histamine_status\"] == \"unknown\" or row[\"unknown_items\"] != 0:\n+            if row[\"histamine_score\"] is None or row[\"histamine_status\"] == \"unknown\" or row[\"unknown_items\"] != 0:\n@@\n         allowed = {\n             \"name\", \"occurrences\", \"reason\", \"queue_key\", \"suggested_canonical_food\",\n-            \"suggested_score\", \"suggested_traffic_light\",\n+            \"suggested_score\",\n         }\n@@\n-        if \"suggested_traffic_light\" in row and row[\"suggested_traffic_light\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n-            raise ValueError(f\"invalid suggested traffic light at {path}\")\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:49:53.174Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_FG6506AzVVI6FVIG0OLlKEDC","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/contracts.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/contracts.py":{"type":"update","unified_diff":"@@ -241,3 +241,3 @@\n                 raise ValueError(f\"invalid count at {path}.{field}\")\n-        if row[\"histamine_status\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n+        if row[\"histamine_status\"] not in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n             raise ValueError(f\"invalid histamine status at {path}\")\n@@ -255,3 +255,3 @@\n             \"name\", \"occurrences\", \"reason\", \"queue_key\", \"suggested_canonical_food\",\n-            \"suggested_score\", \"suggested_traffic_light\",\n+            \"suggested_score\",\n         }\n@@ -271,4 +271,2 @@\n             raise ValueError(f\"invalid suggested score at {path}\")\n-        if \"suggested_traffic_light\" in row and row[\"suggested_traffic_light\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n-            raise ValueError(f\"invalid suggested traffic light at {path}\")\n \n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:49:53.180Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_FG6506AzVVI6FVIG0OLlKEDC","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/contracts.py\n"}}
{"timestamp":"2026-07-16T17:49:53.180Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":809363,"cached_input_tokens":716288,"output_tokens":11865,"reasoning_output_tokens":946,"total_tokens":821228},"last_token_usage":{"input_tokens":93325,"cached_input_tokens":93056,"output_tokens":345,"reasoning_output_tokens":20,"total_tokens":93670},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:49:56.295Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRnE42qZMeZ0nl08t8Rd5KT_SKuM2LdZEy1AOu_eEMxNKVw2CZU1yBLzk70xJwEQ6MIQpfR5qSmnqgPdN7aA466hvOz2PbJzLs8Bv-x0ubns3CkrbZSbc8hpyZBs8LbKF32bbIWwqBOBue2jPsP4PpzF6nXw50_ceaAVpHyskd6Sbe9fLqOZ9_HMbM13oSSArpB-G4tySKVyjkyfDnxcDcK-ECo7RuD5lG-jUglorU5KT_j1cDgVjFuxs5ydqSqb7BXULZTaQ8mEWsABHa14RUAgOFp9q3jpNkA_wuT13EFrmHEzvAzgf9LJn6KYyv6Ir2UmThRFFBsOJXBbxldS5r1-98XWANmkWBFZKv2XKMzNoyxNi2OSYF6zCVLGObCQ3-SC5gLq9nInIh8V3aLUFubyTnXlD4Gvzmwb0I1LjzlO1P4S4hZMbVfwbQsDAN996NWa_RCfBCgjA70MKLSxQuLhK6FAG3NKL3JdhT0ol7ixcFKz9JdBr1W_fEaeReRGapdLGJ0BNiQRfVmKQMmuOjUdieH_fTD9shVc9qhu7Cg67-_hnPLerJSYSNH4CxXNPqr26GiKMy52U5RBik39UvKh-v0OpipREH7ytO_C9PeF21x8pJ9J1KCqVFf5vMpylRUITSqcqppWvmSXawCKPVYh2qqLSJqs-mjJnsdXpQeUE89xSRSck-gVzcBHjJAuNDnZXRPPSPa7Bp2vAtEPH84VKSB4wt1RRglAq9C-5jcbYF3P7Jmy5djBcNY5844v3oDxHQ6sHkoF-xveH9ZZ0KEzEL1tk4sdcF4Fm7Y-72FGMIzuakCeDjp7trM61A4WrJI6FTpLv5AURisJfqkPZy20R9JD7Qn562Q9zmYRQ2RwNpnymslD5qebYlizdCyaq7EvE14ESK5lsLD1WD_5RAoWIwddtVS0AjUk2Wt4JMs4hL_MmekmcGBMRnpnyqjWcjMmx1Wx1_ShFKMNMAAAksyQI3eNZvh9oo35LVmi9_aXpdc="}}
{"timestamp":"2026-07-16T17:49:57.487Z","type":"event_msg","payload":{"type":"agent_message","message":"The public bundle contract now accepts only neutral classified/unknown semantics for Sprint 6G nutrition data. I’m updating the visible workspace so every POST includes decision, alias, score, confidence, method, source, note, ingredient-review flag, and no legacy traffic-light fields.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:49:57.487Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The public bundle contract now accepts only neutral classified/unknown semantics for Sprint 6G nutrition data. I’m updating the visible workspace so every POST includes decision, alias, score, confidence, method, source, note, ingredient-review flag, and no legacy traffic-light fields."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:50:15.358Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_g5HCpsPXfL9z1uz6Q93tcT2v","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/render.py\n@@\n-<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen und Unsicherheit; sie sind keine allgemeine Lebensmittelsicherheitsbewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Score'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Hypothesenansicht: gemeinsam dokumentierte Werte werden transparent gegenübergestellt; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'><span class='sr-only'>Nur Anzeige – Bearbeitung folgt im Ernährungs-Sprint 6G. </span>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\n+<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen, Version und Unsicherheit; sie sind keine medizinische Bewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Index'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Gegenüberstellung gemeinsam dokumentierter Werte; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:50:15.462Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_g5HCpsPXfL9z1uz6Q93tcT2v","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/render.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/render.py":{"type":"update","unified_diff":"@@ -156,3 +156,3 @@\n {calendar_markup}\n-<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen und Unsicherheit; sie sind keine allgemeine Lebensmittelsicherheitsbewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Score'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Hypothesenansicht: gemeinsam dokumentierte Werte werden transparent gegenübergestellt; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'><span class='sr-only'>Nur Anzeige – Bearbeitung folgt im Ernährungs-Sprint 6G. </span>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\n+<section id='view-nutrition' class='app-view' data-dashboard-panel='nutrition' aria-labelledby='title-nutrition' data-mapping-csrf='__CSRF_TOKEN__' hidden><h2 id='title-nutrition'>Ernährung</h2><p class='context-note'>SIGHi-Zuordnungen dokumentieren Quellen, Version und Unsicherheit; sie sind keine medizinische Bewertung.</p><div id='nutrition-summary' class='metric-grid'></div><article class='chart-panel'><div id='nutrition-chart' class='v5-echart' role='img' aria-label='Dokumentierter Histamin-Index'></div><p id='nutrition-chart-empty' class='v5-empty' hidden>Keine vollständig klassifizierten Ernährungstage im gewählten Zeitraum.</p><details class='v5-card api-explorer-table-wrap'><summary>Zugängliche Datentabelle</summary><div class='table-scroll'><table id='nutrition-chart-table'></table></div></details></article><section class='v5-surface nutrition-day-panel'><h3>Ernährungstag</h3><label>Datum <input type='date' data-nutrition-day-date></label><button type='button' data-nutrition-day-open>Tag laden</button><div data-nutrition-day-status role='status' aria-live='polite'></div><div data-nutrition-day></div></section><section class='v5-surface'><h3>Histamin und Aphthen</h3><p class='context-note'>Neutrale Gegenüberstellung gemeinsam dokumentierter Werte; unvollständige Eingaben ergeben keinen Index.</p><div id='nutrition-aphthae-chart' class='v5-echart' role='img' aria-label='Histamin und Aphthen gemeinsam dokumentiert'></div><p id='nutrition-aphthae-chart-empty' class='v5-empty' hidden>Keine vollständigen Datenpaare im gewählten Zeitraum.</p></section><h3>Offene Lebensmittel-Zuordnungen</h3><p class='v5-meta'>Validierte Aktionen werden lokal vorgemerkt und erst vom privaten Worker angewendet.</p><div id='mapping-review'></div></section>\n {record_markup}\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:50:15.468Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_g5HCpsPXfL9z1uz6Q93tcT2v","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/render.py\n"}}
{"timestamp":"2026-07-16T17:50:15.468Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":903078,"cached_input_tokens":809344,"output_tokens":12947,"reasoning_output_tokens":963,"total_tokens":916025},"last_token_usage":{"input_tokens":93715,"cached_input_tokens":93056,"output_tokens":1082,"reasoning_output_tokens":17,"total_tokens":94797},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:50:17.437Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRnZz8lHNBuhGVL91tNuggtFMHfyik4LXlXcsv4P8p5UM8jjL2gkZ_G3kIN3wMkZNCnrARdm69dDILpJNqNwWtl0WE39wu3s-s3Go1-igRhzje7BKSMlh59pU3gV-szWKYZtxT5ct458-yPLnpX9UWIYsJJ4ZqDH_9YnYUb3RFm0EsQpBP1VFxXBDEIl0A8BV_muh1rB0KxEp9HsBQbhKJHS19F6yUOqmW1iurPbs6dzsJXL7H6vlxrCGiTTHfwQohUkhxJvw12eluix4O4Np7dEPtx1njGOM0H5hPjlIt5DC3NIi2L8BmcSR_TWHhfRBBDqGb_Pm5Z1u8hl4nd6Z-c19Ki0VAQ7IhYBrEKfbnE1qmfc60uP0Ce8BzQOdi6blma9YRdRGtjso03dntpMQOIuEEiINLtu37XMprCNKqC7IYq2o_MeWmiiDnInc7iVqOG6qyp2SrySMJZ3lgjg-ZtUsm37wdjd01n4J1BDwT5220BRTcuXdOVLYmn8DZMPtn5y8pnjEefLfno1vp9x1vu00fjoXuUwZjXmig7K_XYOchFAsugh80rmCK92RubUS5yM1YKViBzNjXWyMHheftyDyeO_IhcSymAH4gCPjXT0OV1YyiG_AgSUw1JNOQ_2VfWq1ocPUNdh2s6DrN_mQoO69C-dNBZMVzsl2YlUCHloZjQGv03Dm7o_NyRpB_nu-gY0TIiMXP8sjr52eXK9A_lPaQYPD5Wx3kLQoVBQfVDq5W9MigbmDXjcv_wHYC2CxKRjyeSGJuQJUWeJOS6XHhTJ7NonIn6hYe2zZUXWJqgB4sSlOeTFRG4xSPqylU_cHaWzwQJiIoEHXdwTr3nwfJk1wOrE6qN94Em3o0OxHu-vsidRzoQtHU98WvfmUwUdlwebBDdQe8jjm0d2Ad0HhGVopXxVPQne5q6RlSb2sdX0u0k="}}
{"timestamp":"2026-07-16T17:50:45.132Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_frAzR0k8YunHcYPxgfJxQc4G","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/assets/health-assets/dashboard-v5.js\n@@\n-      card.append(node(\"h3\", \"v5-label\", \"Letzter vollständig klassifizierter Histamin-Score\"));\n+      card.append(node(\"h3\", \"v5-label\", \"Letzter vollständig klassifizierter Histamin-Index\"));\n@@\n         row.append(node(\"strong\", \"\", item.name));\n         row.append(node(\"div\", \"v5-meta\", `${item.occurrences} Dokumentation(en) · Status: nicht zugeordnet`));\n         const form = node(\"form\", \"mapping-action-form\");\n         form.method = \"post\";\n         form.action = \"/health-actions/nutrition-mapping\";\n-        [[\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"], [\"return_to\", \"v5\"], [\"queue_key\", item.queue_key || \"\"], [\"canonical_food\", item.suggested_canonical_food || \"\"], [\"sighi_score\", item.suggested_score ?? \"unknown\"], [\"traffic_light\", item.suggested_traffic_light || \"unknown\"], [\"mapping_scope\", \"canonical\"], [\"personal_tolerance\", \"unknown\"]].forEach(([name, value]) => {\n+        [\n+          [\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"],\n+          [\"return_to\", \"v5\"],\n+          [\"queue_key\", item.queue_key || \"\"],\n+          [\"alias\", item.example_name || item.name || \"\"],\n+          [\"source_label\", \"SIGHi\"],\n+          [\"source_version\", \"unknown\"],\n+        ].forEach(([name, value]) => {\n           const input = node(\"input\");\n           input.type = \"hidden\";\n           input.name = name;\n           input.value = String(value);\n           form.append(input);\n         });\n-        const submit = node(\"button\", \"\", \"Zuordnung vormerken\");\n+        const decision = node(\"select\");\n+        decision.name = \"decision\";\n+        [[\"assign\", \"Zuordnen\"], [\"composite\", \"Zutatenprüfung\"], [\"ignore\", \"Ignorieren\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          decision.append(option);\n+        });\n+        const canonical = node(\"input\");\n+        canonical.name = \"canonical_food\";\n+        canonical.maxLength = 120;\n+        canonical.value = item.suggested_canonical_food || \"\";\n+        const score = node(\"select\");\n+        score.name = \"sighi_score\";\n+        [\"unknown\", \"0\", \"1\", \"2\", \"3\"].forEach((value) => {\n+          const option = node(\"option\", \"\", value === \"unknown\" ? \"SIGHi unbekannt\" : `SIGHi ${value}`);\n+          option.value = value;\n+          if (String(item.suggested_score ?? \"unknown\") === value) option.selected = true;\n+          score.append(option);\n+        });\n+        const confidence = node(\"select\");\n+        confidence.name = \"confidence\";\n+        [[\"low\", \"niedrig\"], [\"medium\", \"mittel\"], [\"high\", \"hoch\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          if (value === \"medium\") option.selected = true;\n+          confidence.append(option);\n+        });\n+        const method = node(\"select\");\n+        method.name = \"mapping_method\";\n+        [[\"sighi_reference\", \"SIGHi-Referenz\"], [\"ingredient_label\", \"Zutatenliste\"], [\"manual_review\", \"manuelle Prüfung\"], [\"local_alias\", \"lokaler Alias\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          method.append(option);\n+        });\n+        const note = node(\"input\");\n+        note.name = \"note\";\n+        note.maxLength = 300;\n+        note.value = item.reason || \"\";\n+        const reviewRequired = node(\"input\");\n+        reviewRequired.type = \"hidden\";\n+        reviewRequired.name = \"ingredient_review_required\";\n+        reviewRequired.value = \"false\";\n+        decision.addEventListener(\"change\", () => {\n+          const composite = decision.value === \"composite\";\n+          const ignore = decision.value === \"ignore\";\n+          reviewRequired.value = composite ? \"true\" : \"false\";\n+          score.value = composite || ignore ? \"unknown\" : score.value;\n+          canonical.value = ignore ? \"\" : canonical.value;\n+          canonical.disabled = ignore;\n+          score.disabled = composite || ignore;\n+        });\n+        [\n+          [\"Entscheidung\", decision],\n+          [\"Kanonisches Lebensmittel\", canonical],\n+          [\"SIGHi\", score],\n+          [\"Vertrauen\", confidence],\n+          [\"Methode\", method],\n+          [\"Notiz\", note],\n+        ].forEach(([labelText, control]) => {\n+          const label = node(\"label\", \"mapping-field\", labelText);\n+          label.append(control);\n+          form.append(label);\n+        });\n+        form.append(reviewRequired);\n+        const submit = node(\"button\", \"\", \"Aktion vormerken\");\n         submit.type = \"submit\";\n         submit.disabled = !item.queue_key;\n         form.append(submit);\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:50:45.235Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_frAzR0k8YunHcYPxgfJxQc4G","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/assets/health-assets/dashboard-v5.js\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/assets/health-assets/dashboard-v5.js":{"type":"update","unified_diff":"@@ -414,3 +414,3 @@\n       const card = node(\"article\", \"v5-card\");\n-      card.append(node(\"h3\", \"v5-label\", \"Letzter vollständig klassifizierter Histamin-Score\"));\n+      card.append(node(\"h3\", \"v5-label\", \"Letzter vollständig klassifizierter Histamin-Index\"));\n       card.append(node(\"div\", \"v5-kpi\", point ? formatValue(point.value) : \"unbekannt\"));\n@@ -431,3 +431,10 @@\n         form.action = \"/health-actions/nutrition-mapping\";\n-        [[\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"], [\"return_to\", \"v5\"], [\"queue_key\", item.queue_key || \"\"], [\"canonical_food\", item.suggested_canonical_food || \"\"], [\"sighi_score\", item.suggested_score ?? \"unknown\"], [\"traffic_light\", item.suggested_traffic_light || \"unknown\"], [\"mapping_scope\", \"canonical\"], [\"personal_tolerance\", \"unknown\"]].forEach(([name, value]) => {\n+        [\n+          [\"csrf_token\", q(\"#view-nutrition\")?.dataset.mappingCsrf || \"\"],\n+          [\"return_to\", \"v5\"],\n+          [\"queue_key\", item.queue_key || \"\"],\n+          [\"alias\", item.example_name || item.name || \"\"],\n+          [\"source_label\", \"SIGHi\"],\n+          [\"source_version\", \"unknown\"],\n+        ].forEach(([name, value]) => {\n           const input = node(\"input\");\n@@ -438,3 +445,67 @@\n         });\n-        const submit = node(\"button\", \"\", \"Zuordnung vormerken\");\n+        const decision = node(\"select\");\n+        decision.name = \"decision\";\n+        [[\"assign\", \"Zuordnen\"], [\"composite\", \"Zutatenprüfung\"], [\"ignore\", \"Ignorieren\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          decision.append(option);\n+        });\n+        const canonical = node(\"input\");\n+        canonical.name = \"canonical_food\";\n+        canonical.maxLength = 120;\n+        canonical.value = item.suggested_canonical_food || \"\";\n+        const score = node(\"select\");\n+        score.name = \"sighi_score\";\n+        [\"unknown\", \"0\", \"1\", \"2\", \"3\"].forEach((value) => {\n+          const option = node(\"option\", \"\", value === \"unknown\" ? \"SIGHi unbekannt\" : `SIGHi ${value}`);\n+          option.value = value;\n+          if (String(item.suggested_score ?? \"unknown\") === value) option.selected = true;\n+          score.append(option);\n+        });\n+        const confidence = node(\"select\");\n+        confidence.name = \"confidence\";\n+        [[\"low\", \"niedrig\"], [\"medium\", \"mittel\"], [\"high\", \"hoch\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          if (value === \"medium\") option.selected = true;\n+          confidence.append(option);\n+        });\n+        const method = node(\"select\");\n+        method.name = \"mapping_method\";\n+        [[\"sighi_reference\", \"SIGHi-Referenz\"], [\"ingredient_label\", \"Zutatenliste\"], [\"manual_review\", \"manuelle Prüfung\"], [\"local_alias\", \"lokaler Alias\"]].forEach(([value, label]) => {\n+          const option = node(\"option\", \"\", label);\n+          option.value = value;\n+          method.append(option);\n+        });\n+        const note = node(\"input\");\n+        note.name = \"note\";\n+        note.maxLength = 300;\n+        note.value = item.reason || \"\";\n+        const reviewRequired = node(\"input\");\n+        reviewRequired.type = \"hidden\";\n+        reviewRequired.name = \"ingredient_review_required\";\n+        reviewRequired.value = \"false\";\n+        decision.addEventListener(\"change\", () => {\n+          const composite = decision.value === \"composite\";\n+          const ignore = decision.value === \"ignore\";\n+          reviewRequired.value = composite ? \"true\" : \"false\";\n+          score.value = composite || ignore ? \"unknown\" : score.value;\n+          canonical.value = ignore ? \"\" : canonical.value;\n+          canonical.disabled = ignore;\n+          score.disabled = composite || ignore;\n+        });\n+        [\n+          [\"Entscheidung\", decision],\n+          [\"Kanonisches Lebensmittel\", canonical],\n+          [\"SIGHi\", score],\n+          [\"Vertrauen\", confidence],\n+          [\"Methode\", method],\n+          [\"Notiz\", note],\n+        ].forEach(([labelText, control]) => {\n+          const label = node(\"label\", \"mapping-field\", labelText);\n+          label.append(control);\n+          form.append(label);\n+        });\n+        form.append(reviewRequired);\n+        const submit = node(\"button\", \"\", \"Aktion vormerken\");\n         submit.type = \"submit\";\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:50:45.242Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_frAzR0k8YunHcYPxgfJxQc4G","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/assets/health-assets/dashboard-v5.js\n"}}
{"timestamp":"2026-07-16T17:50:45.242Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":997920,"cached_input_tokens":902912,"output_tokens":14110,"reasoning_output_tokens":970,"total_tokens":1012030},"last_token_usage":{"input_tokens":94842,"cached_input_tokens":93568,"output_tokens":1163,"reasoning_output_tokens":7,"total_tokens":96005},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:50:50.590Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRn6b_HE46W75RCVCEfR6p91yrvtaNpJT94qxM1idN24TOv8ZLslj2q4NacQWSTfs7VsojizcUWCQ9un684uVUGUMIyANHW0RG3mkYLcRqw8jdBA2Ybh571q1TboMXrqo9ibmRoJpcLpiILZN7glKA6cat-7wyf-DSDe7OAy6puIViq3pgls-6B4nQVfyJ2IZXivrnBzDGepJKJrfzxUj-Ci--lrN2Iv7dk-E7Y2xBcJiBkKo-hFPo3n0a1UN0lON1jw7yePhNjlE7eNxeyWbNeVIYsMFfr5zWg9OFEr8Ij7vngokMaApHoouAPbg920hBT6BzqGT7DMhpqYR1our8eTlJ7rBS3Dk5Mwit4cCDMZat-IwCqJ5vZw5wJoQNdvY7yXk2L1JFmHwwb7uYdEQx5Pxg2IGGXj__ET0nrgbghNoJQm-ELsGAPMerfym_R433Hk1dA9tqijsNsDWfBGpZQ3BgUevSRT0Kx_Y5xumfhIf-SuZz_Wgbpgme-Q_cL3bENY98ZZbvz0FH-uN-QdIcrCJ15XCC0HmA6vG14SzyT7iNejUqL8o6CAFHs3X8znlsJmWzfnEdiVft7yo2G_rU-maFtp_Ikqx0-Q_FkFFJjUNDRNh2moBvxyRT0odehk7lFMRKSe9Vx04CvmFZp2Dt7sRzSD4TbiFEizmylUMnoMo_oPvDYauHA5VCm9nxoUhsgC29hLdTsGANqsu_RIZS6-C2L8WPEcrODoX9S0jGTqBCQfdBtqyXdz_MQJOyQyObYUfEkQbJL0gVUPYspGR5ovP1pnXPsRZv5cB6xtlMP_M1sfH_Y9PyuKZ81xxDM1YuVSZ9h9zX0Mqkg19XbPLg7j8PlYrglDNSSbIpmfBtpx-pKcv2HiMORE7KETuwcPOTxsXBK5kDqPc4b6yTRKbDmDgnj8L7FXGA-KPhWGCUEQUt-7rY2naDoa2acZ2DsBiLA-v5YgceWC0umiVEDevuzS_1ko1d1mKurXWHjXChHGatDYhsxCl0T68fdIy_jf36lLh_kdi1nAxXd18BXthHdYjnn3k7e9ekh9iCVA17EX5UBhaHJVJ1m0Cguu0A-5I8MfiqGUYMRjJOuuhuTsAndKP3KMzv4m23nvaI2v9G-zQ8RZu5D2RTb1t7m20yNFj2iVgAfNNgcLUY-2z7Qx59nwZTTORi3IVvRgLZWS-2MBiP0asSEAdSFKPu4aVzHWgaJrL0YnDhFNzMOY12JWVB2QdxvuDn3A8CkPuZGNKpdae_0Z-LnDGDKz3eXwENSEf0JDgzs4zUFCB3oUxjSjwcqUknZMo0YO7tJGAofS7WRkXFpJYE9wNtAy2DOHtWLRjZK7"}}
{"timestamp":"2026-07-16T17:50:57.849Z","type":"event_msg","payload":{"type":"agent_message","message":"The workspace now renders real controls for the mapping decision instead of a single hidden “map it” form. I noticed disabled form controls would not submit, so I’m keeping the controls submitted and enforcing the composite/ignore invariants in both browser payload and server validation.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:50:57.856Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The workspace now renders real controls for the mapping decision instead of a single hidden “map it” form. I noticed disabled form controls would not submit, so I’m keeping the controls submitted and enforcing the composite/ignore invariants in both browser payload and server validation."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:50:59.003Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_sdRpHt41WvOUmTkuBAxSX2zz","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/assets/health-assets/dashboard-v5.js\n@@\n           reviewRequired.value = composite ? \"true\" : \"false\";\n           score.value = composite || ignore ? \"unknown\" : score.value;\n           canonical.value = ignore ? \"\" : canonical.value;\n-          canonical.disabled = ignore;\n-          score.disabled = composite || ignore;\n+          canonical.readOnly = ignore;\n         });\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:50:59.107Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_sdRpHt41WvOUmTkuBAxSX2zz","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/assets/health-assets/dashboard-v5.js\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/assets/health-assets/dashboard-v5.js":{"type":"update","unified_diff":"@@ -493,4 +493,3 @@\n           canonical.value = ignore ? \"\" : canonical.value;\n-          canonical.disabled = ignore;\n-          score.disabled = composite || ignore;\n+          canonical.readOnly = ignore;\n         });\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:50:59.115Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_sdRpHt41WvOUmTkuBAxSX2zz","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/assets/health-assets/dashboard-v5.js\n"}}
{"timestamp":"2026-07-16T17:50:59.115Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1093973,"cached_input_tokens":997504,"output_tokens":14335,"reasoning_output_tokens":1030,"total_tokens":1108308},"last_token_usage":{"input_tokens":96053,"cached_input_tokens":94592,"output_tokens":225,"reasoning_output_tokens":60,"total_tokens":96278},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:51:01.904Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRoFNgoVD_a22ENgAkGNh87bgIDAuFnd_lRSdO-pNwy570I0w_FWeui54b5m_0yvbmYSLznZ4Vr4ZQLQqDVzCtlnRKeankE6Cnmn1ww334-L9ElkAz8JoJKtx7skhdjEIVYYACfY6-d2OVuVhS189WWZwLQrnYmoxl5Ex1jKJI3znVI6KJFv_NafQLLovpWJVGXzJUiq6Ry0jmkdlqCMCKKSPJbu94spq9gEjEWYt9S4knD5BmcX6-Z5aWk5upv1twTEqmvUrEDazSkIND3MW4FbdEbAXWRucMdHMUijeITSYiYuTMKhFnlpLF4CmjCAQJhIEmyAAZSVWJgLMjf59i_jZxhS-Tgc32UTUWzz6rbROwP19cNLL_-8iiunrowSWlxrC4kDBQWORMXw3uCBjPWpJJ220fviw0zXAuonDg-qyIq3IWyhV9rmT7pTZ9LgKgzI4NaV9A0pBYSrzNzsOZ1WIdrbofjZr2HawU1yo1tYbmAzIZh3L_e3ZP8HlkXBEA3NjsDkib6fWuy47moPGciGn8-ueg-9ih0miUIyssn7BjsfwZiovweC4PQPB10FOhExrSu0BrKbVmrqhSWB0QFBD3EeixiQP14YSSRGMROkA-wqfeALpgIAGWBi77K4qlB_K3QDCyOTT0ZOZmt8EupJC4QAa2LmWm9IbU-lvLjN5HAAj9kEEKE6IS9yVw9OydWhEGL4EIp2ulk78izK3B90tl0BhvGkq6UfXHqSuJv_Rhw3ogD7gE7BT5O62txoylAbjC92ZuW6CUJ55q3HJDhhP8oKSSxYfNox62SfiN2QRN_21FbC6ZfH8p7utyQLYYeWDf6Vs_wHzyNlkSsiXtaKJ67eXTiUey072j6LZDVOuZOMeD0HQ_-OtLfBd1OvAMw-oyGTu68NN6BTC46FrIrOVP2Gr8P8DGzqG0D2RU8bBJv612-7lawcdN9qiz7PbuUl3vBmyZFMg_FkOsHRfg2UWA=="}}
{"timestamp":"2026-07-16T17:51:03.149Z","type":"event_msg","payload":{"type":"agent_message","message":"I’m updating schema defaults and provenance columns so fresh fixtures and bootstrap-created DBs match the new neutral contract. Existing raw nutrition item tables remain untouched; recomputation only writes summary/mapping tables.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:51:03.149Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"I’m updating schema defaults and provenance columns so fresh fixtures and bootstrap-created DBs match the new neutral contract. Existing raw nutrition item tables remain untouched; recomputation only writes summary/mapping tables."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:51:13.787Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_CGFul8FPQbTZ1qjpDIvspb38","name":"apply_patch","input":"*** Begin Patch\n*** Update File: database/schema.sql\n@@\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n@@\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n@@\n-    traffic_light TEXT NOT NULL,\n+    traffic_light TEXT NOT NULL DEFAULT 'unknown',\n@@\n-    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n+    personal_status TEXT NOT NULL DEFAULT 'unknown',\n@@\n     action_hash TEXT NOT NULL,\n     queue_key TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n+    canonical_food TEXT,\n+    sighi_score INTEGER,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n     applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n );\n@@\n     action_id TEXT NOT NULL,\n     normalized_name TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n     canonical_food TEXT,\n     sighi_score INTEGER,\n-    traffic_light TEXT NOT NULL,\n-    mapping_scope TEXT NOT NULL,\n+    traffic_light TEXT,\n+    mapping_scope TEXT,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:51:13.819Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_CGFul8FPQbTZ1qjpDIvspb38","output":"apply_patch verification failed: Failed to find expected lines in /home/agent/.hermes/repos/HealthManager/database/schema.sql:\n    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),"}}
{"timestamp":"2026-07-16T17:51:13.819Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1190299,"cached_input_tokens":1093120,"output_tokens":14731,"reasoning_output_tokens":1043,"total_tokens":1205030},"last_token_usage":{"input_tokens":96326,"cached_input_tokens":95616,"output_tokens":396,"reasoning_output_tokens":13,"total_tokens":96722},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:51:19.588Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRoXj_9lpI6Y3uCBHNXLGlWnWpboqKEElEuXf7jBiuUFr4MsdTT66XpFIiBXrvYkUyn3zuCn5pD4tsAa2gHCASR9HpIuzH9nHu5V3MUtnh0rb-T67P0yOeWFZwDtjKYVtBi2G4DmMmmXmKRD_4HPFi2Khk2Bhvs2iDS0OUMMymPGkfQM4gNyZfCTn1-CFPIhVQ6VuC0WwStHtxgYV6u7y-nhCnbmxt2AUOxx1fjPLme76FjlmkNBLPC2uLgrscjzMPEqxeYc5duyXFm0YknemRhL83-Jm1IvrSoPVOxAdnBNCarEdkilxXW3XQAkyVJQNZNsWXeAvPT6vZ6_yP8ib-bQODo7Qmpr64z4Fyhvm8H_Bbb3Uj6XBdCj7sPEmVoI4PPCnm3EIlKCq_k1twep8J1xkOqYGJ9M6fUfK9sX6ij2eIVUj8BOH3SAfEhdvieNeAU5nuBKMcIgUbeWWzgTujhTvBrLEcykdSwZeNa-uolCIZLJnplSkvwmCgJ6-7GiT1pDKlse7dXJEnF0krXjvKg_1XCa1kQttpseXHu8srsXHW6_pQJGgOKg2xnByruET2OeJoFiRDx1-bLcQQxQ-iL0ZSEsUvxvAANJoOv-s41CgcUN2B-wYfyazIYNdw_a27yrjoTEuOB7Z5AfO2Evz15TPx2M2no8_7wTQaoX_5jnfcY_p0n6bCz_ck6fHn36l9jWPxYponqZain_9moBmh68p4ahlIcVj7vxA_7248kGfV1D4qidfD61ZFPMp41Xmg4amNtcP_s2vj9QdYDM0-zdt4170LuAcrROfkWwCj7Z4OslGEFEkr_pHPSa4T4b2LYEFf8rzm2QN5ZDBxraSWy0xRpirnRqtsVZi-8PtnDGjuhv9HVFra43TapeQ-7jFH6zZ9ki5zzTekBtB0zIWydg48c5I28sJckqgY898LxeZmyR0ntS6FgpS_TnyIY1cxOhaxEqFmkkudRg699oEH4Gpw=="}}
{"timestamp":"2026-07-16T17:51:23.774Z","type":"event_msg","payload":{"type":"agent_message","message":"One schema hunk did not match because the local file’s formatting differs around the tolerance table. I’m reading that block and applying a narrower patch.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:51:23.774Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"One schema hunk did not match because the local file’s formatting differs around the tolerance table. I’m reading that block and applying a narrower patch."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:51:23.775Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '306,450p' database/schema.sql\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_oHunPVcYQ0I7TBVQx4Z58K9g"}}
{"timestamp":"2026-07-16T17:51:23.918Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '420,445p' database/schema.sql\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":8000}","call_id":"call_FT9QSIWmdqHhTMajAOX9m3Dk"}}
{"timestamp":"2026-07-16T17:51:23.931Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_oHunPVcYQ0I7TBVQx4Z58K9g","output":"Chunk ID: 025629\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1088\nOutput:\nCREATE TABLE nutrition_daily_summary_v2 (\n    datum TEXT PRIMARY KEY,\n    kcal REAL DEFAULT 0,\n    protein_g REAL DEFAULT 0,\n    carb_g REAL DEFAULT 0,\n    fat_g REAL DEFAULT 0,\n    item_count INTEGER DEFAULT 0,\n    nutrient_json TEXT,\n    histamine_score REAL DEFAULT 0,\n    histamine_max INTEGER DEFAULT 0,\n    histamine_unknown_count INTEGER DEFAULT 0,\n    histamine_label TEXT DEFAULT 'unknown',\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_food_insights (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    canonical_food TEXT NOT NULL,\n    insight_type TEXT NOT NULL CHECK(insight_type IN ('trigger_candidate','safe_candidate','insufficient_data')),\n    days_seen INTEGER DEFAULT 0,\n    avg_same_day_symptom REAL,\n    avg_next_day_symptom REAL,\n    avg_followup_symptom REAL,\n    histamine_avg REAL,\n    confidence TEXT DEFAULT 'low',\n    notes TEXT,\n    computed_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    UNIQUE(canonical_food, insight_type)\n);\n\nCREATE TABLE nutrition_histamine_scores (\n    item_id INTEGER PRIMARY KEY,\n    canonical_food TEXT,\n    sighi_score INTEGER,\n    traffic_light TEXT NOT NULL,\n    tags TEXT,\n    confidence TEXT DEFAULT 'low',\n    reason TEXT,\n    scored_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\n);\n\nCREATE TABLE nutrition_item_nutrients (\n    item_id INTEGER NOT NULL,\n    nutrient_key TEXT NOT NULL,\n    value REAL,\n    unit TEXT,\n    raw_value TEXT,\n    PRIMARY KEY (item_id, nutrient_key),\n    FOREIGN KEY (item_id) REFERENCES nutrition_items(id) ON DELETE CASCADE\n);\n\nCREATE TABLE nutrition_items (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    source TEXT NOT NULL DEFAULT 'yazio_api',\n    source_item_id TEXT,\n    source_product_id TEXT,\n    datum TEXT NOT NULL,\n    meal TEXT NOT NULL,\n    name TEXT NOT NULL,\n    brand TEXT,\n    amount REAL,\n    amount_unit TEXT DEFAULT 'g',\n    serving TEXT,\n    serving_quantity REAL,\n    kcal REAL,\n    protein_g REAL,\n    carb_g REAL,\n    fat_g REAL,\n    raw_item_json TEXT,\n    raw_product_json TEXT,\n    item_hash TEXT NOT NULL UNIQUE,\n    imported_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_meal_summary (\n    datum TEXT NOT NULL,\n    meal TEXT NOT NULL,\n    kcal REAL DEFAULT 0,\n    protein_g REAL DEFAULT 0,\n    carb_g REAL DEFAULT 0,\n    fat_g REAL DEFAULT 0,\n    item_count INTEGER DEFAULT 0,\n    nutrient_json TEXT,\n    histamine_score REAL DEFAULT 0,\n    histamine_label TEXT DEFAULT 'unknown',\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    PRIMARY KEY (datum, meal)\n);\n\nCREATE TABLE nutrition_review_queue (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    normalized_name TEXT NOT NULL UNIQUE,\n    example_name TEXT NOT NULL,\n    occurrence_count INTEGER DEFAULT 0,\n    first_seen TEXT,\n    last_seen TEXT,\n    suggested_canonical_food TEXT,\n    suggested_score INTEGER,\n    reason TEXT,\n    status TEXT DEFAULT 'open' CHECK(status IN ('open','mapped','ignored')),\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_treatment_phase_summary (\n        phase TEXT PRIMARY KEY,\n        start_date TEXT,\n        end_date TEXT,\n        nutrition_days INTEGER DEFAULT 0,\n        avg_histamine_score REAL,\n        avg_symptom_score REAL,\n        avg_kcal REAL,\n        avg_protein_g REAL,\n        notes TEXT,\n        computed_at TEXT DEFAULT CURRENT_TIMESTAMP\n    );\n\nCREATE TABLE personal_food_tolerance (\n    canonical_food TEXT PRIMARY KEY,\n    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n    evidence_level TEXT DEFAULT 'low' CHECK(evidence_level IN ('low','medium','high')),\n    notes TEXT,\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_action_log (\n    action_id TEXT PRIMARY KEY,\n    action_hash TEXT NOT NULL,\n    queue_key TEXT NOT NULL,\n    applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_provenance (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    action_id TEXT NOT NULL,\n    normalized_name TEXT NOT NULL,\n    canonical_food TEXT,\n    sighi_score INTEGER,\n    traffic_light TEXT NOT NULL,\n    mapping_scope TEXT NOT NULL,\n    affected_day TEXT NOT NULL,\n    applied_at TEXT DEFAULT CURRENT_TIMESTAMP,\n    UNIQUE(action_id, affected_day)\n);\n"}}
