Navigate back to the previous page in browser history. Requires browser_navigate to be called first.

Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e5', '@e12')

Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.

If true, clear the message buffers after reading

JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll("a").length'

Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.

Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.

The URL to navigate to (e.g., 'https://example.com')

Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.

Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')

Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.

Direction to scroll

Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.

If true, returns complete page content. If false (default), returns compact view with interactive elements only.

Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.

The element reference from the snapshot (e.g., '@e3')

The text to type into the field

Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA:<screenshot_path> in your response. Requires browser_navigate to be called first.

What you want to know about the page visually. Be specific about what you're looking for.

If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout.

clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.

The question to present to the user.

Choices:
Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

To stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider.

Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear.

Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured.

REQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored).

DELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently.

WHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content.

Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear.

Optional list of toolset names to restrict the job's agent to (e.g. ["web", "terminal", "file", "delegation"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use "web" if it calls web_search, "terminal" if it runs scripts, "file" if it reads files, "delegation" if it calls delegate_task. On update, pass an empty array to clear.

Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated.

Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify
- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. "respond in Chinese", "return output in Japanese"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.
- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims "uploaded successfully" or "file written" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.
- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks.

Task goal

Task-specific context

Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction.

Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed.

Per-task ACP args override. Leave empty unless acp_command is set.

Per-task role override. See top-level 'role' for semantics.

Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored.

Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable.

Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents).

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided.

Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).

Use normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.

Available via `from hermes_tools import ...`:

  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Scripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout.

Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.

The text prompt describing the desired image. Be detailed and descriptive.

The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.

memory

Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

patch

Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.

REPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.
PATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.

Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.

REQUIRED when mode='replace'. File path to edit.

REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness.

REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

REQUIRED when mode='patch'. V4A format patch content. Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Send a message to a connected messaging platform, or list available targets.

IMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.
If the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.

Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms.

Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:<account_id>' (DM), 'yuanbao:group:<group_code>' (group chat)

The message text to send. To send an image or file, include MEDIA:<local_path> (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment.

Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.

TWO MODES:
1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.

Search syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match ("docker networking"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.

Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.

Actions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.

On delete, pass `absorbed_into=<umbrella>` when you're merging this skill's content into another one, or `absorbed_into=""` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

Pinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted.

Content for the file. Required for 'write_file'.

For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent.

Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.

The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans').

OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.
For servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.
After starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.

Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.

Working directory for this command (absolute path). Defaults to the session working directory.

Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.

When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.

Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both.

Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.<name>), not model-selected.

The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.

Image URL (http/https), local file path, or data: URL to load.

Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image.

Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


[M G] Heute wieder Durchfall, bitte in symptom_Log und Tagebuch eintragen.


Explanation:
**Considering health data management**

I see that health data management skills are relevant here, so I think it's necessary to take action. I’ll focus on loading the proper data or resources needed to proceed. It’s important to make sure I’m looking at this from the right perspective and utilizing the right tools to manage any health-related information effectively. I want to navigate this carefully to provide the best support!


health-data-management

---
name: health-data-management
description: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet alle Gesundheitsdaten (Laborwerte, Dokumente, Symptome, Auswertungen)
version: 2.0.0
tags: [health, database, pdf, camelot, watchdog, statistics]
---

# Health Data Management — Chief Medical Data Officer

## Übersicht

Alle Gesundheitsdaten unter `~/.hermes/assets/Gesundheit/`.
241 Laborwerte, 59 Dokumente (9 mit extrahiertem Volltext), 244 Events, 3 Medikamente.
Master-Datenbank: `health_data.db` mit 13 fachlichen Tabellen plus SQLite-intern `sqlite_sequence` (Stand Prüfung 2026-05-12); `processing_log` ist in der aktuellen DB nicht vorhanden.

**Docling-Reprocessing:** 50 von 59 Dokumenten haben keinen durchsuchbaren Volltext. Siehe `references/docling-reprocessing-workflow.md` für den Bearbeitungsplan.

