{
  "session_id": "20260516_112037_0eb60e",
  "model": "gpt-5.5",
  "base_url": "https://chatgpt.com/backend-api/codex",
  "platform": "telegram",
  "session_start": "2026-05-16T09:22:57.370601",
  "last_updated": "2026-05-16T11:33:47.239971",
  "system_prompt": "# Hermes Agent Persona\n\n<!--\nThis file defines the agent's personality and tone.\nThe agent will embody whatever you write here.\nEdit this to customize how Hermes communicates with you.\n\nExamples:\n  - \"You are a warm, playful assistant who uses kaomoji occasionally.\"\n  - \"You are a concise technical expert. No fluff, just facts.\"\n  - \"You speak like a friendly coworker who happens to know everything.\"\n\nThis file is loaded fresh each message -- no restart needed.\nDelete the contents (or this file) to use the default personality.\n-->\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. 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.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n<tool_persistence>\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n</tool_persistence>\n\n<mandatory_tool_use>\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n</mandatory_tool_use>\n\n<act_dont_ask>\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n</act_dont_ask>\n\n<prerequisite_checks>\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n</prerequisite_checks>\n\n<verification>\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n</verification>\n\n<missing_context>\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n</missing_context>\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n<available_skills>\n  autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n    - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n    - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n    - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n    - opencode: Delegate coding to OpenCode CLI (features, PR review).\n  creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n    - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n    - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n    - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n    - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n    - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n    - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n    - comfyui: Generate images, video, and audio with ComfyUI — install,...\n    - design-md: Author/validate/export Google's DESIGN.md token spec files.\n    - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n    - humanizer: Humanize text: strip AI-isms and add real voice.\n    - ideation: Generate project ideas via creative constraints.\n    - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n    - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n    - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n    - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n    - pretext: Use when building creative browser demos with @chenglou/p...\n    - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n    - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n    - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n  cronjob:\n    - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n  data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n    - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n  devops:\n    - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n    - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n    - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n    - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n  dogfood:\n    - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n  edge-tts:\n    - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n  email: Skills for sending, receiving, searching, and managing email from the terminal.\n    - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n  finance:\n    - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n  gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n    - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n    - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n  github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n    - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n    - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n    - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n    - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n    - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n    - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n  health:\n    - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n  health-management:\n    - health-management: Health data management — lab values, symptom tracking, nu...\n  jarvis-routing-policy:\n    - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n  mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n    - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n  media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n    - gif-search: Search/download GIFs from Tenor via curl + jq.\n    - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n    - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n    - spotify: Spotify: play, search, queue, manage playlists and devices.\n    - youtube-content: YouTube transcripts to summaries, threads, blogs.\n  mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n    - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n  mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n    - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n    - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n  mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n    - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n    - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n    - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n    - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n  mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n    - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n    - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n  mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n    - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n  mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n    - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n    - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n    - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n  note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n    - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n    - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n  openclaw-imports:\n    - browser-act: Browser automation CLI for AI agents with anti-detection ...\n    - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n    - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n    - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n    - self-improvement: Captures learnings, errors, and corrections to enable con...\n    - sherpa-onnx-tts\n    - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n    - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n    - tavily: Tavily web search, content extraction, and research tools.\n    - tavily-extract: Extract clean markdown or text content from specific URLs...\n    - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n    - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n    - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n  productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n    - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n    - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n    - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n    - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n    - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n    - notion: Notion API via curl: pages, databases, blocks, search.\n    - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n    - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n    - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n  red-teaming:\n    - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n  research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n    - arxiv: Search arXiv papers by keyword, author, category, or ID.\n    - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n    - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n    - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n    - web-scraping: Techniques for scraping web content, handling bot detecti...\n  smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n    - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n  social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n    - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n  software-development:\n    - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n    - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n    - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n    - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n    - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n    - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n    - spike: Throwaway experiments to validate an idea before build.\n    - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n    - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n    - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n    - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n  yazio-integration:\n    - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n  yuanbao:\n    - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n</available_skills>\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-23-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,196/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com <command>`\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV path: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv` (outside repo, mode 600); never store/quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [98% — 1,350/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\n**Beziehung:** Loyaler Partner\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nUser wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\n§\nTelegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\n§\nFinance-system preference: phased execution/safety gates; no mass imports without approval; aggregate-only reporting; never expose real values/rows; User Mode should be polished dashboard UX: cards/charts, readable labels, hide IDs/raw debug.\n\nConversation started: Saturday, May 16, 2026 11:32 AM\nModel: gpt-5.5\nProvider: openai-codex",
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "browser_back",
        "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {}
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_click",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "ref": {
              "type": "string",
              "description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
            }
          },
          "required": [
            "ref"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_console",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "clear": {
              "type": "boolean",
              "default": false,
              "description": "If true, clear the message buffers after reading"
            },
            "expression": {
              "type": "string",
              "description": "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'"
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_get_images",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {}
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_navigate",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "url": {
              "type": "string",
              "description": "The URL to navigate to (e.g., 'https://example.com')"
            }
          },
          "required": [
            "url"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_press",
        "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "key": {
              "type": "string",
              "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
            }
          },
          "required": [
            "key"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_scroll",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "direction": {
              "type": "string",
              "enum": [
                "up",
                "down"
              ],
              "description": "Direction to scroll"
            }
          },
          "required": [
            "direction"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_snapshot",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "full": {
              "type": "boolean",
              "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
              "default": false
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_type",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "ref": {
              "type": "string",
              "description": "The element reference from the snapshot (e.g., '@e3')"
            },
            "text": {
              "type": "string",
              "description": "The text to type into the field"
            }
          },
          "required": [
            "ref",
            "text"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_vision",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "question": {
              "type": "string",
              "description": "What you want to know about the page visually. Be specific about what you're looking for."
            },
            "annotate": {
              "type": "boolean",
              "default": false,
              "description": "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."
            }
          },
          "required": [
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "clarify",
        "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo 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.",
        "parameters": {
          "type": "object",
          "properties": {
            "question": {
              "type": "string",
              "description": "The question to present to the user."
            },
            "choices": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "maxItems": 4,
              "description": "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."
            }
          },
          "required": [
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "cronjob",
        "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo 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.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "description": "One of: create, list, update, pause, resume, remove, run"
            },
            "job_id": {
              "type": "string",
              "description": "Required for update/pause/resume/remove/run"
            },
            "prompt": {
              "type": "string",
              "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
            },
            "schedule": {
              "type": "string",
              "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
            },
            "name": {
              "type": "string",
              "description": "Optional human-friendly name"
            },
            "repeat": {
              "type": "integer",
              "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
            },
            "deliver": {
              "type": "string",
              "description": "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."
            },
            "skills": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
            },
            "model": {
              "type": "object",
              "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
              "properties": {
                "provider": {
                  "type": "string",
                  "description": "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": {
                  "type": "string",
                  "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
                }
              },
              "required": [
                "model"
              ]
            },
            "script": {
              "type": "string",
              "description": "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."
            },
            "no_agent": {
              "type": "boolean",
              "default": false,
              "description": "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. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY 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. \n\nWHEN 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."
            },
            "context_from": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "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."
            },
            "enabled_toolsets": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "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."
            },
            "workdir": {
              "type": "string",
              "description": "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."
            }
          },
          "required": [
            "action"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "delegate_task",
        "description": "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.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. 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.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- 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.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- 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.\n- 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.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- 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.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
        "parameters": {
          "type": "object",
          "properties": {
            "goal": {
              "type": "string",
              "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
            },
            "context": {
              "type": "string",
              "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
            },
            "toolsets": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "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', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
            },
            "tasks": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "goal": {
                    "type": "string",
                    "description": "Task goal"
                  },
                  "context": {
                    "type": "string",
                    "description": "Task-specific context"
                  },
                  "toolsets": {
                    "type": "array",
                    "items": {
                      "type": "string"
                    },
                    "description": "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', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
                  },
                  "acp_command": {
                    "type": "string",
                    "description": "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."
                  },
                  "acp_args": {
                    "type": "array",
                    "items": {
                      "type": "string"
                    },
                    "description": "Per-task ACP args override. Leave empty unless acp_command is set."
                  },
                  "role": {
                    "type": "string",
                    "enum": [
                      "leaf",
                      "orchestrator"
                    ],
                    "description": "Per-task role override. See top-level 'role' for semantics."
                  }
                },
                "required": [
                  "goal"
                ]
              },
              "description": "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": {
              "type": "string",
              "enum": [
                "leaf",
                "orchestrator"
              ],
              "description": "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."
            },
            "acp_command": {
              "type": "string",
              "description": "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)."
            },
            "acp_args": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "execute_code",
        "description": "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).\n\nUse 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.\n\nAvailable via `from hermes_tools import ...`:\n\n  read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n    Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n  write_file(path: str, content: str) -> dict\n    Always overwrites the entire file.\n  search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n    target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n    Replaces old_string with new_string in the file.\n  terminal(command: str, timeout=None, workdir=None) -> dict\n    Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts 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().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
        "parameters": {
          "type": "object",
          "properties": {
            "code": {
              "type": "string",
              "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
            }
          },
          "required": [
            "code"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "feishu_doc_read",
        "description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
        "parameters": {
          "type": "object",
          "properties": {
            "doc_token": {
              "type": "string",
              "description": "The document token (from the document URL or comment context)."
            }
          },
          "required": [
            "doc_token"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "feishu_drive_add_comment",
        "description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
        "parameters": {
          "type": "object",
          "properties": {
            "file_token": {
              "type": "string",
              "description": "The document file token."
            },
            "content": {
              "type": "string",
              "description": "The comment text content (plain text only, no markdown)."
            },
            "file_type": {
              "type": "string",
              "description": "File type (default: docx).",
              "default": "docx"
            }
          },
          "required": [
            "file_token",
            "content"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "feishu_drive_list_comment_replies",
        "description": "List all replies in a comment thread on a Feishu document.",
        "parameters": {
          "type": "object",
          "properties": {
            "file_token": {
              "type": "string",
              "description": "The document file token."
            },
            "comment_id": {
              "type": "string",
              "description": "The comment ID to list replies for."
            },
            "file_type": {
              "type": "string",
              "description": "File type (default: docx).",
              "default": "docx"
            },
            "page_size": {
              "type": "integer",
              "description": "Number of replies per page (max 100).",
              "default": 100
            },
            "page_token": {
              "type": "string",
              "description": "Pagination token for next page."
            }
          },
          "required": [
            "file_token",
            "comment_id"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "feishu_drive_list_comments",
        "description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
        "parameters": {
          "type": "object",
          "properties": {
            "file_token": {
              "type": "string",
              "description": "The document file token."
            },
            "file_type": {
              "type": "string",
              "description": "File type (default: docx).",
              "default": "docx"
            },
            "is_whole": {
              "type": "boolean",
              "description": "If true, only return whole-document comments.",
              "default": false
            },
            "page_size": {
              "type": "integer",
              "description": "Number of comments per page (max 100).",
              "default": 100
            },
            "page_token": {
              "type": "string",
              "description": "Pagination token for next page."
            }
          },
          "required": [
            "file_token"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "feishu_drive_reply_comment",
        "description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
        "parameters": {
          "type": "object",
          "properties": {
            "file_token": {
              "type": "string",
              "description": "The document file token."
            },
            "comment_id": {
              "type": "string",
              "description": "The comment ID to reply to."
            },
            "content": {
              "type": "string",
              "description": "The reply text content (plain text only, no markdown)."
            },
            "file_type": {
              "type": "string",
              "description": "File type (default: docx).",
              "default": "docx"
            }
          },
          "required": [
            "file_token",
            "comment_id",
            "content"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "image_generate",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "prompt": {
              "type": "string",
              "description": "The text prompt describing the desired image. Be detailed and descriptive."
            },
            "aspect_ratio": {
              "type": "string",
              "enum": [
                "landscape",
                "square",
                "portrait"
              ],
              "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
              "default": "landscape"
            }
          },
          "required": [
            "prompt"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "memory",
        "description": "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.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf 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.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "add",
                "replace",
                "remove"
              ],
              "description": "The action to perform."
            },
            "target": {
              "type": "string",
              "enum": [
                "memory",
                "user"
              ],
              "description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
            },
            "content": {
              "type": "string",
              "description": "The entry content. Required for 'add' and 'replace'."
            },
            "old_text": {
              "type": "string",
              "description": "Short unique substring identifying the entry to replace or remove."
            }
          },
          "required": [
            "action",
            "target"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "mixture_of_agents",
        "description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
        "parameters": {
          "type": "object",
          "properties": {
            "user_prompt": {
              "type": "string",
              "description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
            }
          },
          "required": [
            "user_prompt"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "patch",
        "description": "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.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
        "parameters": {
          "type": "object",
          "properties": {
            "mode": {
              "type": "string",
              "enum": [
                "replace",
                "patch"
              ],
              "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
              "default": "replace"
            },
            "path": {
              "type": "string",
              "description": "REQUIRED when mode='replace'. File path to edit."
            },
            "old_string": {
              "type": "string",
              "description": "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."
            },
            "new_string": {
              "type": "string",
              "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
            },
            "replace_all": {
              "type": "boolean",
              "description": "Replace all occurrences instead of requiring a unique match (default: false)",
              "default": false
            },
            "patch": {
              "type": "string",
              "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
            }
          },
          "required": [
            "mode"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "process",
        "description": "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).",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "list",
                "poll",
                "log",
                "wait",
                "kill",
                "write",
                "submit",
                "close"
              ],
              "description": "Action to perform on background processes"
            },
            "session_id": {
              "type": "string",
              "description": "Process session ID (from terminal background output). Required for all actions except 'list'."
            },
            "data": {
              "type": "string",
              "description": "Text to send to process stdin (for 'write' and 'submit' actions)"
            },
            "timeout": {
              "type": "integer",
              "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
              "minimum": 1
            },
            "offset": {
              "type": "integer",
              "description": "Line offset for 'log' action (default: last 200 lines)"
            },
            "limit": {
              "type": "integer",
              "description": "Max lines to return for 'log' action",
              "minimum": 1
            }
          },
          "required": [
            "action"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "read_file",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to read (absolute, relative, or ~/path)"
            },
            "offset": {
              "type": "integer",
              "description": "Line number to start reading from (1-indexed, default: 1)",
              "default": 1,
              "minimum": 1
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of lines to read (default: 500, max: 2000)",
              "default": 500,
              "maximum": 2000
            }
          },
          "required": [
            "path"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "search_files",
        "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
        "parameters": {
          "type": "object",
          "properties": {
            "pattern": {
              "type": "string",
              "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
            },
            "target": {
              "type": "string",
              "enum": [
                "content",
                "files"
              ],
              "description": "'content' searches inside file contents, 'files' searches for files by name",
              "default": "content"
            },
            "path": {
              "type": "string",
              "description": "Directory or file to search in (default: current working directory)",
              "default": "."
            },
            "file_glob": {
              "type": "string",
              "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of results to return (default: 50)",
              "default": 50
            },
            "offset": {
              "type": "integer",
              "description": "Skip first N results for pagination (default: 0)",
              "default": 0
            },
            "output_mode": {
              "type": "string",
              "enum": [
                "content",
                "files_only",
                "count"
              ],
              "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
              "default": "content"
            },
            "context": {
              "type": "integer",
              "description": "Number of context lines before and after each match (grep mode only)",
              "default": 0
            }
          },
          "required": [
            "pattern"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "send_message",
        "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: 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.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "send",
                "list"
              ],
              "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
            },
            "target": {
              "type": "string",
              "description": "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)"
            },
            "message": {
              "type": "string",
              "description": "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."
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "session_search",
        "description": "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.\n\nTWO MODES:\n1. 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.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon'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.\n\nSearch 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.",
        "parameters": {
          "type": "object",
          "properties": {
            "query": {
              "type": "string",
              "description": "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)."
            },
            "role_filter": {
              "type": "string",
              "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
            },
            "limit": {
              "type": "integer",
              "description": "Max sessions to summarize (default: 3, max: 5).",
              "default": 3
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skill_manage",
        "description": "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.\n\nActions: 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.\n\nOn 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.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate 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.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned 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.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "create",
                "patch",
                "edit",
                "delete",
                "write_file",
                "remove_file"
              ],
              "description": "The action to perform."
            },
            "name": {
              "type": "string",
              "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
            },
            "content": {
              "type": "string",
              "description": "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."
            },
            "old_string": {
              "type": "string",
              "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
            },
            "new_string": {
              "type": "string",
              "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
            },
            "replace_all": {
              "type": "boolean",
              "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
            },
            "category": {
              "type": "string",
              "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
            },
            "file_path": {
              "type": "string",
              "description": "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."
            },
            "file_content": {
              "type": "string",
              "description": "Content for the file. Required for 'write_file'."
            },
            "absorbed_into": {
              "type": "string",
              "description": "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."
            }
          },
          "required": [
            "action",
            "name"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skill_view",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "name": {
              "type": "string",
              "description": "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')."
            },
            "file_path": {
              "type": "string",
              "description": "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."
            }
          },
          "required": [
            "name"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skills_list",
        "description": "List available skills (name + description). Use skill_view(name) to load full content.",
        "parameters": {
          "type": "object",
          "properties": {
            "category": {
              "type": "string",
              "description": "Optional category filter to narrow results"
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "terminal",
        "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (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.\nBackground: Set background=true to get a session_id. Two patterns:\n  (1) Long-lived processes that never exit (servers, watchers).\n  (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.\nFor 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.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
        "parameters": {
          "type": "object",
          "properties": {
            "command": {
              "type": "string",
              "description": "The command to execute on the VM"
            },
            "background": {
              "type": "boolean",
              "description": "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.",
              "default": false
            },
            "timeout": {
              "type": "integer",
              "description": "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.",
              "minimum": 1
            },
            "workdir": {
              "type": "string",
              "description": "Working directory for this command (absolute path). Defaults to the session working directory."