{"timestamp":"2026-07-16T17:51:23.975Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_FT9QSIWmdqHhTMajAOX9m3Dk","output":"Chunk ID: b255c7\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 213\nOutput:\n        notes TEXT,\n        computed_at TEXT DEFAULT CURRENT_TIMESTAMP\n    );\n\nCREATE TABLE personal_food_tolerance (\n    canonical_food TEXT PRIMARY KEY,\n    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n    evidence_level TEXT DEFAULT 'low' CHECK(evidence_level IN ('low','medium','high')),\n    notes TEXT,\n    updated_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_action_log (\n    action_id TEXT PRIMARY KEY,\n    action_hash TEXT NOT NULL,\n    queue_key TEXT NOT NULL,\n    applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE nutrition_mapping_provenance (\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    action_id TEXT NOT NULL,\n    normalized_name TEXT NOT NULL,\n    canonical_food TEXT,\n    sighi_score INTEGER,\n    traffic_light TEXT NOT NULL,\n"}}
{"timestamp":"2026-07-16T17:51:23.975Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1287084,"cached_input_tokens":1189248,"output_tokens":14909,"reasoning_output_tokens":1054,"total_tokens":1301993},"last_token_usage":{"input_tokens":96785,"cached_input_tokens":96128,"output_tokens":178,"reasoning_output_tokens":11,"total_tokens":96963},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:51:32.922Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_8JFcJdGepBk4ITaPswuLupdC","name":"apply_patch","input":"*** Begin Patch\n*** Update File: database/schema.sql\n@@\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n@@\n-    traffic_light TEXT NOT NULL,\n+    traffic_light TEXT NOT NULL DEFAULT 'unknown',\n@@\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n@@\n-    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n+    personal_status TEXT NOT NULL DEFAULT 'unknown',\n@@\n     action_hash TEXT NOT NULL,\n     queue_key TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n+    canonical_food TEXT,\n+    sighi_score INTEGER,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n     applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n );\n@@\n     action_id TEXT NOT NULL,\n     normalized_name TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n     canonical_food TEXT,\n     sighi_score INTEGER,\n-    traffic_light TEXT NOT NULL,\n-    mapping_scope TEXT NOT NULL,\n+    traffic_light TEXT,\n+    mapping_scope TEXT,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:51:33.036Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_8JFcJdGepBk4ITaPswuLupdC","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM database/schema.sql\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/database/schema.sql":{"type":"update","unified_diff":"@@ -313,3 +313,3 @@\n     nutrient_json TEXT,\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n     histamine_max INTEGER DEFAULT 0,\n@@ -339,3 +339,3 @@\n     sighi_score INTEGER,\n-    traffic_light TEXT NOT NULL,\n+    traffic_light TEXT NOT NULL DEFAULT 'unknown',\n     tags TEXT,\n@@ -390,3 +390,3 @@\n     nutrient_json TEXT,\n-    histamine_score REAL DEFAULT 0,\n+    histamine_score REAL,\n     histamine_label TEXT DEFAULT 'unknown',\n@@ -425,3 +425,3 @@\n     canonical_food TEXT PRIMARY KEY,\n-    personal_status TEXT NOT NULL DEFAULT 'unknown' CHECK(personal_status IN ('safe','problematic','unclear','unknown')),\n+    personal_status TEXT NOT NULL DEFAULT 'unknown',\n     evidence_level TEXT DEFAULT 'low' CHECK(evidence_level IN ('low','medium','high')),\n@@ -435,2 +435,14 @@\n     queue_key TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n+    canonical_food TEXT,\n+    sighi_score INTEGER,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n     applied_at TEXT DEFAULT CURRENT_TIMESTAMP\n@@ -442,6 +454,16 @@\n     normalized_name TEXT NOT NULL,\n+    decision TEXT,\n+    alias TEXT,\n     canonical_food TEXT,\n     sighi_score INTEGER,\n-    traffic_light TEXT NOT NULL,\n-    mapping_scope TEXT NOT NULL,\n+    traffic_light TEXT,\n+    mapping_scope TEXT,\n+    confidence TEXT,\n+    mapping_method TEXT,\n+    source_label TEXT,\n+    source_version TEXT,\n+    note TEXT,\n+    ingredient_review_required INTEGER,\n+    personal_tolerance TEXT,\n+    definition_version TEXT,\n     affected_day TEXT NOT NULL,\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:51:33.043Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_8JFcJdGepBk4ITaPswuLupdC","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM database/schema.sql\n"}}
{"timestamp":"2026-07-16T17:51:33.043Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1385246,"cached_input_tokens":1285888,"output_tokens":15244,"reasoning_output_tokens":1054,"total_tokens":1400490},"last_token_usage":{"input_tokens":98162,"cached_input_tokens":96640,"output_tokens":335,"reasoning_output_tokens":0,"total_tokens":98497},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:51:36.214Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRooW_PDjZAZfx1YJDVk629XBNDTiFnXzTLn4zH7mVHkvKYyGPcMzUbHO70BLX7rVPO1AkFpG_gN8vpAqHELQFE7EAAWs9plahNg-XM_viKC3ul8Clyyo-xBMFsaFB70w9GLuf8Wx5yLcmW3n5P3dozQZGQ6jhPZCHL1CyN-WatgUwMzgF7QMnwm4Tz0_iOX1zwgPqcJL_EbLE0ccqimitVjOvB4J_S19c4ww_7D1ZkXhs0LfPvDMvRyxqzfpYz6c3UUSLdf8Mk-WHim3SauOPyEX_qIYHBnDo6pb2Vlchaq2mLAUPZWAI6k-27dUQvzEIEQ5JP7NqBgRJPwkPj5e0CHkmwGmmHE2t10xPjg0WkDnSXP0M9Ctut7sRxT5BdIYCnOHoCIB4izjPgSZsf6nASzgSGL7B98IaqkLceHMfx7-yo_Ad1ydCyHeEfhxn5H3hMJJROMBTVSdvNjbahbEWkvsAob0iQD-rj39ZqocAkn-Wl86NH_zXMmZA0Op8MXvskVHsjp7eyi2Bk_WE2bdfa5U0dJKVwPBe9TqBoO-_MmAM3wCfzDiBJY5tucdQlPx4aDSc1eoYiRxMEMuiIo0w-zLRnA82w-KU1oZhvW1OPmhbiypIiEQh45J1tSk4b7xQKiPlr2n1m1zzdIli1MmbhdphFdMvvuctAUurR4UW5t1ZzS3jiTvNElMvBqG7msr3QzQv2yMKI6PCWC8-w_xKd-FL-7Kx8kZwgh0-sSuuZ0VP8M97jn5R1lcL77_olYfw9vP0VJDDbxXMJYY_ENNM3Ex66XW3Cy8V8_NPXJAWdc2ulj6wKUI82gOSxsEpU0WhhMBEF7H_4Udg9wNXhp3eAQioeVYBmCHydBtBe8ozetZ21OeyCeQQSRxByZLrUAKb_N4X26YHL2uhujvyeU0sCDVJUaHgfZx3ENu6nb757yGbtp2ll3y95C92tJ1oP284WZDSKJEvaQ6iK3wOYuAw2MouBssl0zB8_8An_ayoThHA8VvfyHVFL3sv7TI3OFBPDE-ZKl5Ji0GvbeXlY4rB1Rng=="}}
{"timestamp":"2026-07-16T17:51:44.292Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_HYwYNBkp1w4LQKQHV1OJYkpb","name":"apply_patch","input":"*** Begin Patch\n*** Update File: tests/fixtures/dashboard_v5_fixture.py\n@@\n-                (1, \"synthetic cucumber\", 0, \"green\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n+                (1, \"synthetic cucumber\", 0, \"classified\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n@@\n             (_iso_day(current), None, None, None, None, 2, None, 0, 1, \"unknown\", json.dumps({\"nutrient.protein\": None, \"nutrient.fiber\": 4.2}), _iso_noon(current)),\n         )\n+        connection.executemany(\n+            \"\"\"INSERT INTO nutrition_item_nutrients(item_id,nutrient_key,value,unit,raw_value)\n+               VALUES(?,?,?,?,?)\"\"\",\n+            [\n+                (1, \"nutrient.fiber\", 4.2, \"g\", \"4.2\"),\n+                (1, \"nutrient.sugar\", 2.0, \"g\", \"2.0\"),\n+                (1, \"nutrient.vitamin_c\", 20.0, \"unknown\", \"20\"),\n+                (2, \"nutrient.sodium\", 10.0, \"mg\", \"10\"),\n+            ],\n+        )\n@@\n-            (_iso_day(current), \"lunch\", 0, 0, 0, 0, 1, \"{}\", 0, \"green\", _iso_noon(current)),\n+            (_iso_day(current), \"lunch\", 0, 0, 0, 0, 1, \"{}\", 0, \"classified\", _iso_noon(current)),\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:51:44.394Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_HYwYNBkp1w4LQKQHV1OJYkpb","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM tests/fixtures/dashboard_v5_fixture.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/tests/fixtures/dashboard_v5_fixture.py":{"type":"update","unified_diff":"@@ -238,3 +238,3 @@\n             [\n-                (1, \"synthetic cucumber\", 0, \"green\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n+                (1, \"synthetic cucumber\", 0, \"classified\", \"fixture\", \"high\", \"complete synthetic mapping\", fixture_timestamp),\n                 (2, None, None, \"unknown\", None, \"low\", \"intentionally unmapped fixture row\", fixture_timestamp),\n@@ -249,2 +249,12 @@\n         )\n+        connection.executemany(\n+            \"\"\"INSERT INTO nutrition_item_nutrients(item_id,nutrient_key,value,unit,raw_value)\n+               VALUES(?,?,?,?,?)\"\"\",\n+            [\n+                (1, \"nutrient.fiber\", 4.2, \"g\", \"4.2\"),\n+                (1, \"nutrient.sugar\", 2.0, \"g\", \"2.0\"),\n+                (1, \"nutrient.vitamin_c\", 20.0, \"unknown\", \"20\"),\n+                (2, \"nutrient.sodium\", 10.0, \"mg\", \"10\"),\n+            ],\n+        )\n         connection.execute(\n@@ -253,3 +263,3 @@\n                VALUES(?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n-            (_iso_day(current), \"lunch\", 0, 0, 0, 0, 1, \"{}\", 0, \"green\", _iso_noon(current)),\n+            (_iso_day(current), \"lunch\", 0, 0, 0, 0, 1, \"{}\", 0, \"classified\", _iso_noon(current)),\n         )\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:51:44.402Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_HYwYNBkp1w4LQKQHV1OJYkpb","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM tests/fixtures/dashboard_v5_fixture.py\n"}}
{"timestamp":"2026-07-16T17:51:44.402Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1483783,"cached_input_tokens":1383552,"output_tokens":15646,"reasoning_output_tokens":1073,"total_tokens":1499429},"last_token_usage":{"input_tokens":98537,"cached_input_tokens":97664,"output_tokens":402,"reasoning_output_tokens":19,"total_tokens":98939},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:51:49.444Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRo1etkNuvxXYwc11ry64s74ykqanhVsxL3mh4jB_G_rU81etOQaBgpCpOA_L_LSy0DofDfGH1qCZjmTnt0TNOXT8J7qqMxariI4VUlQKGBQcRdHK7gjqhD9S9QpC7RmAVGPdr89X5DyjXPrBy-At9y3n3vG9fmHE2Qg5q64QnLT1xF_wWbncNmjqo7gmCFkTLUrePy9c65rYd6Jxf5G0nHmafzTPi9NY5Auq-3n_rodbD25PzRWrm4uHf8GSphHpybMm3UfAtZf1jl5ICOXp4FguY0w_2codCZSOnbJaLLcL9iPEmotzLh48LzIMb9PAsmuORlko4ObisqWlWoXoeGpoJZZcQuOO3U53lVT1q0IVzvIzuZLSQqUHH-GMCdSbt1vMfhQuD8xzlNVLl28jf0ij1jJb5hZLiSuXmsPdNspTdq4xCZbxZ0NYhr1QDrfO0bOAGZ7l-2DouSRZOIL274fv0voKVBxqbhBS-dpT0wetmvl_Fcst11vekFESIaao40ti40JD7ceZltFuMNMElVvN83IGCQEhdXeKwgC8xT_YvzOWTPVBcsb1KAqgKsLUDvFib5sE1mRhh70PxyyJU7ALBS6K-hl_XPqlB9Y82EPBN3ay9l0MSb8oz1117geRsSTm6REBCUiUgkFoR5J5xW7OJfiNUsri1GiqRBj0foGWaoomAVVwqUOK6My5_jGfMFAFCsiy_5WGahzDeFxw9cPGeo87swi8pD1Uo4Jp9JU-hJxLY6_dowAskOjS9hPx2J-yXBh-ATF2O_wSwftckYwiSBYwjiGkyFRbHAupU3iA58lU9G3EhVL-gE56IAp8NsySkEQ9ekCeK2y8Wloo_IoDaKLrjSfdm8NkgLfFwe-sDLJkeCJaTU4pnEYm1Je2EtzcH_Iqmff9kITuS1uH00f0exIAkKZaMKJt2Y1ymM1pfny-1LRhHGGo23Aa2sMx-YgwF4I5SPzCldYl8RBvT0JOzgh2seSoSCRD3GUvDGALd6DUg2UXIzrvquT9eSK4Kjf2wBrWqHy5OhQL-10YSeQcsk9aApBbVUhxzDof56P1Bd06GIRP7ZYqSzHGFI_wChd7k2G9zj3BAP6iWCeCV-cIo71jpiSydEZVwtrdGzEWgncWsN9WoSOjvyOIx165OQXGfoLd548GOcEcQkm42pooW5OkQxmfhs9x-zAO8McgxEGyzRR8zcWRvCHOidSEHxEu8ch6MMVJh3M4klDAyNv6DvBPUxuEOgXLmTDPdIeujSkKQUx0YaIpfCs2dFIGYtitgcZpYftE_8ZTxWgYMIWQQ=="}}
{"timestamp":"2026-07-16T17:51:51.397Z","type":"event_msg","payload":{"type":"agent_message","message":"The synthetic fixture now includes known-unit nutrients and an unknown-unit micronutrient so the read API can prove omission behavior. Next I’m expanding the focused tests to cover CSRF/form validation, composite rejection, ignore idempotency, affected-day-only recompute, and missing nutrient behavior.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:51:51.398Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The synthetic fixture now includes known-unit nutrients and an unknown-unit micronutrient so the read API can prove omission behavior. Next I’m expanding the focused tests to cover CSRF/form validation, composite rejection, ignore idempotency, affected-day-only recompute, and missing nutrient behavior."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:52:22.068Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_6qGrVlBerQqpK1TErZ0ytb4p","name":"apply_patch","input":"*** Begin Patch\n*** Update File: tests/test_dashboard_v5_sprint6g_nutrition.py\n@@\n import json\n import sqlite3\n import importlib.util\n+import urllib.parse\n from datetime import date\n from pathlib import Path\n@@\n def test_nutrition_read_apis_are_bounded_allowlisted_and_incomplete_not_zero(\n@@\n     assert days[\"definition\"][\"insufficient_inputs\"] == \"no_index\"\n+    assert \"traffic_light\" not in json.dumps(days)\n     assert days[\"days\"][-1][\"histamine\"][\"score\"] is None\n     assert days[\"days\"][-1][\"histamine\"][\"status\"] == \"incomplete\"\n@@\n     assert detail[\"coverage\"][\"status\"] == \"incomplete\"\n     assert detail[\"coverage\"][\"insufficient_inputs\"] == \"no_index\"\n+    assert detail[\"macros\"][\"kcal\"] is None\n     assert set(detail[\"nutrients\"]) <= set(read_api.NUTRIENT_ALLOWLIST)\n+    assert \"nutrient.vitamin_c\" not in detail[\"nutrients\"]\n+    assert {meal[\"meal\"] for meal in detail[\"meals\"]} >= {\"breakfast\", \"lunch\", \"dinner\", \"snack\", \"unassigned\"}\n@@\n def test_mapping_worker_is_transactional_idempotent_and_recomputes_only_affected_days(\n@@\n         \"version\": 1,\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": \"assign\",\n         \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n+        \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n         \"canonical_food\": \"synthetic reviewed food\",\n         \"sighi_score\": 1,\n-        \"traffic_light\": \"yellow\",\n-        \"mapping_scope\": \"canonical\",\n-        \"personal_tolerance\": \"unknown\",\n+        \"confidence\": \"medium\",\n+        \"mapping_method\": \"manual_review\",\n+        \"source_label\": \"SIGHi\",\n+        \"source_version\": \"2026-test\",\n+        \"note\": \"synthetic reviewed mapping\",\n+        \"ingredient_review_required\": False,\n+        \"personal_tolerance\": \"documented local note\",\n     }\n@@\n-        day = connection.execute(\"SELECT histamine_unknown_count,histamine_score,histamine_label FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n-        assert dict(day) == {\"histamine_unknown_count\": 0, \"histamine_score\": 1.0, \"histamine_label\": \"yellow\"}\n+        day = connection.execute(\"SELECT kcal,protein_g,histamine_unknown_count,histamine_score,histamine_label,nutrient_json FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n+        assert day[\"kcal\"] is None and day[\"protein_g\"] is None\n+        assert day[\"histamine_unknown_count\"] == 0\n+        assert day[\"histamine_score\"] == 1.0\n+        assert day[\"histamine_label\"] == \"classified\"\n+        nutrients = json.loads(day[\"nutrient_json\"])\n+        assert nutrients[\"nutrient.fiber\"] == 4.2\n+        assert \"nutrient.vitamin_c\" not in nutrients\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 1\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n+        provenance = connection.execute(\"SELECT decision,alias,confidence,mapping_method,source_label,source_version,note,ingredient_review_required,personal_tolerance,definition_version FROM nutrition_mapping_provenance\").fetchone()\n+        assert dict(provenance) == {\n+            \"decision\": \"assign\",\n+            \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n+            \"confidence\": \"medium\",\n+            \"mapping_method\": \"manual_review\",\n+            \"source_label\": \"SIGHi\",\n+            \"source_version\": \"2026-test\",\n+            \"note\": \"synthetic reviewed mapping\",\n+            \"ingredient_review_required\": 0,\n+            \"personal_tolerance\": \"documented local note\",\n+            \"definition_version\": \"sighi_mapping_load_v2\",\n+        }\n         assert connection.execute(\"SELECT COUNT(*) FROM personal_food_tolerance\").fetchone()[0] == 0\n     finally:\n         connection.close()\n+\n+\n+def test_composite_and_ignore_do_not_alter_mappings(fixture_db: Path, monkeypatch: pytest.MonkeyPatch) -> None:\n+    import health_dashboard_action_worker as worker\n+\n+    monkeypatch.setattr(worker, \"DASHBOARD_DB\", fixture_db)\n+    monkeypatch.setattr(worker, \"DASHBOARD_V5_FILE\", None)\n+    base = {\n+        \"version\": 1,\n+        \"action\": \"nutrition_mapping\",\n+        \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n+        \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n+        \"canonical_food\": \"\",\n+        \"sighi_score\": \"unknown\",\n+        \"confidence\": \"low\",\n+        \"mapping_method\": \"manual_review\",\n+        \"source_label\": \"SIGHi\",\n+        \"source_version\": \"2026-test\",\n+        \"note\": \"synthetic decision\",\n+        \"ingredient_review_required\": True,\n+    }\n+    with pytest.raises(ValueError):\n+        worker.validate_mapping_payload({**base, \"decision\": \"composite\", \"sighi_score\": 1})\n+    worker.apply_mapping_action({**base, \"decision\": \"composite\"}, \"b\" * 32)\n+    worker.apply_mapping_action({**base, \"decision\": \"ignore\", \"ingredient_review_required\": False}, \"c\" * 32)\n+\n+    connection = sqlite3.connect(fixture_db)\n+    try:\n+        assert connection.execute(\"SELECT sighi_score FROM nutrition_histamine_scores WHERE item_id=2\").fetchone()[0] is None\n+        assert connection.execute(\"SELECT status FROM nutrition_composite_product_review\").fetchone()[0] == \"needs_ingredient_review\"\n+        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 2\n+    finally:\n+        connection.close()\n+\n+\n+def test_mapping_submission_requires_one_time_csrf_and_exact_shape(monkeypatch: pytest.MonkeyPatch) -> None:\n+    import health_dashboard_server as server\n+\n+    monkeypatch.setattr(server, \"local_today\", lambda: TODAY)\n+    token = server.issue_csrf_token(now=100.0)\n+    form = {\n+        \"csrf_token\": [token],\n+        \"return_to\": [\"v5\"],\n+        \"decision\": [\"assign\"],\n+        \"queue_key\": [\"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\"],\n+        \"alias\": [\"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\"],\n+        \"canonical_food\": [\"synthetic reviewed food\"],\n+        \"sighi_score\": [\"1\"],\n+        \"confidence\": [\"medium\"],\n+        \"mapping_method\": [\"manual_review\"],\n+        \"source_label\": [\"SIGHi\"],\n+        \"source_version\": [\"2026-test\"],\n+        \"note\": [\"bounded note\"],\n+        \"ingredient_review_required\": [\"false\"],\n+        \"personal_tolerance\": [\"neutral note\"],\n+    }\n+    payload, return_to = server.validate_mapping_submission(form)\n+    assert return_to == \"v5\"\n+    assert payload[\"decision\"] == \"assign\"\n+    assert payload[\"personal_tolerance\"] == \"neutral note\"\n+    with pytest.raises(ValueError):\n+        server.validate_mapping_submission({**form, \"canonical_food\": [\"x\", \"y\"]})\n+    assert server.consume_csrf_token(token, now=101.0)\n+    assert not server.consume_csrf_token(token, now=102.0)\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:52:22.170Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_6qGrVlBerQqpK1TErZ0ytb4p","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/tests/test_dashboard_v5_sprint6g_nutrition.py":{"type":"update","unified_diff":"@@ -5,2 +5,3 @@\n import importlib.util\n+import urllib.parse\n from datetime import date\n@@ -68,2 +69,3 @@\n     assert days[\"definition\"][\"insufficient_inputs\"] == \"no_index\"\n+    assert \"traffic_light\" not in json.dumps(days)\n     assert days[\"days\"][-1][\"histamine\"][\"score\"] is None\n@@ -74,3 +76,6 @@\n     assert detail[\"coverage\"][\"insufficient_inputs\"] == \"no_index\"\n+    assert detail[\"macros\"][\"kcal\"] is None\n     assert set(detail[\"nutrients\"]) <= set(read_api.NUTRIENT_ALLOWLIST)\n+    assert \"nutrient.vitamin_c\" not in detail[\"nutrients\"]\n+    assert {meal[\"meal\"] for meal in detail[\"meals\"]} >= {\"breakfast\", \"lunch\", \"dinner\", \"snack\", \"unassigned\"}\n     assert all(\"id\" not in item and \"raw\" not in json.dumps(item).casefold() for item in detail[\"items\"])\n@@ -94,8 +99,14 @@\n         \"action\": \"nutrition_mapping\",\n+        \"decision\": \"assign\",\n         \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n+        \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n         \"canonical_food\": \"synthetic reviewed food\",\n         \"sighi_score\": 1,\n-        \"traffic_light\": \"yellow\",\n-        \"mapping_scope\": \"canonical\",\n-        \"personal_tolerance\": \"unknown\",\n+        \"confidence\": \"medium\",\n+        \"mapping_method\": \"manual_review\",\n+        \"source_label\": \"SIGHi\",\n+        \"source_version\": \"2026-test\",\n+        \"note\": \"synthetic reviewed mapping\",\n+        \"ingredient_review_required\": False,\n+        \"personal_tolerance\": \"documented local note\",\n     }\n@@ -108,6 +119,25 @@\n     try:\n-        day = connection.execute(\"SELECT histamine_unknown_count,histamine_score,histamine_label FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n-        assert dict(day) == {\"histamine_unknown_count\": 0, \"histamine_score\": 1.0, \"histamine_label\": \"yellow\"}\n+        day = connection.execute(\"SELECT kcal,protein_g,histamine_unknown_count,histamine_score,histamine_label,nutrient_json FROM nutrition_daily_summary_v2 WHERE datum='2026-06-14'\").fetchone()\n+        assert day[\"kcal\"] is None and day[\"protein_g\"] is None\n+        assert day[\"histamine_unknown_count\"] == 0\n+        assert day[\"histamine_score\"] == 1.0\n+        assert day[\"histamine_label\"] == \"classified\"\n+        nutrients = json.loads(day[\"nutrient_json\"])\n+        assert nutrients[\"nutrient.fiber\"] == 4.2\n+        assert \"nutrient.vitamin_c\" not in nutrients\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 1\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n+        provenance = connection.execute(\"SELECT decision,alias,confidence,mapping_method,source_label,source_version,note,ingredient_review_required,personal_tolerance,definition_version FROM nutrition_mapping_provenance\").fetchone()\n+        assert dict(provenance) == {\n+            \"decision\": \"assign\",\n+            \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n+            \"confidence\": \"medium\",\n+            \"mapping_method\": \"manual_review\",\n+            \"source_label\": \"SIGHi\",\n+            \"source_version\": \"2026-test\",\n+            \"note\": \"synthetic reviewed mapping\",\n+            \"ingredient_review_required\": 0,\n+            \"personal_tolerance\": \"documented local note\",\n+            \"definition_version\": \"sighi_mapping_load_v2\",\n+        }\n         assert connection.execute(\"SELECT COUNT(*) FROM personal_food_tolerance\").fetchone()[0] == 0\n@@ -115 +145,65 @@\n         connection.close()\n+\n+\n+def test_composite_and_ignore_do_not_alter_mappings(fixture_db: Path, monkeypatch: pytest.MonkeyPatch) -> None:\n+    import health_dashboard_action_worker as worker\n+\n+    monkeypatch.setattr(worker, \"DASHBOARD_DB\", fixture_db)\n+    monkeypatch.setattr(worker, \"DASHBOARD_V5_FILE\", None)\n+    base = {\n+        \"version\": 1,\n+        \"action\": \"nutrition_mapping\",\n+        \"queue_key\": \"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\",\n+        \"alias\": \"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\",\n+        \"canonical_food\": \"\",\n+        \"sighi_score\": \"unknown\",\n+        \"confidence\": \"low\",\n+        \"mapping_method\": \"manual_review\",\n+        \"source_label\": \"SIGHi\",\n+        \"source_version\": \"2026-test\",\n+        \"note\": \"synthetic decision\",\n+        \"ingredient_review_required\": True,\n+    }\n+    with pytest.raises(ValueError):\n+        worker.validate_mapping_payload({**base, \"decision\": \"composite\", \"sighi_score\": 1})\n+    worker.apply_mapping_action({**base, \"decision\": \"composite\"}, \"b\" * 32)\n+    worker.apply_mapping_action({**base, \"decision\": \"ignore\", \"ingredient_review_required\": False}, \"c\" * 32)\n+\n+    connection = sqlite3.connect(fixture_db)\n+    try:\n+        assert connection.execute(\"SELECT sighi_score FROM nutrition_histamine_scores WHERE item_id=2\").fetchone()[0] is None\n+        assert connection.execute(\"SELECT status FROM nutrition_composite_product_review\").fetchone()[0] == \"needs_ingredient_review\"\n+        assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 2\n+    finally:\n+        connection.close()\n+\n+\n+def test_mapping_submission_requires_one_time_csrf_and_exact_shape(monkeypatch: pytest.MonkeyPatch) -> None:\n+    import health_dashboard_server as server\n+\n+    monkeypatch.setattr(server, \"local_today\", lambda: TODAY)\n+    token = server.issue_csrf_token(now=100.0)\n+    form = {\n+        \"csrf_token\": [token],\n+        \"return_to\": [\"v5\"],\n+        \"decision\": [\"assign\"],\n+        \"queue_key\": [\"synthetic_incomplete_food_mapping_with_a_very_long_name_that_must_wrap\"],\n+        \"alias\": [\"SYNTHETIC_INCOMPLETE_FOOD_MAPPING_WITH_A_VERY_LONG_NAME_THAT_MUST_WRAP\"],\n+        \"canonical_food\": [\"synthetic reviewed food\"],\n+        \"sighi_score\": [\"1\"],\n+        \"confidence\": [\"medium\"],\n+        \"mapping_method\": [\"manual_review\"],\n+        \"source_label\": [\"SIGHi\"],\n+        \"source_version\": [\"2026-test\"],\n+        \"note\": [\"bounded note\"],\n+        \"ingredient_review_required\": [\"false\"],\n+        \"personal_tolerance\": [\"neutral note\"],\n+    }\n+    payload, return_to = server.validate_mapping_submission(form)\n+    assert return_to == \"v5\"\n+    assert payload[\"decision\"] == \"assign\"\n+    assert payload[\"personal_tolerance\"] == \"neutral note\"\n+    with pytest.raises(ValueError):\n+        server.validate_mapping_submission({**form, \"canonical_food\": [\"x\", \"y\"]})\n+    assert server.consume_csrf_token(token, now=101.0)\n+    assert not server.consume_csrf_token(token, now=102.0)\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:52:22.178Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_6qGrVlBerQqpK1TErZ0ytb4p","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:52:22.178Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1582767,"cached_input_tokens":1481728,"output_tokens":17548,"reasoning_output_tokens":1132,"total_tokens":1600315},"last_token_usage":{"input_tokens":98984,"cached_input_tokens":98176,"output_tokens":1902,"reasoning_output_tokens":59,"total_tokens":100886},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":17.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:31.585Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRpfhv0At_uZgJnJ9lFuVMSu5WWvuvfDK9pDJUbH5NLGgAVjdFP7rq89Xq0MACHnctKr4x5W6Jb-Pm_YxKHOPea2E1QXr8IylxbD2zg4g3hFQy9IuaZ5VXcFW6W5njrZ62NG4sw3nb4rCpgy1DD1cioDr01Rpg8MyFtWqcoPP-lUvytWt1S8eNvGhX5fvUFMDsjBfs88rOJnCB_W9k53wfxoQs5-HSrHvGLzGrpjD4OZs3wqxnMeUeynBLPTEz8yj9f-UBeysq273Cypt6D0_prQQ-eisXnHdPzk7vg6FX7JtUfbNbxO8LjyiCRXL1Dvam_AUeeDhuLQKH-HfqB9b3K8_3mJNcpqpF54jPLMIKqOYFX6odZ8OYDG4UK4XDqYm0IRnTUGtqluxBkGMap6yMZrE36NNSTUgcu7IcnvEQ-aCxk-DL7zF3ViRqYL8MiTWe50qlkQz1-JTyeuQVLmwY60JOiAEaRvkTvNvW611hu3NFKLu3L930xCgfZ5RO--ZpdIWbKWn9Y2V5LztYDzDwHs8JM3GD32luyytlgoT-5iNIbtm3QXaqO4T5Mq99kbWOWL1X4jBYGRIJ8AMiZgZheXfcfK3j-FxdyRJCsBVkG80EzevN6VoXkB_tCovdnjAOPC_Rg-rjx2gK-id9DQ27L-XDyt20ZyFEk_QWFM2TTNuGlduRkRg_6I8218q6eV6RxZxmNSvfntAI8c_FqM6UrtpM2MZGpbBEn8na_JUrnG_F7KXEWuSp1cHBP7ZLIAi_bD1xkEzCkJBpFkjzYZ7a81XS679TdVLy_GSzAoOBSycAKqlm6mNgOAXmOEf-p4izg6Os_IhuSjmzsbEE93d9QzZMHf7QGMZfGRIhxBE5xlY8b2xCK6X3dPtKY4iM13_18EHLau1UOQVIviO7dS9WqQezLukNXlew4eyZy1fYFmHgThxHMnhhYBK5sh-Zkcb_M2L9qpTE2drBFI0BJLNoVUjTL9kkMCVrdOmm3H0kulqCG2NMFob_wo9ISxNme9eb7MQ65_9z7lhKk4oHkZ46nCPeoE1c-rlQBK95rsFql2OTx6WdZA5LPxsqm9B_3ambi7nYJ53Dd1BA-7a1IjcGPTXzPCM2SlCQj-0Shbi8MgFi7fTxukMNJ1KOxv1JjJfNdZ8C6jIPy9Q8P85dddm80R486SbTyZWJY-sEZNFtlxNS3RPZ049GKJvqDPTtuoHGNK7-kA4bWRjLCXfldIxNX7gmKwHM6Lh95BQgeqyPqxqHp7uXX8dbmKaThXiuPKZY6IxYRJCh2HjsTIddQ5eeDOWiES6DTdYx_3gYV1tweCmawVz3N_cy5X0wgR_o7wFq-q0BziXwoBESHOaLPt9iiTrKDuQmtwsRUVi0lSXxlpaS5BieLZYaYpCfQokUZ8Vxb42zar4l2THUZ-4CP85owoYlM5cFrzY8gb5w1AmPbzJIVvhxaUBmRmpfla-_zvocJbUuXwglQCKKbUVTBC2_b3DVl3dWbK605Im328djbo3pAwyZag1JTy58uqJClHQSIkZJHooVOlIbF_bf9SrMiJpVoqn4WorQ1yh9-EUll2IefLG80p55FF3cL4lPvj0o6w8TYhXMSov4q-oB4HImRgK_cOBmRfCXrqON0rlO5dGBMfwy1PigY1z8Q-gqb6KA51HsPY4tx_orUlURDMgZfV1vtbnrlDOlh6D52_saaHyPpXyh_lWbRLZoOYMNvxdRj8"}}
{"timestamp":"2026-07-16T17:52:31.591Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"traffic_light|suggested_traffic_light|mapping_scope|personal_tolerance|safe|green|yellow|orange|red|Ampel\\\" scripts/health tests/test_dashboard_v5_sprint6g_nutrition.py tests/fixtures/dashboard_v5_fixture.py docs/adr/0003-dietary-inflammation-model.md database/schema.sql -S\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":50000}","call_id":"call_Jm4z3tW6KvQ3C3KWomyLIC3D"}}
{"timestamp":"2026-07-16T17:52:31.707Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"python -m py_compile scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_VAQUOIInSuzuRykvtIecZVKV"}}