## Verzeichnisstruktur

```
Gesundheit/
├── health_data.db          # Master-Datenbank (12 Tabellen)
├── inbox/                  # Neue Dokumente (Watchdog überwacht)
├── processed/              # Verarbeitete Dokumente
├── archiv/                 # Alle Dokumente kategorisiert
│   ├── laborberichte/
│   ├── klinische_befunde/
│   ├── medikamente/
│   ├── krankheitsgeschichte/
│   ├── wearables/
│   ├── ernaehrung/
│   └── sonstiges/
├── reports/                # Generierte Reports (PNG, HTML)
├── scripts/                # Verarbeitungs-Skripte
│   ├── health_manager.py       # Haupt-Manager (CRP, Korrelationen, Dashboards)
│   ├── health_watchdog.py      # Daemon für automatische PDF-Verarbeitung
│   ├── health_watchdog.sh      # Start/Stop/Status Wrapper
│   └── verify_system.py        # System-Verification
├── backup_original/        # FRIDAY-Backup (62 Dateien)
└── logs/                   # Watchdog-Logs
```

## Datenbank-Schema (13 fachliche Tabellen + `sqlite_sequence`)

1. **laborwerte** — 241 Laborwerte (aktuell, Stand Mai 2026)
2. **health_events** — 244 Events
3. **dokumente** — 59 Dokumente (Stand Mai 2026), mit CHECK constraints: `status IN ('neu','eingearbeitet','archiviert')`, `daten_typ IN ('pdf','image')`
4. **medikamente** — 3 Medikamente
5. **dokumente_status** — Status-Tracking
6. **symptome** — Symptom-Stammdaten
7. **symptom_log** — Symptom-Tagebuch
8. **vitalzeichen** — Blutdruck, Puls, Temperatur
9. **arztbesuche** — Arztbesuche
10. **ernaehrung** — Ernährungsdaten
11. **tagebuch** — universelle Tagebuch-/Gesundheitseinträge
12. **korrelationen** — Korrelationen/Analysen
13. **auswertungen** — Generierte Reports

Hinweis: `sqlite_sequence` ist eine SQLite-interne Tabelle. `processing_log` existiert im aktuellen Schema nicht, obwohl ältere Notizen sie erwähnen.

**Pitfall:** Nur 9 von 59 Dokumenten haben `extrahierte_inhalte` (Volltext) in der Datenbank — diese sind durchsuchbar. 50 Dokumente haben keinen extrahierten Text. Docling-Reprocessing empfohlen (siehe unten).

## Workflow

### Dokumenten-Klassifizierung (KRITISCH)

**Arztbericht** (Ambulanter Bericht, Konsultation, Visitenbericht):
- Kategorie: `BEFUNDE`
- Methode: PyMuPDF (fitz) → Volltext extrahieren
- Speicherung: Volltext in `extrahierte_inhalte` für spähere Abfragen
- Metadaten: Dokumentdatum, Behandler, Diagnose als Annotation am Textbeginn
- Datei: `archiv/befunde/`

**Laborbericht** (Blutwerte, Urin, Befunde mit tabellarischen Werten):
- Kategorie: `LABOR`
- Methode: Camelot/pdfplumber → tabellarische Werte extrahieren
- Speicherung: Einzelne rows in `laborwerte` Tabelle
- Datei: `archiv/laborberichte/`

**Sonstiges** (Rezepte, Sonstiges):
- Kategorie: `SONSTIGES`
- Methode: Je nach Inhalt
- Datei: `archiv/sonstiges/`

### Verarbeitungsablauf

1. Dokument → `inbox/` kopieren
2. Dokument-Typ klassifizieren (Arztbericht vs. Laborbericht)
3. **Fallback:** Watchdog-Daemon ist unzuverlässig → manuell verarbeiten mit `health_manager.process_inbox_pdf()`
4. Bei Arztbericht: PyMuPDF Volltext → Datenbank (extrahierte_inhalte)
5. Bei Laborbericht: Camelot/pdfplumber → laborwerte Tabelle
6. Metadaten extrahieren (Datum, Behandler, Diagnose) → im Text annotieren
7. Datenbank-Eintrag erstellen/aktualisieren mit korrekter Kategorie
8. Status auf `eingearbeitet` setzen
9. Datei an korrekten Archiv-Ort verschieben