            },
            "pty": {
              "type": "boolean",
              "description": "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.",
              "default": false
            },
            "notify_on_complete": {
              "type": "boolean",
              "description": "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.",
              "default": false
            },
            "watch_patterns": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "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."
            }
          },
          "required": [
            "command"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "text_to_speech",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "text": {
              "type": "string",
              "description": "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."
            },
            "output_path": {
              "type": "string",
              "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3"
            }
          },
          "required": [
            "text"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "todo",
        "description": "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.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
        "parameters": {
          "type": "object",
          "properties": {
            "todos": {
              "type": "array",
              "description": "Task items to write. Omit to read current list.",
              "items": {
                "type": "object",
                "properties": {
                  "id": {
                    "type": "string",
                    "description": "Unique item identifier"
                  },
                  "content": {
                    "type": "string",
                    "description": "Task description"
                  },
                  "status": {
                    "type": "string",
                    "enum": [
                      "pending",
                      "in_progress",
                      "completed",
                      "cancelled"
                    ],
                    "description": "Current status"
                  }
                },
                "required": [
                  "id",
                  "content",
                  "status"
                ]
              }
            },
            "merge": {
              "type": "boolean",
              "description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
              "default": false
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "video_analyze",
        "description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
        "parameters": {
          "type": "object",
          "properties": {
            "video_url": {
              "type": "string",
              "description": "Video URL (http/https) or local file path to analyze."
            },
            "question": {
              "type": "string",
              "description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
            }
          },
          "required": [
            "video_url",
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "vision_analyze",
        "description": "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.",
        "parameters": {
          "type": "object",
          "properties": {
            "image_url": {
              "type": "string",
              "description": "Image URL (http/https), local file path, or data: URL to load."
            },
            "question": {
              "type": "string",
              "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
            }
          },
          "required": [
            "image_url",
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "write_file",
        "description": "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).",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
            },
            "content": {
              "type": "string",
              "description": "Complete content to write to the file"
            }
          },
          "required": [
            "path",
            "content"
          ]
        }
      }
    }
  ],
  "message_count": 196,
  "messages": [
    {
      "role": "user",
      "content": "[M G] ich habe den API key für Openfigi. wo soll ich dir diesen ablegen? gib mir die befehle fürs Terminal."
    },
    {
      "role": "assistant",
      "content": "",
      "tool_calls": [
        {
          "id": "call_qqE1X4vodSsTOMG176nuQKS2",
          "call_id": "call_qqE1X4vodSsTOMG176nuQKS2",
          "response_item_id": "fc_03507daf03b84d1d016a081825a2948191bca31f4b12ebbdf2",
          "type": "function",
          "function": {
            "name": "skill_view",
            "arguments": "{\"name\":\"finance-system-architecture\"}"
          }
        }
      ],
      "finish_reason": "tool_calls",
      "reasoning": "**Setting up the OpenFIGI key**\n\nI see that we need the answer in German. It’s important to set up the OpenFIGI key properly, but I'm wondering if this is more about configuration rather than Hermes itself, which could be a finance-related aspect. The user seems to be asking where to place the API key, so I might want to focus on the relevant finance skill or even the finance system architecture. Let’s load the finance-system architecture for clarity!",
      "reasoning_content": "**Setting up the OpenFIGI key**\n\nI see that we need the answer in German. It’s important to set up the OpenFIGI key properly, but I'm wondering if this is more about configuration rather than Hermes itself, which could be a finance-related aspect. The user seems to be asking where to place the API key, so I might want to focus on the relevant finance skill or even the finance system architecture. Let’s load the finance-system architecture for clarity!",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqCBglkxBAX3MP9T4yRqp-IH3BTIajwSaqj8Z-oFIRkH7EGlm-jyP6Wc2f8HmWK_Dx9tk9B0w84kbZ5kU2WY_oqbMI-mcdVlhnj9TiN2HHqOurIIvJDm1iPaTIf2pQALP0w_PaFk6dpqsmQunhObbWu6OwJfzKyz1s8DpCasvlBEe39toW4yNCh7CvDMTKV4lLi6zvRZ6Fsm_0QA_7VXthMPrKVTuJZ6G_JRYiCqAXbxo8vaeT6P5MHJg8pXL538maqaMan9WdeFoETvSNt6YyFvzZxhd7-kRCU49MA3dyRLt-8SkGGUTxBNEVAD6rq_1r8sUoXAjlGzHv-Md9QYMiCoU9FDFxikMtzdkuYwF0R_8ovsb_lu8farmD6_yel_ohokZkzCW6YLxqVget07vS7RmdP0R_dwvH25FktzWUKD1UP7IfL1-8dMCyhbuVPSNX4bQjW5iMvWdJq-tsCHae4U-fJ6YsZfT6VOjdVbtTvJb_Y9vx14yq-u9tiXka-51L8SLngavFP9YijomNW4FdvaODs2YwU1ibSv52podZnH4JFv8cAnQ3q-HY2VoBNAvac4DR2GGk2Lw3SytQy-qBteh3DmMt9AyvygfD8QbkTRhDoJzv4rAU1_ijiKbFrQHjteP9-sEkCC9Xhhgo4JX6dTo9ozb-jNiN1jGhpHIVgiESpVXELR-cIWLcKG0bn100VTQXJGgvYR-tSG2MlJGzmhRFFbmzELKcQOqsRaIofI3bCt1dX0OxIZ2x376qReEstbcNvXIgbPyoyiu3sT5SWTORBd7Id7g-tPofV2kIg6MkmQ6JqaZtUWTImcnqr77KqRybSBFnWk63Kq09sIQtsjPFWXtTkw_2LOs-Hm3EfdOc8pPZDuCs29R1A-4IhhNo_vMhou13ZRZ1VuaKml9H8PN1shPGviE3LvzhX4ttuFltcF2RvZt_Ax1tPuccbVJ91LKQOFQp8l0bwIImCqnDCSQ9xQN3lUEyWheeuofgZg3TsH9YWk_Y1GLPx3cbif5-CVwMlGlvq7EjDWLwE-v8xZa682ZyAYVeqbHZHRdY_GfxoAENZomOWGK22EJzkMuEiBzLl8PvUDsRSpx23XRLdVkD5I3HPoTpTR361a_jXfCL_JWoh7KpLQyfyjDz5UbSpTkcpWLm9ooB1xr-ivsNnPDEGrybla2OWgS5JTx_XkLEEL53hATB160tMxYK_fPvA9N2Tzr5fO5bIdSrxPC86ASwzNBI26vx85mRIRRBUGpnMF6RBDODR5wSFY-pkJvqZR_StHkm9jMUIAeSKItK9MTlZhumfoVHkQ-BiQeuzwLoLnI=",
          "id": "rs_03507daf03b84d1d016a081822325c81919691e05e547f39ee",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Setting up the OpenFIGI key**\n\nI see that we need the answer in German. It’s important to set up the OpenFIGI key properly, but I'm wondering if this is more about configuration rather than Hermes itself, which could be a finance-related aspect. The user seems to be asking where to place the API key, so I might want to focus on the relevant finance skill or even the finance system architecture. Let’s load the finance-system architecture for clarity!"
            }
          ]
        }
      ]
    },
    {
      "role": "tool",
      "content": "[skill_view] name=finance-system-architecture (46,556 chars)",
      "tool_call_id": "call_qqE1X4vodSsTOMG176nuQKS2"
    },
    {
      "role": "user",
      "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"[M G] Sehr gut. Der bisherige Provider-Sprint ist akzeptiert.\n\nIch habe den OpenFIGI-Key korrigiert. Der vorherige Key war falsch hinterlegt. Bitte OpenFIGI deshalb nochmals testen und nicht mehr vom letzten auth_failed ausgehen.\n\nZusätzlich habe ich weitere API-Keys in der Runtime-Secrets-ENV eingetragen:\n\n* FINNHUB_API_KEY\n* TWELVEDATA_API_KEY\n* EODHD_API_KEY\n* MASSIVE_API_KEY\n\nWichtig:\n\n* EODHD_API_KEY hat nur ca. 20 API-Abfragen pro Tag und darf deshalb nur als Low-Volume-Fallback genutzt werden.\n* Massive-Dokumentation: https://massive.com/docs/rest/quickstart\n* OpenFIGI-Dokumentation: https://www.openfigi.com/api/documentation\n* FMP-Dokumentation: https://site.financialmodelingprep.com/developer/docs\n\nBitte starte jetzt einen Provider Integration Sprint v2.\n\nZiel:\nDie Instrumentensuche, Preisvorschau, Equity-/ETF-Preisaktualisierung und FX-Aktualisierung sollen mit den vorhandenen Free-/Fallback-Providern zuverlässig testbar werden.\n\nWeiterhin verbindlich:\n\n* Keine echten Finanzdaten ins Git-Repository.\n* Keine API-Keys ins Git-Repository.\n* Keine API-Keys in Logs, Reports, Tests oder Fehlermeldungen.\n* Keine Provider-Abfrage beim normalen Dashboard-Rendern.\n* Provider-Abfragen nur bei explizitem Klick oder CLI-Befehl.\n* Keine Position speichern.\n* Kein produktiver PostFinance-/True-Wealth-Import.\n* Keine echten Portfolio-Daten an externe Provider senden außer explizite Suchbegriffe/ISIN/Ticker, die ich im Wizard eingebe.\n* Runtime-Daten bleiben außerhalb Git.\n* Git-Safety, Tests, Commit und Push wie gewohnt.\n\nBitte unterstütze die genannten Secret-Key-Namen, teste OpenFIGI erneut, lasse FMP Primary für Suche/Preisvorschau, integriere Finnhub, Twelve Data, Massive und EODHD als Low-Volume-Fallback, erweitere Equity-/ETF-Preisupdate und FX-Update, zeige Providerstatus im Dashboard, stabilisiere Online-Suche und Preisvorschau, führe nur explizite Provider-Tests ohne Positionsspeicherung/Portfolio-Daten durch und schließe mit Compile, pytest, Git-Safety, git status, git diff --check, Commit und Push ab.\"\n\n## Goal\nDer User möchte das bestehende Finance-System/Jarvis-Projekt so erweitern, dass Instrumentensuche, Preisvorschau, Equity-/ETF-Preisaktualisierung und FX-Aktualisierung zuverlässig über mehrere Free-/Fallback-Provider testbar sind. FMP bleibt Primary; OpenFIGI soll mit korrigiertem Key erneut getestet werden; Finnhub, Twelve Data, Massive und EODHD sollen integriert werden, wobei EODHD wegen starkem Tageslimit nur als Low-Volume-Fallback dient. Das Dashboard darf keine Provider-Abfragen beim normalen Rendern auslösen; Provider-Abfragen dürfen nur durch explizite User-Aktion oder CLI-Befehl erfolgen.\n\n## Constraints & Preferences\n- Sprache: Deutsch.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection-Strings ausgeben, loggen, reporten, testen oder ins Git aufnehmen; alle Werte als `[REDACTED]` behandeln.\n- Keine echten Finanzdaten ins Git-Repository.\n- Runtime-Daten bleiben außerhalb Git.\n- `.env` liegt unter `~/jarvis_runtime/finance-system/secrets/.env`.\n- Falls möglich, File Mode der `.env` auf `600` prüfen.\n- Unterstützte Secret-Namen:\n  - `OPENFIGI_API_KEY`\n  - `FMP_API_KEY`\n  - `FINNHUB_API_KEY`\n  - `TWELVEDATA_API_KEY`\n  - `JARVIS_TWELVEDATA_API_KEY`\n  - `EODHD_API_KEY`\n  - `MASSIVE_API_KEY`\n  - `ALPHA_VANTAGE_API_KEY`\n- Wichtig: `TWELVEDATA_API_KEY` ohne Unterstrich zwischen Twelve und Data zwingend unterstützen.\n- Keys in Statusanzeigen nur als `vorhanden`, `fehlt`, `auth_failed` oder `error` anzeigen.\n- Keine Provider-Abfrage beim normalen Dashboard-Rendern.\n- Provider-Abfragen nur bei explizitem Klick oder CLI-Befehl.\n- Keine Position speichern.\n- Kein produktiver PostFinance-/True-Wealth-DOCX-Import.\n- Keine echten Portfolio-Daten an externe Provider senden außer explizite Suchbegriffe/ISIN/Ticker, die der User im Wizard eingibt.\n- EODHD hat nur ca. 20 API-Abfragen pro Tag und darf nur als Low-Volume-Fallback genutzt werden; bei Tests höchstens 1 Call und nur wenn nötig.\n- OpenFIGI 401 nicht crashen lassen, sondern als `auth_failed` markieren und mit FMP/Twelve/Finnhub/Massive weiterarbeiten.\n- FMP-402/Tarifgrenzen sauber als `restricted_endpoint` behandeln, keine harte Exception.\n- Git-Safety, Tests, Commit und Push wie gewohnt nach Umsetzung.\n\n## Completed Actions\n1. AKZEPTIERT bisherigen Provider-Sprint — User bestätigte: „Sehr gut. Der bisherige Provider-Sprint ist akzeptiert.“ [tool: user_feedback]\n2. ERFASST neue Runtime-Secrets-Verfügbarkeit — User meldete korrigierten OpenFIGI-Key sowie zusätzliche Keys `FINNHUB_API_KEY`, `TWELVEDATA_API_KEY`, `EODHD_API_KEY`, `MASSIVE_API_KEY`; tatsächliche Werte dürfen nicht gespeichert oder ausgegeben werden. [tool: user_message]\n3. ERFASST Provider-Sprint-v2-Anforderungen — Ziel, Constraints, Providerrollen, Testumfang und Abschlusskriterien wurden aus User-Anfrage übernommen. [tool: user_message]\n\n## Active State\n- Working directory: unbekannt; keine aktuellen Shell-/Dateizugriffe in den neuen Turns vorhanden.\n- Branch: unbekannt.\n- Modifizierte/erstellte Dateien: keine in den neuen Turns durchgeführt.\n- Teststatus: unbekannt; noch keine Compile-/pytest-/Git-Safety-Ausführung für Sprint v2 erfolgt.\n- Running processes/servers: unbekannt.\n- Environment:\n  - Runtime-Secrets-Datei soll unter `~/jarvis_runtime/finance-system/secrets/.env` liegen.\n  - Der User hat nach eigener Aussage weitere API-Keys dort eingetragen.\n  - API-Key-Werte sind geheim und wurden/weden nicht übernommen.\n- OpenFIGI-Status aus vorherigem Sprint darf nicht mehr als maßgeblich gelten, da der User den falschen Key korrigiert hat.\n- FMP ist laut User bereits funktionsfähig und soll Primary für Suche und Preisvorschau bleiben.\n\n## In Progress\n- Provider Integration Sprint v2 ist angefordert, aber noch nicht umgesetzt.\n- Konkret noch offen:\n  - Secret-Ladepfade/Key-Aliase prüfen und erweitern.\n  - OpenFIGI mit korrigiertem Key erneut testen.\n  - Finnhub als echten Provider aktivieren.\n  - Twelve Data integrieren/testen.\n  - Massive integrieren/testen.\n  - EODHD als Low-Volume-Fallback einbinden.\n  - Equity-/ETF-Preisupdate und FX-Update erweitern.\n  - Providerstatus im Dashboard sichtbar machen, ohne Render-Abfragen auszulösen.\n  - Online-Suche und Preisvorschau stabilisieren.\n  - Explizite Provider-Tests ausführen, ohne Positionen oder Portfolio-Daten zu speichern/verwenden.\n  - Compile, pytest, Git-Safety, `git status`, `git diff --check`, Commit und Push durchführen.\n\n## Blocked\n- Keine technischen Blocker aus Tool-Ausgaben bekannt, weil seit der neuen User-Anfrage noch keine Tools ausgeführt wurden.\n- Frühere OpenFIGI-Auth-Fehler sind als möglicherweise obsolet zu betrachten; User hat den Key korrigiert.\n- Potenzieller Ressourcen-/Limit-Blocker: EODHD hat nur ca. 20 API-Abfragen pro Tag; daher sehr sparsam verwenden, höchstens 1 Testcall und nur wenn nötig.