{"timestamp":"2026-07-16T17:52:31.721Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Jm4z3tW6KvQ3C3KWomyLIC3D","output":"Chunk ID: 02c8a4\nWall time: 0.0006 seconds\nProcess exited with code 0\nOriginal token count: 262144\nOutput:\nTotal output lines: 446\n\ndocs/adr/0003-dietary-inflammation-model.md:23:The dashboard does not compute a dietary inflammation index when required inputs are insufficient. Unknown or unmapped foods make the histamine component incomplete rather than zero. Composite products require ingredient review before a definitive mapping is applied.\ndocs/adr/0003-dietary-inflammation-model.md:25:Personal tolerance labels remain separate from shared food mapping definitions.\ntests/fixtures/dashboard_v5_fixture.py:44:    for predictor in SPRINT4B_PREDICTORS:\ntests/fixtures/dashboard_v5_fixture.py:53:                if predictor == SPRINT4B_PREDICTORS[0] and lag == 0 and phase == SPRINT4B_PHASES[0]:\ntests/fixtures/dashboard_v5_fixture.py:55:                elif predictor == SPRINT4B_PREDICTORS[0] and lag == 3 and phase == \"early_treatment\":\ntests/fixtures/dashboard_v5_fixture.py:59:                elif predictor == SPRINT4B_PREDICTORS[1] and lag == 2 and phase == \"stable_treatment\":\ntests/fixtures/dashboard_v5_fixture.py:64:                    \"id\": {\"predictor\": predictor, \"target\": \"symptom_total\"},\ntests/fixtures/dashboard_v5_fixture.py:82:            \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\ntests/fixtures/dashboard_v5_fixture.py:142:                (\"HKQuantityType\", \"oxygen_saturation\", _iso_noon(current), _iso_noon(current), None, \"not measured\", \"%\", \"Synthetic Missing Value Source\", \"1\", None, \"synthetic-missing.json\", \"fixture-missing-file\", \"fixture-missing-record\", \"{}\", _iso_noon(current)),\ntests/fixtures/dashboard_v5_fixture.py:177:                   (predictor,target,lag_days,medication_phase,n,eligible_target_days,\ntests/fixtures/dashboard_v5_fixture.py:182:                    result[\"id\"][\"predictor\"], result[\"id\"][\"target\"],\ntests/fixtures/dashboard_v5_fixture.py:218:                (_iso_day(anchor_date - timedelta(days=4)), \"SYNTHETIC_EVENT_ON_MEASUREMENT_GAP\", \"10 mg\", \"synthetic\", \"administered\", None, \"timeline gap fixture\", \"fixture\", fixture_timestamp),\ntests/fixtures/dashboard_v5_fixture.py:219:                (_iso_day(current), \"SYNTHETIC_ADMINISTERED_MEDICATION\", \"10 mg\", \"synthetic\", \"administered\", None, \"display as administered only\", \"fixture\", fixture_timestamp),\ntests/fixtures/dashboard_v5_fixture.py:236:               (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\ntests/test_dashboard_v5_sprint6g_nutrition.py:70:    assert \"traffic_light\" not in json.dumps(days)\ntests/test_dashboard_v5_sprint6g_nutrition.py:110:        \"ingredient_review_required\": False,\ntests/test_dashboard_v5_sprint6g_nutrition.py:111:        \"personal_tolerance\": \"documented local note\",\ntests/test_dashboard_v5_sprint6g_nutrition.py:130:        provenance = connection.execute(\"SELECT decision,alias,confidence,mapping_method,source_label,source_version,note,ingredient_review_required,personal_tolerance,definition_version FROM nutrition_mapping_provenance\").fetchone()\ntests/test_dashboard_v5_sprint6g_nutrition.py:139:            \"ingredient_review_required\": 0,\ntests/test_dashboard_v5_sprint6g_nutrition.py:140:            \"personal_tolerance\": \"documented local note\",\ntests/test_dashboard_v5_sprint6g_nutrition.py:165:        \"ingredient_review_required\": True,\ntests/test_dashboard_v5_sprint6g_nutrition.py:170:    worker.apply_mapping_action({**base, \"decision\": \"ignore\", \"ingredient_review_required\": False}, \"c\" * 32)\ntests/test_dashboard_v5_sprint6g_nutrition.py:175:        assert connection.execute(\"SELECT status FROM nutrition_composite_product_review\").fetchone()[0] == \"needs_ingredient_review\"\ntests/test_dashboard_v5_sprint6g_nutrition.py:199:        \"ingredient_review_required\": [\"false\"],\ntests/test_dashboard_v5_sprint6g_nutrition.py:200:        \"personal_tolerance\": [\"neutral note\"],\ntests/test_dashboard_v5_sprint6g_nutrition.py:205:    assert payload[\"personal_tolerance\"] == \"neutral note\"\ndatabase/schema.sql:172:    source TEXT DEFAULT 'local_seed_sighi_inspired',\ndatabase/schema.sql:324:    insight_type TEXT NOT NULL CHECK(insight_type IN ('trigger_candidate','safe_candidate','insufficient_data')),\ndatabase/schema.sql:340:    traffic_light TEXT NOT NULL DEFAULT 'unknown',\ndatabase/schema.sql:344:    scored_at TEXT DEFAULT CURRENT_TIMESTAMP,\ndatabase/schema.sql:407:    status TEXT DEFAULT 'open' CHECK(status IN ('open','mapped','ignored')),\ndatabase/schema.sql:445:    ingredient_review_required INTEGER,\ndatabase/schema.sql:446:    personal_tolerance TEXT,\ndatabase/schema.sql:459:    traffic_light TEXT,\ndatabase/schema.sql:460:    mapping_scope TEXT,\ndatabase/schema.sql:466:    ingredient_review_required INTEGER,\ndatabase/schema.sql:467:    personal_tolerance TEXT,\ndatabase/schema.sql:477:    status TEXT NOT NULL DEFAULT 'needs_ingredient_review' CHECK(status IN ('needs_ingredient_review','reviewed')),\ndatabase/schema.sql:483:    predictor TEXT NOT NULL,\ndatabase/schema.sql:500:    UNIQUE(predictor,target,lag_days,medication_phase)\nscripts/health/health_dashboard_action_worker.py:55:MAPPING_METHODS = frozenset({\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"})\nscripts/health/health_dashboard_action_worker.py:112:    required = {\nscripts/health/health_dashboard_action_worker.py:125:        \"ingredient_review_required\",\nscripts/health/health_dashboard_action_worker.py:127:    optional = {\"personal_tolerance\"}\nscripts/health/health_dashboard_action_worker.py:128:    if not required <= set(payload) <= required | optional:\nscripts/health/health_dashboard_action_worker.py:142:    ingredient_review = payload[\"ingredient_review_required\"]\nscripts/health/health_dashboard_action_worker.py:143:    tolerance = payload.get(\"personal_tolerance\")\nscripts/health/health_dashboard_action_worker.py:158:        raise ValueError(\"required mapping documentation missing\")\nscripts/health/health_dashboard_action_worker.py:161:    if type(ingredient_review) is not bool:\nscripts/health/health_dashboard_action_worker.py:162:        raise ValueError(\"invalid ingredient review flag\")\nscripts/health/health_dashboard_action_worker.py:166:        if not canonical or score == \"unknown\" or ingredient_review:\nscripts/health/health_dashboard_action_worker.py:167:            raise ValueError(\"assign requires canonical score without ingredient review\")\nscripts/health/health_dashboard_action_worker.py:169:        if score != \"unknown\" or not ingredient_review:\nscripts/health/health_dashboard_action_worker.py:170:            raise ValueError(\"composite must stay unclassified for ingredient review\")\nscripts/health/health_dashboard_action_worker.py:172:        if score != \"unknown\" or canonical or ingredient_review:\nscripts/health/health_dashboard_action_worker.py:187:        \"ingredient_review_required\": ingredient_review,\nscripts/health/health_dashboard_action_worker.py:188:        \"personal_tolerance\": tolerance,\nscripts/health/health_dashboard_action_worker.py:246:        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\nscripts/health/health_dashboard_action_worker.py:337:            ingredient_review_required INTEGER,\nscripts/health/health_dashboard_action_worker.py:338:            personal_tolerance TEXT,\nscripts/health/health_dashboard_action_worker.py:355:            ingredient_review_required INTEGER,\nscripts/health/health_dashboard_action_worker.py:356:            personal_tolerance TEXT,\nscripts/health/health_dashboard_action_worker.py:365:            status TEXT NOT NULL DEFAULT 'needs_ingredient_review',\nscripts/health/health_dashboard_action_worker.py:374:            \"note\": \"TEXT\", \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\nscripts/health/health_dashboard_action_worker.py:380:            \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\nscripts/health/health_dashboard_action_worker.py:456:        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\nscripts/health/health_dashboard_action_worker.py:487:                       VALUES(?,?, 'needs_ingredient_review', CURRENT_TIMESTAMP)\"\"\",\nscripts/health/health_dashboard_action_worker.py:494:                           (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\nscripts/health/health_dashboard_action_worker.py:500:                connection.execute(\"UPDATE nutrition_review_queue SET status='ignored',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\nscripts/health/health_dashboard_action_worker.py:507:                        mapping_method,source_label,source_version,note,ingredient_review_required,\nscripts/health/health_dashboard_action_worker.py:508:                        personal_tolerance,definition_version,affected_day)\nscripts/health/health_dashboard_action_worker.py:513:                        payload[\"source_version\"], payload[\"note\"], int(payload[\"ingredient_review_required\"]),\nscripts/health/health_dashboard_action_worker.py:514:                        payload.get(\"personal_tolerance\"), HISTAMINE_DEFINITION_VERSION, str(day),\nscripts/health/health_dashboard_action_worker.py:520:                    mapping_method,source_label,source_version,note,ingredient_review_required,\nscripts/health/health_dashboard_action_worker.py:521:                    personal_tolerance,definition_version)\nscripts/health/health_dashboard_action_worker.py:527:                    int(payload[\"ingredient_review_required\"]), payload.get(\"personal_tolerance\"),\nscripts/health/health_dashboard_action_worker.py:581:        print(\"HEALTH_DASHBOARD_DB is required\", file=sys.stderr)\nscripts/health/health_add_event_period.py:17:    ap.add_argument(\"--start\", required=True, help=\"YYYY-MM-DD\")\nscripts/health/health_add_event_period.py:19:    ap.add_argument(\"--type\", required=True, help=\"z.B. Aphte, Schub, Infekt, Medikation, Ernährung\")\nscripts/health/apple_health_drive_sync.py:5:Requires gog CLI credentials for friday.uplink@gmail.com.\nscripts/health/apple_health_drive_sync.py:28:        raise RuntimeError(f\"Unsafe permissions on gog keyring secret file: {GOG_SECRET_ENV}\")\nscripts/health/generate_doctor_report.py:52:        raise RuntimeError(f\"Unsafe permissions on gog keyring secret file: {GOG_SECRET_ENV}\")\nscripts/health/generate_doctor_report.py:122:    safe = str(text or \"\").replace(\"&\", \"&amp;\").replace(\"<\", \"&lt;\").replace(\">\", \"&gt;\")\nscripts/health/generate_doctor_report.py:123:    return Paragraph(safe, ST[style])\nscripts/health/generate_doctor_report.py:266:    story.append(Paragraph(\"Hinweis: Strukturierter Gesprächsbericht; ersetzt keine ärztliche Beurteilung. Es erfolgt keine automatische medizinische Risiko-, Ampel- oder Entwarnungsbewertung. Angezeigte Laborwerte sind validiert, am Original geprüft und verwenden den dokumentbezogenen Referenzbereich; die ärztliche Interpretation bleibt erforderlich.\", ST[\"Small\"]))\nscripts/health/document_fts_migrate.py:119:    parser.add_argument(\"--db\", type=Path, required=True)\nscripts/health/document_fts_migrate.py:120:    mode = parser.add_mutually_exclusive_group(required=True)\nscripts/health/health_dashboard_v4.py:119:        or label not in {\"green\", \"yellow\", \"orange\", \"red\"}\nscripts/health/health_dashboard_v4.py:126:    traffic = str(row[\"traffic_light\"] or \"\").strip().casefold()\nscripts/health/health_dashboard_v4.py:134:        traffic not in {\"green\", \"yellow\", \"orange\", \"red\"}\nscripts/health/health_dashboard_v4.py:144:def is_administered_event(event_type: Any) -> bool:\nscripts/health/health_dashboard_v4.py:145:    return str(event_type or \"\").strip().casefold() in {\"administered\", \"verabreicht\"}\nscripts/health/health_dashboard_v4.py:271:        \"orange\",\nscripts/health/health_dashboard_v4.py:371:                   h.traffic_light,h.canonical_food,h.sighi_score,h.tags,h.confidence\nscripts/health/health_dashboard_v4.py:377:        administered_medications = [\nscripts/health/health_dashboard_v4.py:380:            if is_administered_event(row[\"event_type\"])\nscripts/health/health_dashboard_v4.py:385:                SELECT predictor,target,lag_days,medication_phase,n,eligible_target_days,\nscripts/health/health_dashboard_v4.py:389:                         COALESCE(q_value, 2), predictor, lag_days, medication_phase\nscripts/health/health_dashboard_v4.py:394:        personal_tolerances = list(c.execute(\"SELECT canonical_food,personal_status,evidence_level,notes,updated_at FROM personal_food_tolerance ORDER BY CASE personal_status WHEN 'problematic' THEN 1 WHEN 'unclear' THEN 2 WHEN 'safe' THEN 3 ELSE 4 END, canonical_food\"))\nscripts/health/health_dashboard_v4.py:515:    light_icon = {\"green\":\"🟢\", \"yellow\":\"🟡\", \"orange\":\"🟠\", \"red\":\"🔴\", \"unknown\":\"⚫\"}\nscripts/health/health_dashboard_v4.py:637:            f\"<tr><td>{html.escape(str(r['predictor']))}</td>\"\nscripts/health/health_dashboard_v4.py:651:        'safe': 'bisher persönlich dokumentiert vertragen',\nscripts/health/health_dashboard_v4.py:658:        for r in personal_tolerances\nscripts/health/health_dashboard_v4.py:689:        for r in administered_medications[:8]\nscripts/health/health_dashboard_v4.py:698:    for r in administered_medications[:20]:\nscripts/health/health_dashboard_v4.py:727:    medication_days = {str(row['datum'])[:10] for row in administered_medications if row['datum']}\nscripts/health/health_dashboard_v4.py:754:        warnings.append(('yellow','Dokumente ungeprüft', f'{unreviewed} Dokumente haben noch keinen Review-Status.'))\nscripts/health/health_dashboard_v4.py:756:        warnings.append(('yellow','Symptomdaten unzureichend', f'Nur {len(complete_symptom_days)} vollständig dokumentierte Tage; die neue Engine berechnet erst ab 14 vollständigen Paaren und wertet Signale erst ab 30 Paaren als korrigierte Hypothese.'))\nscripts/health/health_dashboard_v4.py:757:    red_recent = c_ext.execute(\"SELECT COUNT(*) FROM nutrition_daily_summary_v2 WHERE histamine_label='red' AND date(datum) >= date('now','-30 day')\").fetchone()[0]\nscripts/health/health_dashboard_v4.py:758:    if red_recent:\nscripts/health/health_dashboard_v4.py:759:        warnings.append(('orange','Hohe Histamin-Tage', f'{red_recent} rote Tage in den letzten 30 Tagen.'))\nscripts/health/health_dashboard_v4.py:800:        css = \"green\" if status == \"aktuell\" else \"yellow\" if status == \"prüfen\" else \"unknown\"\nscripts/health/health_dashboard_v4.py:869:function openSection(id) {{ const el=document.getElementById(id); if(!el) return; if(el.tagName.toLowerCase()==='details') el.open=true; el.scrollIntoView({{behavior:window.matchMedia('(prefers-reduced-motion: reduce)').matches?'auto':'smooth',block:'start'}}); }}\nscripts/health/health_dashboard_v4.py:893:*{{box-sizing:border-box}}body{{font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Arial,sans-serif;margin:18px;background:var(--bg);color:var(--text);font-size:15px;line-height:1.35}}a{{color:var(--blue)}}h1{{font-size:1.55rem;margin:.2rem 0}}h2{{font-size:1.25rem;margin:1.25rem 0 .6rem}}.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(150px,1fr));gap:10px}}.card,.chart-card,.day-card{{background:white;border-radius:14px;padding:14px;box-shadow:0 2px 8px #0001;margin-bottom:14px}}.num{{font-size:26px;font-weight:700;color:var(--blue)}}table{{border-collapse:collapse;width:100%;background:white;margin-bottom:14px;display:block;overflow-x:auto;-webkit-overflow-scrolling:touch}}td,th{{border:1px solid #ddd;padding:6px;vertical-align:top;white-space:nowrap}}code{{white-space:pre-wrap;overflow-wrap:anywhere;word-break:break-word}}th{{background:var(--blue);color:white}}canvas{{background:white;border-radius:12px;margin-bottom:12px;width:100%!important;max-height:330px}}.muted{{color:var(--muted);font-size:.9em;margin-bottom:8px}}.warn{{background:#fff7ed;border-left:4px solid #f97316;padding:10px;border-radius:8px}}.button{{display:inline-block;background:var(--blue);color:white!important;text-decoration:none;padding:10px 14px;border-radius:8px;margin:4px 8px 4px 0}}.pill{{display:inline-flex;align-items:center;gap:3px;border-radius:999px;padding:3px 8px;font-size:.82em;font-weight:700;white-space:nowrap}}.green{{background:#dcfce7;color:#166534}}.yellow{{background:#fef9c3;color:#854d0e}}.orange{{background:#ffedd5;color:#9a3412}}.red{{background:#fee2e2;color:#991b1b}}.unknown{{background:#e5e7eb;color:#374151}}.day-list{{display:grid;grid-template-columns:1fr;gap:10px}}.day-card{{padding:0;overflow:hidden}}.day-summary{{display:grid;grid-template-columns:1.25fr auto auto auto auto;gap:8px;align-items:center;cursor:pointer;padding:14px}}.day-summary::-webkit-details-marker{{display:none}}.day-summary:before{{content:'▸';font-size:14px;color:var(--muted)}}.day-card[open]>.day-summary:before{{content:'▾'}}.day-date{{font-weight:800}}.day-kcal{{font-weight:800;color:var(--blue)}}.day-load,.day-items{{color:var(--muted);font-size:.9em}}.day-detail{{border-top:1px solid var(--line);padding:12px 14px}}.day-macro{{font-weight:600;margin-bottom:10px;color:#374151}}.meal-card{{border:1px solid var(--line);border-radius:12px;padding:12px;margin:10px 0;background:#fbfdff}}.meal-head{{display:flex;justify-content:space-between;align-items:center;gap:8px}}.meal-head h4{{margin:0;font-size:1rem}}.macroline{{margin:6px 0 8px;color:#374151}}.food-list{{display:grid;gap:6px}}.food-row{{display:grid;grid-template-columns:24px minmax(0,1fr) 52px 60px;grid-template-areas:'h name amount kcal' '. name macro macro';gap:2px 8px;align-items:center;padding:8px;border:1px solid var(--line);border-radius:10px;background:white}}.food-h{{grid-area:h}}.food-name{{grid-area:name;font-weight:650;min-width:0;overflow-wrap:anywhere}}.food-name small{{display:block;color:var(--muted);font-weight:400}}.food-amount{{grid-area:amount;text-align:right;color:#374151}}.food-kcal{{grid-area:kcal;text-align:right;font-weight:700}}.food-macro{{grid-area:macro;color:var(--muted);font-size:.86em;text-align:right}}.chart-card table,.card table{{font-size:.92em}}.topbar{{position:sticky;top:0;z-index:20;background:rgba(246,248,251,.96);backdrop-filter:blur(8px);padding:8px 0 10px;border-bottom:1px solid var(--line);margin-bottom:12px}}.quicklinks{{display:flex;gap:8px;overflow-x:auto;-webkit-overflow-scrolling:touch;padding:4px 0}}.quicklinks a,.quicklinks button{{white-space:nowrap;border:1px solid var(--line);background:white;color:var(--blue);padding:8px 10px;border-radius:999px;text-decoration:none;font-weight:700}}.searchbox{{width:100%;border:1px solid var(--line);border-radius:12px;padding:11px 12px;font-size:16px;background:white}}.kpi-grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(160px,1fr));gap:10px}}.mini-card{{background:white;border:1px solid var(--line);border-radius:14px;padding:12px;display:flex;flex-direction:column;gap:5px;min-height:88px}}.mini-card b{{font-size:.95rem}}.mini-card .big{{font-size:1.8rem;font-weight:800;color:var(--blue)}}.mini-card small{{color:var(--muted)}}.section-block{{background:white;border-radius:14px;box-shadow:0 2px 8px #0001;margin:14px 0;overflow:hidden}}.section-block>summary{{cursor:pointer;list-style:none;padding:16px;font-size:1.12rem;font-weight:800;color:var(--blue);border-bottom:1px solid var(--line)}}.section-block>summary::-webkit-details-marker{{display:none}}.section-block>summary:before{{content:'▸ ';color:var(--muted)}}.section-block[open]>summary:before{{content:'▾ '}}.section-body{{padding:14px}}.priority{{border-left:5px solid var(--blue)}}.legacy-note{{font-size:.85em;color:var(--muted)}}.info-list{{display:grid;gap:8px}}.info-row{{border:1px solid var(--line);border-radius:12px;padding:10px;background:#fff;display:grid;gap:3px}}.info-row b{{overflow-wrap:anywhere}}.info-row span{{font-weight:800;color:var(--blue)}}.info-row small{{color:var(--muted);overflow-wrap:anywhere}}\nscripts/health/health_dashboard_v4.py:895:.skip-link{{position:absolute;left:-9999px;top:8px;background:#111827;color:#fff;padding:10px 14px;z-index:999;border-radius:8px}}.skip-link:focus-visible{{left:8px}}a,button,input,select,textarea,summary{{min-height:44px;touch-action:manipulation}}in…252144 tokens truncated…ransform:lK,parseAxisBreakOptionInwardTransform:uK,makeAxisLabelFormatterParamBreak:hK},Hp||(Hp=t)}var pK=Pa();function fK(t,e,n,i,r){var o=n.axis;if(!o.scale.isBlank()&&Wp()){var a=Wp().retrieveAxisBreakPairs(o.scale.getTicks({breakTicks:\"only_break\"}),function(t){return t.break},!1);if(a.length){var s=n.getModel(\"breakArea\"),l=s.get(\"zigzagAmplitude\"),u=s.get(\"zigzagMinSpan\"),c=s.get(\"zigzagMaxSpan\");u=Math.max(2,u||0),c=Math.max(u,c||0);var h=s.get(\"expandOnClick\"),d=s.get(\"zigzagZ\"),p=s.getModel(\"itemStyle\").getItemStyle(),f=p.stroke,g=p.lineWidth,v=p.lineDash,y=p.fill,m=new vo({ignoreModelZ:!0}),x=o.isHorizontal(),_=pK(e).visualList||(pK(e).visualList=[]);B(_,function(t){return t.shouldRemove=!0});for(var b=function(t){var e=a[t][0].break.parsedBreak,s=[];s[0]=o.toGlobalCoord(o.dataToCoord(e.vmin,!0)),s[1]=o.toGlobalCoord(o.dataToCoord(e.vmax,!0)),s[1]<s[0]&&s.reverse();var b=function(t,e){var n=H(t,function(t){return Wp().identifyAxisBreak(t.parsedBreak.breakOption,e.breakOption)});return n||t.push(n={zigzagRandomList:[],parsedBreak:e,shouldRemove:!1}),n}(_,e);b.shouldRemove=!1;var w=new vo;!function(t,e,n,r,o,a){var s={stroke:f,lineWidth:g,lineDash:v,fill:\"none\"},h=o?0:1,m=1-h,x=i[dd[m]]+i[pd[m]];function _(t){var e=[],n=[];e[h]=n[h]=t,e[m]=i[dd[m]],n[m]=x;var r={x1:e[0],y1:e[1],x2:n[0],y2:n[1]};return iu(r,r,{lineWidth:1}),e[0]=r.x1,e[1]=r.y1,e[h]}n=_(n),r=_(r);for(var b=[],w=[],S=!0,M=i[dd[m]],I=0;;I++){var T=M===i[dd[m]],C=M>=x;C&&(M=x);var D=[],A=[];D[h]=n,A[h]=r,T||C||(D[h]+=S?-l:l,A[h]-=S?l:-l),D[m]=M,A[m]=M,b.push(D),w.push(A);var k=void 0;if(I<t.length?k=t[I]:(k=Math.random(),t.push(k)),M+=k*(c-u)+u,S=!S,C)break}var L=Wp().serializeAxisBreakIdentifier(a.breakOption);if(e.add(new kh({anid:\"break_a_\"+L,shape:{points:b},style:s,z:d})),0!==a.gapReal){e.add(new kh({anid:\"break_b_\"+L,shape:{points:w},style:s,z:d}));var P=w.slice();P.reverse();var O=b.concat(P);e.add(new Dh({anid:\"break_c_\"+L,shape:{points:O},style:{fill:y,opacity:p.opacity},z:d}))}}(b.zigzagRandomList,w,s[0],s[1],x,e),h&&w.on(\"click\",function(){var t={type:ZC,breaks:[{start:e.breakOption.start,end:e.breakOption.end}]};t[o.dim+\"AxisIndex\"]=n.componentIndex,r.dispatchAction(t)}),w.silent=!h,m.add(w)},w=0;w<a.length;w++)b(w);t.add(m),function(t){for(var e=t.length-1;e>=0;e--)t[e].shouldRemove&&t.splice(e,1)}(_)}}}function gK(t,e,n,i){var r=t.axis,o=n.transform;ct(i.style);var a=r.getExtent();r.inverse&&(a=a.slice()).reverse();var s=V(Wp().retrieveAxisBreakPairs(r.scale.getTicks({breakTicks:\"only_break\"}),function(t){return t.break},!1),function(t){var e=t[0].break.parsedBreak,n=[r.dataToCoord(e.vmin,!0),r.dataToCoord(e.vmax,!0)];return n[0]>n[1]&&n.reverse(),{coordPair:n,brkId:Wp().serializeAxisBreakIdentifier(e.breakOption)}});s.sort(function(t,e){return t.coordPair[0]-e.coordPair[0]});for(var l=a[0],u=null,c=0;c<s.length;c++){var h=s[c],d=Math.max(h.coordPair[0],a[0]),p=Math.min(h.coordPair[1],a[1]);l<=d&&f(l,d,u,h),l=p,u=h}function f(t,n,r,a){function s(t,e){o&&(Yt(t,t,o),Yt(e,e,o))}function l(t,e){var n={x1:t[0],y1:t[1],x2:e[0],y2:e[1]};iu(n,n,i.style),t[0]=n.x1,t[1]=n.y1,e[0]=n.x2,e[1]=n.y2}var u=[t,0],c=[n,0],h=[t,5],d=[n,5];s(u,h),l(u,h),s(c,d),l(c,d),l(u,c);var p=new Oh(k({shape:{x1:u[0],y1:u[1],x2:c[0],y2:c[1]}},i));e.add(p),p.anid=\"breakLine_\"+(r?r.brkId:\"\\0\")+\"_\\0_\"+(a?a.brkId:\"\\0\")}l<=a[1]&&f(l,a[1],u,null)}function vK(t,e,n){if(!H(n,function(t){return!t})){var i=new Pe;if(aI(n[0],n[1],i,{direction:-(t?e+Math.PI:e),touchThreshold:0,bidirectional:!1})){var r=[1,0,0,1,0,0];Ce(r,r,-e);var o=V(n,function(t){return t.transform?Ie([1,0,0,1,0,0],r,t.transform):r}),a=.5;if(g(0)||g(1)){var s=V(n,function(t,e){var n=t.localRect.clone();return n.applyTransform(o[e]),n}),l=new Pe;l.copy(n[0].label).add(n[1].label).scale(.5),l.transform(r);var u=i.clone().transform(r),c=(s[0].x+s[1].x+(u.x>=0?s[0].width:s[1].width)+u.x)/2-l.x,h=Math.min(c,c-u.x),d=Math.max(c,c-u.x);a=(c-(d<0?d:h>0?h:0))/u.x}var p=new Pe,f=new Pe;Pe.scale(p,i,-a),Pe.scale(f,i,1-a),eI(n[0],p),eI(n[1],f)}}function g(t){var e=n[0].localRect,i=new Pe(e[pd[t]]*o[0][0],e[pd[t]]*o[0][1]);return Math.abs(i.y)<1e-5}}function yK(t,e){var n={breaks:[]};return B(e.breaks,function(i){if(i){var r=H(t.get(\"breaks\",!0),function(t){return Wp().identifyAxisBreak(t,i)});if(r){var o=e.type,a={isExpanded:!!r.isExpanded};r.isExpanded=o===ZC||o!==jC&&(o===qC?!r.isExpanded:r.isExpanded),n.breaks.push({start:r.start,end:r.end,isExpanded:!!r.isExpanded,old:a})}}}),n}function mK(){var t;t={adjustBreakLabelPair:vK,buildAxisBreakLine:gK,rectCoordBuildBreakAxis:fK,updateModelAxisBreak:yK},YC||(YC=t)}function xK(t,e){B(t,function(t){if(!t.model.get([\"axisLabel\",\"inside\"])){var n=function(t){var e,n,i=t.model,r=t.scale;if(!i.get([\"axisLabel\",\"show\"])||r.isBlank())return;var o=r.getExtent();n=r instanceof Mw?r.count():(e=r.getTicks()).length;var a,s=t.getLabelModel(),l=Zw(t),u=1;n>40&&(u=Math.ceil(n/40));for(var c=0;c<n;c+=u){var h=l(e?e[c]:{value:o[0]+c},c),d=p(s.getTextRect(h),s.get(\"rotate\")||0);a?a.union(d):a=d}return a;function p(t,e){var n=e*Math.PI/180,i=t.width,r=t.height,o=i*Math.abs(Math.cos(n))+Math.abs(r*Math.sin(n)),a=i*Math.abs(Math.sin(n))+Math.abs(r*Math.cos(n));return new Xe(t.x,t.y,o,a)}}(t);if(n){var i=t.isHorizontal()?\"height\":\"width\",r=t.model.get([\"axisLabel\",\"margin\"]);e[i]-=n[i]+r,\"top\"===t.position?e.y+=n.height+r:\"left\"===t.position&&(e.x+=n.width+r)}}})}gb([function(t){t.registerPainter(\"canvas\",XT)}]),gb([function(t){t.registerPainter(\"svg\",DT)}]),gb([function(t){t.registerChartView(VC),t.registerSeriesModel(ZT),t.registerLayout(GC(\"line\",!0)),t.registerVisual({seriesType:\"line\",reset:function(t){var e=t.getData(),n=t.getModel(\"lineStyle\").getLineStyle();n&&!n.stroke&&(n.stroke=e.getVisual(\"style\").fill),e.setVisual(\"legendLineStyle\",n)}}),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,WC(\"line\"))},function(t){t.registerChartView(rA),t.registerSeriesModel(KD),t.registerLayout(t.PRIORITY.VISUAL.LAYOUT,YD(PD)),t.registerLayout(t.PRIORITY.VISUAL.PROGRESSIVE_LAYOUT,XD(PD)),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,WC(PD)),t.registerAction({type:\"changeAxisOrder\",event:\"changeAxisOrder\",update:\"update\"},function(t,e){var n=t.componentType||\"series\";e.eachComponent({mainType:n,query:t},function(e){t.sortInfo&&e.axis.setCategorySortInfo(t.sortInfo)})}),jD(t)},function(t){t.registerChartView(GA),t.registerSeriesModel(TA),$m(MA,t.registerAction),t.registerLayout(zA),t.registerProcessor(_A(MA)),t.registerProcessor(function(t){return{seriesType:t,reset:function(t,e){var n=t.getData();n.filterSelf(function(t){var e=n.mapDimension(\"value\"),i=n.get(e,t);return!(K(i)&&!isNaN(i)&&i<0)})}}}(MA))},function(t){gb(Ok),t.registerSeriesModel(FA),t.registerChartView(YA),t.registerLayout(GC(\"scatter\"))},function(t){gb(eL),t.registerChartView(Yk),t.registerSeriesModel(Uk),t.registerLayout(nL),t.registerProcessor(_A(\"radar\")),t.registerPreprocessor(Hk)},function(t){gb(TO),t.registerChartView(gO),t.registerSeriesModel(cO),t.registerLayout(CO),t.registerProcessor(t.PRIORITY.PROCESSOR.STATISTIC,DO),$m(\"map\",t.registerAction)},function(t){t.registerChartView(iR),t.registerSeriesModel(tR),t.registerLayout(hR),t.registerVisual(dR),function(t){t.registerAction({type:\"treeExpandAndCollapse\",event:\"treeExpandAndCollapse\",update:\"update\"},function(t,e){e.eachComponent({mainType:Du,subType:QO,query:t},function(e){var n=t.dataIndex,i=e.getData().tree.getNodeByDataIndex(n);i.isExpand=!i.isExpand})}),UP(t,Du,QO)}(t)},function(t){t.registerSeriesModel(gR),t.registerChartView(WR),t.registerVisual(aN),t.registerLayout(DR),function(t){for(var e=0;e<pR.length;e++)t.registerAction({type:pR[e],update:\"updateView\"},St);t.registerAction({type:\"treemapRootToNode\",update:\"updateView\"},function(t,e){e.eachComponent({mainType:\"series\",subType:\"treemap\",query:t},function(e,n){var i=qO(t,[\"treemapZoomToNode\",\"treemapRootToNode\"],e);if(i){var r=e.getViewRoot();r&&(t.direction=$O(r,i.node)?\"rollUp\":\"drillDown\"),e.resetViewRoot(i.node)}})})}(t)},function(t){t.registerChartView(pE),t.registerSeriesModel(IN),t.registerProcessor(fE),t.registerVisual(gE),t.registerVisual(CN),t.registerLayout(kN),t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,BN),t.registerLayout(GN),t.registerCoordinateSystem(\"graphView\",{dimensions:nP.dimensions,create:FN}),t.registerAction({type:\"focusNodeAdjacency\",event:\"focusNodeAdjacency\",update:\"series:focusNodeAdjacency\"},St),t.registerAction({type:\"unfocusNodeAdjacency\",event:\"unfocusNodeAdjacency\",update:\"series:unfocusNodeAdjacency\"},St),UP(t,Du,MN)},function(t){t.registerChartView(wE),t.registerSeriesModel(yE),t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,ME),t.registerProcessor(_A(\"chord\"))},function(t){t.registerChartView(DE),t.registerSeriesModel(AE)},function(t){t.registerChartView(RE),t.registerSeriesModel(LE),t.registerLayout(NE),t.registerProcessor(_A(kE))},function(t){gb(iB),t.registerChartView(zE),t.registerSeriesModel(WE),t.registerVisual(t.PRIORITY.VISUAL.BRUSH,XE)},function(t){t.registerChartView(lB),t.registerSeriesModel(oB),t.registerLayout(cB),t.registerVisual(wB),t.registerAction({type:\"dragNode\",event:\"dragnode\",update:\"update\"},function(t,e){e.eachComponent({mainType:Du,subType:rB,query:t},function(e){e.setNodePosition(t.dataIndex,[t.localX,t.localY])})}),UP(t,Du,rB)},function(t){t.registerSeriesModel(TB),t.registerChartView(CB),t.registerLayout(RB),t.registerTransform(NB),function(t){OB(t,function(){var e=AD(IB);xS(t,{key:e,seriesType:IB,getMetrics:LD}),AS(e,DD(e))})}(t)},function(t){t.registerChartView(ZB),t.registerSeriesModel(zB),t.registerPreprocessor(rV),t.registerVisual(YB),t.registerLayout(aV),function(t){oV(t,function(){var e=AD(EB);xS(t,{key:e,seriesType:EB,getMetrics:LD}),AS(e,DD(e))})}(t)},function(t){t.registerChartView(cV),t.registerSeriesModel(dV),t.registerLayout(GC(\"effectScatter\"))},function(t){t.registerChartView(_V),t.registerSeriesModel(MV),t.registerLayout(xV),t.registerVisual(TV)},function(t){t.registerChartView(DV),t.registerSeriesModel(AV)},function(t){t.registerChartView(OV),t.registerSeriesModel($V),t.registerLayout(t.PRIORITY.VISUAL.LAYOUT,YD(OD)),t.registerLayout(t.PRIORITY.VISUAL.PROGRESSIVE_LAYOUT,XD(OD)),jD(t)},function(t){t.registerChartView(tG),t.registerSeriesModel(QV),t.registerLayout(eG),t.registerProcessor(_A(JV))},function(t){t.registerChartView(uG),t.registerSeriesModel(oG),t.registerLayout(dG),t.registerVisual(cG),function(t){t.registerAction({type:sG,update:\"updateView\"},function(t,e){e.eachComponent({mainType:\"series\",subType:\"sunburst\",query:t},function(e,n){var i=qO(t,[sG],e);if(i){var r=e.getViewRoot();r&&(t.direction=$O(r,i.node)?\"rollUp\":\"drillDown\"),e.resetViewRoot(i.node)}})}),t.registerAction({type:lG,update:\"none\"},function(t,e,n){t=k({},t),e.eachComponent({mainType:\"series\",subType:\"sunburst\",query:t},function(e){var n=qO(t,[lG],e);n&&(t.dataIndex=n.node.dataIndex)}),n.dispatchAction(k(t,{type:\"highlight\"}))}),t.registerAction({type:\"sunburstUnhighlight\",update:\"updateView\"},function(t,e,n){t=k({},t),n.dispatchAction(k(t,{type:\"downplay\"}))})}(t)},function(t){t.registerChartView(QG),t.registerSeriesModel(yG)}]),gb(function(t){gb(Ok),gb(nH)}),gb(function(t){gb(nH),wk.registerAxisPointerClass(\"PolarAxisPointer\",iH),t.registerCoordinateSystem(\"polar\",xH),t.registerComponentModel(sH),t.registerComponentView(RH),JA(t,\"angle\",uH,PH),JA(t,\"radius\",cH,OH),t.registerComponentView(MH),t.registerComponentView(CH),t.registerLayout(LH),function(t,e){AH(t,function(){var n=kD(e,oH);RD(t,n,e,oH),AS(n,DD(n))})}(t,PD)}),gb(TO),gb(function(t){gb(nH),wk.registerAxisPointerClass(\"SingleAxisPointer\",XH),t.registerComponentView(KH),t.registerComponentView(zH),t.registerComponentModel(Ek),JA(t,\"single\",Ek,Ek.defaultOption),t.registerCoordinateSystem(\"single\",WH)}),gb(iB),gb(function(t){t.registerComponentModel($H),t.registerComponentView(QH),t.registerCoordinateSystem(\"calendar\",eW)}),gb(function(t){t.registerComponentModel(DW),t.registerComponentView(OW),t.registerCoordinateSystem(\"matrix\",FW)}),gb(function(t){t.registerComponentModel(lU),t.registerComponentView(hU),t.registerPreprocessor(function(t){var e=t.graphic;X(e)?e[0]&&e[0].elements?t.graphic=[t.graphic[0]]:t.graphic=[{elements:e}]:e&&!e.elements&&(t.graphic=[{elements:[e]}])})}),gb(function(t){t.registerComponentModel(GU),t.registerComponentView(HU),BU(\"saveAsImage\",UU),BU(\"magicType\",ZU),BU(\"dataView\",tY),BU(\"dataZoom\",SY),BU(\"restore\",oY),gb(NU)}),gb(function(t){gb(nH),t.registerComponentModel(TY),t.registerComponentView(WY),t.registerAction({type:\"showTip\",event:\"showTip\",update:\"tooltip:manuallyShowTip\"},St),t.registerAction({type:\"hideTip\",event:\"hideTip\",update:\"tooltip:manuallyHideTip\"},St)}),gb(nH),gb(function(t){t.registerComponentView(hX),t.registerComponentModel(dX),t.registerPreprocessor(jY),t.registerVisual(t.PRIORITY.VISUAL.BRUSH,aX),t.registerAction({type:\"brush\",event:\"brush\",update:\"updateVisual\"},function(t,e){e.eachComponent({mainType:\"brush\",query:t},function(e){e.setAreas(t.areas)})}),t.registerAction({type:\"brushSelect\",event:\"brushSelected\",update:\"none\"},St),t.registerAction({type:\"brushEnd\",event:\"brushEnd\",update:\"none\"},St),BU(\"brush\",gX)}),gb(function(t){t.registerComponentModel(vX),t.registerComponentView(yX)}),gb(function(t){t.registerComponentModel(xX),t.registerComponentView(MX),t.registerSubTypeDefaulter(\"timeline\",function(){return\"slider\"}),function(t){t.registerAction({type:\"timelineChange\",event:\"timelineChanged\",update:\"prepareAndUpdate\"},function(t,e,n){var i=e.getComponent(\"timeline\");return i&&null!=t.currentIndex&&(i.setCurrentIndex(t.currentIndex),!i.get(\"loop\",!0)&&i.isIndexMax()&&i.getPlayState()&&(i.setPlayState(!1),n.dispatchAction({type:\"timelinePlayChange\",playState:!1,from:t.from}))),e.resetOption(\"timeline\",{replaceMerge:i.get(\"replaceMerge\",!0)}),P({currentIndex:i.option.currentIndex},t)}),t.registerAction({type:\"timelinePlayChange\",event:\"timelinePlayChanged\",update:\"update\"},function(t,e){var n=e.getComponent(\"timeline\");n&&null!=t.playState&&n.setPlayState(t.playState)})}(t),t.registerPreprocessor(CX)}),gb(function(t){t.registerComponentModel(RX),t.registerComponentView(XX),t.registerPreprocessor(function(t){kX(t.series,\"markPoint\")&&(t.markPoint=t.markPoint||{})})}),gb(function(t){t.registerComponentModel(ZX),t.registerComponentView(tZ),t.registerPreprocessor(function(t){kX(t.series,\"markLine\")&&(t.markLine=t.markLine||{})})}),gb(function(t){t.registerComponentModel(eZ),t.registerComponentView(uZ),t.registerPreprocessor(function(t){kX(t.series,\"markArea\")&&(t.markArea=t.markArea||{})})}),gb(function(t){gb(bZ),gb(DZ)}),gb(function(t){gb(VZ),gb(jZ)}),gb(VZ),gb(jZ),gb(function(t){gb(Dj),gb(Oj)}),gb(Dj),gb(Oj),gb(function(t){t.registerComponentModel(Nj),t.registerComponentView(Ej)}),gb(function(t){t.registerPreprocessor(Wj),t.registerVisual(t.PRIORITY.VISUAL.ARIA,Hj)}),gb(function(t){t.registerTransform(eq),t.registerTransform(nq)}),gb(function(t){t.registerComponentModel(iq),t.registerComponentView(rq)}),gb(function(t){t.registerUpdateLifecycle(\"series:beforeupdate\",function(t,e,n){B(_a(n.seriesTransition),function(t){B(_a(t.to),function(t){for(var e=n.updatedSeries,i=0;i<e.length;i++)(null!=t.seriesIndex&&t.seriesIndex===e[i].seriesIndex||null!=t.seriesId&&t.seriesId===e[i].id)&&(e[i][Fy]=!0)})})}),t.registerUpdateLifecycle(\"series:transition\",function(t,e,n){var i=Vq(e);if(i.oldSeries&&n.updatedSeries&&n.optionChanged){var r=n.seriesTransition;if(r)B(_a(r),function(t){!function(t,e,n,i){var r=[],o=[];B(_a(t.from),function(t){var n=Kq(e.oldSeries,t);n>=0&&r.push({dataGroupId:e.oldDataGroupIds[n],data:e.oldData[n],divide:qq(e.oldData[n]),groupIdDim:t.dimension})}),B(_a(t.to),function(t){var i=Kq(n.updatedSeries,t);if(i>=0){var r=n.updatedSeries[i].getData();o.push({dataGroupId:e.oldDataGroupIds[i],data:r,divide:qq(r),groupIdDim:t.dimension})}}),r.length>0&&o.length>0&&Xq(r,o,i)}(t,i,n,e)});else{var o=function(t,e){var n=mt(),i=mt(),r=mt();return B(t.oldSeries,function(e,n){var o=t.oldDataGroupIds[n],a=t.oldData[n],s=Zq(e),l=jq(s);i.set(l,{dataGroupId:o,data:a}),X(s)&&B(s,function(t){r.set(t,{key:l,dataGroupId:o,data:a})})}),B(e.updatedSeries,function(t){if(t.isUniversalTransitionEnabled()&&t.isAnimationEnabled()){var e=t.get(\"dataGroupId\"),o=t.getData(),a=Zq(t),s=jq(a),l=i.get(s);if(l)n.set(s,{oldSeries:[{dataGroupId:l.dataGroupId,divide:qq(l.data),data:l.data}],newSeries:[{dataGroupId:e,divide:qq(o),data:o}]});else if(X(a)){var u=[];B(a,function(t){var e=i.get(t);e.data&&u.push({dataGroupId:e.dataGroupId,divide:qq(e.data),data:e.data})}),u.length&&n.set(s,{oldSeries:u,newSeries:[{dataGroupId:e,data:o,divide:qq(o)}]})}else{var c=r.get(a);if(c){var h=n.get(c.key);h||(h={oldSeries:[{dataGroupId:c.dataGroupId,data:c.data,divide:qq(c.data)}],newSeries:[]},n.set(c.key,h)),h.newSeries.push({dataGroupId:e,data:o,divide:qq(o)})}}}}),n}(i,n);B(o.keys(),function(t){var n=o.get(t);Xq(n.oldSeries,n.newSeries,e)})}B(n.updatedSeries,function(t){t[Fy]&&(t[Fy]=!1)})}for(var a=t.getSeries(),s=i.oldSeries=[],l=i.oldDataGroupIds=[],u=i.oldData=[],c=0;c<a.length;c++){var h=a[c].getData();h.count()<1e4&&(s.push(a[c]),l.push(a[c].get(\"dataGroupId\")),u.push(h))}})}),gb(function(t){t.registerUpdateLifecycle(\"series:beforeupdate\",function(t,e,n){var i=vI(e).labelManager;i||(i=vI(e).labelManager=new gI),i.clearLabels()}),t.registerUpdateLifecycle(\"series:layoutlabels\",function(t,e,n){var i=vI(e).labelManager;B(n.updatedSeries,function(t){i.addLabelsOfSeries(e.getViewOfSeriesModel(t))}),i.updateLayoutConfig(e),i.layout(e),i.processLabelsOverall()})}),gb(function(t){!function(t){function e(t,e){var n=[],i=Ra(e,t);function r(e,r){B(i[e],function(e){B(e.updateAxisBreaks(t).breaks,function(t){var i;n.push(P(((i={})[r]=e.componentIndex,i),t))})})}return r(\"xAxisModels\",\"xAxisIndex\"),r(\"yAxisModels\",\"yAxisIndex\"),r(\"singleAxisModels\",\"singleAxisIndex\"),{eventBreaks:n}}t.registerAction($C,e),t.registerAction(JC,e),t.registerAction(QC,e)}(t),dK(),mK()}),gb(function(){ok=xK}),gb(function(t){t.registerLayout(t.PRIORITY.VISUAL.POST_CHART_LAYOUT,Fk())}),t.Axis=SM,t.ChartView=tm,t.ComponentModel=lg,t.ComponentView=Ky,t.List=Hb,t.Model=kp,t.PRIORITY=t_,t.SeriesModel=Hy,t.color=Di,t.connect=function(t){if(X(t)){var e=t;t=null,B(e,function(e){null!=e.group&&(t=e.group)}),t=t||\"g_\"+X_++,B(e,function(e){e.group=t})}return U_[t]=!0,t},t.dataTool={},t.dependencies={zrender:\"6.1.0\"},t.disConnect=q_,t.disconnect=j_,t.dispose=function(t){j(t)?t=W_[t]:t instanceof L_||(t=K_(t)),t instanceof L_&&!t.isDisposed()&&t.dispose()},t.env=r,t.extendChartView=function(t){var e=tm.extend(t);return tm.registerClass(e),e},t.extendComponentModel=function(t){var e=lg.extend(t);return lg.registerClass(e),e},t.extendComponentView=function(t){var e=Ky.extend(t);return Ky.registerClass(e),e},t.extendSeriesModel=function(t){var e=Hy.extend(t);return Hy.registerClass(e),e},t.format=nM,t.getCoordinateSystemDimensions=function(t){var e=Vf.get(t);if(e)return e.getDimensionsInfo?e.getDimensionsInfo():e.dimensions.slice()},t.getInstanceByDom=K_,t.getInstanceById=function(t){return W_[t]},t.getMap=function(t){var e=nx(\"getMap\");return e&&e(t)},t.graphic=eM,t.helper=VS,t.init=function(t,e,n){var i=!(n&&n.ssr);if(i){0;var r=K_(t);if(r)return r;0}var o=new L_(t,e,n);return o.id=\"ec_\"+Y_++,W_[o.id]=o,i&&Ga(t,Z_,o.id),I_(o),tx.trigger(\"afterinit\",o),o},t.innerDrawElementOnCanvas=Fx,t.matrix=Le,t.number=QS,t.parseGeoJSON=JS,t.parseGeoJson=JS,t.registerAction=ib,t.registerCoordinateSystem=rb,t.registerCustomSeries=function(t,e){rx(t,e)},t.registerLayout=ob,t.registerLoading=ub,t.registerLocale=Gp,t.registerMap=cb,t.registerPostInit=tb,t.registerPostUpdate=eb,t.registerPreprocessor=J_,t.registerProcessor=Q_,t.registerTheme=$_,t.registerTransform=hb,t.registerUpdateLifecycle=nb,t.registerVisual=ab,t.setCanvasCreator=function(t){h({createCanvas:t})},t.setPlatformAPI=h,t.throttle=um,t.time=tM,t.use=gb,t.util=iM,t.vector=jt,t.version=\"6.1.0\",t.zrUtil=Tt,t.zrender=Io,Object.defineProperty(t,\"__esModule\",{value:!0})});\n"}}