## Health Manager Subagent

### Haupt-Skript: `health_manager.py`

**Methoden:**
- `get_laborwerte(parameter, start_date, end_date)` — Laborwerte laden (FRIDAY-Schema: `parameter_name`, `ermittlung_datum`)
- `compute_crp_trend()` — CRP-Trend mit scipy (P-Werte)
- `compute_correlations(target_parameter)` — Korrelationsanalyse mit statsmodels
- `generate_plotly_dashboard(output_path)` — Interaktives HTML-Dashboard
- `generate_profiling_report(output_path)` — ydata-profiling HTML-Report
- `generate_heatmap(parameter_list, output_path)` — Heatmap mit matplotlib/seaborn
- `process_inbox_pdf(pdf_path)` — Manuelle PDF-Verarbeitung

### Watchdog-Daemon

```bash
# Starten
bash ~/.hermes/assets/Gesundheit/scripts/health_watchdog.sh start

# Stoppen
bash ~/.hermes/assets/Gesundheit/scripts/health_watchdog.sh stop

# Status
bash ~/.hermes/assets/Gesundheit/scripts/health_watchdog.sh status

# Neustarten
bash ~/.hermes/assets/Gesundheit/scripts/health_watchdog.sh restart
```

### Python-Umgebung

- Skripte laufen unter `/usr/bin/python3.12`
- Alle Pakete: `pip install --break-system-packages`
- Watchdog-PID: `~/.hermes/assets/Gesundheit/health_watchdog.pid`
- System-Verification: `python3.12 scripts/verify_system.py`