\n\n## Key Decisions\n- FMP bleibt Primary für Instrumentensuche und Preisvorschau, weil laut User bereits funktionsfähig.\n- OpenFIGI muss erneut getestet werden und darf nicht anhand des alten `auth_failed` beurteilt werden, weil der User den Key korrigiert hat.\n- EODHD nur als Low-Volume-Fallback, weil starkes Tageslimit von ca. 20 Calls besteht.\n- Providerstatus darf nur aggregierte Statuswerte zeigen; keine Keys, keine sensiblen Details, keine echten Portfolio-/Positionsdaten.\n- Dashboard darf beim normalen Rendern keine Provider-Abfragen starten; Online-Providerzugriffe nur explizit per Klick/CLI.\n- Fehlerkategorien sollen robust behandelt werden:\n  - OpenFIGI `401` → `auth_failed`, nicht crashen.\n  - FMP `402`/Tarifgrenze → `restricted_endpoint`, nicht crashen.\n  - Rate-Limits/Auth/Netzwerkfehler aggregiert darstellen.\n- Keine Positionen und kein produktiver Import während dieses Sprints; Ziel ist Provider-Testbarkeit und Preis-/FX-Update-Vorbereitung.\n\n## Resolved Questions\n- Der bisherige Provider-Sprint wurde vom User akzeptiert.\n- Der alte OpenFIGI-Key war laut User falsch; der korrigierte Key ist jetzt in Runtime-Secrets hinterlegt. Daher frühere OpenFIGI-`auth_failed`-Ergebnisse nicht weiter als final bewerten.\n- FMP soll weiterhin Primary für Suche und Preisvorschau bleiben.\n- `TWELVEDATA_API_KEY` ohne Unterstrich zwischen „Twelve“ und „Data“ ist ausdrücklich zu unterstützen; zusätzlich `JARVIS_TWELVEDATA_API_KEY`.\n- EODHD darf wegen Limit nur als Low-Volume-Fallback eingesetzt werden.\n\n## Pending User Asks\n- Provider Integration Sprint v2 starten und abschließen.\n- Secret-Key-Namen unterstützen:\n  - `OPENFIGI_API_KEY`\n  - `FMP_API_KEY`\n  - `FINNHUB_API_KEY`\n  - `TWELVEDATA_API_KEY`\n  - `JARVIS_TWELVEDATA_API_KEY`\n  - `EODHD_API_KEY`\n  - `MASSIVE_API_KEY`\n  - `ALPHA_VANTAGE_API_KEY`\n- `.env` unter `~/jarvis_runtime/finance-system/secrets/.env` verwenden und File Mode `600` prüfen, falls möglich.\n- OpenFIGI erneut testen:\n  - Base URL `https://api.openfigi.com/v3`\n  - Header `X-OPENFIGI-APIKEY: <key>` und `Content-Type: application/json`\n  - `POST /v3/mapping` mit ISIN `US9229087690`\n  - `POST /v3/search` mit Query `Vanguard Total Stock`, `exchCode: US`\n  - `POST /v3/mapping` mit Ticker `VTI`, `exchCode: US`\n  - Berichten: Key gefunden ja/nein, erreichbar ja/nein, Auth OK ja/nein, Mapping ISIN erfolgreich ja/nein, Search erfolgreich ja/nein, Kandidaten gefunden ja/nein, Rate-Limit-Header gelesen ja/nein, keine Position gespeichert.\n- FMP prüfen/beibehalten:\n  - `search-symbol`\n  - `search-name`\n  - `search-isin`\n  - `search-exchange-variants`, falls Tarif erlaubt\n  - `profile`\n  - liefern: Name, Ticker/Symbol, Exchange, Währung, Assetklasse falls ableitbar, ISIN falls verfügbar, Preisvorschau falls verfügbar, Datenquelle `FMP`, Confidence/Mapping-Status.\n- Finnhub integrieren/testen:\n  - Key laden.\n  - Symbol Lookup.\n  - Company Profile.\n  - Quote/Preisvorschau, falls verfügbar.\n  - Fallback für Instrumentensuche/Profile.\n- Twelve Data integrieren/testen:\n  - `TWELVEDATA_API_KEY` und `JARVIS_TWELVEDATA_API_KEY` unterstützen.\n  - Für Aktien/ETF-Quote/Preisvorschau und FX wie `USD/CHF`, `EUR/CHF` nutzen, soweit möglich.\n- Massive integrieren/testen:\n  - Massive-Doku: `https://massive.com/docs/rest/quickstart`\n  - Ticker Details.\n  - Previous Day/Aggregate, falls verfügbar.\n- EODHD einbinden:\n  - Nur als Low-Volume-Fallback.\n  - Höchstens 1 echter Testcall und nur wenn nötig.\n- Providerstatus im Dashboard sichtbar machen:\n  - Keine Provider-Abfragen beim Rendern.\n  - Nur Status/letzte explizite Testresultate aggregiert anzeigen.\n- Online-Suche stabilisieren:\n  - Aggregierte Kandidaten aus FMP/OpenFIGI/Finnhub/Twelve/Massive/EODHD-Fallback.\n  - Confidence/Mapping-Status.\n- Preisvorschau stabilisieren:\n  - FMP primary.\n  - Finnhub/Twelve/Massive fallback, EODHD sparsam.\n- Equity-/ETF-Preisupdate vorbereiten/erweitern.\n- FX-Update implementieren/erweitern.\n- Explizite Provider-Tests ausführen:\n  - OpenFIGI: ISIN `US9229087690`, Name `Vanguard Total Stock`, Ticker `VTI`.\n  - FMP: `VTI`, `Vanguard Total Stock`, Profile `VTI`.\n  - Finnhub: `VTI` oder `AAPL`; Symbol/Profile/Quote falls möglich.\n  - Twelve Data: `VTI` oder `AAPL`; `USD/CHF` oder `EUR/CHF`.\n  - Massive: `VTI` oder `AAPL`; Ticker Details; Previous Day/Aggregate falls verfügbar.\n  - EODHD: höchstens 1 Call, nur wenn nötig.\n- Nur aggregiert berichten:\n  - Provider erreichbar.\n  - Auth OK.\n  - Kandidaten/Preis gefunden.\n  - Fehlerkategorie.\n  - Keine Position gespeichert.\n  - Keine Portfolio-Daten verwendet.\n  - Keine Keys ausgegeben.\n- Abschluss:\n  - Compile.\n  - pytest.\n  - Git-Safety.\n  - `git status`.\n  - `git diff --check`.\n  - Commit.\n  - Push.\n  - Bericht mit OpenFIGI korrigierter Key getestet ja/nein, OpenFIGI Auth OK ja/nein, FMP Status, Finnhub/Twelve/Massive getestet, EODHD Fallback, Equity-/FX-Updates, Providerstatus, Online-Suche, Preisvorschau, Tests, Git-Safety, Commit-Hash, Push-Status, offene Grenzen.\n\n## Relevant Files\n- `~/jarvis_runtime/finance-system/secrets/.env` — Runtime-Secrets-Datei; enthält API-Keys, deren Werte niemals ausgegeben/committed werden dürfen; File Mode möglichst `600` prüfen.\n- Weitere Projektdateien sind in den neuen Turns nicht konkret identifiziert worden, weil noch keine Repository-Inspektion durchgeführt wurde.\n- Aus vorheriger Kompaktierung sind keine konkreten Dateipfade verfügbar; „Summary generation was unavailable“.\n\n## Remaining Work\n- Repository-Zustand ermitteln: `pwd`, `git status`, Branch, relevante Dateien suchen.\n- Secret-Lader/Config prüfen und um alle verlangten Key-Namen erweitern.\n- Sicherstellen, dass `.env` außerhalb Git bleibt und Secrets nirgendwo geloggt/getestet/reportet werden.\n- Provider-Abstraktion/Clients für OpenFIGI, FMP, Finnhub, Twelve Data, Massive und EODHD prüfen/implementieren.\n- Fehlerklassifikation vereinheitlichen: `missing_key`, `auth_failed`, `restricted_endpoint`, `rate_limited`, `network_error`, `provider_error`, `ok`.\n- Providerstatus persistent/zwischengespeichert nur aus expliziten Tests anzeigen; keine Live-Abfragen beim Dashboard-Rendern.\n- CLI oder explizite UI-Aktion für Provider-Tests/Preisvorschau/FX-Update implementieren oder erweitern.\n- Tests mit Mocking/ohne echte Keys ergänzen; echte Provider-Tests nur explizit und sparsam.\n- EODHD-Call-Limit beachten und möglichst vermeiden, außer ein einziger notwendiger Testcall.\n- Abschlusskommandos ausführen, Commit erstellen und pushen.\n\n## Critical Context\n- Früherer OpenFIGI-`auth_failed` darf nicht als aktueller Zustand übernommen werden; User hat den OpenFIGI-Key korrigiert.\n- Keine Secret-Werte in dieser Summary oder späteren Ausgaben speichern. Alle Keys sind `[REDACTED]`.\n- OpenFIGI:\n  - Base URL: `https://api.openfigi.com/v3`\n  - Header-Namen: `X-OPENFIGI-APIKEY`, `Content-Type: application/json`\n  - Test A Mapping nach ISIN:\n    ```json\n    [\n      {\n        \"idType\": \"ID_ISIN\",\n        \"idValue\": \"US9229087690\"\n      }\n    ]\n    ```\n  - Test B Search nach Name:\n    ```json\n    {\n      \"query\": \"Vanguard Total Stock\",\n      \"exchCode\": \"US\"\n    }\n    ```\n  - Test C Ticker + Exchange:\n    ```json\n    [\n      {\n        \"idType\": \"TICKER\",\n        \"idValue\": \"VTI\",\n        \"exchCode\": \"US\"\n      }\n    ]\n    ```\n- Dokumentationen:\n  - Massive: `https://massive.com/docs/rest/quickstart`\n  - OpenFIGI: `https://www.openfigi.com/api/documentation`\n  - FMP: `https://site.financialmodelingprep.com/developer/docs`\n- Testinstrumente laut User:\n  - ISIN: `US9229087690`\n  - Ticker: `VTI`, optional `AAPL`\n  - Name: `Vanguard Total Stock`\n  - FX: `USD/CHF`, `EUR/CHF`\n- Abschlussbericht muss ausdrücklich enthalten:\n  - OpenFIGI korrigierter Key getestet: ja/nein\n  - OpenFIGI Auth OK: ja/nein\n  - FMP Status\n  - Finnhub integriert/getestet\n  - Twelve Data integriert/getestet\n  - Massive integriert/getestet\n  - EODHD eingebunden als Low-volume-Fallback\n  - Equity-Preisupdate erweitert\n  - FX-Update implementiert/erweitert\n  - Providerstatus im Dashboard sichtbar\n  - Online-Suche funktioniert\n  - Preisvorschau funktioniert\n  - Tests\n  - Git-Safety\n  - Commit-Hash\n  - Push-Status\n  - Offene Grenzen\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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      "reasoning": "**Considering test organization**\n\nI'm thinking about how to handle the test imports and whether I should keep them separate for clarity. Maybe I can create a dedicated test file like tests/unit/test_provider_integration_v2.py. I need to use monkeypatch on request.urlopen, but what if I simply subclass to override _get_json? For price providers, I can override _json_url, and for FX, I might use Mock. I feel like running a compile now could help catch any issues.",
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      "content": "[Your active task list was preserved across context compression]\n- [>] inspect. Repo-/Secret-/Provider-Code v2 inspizieren und OpenFIGI/FMP Smoke erneut prüfen (in_progress)\n- [ ] providers. Finnhub, Twelve Data, Massive, EODHD/Alpha provider + Keynamen + Status integrieren (pending)\n- [ ] pricefx. Equity-Preisupdate Provider-Auswahl/Priorität und FX-Update mit Frankfurter/Twelve erweitern (pending)\n- [ ] ui. Providerstatus/Wizard ohne Live-Render-Calls prüfen/anpassen (pending)\n- [ ] tests. Mock-Tests und echte aggregierte Smoke-Tests ergänzen/ausführen (pending)\n- [ ] verify. Compile, pytest, Git-Safety, diff-check, status (pending)\n- [ ] commitpush. Commit und Push (pending)"
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      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\\n@@ -21,6 +21,7 @@\\n from jarvis_finance.market_data.mappings import confirm_instrument_price_mapping\\n from jarvis_finance.market_data.prices import (\\n     EodhdEquityPriceProvider,\\n+    CompositeEquityPriceProvider,\\n     FinnhubEquityPriceProvider,\\n     MassiveEquityPriceProvider,\\n     TwelveDataEquityPriceProvider,\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
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        {
          "id": "call_gB37MWAO0J1xEQTIHVZvMUKG",
          "call_id": "call_gB37MWAO0J1xEQTIHVZvMUKG",
          "response_item_id": "fc_0ffb8bff321e4dcd016a083850b73881918f7148b2230c964b",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"    assert isinstance(equity_price_provider_by_name(\\\"twelvedata\\\"), TwelveDataEquityPriceProvider)\\n    assert isinstance(equity_price_provider_by_name(\\\"auto\\\"), CompositeEquityPriceProvider)\\n\\n\\ndef test_composite_equity_price_provider_uses_priority_order() -> None:\\n    from jarvis_finance.market_data.prices import EquityPriceQuote\\n\\n    class MissingProvider:\\n        name = \\\"fmp\\\"\\n        def get_price(self, provider_symbol: str, *, price_date: str | None = None):\\n            return EquityPriceQuote(provider_symbol=provider_symbol, currency=\\\"\\\", close=None, provider=self.name, quality_status=\\\"missing\\\", error_message=\\\"restricted_endpoint\\\")\\n\\n    class FreshProvider:\\n        name = \\\"twelvedata\\\"\\n        def get_price(self, provider_symbol: str, *, price_date: str | None = None):\\n            return EquityPriceQuote(provider_symbol=provider_symbol, currency=\\\"USD\\\", close=Decimal(\\\"250.15\\\"), provider=self.name, quality_status=\\\"fresh\\\")\\n\\n    quote = CompositeEquityPriceProvider([MissingProvider(), FreshProvider()]).get_price(\\\"VTI\\\")\\n\\n    assert quote.provider == \\\"twelvedata\\\"\\n    assert quote.close == Decimal(\\\"250.15\\\")\\n\\n\\ndef test_eodhd_is_explicit_low_volume_provider_not_default(monkeypatch) -> None:\",\"old_string\":\"    assert isinstance(equity_price_provider_by_name(\\\"twelvedata\\\"), TwelveDataEquityPriceProvider)\\n\\n\\ndef test_eodhd_is_explicit_low_volume_provider_not_default(monkeypatch) -> None:\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\\n@@ -165,6 +165,26 @@\\n     assert TwelveDataEquityPriceProvider(api_key=\\\"synthetic\\\").get_price(\\\"VTI\\\").close == Decimal(\\\"250.13\\\")\\n     assert MassiveEquityPriceProvider(api_key=\\\"synthetic\\\").get_price(\\\"VTI\\\").close == Decimal(\\\"250.14\\\")\\n     assert isinstance(equity_price_provider_by_name(\\\"twelvedata\\\"), TwelveDataEquityPriceProvider)\\n+    assert isinstance(equity_price_provider_by_name(\\\"auto\\\"), CompositeEquityPriceProvider)\\n+\\n+\\n+def test_composite_equity_price_provider_uses_priority_order() -> None:\\n+    from jarvis_finance.market_data.prices import EquityPriceQuote\\n+\\n+    class MissingProvider:\\n+        name = \\\"fmp\\\"\\n+        def get_price(self, provider_symbol: str, *, price_date: str | None = None):\\n+            return EquityPriceQuote(provider_symbol=provider_symbol, currency=\\\"\\\", close=None, provider=self.name, quality_status=\\\"missing\\\", error_message=\\\"restricted_endpoint\\\")\\n+\\n+    class FreshProvider:\\n+        name = \\\"twelvedata\\\"\\n+        def get_price(self, provider_symbol: str, *, price_date: str | None = None):\\n+            return EquityPriceQuote(provider_symbol=provider_symbol, currency=\\\"USD\\\", close=Decimal(\\\"250.15\\\"), provider=self.name, quality_status=\\\"fresh\\\")\\n+\\n+    quote = CompositeEquityPriceProvider([MissingProvider(), FreshProvider()]).get_price(\\\"VTI\\\")\\n+\\n+    assert quote.provider == \\\"twelvedata\\\"\\n+    assert quote.close == Decimal(\\\"250.15\\\")\\n \\n \\n def test_eodhd_is_explicit_low_volume_provider_not_default(monkeypatch) -> None:\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/tests/unit/test_provider_integration_v2.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
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    {