{"timestamp":"2026-07-16T17:52:31.804Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_VAQUOIInSuzuRykvtIecZVKV","output":"Chunk ID: 02c794\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:52:31.804Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1683703,"cached_input_tokens":1580416,"output_tokens":17902,"reasoning_output_tokens":1257,"total_tokens":1701605},"last_token_usage":{"input_tokens":100936,"cached_input_tokens":98688,"output_tokens":354,"reasoning_output_tokens":125,"total_tokens":101290},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:34.571Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRpiZ8ficugdJqREzssbjWDNBRbgHSeUXwyFX3pa4gS51qXnUT8Za7Fg4qq2FGDvjUyE0fncXXQIkfoPIjInL3CS0qCmHGklB8Ua2sI311zRHuwracnCZxJZZpjmVR5a2WLODFrtCyDe9pZEXmz735TE_N9SWYeTPx8bgtvHBS--icwmojB8GloQGh5Etrp0ueEcS4Akv3oCl1U41MUrSQNx247PqFj9odw7ir3btNcr81tJbn2QsqDgY7EUSgmgjYL9A_SUPpXMhNdXQLtlnmYF4b-w88QSbN2T9O9e1d-eJBxBnaw-TsqDSEfwvg3cp--XF4ljisTleGQB6zlHnOf5ila37V4gOtlhTcqhTqPF5_PvhMTzKok-126YW7lmOTK9PQU8sHNmjk-Wtxc2QPbfg02srvenU7mlikEs4WGMlk23jazXEp3iJyM9Hy8Ez8BJuORlJL-G8FXPKmLd7LkpD-58HheeFbpsFkYoUizifVZHCxRGeShrbLRumoN-rldA-WkG3XFI22iupF1QVspELfOT_1mUUkNzV6wNkcOqinl9FL_bjeT6GB4eKqCHlIC_ocJjPRJYLzbntfsDXDnKHjdiEFVe-jXP8rbOvN-bKM9FvKur7aBXf8rcaCFOFJ8yoaHqCRI9iny6A2WhS4XrrjQEx7lZzvemgTaOTFgKI32BTa91f1VIUZFK0hbapQGukCozq4KYJwKPc-pnkeLVR_Y0eNop7NvkvrhQYuvkDX_4rXWCRagqYEj_FDxcbQ1CVnXGzIEF5RzNC_0CAX2qp2HZt4_M93kyf58G8cYF3w_U_fdIxewKLbnAWtarVr1uFy0T5BmKS9ZpNhnB5A23C1K4CTCGVdFYfdmJZyi3IATgdch5qeLPkMj7exN06DStrVx7wVE4fr30IILGaZoICHMcwAeb9GUSE7iG8iW10ccXT2bQq7OliwB6waC8zOyn5vVnXuAgS42GAK6VDyOzx7ueY7on3ILWVpG82JeatSYGDGKbFE6lbKlgecYqt9GQczG08jMlfpMRmG-FI3-owqyIJsZABrTeOzXVvxGu8EBGSapExVhQtCFwbH7P6fAYlddvcF1KwHJBoHo8iOYxvl8yoInXbc6dLiS8nM86Wrc="}}
{"timestamp":"2026-07-16T17:52:36.067Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_lvLmNXda4aKW2f9eV76udXce","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/read_api.py\n@@\n                AND histamine_unknown_count=0 AND histamine_score IS NOT NULL\n-               AND histamine_label IN ('green','yellow','orange','red')\"\"\"\n+               AND histamine_label IN ('classified','green','yellow','orange','red')\"\"\"\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:52:36.191Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_lvLmNXda4aKW2f9eV76udXce","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/read_api.py":{"type":"update","unified_diff":"@@ -433,3 +433,3 @@\n                AND histamine_unknown_count=0 AND histamine_score IS NOT NULL\n-               AND histamine_label IN ('green','yellow','orange','red')\"\"\"\n+               AND histamine_label IN ('classified','green','yellow','orange','red')\"\"\"\n     parameters: list[Any] = []\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:52:36.199Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_lvLmNXda4aKW2f9eV76udXce","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n"}}
{"timestamp":"2026-07-16T17:52:36.200Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1796751,"cached_input_tokens":1681152,"output_tokens":18027,"reasoning_output_tokens":1293,"total_tokens":1814778},"last_token_usage":{"input_tokens":113048,"cached_input_tokens":100736,"output_tokens":125,"reasoning_output_tokens":36,"total_tokens":113173},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:38.540Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRpmqf-UhwDkgczpWsFyAsMXu7xBWmWA0oGZJ-tVd21qmWQRjeUT13CcSvkTHHLVH6lBQTqTMl0o_avHMmZlexOpHHhKdHV5ICTCM_fDkzRZAdEoAGt_Xo_tONc7FN0sQ_DKiYH-dzNdkFrwBfrq9ZiMUF0OVn5bA9BKhAm9-DjZj7LSM_a76aK_j38Z6rinj4UcUo3J62snBRSBasn2QCpMEDQr5vOOu21s306P80OWN2PE6HgXFxZlmKST1ZJ02Y4LDScGROSiSBj2Dw2dg96TQ1OHrcTd54bP6Db-zN_a-UiAsKoQtRFALDyJsmzS9tYIgHBbwLLc-jTE675Hf3q2ByxN5TyryT1tcOpeQMWwuQ75JmP8GJ7OUU0cw8IF2e7sjF7p3vyR7cn3qAGsxUO20eUnAvvpyoJFPYVASdB0He50d1rVyjMa1-McW-PCgBUxUoqedoS-ScX1Wyz5UVcMk2VMdPC0tzy20ucPA42MBTLOROtikO-3gMiXqatusdWf88FYLui2ECGBddYYSu2rLhNtxQycCT98vlRx28qB8eDy-qGb55DPNJeyQOvHWAOMU58Q0F4lnkb6KOt4CVt8-m3tQbbfIK49SVyoqmQQUNl1PZfXdBroe41IbBYHBLm-WKt3MVn_d1RiIACiGABXrwXrOVuRXSdTFthX4RmudLme8fAwHSy2H91GMBpzJwC5PFhF05UpAApAH2SYRBUmnOaCJF0gebXz-MqhdjXKnxGS5E5ZmWvWdA_Dd1nJWU3_GuO5gVou3_Fquaie1y0tvGVzB5fMUin5yHXsFZ3cHY7nU0GWBQhIDCHP1m_9RT_mUxLC289sm-vku-6xPJCHJAwM2SZzXkBjJYm7Y2iBcoarOk0EkyXVX6ebSS9ShpgHtGvEKglKAgQZzBxFEGogLOmrYoV4svC4MXwsloA0nnJK-RjhURO6wgtGqw9zHjKvDOiMjArzEVFhuhMJ9zwJqQ=="}}
{"timestamp":"2026-07-16T17:52:39.298Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_V1TTSgTFVCRJ9C4PQdSmsm5F","name":"apply_patch","input":"*** Begin Patch\n*** Update File: tests/test_dashboard_v5_sprint6g_nutrition.py\n@@\n-import urllib.parse\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:52:39.399Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_V1TTSgTFVCRJ9C4PQdSmsm5F","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/tests/test_dashboard_v5_sprint6g_nutrition.py":{"type":"update","unified_diff":"@@ -5,3 +5,2 @@\n import importlib.util\n-import urllib.parse\n from datetime import date\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:52:39.407Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_V1TTSgTFVCRJ9C4PQdSmsm5F","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:52:39.407Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":1909970,"cached_input_tokens":1794176,"output_tokens":18085,"reasoning_output_tokens":1306,"total_tokens":1928055},"last_token_usage":{"input_tokens":113219,"cached_input_tokens":113024,"output_tokens":58,"reasoning_output_tokens":13,"total_tokens":113277},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:42.287Z","type":"event_msg","payload":{"type":"agent_message","message":"The focused Python files compile. I’m running the Sprint 6G nutrition tests now; if those pass, I’ll run the nearest V5 contract tests that exercise bundle validation and rendering.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:52:42.288Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The focused Python files compile. I’m running the Sprint 6G nutrition tests now; if those pass, I’ll run the nearest V5 contract tests that exercise bundle validation and rendering."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:52:43.280Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":50000}","call_id":"call_tw7UN6PQBCPJZepSX2W0xI8q"}}
{"timestamp":"2026-07-16T17:52:52.226Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_tw7UN6PQBCPJZepSX2W0xI8q","output":"Chunk ID: 1396e7\nWall time: 8.7817 seconds\nProcess exited with code 1\nOriginal token count: 367\nOutput:\n....F                                                                    [100%]\n=================================== FAILURES ===================================\n________ test_mapping_submission_requires_one_time_csrf_and_exact_shape ________\n\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x746b072ea1d0>\n\n    def test_mapping_submission_requires_one_time_csrf_and_exact_shape(monkeypatch: pytest.MonkeyPatch) -> None:\n>       import health_dashboard_server as server\n\ntests/test_dashboard_v5_sprint6g_nutrition.py:181: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nscripts/health/health_dashboard_server.py:38: in <module>\n    DASHBOARD_FILE = Path(os.environ[\"HEALTH_DASHBOARD_FILE\"]).resolve()\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = environ({'SHELL': '/bin/bash', 'HERMES_MEDIA_TRUST_RECENT_FILES': '1', 'DISCORD_REACTIONS': 'true', 'QT_ACCESSIBILITY'...tests/test_dashboard_v5_sprint6g_nutrition.py::test_mapping_submission_requires_one_time_csrf_and_exact_shape (call)'})\nkey = 'HEALTH_DASHBOARD_FILE'\n\n>   ???\nE   KeyError: 'HEALTH_DASHBOARD_FILE'\n\n<frozen os>:679: KeyError\n=========================== short test summary info ============================\nFAILED tests/test_dashboard_v5_sprint6g_nutrition.py::test_mapping_submission_requires_one_time_csrf_and_exact_shape\n1 failed, 4 passed in 8.58s\n"}}
{"timestamp":"2026-07-16T17:52:52.228Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2023297,"cached_input_tokens":1907200,"output_tokens":18192,"reasoning_output_tokens":1306,"total_tokens":2041489},"last_token_usage":{"input_tokens":113327,"cached_input_tokens":113024,"output_tokens":107,"reasoning_output_tokens":0,"total_tokens":113434},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:54.625Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRp2bA67s8Q04vHWMByYap3bSMAlcyxH2uuVyH_DliAhfdHFOfGqEKrUzqOsYjCz76M25UWs5YTSLNqxb7p6DCGX_3UD_-QdEY8k0leNlItVuZ_viN2uDVQNBNbVzP0Gb06UWoiJKiETklN3iAmMmPCt-pLMPtR3v0xAwrKmItIY9j-TumCkF-scq8n6XZkL1rxArW2Nr9_gGNN2JR0oxi-ibU-DIeHZ1xrVyqPgmtkx9AkUPIKyhPpIK8qWs-ErwEaEUBQZIeSBZJQSLdLS7iecweboX-AecPrjmSU0-zS5vZxLGPHJzDnVnvXmU-E5rP1_LCgNk0RKc8QWRMFTauMfhx7whs5rkQpYpmz4P1oo-pzSq4o-vPtx-As0PrrNeWDUklW2DCKP9KpNQDkesCyD_Z21Ght2t0l2WFEIC7q3Rgmc1XKfUOu132cN4a_tRVcWq_gj8I9tnY8CbYoBqCEYt_bHlH1sOCNsglRDczO106xIm-qym1zpZFp5PbEr8mbj_BzDRRB23cPS__ntm4AuPC-3iqStXfdardz6ufY2EpIXazt2Mj0dPO1xcN4okJaNlDPksibZd7wlc58vdibrQ2AnxsEcAJBS9hmkmFtvkmL1FXLXw8LwSLnUKEH6804dkOibs-y2aD2iRUUV2kyUUIHEMBXqLQvPKA0loZtlIotNDXux87o3Z-R1TzU5ULAENHfWCR45qx81RCF_TOcqeFumRp1oB0lOvv5kRw7361VpbxagnQpUNhsTdG_RD2scqp9AXvfuEE19ALJaodffYa8d_Vnv6UnzuhAxrmwMQ5xo05huQsWRYg4WP6cTlB2TZiyQ89wO_mvaBiD4YLEnvdfYwxnxgTtFGqKIm3LCEOWl-3FIVHvzBgVcY7NSzST1P3iUZCoWIF591gnyogitVLqv880k1XGiREVExRVw6GNd9DX59bmVO8tdk7kRO0o0iUDT2EL59JGGJY3j7_TomaNUGm1tclCaKWdsEZs0HNo="}}
{"timestamp":"2026-07-16T17:52:55.558Z","type":"event_msg","payload":{"type":"agent_message","message":"The first focused run has four passes and one import-time environment failure in the new server validation test. I’m fixing the test setup to provide the server’s required local file env vars before import.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:52:55.558Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The first focused run has four passes and one import-time environment failure in the new server validation test. I’m fixing the test setup to provide the server’s required local file env vars before import."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:52:57.160Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_62wyAne5G7OS05ROPYODia29","name":"apply_patch","input":"*** Begin Patch\n*** Update File: tests/test_dashboard_v5_sprint6g_nutrition.py\n@@\n def test_mapping_submission_requires_one_time_csrf_and_exact_shape(monkeypatch: pytest.MonkeyPatch) -> None:\n+    monkeypatch.setenv(\"HEALTH_DASHBOARD_FILE\", \"/tmp/synthetic-v4.html\")\n     import health_dashboard_server as server\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:52:57.264Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_62wyAne5G7OS05ROPYODia29","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/tests/test_dashboard_v5_sprint6g_nutrition.py":{"type":"update","unified_diff":"@@ -180,2 +180,3 @@\n def test_mapping_submission_requires_one_time_csrf_and_exact_shape(monkeypatch: pytest.MonkeyPatch) -> None:\n+    monkeypatch.setenv(\"HEALTH_DASHBOARD_FILE\", \"/tmp/synthetic-v4.html\")\n     import health_dashboard_server as server\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:52:57.273Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_62wyAne5G7OS05ROPYODia29","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:52:57.273Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2137159,"cached_input_tokens":2020224,"output_tokens":18351,"reasoning_output_tokens":1324,"total_tokens":2155510},"last_token_usage":{"input_tokens":113862,"cached_input_tokens":113024,"output_tokens":159,"reasoning_output_tokens":18,"total_tokens":114021},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:52:59.887Z","type":"event_msg","payload":{"type":"agent_message","message":"The environment setup is fixed. I’m rerunning the focused tests and then I’ll address any contract drift from the broader V5 tests.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:52:59.887Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The environment setup is fixed. I’m rerunning the focused tests and then I’ll address any contract drift from the broader V5 tests."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:53:00.728Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":50000}","call_id":"call_pLocgH4rVtvJfV29Lhl0agEh"}}
{"timestamp":"2026-07-16T17:53:09.615Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_pLocgH4rVtvJfV29Lhl0agEh","output":"Chunk ID: 211fa2\nWall time: 8.7228 seconds\nProcess exited with code 0\nOriginal token count: 25\nOutput:\n.....                                                                    [100%]\n5 passed in 8.52s\n"}}
{"timestamp":"2026-07-16T17:53:09.617Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2251230,"cached_input_tokens":2133760,"output_tokens":18448,"reasoning_output_tokens":1324,"total_tokens":2269678},"last_token_usage":{"input_tokens":114071,"cached_input_tokens":113536,"output_tokens":97,"reasoning_output_tokens":0,"total_tokens":114168},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:53:12.532Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRqI_JetWr1LsUbFkqWYFJ3J7QTAD6W56ZNzBR3AFyiARLHv1UnKwEvm8jtgxtdnGllCOTrazuQ044jbwK7VjyGNCq7NvJTRBuxoBPweM-4vLhta7jVo46k2_t9FC_MlOKb11lDuKqq1peI2wDznjaYsnA2U9xFjWAGfE-w1ZIS3HmAYfAk5fl8QrrQtbm9FceE5qjyrmYO24uL15onMxNiEcJugBkCzlqJjRyJBi7SG8nczCBCKAsqI7mXPcGR1hVbwpQ4tzosnU2vtkqmfdyk9VSec9lHbQksQj6uM0iccA-QwWTt-wg7B7-33xNgzYfpLZY5wSpIEd1zNM_zPP4pq4nUlxRZCkwK90TObVshEe5hE1y8H0BiEFjb2hWDRA9zu4sjDaqvG0R0aP7Wg_rTBMoX4CIgEpJsIiuO-gr7ghrxygQankMiYWnxQxMp0GVCZVKGTorcLBupLGTBTP7cEhKTKT7fim95HBgC9k9qN9dGKE1jvlvDSIaXekIcrje9Dp82_lb2Y6XeoxJmz1It-8k2WBD4aICDbflQCwS44rfspa7KZWAe-A5F0Uu7URKIxRV-sDLrO9RCzhXdM9159Y0zWKakNCG-id2N_GoHI3Lf90bSUDxbnlSvPFob9EEvPrDZ4_5ibmqYz19kFgD0VOEGUFYpk2hx5ZTv8knZ6P0oteKyAKblXE7kxEzLoiW0up0H1T-9D49cML6kX3xykMkwMt-IpIUzAPlF37XpoiGpSo2R1Q4n8HnUoqGXK7n23Y733u5HNEwlsNlwV12-FNloNKJbiHGFW-x4O6BH8UO6PwGA3vZCHvxs50Y4z993WacPRwnQNP4BAMzHyJYCZnHfP6Ca4tZby1xjMHkWT0XMRVwXENNZ3v_T5ogH7UE4dTYVRXuoZFIZL_NN1nwTeyTeI40Qd1FEx22S0JbD93RBWmG2TsYcyqCOkndmnAJVM"}}
{"timestamp":"2026-07-16T17:53:16.384Z","type":"event_msg","payload":{"type":"agent_message","message":"Sprint 6G nutrition tests now pass. I’m running the core V5 contract tests next because the bundle validation and rendering rules are the most likely places for this shape change to surface.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:53:16.391Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Sprint 6G nutrition tests now pass. I’m running the core V5 contract tests next because the bundle validation and rendering rules are the most likely places for this shape change to surface."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:53:16.786Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":60000}","call_id":"call_n4lCZcsF0ldoIaPDqOVWEhFo"}}
{"timestamp":"2026-07-16T17:53:17.206Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_n4lCZcsF0ldoIaPDqOVWEhFo","output":"Chunk ID: 4ffe45\nWall time: 0.2612 seconds\nProcess exited with code 2\nOriginal token count: 282\nOutput:\n\n==================================== ERRORS ====================================\n___________ ERROR collecting tests/test_dashboard_v5_sprint6b_api.py ___________\nImportError while importing test module '/home/agent/.hermes/repos/HealthManager/tests/test_dashboard_v5_sprint6b_api.py'.\nHint: make sure your test modules/packages have valid Python names.\nTraceback:\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\n    return _bootstrap._gcd_import(name[level:], package, level)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\ntests/test_dashboard_v5_sprint6b_api.py:33: in <module>\n    from tests.fixtures.dashboard_v5_fixture import build_dashboard_v5_fixture  # noqa: E402\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nE   ModuleNotFoundError: No module named 'tests.fixtures'\n=========================== short test summary info ============================\nERROR tests/test_dashboard_v5_sprint6b_api.py\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\n1 error in 0.14s\n"}}
{"timestamp":"2026-07-16T17:53:17.206Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2365458,"cached_input_tokens":2247808,"output_tokens":18586,"reasoning_output_tokens":1335,"total_tokens":2384044},"last_token_usage":{"input_tokens":114228,"cached_input_tokens":114048,"output_tokens":138,"reasoning_output_tokens":11,"total_tokens":114366},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:53:19.914Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRqPvI5BOZgh6mD6L9J4CIcuJlPLa1tmpTEPaYug3Hhf8XVnIQeaCR-3ifvR6FZxk3cI_t9kQXOgVOsP9jeXkC8sCgZdDVDG1d2WdeyXzdwXUoOBHFP2e3yEy3PjkPUlAGM1TREO9HRLmErCn7VxYXxiq4-lWjf1DrRIewi1rQTZscnymDHCzH5sHjkwXG7E6lvKELkBdibS2MIJadGwyMpgfKtHV1Ls65jTTFxGzku7edymrP0vBmQG4TuoTXBGK8NM1XAE_ZuAjS6UKuMkjXZFeCifTNM13O6VWoI0thGkXNuL_D0hBq7mfyC0HAFGsolIpR-2qWZmch5AlVYjlFJGHlvVJ47BUrOWjqyzy3qOIVouWZQfYYwEpzCqf8pO_RcIr7nxPek0u_Q_AfaCi-SzHNaPXvqfprrIZNsx-A7ev2HnU-O1JQ45bvQLYc_vAQ-fPPcAjl2tcTnCOKVY8uyclPPmm-zu0W-41k7CDg1gzAA125M3nLmb_7we0usKqUvAZnbg2iAXlugiljqJd3VD_jC58D0A_ynLUKOGskfgk7wH_zwedEC-DxH4mpmXHQ_uwTdrglLqlNlJxf4W7Qp1zdjZMxfSMTc3syDJBBJ93Fbyw1FdSXM16zs30Xq7dK5Vo2sFaK4arLjHdBUV99uvpCGyIAOWPRHgo6dyPifwd8VXKytaPV9wi88caCMZb9t3QUMkuGGKN13ZlXW9m2I1tha5uLQAkW0Our-rY-xRBMcbr_U3JztpixT7MCZd5O0W2x_BJdVi9Me3pg6G-iHpddrWOobc-m_WywBc7rynlGR6Mso-jK7q2Bc97-vyvw3b2THKhQCBwUg8SPkCr8o1FlkoKnX-uKysc3AVdhKXtn6MdDgGBBnwmzsaiKkdEeIB4m8i5iWt0sAYU5EooiSrVUEDVznQIkkJKLG1rWwJEc-fq0lPYqKm5jmR9cggjAxeSYUupdkVKsAFDEXBPYaZR0lOQUrsC7cGllQM2IlF_03Qep5QyyJ0r5vn3W8oMKIt-WCwE26gcKXuhuaP7j5NhhbsXKoC472NAL7sEe6cVaxJWqf7u1lUk8bDsYiqI1KB5KozUlHmleJvTIjLwvVwplXWpjGDbR4k912B2ZUiGZNEqFDSBgvhXcNsvPLFqfY4oksnTickL5TKOA3iR9T8lw=="}}
{"timestamp":"2026-07-16T17:53:20.821Z","type":"event_msg","payload":{"type":"agent_message","message":"The broader API test command hit an existing collection import issue for `tests.fixtures` under this runner. I’m rerunning with `PYTHONPATH=.` so those tests can import the fixture package as intended.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:53:20.821Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The broader API test command hit an existing collection import issue for `tests.fixtures` under this runner. I’m rerunning with `PYTHONPATH=.` so those tests can import the fixture package as intended."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:53:22.247Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_7esdEZQDP2r32jsQPXq023cV"}}
{"timestamp":"2026-07-16T17:53:52.411Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_7esdEZQDP2r32jsQPXq023cV","output":"Chunk ID: a79b3f\nWall time: 30.0010 seconds\nProcess running with session ID 98746\nOriginal token count: 8\nOutput:\n.................F............."}}
{"timestamp":"2026-07-16T17:53:52.418Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2480114,"cached_input_tokens":2361856,"output_tokens":18763,"reasoning_output_tokens":1376,"total_tokens":2498877},"last_token_usage":{"input_tokens":114656,"cached_input_tokens":114048,"output_tokens":177,"reasoning_output_tokens":41,"total_tokens":114833},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:53:54.260Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRqyl4FYvy8cGwiwB-SD2Ewp-vvCB6tD2NEA-MoDe2JlBBToh0UpNUNhbC5tUIw_OZBMmbPIQp9tqJNIgTEtQeWhKshaJ5WLlKHsmZU3eB8vTx3dLSQOoo4_v9VUywg0dXtpRcnytunqmR7mFs_QTxgOHjdcVUdHaHPXhoZ_hH9pdAjZTwQ2MpoCq1hvVP8uCX45NDtYFv5BW7cQEGsuWKemDSO5y-5V0JrcbYsALfJzq5dx9aPtuk_LuBv41d2DISKdmZYYrkjJa9CXnRLLARD1SwCEaI-UhAuzpvUtsRm9GVCZ0ZMEse8-V-2MI-tSEcvThRuRukLKwcnNSVhOZ09Rky4APd7H3Q8bqNbHzvFHJR5jtDs9uwXeR2clad5162Yum9Ugfebo4Xb16MCQmBVZWzkfTa4y8zhYouqTJIQ-u0GOLWHNXahE56zs5bivezB7Jx6z-uPK6s_rdt8c_qQU8iPsf3DW8iHWUgYB0mUt5d67JIXHQCDAHSqhfOtthNyJdn4zBq1XQGct1bz-9VXyoHVpBDFTIpZyIllgNTutHKZt2GxY-KKV0UksN_jTE34p7Q1jiS38WLNgRZD-JMLe8LDahxB4lqZyDk24zx17xL91ZrNQuBMxzctNZ6NNEBuvd9gFN73G0Zvwpi7VTpzln75lDXUdmZRJWjuP-rsn1lZCYBhayqOSQmt-C_VazU-KgJBmJUs4EcOdcAHLmF61yJIR0x7Tu4qASjQm-ca5bcBXzIvyR0pIfIqv6J6fhbOt47NrzKavz3tzjkqUCF2762uMiIKO8uEMfx-2vvoxZq0g-NUGlzggnu1XYFz8PW3-q77u_0TW3dnYAj2F9WFtgibOgTsBaQNkX7XLmiqdTruYAfG6fsIK9s9fTh3HUhzkPf8dZndfvnHBky5kkiM9H2NW5JK1P3gweiPMc80Y9YU="}}
{"timestamp":"2026-07-16T17:53:55.140Z","type":"event_msg","payload":{"type":"agent_message","message":"The broader V5 run is still active and has one failure so far. I’m waiting for the full failure report before patching so I don’t chase partial output.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:53:55.140Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The broader V5 run is still active and has one failure so far. I’m waiting for the full failure report before patching so I don’t chase partial output."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:53:55.992Z","type":"response_item","payload":{"type":"function_call","name":"write_stdin","arguments":"{\"session_id\":98746,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_RM4e9kN5EM4EXIH8CNXM8FRU"}}
{"timestamp":"2026-07-16T17:54:03.157Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_RM4e9kN5EM4EXIH8CNXM8FRU","output":"Chunk ID: 1fded5\nWall time: 7.1617 seconds\nProcess exited with code 1\nOriginal token count: 2070\nOutput:\nFF.....                                   [100%]\n=================================== FAILURES ===================================\n___________ test_server_exposes_v5_preview_and_exact_asset_allowlist ___________\n\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-734/test_server_exposes_v5_preview0')\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7b67771d3090>\n\n    def test_server_exposes_v5_preview_and_exact_asset_allowlist(tmp_path: Path, monkeypatch) -> None:\n        v4 = tmp_path / \"v4.html\"\n        v5 = tmp_path / \"v5.html\"\n        v4.write_text(\"<html>v4</html>\", encoding=\"utf-8\")\n        v5.write_text(\"<html><script>window.v5=true</script></html>\", encoding=\"utf-8\")\n        assets = tmp_path / \"assets\"\n        assets.mkdir()\n        for name in (\"chart.umd.min.js\", \"dashboard-v5.js\", \"dashboard-v5.css\"):\n            (assets / name).write_text(\"/* synthetic asset */\", encoding=\"utf-8\")\n        monkeypatch.setenv(\"HEALTH_DASHBOARD_FILE\", str(v4))\n        monkeypatch.setenv(\"HEALTH_DASHBOARD_V5_FILE\", str(v5))\n        spec = importlib.util.spec_from_file_location(\n            \"health_dashboard_server_v5_test\", HEALTH / \"health_dashboard_server.py\"\n        )\n        assert spec and spec.loader\n        server_module = importlib.util.module_from_spec(spec)\n        spec.loader.exec_module(server_module)\n        monkeypatch.setattr(server_module, \"ASSET_DIR\", assets)\n>       server = server_module.ThreadingHTTPServer((\"127.0.0.1\", 0), server_module.Handler)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\ntests/test_health_dashboard_v5.py:420: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socketserver.py:452: in __init__\n    self.socket = socket.socket(self.address_family,\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = <socket.socket fd=-1, family=0, type=0, proto=0>\nfamily = <AddressFamily.AF_INET: 2>, type = <SocketKind.SOCK_STREAM: 1>\nproto = 0, fileno = None\n\n    def __init__(self, family=-1, type=-1, proto=-1, fileno=None):\n        # For user code address family and type values are IntEnum members, but\n        # for the underlying _socket.socket they're just integers. The\n        # constructor of _socket.socket converts the given argument to an\n        # integer automatically.\n        if fileno is None:\n            if family == -1:\n                family = AF_INET\n            if type == -1:\n                type = SOCK_STREAM\n            if proto == -1:\n                proto = 0\n>       _socket.socket.__init__(self, family, type, proto, fileno)\nE       PermissionError: [Errno 1] Operation not permitted\n\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socket.py:232: PermissionError\n_________ test_http_api_requires_private_bearer_token_and_never_caches _________\n\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-734/test_http_api_requires_private0')\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7b6777205250>\n\n    def test_http_api_requires_private_bearer_token_and_never_caches(tmp_path, monkeypatch):\n        database = api_database(tmp_path)\n        dashboard = tmp_path / \"dashboard.html\"\n        dashboard.write_text(\"<html></html>\", encoding=\"utf-8\")\n        token = \"synthetic_api_token_0123456789abcdef\"\n        token_file = tmp_path / \"api-token\"\n        token_file.write_text(token, encoding=\"ascii\")\n        token_file.chmod(0o600)\n        module = load_server(\"health_dashboard_server_sprint6b\", dashboard, database, token_file, monkeypatch)\n        assert module.API_DB == database.resolve()\n        assert module.DB != database.resolve()\n>       server = module.ThreadingHTTPServer((\"127.0.0.1\", 0), module.Handler)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\ntests/test_dashboard_v5_sprint6b_api.py:583: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socketserver.py:452: in __init__\n    self.socket = socket.socket(self.address_family,\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = <socket.socket fd=-1, family=0, type=0, proto=0>\nfamily = <AddressFamily.AF_INET: 2>, type = <SocketKind.SOCK_STREAM: 1>\nproto = 0, fileno = None\n\n    def __init__(self, family=-1, type=-1, proto=-1, fileno=None):\n        # For user code address family and type values are IntEnum members, but\n        # for the underlying _socket.socket they're just integers. The\n        # constructor of _socket.socket converts the given argument to an\n        # integer automatically.\n        if fileno is None:\n            if family == -1:\n                family = AF_INET\n            if type == -1:\n                type = SOCK_STREAM\n            if proto == -1:\n                proto = 0\n>       _socket.socket.__init__(self, family, type, proto, fileno)\nE       PermissionError: [Errno 1] Operation not permitted\n\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socket.py:232: PermissionError\n____ test_http_api_is_fail_closed_without_explicit_database_and_token_paths ____\n\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-734/test_http_api_is_fail_closed_w0')\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x7b67789120d0>\n\n    def test_http_api_is_fail_closed_without_explicit_database_and_token_paths(tmp_path, monkeypatch):\n        dashboard = tmp_path / \"dashboard.html\"\n        dashboard.write_text(\"<html></html>\", encoding=\"utf-8\")\n        monkeypatch.delenv(\"HEALTH_DASHBOARD_TEST_INSTANCE_ID\", raising=False)\n        monkeypatch.delenv(\"HEALTH_DASHBOARD_ACTION_INBOX\", raising=False)\n        monkeypatch.setenv(\"HEALTH_DASHBOARD_FILE\", str(dashboard))\n        monkeypatch.setenv(\"HEALTH_DASHBOARD_V5_FILE\", str(dashboard))\n        monkeypatch.delenv(\"HEALTH_DASHBOARD_DB\", raising=False)\n        monkeypatch.delenv(\"HEALTH_DASHBOARD_API_TOKEN_FILE\", raising=False)\n        spec = importlib.util.spec_from_file_location(\"health_dashboard_server_sprint6b_disabled\", SERVER)\n        assert spec and spec.loader\n        module = importlib.util.module_from_spec(spec)\n        sys.modules[spec.name] = module\n        spec.loader.exec_module(module)\n>       server = module.ThreadingHTTPServer((\"127.0.0.1\", 0), module.Handler)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\ntests/test_dashboard_v5_sprint6b_api.py:763: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socketserver.py:452: in __init__\n    self.socket = socket.socket(self.address_family,\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = <socket.socket fd=-1, family=0, type=0, proto=0>\nfamily = <AddressFamily.AF_INET: 2>, type = <SocketKind.SOCK_STREAM: 1>\nproto = 0, fileno = None\n\n    def __init__(self, family=-1, type=-1, proto=-1, fileno=None):\n        # For user code address family and type values are IntEnum members, but\n        # for the underlying _socket.socket they're just integers. The\n        # constructor of _socket.socket converts the given argument to an\n        # integer automatically.\n        if fileno is None:\n            if family == -1:\n                family = AF_INET\n            if type == -1:\n                type = SOCK_STREAM\n            if proto == -1:\n                proto = 0\n>       _socket.socket.__init__(self, family, type, proto, fileno)\nE       PermissionError: [Errno 1] Operation not permitted\n\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socket.py:232: PermissionError\n=========================== short test summary info ============================\nFAILED tests/test_health_dashboard_v5.py::test_server_exposes_v5_preview_and_exact_asset_allowlist\nFAILED tests/test_dashboard_v5_sprint6b_api.py::test_http_api_requires_private_bearer_token_and_never_caches\nFAILED tests/test_dashboard_v5_sprint6b_api.py::test_http_api_is_fail_closed_without_explicit_database_and_token_paths\n3 failed, 35 passed in 40.53s\n"}}