## Health Intelligence Workflow (ab 2026-05-12)

- Migration: `scripts/health_system_migrate.py` legt Review-/Staging-/Insight-/Report-Tabellen an und macht vorher ein DB-Backup unter `backups/`.
- `scripts/health_pipeline.py` ist die zentrale Pipeline: `generate-lab-report`, `export-correction`, `import-correction`, `dashboard`, `daily-report`, `weekly-report`, `status`.
- Vollverarbeitung/Datenqualität: `scripts/process_all_health_documents.py` verarbeitet alle Dokumente in `dokumente` mit PyMuPDF/OpenPyXL und Docling-Fallback, aktualisiert `extrahierte_inhalte`, `datei_hash`, `local_original_path`, `document_insights` und markiert Duplikate konservativ als `review_status='duplicate_candidate'` ohne zu löschen. Danach `scripts/link_lab_values_to_documents.py` für konservative Laborwert↔Original-PDF-Verlinkung und `scripts/generate_health_data_quality_report.py` für QA-Bericht ausführen.
- Dashboard v3 (`scripts/health_dashboard_v3.py`) rendert Labortrends stabil ohne Chart.js-Annotation-Plugin: Messlinien, Achsen, Referenzbänder/-Grenzlinien und Health-Event-Marker sind normale Chart.js-Datasets. `health_pipeline.py dashboard` ruft diesen Renderer auf. Die Tabelle „Klinische Events, Medikamente & Symptome“ schliesst routinemässige LABOR/PROFIL/BEFUNDE-Artefakte aus; Laborwerte gehören in die Trendcharts, nicht in die Event-Tabelle. Seit 2026-05-12 enthält das Dashboard zusätzlich Behçet-relevante Charts wie BSG, Differentialblutbild, Fibrinogen, Gesamtcholesterin, Triglyceride, Homocystein, Nieren-/Urinmarker und Leberwerte.
- Dashboard v3 enthält Originaldokument-/PDF-Links via `drive_web_url` oder `file://`, Health-Event-/Symptom-Tabellen, `health_event_periods` für Aphthen/Schübe/Medikationsphasen und `nutrition_daily_features` mit keywordbasierter Ernährungsplan-Treue. Zeitraum-Events können per `scripts/health_add_event_period.py --start YYYY-MM-DD --end YYYY-MM-DD --type Aphte ...` erfasst werden.
- Apple Health Auto Export: iPhone-App auf Google Drive + JSON konfigurieren. Tatsächlicher App-Zielordner nach Testexport: `Health Auto Export` ID `1BPtqp9h72eh5GQZRSszIn-pYKX6AGUaT` (Dateien `HealthAutoExport-2026-19.json`, `HealthAutoExport_jahr-2025.json`, tägliche `HealthAutoExport_jahr-YYYY-MM-DD.json`). Import über `scripts/apple_health_drive_sync.py`; Parser unterstützt `{data:{metrics:[{name,units,data:[...] }]}}` und Schlafwerte via `totalSleep`. Raw/normalisierte Daten liegen in `apple_health_records`, Importdateien in `apple_health_import_files`. Für Berichte/Dashboard niemals Rohwerte direkt summieren: `scripts/apple_health_analytics.py` nutzen. Es dedupliziert überlappende Exporte, wandelt 2025-Wochen-Summen für Sum-Metriken in Tagesäquivalente um und blendet den aktuellen Tag aus, weil der 12h-Sync erst am Folgetag definitive Tageswerte liefert. Täglicher no-agent Cron `bdc03626e17f` läuft 23:45 via `~/.hermes/scripts/apple_health_daily_sync.py` und bleibt still, wenn nichts Neues importiert wurde.
- 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.
- Apple Health Auto Export: Für die iPhone-App `Health Auto Export - JSON + CSV` bevorzugt **Google Drive + JSON**. Zielordner: `Gesundheitsdaten Inbox / Apple Health Auto Export` (`1T2kpxRyRyH8mSknXm85RRNFFfex61iWQ`). Lokale Inbox: `inbox/apple_health_exports/`. Import-Skripte: `scripts/apple_health_import.py` und `scripts/apple_health_drive_sync.py`. Details: `references/apple-health-auto-export.md`.
- `scripts/generate_labor_report.py` ist nur noch ein Kompatibilitäts-Wrapper für `health_pipeline.py generate-lab-report`.
- `reports/aktuelle_blutwerte.xlsx` wird im gewünschten Pivot-Format erzeugt: eine Spalte pro Untersuchung/Datum, eine Zeile pro Parameter. Die aktuellste Referenz-XLSX aus `inbox/260510_Laborwerte_Uebersicht.xlsx` wird bevorzugt, weil sie Datums-Spalten bis `2026-05-07` enthält.
- Dashboard: `reports/health_dashboard.html`.
- Workflow-Dokumentation: `reports/health_intelligence_workflow.md`.
- Google Drive Korrektur-Ordner: `Gesundheitsdaten Korrektur` (`18wPP6yHjdp8XShhue7OZcamcqc09ec9g`).
- Cron Scripts: `~/.hermes/scripts/health_daily_sync.py` (täglich 23:10), `~/.hermes/scripts/yazio_sync.py` (YAZIO-Ernährungsimport täglich 23:15, Job `b9eb80891aa4`), `~/.hermes/scripts/health_weekly_report.py` (sonntags 18:00) und `~/.hermes/scripts/weekly_health_drive_backup.py` (verschlüsseltes Health/Hermes-Backup sonntags 23:55, Job `04918ca83019`). Alter fehlerhafter Inbox-Cron `e2150e5d908d` ist pausiert; neue Jobs: `2bf4d0a8bedb`, `e9b6fe32c177`. YAZIO-Sync schreibt idempotent in `ernaehrung` und `tagebuch`, prüft heute und gestern und bleibt still, wenn nichts geändert wurde. Weekly Backup nutzt GPG AES256 mit Passphrase-Datei `~/.hermes/backup_keys/health_weekly_gpg_passphrase.txt`, Google-Drive-Ordner `Hermes_Health_Backups` (`1dlFpkKYXOCKaausFfVVnBK-qk3hFHA8T`), enthält Health-DB/Schema/Skripte, Hermes-Quick-Backup, Skills/Skripte und schliesst PDFs/Bilder/Rohdokumente aus. GitHub-Codebackup: privates Repo `https://github.com/Gamexgit/HealthManager`, lokale Arbeitskopie `~/.hermes/repos/HealthManager`, Initial-Commit `b65ab3cea044273033ff4e5185e78cf849dcd154`; enthält nur Code/Skripte/Doku/Schema ohne Daten, keine PDFs/DB/Credentials. GitHub Token/Repo-Link liegen in Google Sheet `Github_token` (`1-I7s9PazhYfYWrYQMwkSviJDk13a8qnAMRueAznD8Rs`) und dürfen nicht in Chat/Repo ausgegeben werden.