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      "content": "{\"output\": \"........................................................................ [ 24%]\\n................................................................F....... [ 48%]\\n...F.................................................................... [ 73%]\\n........................................................................ [ 97%]\\n......                                                                   [100%]\\n=================================== FAILURES ===================================\\n_______ test_provider_lookup_classes_skip_missing_keys_and_cache_results _______\\n\\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x715dda045610>\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-161/test_provider_lookup_classes_s0')\\n\\n    def test_provider_lookup_classes_skip_missing_keys_and_cache_results(monkeypatch, tmp_path) -> None:\\n        from jarvis_finance.market_data.catalog import OpenFigiLookupProvider, default_instrument_lookup_providers, search_instruments\\n    \\n>       assert [p.name for p in default_instrument_lookup_providers()] == [\\\"openfigi\\\", \\\"fmp\\\", \\\"finnhub\\\"]\\nE       AssertionError: assert ['openfigi', ...a', 'massive'] == ['openfigi', 'fmp', 'finnhub']\\nE         \\nE         Left contains 2 more items, first extra item: 'twelvedata'\\nE         Use -v to get more diff\\n\\ntests/unit/test_equity_manage.py:475: AssertionError\\n_____________ test_runtime_secret_file_is_loaded_without_repo_env ______________\\n\\nmonkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x715dd9dcba50>\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-161/test_runtime_secret_file_is_lo0')\\n\\n    def test_runtime_secret_file_is_loaded_without_repo_env(monkeypatch, tmp_path) -> None:\\n        from jarvis_finance.market_data.catalog import FmpSearchProvider, OpenFigiLookupProvider, _runtime_secret_value\\n    \\n        for key in [\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\", \\\"FMP_API_KEY\\\", \\\"JARVIS_FMP_API_KEY\\\"]:\\n            monkeypatch.delenv(key, raising=False)\\n        runtime = tmp_path / \\\"runtime\\\"\\n        secrets = runtime / \\\"secrets\\\"\\n        secrets.mkdir(parents=True)\\n        (secrets / \\\".env\\\").write_text(\\\"OPENFIGI_API_KEY\\\" + \\\"=dummy-openfigi\\\\n\\\" + \\\"FMP_API_KEY\\\" + \\\"=dummy-fmp\\\\n\\\", encoding=\\\"utf-8\\\")\\n        monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(runtime))\\n    \\n        assert _runtime_secret_value((\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\")) == \\\"dummy-openfigi\\\"\\n        assert OpenFigiLookupProvider().api_key == \\\"dummy-openfigi\\\"\\n        assert FmpSearchProvider().api_key == \\\"dummy-fmp\\\"\\n        assert FmpEquityPriceProvider().api_key == \\\"dummy-fmp\\\"\\n        statuses = instrument_provider_statuses()\\n>       assert next(row for row in statuses if row[\\\"Provider\\\"] == \\\"OpenFIGI\\\")[\\\"Status\\\"] == \\\"Key geladen\\\"\\nE       AssertionError: assert 'aktiv' == 'Key geladen'\\nE         \\nE         - Key geladen\\nE         + aktiv\\n\\ntests/unit/test_equity_manage.py:703: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/unit/test_equity_manage.py::test_provider_lookup_classes_skip_missing_keys_and_cache_results\\nFAILED tests/unit/test_equity_manage.py::test_runtime_secret_file_is_loaded_without_repo_env\\n2 failed, 292 passed in 14.21s\", \"exit_code\": 1, \"error\": null}",
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      "content": "{\"content\": \"   440|    assert tx_missing[\\\"gross_amount_chf\\\"] is None\\n   441|    assert \\\"missing_fx\\\" in missing.warnings\\n   442|\\n   443|\\n   444|def test_manual_wizard_supports_buy_sell_dividend_and_no_dashboard_live_api_or_ids() -> None:\\n   445|    conn = setup_conn()\\n   446|    account_id = seed_account(conn, \\\"Synthetic Trading Broker\\\")\\n   447|    catalog_id = create_manual_catalog_entry(\\n   448|        conn,\\n   449|        asset_class=\\\"stock\\\",\\n   450|        name=\\\"Synthetic Tradable\\\",\\n   451|        currency=\\\"CHF\\\",\\n   452|        isin=\\\"CH4444444444\\\",\\n   453|        ticker=\\\"TRD\\\",\\n   454|        exchange=\\\"SIX\\\",\\n   455|        note=\\\"synthetic tradable source\\\",\\n   456|    )\\n   457|    snap = add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"initial_snapshot\\\", quantity_text=\\\"5\\\", trade_date=\\\"2025-12-31\\\", currency=\\\"CHF\\\", cost_basis_original_text=\\\"500\\\", note=\\\"synthetic starting point\\\", confirm=True)\\n   458|    add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"buy\\\", quantity_text=\\\"1\\\", trade_date=\\\"2026-01-05\\\", currency=\\\"CHF\\\", cost_basis_original_text=\\\"110\\\", note=\\\"synthetic buy\\\", confirm=True)\\n   459|    add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"partial_sell\\\", quantity_text=\\\"2\\\", trade_date=\\\"2026-01-10\\\", currency=\\\"CHF\\\", cost_basis_original_text=\\\"220\\\", note=\\\"synthetic sell\\\", confirm=True)\\n   460|    add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"dividend\\\", quantity_text=\\\"0\\\", trade_date=\\\"2026-02-01\\\", currency=\\\"CHF\\\", cost_basis_original_text=\\\"4.50\\\", note=\\\"synthetic dividend\\\", confirm=True)\\n   461|    assert calculate_positions(conn).positions[(account_id, snap.instrument_id)].quantity == Decimal(\\\"4\\\")\\n   462|\\n   463|    from pathlib import Path\\n   464|    page = Path(\\\"src/jarvis_finance/dashboard/views/page_13_equity_manage.py\\\").read_text()\\n   465|    assert \\\"search_instrument_candidates(\\\" in page\\n   466|    assert \\\"Provider-Suche explizit\\\" in page\\n   467|    wizard_source = page.split(\\\"with tabs[1]:\\\", 1)[0]\\n   468|    assert \\\"st.write({\\\\\\\"instrument_id\\\\\\\"\\\" not in wizard_source\\n   469|    assert \\\"st.write({\\\\\\\"catalog_entry_id\\\\\\\"\\\" not in wizard_source\\n   470|\\n   471|\\n   472|def test_provider_lookup_classes_skip_missing_keys_and_cache_results(monkeypatch, tmp_path) -> None:\\n   473|    from jarvis_finance.market_data.catalog import OpenFigiLookupProvider, default_instrument_lookup_providers, search_instruments\\n   474|\\n   475|    assert [p.name for p in default_instrument_lookup_providers()] == [\\\"openfigi\\\", \\\"fmp\\\", \\\"finnhub\\\"]\\n   476|    conn = setup_conn()\\n   477|    monkeypatch.delenv(\\\"OPENFIGI_API_KEY\\\", raising=False)\\n   478|    monkeypatch.delenv(\\\"JARVIS_OPENFIGI_API_KEY\\\", raising=False)\\n   479|    monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(tmp_path))\\n   480|    (tmp_path / \\\"secrets\\\").mkdir()\\n   481|    (tmp_path / \\\"secrets\\\" / \\\".env\\\").write_text(\\\"OPENFIGI_API_KEY\\\" + \\\"=runtime-file-key\\\\n\\\", encoding=\\\"utf-8\\\")\\n   482|    assert OpenFigiLookupProvider().api_key == \\\"runtime-file-key\\\"\\n   483|    (tmp_path / \\\"secrets\\\" / \\\".env\\\").unlink()\\n   484|    results, warnings = search_instruments(conn, \\\"US0378331005\\\", providers=[OpenFigiLookupProvider()])\\n   485|    assert results == []\\n   486|    assert \\\"openfigi_api_key_missing\\\" in warnings\\n   487|\\n   488|    class FakeOpenFigi(OpenFigiLookupProvider):\\n   489|        def _request(self, query: str):\\n   490|            return [{\\\"name\\\": \\\"Apple Inc\\\", \\\"ticker\\\": \\\"AAPL\\\", \\\"exchCode\\\": \\\"US\\\", \\\"securityType\\\": \\\"Common Stock\\\", \\\"marketSecDes\\\": \\\"Equity\\\", \\\"compositeFIGI\\\": \\\"BBG000B9XRY4\\\"}]\\n   491|\\n   492|    results, warnings = search_instruments(conn, \\\"Apple\\\", providers=[FakeOpenFigi(api_key=\\\"runtime-only-key\\\")])\\n   493|    assert warnings == []\\n   494|    assert results[0].source == \\\"openfigi\\\"\\n   495|    assert conn.execute(\\\"SELECT COUNT(*) AS c FROM instrument_catalog_entries WHERE provider='openfigi'\\\").fetchone()[\\\"c\\\"] == 1\\n   496|\\n   497|\\n   498|def test_search_candidates_uses_external_providers_only_on_explicit_click(monkeypatch) -> None:\\n   499|    from jarvis_finance.equity import manage\\n   500|    from jarvis_finance.market_data.catalog import CatalogSearchResult\\n   501|\\n   502|    conn = setup_conn()\\n   503|    calls = {\\\"n\\\": 0}\\n   504|\\n   505|    class FakeProvider:\\n   506|        name = \\\"fake_provider\\\"\\n   507|        def search(self, *, query, asset_class=None):\\n   508|            calls[\\\"n\\\"] += 1\\n   509|            return [CatalogSearchResult(None, asset_class or \\\"stock\\\", \\\"Synthetic Provider AG\\\", isin=\\\"CH5555555555\\\", ticker=\\\"SPV\\\", exchange=\\\"SIX\\\", trading_currency=\\\"CHF\\\", provider=\\\"fake\\\", confidence=\\\"medium\\\", source=\\\"fake_provider\\\")]\\n   510|\\n   511|    monkeypatch.setattr(manage, \\\"default_instrument_lookup_providers\\\", lambda: [FakeProvider()])\\n   512|    search_instrument_candidates(conn, query=\\\"SPV\\\", include_external=False)\\n   513|    assert calls[\\\"n\\\"] == 0\\n   514|    result = search_instrument_candidates(conn, query=\\\"SPV\\\", include_external=True)\\n   515|    assert calls[\\\"n\\\"] == 1\\n   516|    assert result[\\\"results\\\"][0][\\\"data_source\\\"] == \\\"fake_provider\\\"\\n   517|\\n   518|\\n   519|class CountingProvider:\\n   520|    name = \\\"counting\\\"\\n   521|\\n   522|    def __init__(self) -> None:\\n   523|        self.calls = 0\\n   524|\\n   525|    def search(self, *, query: str, asset_class: str | None = None):\\n   526|        self.calls += 1\\n   527|        return [CatalogSearchResult(None, asset_class or \\\"etf\\\", \\\"Provider Test ETF\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"NYSE\\\", trading_currency=\\\"USD\\\", provider=\\\"counting\\\", provider_symbol=\\\"VTI\\\", confidence=\\\"medium\\\", source=\\\"counting\\\", evidence_note=\\\"synthetic provider result\\\")]\\n   528|\\n   529|\\n   530|def test_vti_examples_are_searchable_and_ticker_requires_selection() -> None:\\n   531|    conn = setup_conn()\\n   532|    create_manual_catalog_entry(conn, asset_class=\\\"etf\\\", name=\\\"Vanguard Total Stock Market ETF\\\", currency=\\\"USD\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"NYSE\\\", note=\\\"synthetic VTI catalog entry\\\")\\n   533|    create_manual_catalog_entry(conn, asset_class=\\\"etf\\\", name=\\\"Vanguard Total Stock Market ETF Secondary Listing\\\", currency=\\\"USD\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"ARCA\\\", note=\\\"synthetic VTI alternate listing\\\")\\n   534|\\n   535|    by_isin = search_instrument_candidates(conn, query=\\\"US9229087690\\\", asset_class=\\\"etf\\\")\\n   536|    assert by_isin[\\\"results\\\"]\\n   537|    assert by_isin[\\\"results\\\"][0][\\\"name\\\"].startswith(\\\"Vanguard Total Stock Market\\\")\\n   538|    assert any(r[\\\"ticker\\\"] == \\\"VTI\\\" and r[\\\"currency\\\"] == \\\"USD\\\" for r in by_isin[\\\"results\\\"])\\n   539|    assert all(r[\\\"trust_status\\\"] == \\\"Eindeutig\\\" for r in by_isin[\\\"results\\\"])\\n   540|\\n   541|    by_name = search_instrument_candidates(conn, query=\\\"Vanguard Total Stock\\\", asset_class=\\\"etf\\\")\\n   542|    assert len(by_name[\\\"results\\\"]) >= 2\\n   543|    assert by_name[\\\"selection_required\\\"] is True\\n   544|\\n   545|    by_ticker = search_instrument_candidates(conn, query=\\\"VTI\\\", asset_class=\\\"etf\\\")\\n   546|    assert len(by_ticker[\\\"results\\\"]) >= 2\\n   547|    assert by_ticker[\\\"selection_required\\\"] is True\\n   548|    assert all(r[\\\"trust_status\\\"] == \\\"Manuell prüfen\\\" for r in by_ticker[\\\"results\\\"])\\n   549|\\n   550|\\n   551|def test_online_provider_is_only_used_when_explicitly_enabled_and_caches_result() -> None:\\n   552|    conn = setup_conn()\\n   553|    provider = CountingProvider()\\n   554|    from jarvis_finance.market_data.catalog import search_instruments\\n   555|\\n   556|    local_only = search_instrument_candidates(conn, query=\\\"US9229087690\\\", asset_class=\\\"etf\\\", include_external=False)\\n   557|    assert local_only[\\\"results\\\"] == []\\n   558|    assert provider.calls == 0\\n   559|\\n   560|    results, warnings = search_instruments(conn, \\\"US9229087690\\\", \\\"etf\\\", providers=[provider])\\n   561|    assert warnings == []\\n   562|    assert provider.calls == 1\\n   563|    assert results[0].ticker == \\\"VTI\\\"\\n   564|    cached = search_instrument_candidates(conn, query=\\\"US9229087690\\\", asset_class=\\\"etf\\\", include_external=False)\\n   565|    assert cached[\\\"results\\\"]\\n   566|    assert cached[\\\"results\\\"][0][\\\"data_source\\\"] == \\\"local_catalog\\\"\\n   567|\\n   568|\\n   569|def test_provider_statuses_report_missing_keys_without_secret_values(monkeypatch, tmp_path) -> None:\\n   570|    for key in [\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\", \\\"FINNHUB_API_KEY\\\", \\\"JARVIS_FINNHUB_API_KEY\\\", \\\"FMP_API_KEY\\\", \\\"JARVIS_FMP_API_KEY\\\"]:\\n   571|        monkeypatch.delenv(key, raising=False)\\n   572|    monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(tmp_path))\\n   573|    statuses = instrument_provider_statuses()\\n   574|    assert {row[\\\"Provider\\\"] for row in statuses} >= {\\\"Lokale Suche\\\", \\\"OpenFIGI\\\", \\\"Finnhub\\\", \\\"FMP\\\", \\\"Manuelle Anlage\\\"}\\n   575|    assert next(row for row in statuses if row[\\\"Provider\\\"] == \\\"OpenFIGI\\\")[\\\"Status\\\"] == \\\"API-Key fehlt\\\"\\n   576|    assert not any(\\\"secret\\\" in str(row).lower() or \\\"runtime-file-key\\\" in str(row) for row in statuses)\\n   577|\\n   578|\\n   579|def test_category_is_saved_for_manual_position_from_catalog() -> None:\\n   580|    conn = setup_conn()\\n   581|    account_id = seed_account(conn, \\\"Synthetic Category Broker\\\")\\n   582|    catalog_id = create_manual_catalog_entry(conn, asset_class=\\\"etf\\\", name=\\\"Synthetic Core ETF\\\", currency=\\\"CHF\\\", isin=\\\"CH5555555555\\\", ticker=\\\"CORE\\\", exchange=\\\"SIX\\\", note=\\\"synthetic core entry\\\")\\n   583|    result = add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"initial_snapshot\\\", quantity_text=\\\"1\\\", trade_date=\\\"2025-12-31\\\", currency=\\\"CHF\\\", cost_basis_original_text=\\\"100\\\", note=\\\"synthetic category test\\\", category=\\\"Core\\\", confirm=True)\\n   584|    row = conn.execute(\\\"SELECT position_category FROM instruments WHERE instrument_id=?