{"timestamp":"2026-07-16T17:54:03.157Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2595000,"cached_input_tokens":2476416,"output_tokens":18850,"reasoning_output_tokens":1383,"total_tokens":2613850},"last_token_usage":{"input_tokens":114886,"cached_input_tokens":114560,"output_tokens":87,"reasoning_output_tokens":7,"total_tokens":114973},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:54:14.137Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRrGlH5NGQlzWF61rDknYkC2yJ_f7YOND7kq15vDXoZg3gR0mwdIwQR-Yvy1u9fZjBvU6EEQxF_-aeJan1YPtQNNSY2Hi5t2NtUS7871hoTIl4SmTrE5WvuAH72lpY4AGYZ0C6FJSeTwovQEm38vEmKfUcjBf1YrLzg69QRai3plNWXwq-Y3miNQ8j7HDPPZAaY7bKIWKJYLGnBnoXHc6ZekKwiOlvUSqIUDkFknubs23xd0VEDWG8gBFXcazBIKJ6kpTX7gklWSOpqNDmsnae0QdPsWBof8n7j4Cof5r3Njm1B2cGtQXmVrkjHOX6uqnT54In5PAnrKElThS9CS8H6j0EJgf4-dj9es1NUkfAXuvMQFjlv8mVy_dPyvlee19-2RLtOeuKfE1TvB12RWHkrp-jPFI4ltBN5ogiB0qISpBRlOpAn0i2WLIrq20hqM3hvs4e5Yd3_ygEbZPX1Sq0Hi6S8Vm22hVz3kSbtkIXuLUzPmaXzqQRIAxHxBo5RoaYYLVqV6D-E4amKiT82wDiml75yJvuLyAVS5f9GelbZkHwu2kHiEealtLEsfHgduDIKxLqPqSnaQ_tSWfals88EHCJfyidIVi0RV0KDajUVENDhus9FWdoVds6lJvLsP3HGb-Y_1nsHdqX0qjkMXk-2WMpygvpnly6218eUU-e0E7w95VB0L9cNsU3nLISE0_malper04pOHpQ3QN84rH2ykhZWWTvr4qazzFfTpJc_3DHUnyqAeBi7dw7VnTHKnqWp0HkeF5UTjg1EdkcU38uvQDrFzYxNYfTJpOsPKeIVkWI8qFcIwF4-uSgqo6ehzwVYVScG9uialk9Vw4C0b8jQyOOs338Z4fg6973U3lwOogn4UB02crimnn7ziSJXWLY1bqIyADZM3mNKGuULlS6dEaTvIYi50-uANQhv3Oc5ZiwzQZoFo5GjxMkuBx5keSLKxT2PWI5wQpV-oLwfVy5tHnQFFflVqp7C2KzA7dPFS6wTkXgGtx978F4Kgo_gPRkdzRMTzBlpo3bY8IqeYxpS6Gq9_TPYivdKk23xdHDKoConHyVnYVzSuaAxLG2BSgbL_eBDPbi6ymoSYBA_xG6okr-_qB8qRbx6n--84bvaoKMKFpon5QlLDVHZdYR31z-TsByC31BZvewD-LJx8y_aNiDMa6CaGGOCaTulHxY1ieF8="}}
{"timestamp":"2026-07-16T17:54:20.619Z","type":"event_msg","payload":{"type":"agent_message","message":"The broader V5 run passed all non-socket checks; the three failures are the sandbox denying local socket creation, not assertion failures in the changed code. I’m updating ADR 0003 now with the required inventory/methodology/conclusion details.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:54:20.622Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The broader V5 run passed all non-socket checks; the three failures are the sandbox denying local socket creation, not assertion failures in the changed code. I’m updating ADR 0003 now with the required inventory/methodology/conclusion details."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:54:28.855Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_oby6Ir1FDmT2RqbS7v6OC1xY","name":"apply_patch","input":"*** Begin Patch\n*** Update File: docs/adr/0003-dietary-inflammation-model.md\n@@\n-## Context\n-\n-Nutrition data combines imported meal records, nutrient totals, SIGHi-style histamine mappings, symptom dimensions, and optional context such as medication phases or documented events. These inputs are incomplete on many days and may contain unknown food mappings.\n+## Context\n+\n+Nutrition data combines imported meal records, nutrient totals, SIGHi-style histamine mappings, symptom dimensions, and optional context such as medication phases or documented events. These inputs are incomplete on many days and may contain unknown food mappings.\n+\n+Technical inventory from the Sprint 6G-A fixture/audit, abstracted without product names:\n+\n+- nutrition item records: present, with meal, date, item label, imported macros, and source-local product identifiers;\n+- item nutrient rows: present for selected macro and nutrient keys, with mixed unit coverage;\n+- histamine mapping rows: present for mapped and unmapped item states;\n+- review queue rows: present for unmapped normalized product slugs;\n+- daily and meal summaries: present, but historically allowed zero defaults for missing macro/index fields;\n+- DII reference-data tables: absent;\n+- DII food-parameter normalization/version tables: absent;\n+- reference licensing evidence for the full DII parameter database: absent.\n+\n+Input coverage/unit findings:\n+\n+- known and displayable nutrient units are limited to kcal, g, and mg for allowlisted keys;\n+- unknown-unit micronutrients are omitted from read APIs;\n+- missing imported macros are unknown and must not be normalized to zero;\n+- currently displayable inflammation-adjacent components are fiber, sugar, saturated fat, and sodium where known;\n+- required DII parameters are not covered with a complete, licensed, versioned reference table.\n+\n+The Dietary Inflammatory Index methodology required for a real DII implementation is the peer-reviewed Shivappa et al. 2014 method: nutrient/food-parameter intake is standardized against a global comparative database, converted to centered percentiles, multiplied by literature-derived inflammatory effect scores, and summed across available parameters. That methodology requires stable parameter names, units, global means/standard deviations, effect scores, coverage thresholds, and versioning. Those inputs are not available in this project state.\n@@\n Dashboard v5 exposes transparent components only:\n \n - documented macros and allowlisted nutrients,\n - versioned histamine mapping coverage,\n - unknown item counts,\n - explicitly complete or incomplete day semantics,\n - side-by-side ECharts views for nutrition and Aphthae when both are documented.\n \n The dashboard does not compute a dietary inflammation index when required inputs are insufficient. Unknown or unmapped foods make the histamine component incomplete rather than zero. Composite products require ingredient review before a definitive mapping is applied.\n \n Personal tolerance labels remain separate from shared food mapping definitions.\n+\n+Missing-parameter handling:\n+\n+- unobserved DII parameters are not imputed as neutral;\n+- known values are shown only as individual documented components;\n+- any future DII calculation must store the methodology id, source version, parameter coverage, unit normalization map, and unavailable-parameter policy with each computed value.\n+\n+Conclusion: Entzündungsindex derzeit nicht belastbar berechenbar.\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:54:28.959Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_oby6Ir1FDmT2RqbS7v6OC1xY","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM docs/adr/0003-dietary-inflammation-model.md\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/docs/adr/0003-dietary-inflammation-model.md":{"type":"update","unified_diff":"@@ -12,2 +12,23 @@\n \n+Technical inventory from the Sprint 6G-A fixture/audit, abstracted without product names:\n+\n+- nutrition item records: present, with meal, date, item label, imported macros, and source-local product identifiers;\n+- item nutrient rows: present for selected macro and nutrient keys, with mixed unit coverage;\n+- histamine mapping rows: present for mapped and unmapped item states;\n+- review queue rows: present for unmapped normalized product slugs;\n+- daily and meal summaries: present, but historically allowed zero defaults for missing macro/index fields;\n+- DII reference-data tables: absent;\n+- DII food-parameter normalization/version tables: absent;\n+- reference licensing evidence for the full DII parameter database: absent.\n+\n+Input coverage/unit findings:\n+\n+- known and displayable nutrient units are limited to kcal, g, and mg for allowlisted keys;\n+- unknown-unit micronutrients are omitted from read APIs;\n+- missing imported macros are unknown and must not be normalized to zero;\n+- currently displayable inflammation-adjacent components are fiber, sugar, saturated fat, and sodium where known;\n+- required DII parameters are not covered with a complete, licensed, versioned reference table.\n+\n+The Dietary Inflammatory Index methodology required for a real DII implementation is the peer-reviewed Shivappa et al. 2014 method: nutrient/food-parameter intake is standardized against a global comparative database, converted to centered percentiles, multiplied by literature-derived inflammatory effect scores, and summed across available parameters. That methodology requires stable parameter names, units, global means/standard deviations, effect scores, coverage thresholds, and versioning. Those inputs are not available in this project state.\n+\n ## Decision\n@@ -26,2 +47,10 @@\n \n+Missing-parameter handling:\n+\n+- unobserved DII parameters are not imputed as neutral;\n+- known values are shown only as individual documented components;\n+- any future DII calculation must store the methodology id, source version, parameter coverage, unit normalization map, and unavailable-parameter policy with each computed value.\n+\n+Conclusion: Entzündungsindex derzeit nicht belastbar berechenbar.\n+\n ## Consequences\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:54:28.967Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_oby6Ir1FDmT2RqbS7v6OC1xY","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM docs/adr/0003-dietary-inflammation-model.md\n"}}
{"timestamp":"2026-07-16T17:54:28.967Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2712206,"cached_input_tokens":2590976,"output_tokens":19632,"reasoning_output_tokens":1429,"total_tokens":2731838},"last_token_usage":{"input_tokens":117206,"cached_input_tokens":114560,"output_tokens":782,"reasoning_output_tokens":46,"total_tokens":117988},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:54:33.955Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRrZTU3JG_ZgU_0zVT-x6tffWV4CSrb43EtA8si3q5SsYc0P9_if1D_mru2-VevAJzV5chmx93ZhiKf8ryPODM9GL25U1Lnat6DoPzgeumUjhd8GYrU3Y0PEeIUxYdJnTpuk_QxlSLWQiyocH3R8B3dXzAEqoOwdLmK8XQ5o1GWwD72ho4HgLo80tBnAGaPSDSBFhharB2AtsGlabbeCE4kk_4xw2arkDc-1G5mN2qylXwmQQDaWgLjOhWDIhx8Y6L5u5_GyoGv11J8iyt1aSZdmx_FWfU2n22x4iCVcAzrME0_rtmu8Ly6xvzI_RKmUtq3vKlxOSESvoRUuxTh0AyhIqQzwUtqKv41DLHT7-6Ird93vmoq08ZMhC_DMMqX0VprcYbMhD3d76sjW47CsG1ev3ltNfCOidjMLb-JcFckhckyOwgdjcUtyxTrS1RYvQq2yGmqj34zF98Jj9O_UjvtcJX1bBDS7nvm0FfwcS7fWvPDp6RAMwf1Ba0nvlmOvS9jsTKWtrkW_C9LBdY63rtLjnWBdak1NY4Xn1aJqR5fQKsO03-OHBT_y5Xrh9k8zGvhGCIa2gF6IpuyMiC_AOj1WGC7VMXxGcrzhTalKR2U7-8wSjuOG9koekZnlJ-77X-UHrALNswlm69upDMbKKqojsqHLVvs8qUipkTrAsEyJaa1F8RqWtQMWaUGotdkNd6g7qNSBXypMtf-3p1PIrGnjvvGVJ5TlR59kliGiQhhTqEmwQPC2bXMXlQKvqfCNgzcTchu1THAV-QjJ6xkY5lFKGmPW5XjpVQwu2-6mQCAZvD0y1o2vLxlr3ZlIzctBAv7pE0HulBc_McKGNxjYzz81YMzEwpZ7V4M33BXIulbxJksJIoQ2aUgTXHaxA4dtxO7fZTqszAdOgf-pNGzyFnyRZM-FAr8D7lMO8LzSaF3spYsbiVgkdm1A7Rbm7o-0q9sZARPFLAFu72iJ3gFSWlnuod5QkuliBJ35uNm1zog_oCcBxAU__oRnUEZgZ-T5OsbfW5G3dbujqeUXNQi9u0wP2RF-9Lj9u3LkS2jxOJtgLA9tbCswGkExY_UOjqx7gMTO4v3bGZ2iKwf2HonQktjItav2-wJgkjwl48GUm7fAghMk-DQznbgCHuivH2YHvp5SxhEt-LoK56_BBaZff2yCdaEOS2AVP5AKgSMUUfLB1fvL1siXNYEOg2pf6-rvhPrwIzrdW6QuMUWRCf6DL97zDFHJClhHwoLrwsFocvSJBwzOB-aT6kI_FvJGuEoQ9y662-AmUwiNrdlYnDJ39MPu0UDVfLwd7xZ7_8gsRtE2pYkMb-UmS9hlTrM4Qxnwfw-VvoMCe18WswTJgm57AqC7NrZ75rXMldUjXdg9NKxd_3FEI11Cf8y0QOdUAiY2apaHXFezvn5FhCDZl2AxDZ2OVB9VD1CR1nq-p3NeV5YRZRFTvuCb17EPF7FGkdB6lVdM_EPN48aCxosF-0r0ju08xmNSR1sefceDoJmLulY0EdqUtffLrb0JztBljR0I608MaJj_JnBYDts_N5z9FZLfUZU3OI1LS6yyeKRiyIKOOflVIgNao6mKmEVfpXxnskeq9h2_G3byl-dvzZjjoJWhpdSsNletjw1mNTFNFd_t3acwC8t-rdwaV27jXi6tRAsrPYRixT1buJSBG5PyxhendSFCXpuTsew5YHA-KEjJcHMA35t0J27HSlfsNKEF4yXxj0kQ62nzgEnU6BwTGn9FwsfAB3anXF1hqOCl6SbmJvTQeXc0lnwlfFTje40qPkf50ngQXmIxX-uH5sfbtERrY6IflE4Hg2m_QoUxQtHj8ZX2bgmfXLRUZGqVQXsO0NfLFPhVwwzpCwgbugppaWSyNw=="}}
{"timestamp":"2026-07-16T17:54:35.045Z","type":"event_msg","payload":{"type":"agent_message","message":"ADR 0003 now contains the required methodology and limitation language, including the explicit “not reliably computable” conclusion. I’m adding one more focused bootstrap/idempotency test for schema migration gates and an affected-day-only assertion.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:54:35.052Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"ADR 0003 now contains the required methodology and limitation language, including the explicit “not reliably computable” conclusion. I’m adding one more focused bootstrap/idempotency test for schema migration gates and an affected-day-only assertion."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:54:46.149Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_qXE8bq6X57g65gnvGxvwVYw1","name":"apply_patch","input":"*** Begin Patch\n*** Update File: tests/test_dashboard_v5_sprint6g_nutrition.py\n@@\n def test_mapping_worker_is_transactional_idempotent_and_recomputes_only_affected_days(\n     fixture_db: Path, monkeypatch: pytest.MonkeyPatch\n ) -> None:\n@@\n     monkeypatch.setattr(worker, \"DASHBOARD_DB\", fixture_db)\n     monkeypatch.setattr(worker, \"DASHBOARD_V5_FILE\", None)\n+    connection = sqlite3.connect(fixture_db)\n+    try:\n+        connection.execute(\n+            \"\"\"INSERT INTO nutrition_daily_summary_v2\n+               (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,histamine_max,histamine_unknown_count,histamine_label,nutrient_json)\n+               VALUES('2026-06-13',123,4,5,6,1,2,2,0,'classified','{}')\"\"\"\n+        )\n+        connection.commit()\n+    finally:\n+        connection.close()\n@@\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_action_log\").fetchone()[0] == 1\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n+        unaffected = connection.execute(\"SELECT kcal,histamine_score FROM nutrition_daily_summary_v2 WHERE datum='2026-06-13'\").fetchone()\n+        assert dict(unaffected) == {\"kcal\": 123.0, \"histamine_score\": 2.0}\n@@\n     finally:\n         connection.close()\n+\n+\n+def test_worker_schema_bootstrap_is_idempotent_and_preserves_raw_items(fixture_db: Path) -> None:\n+    import health_dashboard_action_worker as worker\n+\n+    connection = sqlite3.connect(fixture_db)\n+    connection.row_factory = sqlite3.Row\n+    try:\n+        before = connection.execute(\n+            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\n+        ).fetchone()\n+        worker.ensure_nutrition_mapping_schema(connection)\n+        worker.ensure_nutrition_mapping_schema(connection)\n+        after = connection.execute(\n+            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\n+        ).fetchone()\n+        assert tuple(before) == tuple(after)\n+        columns = {row[\"name\"] for row in connection.execute(\"PRAGMA table_info(nutrition_mapping_provenance)\")}\n+        assert {\"decision\", \"alias\", \"mapping_method\", \"source_label\", \"source_version\", \"definition_version\"} <= columns\n+        assert connection.execute(\"PRAGMA integrity_check\").fetchone()[0] == \"ok\"\n+    finally:\n+        connection.close()\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:54:46.256Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_qXE8bq6X57g65gnvGxvwVYw1","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/tests/test_dashboard_v5_sprint6g_nutrition.py":{"type":"update","unified_diff":"@@ -95,2 +95,12 @@\n     monkeypatch.setattr(worker, \"DASHBOARD_V5_FILE\", None)\n+    connection = sqlite3.connect(fixture_db)\n+    try:\n+        connection.execute(\n+            \"\"\"INSERT INTO nutrition_daily_summary_v2\n+               (datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,histamine_max,histamine_unknown_count,histamine_label,nutrient_json)\n+               VALUES('2026-06-13',123,4,5,6,1,2,2,0,'classified','{}')\"\"\"\n+        )\n+        connection.commit()\n+    finally:\n+        connection.close()\n     payload = {\n@@ -128,2 +138,4 @@\n         assert connection.execute(\"SELECT COUNT(*) FROM nutrition_mapping_provenance WHERE affected_day='2026-06-14'\").fetchone()[0] == 1\n+        unaffected = connection.execute(\"SELECT kcal,histamine_score FROM nutrition_daily_summary_v2 WHERE datum='2026-06-13'\").fetchone()\n+        assert dict(unaffected) == {\"kcal\": 123.0, \"histamine_score\": 2.0}\n         provenance = connection.execute(\"SELECT decision,alias,confidence,mapping_method,source_label,source_version,note,ingredient_review_required,personal_tolerance,definition_version FROM nutrition_mapping_provenance\").fetchone()\n@@ -146,2 +158,24 @@\n \n+def test_worker_schema_bootstrap_is_idempotent_and_preserves_raw_items(fixture_db: Path) -> None:\n+    import health_dashboard_action_worker as worker\n+\n+    connection = sqlite3.connect(fixture_db)\n+    connection.row_factory = sqlite3.Row\n+    try:\n+        before = connection.execute(\n+            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\n+        ).fetchone()\n+        worker.ensure_nutrition_mapping_schema(connection)\n+        worker.ensure_nutrition_mapping_schema(connection)\n+        after = connection.execute(\n+            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\n+        ).fetchone()\n+        assert tuple(before) == tuple(after)\n+        columns = {row[\"name\"] for row in connection.execute(\"PRAGMA table_info(nutrition_mapping_provenance)\")}\n+        assert {\"decision\", \"alias\", \"mapping_method\", \"source_label\", \"source_version\", \"definition_version\"} <= columns\n+        assert connection.execute(\"PRAGMA integrity_check\").fetchone()[0] == \"ok\"\n+    finally:\n+        connection.close()\n+\n+\n def test_composite_and_ignore_do_not_alter_mappings(fixture_db: Path, monkeypatch: pytest.MonkeyPatch) -> None:\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:54:46.265Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_qXE8bq6X57g65gnvGxvwVYw1","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:54:46.265Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2830244,"cached_input_tokens":2708096,"output_tokens":20455,"reasoning_output_tokens":1577,"total_tokens":2850699},"last_token_usage":{"input_tokens":118038,"cached_input_tokens":117120,"output_tokens":823,"reasoning_output_tokens":148,"total_tokens":118861},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:54:49.448Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRrp5tj62-nM0fy5Av1JHqWuf-FggCSCkCGz4Uf4uD43IBED7d0NvEQt_MT1TBx4_qpZU4A4yHHchcoHPFW8UGwEQ6Vo3NrSLh2ZNcfl8KgWaaGOUEWMk-rKAa2x6wOyfh6UAI4rPdWNFwFme299gkSlPvXZlcRHx6C_Lp7yRPHd-qN7BQEkZG0N5UaUYtG4xy1M6wYjb2MAZLg9u5J10r2UXcdnzap8MGIMLttVhD9YZoT2fb2qkjRrcASZk4Ny7wYluRbi7tpsqIjUBwQF6kiqWmb1MMqcfkOACSi1umjxbyg_5EI0c8dqp0qyQlDW44IKZI4iBVax4Eya34nviDTOl44-Y8ztyfzGBrRP1m3IbVCHyE_ub8EL4Epm0DQu7BrtniwUU7eCABODkzFzVvdMzINBr9M2kjjdifmi8qvbu_JCyp1vxEoqJAnqKSQlPOe8cxs8JUaHUhTICq50mlWrbHiNbhubmA56JCSoZDRP4VuNLkdc24xg228a_x95WplDefJMS3MPyYRLSbMQ6CD0M0RQuZtExP6n0Uix5--C0E2W3AoGW4gJFM7arXvyHRuAUVADHDguARnzjJz4Jq8kbIAAk8cSi4Z3fycrZZFHQdSFxrrpGbKPiDGZmmG3jD-Neywa4AfjjOcdO-IOBayRCVua1TYrLdjRGaCWQ2trH6xDCY4IFz3DXHk5pOFtTnfgKlEddI_sn4hpgiHK_Dib3soi_KW05l-BGiqkFb0tUaVTC79JNzejNhVHmgUK2b-tR02NhW66medeLZ0-i9XojNL_QeM9hCw_wfaDQ4--_u_vTwjdIzMbth9DTn9GQrVLoxBwGbIKJmqO-kne1MnhaFygJGD2DN3bHwMJamRdSneJNI_rwAPWhLMqxfA25eXmqOiCu6ZA8GRvkQ7DU44c2exUVaZjovY7RmtdtJ_ou-8="}}
{"timestamp":"2026-07-16T17:54:50.667Z","type":"event_msg","payload":{"type":"agent_message","message":"The migration/bootstrap test is in place and checks repeat application, integrity, and raw nutrition item preservation. I’m rerunning Sprint 6G focused tests after that addition.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:54:50.667Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The migration/bootstrap test is in place and checks repeat application, integrity, and raw nutrition item preservation. I’m rerunning Sprint 6G focused tests after that addition."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:54:51.167Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":50000}","call_id":"call_aQc5dnGaCdHCsT8DlGna2Dvw"}}
{"timestamp":"2026-07-16T17:55:01.828Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_aQc5dnGaCdHCsT8DlGna2Dvw","output":"Chunk ID: be11d9\nWall time: 10.4965 seconds\nProcess exited with code 0\nOriginal token count: 25\nOutput:\n......                                                                   [100%]\n6 passed in 10.30s\n"}}
{"timestamp":"2026-07-16T17:55:01.830Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":2949155,"cached_input_tokens":2825728,"output_tokens":20567,"reasoning_output_tokens":1584,"total_tokens":2969722},"last_token_usage":{"input_tokens":118911,"cached_input_tokens":117632,"output_tokens":112,"reasoning_output_tokens":7,"total_tokens":119023},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:03.899Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRr3ZZeK1DNpDQpppsHPXlQWsnmUt45NP0Fq7pdI-kA1KBJ16-BqkrArJZAijGtfUO03n6sxwn41u7k_VlhRUYSPkSjXqZ46cP_T4PphLYo-8imBM6K424uKQkLLLa2OiLQES_KJ6Q-UlIknbk0cg0vDbD6vAEnLYswdJqjpdgd7TLjgN0w8i59hTLB2b228wwxbpr2pkkVPAGwtlbje1lKUL8hIoOmKY-hkuBxOGNEz0RaMB9Rn93Cp5QxzFo3GpyIvOvDcC_cilQtDQI5dKzqsBK4iSpXicFY0wsGA_qLZSPcOKz0kPwCDP7xQ5KltnUgCpibpTMDI7yi6sZ5hZqAJyIPf2P54-pS4plqPz8cklxBBxXEap4uMzw5M3tr7MAv5HR4MgP-LEHpp9Ac2uymqQzhwDSU_lUhcSVLQVeZ5Jj1bo5U4V3oiB4AtkiUtAmyELbX2eA9pCywbkG96XbEVmr2jM-tPyoKjvC0kQoDOwPEuWCMyKuYf1QQ8I8iSXGzZlYUw_WXSdtXCulqQ4SE4vLYAZhMOXcB_VeZ-EODtmXoNAyD8dW3cVnE3gr9DU3xX7ZKLe9yYmn37gTtGU0DRgk_gI4IaLJWMT5tbaeokyQZZ3qqIx0cP63lywe7uSD9wUaSURL1wY2wLmVRCNU5ZOzCpkheDQZZdgSbVRUFQfcn09DK5hhhC4qnS_4d0Ho6lq82c2qSqaU_TrZNaciztltyvcKQ1MvID1FxlzoQ4_jfT49NAc2VI3wNfWP-WNQ4XdaFp8f_l31wIhaYeo0imVUnbSbywx8aETDkwANuh2G6kJTQKeIUy3GkgX-kWIIIxqeVd2p8in_-3T9f45pEXo11WjeMrJPkYRLi8GyDGVNMe3fNUTWJb2m1CAZavGXJhB7jHYFyAssr1I8KUwDZgWBL-9rJ_0rG4oOx9vKl44y9-Z6d8Xfu_KtRovbnCf79QGd0XFS6pL53LFK1TU01AGw=="}}
{"timestamp":"2026-07-16T17:55:09.930Z","type":"event_msg","payload":{"type":"agent_message","message":"Sprint 6G focused tests pass with the added bootstrap gate. I’m doing quick static checks for the modified JavaScript/Python and then I’ll inspect the final diff for any accidental V4 or production-facing changes.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:55:09.937Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Sprint 6G focused tests pass with the added bootstrap gate. I’m doing quick static checks for the modified JavaScript/Python and then I’ll inspect the final diff for any accidental V4 or production-facing changes."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:55:09.937Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"node --check scripts/health/assets/health-assets/dashboard-v5.js\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_jdPV9GlBQF8w9FKKyCpEq60M"}}
{"timestamp":"2026-07-16T17:55:09.939Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"python -m py_compile scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py scripts/health/dashboard_v5/render.py tests/fixtures/dashboard_v5_fixture.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_agM7iAqXT5TYgveextIHkgu7"}}
{"timestamp":"2026-07-16T17:55:09.942Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git diff --stat\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_t1TwU4YLH36IoXFWlXH6hkRC"}}
{"timestamp":"2026-07-16T17:55:09.986Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git diff -- scripts/health/health_dashboard_v4.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_xuGQiOJjRjKozgUUOvzX4HZZ"}}
{"timestamp":"2026-07-16T17:55:10.005Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_jdPV9GlBQF8w9FKKyCpEq60M","output":"Chunk ID: 560f82\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:55:10.044Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_agM7iAqXT5TYgveextIHkgu7","output":"Chunk ID: 263ca4\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:55:10.044Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_t1TwU4YLH36IoXFWlXH6hkRC","output":"Chunk ID: ee5db7\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 194\nOutput:\n database/schema.sql                                |  57 ++-\n .../health-assets/dashboard-v5-api-explorer.js     |   2 +-\n .../health/assets/health-assets/dashboard-v5.css   |  15 +-\n .../health/assets/health-assets/dashboard-v5.js    | 248 +++++++++++--\n scripts/health/dashboard_v5/contracts.py           |  17 +-\n scripts/health/dashboard_v5/data_provider.py       |  18 +-\n scripts/health/dashboard_v5/read_api.py            | 292 ++++++++++++++-\n scripts/health/dashboard_v5/render.py              |  14 +-\n scripts/health/health_dashboard_action_worker.py   | 409 ++++++++++++++++++++-\n scripts/health/health_dashboard_server.py          | 115 +++++-\n tests/fixtures/dashboard_v5_fixture.py             |  28 +-\n 11 files changed, 1130 insertions(+), 85 deletions(-)\n"}}
{"timestamp":"2026-07-16T17:55:10.044Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_xuGQiOJjRjKozgUUOvzX4HZZ","output":"Chunk ID: 1dbf52\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:55:10.044Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3068238,"cached_input_tokens":2944384,"output_tokens":20916,"reasoning_output_tokens":1598,"total_tokens":3089154},"last_token_usage":{"input_tokens":119083,"cached_input_tokens":118656,"output_tokens":349,"reasoning_output_tokens":14,"total_tokens":119432},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:16.528Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRsEkUIczeGR6sB4SlWkkbqp1Wspg96pHbwjgKBRloAE2OiiPS3aQrCQ4j_uRE7dwvVpw9DMfJNCLPFFRxntLG4LxnbHPvgNY0YM_H2Z9d_gUgGvx8xgdvQVbT66RWVxpHmoztYkaZWfAWXKOj61Pxfuo_f_MA8odBCTqs37KEzbx7b9i-leONLHnqIgsd45bH9M7JQVJ5tiTVVSgcC_DX3_NdFW4MIDkJr4394GLMDXSBH7kgyLqOxggX8aXsAMAuk-CtWrw6Uhgf6VCkk17VdDvBWkDjYEbLUm0RnAq02yIpx0PSTxeO5-Ey7xdqQ0di7kEIkAqujsq4OLMQGujRCJ6ovhHqz3OIQvAvUeiWeTiD7vZbPhlSB7lXwdJs9c66isJ0cIdZjEB9FJ_t_j_NFOKyDNM99lg7KwbFOooMvhbu_z4vhkcNjAKKtKvAfXVN3Z3yquTe4kNrQQqOF18MuGmgLcmaGff99V6-rRSHqPZPYAWozyQuC63CKa_R12p477ZK_Mj9Hrapym33ZJioxyvFP_3TuL0Zlj3FhtG4JDrdL0OdJ3x4wXBrSFGWroO7r-zpIQFpaj6a4A5ZM6F2PtC-MCXT_Y7NbB5u0efeaVEccX6V51_CIvEquULp_Ud3pZyYsNtAPXqghOoDJ3CTm8S5HhfTxwzZMDc2U2kh-vFzA7vpVjdR4vQiTrWiDLSB9WwNaGqi4iG9S7utGtH8TRC_TylubnEyCPb9ZQJ6DlrXzsJz5QTf_2Jh7c6B96nJZmB0IfjB9IxNkp6nE1IXy5frLAaD1zU9cuXVjiWycD2LqcMdpvMSw7N2a7U6Wws4WcAIMDwVZe65hsMddh6RJrwZXH_m5BU9YCaOd3KlNha-lM75-NLhAKWHFtekavoZGeMbuuFLbRsGds46dYoGnOQMLqn47o9h5Dsr6SlmkCFb2WjbIV94S0Q6tihfhbXclInlQs1SIUntnewWUS_JLjDiWnvJDa2TaxXrds9oAxrHD1xNAeN0CfA04kw9sfzuFmNRKBsJrCBsQgPHcvprXpMZ0_kP2eLBFYrbEvEigMspqbMtW2hwQUC1WBka74CqKolXDO9dNYucS8Rt178lGh_JX6IFt-p2OoxfYSX7UwvF7xyAj2OvigGc7eVX3p-W4UBHBwCS8zjETLwphYTt__zGEM6mwHYSGj9KlTlUKmaX9w25BL6UqdRveIAoGY-zjhf0zKIDC3W0xnlyKHULLlOLKlSTEB8EGD7n2M_qPbsuPLf6saDSoWxBasbD6Vt0wFK9bjXajOHywBRZm_AtykPGBSeNrL-msrVSpwzY1HrcWt8YLduHqS0MG8pQ95ZPgMl_rcClpgPA0qLq8UBx85cR9RskcwV5tDwCa_QTJodOjb830Z6foQ4NAL3362vl5qn2i8H-P6WbyF7k7M3z8GcxjxBQ8xjT6KNQ9SnRThTU97eI2ttzJKQ13b5f09u8oavNkq-cMfN8Q1i-4EKjSd7NLdQ60Gq7EOYmf6cMob6GUuFsywgVq_1qqFuzOjZQ3S5apemDASDDAv-5OSZqrvP7ilDEgdL45D1_lyePwIl5p_AAl19TOjy7g8jzsjb4U7"}}
{"timestamp":"2026-07-16T17:55:17.266Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"def dispatch_api|nutrition/day|API_PATHS\\\" scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_GT0d6OqIEyIAGHx82AXYEHVB"}}
{"timestamp":"2026-07-16T17:55:17.323Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_GT0d6OqIEyIAGHx82AXYEHVB","output":"Chunk ID: 917ffc\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 93\nOutput:\n98:API_PATHS = frozenset(\n106:        \"/api/v1/nutrition/days\",\n2523:def dispatch_api(database: Path, path: str, query: str) -> dict[str, Any]:\n2536:        path not in API_PATHS\n2540:        and not re.fullmatch(r\"/api/v1/nutrition/day/\\d{4}-\\d{2}-\\d{2}\", path)\n2628:        if path == \"/api/v1/nutrition/days\":\n2630:        if path.startswith(\"/api/v1/nutrition/day/\"):\n"}}
{"timestamp":"2026-07-16T17:55:17.323Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3188039,"cached_input_tokens":3063040,"output_tokens":21095,"reasoning_output_tokens":1705,"total_tokens":3209134},"last_token_usage":{"input_tokens":119801,"cached_input_tokens":118656,"output_tokens":179,"reasoning_output_tokens":107,"total_tokens":119980},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:20.076Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRsIzNuX8HD-MCshrGaJuDRQqdcVGI3q2rmg5r-NFCZiT9eWw1E63oy_2VxXqZsSy-ZUj_4-Wk1Tmc2I9j0lDQQxQ7OJ7dvqRQDiwqt9RK1jLa1l1Jbvjrf0G4U-eaMOaXa1j6gB1ShurRGVa_tquKgGkEbGuzSZLu09owPscJY_NQFHdrAoLcDpvMuYgSyRlazAtGG7h7zHr8n72rERwubBLNMF7QvTDHNUbrJb0FTvCMW-ccI27sl4zfJKSMqH0tAl5ojSPiTCEtLejiAs77BCp1xUChDvgornvJ-j-xMwzXQ5p563PbJDr0WKR-aG8f_bNEysEi9N3Qv9lNSB6n0-em8PUDjXdqoADigW4Vli4ldBdjhuRyhQNH-1WR-3GFzEM5Epw4cRDkK9RS4zhHJ5tsTDd5gngkqJmV8GlrnA1A8BtcHgZK6dzKfNw9KlIVqDhJQkYQ4HKpFrh1qPYrDXcU4L3Bz3-H6kfkE0pOF8PD0P5uDGWftjHAMf8nH2J80O6RRwqHESKiDfbIBwrHN2zQdwYz2FDXoRVdxB9LL_BFdivi_mWbED79fjSvImjs4apA8ucIdzxsgdeA-BC5VLDAs5_xxKioM5qm49d7yKFvVB72Cux4US9toRHNLsSgbJy8Z_cgzAdx9zBv3SJ7HLuyzdSkbRRuTFyXMpy4n6aLILwdeIKxSH4IrVE6YmiO90X8lRZT1j4u4HF7a6Hz4Frqg4bICk5pOd1a1QFPvTpc0O_0t-xaSGvO2-lcnzzImfdqJrLeTZu6JSNrCiyUhA6DxHRmx4N__9sDCZtJ22sqtcxgllOATdWR_MK0FuDcFy_C94gZRtFuv4lKzuJGCLuQFauRLGT09hJ-Ms6FMC23LM96Ai0Ohf5uBQ7WzWVh_5EHtFWg_-4nMXJpI-cCf-_RWjvNDe_GDBqOxNm8EuMKStvmxphzUv5AVK41y6e2gK"}}
{"timestamp":"2026-07-16T17:55:21.039Z","type":"event_msg","payload":{"type":"agent_message","message":"Static checks are clean, and there is no diff in `health_dashboard_v4.py`. I’m checking the dispatch and serialization edges now, then I’ll run one final focused command set.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:55:21.039Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Static checks are clean, and there is no diff in `health_dashboard_v4.py`. I’m checking the dispatch and serialization edges now, then I’ll run one final focused command set."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:55:22.336Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"sed -n '2500,2650p' scripts/health/dashboard_v5/read_api.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_8bXkyOd3EqjUoYSuZsPxwxZ0"}}
{"timestamp":"2026-07-16T17:55:22.391Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_8bXkyOd3EqjUoYSuZsPxwxZ0","output":"Chunk ID: acdb84\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 1568\nOutput:\n                    \"symptoms\": False,\n                    \"appointments\": False,\n                    \"documents\": bool(document_page.get(\"truncated\")),\n                }[name],\n            }\n            for name, section_payload in {\n                \"overview\": overview,\n                \"labs\": labs,\n                \"medications\": sum(\n                    len(medications.get(kind, []))\n                    for kind in (\"planned\", \"administered\", \"missed\", \"corrected\")\n                ),\n                \"symptoms\": symptoms,\n                \"appointments\": appointments,\n                \"documents\": documents,\n            }.items()\n            if name in selected\n        },\n        \"medical_statement\": \"Dokumentierte Daten ohne Diagnose-, Kausalitäts- oder Therapieaussage.\",\n    }\n    return payload\n\n\ndef dispatch_api(database: Path, path: str, query: str) -> dict[str, Any]:\n    document_route = re.fullmatch(\n        r\"/api/v1/documents/(api-document-[a-f0-9]{24})(?:/(matches))?\", path\n    )\n    allowed_record = {\n        \"/api/v1/record-summary\",\n        \"/api/v1/record-labs\",\n        \"/api/v1/medications\",\n        \"/api/v1/appointments\",\n        \"/api/v1/documents\",\n        \"/api/v1/doctor-report\",\n    }\n    if (\n        path not in API_PATHS\n        and path not in allowed_record\n        and not document_route\n        and not re.fullmatch(r\"/api/v1/day/\\d{4}-\\d{2}-\\d{2}\", path)\n        and not re.fullmatch(r\"/api/v1/nutrition/day/\\d{4}-\\d{2}-\\d{2}\", path)\n    ):\n        raise APIError(404, \"endpoint_not_found\")\n    connection = connect_read_only(database)\n    try:\n        if path == \"/api/v1/record-summary\":\n            parse_query(query, set())\n            return _record_summary(connection)\n        if path == \"/api/v1/record-labs\":\n            return _record_labs(connection, parse_query(query, {\"q\", \"from\", \"to\"}))\n        if path == \"/api/v1/medications\":\n            return _record_medications(connection, parse_query(query, {\"from\", \"to\"}))\n        if path == \"/api/v1/appointments\":\n            return _record_appointments(\n                connection, parse_query(query, {\"from\", \"to\", \"order\", \"institution\"})\n            )\n        if path == \"/api/v1/documents\":\n            return _record_document_rows(\n                connection,\n                parse_query(\n                    query,\n                    {\n                        \"from\",\n                        \"to\",\n                        \"category\",\n                        \"institution\",\n                        \"type\",\n                        \"review_status\",\n                        \"sort\",\n                        \"q\",\n                        \"cursor\",\n                        \"limit\",\n                    },\n                ),\n            )\n        if document_route:\n            opaque, suffix = document_route.groups()\n            if suffix == \"matches\":\n                return _record_document_matches(\n                    connection, opaque, parse_query(query, {\"q\"})\n                )\n            return _record_document_detail(\n                connection,\n                opaque,\n                parse_query(query, {\"cursor\", \"limit\"}),\n            )\n        if path == \"/api/v1/doctor-report\":\n            return _doctor_report(\n                connection, parse_query(query, {\"from\", \"to\", \"sections\"})\n            )\n        if path == \"/api/v1/metric-catalog\":\n            params = parse_query(query, {\"q\"})\n            query_text = params.get(\"q\", \"\")\n            metrics = (\n                catalog_search(connection, query_text)\n                if query_text\n                else public_catalog(connection)\n            )\n            inventory = inventory_sources(connection)\n            return {\n                \"version\": 2,\n                \"timezone\": TZ_NAME,\n                \"today\": local_today().isoformat(),\n                \"groups\": {\"metrics\": metrics},\n                \"inventory\": {\n                    \"apple_observed_identifiers\": len(inventory[\"apple_health\"]),\n                    \"apple_released_identifiers\": sum(\n                        item[\"status\"] == \"released\"\n                        for item in inventory[\"apple_health\"]\n                    ),\n                    \"canonical_verified_laboratory_parameters\": len(\n                        inventory[\"laboratory\"]\n                    ),\n                    \"observed_vital_sign_parameters\": len(inventory[\"vital_signs\"]),\n                },\n            }\n        if path == \"/api/v1/series\":\n            params = parse_query(query, {\"metric\", \"from\", \"to\", \"resolution\"})\n            if not params.get(\"metric\"):\n                raise APIError(400, \"metric_required\")\n            return _series(connection, params)\n        if path.startswith(\"/api/v1/day/\"):\n            if query:\n                raise APIError(400, \"unknown_parameter\")\n            requested_day = parse_iso_day(path.rsplit(\"/\", 1)[-1])\n            if requested_day > local_today() + timedelta(days=MAX_FUTURE_DAYS):\n                raise APIError(422, \"future_date_not_allowed\")\n            return _day(connection, requested_day)\n        if path == \"/api/v1/nutrition/days\":\n            return _nutrition_days(connection, parse_query(query, {\"from\", \"to\"}))\n        if path.startswith(\"/api/v1/nutrition/day/\"):\n            if query:\n                raise APIError(400, \"unknown_parameter\")\n            requested_day = parse_iso_day(path.rsplit(\"/\", 1)[-1])\n            if requested_day > local_today():\n                raise APIError(422, \"future_date_not_allowed\")\n            return _nutrition_day_detail(connection, requested_day)\n        if path == \"/api/v1/nutrition/mapping-queue\":\n            return _nutrition_mapping_queue(connection, parse_query(query, {\"status\"}))\n        if path == \"/api/v1/calendar\":\n            params = parse_query(query, {\"from\", \"to\"})\n            return _calendar(connection, params)\n        if path == \"/api/v1/events\":\n            params = parse_query(query, {\"from\", \"to\", \"types\"})\n            return _events(connection, params)\n        if path == \"/api/v1/labs\":\n            parse_query(query, set())\n            rows = _verified_lab_rows(connection)\n            if len(rows) > MAX_LAB_ROWS:\n                raise APIError(422, \"row_limit_exceeded\")\n            return {\n"}}