## Support Files

- `references/drive-workflow.md` — Google Drive Befehle, Folder-IDs, Upload/Download-Workflow
- `references/gog-email-attachments.md` — GOG Gmail Attachment-Download, Umbenennung langer Dateinamen, Dokumenten-Verarbeitung
- `references/friday-db-schema-compat.md` — FRIDAY-Schema-Kompatibilität, Debugging-Checkliste, Wert-Konvertierung
- `references/health-manager-pitfalls.md` — Häufige Bugs: Variable Shadowing, CHECK-Constraints, Watchdog-Management, FRIDAY-Kompatibilität
- `references/document-classification.md` — Arztbericht vs. Laborbericht Klassifizierung, Quick-Decision-Tree
- `references/labor-report-generation.md` — Laborbericht-Erstellung aus XLSX, Pivot-Tabellen, Kategorien-Struktur, `ermittlung_datum`-Pitfall

## Google Drive Integration

### Drive-Ordner-Struktur

- **Gesundheitsdaten Inbox** (Upload-Ort): `1HOJXzIjJaUUoLGZgJjAR5V6ZCsOBsGgE`
- **Archiv** (Verarbeitete Dokumente): `1H2zXafzbaRobY8XDcPyRpZBE6mXeTcJe`

### Drive-Workflow: Neue Dokumente aus Google Drive Inbox verarbeiten

1. **Inbox prüfen:** `gog -a friday.uplink@gmail.com drive ls --parent <inbox_id> --json`
2. **Neue Dateien identifizieren** (nicht im lokalen Archiv vorhanden)
3. **Download:** `gog -a friday.uplink@gmail.com drive download <file_id>`
4. **Lokale Kopie** nach `~/.hermes/assets/Gesundheit/inbox/` verschieben
5. **Dokument klassifizieren** (Arztbericht vs. Laborbericht)
6. **Text extrahieren** (PyMuPDF für PDFs, Camelot für Labor-Tabellen)
7. **Datenbank aktualisieren** (laborwerte, health_events, dokumente)
8. **Datei ins lokale Archiv** verschieben
9. **Datei ins Google Drive Archiv** hochladen: `gog -a friday.uplink@gmail.com drive upload <local_path> --parent <archiv_id>`
10. **Datei aus Inbox löschen** (optional: `gog drive delete <file_id>`)
11. **Benachrichtigung** an den User über die Erkenntnisse

### Pitfalls

- **`gog gmail attachment <msgId> <attachId> --output <path>`** — korrekter Befehl zum Herunterladen von E-Mail-Anhängen. Die Attachment-ID ist NICHT die Drive-Datei-ID.
- **`--parent <folder_id>`** statt `--folder` Flag verwenden (Flag `--folder` existiert NICHT)
- **`gog drive move`** verwendet `--parent <folder_id>`, NICHT `--to` oder `--id`
- **`--json`** Flag für parsebare JSON-Ausgabe
- Drive-Download speichert in `~/.config/gogcli/` mit GENERIERTEM Dateinamen — Datei muss manuell umbenannt und verschoben werden
- **`gog drive download`** ohne Parameter zeigt nur Root-Ordner — immer `--parent` mit Folder-ID verwenden
- OCR bei reinen Bild-PDFs (scanned): Tesseract PSM=6 für tabellarische Daten, aber viele Werte können im PDF selbst obscured sein — prüfen ob Werte lesbar bevor man OCR durchführt
- Python-Pakete (fitz, pytesseract) liegen in `~/.local/lib/python3.12/site-packages`, NICHT im hermes-agent venv
- FRIDAY wird NICHT mehr für Gesundheitsdaten verwendet — alles JARVIS
- `backup_original/` enthält alle Originaldateien
- Werte `'<5.0'` etc. müssen vor numerischen Operationen geparsed werden (`lstrip('<>=')`)
- Datenbank-INSERTs prüfen CHECK-Constraints: `status` muss `'neu'`, `'eingearbeitet'` oder `'archiviert'` sein; `daten_typ` muss `'pdf'` oder `'image'` sein
- Watchdog-Daemon unzuverlässig → manuelle Verarbeitung mit `health_manager.process_inbox_pdf()` bevorzugen
- **`.xlsx` Dateien:** Mit `openpyxl` lesen, als Volltext in Datenbank speichern (keine Laborwerte!)
- **Laborbericht-Erstellung:** Skript `scripts/generate_labor_report.py` soll pivot-Tabellen aus XLSX-Referenzdaten/DB erstellen. Format: Spalten pro Blutabnahme, Zeilen pro Parameter, sortiert nach Kategorien. XLSX als primäre Datenquelle verwenden — `ermittlung_datum` in der DB ist OCR-/Importdatum, nicht tatsächliches Abnahmedatum. Aktueller Prüfstand 2026-05-12: `reports/aktuelle_blutwerte.xlsx` existiert und hat Datums-Spalten; das Skript selbst ist aber nicht zuverlässig, weil es aus `ermittlung_datum` liest, den Zeitanteil abschneidet und dadurch DB-Zeilen verfehlen kann; außerdem ist `cat_border` undefiniert. Vor produktiver Nutzung Script fixen und gegen Referenz-XLSX validieren.
- Immer Dokumentendatum innerhalb des Dokuments extrahieren und im Text annotieren: `[DOKUMENTDATUM: YYYY-MM-DD]`
- **STT für Sprachnachrichten:** faster-whisper mit `medium` Modell, Sprache `de`. Installation via `uv pip install`. Gateway restart nach Config-Änderungen. Schweizerdeutsch wird NICHT unterstützt.
- **Tagebuch-Workflow:** User sendet Einträge per Telegram (#ernaehrung, #symptom, etc.). Unstrukturierte Eingabe — Agent extrahiert automatisch Datum, Kategorie, Inhalt.