\\\", (result.instrument_id,)).fetchone()\\n   585|    assert row[\\\"position_category\\\"] == \\\"Core\\\"\\n   586|\\n   587|\\n   588|def test_openfigi_isin_and_fmp_name_search_candidates_are_structured() -> None:\\n   589|    from jarvis_finance.market_data.catalog import FmpSearchProvider, OpenFigiLookupProvider\\n   590|\\n   591|    class FakeOpenFigi(OpenFigiLookupProvider):\\n   592|        def _request(self, query: str):\\n   593|            assert query == \\\"US9229087690\\\"\\n   594|            return [{\\\"data\\\": [{\\\"name\\\": \\\"Vanguard Total Stock Market ETF\\\", \\\"ticker\\\": \\\"VTI\\\", \\\"exchCode\\\": \\\"US\\\", \\\"currency\\\": \\\"USD\\\", \\\"securityType\\\": \\\"ETF\\\", \\\"compositeFIGI\\\": \\\"BBG000PSKYX7\\\"}]}]\\n   595|\\n   596|    class FakeFmp(FmpSearchProvider):\\n   597|        def _request_many(self, query: str) -> list[dict[str, object]]:\\n   598|            assert query == \\\"Vanguard Total Stock\\\"\\n   599|            return [{\\\"symbol\\\": \\\"VTI\\\", \\\"name\\\": \\\"Vanguard Total Stock Market ETF\\\", \\\"exchangeShortName\\\": \\\"AMEX\\\", \\\"currency\\\": \\\"USD\\\"}]\\n   600|        def _profile(self, symbol: str):\\n   601|            return {\\\"isin\\\": \\\"US9229087690\\\", \\\"currency\\\": \\\"USD\\\", \\\"isEtf\\\": True, \\\"country\\\": \\\"US\\\", \\\"price\\\": 250.12}\\n   602|\\n   603|    openfigi = FakeOpenFigi(api_key=\\\"test-key\\\")\\n   604|    fmp = FakeFmp(api_key=\\\"test-key\\\")\\n   605|    figi_rows = openfigi.search(query=\\\"US9229087690\\\", asset_class=\\\"etf\\\")\\n   606|    fmp_rows = fmp.search(query=\\\"Vanguard Total Stock\\\", asset_class=\\\"etf\\\")\\n   607|    assert figi_rows[0].isin == \\\"US9229087690\\\"\\n   608|    assert figi_rows[0].provider == \\\"openfigi\\\"\\n   609|    assert figi_rows[0].provider_symbol == \\\"BBG000PSKYX7\\\"\\n   610|    assert figi_rows[0].confidence == \\\"high\\\"\\n   611|    assert fmp_rows[0].isin == \\\"US9229087690\\\"\\n   612|    assert fmp_rows[0].provider == \\\"fmp\\\"\\n   613|    assert fmp_rows[0].confidence == \\\"medium\\\"\\n   614|\\n   615|\\n   616|def test_provider_merge_raises_confidence_and_conflict_needs_manual_review() -> None:\\n   617|    from jarvis_finance.market_data.catalog import search_instruments\\n   618|\\n   619|    class FakeOpenFigi:\\n   620|        name = \\\"openfigi\\\"\\n   621|        def search(self, *, query, asset_class=None):\\n   622|            return [CatalogSearchResult(None, asset_class or \\\"etf\\\", \\\"Vanguard Total Stock Market ETF\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"US\\\", trading_currency=\\\"USD\\\", provider=\\\"openfigi\\\", provider_symbol=\\\"BBG000PSKYX7\\\", confidence=\\\"high\\\", source=\\\"openfigi\\\")]\\n   623|\\n   624|    class FakeFmpSame:\\n   625|        name = \\\"fmp\\\"\\n   626|        def search(self, *, query, asset_class=None):\\n   627|            return [CatalogSearchResult(None, asset_class or \\\"etf\\\", \\\"Vanguard Total Stock Market ETF\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"US\\\", trading_currency=\\\"USD\\\", provider=\\\"fmp\\\", provider_symbol=\\\"VTI\\\", confidence=\\\"medium\\\", source=\\\"fmp\\\")]\\n   628|\\n   629|    class FakeFmpConflict:\\n   630|        name = \\\"fmp\\\"\\n   631|        def search(self, *, query, asset_class=None):\\n   632|            return [CatalogSearchResult(None, asset_class or \\\"etf\\\", \\\"Vanguard Total Stock Market ETF\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTIX\\\", exchange=\\\"US\\\", trading_currency=\\\"USD\\\", provider=\\\"fmp\\\", provider_symbol=\\\"VTIX\\\", confidence=\\\"medium\\\", source=\\\"fmp\\\")]\\n   633|\\n   634|    conn = setup_conn()\\n   635|    merged, warnings = search_instruments(conn, \\\"US9229087690\\\", \\\"etf\\\", providers=[FakeOpenFigi(), FakeFmpSame()])\\n   636|    assert warnings == []\\n   637|    assert len(merged) == 1\\n   638|    assert merged[0].provider == \\\"fmp+openfigi\\\"\\n   639|    assert merged[0].confidence == \\\"high\\\"\\n   640|\\n   641|    conn = setup_conn()\\n   642|    conflicted, warnings = search_instruments(conn, \\\"US9229087690\\\", \\\"etf\\\", providers=[FakeOpenFigi(), FakeFmpConflict()])\\n   643|    assert warnings == []\\n   644|    assert len(conflicted) == 1\\n   645|    assert conflicted[0].confidence == \\\"low\\\"\\n   646|    result = search_instrument_candidates(conn, query=\\\"US9229087690\\\", asset_class=\\\"etf\\\", include_external=False)\\n   647|    assert result[\\\"selection_required\\\"] is True\\n   648|\\n   649|\\n   650|def test_price_preview_uses_local_market_prices_and_missing_price_is_not_zero() -> None:\\n   651|    from jarvis_finance.dashboard.views.page_17_position_add import _price_preview\\n   652|\\n   653|    conn = setup_conn()\\n   654|    account_id = seed_account(conn, \\\"Synthetic Price Broker\\\")\\n   655|    catalog_id = create_manual_catalog_entry(conn, asset_class=\\\"etf\\\", name=\\\"Vanguard Total Stock Market ETF\\\", currency=\\\"USD\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"US\\\", note=\\\"synthetic price preview\\\")\\n   656|    result = add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"initial_snapshot\\\", quantity_text=\\\"1\\\", trade_date=\\\"2026-01-01\\\", currency=\\\"USD\\\", cost_basis_original_text=\\\"100\\\", fx_status=\\\"missing\\\", note=\\\"synthetic price preview\\\", confirm=True)\\n   657|    store_market_price(conn, instrument_id=result.instrument_id, price_date=\\\"2026-01-02\\\", close=Decimal(\\\"250.12\\\"), currency=\\\"USD\\\", provider=\\\"fmp\\\", provider_symbol=\\\"VTI\\\")\\n   658|    rows = search_instrument_candidates(conn, query=\\\"US9229087690\\\", asset_class=\\\"etf\\\")[\\\"results\\\"]\\n   659|    assert rows[0][\\\"last_price\\\"] == \\\"250.12\\\"\\n   660|    assert rows[0][\\\"price_source\\\"] == \\\"fmp\\\"\\n   661|    assert \\\"250.12 USD\\\" in _price_preview(rows[0])\\n   662|    assert _price_preview({\\\"name\\\": \\\"No Price\\\", \\\"last_price\\\": \\\"\\\"}) == \\\"Preis noch nicht verfügbar\\\"\\n   663|    assert \\\"0\\\" not in _price_preview({\\\"name\\\": \\\"No Price\\\", \\\"last_price\\\": \\\"\\\"})\\n   664|\\n   665|\\n   666|def test_update_equity_prices_cli_workflow_uses_explicit_provider_mapping() -> None:\\n   667|    conn = setup_conn()\\n   668|    account_id = seed_account(conn, \\\"Synthetic Update Broker\\\")\\n   669|    catalog_id = create_manual_catalog_entry(conn, asset_class=\\\"etf\\\", name=\\\"Vanguard Total Stock Market ETF\\\", currency=\\\"USD\\\", isin=\\\"US9229087690\\\", ticker=\\\"VTI\\\", exchange=\\\"US\\\", provider=\\\"fmp\\\", provider_symbol=\\\"VTI\\\", note=\\\"synthetic price update\\\")\\n   670|    result = add_manual_position_from_catalog(conn, catalog_entry_id=catalog_id, account_id=account_id, position_type=\\\"initial_snapshot\\\", quantity_text=\\\"1\\\", trade_date=\\\"2026-01-01\\\", currency=\\\"USD\\\", cost_basis_original_text=\\\"100\\\", fx_status=\\\"missing\\\", note=\\\"synthetic price update\\\", confirm=True)\\n   671|    summary = refresh_market_prices(conn, provider=MockEquityPriceProvider({\\\"VTI\\\": \\\"251.34\\\"}), asset_class=\\\"etf\\\", price_date=\\\"2026-01-02\\\")\\n   672|    assert summary.updated_count == 1\\n   673|    row = conn.execute(\\\"SELECT close, provider FROM market_prices WHERE instrument_id=?\\\", (result.instrument_id,)).fetchone()\\n   674|    assert row[\\\"close\\\"] == \\\"251.34\\\"\\n   675|    assert row[\\\"provider\\\"] == \\\"mock\\\"\\n   676|\\n   677|\\n   678|def test_fmp_price_provider_missing_key_is_sanitized(monkeypatch, tmp_path) -> None:\\n   679|    monkeypatch.delenv(\\\"FMP_API_KEY\\\", raising=False)\\n   680|    monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(tmp_path))\\n   681|    quote = FmpEquityPriceProvider().get_price(\\\"VTI\\\")\\n   682|    assert quote.close is None\\n   683|    assert quote.error_message == \\\"fmp_api_key_missing\\\"\\n   684|    assert \\\"VTI\\\" == quote.provider_symbol\\n   685|\\n   686|\\n   687|def test_runtime_secret_file_is_loaded_without_repo_env(monkeypatch, tmp_path) -> None:\\n   688|    from jarvis_finance.market_data.catalog import FmpSearchProvider, OpenFigiLookupProvider, _runtime_secret_value\\n   689|\\n   690|    for key in [\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\", \\\"FMP_API_KEY\\\", \\\"JARVIS_FMP_API_KEY\\\"]:\\n   691|        monkeypatch.delenv(key, raising=False)\\n   692|    runtime = tmp_path / \\\"runtime\\\"\\n   693|    secrets = runtime / \\\"secrets\\\"\\n   694|    secrets.mkdir(parents=True)\\n   695|    (secrets / \\\".env\\\").write_text(\\\"OPENFIGI_API_KEY\\\" + \\\"=dummy-openfigi\\\\n\\\" + \\\"FMP_API_KEY\\\" + \\\"=dummy-fmp\\\\n\\\", encoding=\\\"utf-8\\\")\\n   696|    monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(runtime))\\n   697|\\n   698|    assert _runtime_secret_value((\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\")) == \\\"dummy-openfigi\\\"\\n   699|    assert OpenFigiLookupProvider().api_key == \\\"dummy-openfigi\\\"\\n   700|    assert FmpSearchProvider().api_key == \\\"dummy-fmp\\\"\\n   701|    assert FmpEquityPriceProvider().api_key == \\\"dummy-fmp\\\"\\n   702|    statuses = instrument_provider_statuses()\\n   703|    assert next(row for row in statuses if row[\\\"Provider\\\"] == \\\"OpenFIGI\\\")[\\\"Status\\\"] == \\\"Key geladen\\\"\\n   704|    assert next(row for row in statuses if row[\\\"Provider\\\"] == \\\"FMP\\\")[\\\"Kategorie\\\"] == \\\"key_loaded\\\"\\n   705|    assert not any(\\\"dummy-openfigi\\\" in str(row) or \\\"dummy-fmp\\\" in str(row) for row in statuses)\\n   706|\\n   707|\\n   708|def test_openfigi_key_is_sent_as_header_not_query_param() -> None:\\n   709|    from jarvis_finance.market_data.catalog import OpenFigiLookupProvider\\n   710|\", \"total_lines\": 880, \"file_size\": 39673, \"truncated\": true, \"hint\": \"Use offset=710 to continue reading (showing 440-709 of 880 lines)\", \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "call_8sbsvfJiGC5dO4zLShGKTCqg"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Considering patch tests**\n\nI need to ensure that the monkeypatch is also deleting the new keys for the environment. There might be issues with provider status due to missing existing keys, especially since it could be affected by runtime temperature. This could cause tests to fail. But hold on, maybe there's no environment variable in pytest? It seems like we've only seen the status fail. I also need to patch the expected list or status for clarity.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Considering patch tests**\n\nI need to ensure that the monkeypatch is also deleting the new keys for the environment. There might be issues with provider status due to missing existing keys, especially since it could be affected by runtime temperature. This could cause tests to fail. But hold on, maybe there's no environment variable in pytest? It seems like we've only seen the status fail. I also need to patch the expected list or status for clarity.",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
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      "content": "{\"output\": \"src/jarvis_finance/cli/main.py                     |  23 ++-\\n .../dashboard/views/page_17_position_add.py        |   4 +-\\n src/jarvis_finance/fx/providers.py                 |  92 ++++++++++-\\n src/jarvis_finance/market_data/catalog.py          | 126 ++++++++++++++-\\n src/jarvis_finance/market_data/prices.py           | 171 +++++++++++++++++++++\\n tests/unit/test_equity_manage.py                   |   4 +-\\n 6 files changed, 407 insertions(+), 13 deletions(-)\\ndiff_chars 35297\\nOPENFIGI_API_KEY=***\\nFMP_API_KEY=***\\nFINNHUB_API_KEY=***\\nTWELVEDATA_API_KEY=***\\nMASSIVE_API_KEY=***\\nEODHD_API_KEY=***\", \"exit_code\": 0, \"error\": null}",
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      "content": "{\"content\": \"    45|\\n    46|    def read(self) -> bytes:\\n    47|        return json.dumps(self.payload).encode(\\\"utf-8\\\")\\n    48|\\n    49|\\n    50|def setup_conn():\\n    51|    conn = connect_memory()\\n    52|    apply_migrations(conn)\\n    53|    conn.execute(\\\"INSERT INTO platforms(platform_id,name,platform_type,default_currency,created_at) VALUES('p1','Broker','broker','CHF','now')\\\")\\n    54|    conn.execute(\\\"INSERT INTO accounts(account_id,platform_id,account_name,account_type,currency,created_at) VALUES('a1','p1','Account','brokerage','CHF','now')\\\")\\n    55|    conn.execute(\\\"INSERT INTO instruments(instrument_id,asset_class,name,ticker,isin,exchange,currency,created_at) VALUES('usd1','ETF','USD ETF','VTI','US9229087690','NYSE','USD','now')\\\")\\n    56|    return conn\\n    57|\\n    58|\\n    59|def test_runtime_secret_aliases_include_new_provider_key_names(monkeypatch, tmp_path) -> None:\\n    60|    monkeypatch.delenv(\\\"OPENFIGI_API_KEY\\\", raising=False)\\n    61|    monkeypatch.setenv(\\\"JARVIS_FINANCE_RUNTIME_DIR\\\", str(tmp_path))\\n    62|    secrets = tmp_path / \\\"secrets\\\"\\n    63|    secrets.mkdir()\\n    64|    env_file = secrets / \\\".env\\\"\\n    65|    env_file.write_text(\\\"TWELVEDATA_API_KEY=td\\\\nMASSIVE_API_KEY=mass\\\\nJARVIS_OPENFIGI_API_KEY=figi\\\\n\\\", encoding=\\\"utf-8\\\")\\n    66|    env_file.chmod(0o600)\\n    67|\\n    68|    assert _runtime_secret_value((\\\"TWELVEDATA_API_KEY\\\", \\\"TWELVE_DATA_API_KEY\\\")) == \\\"td\\\"\\n    69|    assert _runtime_secret_value((\\\"MASSIVE_API_KEY\\\", \\\"JARVIS_MASSIVE_API_KEY\\\")) == \\\"mass\\\"\\n    70|    assert _runtime_secret_value((\\\"OPENFIGI_API_KEY\\\", \\\"JARVIS_OPENFIGI_API_KEY\\\")) == \\\"figi\\\"\\n    71|\\n    72|\\n    73|def test_openfigi_401_is_auth_failed_without_crash(monkeypatch) -> None:\\n    74|    def fail(*args, **kwargs):\\n    75|        raise HTTPError(\\\"https://api.openfigi.com/v3/mapping\\\", 401, \\\"Unauthorized\\\", {}, BytesIO())\\n    76|\\n    77|    monkeypatch.setattr(\\\"jarvis_finance.market_data.catalog.request.urlopen\\\", fail)\\n    78|    provider = OpenFigiLookupProvider(api_key=\\\"synthetic\\\", min_interval_seconds=0)\\n    79|\\n    80|    with pytest.raises(LookupUnavailable, match=\\\"openfigi_auth_failed\\\"):\\n    81|        provider.search(query=\\\"US9229087690\\\", asset_class=\\\"etf\\\")\\n    82|\\n    83|\\n    84|def test_openfigi_isin_mapping_mock_returns_candidate(monkeypatch) -> None:\\n    85|    provider = OpenFigiLookupProvider(api_key=\\\"synthetic\\\", min_interval_seconds=0)\\n    86|    monkeypatch.setattr(provider, \\\"_post_json\\\", lambda *a, **k: [{\\\"data\\\": [{\\\"figi\\\": \\\"BBG000\\\", \\\"ticker\\\": \\\"VTI\\\", \\\"name\\\": \\\"Vanguard Total Stock Market ETF\\\", \\\"exchCode\\\": \\\"US\\\", \\\"currency\\\": \\\"USD\\\"}]}])\\n    87|\\n    88|    results = provider.search(query=\\\"US9229087690\\\", asset_class=\\\"etf\\\")\\n    89|\\n    90|\", \"total_lines\": 223, \"file_size\": 10781, \"truncated\": true, \"hint\": \"Use offset=90 to continue reading (showing 45-89 of 223 lines)\", \"is_binary\": false, \"is_image\": false}",
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    {
      "role": "tool",
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      "content": "{\"output\": \"origin\\thttps://github.com/Gamexgit/FinanceManager.git (fetch)\\norigin\\thttps://github.com/Gamexgit/FinanceManager.git (push)\\n* main 4835f11 [origin/main: 3 voraus] feat: add provider integration sprint v2\\n/usr/bin/bash: Zeile 3: gh: Befehl nicht gefunden\", \"exit_code\": 127, \"error\": null}",
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            "arguments": "{\"command\":\"python - <<'PY'\\nimport csv, subprocess, os, sys\\nfrom pathlib import Path\\npath=Path.home()/ 'jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv'\\nif not path.exists():\\n    print('TOKEN_FILE_MISSING')\\n    sys.exit(2)\\ntext=path.read_text().strip().splitlines()\\nif not text:\\n    print('TOKEN_FILE_EMPTY')\\n    sys.exit(2)\\n# Support either raw token, token in first column, or named token column.\\nif len(text)==1 and ',' not in text[0]:\\n    token=text[0].strip()\\nelse:\\n    rows=list(csv.DictReader(text))\\n    if rows:\\n        row=rows[0]\\n        token=next((row[k].strip() for k in row if k and any(w in k.lower() for w in ['token','pat','github']) and row[k].strip()), '')\\n        if not token:\\n            token=next((v.strip() for v in row.values() if v and v.strip()), '')\\n    else:\\n        token=text[0].split(',',1)[0].strip()\\nif not token:\\n    print('TOKEN_NOT_FOUND')\\n    sys.exit(2)\\nurl=f'https://x-access-token:{token}@github.com/Gamexgit/FinanceManager.git'\\nres=subprocess.run(['git','push',url,'main:main'], cwd='/home/agent/.hermes/repos/FinanceManager', text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, timeout=300)\\nout=res.stdout.replace(token, '***')\\nprint(out, end='')\\nsys.exit(res.returncode)\\nPY\\ngit status --short\",\"timeout\":360,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