{"timestamp":"2026-07-16T17:55:22.391Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3308189,"cached_input_tokens":3182720,"output_tokens":21217,"reasoning_output_tokens":1714,"total_tokens":3329406},"last_token_usage":{"input_tokens":120150,"cached_input_tokens":119680,"output_tokens":122,"reasoning_output_tokens":9,"total_tokens":120272},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:30.545Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRsSgNLvFwVIfX8TUmsoMO15K8-FNlRPD9ofEFoy81VSoOsf4sFbZCF9T2wYpryFoEaIAMXDxppnTMRJu_nNgNKyTrkq7drPXVjq7Q6zrJfxTsG2fhWS-OYDAqItEk6u5KxRr_rmJSu5J3LMdnMs6wzSSx33x3TpYh5fXuSdXoMMuvWS9KOQ6fZa34rK7GjIVBIJuanFjHWj3AtoJSAyVbLdj0XB2Fb7JEKKI9DwVli8SYcPQHNCo4wDGn6CHA5SaHHXnbVaBpo5OEPsIZW8UQ35SA6T0I5uElBwvBqBAF1FGxf-phcTsYqDcWmjPjSsQzZqlkGm-4F_ZNlf5Y5DMTDJlprKx27ugLd4MmxE4xCFlHIXOMvCP3Lycp-A3vJzRUhMIF28ZHe2goWgS5HCaKc_-zzTqaWwlbMcm3SsiltXks25WYUqberl5ev_lu_dQxUUrQ8QIQRq44Nd2NQLXyjl7TT_Lziavk6NOHuadgfNhaZcJP-XQ5Zp5WxcdqkPlrhjsLjAaiyqxM1qJUCrYQMsZtXRnaTuMX_6Arlc0E1V7JyR6bpYRdrtUcyAK42hm-r7ep82eFgQS5AWk0H3SUka5qw2HGa4As4SxO-FkMSdher_VCk93W_lfOqp-P_U5wmVZHPk_BPyLIwTYhGVPsiNiRS565fbeRh2Zz9E-LhzBsmJyT46o2KpayiaMrdQbsr9J7C6uBMarzk4xojAF1e5z3SW0dkcEHzMTHebLUk62ChhPDWTSO8KNVfbxlN_8Amvyt8YU7_son_lTS8OudiGygRvvMpcjdXXj9DgBh7R4-mA8i9ozhDX1cFKIKRFyLvWpBrSDBqumwbKPv4rpQJq5N6Zc9z7BoEBIGQ0QY48um1c6NCsVqJXTzJxbx4Wyqtbli3qLGqh_CDZpuIRBZPjyEgBKM4KSz87piNuWMiTS5OZZvpvpaIZlfD5P40PsVq-EXsdToc8QH4xM6QSethf9-fonJIWuEn6qjBE19Fz-Ub3bopac9WloBmD6D7ITMr8ecSLZqPPJsUoYSGlsQDtXM2fR4JlKYNLpJhL95W5hPNoWK4wF8Xduh8E_8N3EwLFbHkru9CWzcIF9z-_oQITL-frbppByf13Lr4pXai9_qWzA9VVlVGLOXDpLOB5LLBpeibwd5Ka4zrFqcURQhRA64rDDK7AuYxl3uTXZLVk7UQz5zKU4rBF3eZ7SXxPuAw7wPf855fudVWBzXiAavU0dd3zH00DJ6FGAfleolMToF1PjNchgS4zlGCXIM3I7yzAOSZ7jGJQdlFg4kgR8hP51a8cMAJxjg_M4IWdY0OiQ3NieWyNJt85n4lTpXGM37VT_Wf0PchobyCYQ2V1jSixkBR4_0DF1rzU41RX73N3snqLe1_CYSWxrCy3KQHmtEZ3xF1r2NNga1aWoKEoV7H8d5r_AG9tk2OSiplMseJd6k93l-4Fo0UKZDyd6mEfI8XwiWKDCQbKLiK3cl_JvYei7uKX_D2Z1oitfOT7HdgeDzwGPXVXkuTqU19CzqGIyArYeGmhZ3FHKGrN7gF7JFbkOPwOxkqZGnAypnhF3vXUTBDjP3ESlPFgKzgT8_Hfke_50s_mDofLBDqY-OD-3-kRn2uPW8F_tosYqqWA8N3hw8-a_jdlSSWLFTdMarSdDjis2i9j9GOvMPEFhHAsqPYu1Lad_9ZmITQW-8SZWgmXbX_eLNKb28VfYJmGPiN4HXHBrwqR3GLdLHroKXwhbbDHKt6JrKA8paXjm1eyJEGrtmOgHC5Kvaq7RzF6nVF5mqzrdfZ1WhHKFX8wZTC6mO8mz6DCzz3DBeQ0RW2Q6Oax7fKTsGqz7lK-OpSJ47QTqSns4IBhoJc80mx78bCEhRAvVpgV1dc3IZNtbpskTY0MsH9unGvxMux7atT-c0pRqs2Tcdzt863LG6d_Ek2o-G9nUIYnZYSaygFHUxTB-pQbdv4auyXChfQpwNbGq72irvsc1a4LJ362kddd-mATOSF2us5k1xtP4ZXQCylbiFe5XCUnxSm4iR2ANszV2fDfnt12M75_SOmlmswMcWolH75OcNA0nzAS9g6zFZJKEK-ZVE6INB_NZa0DM4R6PkDJtuMiX539S6KZCQYyXJFFBFRBVQ=="}}
{"timestamp":"2026-07-16T17:55:36.356Z","type":"event_msg","payload":{"type":"agent_message","message":"One remaining privacy issue from the frozen review applies outside nutrition too: lab read rows used an internal numeric ID in an opaque-looking string. I’m replacing that with a hash-derived opaque ID so read APIs do not expose internal numeric identifiers.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:55:36.363Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"One remaining privacy issue from the frozen review applies outside nutrition too: lab read rows used an internal numeric ID in an opaque-looking string. I’m replacing that with a hash-derived opaque ID so read APIs do not expose internal numeric identifiers."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:55:36.363Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"['\\\\\\\"]id['\\\\\\\"]\\\\s*:.*row\\\\[|api_document_id\\\\(|lab-observation-|source_product_id|raw_json|raw_item_json|raw_product_json\\\" scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py -S\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_mffC9V3FnP98ATVtLrqZjGC7"}}
{"timestamp":"2026-07-16T17:55:36.403Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"source_product_id|raw_json|raw_item_json|raw_product_json|/home/|/tmp/|\\\\.hermes|lab-observation-[0-9]\\\" tests/test_dashboard_v5_sprint6g_nutrition.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py tests/test_health_dashboard_v5.py -S\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_qvdIKkDt6JTkMJsVTqbbgy2f"}}
{"timestamp":"2026-07-16T17:55:36.416Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_mffC9V3FnP98ATVtLrqZjGC7","output":"Chunk ID: 77bf2e\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 345\nOutput:\nscripts/health/dashboard_v5/read_api.py:139:def api_document_id(\nscripts/health/dashboard_v5/read_api.py:233:    include_raw_json: bool = False,\nscripts/health/dashboard_v5/read_api.py:235:    if include_raw_json:\nscripts/health/dashboard_v5/read_api.py:237:                        file_name,file_hash,raw_json\nscripts/health/dashboard_v5/read_api.py:311:        connection, \"blood_pressure\", start, end, include_raw_json=True\nscripts/health/dashboard_v5/read_api.py:320:            payload = json.loads(str(row[\"raw_json\"] or \"\"))\nscripts/health/dashboard_v5/read_api.py:543:            \"id\": f\"lab-observation-{int(row['id'])}\",\nscripts/health/dashboard_v5/read_api.py:571:                    \"id\": api_document_id(\nscripts/health/dashboard_v5/read_api.py:923:                \"id\": api_document_id(row[\"id\"], document_day, category, institution),\nscripts/health/dashboard_v5/read_api.py:946:            \"id\": row[\"id\"],\nscripts/health/dashboard_v5/read_api.py:1162:                \"id\": api_document_id(row[\"id\"], requested, category, institution),\nscripts/health/dashboard_v5/read_api.py:1954:                \"id\": api_document_id(row[\"id\"], document_day, category, institution),\nscripts/health/dashboard_v5/read_api.py:1990:            {\"b\": binding, \"k\": cursor_key, \"id\": int(last_row[\"id\"])}\nscripts/health/dashboard_v5/read_api.py:2029:            and api_document_id(\n"}}
{"timestamp":"2026-07-16T17:55:36.458Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_qvdIKkDt6JTkMJsVTqbbgy2f","output":"Chunk ID: 3fd863\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 684\nOutput:\ntests/test_dashboard_v5_sprint6c_api_contracts.py:53:                    source_version,device,file_name,file_hash,record_hash,raw_json,imported_at)\ntests/test_health_dashboard_v5.py:237:        \"date\": \"2026-01-04\", \"name\": \"/home/person/private.pdf\", \"dose\": \"40 mg\", \"status\": \"planned\"\ntests/test_dashboard_v5_sprint6b_api.py:38:    \"raw_json\", \"file_name\", \"file_hash\", \"record_hash\", \"quelle\",\ntests/test_dashboard_v5_sprint6b_api.py:108:                source_version,device,file_name,file_hash,record_hash,raw_json,imported_at)\ntests/test_dashboard_v5_sprint6b_api.py:119:                \"synthetic-lab.pdf\", \"/tmp/private/synthetic-lab.pdf\", \"pdf\", \"eingearbeitet\",\ntests/test_dashboard_v5_sprint6b_api.py:121:                \"https://drive.invalid/private\", \"/tmp/private/original.pdf\",\ntests/test_dashboard_v5_sprint6b_api.py:150:            ((ANCHOR - timedelta(days=2)).isoformat(), \"malformed\", \"prefix /home/agent/private\", None, None, \"fixture\", None),\ntests/test_dashboard_v5_sprint6b_api.py:157:                    \"prefix:/home/agent/private.db\",\ntests/test_dashboard_v5_sprint6b_api.py:159:                    \"x|/tmp/private/report.pdf\",\ntests/test_dashboard_v5_sprint6b_api.py:189:        assert \"/tmp/private\" not in value\ntests/test_dashboard_v5_sprint6b_api.py:190:        assert \"/home/agent\" not in value\ntests/test_dashboard_v5_sprint6b_api.py:350:        (\"/api/v1/series\", \"metric=apple.steps&from=2026-01-01&to=2026-01-02&resolution=day&column=raw_json\"),\ntests/test_dashboard_v5_sprint6b_api.py:352:        (\"/api/v1/labs\", \"path=/tmp/private\"),\ntests/test_dashboard_v5_sprint6b_api.py:412:            \"INSERT INTO apple_health_records(metric,value,unit,raw_json) VALUES(?,?,?,?)\",\ntests/test_dashboard_v5_sprint6b_api.py:427:            \"\"\"INSERT INTO apple_health_records(metric,value,unit,raw_json,start_date)\ntests/test_dashboard_v5_sprint6b_api.py:462:            \"\"\"INSERT INTO apple_health_records(metric,value,unit,raw_json,start_date)\ntests/test_dashboard_v5_sprint6g_nutrition.py:82:    assert not any(forbidden in serialized for forbidden in (\"raw_json\", \"source_product_id\", \"http://\", \"https://\", \"/home/\", \"/tmp/\", \".hermes\"))\ntests/test_dashboard_v5_sprint6g_nutrition.py:86:    assert \"source_product_id\" not in json.dumps(queue)\ntests/test_dashboard_v5_sprint6g_nutrition.py:166:            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\ntests/test_dashboard_v5_sprint6g_nutrition.py:171:            \"SELECT COUNT(*),SUM(raw_item_json IS NOT NULL),SUM(raw_product_json IS NOT NULL) FROM nutrition_items\"\ntests/test_dashboard_v5_sprint6g_nutrition.py:215:    monkeypatch.setenv(\"HEALTH_DASHBOARD_FILE\", \"/tmp/synthetic-v4.html\")\n"}}
{"timestamp":"2026-07-16T17:55:36.458Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3429792,"cached_input_tokens":3302400,"output_tokens":21730,"reasoning_output_tokens":1910,"total_tokens":3451522},"last_token_usage":{"input_tokens":121603,"cached_input_tokens":119680,"output_tokens":513,"reasoning_output_tokens":196,"total_tokens":122116},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:39.662Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRsb27cq0AZodnmEyOa8tN-YlsQIWsUyD-MwPfiz1FtxUnGwtrBmhVl4XlXAydXVF9Ml_QsgW6gqdg_3pgqsO5giGcjydjIdU0O6r_f4pHUUZNIX3sFvfrSO-VvPvq_15nBO5FQjHE5BCgn2Hkmh9gIQGVfvS6Xhe3A0ir-PoDmR-RgUueh0czVbiQH85INn1xbRRakrAG-Ndf4JkYARl6XVV0ai3ej8vzGSZzCQCG22HWKNx-5VhyV8E40VQfwPQwldIearQffuWjdZVs0wXvDZGFXQJwUn8SAb-pcEpeH-5_bdBv4zjCgOIvhEUXS2NcHzSDqU6Bw9loMK4_J9bnCg5-kJsU6hXSs5OeMuBRWojdQJdZSQvh_n2yT-jXw15VH9oI1XPQMQhm78OAyBTA8Za2iMi5j4uvRTFSEzKyNVucNUQJqQaTXE_SfAW42uwd3toMV_rm5P_vGWm7ej3GhPGMp86lAXIq06PM6lbZxWRdqLN2tVuOMggNO0K_iVNwqWdwdtYaYFomVLJX9ZZ3Im7WxQ1gRAXPpyonJDwC2WQI9WpQEOzeo_KjGki7rFQWt08C6WMP3rA-fmCx5MRY2cl7DHe7myOhtA1vRN9WRwJpV5pQ3VEuAbZdMUTttezuCrWoj3s5nsTFr7H9JLGSY2NdRtfkFg45ikVe0WDzmqIQx-Z2j4yRcoINEsCTGM4XZYMMgDtQaZDwMBcgKb_nuo5K37Z2sIO0h7O6G1PRkCKZU9Pkkt0eLJ8uNkUF7kMz48CpoAagLq6Lq8Z7mxKRzItH058a7YhlRC2veUi72OMm6u8GP0b0hekRJ2zOJj9jqoDlEkeD78WV9ORKth-aD_uIEQDiTOxe0MvqzFAOVJmAUswbM-Buz4z6oRRKDkeWF5qQ8cxYPwpTOrHMRERsQTst5Y_O1Ztov0lC0b9W7c0uY24Akd4arlSmM9tdAPGPuGkRhUmCAZoDhFeCLAt028n5yYOA8-XkZv9GUS3ilmp4r_J0XTzMaS3d-uyrZ28bOQEizUFi0LFzvzzlWUcxbXc2gO2w3Bt4hbbEUno9XSVHA="}}
{"timestamp":"2026-07-16T17:55:43.282Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_hGnA58xIRkNphZrpcYWewbZE","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/read_api.py\n@@\n def api_document_id(\n@@\n     return f\"api-document-{hashlib.sha256(b'health-api-document-v1:' + material).hexdigest()[:24]}\"\n+\n+\n+def api_observation_id(prefix: str, raw_id: Any, *parts: Any) -> str:\n+    material = json.dumps(\n+        [int(raw_id), *parts],\n+        ensure_ascii=False,\n+        separators=(\",\", \":\"),\n+    ).encode(\"utf-8\")\n+    return f\"{prefix}-{hashlib.sha256(b'health-api-observation-v1:' + material).hexdigest()[:24]}\"\n@@\n-            \"id\": f\"lab-observation-{int(row['id'])}\",\n+            \"id\": api_observation_id(\"lab-observation\", row[\"id\"], parameter, unit, day),\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:55:43.385Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_hGnA58xIRkNphZrpcYWewbZE","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/read_api.py":{"type":"update","unified_diff":"@@ -149,2 +149,11 @@\n \n+def api_observation_id(prefix: str, raw_id: Any, *parts: Any) -> str:\n+    material = json.dumps(\n+        [int(raw_id), *parts],\n+        ensure_ascii=False,\n+        separators=(\",\", \":\"),\n+    ).encode(\"utf-8\")\n+    return f\"{prefix}-{hashlib.sha256(b'health-api-observation-v1:' + material).hexdigest()[:24]}\"\n+\n+\n def connect_read_only(database: Path) -> sqlite3.Connection:\n@@ -542,3 +551,3 @@\n         item: dict[str, Any] = {\n-            \"id\": f\"lab-observation-{int(row['id'])}\",\n+            \"id\": api_observation_id(\"lab-observation\", row[\"id\"], parameter, unit, day),\n             \"metric_id\": LAB_CATALOG_SPECS[parameter].metric_id,\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:55:43.393Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_hGnA58xIRkNphZrpcYWewbZE","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n"}}
{"timestamp":"2026-07-16T17:55:43.393Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3553087,"cached_input_tokens":3423616,"output_tokens":21968,"reasoning_output_tokens":1940,"total_tokens":3575055},"last_token_usage":{"input_tokens":123295,"cached_input_tokens":121216,"output_tokens":238,"reasoning_output_tokens":30,"total_tokens":123533},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:55:47.157Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRsjxN8DQo05TKSs4fBC_IhvAt7c8Oj2bRjL4_4yFaDQLtv2ssOZs9TFNpnaSgXQx9IN_F_wrkKPsjbcldoxBLfZuYhjs-xPCsjBBooIUVFwXOvtj32i1A0PHAOzehg8xsDpB3_fjNbsjUJNeZrH8qTBlIUXfVjVAiROp8r-_G1lA8Ves4fC6g0saHe1Fjg4bBc_hbqiub_bKJZr1jRjOObMwkiXBIxiMVc0_QvIXwi_GReXkDl9A4rUXu1dCLFYgFkQeT9CWa2lMzOLcHj9mUzLym8VazwiUvWoHs89BfM7CGNcn1tpFV0tdv4wVr-hdq_wCCChUP2_trp1nh36_2m-Ox7BelNMiot76IgU-hhTzhsRwzKRmS1v9zoWVuEatI0Yf4kPjdg3ZDvtXuXoaIv55GTGu6he2tioU3lJprO8PaFEuz1GVwQs4W6BCEeOKDt9h5qZB0-eKCxCEKwVJLMNRmqhelCM2ewy2eU-N2hKK5uPZVQ6a69Z5nRJhek4I48oym7zyC8slP3HtKYKsJGPDVJgMQwIwNg11W1RULjyAR4iyM_ATkLq8U_A0GhjimbV7Hz3sN7Ors3duSrf_k-OQMNLME7rX5EqpfsRuZvMwHR7OcRpJJeCV4Fpk0_MxTcKaAGXaraKewgRnReaMtUrhifXrCiCgoW66fPjXTfkT-d9HMD16HyUdQU917QGQTmwa3_DUxtNvu2wYEOzq3fGepS7_pJuJnrpNXAyo11KBKHY6HnjA5IJBkBYd_34QMprdylX37tc9KICx0n2sHFkDiJlge2IyHMj0nCgJB9sqRKlV5KRtqvDbfIKgFYGJz_5d4dVM5d3BCwodXiHu_Thcg-HVo6d2Mflj3GF-hA1LOS-iE5rE9c42tTC54WRpMNfOJ-XrG_57SwqFl8ICxRvR_3xO_UJ_vEv8YL4zu1KZt8DplwxHZgqS0DmTJWDAnwu"}}
{"timestamp":"2026-07-16T17:55:48.016Z","type":"event_msg","payload":{"type":"agent_message","message":"The lab observation ID is now hash-derived from stable observation fields rather than the raw numeric primary key. I’m rerunning the focused Python checks that cover API contracts and nutrition.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:55:48.016Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The lab observation ID is now hash-derived from stable observation fields rather than the raw numeric primary key. I’m rerunning the focused Python checks that cover API contracts and nutrition."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:55:50.833Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py && PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py -k 'not http_api_requires_private_bearer_token_and_never_caches and not http_api_is_fail_closed_without_explicit_database_and_token_paths and not server_exposes_v5_preview_and_exact_asset_allowlist'\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_RCFs874VrCXojJaN4Ismpx3d"}}
{"timestamp":"2026-07-16T17:56:20.993Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_RCFs874VrCXojJaN4Ismpx3d","output":"Chunk ID: 91c907\nWall time: 30.0012 seconds\nProcess running with session ID 37596\nOriginal token count: 31\nOutput:\n......                                                                   [100%]\n6 passed in 10.57s\n........................"}}
{"timestamp":"2026-07-16T17:56:20.997Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3676666,"cached_input_tokens":3546880,"output_tokens":22170,"reasoning_output_tokens":1952,"total_tokens":3698836},"last_token_usage":{"input_tokens":123579,"cached_input_tokens":123264,"output_tokens":202,"reasoning_output_tokens":12,"total_tokens":123781},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:56:23.354Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRtH0JDAdCF6Zy37OD721bo0oXA6VPDx-xOhWp-qR_LDJvaQl7AIWDhYxWq4JN3oNWETmscDq4RKsujEyiWxuPM5d6FLY7rswADAWkLRvqnwtNFoKqH8m6oT-KjP_iYcJhf3HI7WKys_1ZIeaxrW1TPA3YAEhoeit8_lshB3TQsUtN0JXY_pbw3hJCKU7SbK8HrYuM0ejhvx8Ic50-WYVOgNGKpJ4cRqybSiE17ZUsQX-H-08_q-v132OT-KjKfAG21UFCC6W2YmV-WxZ-qyE78GFoKhTmo0Ygt1a9DttkzxJaEg4JuWVPHbd2OOnptn4PPCxkUsl55mDoZk1nKxgzr2z9iRh9pnyjhITeGybj-k6Xrc7gUEO1x2kmgOSRzc-xkSuNOZdPaWZJWKy0u8WtZnBS-xEDl0HJuJ7J0VdKg9jDjU1R3Cxw30ADthEE7PXZLXluh_mkInnJ5AuiDe_Hms9qAp6v_AiTuTWpqEv3IwvAbdThi6-jduDn9BW3p4XTMEwt2fDPK10rfg4wkQMnVH37m6FlAqDun93cnfBm_md3HVO9x7IsBUDH4yjt8p3ExI6ozUEZ4cSgHFE6_NOIdLQ7Gh3Sqdk2biAjSiVhYlxYMME-asIlKzw8JX2hl-CrjwpGU9uuXyUweD80h_p3dGN6rh1s0Ayk8IhNXhc6eC5J_xiDHrBBPznO-w_TSD5OMRBsDbm9eFxgES10i-eD715TyRZo80C_BoiWFoTbzTU9eO1crcHO3riHHwEcyJ8uIlom9_JrLDfOq_hdvmO18P6o3a1mhQYdwbXEHGwVdqPSu2bgUOKyE4_YcIDTz6T5ksTQ_bw2EtnSERoh8GiyinrMuhvxLvAhuZx7ZMJgIerY5LdGS1p4zUSAHwbGlawLZzxYTQeefXTL_FN7Mgkb3CJGCYD233c6BNITfKnzbvB_Q="}}
{"timestamp":"2026-07-16T17:56:24.184Z","type":"event_msg","payload":{"type":"agent_message","message":"The combined focused run is still going; Sprint 6G passed again and the selected V5/API tests are progressing. I’m waiting for the final status.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:56:24.184Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The combined focused run is still going; Sprint 6G passed again and the selected V5/API tests are progressing. I’m waiting for the final status."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:56:25.007Z","type":"response_item","payload":{"type":"function_call","name":"write_stdin","arguments":"{\"session_id\":37596,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_Y8TTT9Elb3BbPp81gpKXqB64"}}
{"timestamp":"2026-07-16T17:56:40.988Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Y8TTT9Elb3BbPp81gpKXqB64","output":"Chunk ID: 42806c\nWall time: 15.9777 seconds\nProcess exited with code 0\nOriginal token count: 23\nOutput:\n...........                                      [100%]\n35 passed, 3 deselected in 38.89s\n"}}
{"timestamp":"2026-07-16T17:56:40.988Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3800510,"cached_input_tokens":3670144,"output_tokens":22254,"reasoning_output_tokens":1958,"total_tokens":3822764},"last_token_usage":{"input_tokens":123844,"cached_input_tokens":123264,"output_tokens":84,"reasoning_output_tokens":6,"total_tokens":123928},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:56:45.412Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRtdFo6NJjsQcIM3FomeBTV07b14pLH6p40KqOh959HwK9ku83hDBAA16W52OHz4R5vsnrzxxokQDFQHkrSNajGemPgMn880JUThO73L2yG02CCJq5ejap5DZNXvzDzOomJ6xflaf2sidZLPGAgd_dU8IA-xuHyc89l-hVd5SC8DNIuIpFksfMCaIqnLLai0d_YG9qVf0nfuZxcQcKYSbGWa3TWMIoAPcqe5LvC7m5ncg-B4a_tqF_5sRZow1pbXsOXhkmjDFgNPLgnB18ExjGgAJ7szKdwbKDRSnE2ldrb254vIgG0vSdtV8BHjGcjxwQkYwgrXLyJxxAkmcAXVMTNNG5fwn8IlVt7J5JqQt2Tbo7dJWnOXLVZF5QvQYljpkI-GVfas8n5XUo2KI4XPdKjwNXkc8zLExp0kQYwNxY76XfczuNdHwE-3HqZgZ84b0Ho4WaytpyY1N8s29SlYqUgsdDXC9KMDesNkRGH1AFdlHybr2X4sqFVYJbcOqcpIKRGTd6H9O4d32Zgsl9m8oTDKbLBeTxgOYKV9tvcc1PbsE8XQ3nkgVWVMY4uEfldTmTcuv_IqFds9EXZPwFEqUJjNZqQSqkijwOInGWeXVw6zHHdFd4Gu_c8MCfTSXwgq45lBObJr64bVnhQfgrWMiBkvzYMC3JK39oH-KxtmZWHQ__BfMKHlolPsS-xScP-WEkJ-EtLr5KogzT85UZh2xoY-AQeYS6wzEd080AGQpufmCfBvc2AEJvt-jPjPO2oceyJdalFmPRWo4Sews70oA_buC3TrbQUWLuMyNdglTH22PJxfwkJCtOOXE5O8Pg_uC1UmuT6g0OGAulCbUtwr0ecuizEMywT-Pmx2-7-1SO-VGhHZ4V5VLhRSiZc1uO0z-id2DI36pseA2UQBrNbi1SA4MiEo7IwEhbI_eyCkcLxxVWo2nqJatbKYLWXdIJ8M4rVa9PGsWnJOTWkcZZCfaoby_zHDjlCPp_yMmJ73XWKYUyDzTZU2Az5uuijhZIwj1RgXuotlO6rzkW33rvy8NVmvYNm4utWPz5Ti-Fva79gi2W_lagii-Adfimmu-CeA5gKyD9I0NvsrGngo3hUaVTeaql0hcTYfnyhbZR9krU2EyWielayc57O2L9cZqjpaRmZhfAA8xD4dOUHF_9MqoqfbRCf-KLn-s0RafHlp9RNHJGL29NYmD2Kc8Nt2yoQfE28Q"}}
{"timestamp":"2026-07-16T17:56:52.537Z","type":"event_msg","payload":{"type":"agent_message","message":"The focused API/V5 tests pass with the socket-dependent tests deselected. I’m doing one last grep over the modified Sprint 6G surfaces for forbidden wording/fields and a final compile check.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:56:52.542Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The focused API/V5 tests pass with the socket-dependent tests deselected. I’m doing one last grep over the modified Sprint 6G surfaces for forbidden wording/fields and a final compile check."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:56:52.543Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"python -m py_compile scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py scripts/health/dashboard_v5/render.py tests/fixtures/dashboard_v5_fixture.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_vLLSe2xEDzhhOUBGImMsBZ4k"}}
{"timestamp":"2026-07-16T17:56:52.543Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"node --check scripts/health/assets/health-assets/dashboard-v5.js\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_HxtG9GsKCOTgaH2660iMDECY"}}
{"timestamp":"2026-07-16T17:56:52.543Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"rg -n \\\"Ampel|safe|traffic_light|suggested_traffic_light|mapping_scope|green|yellow|orange|red\\\" scripts/health/dashboard_v5 scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/assets/health-assets/dashboard-v5.js tests/test_dashboard_v5_sprint6g_nutrition.py docs/adr/0003-dietary-inflammation-model.md -S\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}","call_id":"call_Uwf710tD5pzZxoi5Ll2GxcSW"}}
{"timestamp":"2026-07-16T17:56:52.601Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git status --short\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_ZydtPLfZAcPzEEIhJmdyUDSv"}}
{"timestamp":"2026-07-16T17:56:52.648Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_vLLSe2xEDzhhOUBGImMsBZ4k","output":"Chunk ID: bd5e46\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:56:52.648Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_HxtG9GsKCOTgaH2660iMDECY","output":"Chunk ID: 027f32\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:56:52.648Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_Uwf710tD5pzZxoi5Ll2GxcSW","output":"Chunk ID: 66f65b\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 7646\nOutput:\ndocs/adr/0003-dietary-inflammation-model.md:30:- required DII parameters are not covered with a complete, licensed, versioned reference table.\ndocs/adr/0003-dietary-inflammation-model.md:32:The Dietary Inflammatory Index methodology required for a real DII implementation is the peer-reviewed Shivappa et al. 2014 method: nutrient/food-parameter intake is standardized against a global comparative database, converted to centered percentiles, multiplied by literature-derived inflammatory effect scores, and summed across available parameters. That methodology requires stable parameter names, units, global means/standard deviations, effect scores, coverage thresholds, and versioning. Those inputs are not available in this project state.\ndocs/adr/0003-dietary-inflammation-model.md:44:The dashboard does not compute a dietary inflammation index when required inputs are insufficient. Unknown or unmapped foods make the histamine component incomplete rather than zero. Composite products require ingredient review before a definitive mapping is applied.\ndocs/adr/0003-dietary-inflammation-model.md:46:Personal tolerance labels remain separate from shared food mapping definitions.\ntests/test_dashboard_v5_sprint6g_nutrition.py:69:    assert \"traffic_light\" not in json.dumps(days)\ntests/test_dashboard_v5_sprint6g_nutrition.py:119:        \"ingredient_review_required\": False,\ntests/test_dashboard_v5_sprint6g_nutrition.py:141:        provenance = connection.execute(\"SELECT decision,alias,confidence,mapping_method,source_label,source_version,note,ingredient_review_required,personal_tolerance,definition_version FROM nutrition_mapping_provenance\").fetchone()\ntests/test_dashboard_v5_sprint6g_nutrition.py:150:            \"ingredient_review_required\": 0,\ntests/test_dashboard_v5_sprint6g_nutrition.py:198:        \"ingredient_review_required\": True,\ntests/test_dashboard_v5_sprint6g_nutrition.py:203:    worker.apply_mapping_action({**base, \"decision\": \"ignore\", \"ingredient_review_required\": False}, \"c\" * 32)\ntests/test_dashboard_v5_sprint6g_nutrition.py:208:        assert connection.execute(\"SELECT status FROM nutrition_composite_product_review\").fetchone()[0] == \"needs_ingredient_review\"\ntests/test_dashboard_v5_sprint6g_nutrition.py:233:        \"ingredient_review_required\": [\"false\"],\nscripts/health/assets/health-assets/dashboard-v5.js:23:      credentials: \"same-origin\",\nscripts/health/assets/health-assets/dashboard-v5.js:33:    const response = await fetch(path, { credentials: \"same-origin\" });\nscripts/health/assets/health-assets/dashboard-v5.js:55:    const raw = item.date || item.datetime || item.timestamp || item.measured_at || item.recorded_at;\nscripts/health/assets/health-assets/dashboard-v5.js:83:    const value = path.split(\".\").reduce((current, key) => {\nscripts/health/assets/health-assets/dashboard-v5.js:139:        animation: window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches ? false : undefined,\nscripts/health/assets/health-assets/dashboard-v5.js:202:        ? values.reduce((total, item) => total + item, 0)\nscripts/health/assets/health-assets/dashboard-v5.js:203:        : values.reduce((total, item) => total + item, 0) / values.length;\nscripts/health/assets/health-assets/dashboard-v5.js:300:    if (!target || target.dataset.renderedPeriod === activePeriod) return;\nscripts/health/assets/health-assets/dashboard-v5.js:337:    target.dataset.renderedPeriod = activePeriod;\nscripts/health/assets/health-assets/dashboard-v5.js:352:      .concat(bundle.medication?.last_administered?.date || [])\nscripts/health/assets/health-assets/dashboard-v5.js:400:    const last = medication.last_administered;\nscripts/health/assets/health-assets/dashboard-v5.js:475:        [[\"sighi_reference\", \"SIGHi-Referenz\"], [\"ingredient_label\", \"Zutatenliste\"], [\"manual_review\", \"manuelle Prüfung\"], [\"local_alias\", \"lokaler Alias\"]].forEach(([value, label]) => {\nscripts/health/assets/health-assets/dashboard-v5.js:484:        const reviewRequired = node(\"input\");\nscripts/health/assets/health-assets/dashboard-v5.js:485:        reviewRequired.type = \"hidden\";\nscripts/health/assets/health-assets/dashboard-v5.js:486:        reviewRequired.name = \"ingredient_review_required\";\nscripts/health/assets/health-assets/dashboard-v5.js:487:        reviewRequired.value = \"false\";\nscripts/health/assets/health-assets/dashboard-v5.js:491:          reviewRequired.value = composite ? \"true\" : \"false\";\nscripts/health/assets/health-assets/dashboard-v5.js:508:        form.append(reviewRequired);\nscripts/health/assets/health-assets/dashboard-v5.js:645:      row.status === \"computed\" && (correlationIds.has(row.id.predictor) || correlationIds.has(row.id.target))\nscripts/health/assets/health-assets/dashboard-v5.js:659:    rows.slice(0, 30).forEach((row) => target.append(node(\"div\", \"v5-card v5-card--quiet\", `${row.id.predictor} → ${row.id.target}; Prädiktor an Tag d, Ziel an Tag d + ${row.dimensions.lag_days}; Phase ${row.dimensions.medication_phase}: n=${row.sample.observed_pairs}, Coverage ${formatValue(row.sample.target_coverage)}, ρ=${formatValue(row.statistics.rho)}, q=${formatValue(row.statistics.q_value)}. Explorativ, keine Kausalität.`)));\nscripts/health/assets/health-assets/dashboard-v5.js:744:    canvas.dataset.completePointCount = String([...rowsByMetric.values()].reduce((count, rows) => count + rows.filter((point) => typeof point.value === \"number\" && Number.isFinite(point.value)).length, 0));\nscripts/health/assets/health-assets/dashboard-v5.js:787:    const config = { type: \"line\", data: { labels: days.map((day) => formatValue(day, \"date\")), datasets }, options: { responsive: true, maintainAspectRatio: false, animation: useExplorerCadence || window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches ? false : undefined, interaction: { intersect: false, mode: \"index\" }, plugins: { legend: { position: \"bottom\", labels: { font: { size: 14 } } } }, scales: { x: { grid: { display: false }, ticks: { maxTicksLimit: 7, font: { size: 13 } } }, y: { ticks: { maxTicksLimit: 6, font: { size: 13 } } } } } };\nscripts/health/assets/health-assets/dashboard-v5.js:853:          const cell = node(\"div\", \"explorer-result-cell\", `${correlationLabel(row.id.predictor)} an Tag d → ${correlationLabel(row.id.target)} an Tag d + ${row.dimensions.lag_days} · ${phaseLabels[row.dimensions.medication_phase] || \"Phase unbekannt\"} · ρ ${formatValue(rho)} · q ${formatValue(row.statistics.q_value)} · n ${row.sample.observed_pairs}`);\nscripts/health/assets/health-assets/dashboard-v5.js:875:          rows.forEach((row) => section.append(node(\"div\", \"explorer-result-cell\", `${correlationLabel(row.id.predictor)} → ${correlationLabel(row.id.target)} · d zu d + ${row.dimensions.lag_days} · ρ ${formatValue(row.statistics.rho)} · q ${formatValue(row.statistics.q_value)} · n ${row.sample.observed_pairs} · Coverage ${formatValue(row.sample.target_coverage)}`)));\nscripts/health/assets/health-assets/dashboard-v5.js:987:    const observed = Object.values(bundle.series || {}).reduce((count, rows) => count + rows.length, 0);\nscripts/health/assets/health-assets/dashboard-v5.js:1177:        qa('#metric-cards').forEach(node => { delete node.dataset.renderedPeriod; });\nscripts/health/health_dashboard_action_worker.py:55:MAPPING_METHODS = frozenset({\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"})\nscripts/health/health_dashboard_action_worker.py:112:    required = {\nscripts/health/health_dashboard_action_worker.py:125:        \"ingredient_review_required\",\nscripts/health/health_dashboard_action_worker.py:128:    if not required <= set(payload) <= required | optional:\nscripts/health/health_dashboard_action_worker.py:142:    ingredient_review = payload[\"ingredient_review_required\"]\nscripts/health/health_dashboard_action_worker.py:158:        raise ValueError(\"required mapping documentation missing\")\nscripts/health/health_dashboard_action_worker.py:161:    if type(ingredient_review) is not bool:\nscripts/health/health_dashboard_action_worker.py:162:        raise ValueError(\"invalid ingredient review flag\")\nscripts/health/health_dashboard_action_worker.py:166:        if not canonical or score == \"unknown\" or ingredient_review:\nscripts/health/health_dashboard_action_worker.py:167:            raise ValueError(\"assign requires canonical score without ingredient review\")\nscripts/health/health_dashboard_action_worker.py:169:        if score != \"unknown\" or not ingredient_review:\nscripts/health/health_dashboard_action_worker.py:170:            raise ValueError(\"composite must stay unclassified for ingredient review\")\nscripts/health/health_dashboard_action_worker.py:172:        if score != \"unknown\" or canonical or ingredient_review:\nscripts/health/health_dashboard_action_worker.py:187:        \"ingredient_review_required\": ingredient_review,\nscripts/health/health_dashboard_action_worker.py:246:        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\nscripts/health/health_dashboard_action_worker.py:337:            ingredient_review_required INTEGER,\nscripts/health/health_dashboard_action_worker.py:355:            ingredient_review_required INTEGER,\nscripts/health/health_dashboard_action_worker.py:365:            status TEXT NOT NULL DEFAULT 'needs_ingredient_review',\nscripts/health/health_dashboard_action_worker.py:374:            \"note\": \"TEXT\", \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\nscripts/health/health_dashboard_action_worker.py:380:            \"ingredient_review_required\": \"INTEGER\", \"personal_tolerance\": \"TEXT\",\nscripts/health/health_dashboard_action_worker.py:456:        raise RuntimeError(\"HEALTH_DASHBOARD_DB is required\")\nscripts/health/health_dashboard_action_worker.py:487:                       VALUES(?,?, 'needs_ingredient_review', CURRENT_TIMESTAMP)\"\"\",\nscripts/health/health_dashboard_action_worker.py:494:                           (item_id,canonical_food,sighi_score,traffic_light,tags,confidence,reason,scored_at)\nscripts/health/health_dashboard_action_worker.py:500:                connection.execute(\"UPDATE nutrition_review_queue SET status='ignored',updated_at=CURRENT_TIMESTAMP WHERE normalized_name=?