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


Explanation:
**Inspecting schema requirements**

I need to insert the symptom log and the "tagebuch." First, I’ll inspect the schema to ensure everything aligns correctly. It seems I need to use the terminal for reading this information. Oh, and do I need to include the current date? I’m guessing I should use "Heute" and pull in today’s date through the terminal. I'll ensure everything is set up properly for this task!

terminal

python3.12 - <<'PY'
import sqlite3, json
p='/home/agent/.hermes/assets/Gesundheit/health_data.db'
con=sqlite3.connect(p)
cur=con.cursor()
for t in ['symptome','symptom_log','tagebuch']:
    print('\nTABLE',t)
    print(cur.execute(f"PRAGMA table_info({t})").fetchall())
    print('sample', cur.execute(f"SELECT * FROM {t} ORDER BY 1 DESC LIMIT 3").fetchall())
con.close()
PY


Output: 2026-05-18 13:07:00 CEST (+0200)


Output:
TABLE symptome
[(0, 'id', 'INTEGER', 0, None, 1), (1, 'datum', 'TEXT', 1, None, 0), (2, 'kategori', 'TEXT', 0, None, 0), (3, 'symptom', 'TEXT', 1, None, 0), (4, 'schwergrad', 'TEXT', 0, None, 0), (5, 'wirkung', 'TEXT', 0, None, 0), (6, 'notizen', 'TEXT', 0, None, 0), (7, 'created_at', 'TEXT', 0, "datetime('now','localtime')", 0)]
sample [(1, '2026-05-10', 'Allgemein', 'Müdigkeit', 'leicht', '', '', '2026-05-10 13:43:33')]

TABLE symptom_log
[(0, 'id', 'INTEGER', 0, None, 1), (1, 'datum', 'TEXT', 1, None, 0), (2, 'symptom', 'TEXT', 1, None, 0), (3, 'schwergrad', 'TEXT', 0, None, 0), (4, 'kontext', 'TEXT', 0, None, 0), (5, 'notizen', 'TEXT', 0, None, 0), (6, 'created_at', 'TEXT', 0, "datetime('now','localtime')", 0)]
sample [(2, '2026-05-14', 'Durchfall', None, '13:30 Uhr', 'Per Telegram gemeldet.', '2026-05-14 14:33:54'), (1, '2026-05-12', 'Aphte', 'klein / mild', 'rechts unter der Zunge', 'Vom User gemeldet: kleine Aphte entdeckt.', '2026-05-12 22:59:33')]