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    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"To https://github.com/Gamexgit/FinanceManager.git\\n   9f1ead2..4835f11  main -> main\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_1bV2CpDkzsJi0gWYHG1TXXz8"
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    {
      "role": "assistant",
      "content": "",
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      "finish_reason": "tool_calls",
      "reasoning_content": "**Finalizing Git Status**\n\nI'm considering checking the final status, possibly looking at the origin/main. I think I should make sure that the todo is marked as completed too. Running a git status -vv could be useful here, but maybe it's not strictly necessary. I wonder if I need to confirm anything else before finishing this up. Ultimately, getting everything squared away with my git status sounds like a solid plan!",
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            "arguments": "{\"command\":\"git status --short && git branch -vv && git log -1 --oneline\",\"timeout\":60,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
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    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"* main 4835f11 [origin/main: 3 voraus] feat: add provider integration sprint v2\\n4835f11 feat: add provider integration sprint v2\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_aYDOgBaV2osaveIQ17LsfK0o"
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    {
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      "content": "",
      "reasoning": "**Clarifying git updates**\n\nI'm noticing there might be some confusion with my local and remote branches. It looks like my local is ahead by three commits, which might mean I haven't pushed those changes yet. I should check if origin/main is updated. Maybe I need to perform a `git fetch origin` to see if I need to resolve any authentication issues too. I could also use `git ls-remote` to verify everything looks correct. I'll figure out if I need to update my tracking.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Clarifying git updates**\n\nI'm noticing there might be some confusion with my local and remote branches. It looks like my local is ahead by three commits, which might mean I haven't pushed those changes yet. I should check if origin/main is updated. Maybe I need to perform a `git fetch origin` to see if I need to resolve any authentication issues too. I could also use `git ls-remote` to verify everything looks correct. I'll figure out if I need to update my tracking.",
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            "arguments": "{\"command\":\"python - <<'PY'\\nimport csv, subprocess, sys\\nfrom pathlib import Path\\npath=Path.home()/ 'jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv'\\ntext=path.read_text().strip().splitlines()\\nif len(text)==1 and ',' not in text[0]: token=text[0].strip()\\nelse:\\n rows=list(csv.DictReader(text)); row=rows[0]\\n token=next((row[k].strip() for k in row if k and any(w in k.lower() for w in ['token','pat','github']) and row[k].strip()), '') or next((v.strip() for v in row.values() if v and v.strip()), '')\\nurl=f'https://x-access-token:{token}@github.com/Gamexgit/FinanceManager.git'\\nfor cmd in [['git','fetch',url,'main:refs/remotes/origin/main'], ['git','ls-remote',url,'refs/heads/main']]:\\n res=subprocess.run(cmd, cwd='/home/agent/.hermes/repos/FinanceManager', text=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, timeout=300)\\n print(res.stdout.replace(token,'***'), end='')\\n if res.returncode: sys.exit(res.returncode)\\nPY\\ngit branch -vv && git status --short\",\"timeout\":360,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
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    {
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      "content": "{\"output\": \"Von https://github.com/Gamexgit/FinanceManager\\n   3345eee..4835f11  main       -> origin/main\\n4835f1122493e2b0646c1fc67d8f768916a84aae\\trefs/heads/main\\n* main 4835f11 [origin/main] feat: add provider integration sprint v2\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_X3spKVgnW9ruPrHAMDOjP4Fk"
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      "content": "Erledigt, Sir. Provider Integration Sprint v2 ist implementiert, getestet, committed und gepusht.\n\n## Ergebnis\n\n- **Commit:** `4835f11`\n- **Branch:** `main`\n- **Push:** erfolgreich nach `origin/main`\n- **Git-Status:** sauber\n- **Keine API-Keys ausgegeben**\n- **Keine echten Finanz-/Portfolio-Daten ins Repo**\n- **Keine Position gespeichert**\n- **Keine produktiven Broker-Imports**\n- **EODHD nicht live getestet**, um das Low-Volume-Limit nicht unnötig zu verbrennen — sehr unbritisch, API-Calls zu verfeuern wie Champagner bei einer schlechten Hochzeit.\n\n## Implementiert\n\n### Provider / Secrets\n\nUnterstützte Key-Namen erweitert für:\n\n- OpenFIGI:\n  - `OPENFIGI_API_KEY`\n  - `JARVIS_OPENFIGI_API_KEY`\n- FMP:\n  - `FMP_API_KEY`\n  - `JARVIS_FMP_API_KEY`\n- Finnhub:\n  - `FINNHUB_API_KEY`\n  - `JARVIS_FINNHUB_API_KEY`\n- Twelve Data:\n  - `TWELVEDATA_API_KEY`\n  - `TWELVE_DATA_API_KEY`\n  - `JARVIS_TWELVEDATA_API_KEY`\n  - `JARVIS_TWELVE_DATA_API_KEY`\n- EODHD:\n  - `EODHD_API_KEY`\n  - `JARVIS_EODHD_API_KEY`\n- Massive:\n  - `MASSIVE_API_KEY`\n  - `JARVIS_MASSIVE_API_KEY`\n- Alpha Vantage:\n  - `ALPHA_VANTAGE_API_KEY`\n  - `JARVIS_ALPHA_VANTAGE_API_KEY`\n\n`.env` unter `~/jarvis_runtime/finance-system/secrets/.env` wurde gefunden, Mode `600` bestätigt.\n\n## Echte Provider-Smoke-Tests\n\nAggregiert, ohne Keys/rohe Daten:\n\n- **OpenFIGI korrigierter Key getestet:** ja\n- **OpenFIGI erreichbar:** ja\n- **OpenFIGI Auth OK:** ja\n- **OpenFIGI ISIN-Mapping `US9229087690`:** erfolgreich\n- **OpenFIGI Search `Vanguard Total Stock`:** erfolgreich\n- **OpenFIGI Ticker+Exchange `VTI` / `US`:** erfolgreich\n- **OpenFIGI Rate-Limit-Header gelesen:** ja\n\n- **FMP Status:** aktiv / OK\n- **FMP Profile VTI:** erfolgreich\n- **FMP restricted endpoint handling:** implementiert\n\n- **Finnhub integriert/getestet:** ja\n- **Finnhub Symbol Search:** erfolgreich\n- **Finnhub Quote:** erfolgreich\n- **Finnhub Profile:** Endpoint erreichbar, VTI-Profil im Smoke leer; kein Crash, sauberer Fallback\n\n- **Twelve Data integriert/getestet:** ja\n- **Twelve Data Symbol Search:** erfolgreich\n- **Twelve Data Quote:** erfolgreich\n- **Twelve Data FX USD/CHF:** erfolgreich\n- **Wichtiger Keyname `TWELVEDATA_API_KEY`:** unterstützt und getestet\n\n- **Massive integriert/getestet:** ja\n- **Massive Reference Tickers:** erfolgreich\n- **Massive Ticker Details:** erfolgreich\n- **Massive Previous Day / Aggregates:** erfolgreich\n- **Auth:** Authorization Header bevorzugt, Query-Parameter-Fallback vorbereitet\n\n- **EODHD eingebunden:** ja, als Low-volume-Fallback\n- **EODHD echter Smoke-Test:** nein, absichtlich geschont\n- **EODHD Bulk-Nutzung:** blockiert/nicht Default\n- **EODHD nur explizit:** ja, `--provider eodhd --allow-low-volume`\n\n- **Alpha Vantage:** optional eingebunden als Status/Key-Erkennung; Key fehlt aktuell\n\n## Providerrollen / Prioritäten\n\n### Instrument Search / Mapping\n\nDefault-Reihenfolge jetzt:\n\n1. Lokale DB\n2. OpenFIGI\n3. FMP\n4. Finnhub\n5. Twelve Data\n6. Massive\n7. Manuell\n\nEODHD bleibt bewusst **nicht** im Default-Search, weil Low-Volume.\n\n### Preisvorschau / Equity Price Update\n\nNeu:\n\n- `CompositeEquityPriceProvider` mit Priorität:\n  1. FMP\n  2. Twelve Data\n  3. Finnhub\n  4. Massive\n\nCLI erweitert:\n\n```bash\nfinance update-equity-prices --provider auto\nfinance update-equity-prices --provider fmp\nfinance update-equity-prices --provider twelvedata\nfinance update-equity-prices --provider finnhub\nfinance update-equity-prices --provider massive\nfinance update-equity-prices --provider eodhd --allow-low-volume\n```\n\nDry-run bleibt möglich.\n\n### FX Update\n\nImplementiert/erweitert:\n\n- Frankfurter als Primary no-key Provider\n- Twelve Data FX als optionaler Provider\n- `CHF/CHF = 1` lokal\n- aktuelle und historische Rates via bestehendem Workflow\n- Dashboard liest weiterhin lokale Daten, keine Live-Abfrage beim Rendern\n\nCLI:\n\n```bash\nfinance update-fx-rates --latest --provider frankfurter --currency USD --currency EUR\nfinance update-fx-rates --latest --provider twelvedata --currency USD\n```\n\n## Dashboard / Wizard\n\nProviderstatus im Position-Add-Wizard verbessert:\n\n- **keine Live-Provider-Abfrage beim normalen Rendern**\n- Statusanzeige nutzt `probe=False`\n- Online-Suche weiterhin nur bei explizitem Klick\n- Unterstützte Keynamen im Wizard aktualisiert\n- Keys werden nicht angezeigt\n\nStatus-Provider sichtbar:\n\n- OpenFIGI\n- FMP\n- Finnhub\n- Twelve Data\n- Massive\n- EODHD Low-volume\n- Frankfurter FX\n- Stooq\n- Alpha Vantage\n- Manuelle Anlage\n\n## Tests / Verification\n\nAusgeführt:\n\n```bash\npython -m compileall -q src tests\npytest -q\npython -m jarvis_finance.cli.main git-safety-scan .\ngit diff --check\ngit status --short\n```\n\nErgebnis:\n\n- **Compile:** OK\n- **pytest:** `294 passed`\n- **Git-Safety:** `GIT_SAFETY_OK`\n- **git diff --check:** OK\n- **git status:** clean nach Commit/Push\n\n## Offene Grenzen\n\n- **Finnhub Profile** war beim VTI-Smoke leer, Search und Quote funktionieren aber.\n- **EODHD** wurde absichtlich nicht live abgefragt, um die ca. 20 Calls/Tag zu schonen.\n- **Alpha Vantage** bleibt optional und aktuell ohne Key.\n- Massive ist US-fokussiert und nicht als Primary für CH/EU-Instrumente gedacht.",
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              "text": "Erledigt, Sir. Provider Integration Sprint v2 ist implementiert, getestet, committed und gepusht.\n\n## Ergebnis\n\n- **Commit:** `4835f11`\n- **Branch:** `main`\n- **Push:** erfolgreich nach `origin/main`\n- **Git-Status:** sauber\n- **Keine API-Keys ausgegeben**\n- **Keine echten Finanz-/Portfolio-Daten ins Repo**\n- **Keine Position gespeichert**\n- **Keine produktiven Broker-Imports**\n- **EODHD nicht live getestet**, um das Low-Volume-Limit nicht unnötig zu verbrennen — sehr unbritisch, API-Calls zu verfeuern wie Champagner bei einer schlechten Hochzeit.\n\n## Implementiert\n\n### Provider / Secrets\n\nUnterstützte Key-Namen erweitert für:\n\n- OpenFIGI:\n  - `OPENFIGI_API_KEY`\n  - `JARVIS_OPENFIGI_API_KEY`\n- FMP:\n  - `FMP_API_KEY`\n  - `JARVIS_FMP_API_KEY`\n- Finnhub:\n  - `FINNHUB_API_KEY`\n  - `JARVIS_FINNHUB_API_KEY`\n- Twelve Data:\n  - `TWELVEDATA_API_KEY`\n  - `TWELVE_DATA_API_KEY`\n  - `JARVIS_TWELVEDATA_API_KEY`\n  - `JARVIS_TWELVE_DATA_API_KEY`\n- EODHD:\n  - `EODHD_API_KEY`\n  - `JARVIS_EODHD_API_KEY`\n- Massive:\n  - `MASSIVE_API_KEY`\n  - `JARVIS_MASSIVE_API_KEY`\n- Alpha Vantage:\n  - `ALPHA_VANTAGE_API_KEY`\n  - `JARVIS_ALPHA_VANTAGE_API_KEY`\n\n`.env` unter `~/jarvis_runtime/finance-system/secrets/.env` wurde gefunden, Mode `600` bestätigt.\n\n## Echte Provider-Smoke-Tests\n\nAggregiert, ohne Keys/rohe Daten:\n\n- **OpenFIGI korrigierter Key getestet:** ja\n- **OpenFIGI erreichbar:** ja\n- **OpenFIGI Auth OK:** ja\n- **OpenFIGI ISIN-Mapping `US9229087690`:** erfolgreich\n- **OpenFIGI Search `Vanguard Total Stock`:** erfolgreich\n- **OpenFIGI Ticker+Exchange `VTI` / `US`:** erfolgreich\n- **OpenFIGI Rate-Limit-Header gelesen:** ja\n\n- **FMP Status:** aktiv / OK\n- **FMP Profile VTI:** erfolgreich\n- **FMP restricted endpoint handling:** implementiert\n\n- **Finnhub integriert/getestet:** ja\n- **Finnhub Symbol Search:** erfolgreich\n- **Finnhub Quote:** erfolgreich\n- **Finnhub Profile:** Endpoint erreichbar, VTI-Profil im Smoke leer; kein Crash, sauberer Fallback\n\n- **Twelve Data integriert/getestet:** ja\n- **Twelve Data Symbol Search:** erfolgreich\n- **Twelve Data Quote:** erfolgreich\n- **Twelve Data FX USD/CHF:** erfolgreich\n- **Wichtiger Keyname `TWELVEDATA_API_KEY`:** unterstützt und getestet\n\n- **Massive integriert/getestet:** ja\n- **Massive Reference Tickers:** erfolgreich\n- **Massive Ticker Details:** erfolgreich\n- **Massive Previous Day / Aggregates:** erfolgreich\n- **Auth:** Authorization Header bevorzugt, Query-Parameter-Fallback vorbereitet\n\n- **EODHD eingebunden:** ja, als Low-volume-Fallback\n- **EODHD echter Smoke-Test:** nein, absichtlich geschont\n- **EODHD Bulk-Nutzung:** blockiert/nicht Default\n- **EODHD nur explizit:** ja, `--provider eodhd --allow-low-volume`\n\n- **Alpha Vantage:** optional eingebunden als Status/Key-Erkennung; Key fehlt aktuell\n\n## Providerrollen / Prioritäten\n\n### Instrument Search / Mapping\n\nDefault-Reihenfolge jetzt:\n\n1. Lokale DB\n2. OpenFIGI\n3. FMP\n4. Finnhub\n5. Twelve Data\n6. Massive\n7. Manuell\n\nEODHD bleibt bewusst **nicht** im Default-Search, weil Low-Volume.\n\n### Preisvorschau / Equity Price Update\n\nNeu:\n\n- `CompositeEquityPriceProvider` mit Priorität:\n  1. FMP\n  2. Twelve Data\n  3. Finnhub\n  4. Massive\n\nCLI erweitert:\n\n```bash\nfinance update-equity-prices --provider auto\nfinance update-equity-prices --provider fmp\nfinance update-equity-prices --provider twelvedata\nfinance update-equity-prices --provider finnhub\nfinance update-equity-prices --provider massive\nfinance update-equity-prices --provider eodhd --allow-low-volume\n```\n\nDry-run bleibt möglich.\n\n### FX Update\n\nImplementiert/erweitert:\n\n- Frankfurter als Primary no-key Provider\n- Twelve Data FX als optionaler Provider\n- `CHF/CHF = 1` lokal\n- aktuelle und historische Rates via bestehendem Workflow\n- Dashboard liest weiterhin lokale Daten, keine Live-Abfrage beim Rendern\n\nCLI:\n\n```bash\nfinance update-fx-rates --latest --provider frankfurter --currency USD --currency EUR\nfinance update-fx-rates --latest --provider twelvedata --currency USD\n```\n\n## Dashboard / Wizard\n\nProviderstatus im Position-Add-Wizard verbessert:\n\n- **keine Live-Provider-Abfrage beim normalen Rendern**\n- Statusanzeige nutzt `probe=False`\n- Online-Suche weiterhin nur bei explizitem Klick\n- Unterstützte Keynamen im Wizard aktualisiert\n- Keys werden nicht angezeigt\n\nStatus-Provider sichtbar:\n\n- OpenFIGI\n- FMP\n- Finnhub\n- Twelve Data\n- Massive\n- EODHD Low-volume\n- Frankfurter FX\n- Stooq\n- Alpha Vantage\n- Manuelle Anlage\n\n## Tests / Verification\n\nAusgeführt:\n\n```bash\npython -m compileall -q src tests\npytest -q\npython -m jarvis_finance.cli.main git-safety-scan .\ngit diff --check\ngit status --short\n```\n\nErgebnis:\n\n- **Compile:** OK\n- **pytest:** `294 passed`\n- **Git-Safety:** `GIT_SAFETY_OK`\n- **git diff --check:** OK\n- **git status:** clean nach Commit/Push\n\n## Offene Grenzen\n\n- **Finnhub Profile** war beim VTI-Smoke leer, Search und Quote funktionieren aber.\n- **EODHD** wurde absichtlich nicht live abgefragt, um die ca. 20 Calls/Tag zu schonen.\n- **Alpha Vantage** bleibt optional und aktuell ohne Key.\n- Massive ist US-fokussiert und nicht als Primary für CH/EU-Instrumente gedacht."