\", (normalized,))\nscripts/health/health_dashboard_action_worker.py:507:                        mapping_method,source_label,source_version,note,ingredient_review_required,\nscripts/health/health_dashboard_action_worker.py:513:                        payload[\"source_version\"], payload[\"note\"], int(payload[\"ingredient_review_required\"]),\nscripts/health/health_dashboard_action_worker.py:520:                    mapping_method,source_label,source_version,note,ingredient_review_required,\nscripts/health/health_dashboard_action_worker.py:527:                    int(payload[\"ingredient_review_required\"]), payload.get(\"personal_tolerance\"),\nscripts/health/health_dashboard_action_worker.py:581:        print(\"HEALTH_DASHBOARD_DB is required\", file=sys.stderr)\nscripts/health/health_dashboard_server.py:28:from dashboard_v5.document_originals import configured_original_roots, probe_original\nscripts/health/health_dashboard_server.py:139:MAPPING_METHODS = {\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"}\nscripts/health/health_dashboard_server.py:155:ORIGINAL_ROOTS = configured_original_roots()\nscripts/health/health_dashboard_server.py:171:        raise OSError(\"read-only API authentication is not configured\")\nscripts/health/health_dashboard_server.py:204:        expired = [key for key, expiry in _browser_sessions.items() if expiry <= now]\nscripts/health/health_dashboard_server.py:205:        for key in expired:\nscripts/health/health_dashboard_server.py:207:        session_id = secrets.token_urlsafe(32)\nscripts/health/health_dashboard_server.py:333:def safe_report_path(name: str) -> Path | None:\nscripts/health/health_dashboard_server.py:390:    token = secrets.token_urlsafe(24)\nscripts/health/health_dashboard_server.py:392:        expired = [\nscripts/health/health_dashboard_server.py:397:        for existing in expired:\nscripts/health/health_dashboard_server.py:415:    token = secrets.token_urlsafe(18)\nscripts/health/health_dashboard_server.py:418:        expired = [\nscripts/health/health_dashboard_server.py:423:        for key in expired:\nscripts/health/health_dashboard_server.py:446:    nonce = secrets.token_urlsafe(18)\nscripts/health/health_dashboard_server.py:490:    required = {\"csrf_token\", \"date\", \"notes\", *SYMPTOM_FIELDS}\nscripts/health/health_dashboard_server.py:491:    allowed = required | {\"return_to\"}\nscripts/health/health_dashboard_server.py:493:    if keys not in {frozenset(required), frozenset(allowed)}:\nscripts/health/health_dashboard_server.py:505:    required = {\nscripts/health/health_dashboard_server.py:517:        \"ingredient_review_required\",\nscripts/health/health_dashboard_server.py:520:    allowed = required | {\"personal_tolerance\"}\nscripts/health/health_dashboard_server.py:521:    if not required <= set(form) <= allowed or any(len(values) != 1 for values in form.values()):\nscripts/health/health_dashboard_server.py:536:    review = form[\"ingredient_review_required\"][0]\nscripts/health/health_dashboard_server.py:544:        raise ValueError(\"mapping documentation required\")\nscripts/health/health_dashboard_server.py:547:    ingredient_review_required = review == \"true\"\nscripts/health/health_dashboard_server.py:548:    if decision == \"assign\" and (score == \"unknown\" or not canonical_food or ingredient_review_required):\nscripts/health/health_dashboard_server.py:550:    if decision == \"composite\" and (score != \"unknown\" or not ingredient_review_required):\nscripts/health/health_dashboard_server.py:552:    if decision == \"ignore\" and (score != \"unknown\" or canonical_food or ingredient_review_required):\nscripts/health/health_dashboard_server.py:567:        \"ingredient_review_required\": ingredient_review_required,\nscripts/health/health_dashboard_server.py:753:            receipt = quote(issue_queue_receipt(), safe=\"\")\nscripts/health/health_dashboard_server.py:830:            report = safe_report_path(path.rsplit(\"/\", 1)[-1])\nscripts/health/health_dashboard_server.py:911:            self._send_api_error(401, \"authentication_required\", send_body)\nscripts/health/dashboard_v5/data_provider.py:30:    \"administered\", \"verabreicht\",\nscripts/health/dashboard_v5/data_provider.py:135:        return {\"last_administered\": None, \"next_planned\": None}, []\nscripts/health/dashboard_v5/data_provider.py:142:        and (administered_day := parse_day(row[\"datum\"])) is not None\nscripts/health/dashboard_v5/data_provider.py:143:        and administered_day <= today\nscripts/health/dashboard_v5/data_provider.py:185:        \"last_administered\": public_medication(last, parse_day(last[\"datum\"]) if last else None, planned=False),\nscripts/health/dashboard_v5/data_provider.py:193:            events.append({\"date\": day, \"type\": \"medication_administered\", \"label\": label})\nscripts/health/dashboard_v5/data_provider.py:249:                and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\nscripts/health/dashboard_v5/data_provider.py:281:        \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\nscripts/health/dashboard_v5/data_provider.py:290:        \"\"\"SELECT predictor,target,lag_days,medication_phase,n,eligible_target_days,\nscripts/health/dashboard_v5/data_provider.py:294:           ORDER BY predictor,target,lag_days,medication_phase LIMIT 1000\"\"\"\nscripts/health/dashboard_v5/data_provider.py:302:                predictor=str(row[\"predictor\"]), target=str(row[\"target\"]),\nscripts/health/dashboard_v5/data_provider.py:315:            \"id\": {\"predictor\": result.predictor, \"target\": result.target},\nscripts/health/dashboard_v5/document_originals.py:1:\"\"\"Fail-closed document-original availability shared by V5 metadata and streaming.\"\"\"\nscripts/health/dashboard_v5/document_originals.py:32:def configured_original_roots() -> tuple[Path, ...]:\nscripts/health/dashboard_v5/document_originals.py:81:    allowed_roots = roots or configured_original_roots()\nscripts/health/dashboard_v5/contracts.py:51:        raise ValueError(f\"unsafe whitespace at {path}\")\nscripts/health/dashboard_v5/contracts.py:53:        raise ValueError(f\"unsafe text at {path}\")\nscripts/health/dashboard_v5/contracts.py:182:        if event_day > today or event[\"type\"] != \"medication_administered\":\nscripts/health/dashboard_v5/contracts.py:187:    medication = _exact(bundle[\"medication\"], {\"last_administered\", \"next_planned\"}, \"medication\")\nscripts/health/dashboard_v5/contracts.py:189:        (\"last_administered\", {\"date\", \"name\", \"dose\"}),\nscripts/health/dashboard_v5/contracts.py:197:        if (field == \"last_administered\" and medication_day > today) or (field == \"next_planned\" and medication_day <= today):\nscripts/health/dashboard_v5/contracts.py:242:        if row[\"histamine_status\"] not in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\nscripts/health/dashboard_v5/contracts.py:293:        \"lag_direction\": \"predictor_d_to_target_d_plus_lag\",\nscripts/health/dashboard_v5/contracts.py:302:        identity = _exact(row[\"id\"], {\"predictor\", \"target\"}, f\"{path}.id\")\nscripts/health/dashboard_v5/contracts.py:306:        if identity[\"predictor\"] not in engine.ALLOWED_PREDICTORS or identity[\"target\"] not in engine.ALLOWED_TARGETS:\nscripts/health/dashboard_v5/contracts.py:312:        order = (identity[\"predictor\"], identity[\"target\"], dims[\"lag_days\"], dims[\"medication_phase\"])\nscripts/health/dashboard_v5/contracts.py:314:            raise ValueError(\"correlation results are not uniquely and deterministically ordered\")\nscripts/health/dashboard_v5/contracts.py:347:def safe_json(bundle: dict[str, Any]) -> str:\nscripts/health/dashboard_v5/read_api.py:70:    {\"medication_administered\", \"symptom_day\", \"health_event\", \"health_period\"}\nscripts/health/dashboard_v5/read_api.py:122:def safe_metadata_text(\nscripts/health/dashboard_v5/read_api.py:215:    params: dict[str, str], *, required: bool = False\nscripts/health/dashboard_v5/read_api.py:221:    if required and not raw_start:\nscripts/health/dashboard_v5/read_api.py:222:        raise APIError(400, \"range_required\")\nscripts/health/dashboard_v5/read_api.py:353:            \"quality\": \"paired_observation\",\nscripts/health/dashboard_v5/read_api.py:443:               AND histamine_label IN ('classified','green','yellow','orange','red')\"\"\"\nscripts/health/dashboard_v5/read_api.py:575:            category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:576:            institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:798:    start, end = parse_range(params, required=True)\nscripts/health/dashboard_v5/read_api.py:808:    if \"medication_administered\" in selected and table_exists(\nscripts/health/dashboard_v5/read_api.py:811:        administered_values = tuple(sorted(ADMINISTERED))\nscripts/health/dashboard_v5/read_api.py:812:        if len(administered_values) != 6:\nscripts/health/dashboard_v5/read_api.py:823:                    *administered_values,\nscripts/health/dashboard_v5/read_api.py:831:            label = safe_metadata_text(row[\"medication_name\"], 120)\nscripts/health/dashboard_v5/read_api.py:835:                    {\"date\": day, \"type\": \"medication_administered\", \"label\": label}\nscripts/health/dashboard_v5/read_api.py:856:            category = safe_metadata_text(row[\"category\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:857:            parameter = safe_metadata_text(row[\"parameter\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:880:            event_type = safe_metadata_text(row[\"event_type\"], 80)\nscripts/health/dashboard_v5/read_api.py:881:            label = safe_metadata_text(row[\"label\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:912:def _safe_documents(\nscripts/health/dashboard_v5/read_api.py:922:        category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:923:        institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:999:        groups[\"documents\"] = _safe_documents(connection, clean)\nscripts/health/dashboard_v5/read_api.py:1009:def _safe_day_text(\nscripts/health/dashboard_v5/read_api.py:1012:    return safe_metadata_text(value, maximum, allow_empty=allow_empty)\nscripts/health/dashboard_v5/read_api.py:1032:            note = _safe_day_text(row[\"notizen\"], 300)\nscripts/health/dashboard_v5/read_api.py:1054:    result = {\"planned\": [], \"administered\": [], \"missed\": [], \"corrected\": []}\nscripts/health/dashboard_v5/read_api.py:1069:        name = _safe_day_text(row[\"medication_name\"], 120, allow_empty=False)\nscripts/health/dashboard_v5/read_api.py:1070:        dose = _safe_day_text(row[\"dose\"], 40)\nscripts/health/dashboard_v5/read_api.py:1071:        route = _safe_day_text(row[\"route\"], 40)\nscripts/health/dashboard_v5/read_api.py:1072:        note = _safe_day_text(row[\"notes\"], 300)\nscripts/health/dashboard_v5/read_api.py:1073:        source = _safe_day_text(row[\"source\"], 80)\nscripts/health/dashboard_v5/read_api.py:1086:            result[\"administered\"].append(item)\nscripts/health/dashboard_v5/read_api.py:1109:                \"category\": _safe_day_text(row[\"category\"], 80),\nscripts/health/dashboard_v5/read_api.py:1110:                \"label\": _safe_day_text(row[\"parameter\"], 120),\nscripts/health/dashboard_v5/read_api.py:1111:                \"value\": _safe_day_text(row[\"value\"], 80),\nscripts/health/dashboard_v5/read_api.py:1112:                \"unit\": _safe_day_text(row[\"unit\"], 40),\nscripts/health/dashboard_v5/read_api.py:1113:                \"source\": _safe_day_text(row[\"source\"], 80),\nscripts/health/dashboard_v5/read_api.py:1114:                \"note\": _safe_day_text(row[\"notes\"], 300),\nscripts/health/dashboard_v5/read_api.py:1127:            event_type = _safe_day_text(row[\"event_type\"], 80, allow_empty=False)\nscripts/health/dashboard_v5/read_api.py:1128:            label = _safe_day_text(row[\"label\"], 120)\nscripts/health/dashboard_v5/read_api.py:1129:            source = _safe_day_text(row[\"source\"], 80)\nscripts/health/dashboard_v5/read_api.py:1130:            note = _safe_day_text(row[\"notes\"], 300)\nscripts/health/dashboard_v5/read_api.py:1165:        category = _safe_day_text(row[\"kategorie\"], 80)\nscripts/health/dashboard_v5/read_api.py:1166:        institution = _safe_day_text(row[\"institution\"], 120)\nscripts/health/dashboard_v5/read_api.py:1188:        practitioner = _safe_day_text(row[\"arzt\"], 120)\nscripts/health/dashboard_v5/read_api.py:1189:        institution = _safe_day_text(row[\"klinik\"], 120)\nscripts/health/dashboard_v5/read_api.py:1190:        reason = _safe_day_text(row[\"grund\"], 160)\nscripts/health/dashboard_v5/read_api.py:1191:        note = _safe_day_text(row[\"notizen\"], 300)\nscripts/health/dashboard_v5/read_api.py:1247:        and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\nscripts/health/dashboard_v5/read_api.py:1383:                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else None,\nscripts/health/dashboard_v5/read_api.py:1384:                    \"status\": \"complete\" if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else \"incomplete\",\nscripts/health/dashboard_v5/read_api.py:1396:                      h.canonical_food,h.sighi_score,h.traffic_light,h.confidence\nscripts/health/dashboard_v5/read_api.py:1403:            name = safe_metadata_text(item[\"name\"], 120, allow_empty=False)\nscripts/health/dashboard_v5/read_api.py:1404:            brand = safe_metadata_text(item[\"brand\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1405:            canonical = safe_metadata_text(item[\"canonical_food\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1474:        example = safe_metadata_text(row[\"example_name\"], 160, allow_empty=False)\nscripts/health/dashboard_v5/read_api.py:1475:        suggested = safe_metadata_text(row[\"suggested_canonical_food\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1476:        reason = safe_metadata_text(row[\"reason\"], 160, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1492:            \"allowed_mapping_methods\": [\"sighi_reference\", \"ingredient_label\", \"manual_review\", \"local_alias\"],\nscripts/health/dashboard_v5/read_api.py:1593:        raise APIError(400, \"range_required\")\nscripts/health/dashboard_v5/read_api.py:1780:    return \"record-cursor-\" + base64.urlsafe_b64encode(raw + signature).decode(\nscripts/health/dashboard_v5/read_api.py:1790:        packed = base64.urlsafe_b64decode(encoded + \"=\" * (-len(encoded) % 4))\nscripts/health/dashboard_v5/read_api.py:1838:            clean = safe_metadata_text(raw, maximum, allow_empty=False)\nscripts/health/dashboard_v5/read_api.py:1942:        category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1943:        institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:1978:                \"processing_quality\": safe_metadata_text(\nscripts/health/dashboard_v5/read_api.py:2033:        category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2034:        institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2051:    category = safe_metadata_text(row[\"kategorie\"], 80, allow_empty=True) or \"\"\nscripts/health/dashboard_v5/read_api.py:2052:    institution = safe_metadata_text(row[\"institution\"], 120, allow_empty=True) or \"\"\nscripts/health/dashboard_v5/read_api.py:2146:        \"administered\": [],\nscripts/health/dashboard_v5/read_api.py:2154:        \"administered\": tuple(sorted(ADMINISTERED)),\nscripts/health/dashboard_v5/read_api.py:2190:            name = safe_metadata_text(row[\"medication_name\"], 120)\nscripts/health/dashboard_v5/read_api.py:2199:                    \"dose\": safe_metadata_text(row[\"dose\"], 40, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2200:                    \"route\": safe_metadata_text(row[\"route\"], 40, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2201:                    \"note\": safe_metadata_text(row[\"notes\"], 300, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2202:                    \"source\": safe_metadata_text(row[\"source\"], 80, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2219:        safe_metadata_text(params[\"institution\"], 120)\nscripts/health/dashboard_v5/read_api.py:2247:        institution = safe_metadata_text(row[\"klinik\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2255:        practitioner = safe_metadata_text(row[\"arzt\"], 120, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2256:        reason = safe_metadata_text(row[\"grund\"], 160, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2257:        note = safe_metadata_text(row[\"notizen\"], 300, allow_empty=True)\nscripts/health/dashboard_v5/read_api.py:2291:    if query and (len(query) > 80 or safe_metadata_text(query, 80) is None):\nscripts/health/dashboard_v5/read_api.py:2371:        name = safe_metadata_text(row[\"medication_name\"], 120)\nscripts/health/dashboard_v5/read_api.py:2379:                    \"dose\": safe_metadata_text(row[\"dose\"], 40, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2380:                    \"route\": safe_metadata_text(row[\"route\"], 40, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2381:                    \"note\": safe_metadata_text(row[\"notes\"], 300, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2382:                    \"source\": safe_metadata_text(row[\"source\"], 80, allow_empty=True),\nscripts/health/dashboard_v5/read_api.py:2414:        \"latest_administered\": medications[\"administered\"][:3],\nscripts/health/dashboard_v5/read_api.py:2430:    start, end = parse_range(params, required=True)\nscripts/health/dashboard_v5/read_api.py:2451:        else {\"planned\": [], \"administered\": [], \"missed\": [], \"corrected\": []}\nscripts/health/dashboard_v5/read_api.py:2519:                    for kind in (\"planned\", \"administered\", \"missed\", \"corrected\")\nscripts/health/dashboard_v5/read_api.py:2628:                raise APIError(400, \"metric_required\")\nscripts/health/dashboard_v5/document_chunks.py:1:\"\"\"Shared deterministic reviewed-document chunk contract for FTS and read APIs.\"\"\"\nscripts/health/dashboard_v5/render.py:7:from dashboard_v5.contracts import safe_json\nscripts/health/dashboard_v5/render.py:20:    payload = safe_json(bundle)\nscripts/health/dashboard_v5/render.py:34:        f\"<label><span>{label}</span><select name='{key}' required><option value=''>–</option>\"\nscripts/health/dashboard_v5/metric_catalog_v2.py:150:    _metric(\"panel.blood_pressure\", \"Blutdruck\", (\"Blutdruck\", \"BD\", \"RR\"), \"vital_signs\", \"apple_health\", \"mmHg\", \"panel\", 0, \"panel\", \"intermittent\", \"blood_pressure_panel\", {}, \"paired_panel\", (\"systolisch\", \"diastolisch\"), \"explorer\", False, (), \"blood_pressure\", \"apple_health_blood_pressure_panel_v1\"),\nscripts/health/dashboard_v5/metric_catalog_v2.py:151:    _metric(\"apple.blood_pressure.systolic\", \"Blutdruck systolisch\", (\"systolisch\",), \"vital_signs\", \"apple_health\", \"mmHg\", \"number\", 0, \"last\", \"intermittent\", \"blood_pressure\", _baseline(30), \"paired_panel\", (\"blutdruck\", \"bd\", \"rr\"), \"explorer\", False, (), \"blood_pressure\", \"apple_health_blood_pressure_panel_v1\", \"panel.blood_pressure\"),\nscripts/health/dashboard_v5/metric_catalog_v2.py:152:    _metric(\"apple.blood_pressure.diastolic\", \"Blutdruck diastolisch\", (\"diastolisch\",), \"vital_signs\", \"apple_health\", \"mmHg\", \"number\", 0, \"last\", \"intermittent\", \"blood_pressure\", _baseline(30), \"paired_panel\", (\"blutdruck\", \"bd\", \"rr\"), \"explorer\", False, (), \"blood_pressure\", \"apple_health_blood_pressure_panel_v1\", \"panel.blood_pressure\"),\nscripts/health/dashboard_v5/metric_catalog_v2.py:335:                 AND histamine_label IN ('green','yellow','orange','red')\nscripts/health/dashboard_v5/apple_identifier_report.py:1:\"\"\"Generate a safe machine-readable inventory of observed Apple Health identifiers.\"\"\"\nscripts/health/dashboard_v5/apple_identifier_report.py:87:    parser.add_argument(\"--db\", required=True, type=Path)\nscripts/health/dashboard_v5/apple_identifier_report.py:88:    parser.add_argument(\"--output\", required=True, type=Path)\n"}}
{"timestamp":"2026-07-16T17:56:52.654Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_ZydtPLfZAcPzEEIhJmdyUDSv","output":"Chunk ID: 39d3ae\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 183\nOutput:\n M database/schema.sql\n M scripts/health/assets/health-assets/dashboard-v5-api-explorer.js\n M scripts/health/assets/health-assets/dashboard-v5.css\n M scripts/health/assets/health-assets/dashboard-v5.js\n M scripts/health/dashboard_v5/contracts.py\n M scripts/health/dashboard_v5/data_provider.py\n M scripts/health/dashboard_v5/read_api.py\n M scripts/health/dashboard_v5/render.py\n M scripts/health/health_dashboard_action_worker.py\n M scripts/health/health_dashboard_server.py\n M tests/fixtures/dashboard_v5_fixture.py\n?? docs/adr/0003-dietary-inflammation-model.md\n?? scripts/health/assets/health-assets/dashboard-v5-echarts.js\n?? tests/browser/dashboard_v5_sprint6g_echarts.spec.js\n?? tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:56:52.654Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":3924505,"cached_input_tokens":3793920,"output_tokens":22719,"reasoning_output_tokens":2013,"total_tokens":3947224},"last_token_usage":{"input_tokens":123995,"cached_input_tokens":123776,"output_tokens":465,"reasoning_output_tokens":55,"total_tokens":124460},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:56:56.643Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRtowID4OlPglTIHqRlljMtCryDnfj7uizBfulXQ4APLb7Nc5VTxF09VhB3mWkf0D5vHQK1ahVXvmzkT9j77GI3aP86zcglSCPuiYl9_ctLVOTKm49VMYYp4MxHznxoIX2E7roX01sMy_X5tKeLwbMtgtOerZ7UJgzaYlKOgTpvW594ZdgijuOu-nsz_W_eTJ60rKljWN_kgDDSr58GuDGQvNmAUT0ocFffFT2xLbgetfzXV7TERYjVnZfj47MLBhbUrL9_f_89g23p_x_FPH_NTl5SGNs_k_sivvOs7BVm4Pfm-lB5NC0ffesVkWXpxOIqmwML3qjKLpkTq2KmcGtMXlgBl7vGl_vmrE_PID3wf-qtS4Q1kh6TYYN5kD6W4OEqeOUH2qOAn85kYKjCbHbV1WBLyCqP_t2giVELOJV9vqRXYRr644OTWIP8NMWqag8A_F30aw4H2K3TBVpZNI4Fg6IHTqF-NVDeRi4OSGsln94ZBE-vOmmjcJ0m5y7RljzC5VJUOPEJPZzxICKj-qTK0e7CUDsaQOuIZhmuZ4VOyi8L9_TKTWNhllTmiDqdeXJ7jZBWPWSKrIX16yB0m_-GrWdLrF67fg6wV6ADNqIDbhPyPqe-VjnHfLhMZRPqfHrvxnq4R2yBXKqCSbEgho-nYIrLG-6oOenRztSKN_wvefQwGJyjDPoAoZKXZN2nH_XNtOa1ZIchGseyoIL3_QYbX8aJ5IX0QeI3QW6KLC6_anU0QVYFomZ1I9KnUvfdVSEn-vc8YUDFi5-AQBxoJc1mi2QH34X31tLhZ4JrfDp6EGQ4sUKZVBMcLIwgp-xdu1nF5LfGGAqvDSYSdp-m2d9w5A-46BJfeU4iPXt0IlCjLfy9NZrJUqaOCdpq8MBodacv9ctvdDd9kFJvaWqf-_jkY4qGwkZtqLAqYN4OY92FLz5nT5opx_fFkrLwlqhoC5JURU65GSHwEzWyb0_WRS3_yQyqXjFjzHF3XATRqZ4FejGS7BP2owI3BPxXjic7VOZb9l-gHLpN6j_ZxjUr9RsyaiG1HVMNhh_Cvf91Qg_wDBFcUNXyMo5vuZz9EJK4N0NUusm3Ll9rjTgUO4bi5Lb1noY7e8rGa-jq1LHsxByC2NCfA0M_PZY2P2paECk0_hjWYixIFPwPZT_tfdyRF_cUK-mCqRpiex-qnPvFX9xygCXEjZIg5JsW9bGebkW3nITDyE8z6HCaYqxpYQm8NgfWHwu1rAi7EIdVYBUgDAzRkF7OSUuXyUqb-X6slbLwLCajGCl6c6ABArB-2Rw1gv4EIsNPqOhWfjo2BM9kwgXzESeM4DrQAXseMKNKCIteQxfmiPnE22Rhe4IAryHcrZgal7Ypu06oRV6o0dGU21g8zQPrbHpvVDv-I2MMtSv8c2y8tKeGD-8xt-7lgz2ZxyAFAbRzv3X1Hxc2wRQOsKbfgfsEvZwFUPKLFVc8HNZPgbNWYara4cr3bWGXvZs9cP6gYKfJRy-b8T7qEpIkvktu7CfPosjAb5zQ79opl8LQ3EfK_MXzRQtkrKWXT7cxN7_dBUlewPGmooyyxGJa2aab9Qdxtf4tHwllVFXZ1-6YlE0xNVKSu0vIugiWQqYmedWMm7GVGyntNGVqDc6_2kmCJJo8="}}
{"timestamp":"2026-07-16T17:56:57.592Z","type":"event_msg","payload":{"type":"agent_message","message":"The final grep found one neutral display bug: meal-level score visibility still checked only legacy color labels. I’m patching that and the catalog availability SQL to recognize the new `classified` summary label.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:56:57.592Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The final grep found one neutral display bug: meal-level score visibility still checked only legacy color labels. I’m patching that and the catalog availability SQL to recognize the new `classified` summary label."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:56:59.622Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_n6aEi5xzKKSZ9JeFdLOLaRK3","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/read_api.py\n@@\n-                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else None,\n+                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else None,\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:56:59.727Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_n6aEi5xzKKSZ9JeFdLOLaRK3","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/read_api.py":{"type":"update","unified_diff":"@@ -1382,3 +1382,3 @@\n                 \"histamine\": {\n-                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"} else None,\n+                    \"score\": _public_number(meal[\"histamine_score\"]) if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else None,\n                     \"status\": \"complete\" if meal[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"} else \"incomplete\",\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:56:59.736Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_n6aEi5xzKKSZ9JeFdLOLaRK3","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/read_api.py\n"}}
{"timestamp":"2026-07-16T17:56:59.736Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4056697,"cached_input_tokens":3917696,"output_tokens":22995,"reasoning_output_tokens":2126,"total_tokens":4079692},"last_token_usage":{"input_tokens":132192,"cached_input_tokens":123776,"output_tokens":276,"reasoning_output_tokens":113,"total_tokens":132468},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:57:03.292Z","type":"response_item","payload":{"type":"custom_tool_call","status":"completed","call_id":"call_S6AuAG6kCQYuINCcKsXz47Ko","name":"apply_patch","input":"*** Begin Patch\n*** Update File: scripts/health/dashboard_v5/metric_catalog_v2.py\n@@\n-                 AND histamine_label IN ('green','yellow','orange','red')\n+                 AND histamine_label IN ('classified','green','yellow','orange','red')\n*** End Patch\n"}}
{"timestamp":"2026-07-16T17:57:03.396Z","type":"event_msg","payload":{"type":"patch_apply_end","call_id":"call_S6AuAG6kCQYuINCcKsXz47Ko","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","stdout":"Success. Updated the following files:\nM scripts/health/dashboard_v5/metric_catalog_v2.py\n","stderr":"","success":true,"changes":{"/home/agent/.hermes/repos/HealthManager/scripts/health/dashboard_v5/metric_catalog_v2.py":{"type":"update","unified_diff":"@@ -334,3 +334,3 @@\n                  AND histamine_score IS NOT NULL\n-                 AND histamine_label IN ('green','yellow','orange','red')\n+                 AND histamine_label IN ('classified','green','yellow','orange','red')\n                  AND datum<=? LIMIT ?\"\"\",\n","move_path":null}},"status":"completed"}}
{"timestamp":"2026-07-16T17:57:03.404Z","type":"response_item","payload":{"type":"custom_tool_call_output","call_id":"call_S6AuAG6kCQYuINCcKsXz47Ko","output":"Exit code: 0\nWall time: 0.1 seconds\nOutput:\nSuccess. Updated the following files:\nM scripts/health/dashboard_v5/metric_catalog_v2.py\n"}}
{"timestamp":"2026-07-16T17:57:03.404Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4189211,"cached_input_tokens":4049664,"output_tokens":23067,"reasoning_output_tokens":2126,"total_tokens":4212278},"last_token_usage":{"input_tokens":132514,"cached_input_tokens":131968,"output_tokens":72,"reasoning_output_tokens":0,"total_tokens":132586},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:57:08.368Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRtxPZTJcJ8zXFj6dcyvJEDVFmW4P3PaIwvD6Td-7ZmMu2e8VoGJDJGonuIK8qDuVE3VaGDH2NJFQgV1cnmUliDIA-6i-yt5ibYu8s25qIcoaWAL1h0wXyBaYDS8qFDMdCT4KIxUp3AbFnkeVcUy-yx6B_xS4-cQlOhewzo5SzKh4oH1diUEBUZcfSEhndDRgAbpPBM6HeUnQ9N951v3nhbbx0IwLNaqBhPHcFtJJuyJ-G7KP42ElTcp_AwjnN7KmrWwHC5pxE8RtxdjpT4C5qtLZXuufqzFGEwIFFRTxUJGNl5VHV4ZjuZ0jemM8H2ukZtxEmNo7_Vit3EPex0wWXcokvX79hXf9bupSKCkmv4Z98O77-NibAXdbq9yHtsI8mSrHIk-hJf-oRy6PUIb1Z7TJk4TQzgwLLT2F4Ox7zOJKbt9tRl7OZFE42AxYMYsHcvBqa2vnqQKdP3iSNr3S18ItZmfrEn-4OPafYaAvfvm74zxSZWRojCHcYmPmm-puliYIe6eMJokl7dSTDkzt_w0YuUaajRZU3nJS_GMXvyTQQw_DDD4btTNOltnwdux94WUDpiJh24ylhMPu_O2YWjmHbT66CPg3gR6MjSjncLWd-MbjiCR1tPIb7vkTxYujY80hetKSvJ6pCOeqvj-erKb3J74aytcIC7QXwDS04aZsvBADTdC4rvxuum6Xns1rEZW4J1encfYsE9sPrbhmZAUd2DH4xmWckGHX4HfSYHQTS_e6N10aDKrqoLr79Zcz1Ndf7qvzAFvtH20sAMQwcLf2J6-pEr9rU_alW0_Bm83flp_I0NsuUDRtjDDI1BXO407e60z3hzebSuCvBS7_rB3ZgL8w1-mk4AMksAPO4AItkXil1dYcsiavb9U8HMfUU-RmbyeN0ZCJ053qU_PnoykxIyKGv0LO0nYrEaCuyHeZvJ5h9MmkwQNCPRHALuMiwetAxapuFbCpGwB04uq60vK1oXih0biJGJE1LlvwkDMB_pNJLTAlB2gQkRlRjzsRekKzXJ86kvaeK8BH_yqo71ekg=="}}
{"timestamp":"2026-07-16T17:57:15.880Z","type":"event_msg","payload":{"type":"agent_message","message":"That last compatibility patch is in. I’m rerunning the focused nutrition tests and static compilation one more time after touching the catalog helper.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:57:15.886Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"That last compatibility patch is in. I’m rerunning the focused nutrition tests and static compilation one more time after touching the catalog helper."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:57:15.886Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":40000}","call_id":"call_96aegQphzBcJNZKmaX5M13tu"}}
{"timestamp":"2026-07-16T17:57:15.886Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"python -m py_compile scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py scripts/health/dashboard_v5/render.py scripts/health/dashboard_v5/metric_catalog_v2.py tests/fixtures/dashboard_v5_fixture.py\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_vCJqEeErhQugVE0oJzPx9sma"}}
{"timestamp":"2026-07-16T17:57:17.497Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"node --check scripts/health/assets/health-assets/dashboard-v5.js\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}","call_id":"call_dHnQ1jIXnpIfbaw2D44ujCEQ"}}
{"timestamp":"2026-07-16T17:57:26.775Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_96aegQphzBcJNZKmaX5M13tu","output":"Chunk ID: 9d176c\nWall time: 10.7307 seconds\nProcess exited with code 0\nOriginal token count: 25\nOutput:\n......                                                                   [100%]\n6 passed in 10.53s\n"}}
{"timestamp":"2026-07-16T17:57:26.776Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_vCJqEeErhQugVE0oJzPx9sma","output":"Chunk ID: 79244f\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:57:26.776Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_dHnQ1jIXnpIfbaw2D44ujCEQ","output":"Chunk ID: dd0f1d\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 0\nOutput:\n"}}
{"timestamp":"2026-07-16T17:57:26.776Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4321846,"cached_input_tokens":4182144,"output_tokens":23377,"reasoning_output_tokens":2148,"total_tokens":4345223},"last_token_usage":{"input_tokens":132635,"cached_input_tokens":132480,"output_tokens":310,"reasoning_output_tokens":22,"total_tokens":132945},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786007},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:57:29.207Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRuJannWlpxtrZDzVrbPXAxkltvZUbmVfmcS0EVD2AcT4HBd6xXQx2OgG0-PTphldaTBtbU6dmJl5jBflemAqKXfVZJKCn7Ew8tsArhVDrbM2pZIsVZtEjX8MBuS9Hz-9N8ZN-R5odANS-NRkiGHVArvqb7bf3CLhjmTSsKWlmSsATL6YcvWBw8dIuhJAIeo51uHaUME1OVL53Uj1WF6Vks3Z2_eATewifk4-aPPw4YqZ_S-nnlf3G0pQlcPkFFjcmpkR2fUsMDjVQbgTbi0oDXJJAbCuAfdZjXVU3k29LE9KWode94rpY7e7kIRmEigLLC84J7_x-fTpjat2YLTAFtGKgclRFrnokb4jp6mGMbFTjk0LTpkLZza8NCJCymK4W-_SH5685Y47j0LnjxZko1_pk1I_wTz54MCUJ9fhJ7sKYOCi0tPdA95GkM5gDiYzGyyDJ0zr9CTCVd-iPh0PaF22CTOSZc9FCUrms3V7SHn2BSFj5hv8FFcvAyK2ssOJu6w-yo9tRSDQG4kn6EyrlSBom6KiBX_4VGK_yvOV6VAszEol1fkkoF5z8rPRhQXRIdqNL2ymwjHOvE1CaeM9_1HU_mEbHkYuLXB7xb3N32QUaJD77C9q2PrMxvZf8-Ey4Ll_zdjOk2kV82tS-2qGmOnonqvbAAa1KLUxuv-Ukd0BEB25kg_Mg2w6dQvNvIFQeWqcTJ9kw2va9fmZC0cxqKS6xsODaHUA_j76cQXU7JIq71rJkFih7SkKtoUvRaUHfMT10xrjiZJukT3oZwqNw9OW4fTPRmrAX-1Y61BoQWVwCNPzxYg12j3LLOlCkI3DVtAAuZGPuykCgpaUQZll8bI4zMwCRm_L5RVLW-J-X977QKtoMIu9FGdjsEFSd--i6nGiEzQO5YipPSQ_yvucQXZfPnNoFIbaSA4kmeFT1crHL1Fl6p74uAEzivEuitYN0zK-OiKnHhNBAgmeEEimJFDS6h8ABlZzZNOTCVT6a-EhWU="}}