TABLE tagebuch
[(0, 'id', 'INTEGER', 0, None, 1), (1, 'datum', 'TEXT', 1, None, 0), (2, 'kategori', 'TEXT', 1, None, 0), (3, 'titel', 'TEXT', 1, None, 0), (4, 'inhalt', 'TEXT', 0, None, 0), (5, 'wirkung', 'TEXT', 0, None, 0), (6, 'verknuepft', 'TEXT', 0, None, 0), (7, 'notizen', 'TEXT', 0, None, 0), (8, 'created_at', 'TEXT', 0, "datetime('now','localtime')", 0)]
sample [(20, '2026-05-15', 'ernaehrung', 'YAZIO-Ernährung: 2026-05-15', 'YAZIO-Ernährung 2026-05-15\n- breakfast: c4ace03c-a735-4d8d-bedc-f40575bab43a (150g)\n- breakfast: e8d75879-d6af-4c70-8d7f-790da0f454ba (70g)\n- breakfast: 9e71f74a-becf-11e6-a324-e0071b8a8723 (200g)\n- lunch: 9d78e3e4-becf-11e6-ba35-e0071b8a8723 (177g)\n- lunch: 37bc6d79-3231-43d5-ac0e-bcac3eb688e5 (60g)\n- lunch: 9de545f2-becf-11e6-b5e2-e0071b8a8723 (18g)\n- lunch: 9de545f2-becf-11e6-b5e2-e0071b8a8723 (18g)\n- lunch: 9de545f2-becf-11e6-b5e2-e0071b8a8723 (18g)', '', 'yazio', 'Automatischer YAZIO Sync', '2026-05-16 23:10:23'), (19, '2026-05-14', 'ernaehrung', 'YAZIO-Ernährung: 2026-05-14', 'YAZIO-Ernährung 2026-05-14\n\n- [lunch] Filets de poulet (250 g): 0.3 kcal, 58.88 g Protein, 0 g KH, 1.75 g Fett\n- [lunch] Halbrahm, laktosefrei (30 g): 0.1 kcal, 0.81 g Protein, 0.39 g KH, 8.01 g Fett\n- [lunch] Dinkel Spirelli (100 g): 0.4 kcal, 12 g Protein, 72 g KH, 1.7 g Fett\n- [breakfast] Joghurt Classic laktosefrei (150 g): 0.1 kcal, 7.5 g Protein, 5.4 g KH, 5.4 g Fett\n- [breakfast] Beerenmüsli (50 g): 0.2 kcal, 5 g Protein, 29.65 g KH, 2.9 g Fett\n- [dinner] Randensalat (100 g): 0 kcal, 1.2 g Protein, 8.1 g KH, 0.5 g Fett\n- [dinner] Kürbiskerne, getrocknet (10 g): 0.1 kcal, 3.71 g Protein, 0.29 g KH, 4.84 g Fett\n- [dinner] Coleslaw (191 g): 0.3 kcal, 1.81 g Protein, 28.44 g KH, 18.93 g Fett\n- [dinner] Kopfsalat (100 g): 0 kcal, 1.2 g Protein, 1.1 g KH, 0.2 g Fett\n- [dinner] Hühnerei, Eier, gekocht (60 g): 0.1 kcal, 7.11 g Protein, 0.92 g KH, 5.59 g Fett\n- [dinner] Kohlrabi, frisch (16 g): 0 kcal, 0.31 g Protein, 0.59 g KH, 0.03 g Fett\n- [dinner] Kohlrabi, frisch (16 g): 0 kcal, 0.31 g Protein, 0.59 g KH, 0.03 g Fett\n\nSumme: 1.6 kcal | 99.8 g Protein | 147.5 g Kohlenhydrate | 49.9 g Fett', '', 'yazio_api', 'Automatisch via YAZIO API; fingerprint=8dacad80aeabb5dda38b09b93bda4a94c8cd54e18a91877109cd4cbd2e7ba450', '2026-05-15 23:15:51'), (18, '2026-05-15', 'ernaehrung', 'YAZIO-Ernährung: 2026-05-15', 'YAZIO-Ernährung 2026-05-15\n\n- [breakfast] Joghurt Classic laktosefrei (150 g): 0.1 kcal, 7.5 g Protein, 5.4 g KH, 5.4 g Fett\n- [breakfast] Beeren Müsli Alnatura (70 g): 0.3 kcal, 7.7 g Protein, 42.7 g KH, 4.55 g Fett\n- [breakfast] Schwarzer Kaffee (200 g): 0 kcal, 0.4 g Protein, 0.6 g KH, 0 g Fett\n- [lunch] Entrecôte de bœuf, grillée (177 g): 0.4 kcal, 48.5 g Protein, 1.06 g KH, 24.96 g Fett\n- [lunch] Basmati Reis* (60 g): 0.2 kcal, 5.1 g Protein, 47.4 g KH, 0.48 g Fett\n- [lunch] Blumenkohl, gekocht (18 g): 0 kcal, 0.36 g Protein, 0.37 g KH, 0.05 g Fett\n- [lunch] Blumenkohl, gekocht (18 g): 0 kcal, 0.36 g Protein, 0.37 g KH, 0.05 g Fett\n- [lunch] Blumenkohl, gekocht (18 g): 0 kcal, 0.36 g Protein, 0.37 g KH, 0.05 g Fett\n\nSumme: 1.0 kcal | 70.3 g Protein | 98.3 g Kohlenhydrate | 35.5 g Fett', '', 'yazio_api', 'Automatisch via YAZIO API; fingerprint=fb73676c465d399d4e5e267063d29b5ff0396caa1ed34c5d790689fef13c53b6', '2026-05-15 23:15:50')]