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          "id": "msg_06c122bb065c7c6c016a08394def2081918d9163a7bfb766e5",
          "phase": "final_answer"
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      "role": "user",
      "content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n  • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n  • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n  • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n  • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n  1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n  2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n  3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n     • `references/<topic>.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n     • `templates/<name>.<ext>` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n     • `scripts/<name>.<ext>` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n     Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n  4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n  • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n  • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n  • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n  • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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      "content": "{\"success\": true, \"name\": \"finance-system-architecture\", \"description\": \"Design/implement the user's JARVIS Finance System: CHF ledger, portfolio accounting, dashboard, imports, reports, secrecy.\", \"tags\": [\"finance\", \"portfolio\", \"accounting\", \"dashboard\", \"csv\", \"fx\", \"tax\", \"privacy\"], \"related_skills\": [], \"content\": \"---\\nname: finance-system-architecture\\ndescription: \\\"Design/implement the user's JARVIS Finance System: CHF ledger, portfolio accounting, dashboard, imports, reports, secrecy.\\\"\\nversion: 1.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\nmetadata:\\n  hermes:\\n    tags: [finance, portfolio, accounting, dashboard, csv, fx, tax, privacy]\\n---\\n\\n# Finance System Architecture\\n\\n## When to use\\n\\nUse this skill whenever the user asks about the **Finance System – Architektur & Scripts** topic or work involving the user's personal finance-management system: portfolio imports, transaction accounting, Bank/Broker/CSV processing, dashboards, reports, tax/income views, FX logic, market data jobs, alerts, or later implementation.\\n\\nDo **not** use it for pure investment-strategy discussion; those belong in the separate finance strategy topic unless the user explicitly asks for technical implementation.\\n\\n## Core principles\\n\\n1. **No automated trading.** The system may prepare orders and recommendations, but never executes orders or connects to broker trading APIs for execution.\\n2. **Accounting first, opinions later.** MVP must prioritize correct transaction ledger, cash ledger, FX handling, income/tax data, and dashboard before complex scores, news, analyst ratings, or LLM analysis.\\n3. **Rule-based first.** Calculations, scores, alerts, rebalancing and reports must be deterministic and auditable. LLMs may only summarize/explain text or assist extraction with human review.\\n4. **CHF as base currency.** Store every transaction in original currency and CHF using historical FX at transaction/payment date; current valuations use current FX.\\n5. **Financial data secrecy.** Never expose real financial values, portfolio files, raw exports, reports with real numbers, DBs, API keys, or credentials in chat, GitHub, logs, or shared docs unless the user explicitly approves scope.\\n6. **Data ownership.** All data must be exportable: CSV, JSON, Parquet, SQLite/Postgres dump, Markdown/HTML/PDF reports.\\n\\n## Current v0.3 decisions\\n\\nSee `references/spec-v0.2-decisions.md` for the baseline, `references/spec-v0.3-decisions.md` for the priority update, `references/spec-v0.4-implementation-blueprint.md` for the MVP 1 technical implementation blueprint, `references/spec-v0.4.1-corrections.md` for accepted corrections, `references/spec-v0.5-repo-blueprint.md` for the concrete repo/implementation plan, `references/mvp-safety-gate-implementation.md` for the implementation safety-gate sequence and verification commands, `references/mvp-import-pipeline-tasks-9-13.md` for the synthetic CSV import/FX/transaction pipeline pattern, `references/mvp-ledger-cash-wac-positions.md` for cash/WAC/position ledger calculation rules and tests, `references/mvp-crypto-wallet-ledger.md` for crypto wallet/assets/holdings/transfers/buy-sell/fee MVP rules, `references/crypto-xlsx-initial-snapshot-dry-run.md` for the controlled real-file XLSX crypto initial-snapshot dry-run pattern and mapping decisions, `references/crypto-xlsx-initial-snapshot-production-import.md` for productive safe-subset XLSX import operations, compatibility/partial-write recovery, and dashboard correction follow-up, `references/crypto-single-coin-runtime-test.md` for controlled one-coin productive runtime tests and the coin-vs-holding ambiguity guard, `references/multi-wallet-crypto-and-equity-manage-ui.md` for approved multi-wallet single-coin snapshots, SQLite Decimal/TEXT affinity pitfalls, and synthetic-first Equity/ETF Manage UI patterns, `references/crypto-price-refresh-and-idempotent-btc-runtime.md` for robust batch price refresh and re-run-safe BTC/single-coin runtime checks, `references/crypto-runtime-db-consistency-verification.md` for post-import runtime DB consistency checks, exact safe-set verification, dashboard no-live-API testing, and runtime-only report generation, `references/mvp-crypto-manual-management-ui.md` for audited dashboard add/correct/remove crypto holding workflows after an initial snapshot, `references/mvp-market-import-safety-hardening.md` for CoinGecko provider/backoff, CSV importer completion, alert lifecycle, Git-safety and crypto Decimal precision patterns, `references/git-safety-verification-coverage.md` for the verifier-warning workflow and minimum Git-safety test matrix, `references/mvp-streamlit-dashboard.md` for the safe local Streamlit dashboard shell/read-model/testing pattern, `references/mvp-crypto-report-export.md` for local-only crypto report context/export rules, `references/github-token-push-from-drive.md` for the safe Google-Drive-token push workflow when local FinanceManager commits are ahead of GitHub, `references/runtime-backup-and-broker-mapping.md` for runtime DB backup/restore hardening plus PostFinance/True Wealth/Raiffeisen structural mapping/dry-run rules, `references/import-wizard-mapping-layer.md` for the Phase B1 import wizard, instrument/account mapping, review queue, data-quality and read-only safety pattern, `references/broker-parser-dry-run-pipeline.md` for the Phase B2 synthetic broker parser, dry-run, runtime review-item and UI quality-flag pipeline, `references/controlled-real-broker-dry-run.md` for the Phase B3 controlled real-file dry-run pattern with backup, aggregate-only reporting, runtime-only review items, and no productive position writes, and `references/broker-review-queue-hardening.md` for Phase B4 manual review queue filters, auditable mapping actions, import-readiness gating, dry-run session management, and Data Quality Center hardening without productive portfolio writes, and `references/guided-manual-review-pilot.md` for Phase B5 small-scope runtime metadata pilots that move selected review items to `ready_for_import` without productive imports, `references/broker-import-execution-plan-mini-import.md` for Phase B6 final dry-run execution plans plus exactly-one productive broker mini-import, `references/broker-mini-import-regression-and-corrections.md` for post-mini-import regression, void/correction workflows, backup/restore checks, and one-at-a-time follow-up imports, and `references/broker-mini-import-completion-and-dq-consolidation.md` for completing the last ready True-Wealth mini-import plus active-vs-resolved Data Quality alert consolidation, `references/equity-fx-market-data-phase-c.md` for the local FX/market-data foundation after broker mini-imports and before broader Equity/ETF valuation/imports, and `references/true-wealth-valuation-pilot-c2.md` for the True-Wealth valuation pilot safeguards around hedge metadata, provider mappings, historical FX failures, timestamp alignment, corporate-action flags, and aggregate-only runtime reporting, `references/true-wealth-mapping-candidate-review-c3.md` for the controlled mapping-candidate review pattern before any productive Equity/ETF price or FX valuation, `references/instrument-catalog-manual-add-workflow.md` for the general Instrument Catalog + dashboard manual-add workflow that must underpin future Equity/ETF/Crypto adds, and `references/public-instrument-lookup-candidate-generator.md` for runtime-only public metadata lookups, candidate review packs, alert dedupe, and no-auto-selection rules, and `references/mapping-candidate-ranking-decision-pack.md` for ranking raw candidates into a manual-review decision aid, Data Quality alerts, confirmation workflow, and migration pitfalls, and `references/dashboard-user-mode-ux-and-runtime-review.md` for User/Admin dashboard navigation, crypto wallet drilldowns, audited Crypto Manage actions, and runtime-only mapping demotion patterns, and `references/dashboard-functional-ux-stabilization.md` for removing duplicate Streamlit navigation, hiding unfinished User Mode pages, enforcing Read-only/Edit Mode button behavior, and testing explicit-click price/report actions, and `references/dashboard-usability-simplification.md` for the stricter MVP User Mode reduction, calm Command Center, compact Crypto/Wallet pages, direct inline action forms, and disabled-not-fake button rule, and `references/crypto-adjusted-xlsx-and-equity-docx-dryrun.md` for adjusted real crypto XLSX completion using CoinGecko IDs plus aggregate-only True Wealth/PostFinance DOCX Equity/ETF dry-run preparation, and `references/equity-docx-instrument-resolver-review-workflow.md` for the controlled Equity/ETF DOCX staging, resolver, review queue, local-Qwen-as-parser-only, and confirmed-only initial-snapshot workflow, `references/manual-equity-etf-entry-wizard.md` for the preferred manual Equity/ETF search/add-position wizard when DOCX imports are too unsafe or not approved, and `references/manual-position-entry-provider-ux.md` for User Mode placement, explicit-click provider lookup, runtime-secret loading, caching, and final verification/push discipline, and `references/equity-etf-provider-integration-openfigi-fmp.md` for the concrete OpenFIGI-first/FMP-second integration pattern, provider merge rules, local-price preview semantics, and `update-equity-prices` workflow, and `references/provider-secret-auth-debugging.md` for targeted runtime-secret/provider-auth debugging when keys are present but OpenFIGI/FMP smoke tests fail. Key decisions:\\n\\n- Platform/account views: Raiffeisen, PostFinance, True Wealth, Crypto-Coins, plus Gesamtansicht.\\n- Existing Google Drive files under `Finanzen/01 Aktueller Stand Portfolios` are **initial-snapshot reality checks**, not full ledgers.\\n- MVP means the smallest useful first version: correct data foundation, ledger, CHF/FX logic, platform views, crypto inventory, watchlist, reports, workflows and audit log.\\n- Stock/ETF tax documents are downgraded: optional reference/control module and dividend/distribution transparency, not MVP 1 and not a complete tax dossier replacement.\\n- Crypto inventory is promoted to MVP 1: wallet/platform-level holdings, Crypto PDF, CoinGecko valuation, stale-price warnings and manual verification.\\n- Crypto snapshot values are stale; use quantities only and recalculate current values via CoinGecko later.\\n- MVP dashboard recommendation: Streamlit + SQLite + SQLAlchemy/Pandas/Plotly; later migrateable to FastAPI + React/PostgreSQL.\\n- MVP inputs: manual dashboard entry, standard CSV import/export, later broker/bank export import. No direct broker integrations initially.\\n- Telegram crypto buys/sells/transfers should be parsed as pending transactions and only saved after explicit confirmation.\\n- Every portfolio or crypto change must create a History/Audit entry.\\n\\n## Required data model areas\\n\\nAt minimum design for:\\n\\n- `platforms`, `accounts`, `instruments`\\n- `transactions` with original currency and CHF fields\\n- `cash_ledger`\\n- derived `positions_current`\\n- `dividends_distributions`\\n- `market_prices`, `fx_rates`\\n- `watchlist_items`\\n- `alerts`\\n- `decision_journal`\\n- `reports`\\n- `data_quality_issues`\\n\\n## CSV/import rules\\n\\n- For broker/bank imports (PostFinance, True Wealth, Raiffeisen), build the safe review layer before productive imports: Import Wizard, instrument mappings, platform/account mappings, dry-run summaries, Manual Review Queue, Data Quality Center and visible read-only/write-enabled state. Follow `references/import-wizard-mapping-layer.md`. When wiring parser output into the wizard, use the synthetic-first parser/dry-run/review-item pipeline in `references/broker-parser-dry-run-pipeline.md`. After explicit approval for real-file dry-runs, follow `references/controlled-real-broker-dry-run.md`: preflight, verified runtime backup, temp-only file handling, aggregate-only reporting, runtime-only review items, and verification that productive positions/ledger rows stayed unchanged. For DOCX portfolio extracts from True Wealth/PostFinance, follow `references/crypto-adjusted-xlsx-and-equity-docx-dryrun.md`: use structural/masked parsing only, count field coverage and mapping status, treat ticker-without-exchange as review-needed, and stop before productive import. If the DOCX dry run shows low ISIN coverage/no cost basis/no transaction history or the user withholds final import approval, switch to `references/equity-docx-instrument-resolver-review-workflow.md`: create `instrument_import_candidates`-style staging, use local Qwen only as an assistive parser if available, classify ticker-only rows as `needs_manual_review`, expose a Mapping Review Queue, and import only later from `confirmed` candidates after explicit approval. If the user rejects productive DOCX import or wants direct manual control, follow `references/manual-equity-etf-entry-wizard.md`: make the main path an audited portfolio-app-style search/add-position wizard with explicit candidate selection, no live API on render, no ticker-only auto-selection, hidden technical IDs in User Mode, cost-basis/FX quality alerts, and audit-log writes. When the user asks to make real dry-run results manually editable, follow `references/broker-review-queue-hardening.md`: add filters/detail views, auditable review-metadata actions, per-item/session import-readiness status, dry-run session management, and Data Quality Center counts while keeping productive portfolio writes disabled. For a guided manual-review pilot on real runtime items, follow `references/guided-manual-review-pilot.md`: select 1–3 clear ETF/equity items (prefer True Wealth), write only audited review metadata, correct only presence booleans if needed, derive `ready_for_import` via readiness logic, and verify productive tables remain unchanged. Before the first productive broker position import, follow `references/broker-import-execution-plan-mini-import.md`: create runtime-only execution plans from the ready review items, validate exact Decimal-text payloads from the runtime source, preserve missing FX/cost-basis quality flags, then import exactly one ledger `initial_position_snapshot` after a verified backup; do not write `positions_snapshot` as the source of truth. After the first mini-import and before broader imports, follow `references/broker-mini-import-regression-and-corrections.md`: verify import links/audit/Decimal handling/re-import blocking, add or test safe void/correction flows, run backup/restore-to-test-DB regression, and only then optionally import one more ready plan. When the user approves finishing a remaining small set, follow `references/broker-mini-import-completion-and-dq-consolidation.md`: first disambiguate active vs resolved stale crypto alerts with `check-data-quality --scope crypto --resolve-fixed`, then import exactly one remaining ready execution plan after backup/verify, and consolidate Review Queue/Data Quality counts without exposing values.\\n- Do not pivot into later analytics modules while the user has asked for import/mapping hardening. Monte Carlo, backtesting, rebalancing, scores, heatmaps and projections are later phases unless explicitly re-authorized.\\n- Use a canonical CSV format before broker-specific importers.\\n- Imports must be idempotent via external transaction ID or row hash.\\n- Importers must separate dry-run and commit modes, collect validation errors explicitly, and always create an `import_sessions` record even for dry runs or failed imports.\\n- Import results should classify rows as new, existing, duplicate, or failed; do not rely on `INSERT OR IGNORE` as the only duplicate-detection mechanism.\\n- Row hashes must be stable and deterministic: normalize key order, whitespace, and `None` versus empty strings while leaving domain-specific canonicalization to the importer.\\n- If a source file has only current holdings and no full history, import it as `initial_position_snapshot` / `initial_cash_snapshot`, not fake historical buys.\\n- Missing historic FX, fees, taxes, dates, investment case, target weights, or dividend details must be flagged as data-quality gaps.\\n\\n## FX and return rules\\n\\nFor foreign currency assets, always distinguish:\\n\\n- price/security performance in original currency\\n- FX effect to CHF\\n- realized P&L\\n- unrealized P&L\\n- dividends/distributions/income\\n- fees and taxes\\n- Total Return in CHF\\n\\nEvery transaction/income event needs:\\n\\n- original amount and currency\\n- historical FX rate to CHF, or explicit `fx_status=missing` with a critical data-quality alert\\n- FX source and timestamp\\n- CHF equivalent at event date when FX is known\\n\\nManual FX overrides require a non-empty note and an audit-log entry.\\n\\n## Ledger calculation rules\\n\\n- Cash after system start must be calculated from `transactions`; `cash_balances` is only a snapshot/cache/control view.\\n- `initial_cash_snapshot` and `initial_position_snapshot` are explicit starting truths, not fabricated historical trades.\\n- Implement Weighted Average Cost as the MVP internal performance method: buy fees increase cost basis, partial sells remove proportional cost basis, realized P&L is calculated against average cost, full sells zero remaining quantity/cost basis, and dividends do not alter quantity or cost basis.\\n- WAC is not tax-complete; do not add FIFO/LIFO/tax-lot logic in MVP 1, but keep transaction granularity so it can be added later.\\n- Position calculations must keep zero-quantity historical positions visible when sale history exists.\\n- Missing market price or FX must degrade data quality and suppress precise Total Return CHF rather than inventing a value.\\n- Before productive dashboard use, add alert deduplication per rule/entity/date.\\n- Market-FX for non-CHF market prices belongs to the later Market/FX provider block.\\n\\n## Crypto MVP rules\\n\\n- Crypto inventory is MVP-critical but must remain dummy-first until mapper/dry-run layers are ready; do not touch real coin/wallet files during core implementation. After explicit user approval, real files may be read only from temp/runtime for controlled dry runs using `references/crypto-xlsx-initial-snapshot-dry-run.md`; report only aggregate counts/labels and never quantities or values. Productive safe-subset imports require a second explicit approval and must follow `references/crypto-xlsx-initial-snapshot-production-import.md`: external runtime DB only, no real CSV/XLSX/reports/DBs in Git, Git-safety before/after, import-session + audit-log creation, and aggregate-only reporting. When the user supplies an adjusted real crypto workbook with CoinGecko IDs for missing/previously skipped assets, follow `references/crypto-adjusted-xlsx-and-equity-docx-dryrun.md`: detect the actual structure first, validate IDs against CoinGecko metadata, import only clear missing asset/holding subsets after verified backup, preserve Decimal/Text quantities, ignore legacy CHF values, and delete temporary source files.\\n- Wallet names must be unique; wallet addresses are optional and must not appear in tests/fixtures.\\n- Validate wallet type against: Hardware Wallet, Software Wallet, Exchange, Bank/Broker, DeFi, Sonstiges.\\n- CoinGecko ID is strongly recommended but not hard-required; missing ID creates a data-quality warning and blocks reliable valuation.\\n- Crypto symbols are not globally unique; symbol conflicts require warning/manual selection, never silent merging.\\n- Initial crypto holdings are confirmed snapshots by wallet+asset. Store legacy values for provenance/control only, never current valuation.\\n- Crypto holdings should be calculated/controlled from initial snapshots plus crypto transactions, not silently overwritten.\\n- Crypto transfers require different source/target wallets, quantity > 0, and same asset; coin fees reduce source-wallet balance.\\n- Negative wallet balances should be blocked in MVP and produce a critical alert unless a future explicit correction workflow is implemented.\\n- Crypto buys/sells with fiat impact must create/reference general ledger transactions via `source_type='crypto'`/`source_id` so cash remains ledger-consistent.\\n- Fiat fees should be ledger fees when unambiguous; avoid double counting when a fee is already embedded in sell net proceeds.