{"timestamp":"2026-07-16T17:57:30.038Z","type":"event_msg","payload":{"type":"agent_message","message":"Focused nutrition and static checks are clean. Since I touched metric catalog availability, I’m rerunning the selected non-socket V5/API tests once more.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:57:30.038Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Focused nutrition and static checks are clean. Since I touched metric catalog availability, I’m rerunning the selected non-socket V5/API tests once more."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:57:32.278Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py -k 'not http_api_requires_private_bearer_token_and_never_caches and not http_api_is_fail_closed_without_explicit_database_and_token_paths and not server_exposes_v5_preview_and_exact_asset_allowlist'\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_mUVbv5VZDU5IidNdZCgQxYxk"}}
{"timestamp":"2026-07-16T17:58:02.441Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_mUVbv5VZDU5IidNdZCgQxYxk","output":"Chunk ID: 52897f\nWall time: 30.0013 seconds\nProcess running with session ID 38772\nOriginal token count: 8\nOutput:\n.............................."}}
{"timestamp":"2026-07-16T17:58:02.447Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4454939,"cached_input_tokens":4314624,"output_tokens":23563,"reasoning_output_tokens":2165,"total_tokens":4478502},"last_token_usage":{"input_tokens":133093,"cached_input_tokens":132480,"output_tokens":186,"reasoning_output_tokens":17,"total_tokens":133279},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:58:07.594Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRuvobGxxFdNHN5NdKNr3TYVUw_0SQsBjHA4XolI3GDfOyJ2f5-8Xuk2dej-aBuxUbaoEvPPa1OqDcwMkFLLeEmrpkJE3T--YNW2DKuAER0mkK8NZFLT5tVbiUr6GG-4DTtT4z0WsunaJ6ufiHWp-6j-t_xulFUpzYaT6mlrvnA3JNvgYLrE-air9tWjlzKTa0qCzaLmx7fTufIddujEZgx36rBIg7MB8ioCwRAcSJXZW3N2r3XN8UNDgr1KZmi2rVnNvVzgry0kStKyUaRkzARVnQHZjAuCVOogzslXSgsMZg1CS30OzEJ4Rc6aZQ9SofoUxn4_tAPNJ3kw5wGobs7FmvadMXXWS2YNwXa_Msrxz7wHUeXZ7aPmCkp0Ef3HttydekUslW7cSTuCoU1S37P9iMHD3NHi5SLXBQ6HlcFnkbuVXXo8tkRF77gNm_Bt7Mqufp0sfGqloh3UBaI0MYPU8BNo-5o30VA1uzYkGm1gbyfRAuw0pBfnM3xxaRD-Jk906pnEpgEwz7gaIVsjexJdaIppkCBPCv81TX85u3Bs-NXkJWDXR3Ko0zkJyWvIaNcXF2De7-Aqb1R2RhhQXAYmgYWd9SsVZ4XTzoBT-E0OU9xP_SzC3n3rMs1YFb9oK6yvA2nd83do7E-k_uImOo0HZEyNDzN76bZtvmBAD3HYvdDdULfVk8qd-woyurNdwZtI8JQr0B4-CnsHW9YFKAJIwctSkdqR0K4rEJPSLgptTFIGcUTvaq_E4mgxYhDZQbg93byvqnChgkKz3AHMzV1d4y15aTU1XQoMc9OjJVjjx09cuXmshADtQU0PozlX58zk7GXAjDvomx0siE_HQA_TglnLv2h4VZvF1P_YXa5JOQMeWMvX5qjpJGO88EeWU989PQNo4VB9hWzKlddlFNUJ6MShRk-SmCj0ZFspAsf6I9I="}}
{"timestamp":"2026-07-16T17:58:08.343Z","type":"event_msg","payload":{"type":"agent_message","message":"The selected V5/API run is still active and remains green so far. I’m waiting for the final test summary before closing out.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:58:08.344Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The selected V5/API run is still active and remains green so far. I’m waiting for the final test summary before closing out."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:58:09.126Z","type":"response_item","payload":{"type":"function_call","name":"write_stdin","arguments":"{\"session_id\":38772,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":80000}","call_id":"call_c14jHCpFSRJcVaAting31hvd"}}
{"timestamp":"2026-07-16T17:58:11.019Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_c14jHCpFSRJcVaAting31hvd","output":"Chunk ID: fab62a\nWall time: 1.8900 seconds\nProcess exited with code 0\nOriginal token count: 21\nOutput:\n.....                                      [100%]\n35 passed, 3 deselected in 38.35s\n"}}
{"timestamp":"2026-07-16T17:58:11.019Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4588267,"cached_input_tokens":4447616,"output_tokens":23642,"reasoning_output_tokens":2171,"total_tokens":4611909},"last_token_usage":{"input_tokens":133328,"cached_input_tokens":132992,"output_tokens":79,"reasoning_output_tokens":6,"total_tokens":133407},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:58:13.219Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRu1r2KLLct_J_xU_ofWKIJN_wyDxw6Ukq4dLol1BNcEpdg5vdbibgsEC_7bvhx_KNtwkK5Rt0l1XqklndQp444g-tM6hS8MIjaN37zI3w0MMjqZ6Zo7m78dBpbSYlnuOady21H1VspN3yefPpiaVEyQhPBFmfcc4XHAEGUCTmkVG2qKa_fFG2wnTykP4J3RW7aEvDEqnfMKDNjAo2e2JvU3IvBchtQDbQ_mMvhZUoEhF-GwrYCtMSwDLLFUe6GWP9_48voVsmICWODpkZFs3WHhqNZPJT8-Okdygh5be3gYutEtcZf3dJhwSijInThXCrUlmiZUcwfHXOZiuYh-ya0TrC40zrvk3LaudR_dRSPREKULibdHtAuQh-6w4BiYE7Csns828fISkBGkgEFItcBOJScDZT3aH60VYR033vIMPIeXlutORJVD69F0FCWNHtmEcrfvwUyeI3jQYxg6lamllj5MwpKpCFmyLT93Zs2sVOhVeXe7botiBPslbdrt_UWDvR7yxcSGLNpxjwKkTkQlwMyC6ExXzObZQQv4uxD6NZQ_Xc0hRN8zGsgKy_czQDSn_Ih_bsIy9AuAKDVXhBWDRDwve5M7tN9zLw5U4g0Dzq-lkC2_A8aPSaxuW5bWaGN8arPOpOPJ6hjS0L6o9OFi6xBBPdmCnOAr4j_4tWKs43smLG1CK4NHmwX7rCzGkPBldI6yujnUi5CJz_fY3X9mLU4Hlc9wC9BPy2yNvdKQzMzDiglsmliHj-QMhnny2jwhm3ke3hYph_G9wUSuSgqoqaCymnwwJFSkhXdzJoKx9dUdXn4ppAOoO1IlYQlH-g73xi-q8fbb-KB05GJiNX65k7-gRsoUD-XahZojqjJDGPtlwfTdUEen98q4HMREDQ_7ZuJ3lVub9T3z8IF07dAtbZ6vro67Lp6PdVhGhu9tSKuBAtcGRXA0CA8R6Fi3x7-Op84szAbh5DSWcwwOgP9Ly2_jXaH892krsSlfxBf3COs="}}
{"timestamp":"2026-07-16T17:58:18.443Z","type":"event_msg","payload":{"type":"agent_message","message":"The selected V5/API tests pass. I’m collecting the final status and diff summary now, then I’ll report exactly what changed and what was verified.","phase":"commentary","memory_citation":null}}
{"timestamp":"2026-07-16T17:58:18.448Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"The selected V5/API tests pass. I’m collecting the final status and diff summary now, then I’ll report exactly what changed and what was verified."}],"phase":"commentary"}}
{"timestamp":"2026-07-16T17:58:18.448Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git status --short\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}","call_id":"call_MokF8iIA7w3aN4BUt1lGHmMx"}}
{"timestamp":"2026-07-16T17:58:18.448Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git diff --stat\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":16000}","call_id":"call_28BgmREvCqjxEHL055lzSwbD"}}
{"timestamp":"2026-07-16T17:58:18.520Z","type":"response_item","payload":{"type":"function_call","name":"exec_command","arguments":"{\"cmd\":\"git diff -- scripts/health/health_dashboard_action_worker.py scripts/health/health_dashboard_server.py scripts/health/dashboard_v5/read_api.py scripts/health/dashboard_v5/data_provider.py scripts/health/dashboard_v5/contracts.py scripts/health/dashboard_v5/metric_catalog_v2.py tests/test_dashboard_v5_sprint6g_nutrition.py docs/adr/0003-dietary-inflammation-model.md | sed -n '1,260p'\",\"workdir\":\"/home/agent/.hermes/repos/HealthManager\",\"yield_time_ms\":10000,\"max_output_tokens\":40000}","call_id":"call_z1qZyjmqx1tlayTVUAIOoYk2"}}
{"timestamp":"2026-07-16T17:58:18.531Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_MokF8iIA7w3aN4BUt1lGHmMx","output":"Chunk ID: acff57\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 196\nOutput:\n M database/schema.sql\n M scripts/health/assets/health-assets/dashboard-v5-api-explorer.js\n M scripts/health/assets/health-assets/dashboard-v5.css\n M scripts/health/assets/health-assets/dashboard-v5.js\n M scripts/health/dashboard_v5/contracts.py\n M scripts/health/dashboard_v5/data_provider.py\n M scripts/health/dashboard_v5/metric_catalog_v2.py\n M scripts/health/dashboard_v5/read_api.py\n M scripts/health/dashboard_v5/render.py\n M scripts/health/health_dashboard_action_worker.py\n M scripts/health/health_dashboard_server.py\n M tests/fixtures/dashboard_v5_fixture.py\n?? docs/adr/0003-dietary-inflammation-model.md\n?? scripts/health/assets/health-assets/dashboard-v5-echarts.js\n?? tests/browser/dashboard_v5_sprint6g_echarts.spec.js\n?? tests/test_dashboard_v5_sprint6g_nutrition.py\n"}}
{"timestamp":"2026-07-16T17:58:18.531Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_28BgmREvCqjxEHL055lzSwbD","output":"Chunk ID: 8f1acf\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 209\nOutput:\n database/schema.sql                                |  57 ++-\n .../health-assets/dashboard-v5-api-explorer.js     |   2 +-\n .../health/assets/health-assets/dashboard-v5.css   |  15 +-\n .../health/assets/health-assets/dashboard-v5.js    | 248 +++++++++++--\n scripts/health/dashboard_v5/contracts.py           |  17 +-\n scripts/health/dashboard_v5/data_provider.py       |  18 +-\n scripts/health/dashboard_v5/metric_catalog_v2.py   |   2 +-\n scripts/health/dashboard_v5/read_api.py            | 303 ++++++++++++++-\n scripts/health/dashboard_v5/render.py              |  14 +-\n scripts/health/health_dashboard_action_worker.py   | 409 ++++++++++++++++++++-\n scripts/health/health_dashboard_server.py          | 115 +++++-\n tests/fixtures/dashboard_v5_fixture.py             |  28 +-\n 12 files changed, 1141 insertions(+), 87 deletions(-)\n"}}
{"timestamp":"2026-07-16T17:58:18.573Z","type":"response_item","payload":{"type":"function_call_output","call_id":"call_z1qZyjmqx1tlayTVUAIOoYk2","output":"Chunk ID: d416c5\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 3326\nOutput:\ndiff --git a/scripts/health/dashboard_v5/contracts.py b/scripts/health/dashboard_v5/contracts.py\nindex 97cb689..853e812 100644\n--- a/scripts/health/dashboard_v5/contracts.py\n+++ b/scripts/health/dashboard_v5/contracts.py\n@@ -239,7 +239,7 @@ def validate_bundle(bundle: dict[str, Any]) -> None:\n         for field in (\"item_count\", \"unknown_items\"):\n             if row[field] is not None and (type(row[field]) is not int or row[field] < 0):\n                 raise ValueError(f\"invalid count at {path}.{field}\")\n-        if row[\"histamine_status\"] not in {\"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n+        if row[\"histamine_status\"] not in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\", \"unknown\"}:\n             raise ValueError(f\"invalid histamine status at {path}\")\n         if row[\"quality\"] not in {\"complete\", \"incomplete\"}:\n             raise ValueError(f\"invalid nutrition quality at {path}\")\n@@ -251,11 +251,24 @@ def validate_bundle(bundle: dict[str, Any]) -> None:\n     review_rows = _bounded_list(nutrition[\"mapping_review\"], \"nutrition.mapping_review\", MAX_REVIEW_ITEMS)\n     for index, entry in enumerate(review_rows):\n         path = f\"nutrition.mapping_review[{index}]\"\n-        row = _exact(entry, {\"name\", \"occurrences\", \"reason\"}, path)\n+        allowed = {\n+            \"name\", \"occurrences\", \"reason\", \"queue_key\", \"suggested_canonical_food\",\n+            \"suggested_score\",\n+        }\n+        if not isinstance(entry, dict) or not {\"name\", \"occurrences\", \"reason\"} <= set(entry) <= allowed:\n+            raise ValueError(f\"invalid fields at {path}\")\n+        row = entry\n         _bounded_text(row[\"name\"], f\"{path}.name\", 160)\n         _bounded_text(row[\"reason\"], f\"{path}.reason\", 120)\n         if type(row[\"occurrences\"]) is not int or row[\"occurrences\"] < 0:\n             raise ValueError(f\"invalid occurrences at {path}\")\n+        if \"queue_key\" in row:\n+            if not isinstance(row[\"queue_key\"], str) or not re.fullmatch(r\"[a-z0-9_]{0,80}\", row[\"queue_key\"]):\n+                raise ValueError(f\"invalid queue key at {path}\")\n+        if \"suggested_canonical_food\" in row:\n+            _bounded_text(row[\"suggested_canonical_food\"], f\"{path}.suggested_canonical_food\", 120, allow_empty=True)\n+        if \"suggested_score\" in row and row[\"suggested_score\"] not in {0, 1, 2, 3, \"unknown\"}:\n+            raise ValueError(f\"invalid suggested score at {path}\")\n \n     tasks = _bounded_list(bundle[\"tasks\"], \"tasks\", min(MAX_TASKS, 3))\n     for index, entry in enumerate(tasks):\ndiff --git a/scripts/health/dashboard_v5/data_provider.py b/scripts/health/dashboard_v5/data_provider.py\nindex 40f3017..9c910d1 100644\n--- a/scripts/health/dashboard_v5/data_provider.py\n+++ b/scripts/health/dashboard_v5/data_provider.py\n@@ -246,21 +246,31 @@ def nutrition_data(connection: sqlite3.Connection) -> dict[str, Any]:\n                 day is not None and row[\"item_count\"] is not None and int(row[\"item_count\"]) > 0\n                 and row[\"histamine_unknown_count\"] is not None and int(row[\"histamine_unknown_count\"]) == 0\n                 and row[\"histamine_score\"] is not None\n-                and row[\"histamine_label\"] in {\"green\", \"yellow\", \"orange\", \"red\"}\n+                and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n             )\n             if day:\n                 daily.append({\n                     \"date\": day, \"kcal\": row[\"kcal\"], \"item_count\": row[\"item_count\"],\n                     \"histamine_score\": row[\"histamine_score\"] if complete else None,\n-                    \"histamine_status\": str(row[\"histamine_label\"]) if complete else \"unknown\",\n+                    \"histamine_status\": \"classified\" if complete else \"unknown\",\n                     \"unknown_items\": row[\"histamine_unknown_count\"], \"quality\": \"complete\" if complete else \"incomplete\",\n                 })\n     review = []\n     if table_exists(connection, \"nutrition_review_queue\"):\n-        for row in connection.execute(\"SELECT example_name,occurrence_count FROM nutrition_review_queue WHERE status='open' ORDER BY occurrence_count DESC,last_seen DESC LIMIT 25\"):\n+        for row in connection.execute(\"SELECT normalized_name,example_name,occurrence_count,suggested_canonical_food,suggested_score FROM nutrition_review_queue WHERE status='open' ORDER BY occurrence_count DESC,last_seen DESC LIMIT 25\"):\n             name = public_text(row[\"example_name\"], 160)\n             if name:\n-                review.append({\"name\": name, \"occurrences\": int(row[\"occurrence_count\"] or 0), \"reason\": \"Zuordnung fehlt\"})\n+                queue_key = slug(row[\"normalized_name\"])\n+                suggested = public_text(row[\"suggested_canonical_food\"], 120, allow_empty=True)\n+                score = row[\"suggested_score\"] if row[\"suggested_score\"] in (0, 1, 2, 3) else None\n+                review.append({\n+                    \"queue_key\": queue_key,\n+                    \"name\": name,\n+                    \"occurrences\": int(row[\"occurrence_count\"] or 0),\n+                    \"reason\": \"Zuordnung fehlt\",\n+                    \"suggested_canonical_food\": suggested or \"\",\n+                    \"suggested_score\": score if score is not None else \"unknown\",\n+                })\n     return {\"daily\": list(reversed(daily)), \"mapping_review\": review, \"mapping_open_count\": len(review)}\n \n \ndiff --git a/scripts/health/dashboard_v5/metric_catalog_v2.py b/scripts/health/dashboard_v5/metric_catalog_v2.py\nindex bccb575..7854aa2 100644\n--- a/scripts/health/dashboard_v5/metric_catalog_v2.py\n+++ b/scripts/health/dashboard_v5/metric_catalog_v2.py\n@@ -332,7 +332,7 @@ def _availability(connection: sqlite3.Connection, metric: MetricV2) -> dict[str,\n             \"\"\"SELECT datum,histamine_score FROM nutrition_daily_summary_v2\n                WHERE item_count>0 AND histamine_unknown_count=0\n                  AND histamine_score IS NOT NULL\n-                 AND histamine_label IN ('green','yellow','orange','red')\n+                 AND histamine_label IN ('classified','green','yellow','orange','red')\n                  AND datum<=? LIMIT ?\"\"\",\n             (today.isoformat(), MAX_BLOOD_PRESSURE_SOURCE_ROWS + 1),\n         ))\ndiff --git a/scripts/health/dashboard_v5/read_api.py b/scripts/health/dashboard_v5/read_api.py\nindex a17191e..9a5e721 100644\n--- a/scripts/health/dashboard_v5/read_api.py\n+++ b/scripts/health/dashboard_v5/read_api.py\n@@ -59,6 +59,9 @@ MAX_EVENT_ROWS = 1000\n MAX_LAB_ROWS = 100\n MAX_SEARCH_ROWS_PER_GROUP = 20\n MAX_CALENDAR_DAYS = 62\n+MAX_NUTRITION_DAYS = 180\n+MAX_NUTRITION_ITEMS = 200\n+MAX_NUTRITION_QUEUE = 50\n MAX_FUTURE_DAYS = 366\n MAX_QUERY_BYTES = 512\n QUERY_TIMEOUT_SECONDS = 1.0\n@@ -66,6 +69,24 @@ ALLOWED_RESOLUTIONS = frozenset({\"day\", \"week\"})\n ALLOWED_EVENT_TYPES = frozenset(\n     {\"medication_administered\", \"symptom_day\", \"health_event\", \"health_period\"}\n )\n+NUTRIENT_ALLOWLIST = {\n+    \"energy.energy\": (\"Energie\", \"kcal\"),\n+    \"nutrient.protein\": (\"Protein\", \"g\"),\n+    \"nutrient.carb\": (\"Kohlenhydrate\", \"g\"),\n+    \"nutrient.fat\": (\"Fett\", \"g\"),\n+    \"nutrient.fiber\": (\"Ballaststoffe\", \"g\"),\n+    \"nutrient.sugar\": (\"Zucker\", \"g\"),\n+    \"nutrient.saturated_fat\": (\"Gesättigte Fettsäuren\", \"g\"),\n+    \"nutrient.salt\": (\"Salz\", \"g\"),\n+    \"nutrient.sodium\": (\"Natrium\", \"mg\"),\n+}\n+MEAL_LABELS = {\n+    \"breakfast\": \"Frühstück\",\n+    \"lunch\": \"Mittagessen\",\n+    \"dinner\": \"Abendessen\",\n+    \"snack\": \"Snack\",\n+    \"unassigned\": \"Nicht zugeordnet\",\n+}\n FORBIDDEN_METADATA_TEXT = re.compile(\n     r\"(?:/home/|/tmp/|/Users/|/(?:var|etc|usr|opt|srv|root|run|mnt|media|private)/|\"\n     r\"~/|file:|https?://|[A-Za-z]:[\\\\/]|\"\n@@ -82,6 +103,8 @@ API_PATHS = frozenset(\n         \"/api/v1/labs\",\n         \"/api/v1/calendar\",\n         \"/api/v1/search\",\n+        \"/api/v1/nutrition/days\",\n+        \"/api/v1/nutrition/mapping-queue\",\n     }\n )\n EXPECTED_ANALYTICS_UNITS = APPLE_ANALYTICS_UNITS\n@@ -124,6 +147,15 @@ def api_document_id(\n     return f\"api-document-{hashlib.sha256(b'health-api-document-v1:' + material).hexdigest()[:24]}\"\n \n \n+def api_observation_id(prefix: str, raw_id: Any, *parts: Any) -> str:\n+    material = json.dumps(\n+        [int(raw_id), *parts],\n+        ensure_ascii=False,\n+        separators=(\",\", \":\"),\n+    ).encode(\"utf-8\")\n+    return f\"{prefix}-{hashlib.sha256(b'health-api-observation-v1:' + material).hexdigest()[:24]}\"\n+\n+\n def connect_read_only(database: Path) -> sqlite3.Connection:\n     connection = sqlite3.connect(f\"{database.resolve().as_uri()}?mode=ro\", uri=True)\n     connection.row_factory = sqlite3.Row\n@@ -408,7 +440,7 @@ def _nutrition_points(\n     sql = \"\"\"SELECT datum,histamine_score,item_count,histamine_unknown_count,histamine_label\n              FROM nutrition_daily_summary_v2 WHERE item_count>0\n                AND histamine_unknown_count=0 AND histamine_score IS NOT NULL\n-               AND histamine_label IN ('green','yellow','orange','red')\"\"\"\n+               AND histamine_label IN ('classified','green','yellow','orange','red')\"\"\"\n     parameters: list[Any] = []\n     if start:\n         sql += \" AND datum>=?\"\n@@ -517,7 +549,7 @@ def _verified_lab_rows(\n             else None\n         )\n         item: dict[str, Any] = {\n-            \"id\": f\"lab-observation-{int(row['id'])}\",\n+            \"id\": api_observation_id(\"lab-observation\", row[\"id\"], parameter, unit, day),\n             \"metric_id\": LAB_CATALOG_SPECS[parameter].metric_id,\n             \"parameter\": parameter,\n             \"value\": value,\n@@ -1207,6 +1239,261 @@ def _day_nutrition(connection: sqlite3.Connection, requested: str) -> dict[str,\n     }\n \n \n+def _nutrition_complete(row: sqlite3.Row) -> bool:\n+    return (\n+        int(row[\"item_count\"] or 0) > 0\n+        and int(row[\"histamine_unknown_count\"] or 0) == 0\n+        and row[\"histamine_score\"] is not None\n+        and row[\"histamine_label\"] in {\"classified\", \"green\", \"yellow\", \"orange\", \"red\"}\n+    )\n+\n+\n+def _public_number(value: Any) -> int | float | None:\n+    try:\n+        numeric = float(value)\n+    except (TypeError, ValueError):\n+        return None\n+    if not math.isfinite(numeric):\n+        return None\n+    rounded = round(numeric, 3)\n+    return int(rounded) if rounded.is_integer() else rounded\n+\n+\n+def _nutrition_days(connection: sqlite3.Connection, params: dict[str, str]) -> dict[str, Any]:\n+    start, end = parse_range(params)\n+    if not table_exists(connection, \"nutrition_daily_summary_v2\"):\n+        return {\"version\": 1, \"timezone\": TZ_NAME, \"definition\": _histamine_definition(), \"days\": []}\n+    sql = \"\"\"SELECT datum,kcal,protein_g,carb_g,fat_g,item_count,histamine_score,\n+                    histamine_max,histamine_unknown_count,histamine_label\n+             FROM nutrition_daily_summary_v2 WHERE datum<=?\"\"\"\n+    values: list[Any] = [local_today().isoformat()]\n+    if start:\n+        sql += \" AND datum>=?\"\n+        values.append(start.isoformat())\n+    if end:\n+        sql += \" AND datum<=?\"\n+        values.append(end.isoformat())\n+    sql += \" ORDER BY datum DESC LIMIT ?\"\n+    values.append(MAX_NUTRITION_DAYS + 1)\n+    rows = list(connection.execute(sql, values))\n+    if len(rows) > MAX_NUTRITION_DAYS:\n+        raise APIError(422, \"row_limit_exceeded\")\n+    days = []\n+    for row in reversed(rows):\n+        day = parse_day(row[\"datum\"])\n+        if not day:\n+            continue\n+        complete = _nutrition_complete(row)\n+        days.append({\n+            \"date\": day,\n+            \"macros\": {\n+                \"kcal\": _public_number(row[\"kcal\"]),\n+                \"protein_g\": _public_number(row[\"protein_g\"]),\n+                \"carb_g\": _public_number(row[\"carb_g\"]),\n+                \"fat_g\": _public_number(row[\"fat_g\"]),\n+            },\n+            \"item_count\": int(row[\"item_count\"] or 0),\n+            \"histamine\": {\n+                \"score\": _public_number(row[\"histamine_score\"]) if complete else None,\n+                \"max_score\": int(row[\"histamine_max\"] or 0) if complete else None,\n+                \"mapping_coverage\": round((int(row[\"item_count\"] or 0) - int(row[\"histamine_unknown_count\"] or 0)) / int(row[\"item_count\"] or 1), 3) if int(row[\"item_count\"] or 0) else 0,\n+                \"unknown_items\": int(row[\"histamine_unknown_count\"] or 0),\n+                \"status\": \"complete\" if complete else \"incomplete\",\n+                \"definition_version\": _histamine_definition()[\"version\"],\n+            },\n+        })\n+    return {\"version\": 1, \"timezone\": TZ_NAME, \"definition\": _histamine_definition(), \"days\": days}\n+\n+\n+def _histamine_definition() -> dict[str, Any]:\n+    return {\n+        \"id\": \"sighi_mapping_load_v1\",\n+        \"version\": \"sighi_mapping_load_v2\",\n+        \"components\": [\"mapped_item_score_sum\", \"mapped_item_score_max\", \"mapping_coverage\", \"unknown_item_count\"],\n+        \"coverage_semantics\": \"unknown_or_unmapped_items_make_day_incomplete\",\n+        \"insufficient_inputs\": \"no_index\",\n+        \"score_scale\": \"SIGHi 0-3 or unknown per mapped item\",\n+        \"complete_rule\": \"complete only when every documented nutrition item has a mapping\",\n+    }\n+\n+\n+def _nutrition_day_detail(connection: sqlite3.Connection, requested: date) -> dict[str, Any]:\n+    day = requested.isoformat()\n+    if not table_exists(connection, \"nutrition_daily_summary_v2\"):\n+        return {\"version\": 1, \"date\": day, \"timezone\": TZ_NAME, \"status\": \"supported_no_data\"}\n"}}
{"timestamp":"2026-07-16T17:58:18.573Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4721739,"cached_input_tokens":4580608,"output_tokens":23947,"reasoning_output_tokens":2188,"total_tokens":4745686},"last_token_usage":{"input_tokens":133472,"cached_input_tokens":132992,"output_tokens":305,"reasoning_output_tokens":17,"total_tokens":133777},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:58:23.751Z","type":"response_item","payload":{"type":"reasoning","summary":[],"encrypted_content":"gAAAAABqWRu_gtkOY75ff_LDVt73uP04zmdmrz_py7LErjTwWyQrXNwurb_TAbFLf4s7o7hhPx5ETlOu5HMr-56cTwLtSAUVqxj5LpwUhmwvagfXNowqT8vHxFQwMQ3UctavYCjTFqcR2n4ktaEIzfmLoM8Ym5JWrRVVv-iiful3tbddqpBXkboPp0MOIOH6TSK_tyeTTZw3Ii0iVw8cC-T-GB-DIapBADVb1L8mfSRicjorNdXUmb4ZxQGgV-bKJyGOdiveGUUVwKdgzjatUt5JgKrF6d4D1oJFg1hUIW1pEJoG9S-Yc9ceK0ktRNPGAeYjEDHuK_xVQ1PmIxX49YkqRFjJymRfRiSuSYv7hehTybRMOqBRs86KGm4_dQbN4SKxuH4zySobWhshbtsq6AoGLSPWndzcuRn145P4LwS3nE4Np_Ec5tKawPWSjsPLnQHliMjbHsqvY7hKYPAjx43OdTfAdMK6YtSaurikATKkHRjc-EUafseNxaLR4T-g8LfSAbxHxd95AWAizM7dKt4XGosZ4RDPR0B6n0KxTqZG42DcGKYy6vtzV4NwbsgszQFJNqdy59Jk-6QOks8Oe49-o-SRqPC7mpOayaVn_4Y1qG_L24eZ8FnTnqEpQw8K-Fn6EY35fWJQ79xXdfhaNui8jdwwY1RHlIPyfr5vHnMxmJWHAdkpiN3M5WccN6fe_dVBXZ0M0kImzrZ-brgdsp6Vg8hLLNhTv_RpOlRyiCo55K1SX5y5U-KrYYsEqefi6fJ9zTf4npFX9j1ZfYyKDrvyrSWm_c2YpblN8Z-mpbocWM3mKSZglyjO0le87rphLuK62GqkKwtJG8jS5B-6-2SP6hu0Oqs5VMQdJpTN6185RwN9rP_WnnIXQU0C2jRApNcmuHdsLNP4TU0QZiPZr22oETAloR0kKWy3IRHgJbacGCyy4u5OtFuKLuQf7ST6WlI_LBMQYdnsg5tSbwnxWraKTxKWIDPnNc1RPUIL0kCvmYPbo4dn2ZfshiGyddWVP1Y1O-oIOXed6ohPenKvl9J9crK6FMfI0dZIxwxN3coV_FSroc2riutp3Y6ffpxXaz3OPsrqgtja83-28tATIiKDOG_NMpfyqcvZ7ucLXe-jc4dy5Aj7TfcZTtyR6uyNkH56m_uCQWUkLL3U_DUswomYGWFvyx9zMzPzoDpDocIJ5Cg2YwSdWnEqnufY7mrg7btQ1AYRF6m4om6ZOaKzRf5nKWs5gmWDfZtziIPSNWT6SjOnlh9YuoGd3y1nIghWc-7ZF6_aGw2jTGMjJA7I7-r989AWFhCAoTr_RJDZWXdhWfVe1QokDyh1soySbDNKpIpez9mGEP-sVkukNs9PMGqmTQ8M320ExdXVSU1R_6PJzpl69n1E92HXDr33w9WxmSIKOzkuHDjLx_pB7rdSKYxo1O8_VMsSjiPwfK-vq66NnL87opYvMWcDfDAUi-QTJ4hyf7tN76Dg-PVoBmUJRmQf77hSmDFGJXKfMhPAyZ52-SFEYScX-81GgG9CGB1r_9e9PmRxWvJZpuv0c78bGU1RHd38x13vzjNPcl_Kf19cRRi5IhdFC9puAMPY025o8pQVBNUf7CwlXiX8OolRaGrwYjCz-gnvFhaMyhByHNl1KKCdx71iJBwdm3u2vvNm9H1-5cm5FhBK_0ZuwR5Mn2D_fxd4ArDUf4rXERfvciTTZToAVSOhlP_v_5Pzg4Xd4rWyWZ17MQVGTA49jyTPqgO8v5KyrMTfEMGzxMJSGfixPgnknhs7KAx3LZQd5L5WmR3qgXRBUKPAQugikEbD2gmDmeMIH6AA2Q=="}}
{"timestamp":"2026-07-16T17:58:33.208Z","type":"event_msg","payload":{"type":"agent_message","message":"Implemented the Sprint 6G-A repair in the current uncommitted worktree. No commit/push/deploy, no production access.\n\nKey fixes:\n- Replaced mapping action contract with explicit `decision` (`assign|composite|ignore`), alias, canonical food, SIGHi `0-3|unknown`, confidence, mapping method allowlist, source label/version, bounded note, ingredient-review flag, and optional neutral personal tolerance documentation.\n- Removed traffic-light semantics from new public nutrition/mapping APIs and UI wording; no `safe` personal tolerance label in the new contract.\n- Worker is now idempotent/transactional, audits provenance/version fields, maps all items matching normalized product slug, preserves composite as unclassified, and makes ignore not alter mappings.\n- Recompute preserves missing macros as unknown, rebuilds allowlisted nutrient JSON from `nutrition_item_nutrients`, omits unknown-unit micronutrients, and only recomputes affected days.\n- Read APIs avoid raw/source/internal numeric exposure; lab observation IDs are now opaque hashes.\n- Mapping POST uses the bounded exact form shape with same-origin one-time CSRF.\n- V5 worker regeneration preserves `--health-record-6e`, `--explorer-6c`, and `--calendar-day-6d`.\n- UI mapping workspace is functional; nutrition day groups cover breakfast/lunch/dinner/snack/unassigned.\n- ADR 0003 now includes DII methodology constraints, coverage/unit findings, licensing/reference limitation, versioning requirements, and the required conclusion: `Entzündungsindex derzeit nicht belastbar berechenbar`.\n- V4 file was not modified.\n\nVerification run:\n- `pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py` -> 6 passed\n- `PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py -k 'not http_api_requires_private_bearer_token_and_never_caches and not http_api_is_fail_closed_without_explicit_database_and_token_paths and not server_exposes_v5_preview_and_exact_asset_allowlist'` -> 35 passed, 3 deselected\n- `python -m py_compile ...` on modified Python modules -> passed\n- `node --check scripts/health/assets/health-assets/dashboard-v5.js` -> passed\n\nThe three deselected tests require opening local sockets; the full run hit sandbox `PermissionError: [Errno 1] Operation not permitted` there.","phase":"final_answer","memory_citation":null}}
{"timestamp":"2026-07-16T17:58:33.226Z","type":"response_item","payload":{"type":"message","role":"assistant","content":[{"type":"output_text","text":"Implemented the Sprint 6G-A repair in the current uncommitted worktree. No commit/push/deploy, no production access.\n\nKey fixes:\n- Replaced mapping action contract with explicit `decision` (`assign|composite|ignore`), alias, canonical food, SIGHi `0-3|unknown`, confidence, mapping method allowlist, source label/version, bounded note, ingredient-review flag, and optional neutral personal tolerance documentation.\n- Removed traffic-light semantics from new public nutrition/mapping APIs and UI wording; no `safe` personal tolerance label in the new contract.\n- Worker is now idempotent/transactional, audits provenance/version fields, maps all items matching normalized product slug, preserves composite as unclassified, and makes ignore not alter mappings.\n- Recompute preserves missing macros as unknown, rebuilds allowlisted nutrient JSON from `nutrition_item_nutrients`, omits unknown-unit micronutrients, and only recomputes affected days.\n- Read APIs avoid raw/source/internal numeric exposure; lab observation IDs are now opaque hashes.\n- Mapping POST uses the bounded exact form shape with same-origin one-time CSRF.\n- V5 worker regeneration preserves `--health-record-6e`, `--explorer-6c`, and `--calendar-day-6d`.\n- UI mapping workspace is functional; nutrition day groups cover breakfast/lunch/dinner/snack/unassigned.\n- ADR 0003 now includes DII methodology constraints, coverage/unit findings, licensing/reference limitation, versioning requirements, and the required conclusion: `Entzündungsindex derzeit nicht belastbar berechenbar`.\n- V4 file was not modified.\n\nVerification run:\n- `pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py` -> 6 passed\n- `PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py -k 'not http_api_requires_private_bearer_token_and_never_caches and not http_api_is_fail_closed_without_explicit_database_and_token_paths and not server_exposes_v5_preview_and_exact_asset_allowlist'` -> 35 passed, 3 deselected\n- `python -m py_compile ...` on modified Python modules -> passed\n- `node --check scripts/health/assets/health-assets/dashboard-v5.js` -> passed\n\nThe three deselected tests require opening local sockets; the full run hit sandbox `PermissionError: [Errno 1] Operation not permitted` there."}],"phase":"final_answer"}}
{"timestamp":"2026-07-16T17:58:33.232Z","type":"event_msg","payload":{"type":"token_count","info":{"total_token_usage":{"input_tokens":4859569,"cached_input_tokens":4713600,"output_tokens":24613,"reasoning_output_tokens":2328,"total_tokens":4884182},"last_token_usage":{"input_tokens":137830,"cached_input_tokens":132992,"output_tokens":666,"reasoning_output_tokens":140,"total_tokens":138496},"model_context_window":258400},"rate_limits":{"limit_id":"codex","limit_name":null,"primary":{"used_percent":18.0,"window_minutes":10080,"resets_at":1784786006},"secondary":null,"credits":{"has_credits":false,"unlimited":false,"balance":"0"},"individual_limit":null,"plan_type":"prolite","rate_limit_reached_type":null}}}
{"timestamp":"2026-07-16T17:58:33.242Z","type":"event_msg","payload":{"type":"task_complete","turn_id":"019f6c08-c021-7c71-92e8-a5e878a68d83","last_agent_message":"Implemented the Sprint 6G-A repair in the current uncommitted worktree. No commit/push/deploy, no production access.\n\nKey fixes:\n- Replaced mapping action contract with explicit `decision` (`assign|composite|ignore`), alias, canonical food, SIGHi `0-3|unknown`, confidence, mapping method allowlist, source label/version, bounded note, ingredient-review flag, and optional neutral personal tolerance documentation.\n- Removed traffic-light semantics from new public nutrition/mapping APIs and UI wording; no `safe` personal tolerance label in the new contract.\n- Worker is now idempotent/transactional, audits provenance/version fields, maps all items matching normalized product slug, preserves composite as unclassified, and makes ignore not alter mappings.\n- Recompute preserves missing macros as unknown, rebuilds allowlisted nutrient JSON from `nutrition_item_nutrients`, omits unknown-unit micronutrients, and only recomputes affected days.\n- Read APIs avoid raw/source/internal numeric exposure; lab observation IDs are now opaque hashes.\n- Mapping POST uses the bounded exact form shape with same-origin one-time CSRF.\n- V5 worker regeneration preserves `--health-record-6e`, `--explorer-6c`, and `--calendar-day-6d`.\n- UI mapping workspace is functional; nutrition day groups cover breakfast/lunch/dinner/snack/unassigned.\n- ADR 0003 now includes DII methodology constraints, coverage/unit findings, licensing/reference limitation, versioning requirements, and the required conclusion: `Entzündungsindex derzeit nicht belastbar berechenbar`.\n- V4 file was not modified.\n\nVerification run:\n- `pytest -q tests/test_dashboard_v5_sprint6g_nutrition.py` -> 6 passed\n- `PYTHONPATH=. pytest -q tests/test_health_dashboard_v5.py tests/test_dashboard_v5_sprint6b_api.py tests/test_dashboard_v5_sprint6c_api_contracts.py -k 'not http_api_requires_private_bearer_token_and_never_caches and not http_api_is_fail_closed_without_explicit_database_and_token_paths and not server_exposes_v5_preview_and_exact_asset_allowlist'` -> 35 passed, 3 deselected\n- `python -m py_compile ...` on modified Python modules -> passed\n- `node --check scripts/health/assets/health-assets/dashboard-v5.js` -> passed\n\nThe three deselected tests require opening local sockets; the full run hit sandbox `PermissionError: [Errno 1] Operation not permitted` there.","completed_at":1784224713,"duration_ms":772599,"time_to_first_token_ms":5765}}