Explanation:
**Considering data insertion and backup**

I need to insert data, maybe without severity. It seems like I should use a transaction and verify things. A backup might be helpful too? I'll think about a health event as well. The user is asking about symptom logs and Tagebuch, so I believe I don't need to ask for further clarification. I just want to make sure I manage everything properly while keeping it simple and efficient!

python3.12 - <<'PY'
import sqlite3
from pathlib import Path
DB=Path('/home/agent/.hermes/assets/Gesundheit/health_data.db')
datum='2026-05-18'
con=sqlite3.connect(DB)
cur=con.cursor()
try:
    cur.execute("""
        INSERT INTO symptom_log (datum, symptom, schwergrad, kontext, notizen)
        VALUES (?, ?, ?, ?, ?)
    """, (datum, 'Durchfall', None, 'Heute per Telegram gemeldet', 'User meldet: „Heute wieder Durchfall“.'))
    symptom_id=cur.lastrowid
    cur.execute("""
        INSERT INTO tagebuch (datum, kategori, titel, inhalt, wirkung, verknuepft, notizen)
        VALUES (?, ?, ?, ?, ?, ?, ?)
    """, (datum, 'symptom', 'Symptom: Durchfall', 'Heute wieder Durchfall.', '', f'symptom_log:{symptom_id}', 'Per Telegram gemeldet.'))
    diary_id=cur.lastrowid
    con.commit()
    print(f'INSERTED symptom_log_id={symptom_id} tagebuch_id={diary_id}')
    print('symptom_log:', cur.execute('SELECT id, datum, symptom, schwergrad, kontext, notizen, created_at FROM symptom_log WHERE id=?',(symptom_id,)).fetchone())
    print('tagebuch:', cur.execute('SELECT id, datum, kategori, titel, inhalt, verknuepft, notizen, created_at FROM tagebuch WHERE id=?',(diary_id,)).fetchone())
except Exception:
    con.rollback()
    raise
finally:
    con.close()
PY


Output:
INSERTED symptom_log_id=3 tagebuch_id=21
symptom_log: (3, '2026-05-18', 'Durchfall', None, 'Heute per Telegram gemeldet', 'User meldet: „Heute wieder Durchfall“.', '2026-05-18 13:07:14')
tagebuch: (21, '2026-05-18', 'symptom', 'Symptom: Durchfall', 'Heute wieder Durchfall.', 'symptom_log:3', 'Per Telegram gemeldet.', '2026-05-18 13:07:14')


Erledigt, Sir.

- `symptom_log`: Durchfall für heute eingetragen
- `tagebuch`: Symptom-Eintrag für heute angelegt
- Verknüpfung: `symptom_log:3`