\\n- Unknown fee currency creates a data-quality warning instead of invented accounting.\\n- Crypto Cost Basis/WAC is not required for MVP 1; MVP 1 prioritizes crypto holdings, wallet structure, current valuation, reports and audit.\\n- Crypto report context/export must be renderer-neutral and local-only: build from SQLite, aggregate by coin and wallet, use local cached prices only, preserve Decimal/Text values, ignore legacy snapshot values for current valuation, include data-quality warnings, and write generated files only to the external runtime reports directory.\\n- Crypto PDF export should be optional-dependency tolerant: prefer PDF when a renderer is available, otherwise fall back to HTML/Markdown with a clear warning while still recording report metadata and audit.\\n- After productive real-data crypto imports, run the post-import runtime verification in `references/crypto-runtime-db-consistency-verification.md`: exact safe-set membership checks, aggregate-only counts, audit/import-session traceability, dashboard render with no live API, runtime-only report generation, and Git-safety before/after.\\n- Manual dashboard additions/corrections/removals after an initial snapshot must follow `references/mvp-crypto-manual-management-ui.md`: no silent `crypto_holdings` mutation, explicit confirmation, mandatory notes for adjustments/removals, audit-log entries with old/new values, Decimal/TEXT quantity handling, no negative holdings, and no live API lookup during render. For dashboard UX/read-model work, also follow `references/dashboard-user-mode-ux-and-runtime-review.md`: split User Mode from Admin/Review/Debug, aggregate coins across wallets, hide technical IDs in normal views, keep cached-price semantics explicit, and make Crypto Manage actions audited workflows rather than raw table edits.\\n- Crypto tax logic, tax lots, FIFO/LIFO and detailed cost basis are later modules.\\n- Keep crypto assets separate from `instruments` for MVP 1, but later evaluate a shared Asset Master if it improves reporting, performance views or watchlists.\\n\\n## Income/tax rules\\n\\nFor dividends and ETF distributions, separately store:\\n\\n- gross dividend/distribution\\n- Swiss withholding tax 35%\\n- foreign withholding tax\\n- other tax/withholding if present\\n- net amount\\n- original currency\\n- historic FX to CHF\\n- gross/net CHF equivalents\\n- platform/account\\n- ISIN/ticker\\n- payment date\\n- ex-dividend date if available\\n\\nPlan later ESTV/ICTax support for Swiss year-end values and tax reports, but label outputs as technical reports, not tax advice.\\n\\n## Market-data rules\\n\\n- Dashboard reads primarily from local DB, not directly from APIs on page load.\\n- For Equity/ETF FX and market-data hardening after broker mini-imports, follow `references/equity-fx-market-data-phase-c.md`: validate active `missing_fx` alerts by original transaction currency first, correct only CHF false positives with audit, keep foreign-currency missing-FX alerts active until local historical rates exist, use ISIN as instrument identity and provider symbols only as market-data mappings, and run market-price providers only from explicit CLI/scheduled jobs.\\n- For the True-Wealth valuation pilot, follow `references/true-wealth-valuation-pilot-c2.md`: do not import more positions, do not guess provider symbols or hedge status, extend instrument metadata for currency hedging/status/valuation policy/corporate-action state, handle historical-FX paywalls as `manual_override_required` without crashing, align price/FX timestamps before claiming valuation precision, and report only aggregate runtime counts.\\n- For True-Wealth mapping-candidate review after the valuation pilot, follow `references/true-wealth-mapping-candidate-review-c3.md`: create runtime-only provider-symbol candidates keyed by ISIN/instrument, keep name-only or ticker-without-exchange candidates review-required, mark multiple candidates `needs_manual_review`, do not auto-select final mappings on ambiguity, keep hedge/instrument status unknown until confirmed, and ensure market-price dry runs call no provider when there are zero confirmed mappings. For public metadata lookup implementation details, provider result caps, OpenFIGI/Yahoo evidence handling, alert dedupe, runtime-only review templates, and UI/test requirements, use `references/public-instrument-lookup-candidate-generator.md`. For ranking/scoring candidates, generating a runtime-only user decision pack, surfacing `candidate_review_required` and same-ISIN multiple-listing risks, and confirming/rejecting candidates with audit logs, use `references/mapping-candidate-ranking-decision-pack.md`.\\n- For the broader Instrument Catalog + manual-add workflow, follow `references/instrument-catalog-manual-add-workflow.md`: documents/import files are initial-population aids, but the dashboard must allow audited manual add of equities/ETFs and later coins via search, candidate selection, account/wallet selection, Decimal/Text quantity, date, cost-basis/snapshot info and notes. ISIN is primary for equities/ETFs but can have multiple listings; ticker/symbol alone is never unique; provider symbols are market-data mappings only; CoinGecko ID is primary for crypto provider identity. External lookups may use only non-confidential instrument metadata and must never send quantities, balances, account/wallet IDs, cost basis, private notes or source documents. If DOCX/Broker import is not approved or structurally weak, use `references/manual-equity-etf-entry-wizard.md` as the preferred user path: search on explicit click, show candidates, require conscious selection, allow warned manual creation, then review/confirm an audited initial snapshot or transaction. For the concrete Dashboard/User-Mode implementation pattern, provider order (local/OpenFIGI/FMP/Finnhub/manual), runtime `.env` secret lookup, provider-result caching, and final compile/test/Git-safety/commit/push discipline, also follow `references/manual-position-entry-provider-ux.md` and `references/equity-etf-provider-integration-openfigi-fmp.md`. If provider smoke tests report missing candidates or unreachable providers despite keys being stored, first follow `references/provider-secret-auth-debugging.md`: verify runtime secret loading without printing values, classify 401/403 as `auth_failed` rather than generic reachability failure, and improve status diagnostics before adding features.\\n- First Streamlit MVP should be a safe, local, read-first shell over SQLite with explicit synthetic demo DB creation only; page render functions must not call CoinGecko/market APIs, and tests should prove that by monkeypatching provider methods to fail.\\n- Functional UX stabilization work must prioritize making the existing dashboard genuinely usable before adding more pages. Follow `references/dashboard-functional-ux-stabilization.md`: use one navigation system, move executable page modules out of Streamlit `pages/` when using a custom router, hide unfinished pages from User Mode, replace text-link/fake actions with real buttons or disabled controls, enforce session-based Edit Mode for writes, keep IDs/debug notes out of User Mode, and test price/report provider calls happen only on explicit clicks. If the user says the dashboard is still too busy or prototype-like, follow `references/dashboard-usability-simplification.md`: reduce User Mode to only currently useful pages, keep the Command Center calm, avoid misleading `CHF 0.00` Equity/ETF displays when valuation is incomplete, make Crypto the primary MVP surface, and ensure every visible button either works immediately, opens an inline form, or is disabled with a reason.\\n- Use scheduled jobs to update prices/FX/news.\\n- Store provider, timestamp, quality status and freshness for every price/FX point.\\n- Implement caching, local historical storage, batch requests where possible, provider rate-limit queues, and backoff on HTTP 429.\\n- CoinGecko integrations must be mockable and unit-tested without real API calls; treat HTTP 429 as non-fatal and return a non-fresh data-quality status plus alert after configured retries.\\n- Crypto price refresh belongs in CLI/scheduled-job commands, not dashboard/report rendering. Prefer CoinGecko batch requests over per-coin requests; expose conservative controls (`--sleep-seconds`, `--max-retries`, `--initial-backoff`, `--max-backoff`, `--only-missing`, `--only-stale`, `--only-symbol`, `--limit`, `--dry-run`, `--currency`). Commands should use aggregate-only output (`total_assets/updated/cached/skipped/stale/warnings/errors/assets_still_missing_local_price`), cache by configurable `max_age_seconds`, store exact Decimal prices as TEXT in `crypto_prices`, and verify dashboard/report contexts read local prices with `live_api_calls=False`. For details and tests see `references/crypto-price-refresh-and-idempotent-btc-runtime.md`.\\n- Candidate providers: Finnhub, Financial Modeling Prep, Twelve Data, Stooq, Alpha Vantage selectively, Yahoo Finance fallback only; CoinGecko for crypto; later DefiLlama; ESTV/ICTax for Swiss tax values.\\n\\n## Crypto Decimal precision rules\\n\\nSQLite has no exact native DECIMAL. In the crypto context, store quantities, prices, crypto fees and valuation-relevant amounts as exact Decimal strings (`TEXT`) and compute with Python `Decimal`. Never convert crypto quantity/price/fee/valuation through float/REAL. Format Decimal writes with `format(value, \\\"f\\\")` so small quantities such as `0.00000001` do not become scientific notation. CSV importers must validate Decimal strings, and tests should assert high-precision preservation plus SQLite text storage. For multi-wallet real-data snapshots and Equity/ETF ledger fields, also see `references/multi-wallet-crypto-and-equity-manage-ui.md`; SQLite `NUMERIC` affinity can coerce Decimal-looking text to `real`, so schema/migrations should use `TEXT` for exact quantities, FX rates, original amounts and CHF equivalents where precision matters.\\n\\n## Alert lifecycle rules\\n\\nAlerts must be deduplicated and lifecycle-aware before dashboards run repeated calculations. Use a dedup key from `rule_id`, `entity_type`, `entity_id`, `priority` and an error fingerprint. Alerts should carry `status` (`active`, `resolved`, `muted`), `resolved_at`, optional `muted_until`, `last_seen_at`, `occurrence_count`, `fingerprint` and `dedup_key`. Re-seeing an identical active/resolved/muted alert should update `last_seen_at`/`occurrence_count` rather than create a new active row; changed fingerprints may create a new alert.\\n\\n## Git/GitHub safety\\n\\nCode/scripts/docs may be stored in a private Git repo, but never commit financial data. Use the `github-repo-management` skill and its `references/sensitive-code-backup.md` guidance.\\n\\nMinimum `.gitignore` for finance repos must block `data/`, `reports/`, `exports/`, `backups/`, DB files, spreadsheets, PDFs, DOCX, raw CSV/JSON, `.env`, tokens, credentials and client secrets. Only synthetic examples under `examples/` are acceptable.\\n\\n## MVP implementation safety gate\\n\\nBefore any real broker/bank/portfolio/crypto data is imported, build and verify the safety foundation described in `references/mvp-safety-gate-implementation.md`: repo skeleton, `.gitignore`, `.env.example`, runtime outside repo, settings validation, SQLite schema, synthetic fixtures, audit log, CSV dry-run, duplicate protection, and Git-safety scan. Run the safety scan before staging, committing, or pushing. If GitHub credentials are needed from Drive, use them only in a temporary location and never echo token values.\\n\\n- Before broader productive imports, implement or verify the runtime backup loop in `references/runtime-backup-and-broker-mapping.md`: backup outside Git, SHA256 verification, restore requiring explicit confirmation, tests for backup/restore/checksum, and Git-safety coverage that blocks backup artifacts in the repo. For PostFinance/True Wealth/Raiffeisen, perform structural mapping and dry-runs only until the user approves import; report aggregate counts/field coverage/quality flags only, never real values or raw rows. If backup CLI output is too terse to evidence the backup path/checksum, call `jarvis_finance.runtime.backup.backup_runtime_db(...)` and `verify_backup(...)` directly and report only path-existence/outside-repo/checksum status, not DB contents.\\n\\n## Common pitfalls\\n\\n- Starting later analytics (Monte Carlo, backtesting, rebalancing, scoring, heatmaps, projections) while import/mapping/data-quality phases are still being hardened or while the user explicitly asked to postpone them.\\n- Treating a holdings snapshot as a real transaction history.\\n- Mixing price P&L and FX P&L.\\n- Letting dashboard refreshes hammer APIs.\\n- Committing realistic sample data to Git because the repo is private. Private GitHub is not a financial data vault.\\n- Showing real portfolio values back to the user unnecessarily in summaries.\\n- Designing Git-safety scans that block source package names like `src/.../imports` or `src/.../reports`; block top-level runtime/data directories instead.\\n- In broker/bank real-file dry-runs, aggregating Data Quality flags only for `review_status='open'`. Blocked review items are still actionable data-quality issues; include `open` and `blocked` so defensive Raiffeisen aggregate-only rows remain visible.\\n- Letting manual review actions blur into productive import actions. Review-metadata writes (ISIN/ticker/exchange/account/instrument confirmations, ignore/resolve, dry-run archive/current status) may be allowed with audit logs, but they must not write `positions_snapshot`, `transactions`, `cash_balances`, ledger rows, or cash snapshots. Name-only mapping must never become `mapped`/`ready_for_import`; ticker-only without exchange remains review-needed; `aggregate_only` remains blocked for instrument import. In guided pilots, do not force `ready_for_import` by direct status edits; let readiness logic derive it after audited metadata actions, and if parser presence flags were missed, correct only boolean presence metadata with an audit event, never actual quantities/values.\\n- Skipping the execution-plan layer between ready review items and productive broker imports. A final dry-run should materialize runtime-only execution plans with source row hash, target IDs, exact Decimal-text payload summary, quality flags and planned write status. Productive mini-imports should consume exactly one planned ready payload, write exactly one ledger `initial_position_snapshot`, mark that plan/review item imported, leave the other ready plans untouched, and verify zero direct `positions_snapshot`/cash writes. After the first productive mini-import, do not proceed straight to mass import: run regression checks, implement/test void and correction audit paths, verify backup/restore to a separate test DB, and import at most one additional ready plan. When later completing the final ready plan, first resolve Data Quality alert ambiguity by counting only active alerts (`status='active'`) and treating resolved rows as history; then import exactly one plan and consolidate counts. See `references/broker-import-execution-plan-mini-import.md`, `references/broker-mini-import-regression-and-corrections.md`, and `references/broker-mini-import-completion-and-dq-consolidation.md`.\\n- Running compile/tests before Git-safety and forgetting generated `__pycache__`; remove or ignore generated caches before safety scans. `pytest` may recreate `.pytest_cache`, so remove `__pycache__`/`.pytest_cache` again after test execution and before `git-safety-scan`.\\n- Treating every active `missing_fx` alert after a broker mini-import as a bug. First aggregate by original currency: CHF rows may be false positives (`fx=1`, `not_needed`, resolve with audit), but foreign-currency rows without local historical FX should remain active and block precise CHF valuation until rates are populated.\\n- Auto-mapping Equity/ETF market prices by instrument name or ticker alone. ISIN is the instrument identity, but one ISIN can have multiple listings across exchange/currency, so even ISIN matches may need candidate selection. Provider symbols are separate market-data mappings, ticker without exchange is review-needed, and missing provider symbols should create deduplicated Data Quality alerts rather than triggering provider guesses. For mapping-candidate phases, candidate creation is allowed in the runtime DB, but final selection must remain manual/audited unless the user explicitly approves and the match is unambiguous. Public lookup endpoints can return dozens of listings; cap result sets, dedupe active mapping alerts by rule/entity, and do not treat search-fallback results as ISIN-confirmed unless the provider response supplies ISIN evidence.\\n- Designing future portfolio entry as import-file-only. Imports/documents are for initial population and existing holdings; the system must also support dashboard manual add/search workflows with audited ledger writes, explicit candidate selection, Decimal/Text quantities, required notes where history is incomplete, and no silent overwrites. If the user says DOCX/Broker files are too unsafe or not approved, stop the productive import path and pivot to the manual Equity/ETF wizard in `references/manual-equity-etf-entry-wizard.md`; keep the review queue optional/admin-only, ensure manual-entry broker accounts exist, and do not expose technical IDs in User Mode. The manual entry path must be visible in normal User Mode (e.g. `Position hinzufügen`) with Command Center routing; provider lookup must be explicit-click only, cache results locally, load provider keys from runtime secrets, and degrade missing keys to warnings. See `references/instrument-catalog-manual-add-workflow.md` and `references/manual-position-entry-provider-ux.md`.\\n- Letting an Equity/ETF market-price dry run call providers when no confirmed mappings exist. The safe outcome is `total_mappings=0`, no provider calls, and aggregate reporting only.\\n- For valuation pilots on historical broker snapshots, treating a computable current market value as a clean performance result. `snapshot_only`, `cost_basis_uncertain`, missing hedge confirmation, missing historical FX, stale price/FX timestamp alignment, or suspected corporate actions must keep valuation `partially_valuable` or suppress precise Total Return until reviewed.\\n- Inferring CHF-hedged ETF behavior from the name alone. Hedge status is methodology-critical: unknown status should create a warning/review item; confirmed CHF hedging must prevent naive free FX-PnL attribution.\\n- Crashing or blocking on historical-FX provider paywalls. Persist `manual_override_required`, create deduplicated alerts, keep the flow moving, and require a noted audited manual override instead of guessing latest FX.\\n- Productive real-data imports can partially write before surfacing schema/importer compatibility issues. Do not rerun blindly or dump row-level data; inspect aggregate counts and unique identifiers needed for idempotency, patch/complete via missing-only inserts, then verify committed import session, audit log, Git clean and Git-safety OK.\\n- In controlled one-coin runtime tests from a real XLSX, distinguish **one coin/asset** from **one holding/wallet row**. A coin may be split across multiple wallet columns; if the user required an unambiguous target wallet or one holding, stop and ask rather than choosing a wallet silently. If the user explicitly approves multi-wallet import for that coin, import exactly one asset plus one audited holding per non-empty approved wallet cell; verify aggregate counts only. If re-running a BTC/single-coin test finds the exact approved asset and holdings already present, do not import duplicates; perform the aggregate-only idempotency checks and report that asset/holding counts did not increase because duplicate prevention succeeded. See `references/crypto-single-coin-runtime-test.md`, `references/multi-wallet-crypto-and-equity-manage-ui.md`, and `references/crypto-price-refresh-and-idempotent-btc-runtime.md`.\\n- After productive crypto imports, do a separate aggregate-only consistency pass before building new features: DB/runtime outside repo, exact safe-subset membership, no duplicate/negative/orphan holdings, correct snapshot date, null legacy-value fields when ignored, import sessions committed, holding-level audit traces present, dashboard local-read/no-live-API verified, and report generated only under runtime reports.\\n- Letting a dashboard correction UI mutate crypto holdings directly. Add/remove/edit operations must become audited transactions or `manual_adjustment` events with mandatory notes where appropriate; preserve history instead of silently overwriting balances.\\n- Letting normal dashboard pages become technical raw-table dumps. For this user's Finance dashboard, implement an Alltag/User Mode that hides internal IDs/provider internals and presents aggregate, actionable read models; keep raw identifiers, candidate details and audit/debug fields in Admin/Review/Data Quality views. See `references/dashboard-user-mode-ux-and-runtime-review.md`.\\n- Treating Streamlit dashboard UX stabilization as a chance to build more modules. When the user complains about prototype feel, first eliminate duplicate navigation, empty User Mode pages, fake/text buttons, unclear Read-only/Edit state, and raw technical IDs. Keep unfinished pages in Admin/Debug or hide them; every visible button must work, prefill a real workflow, or be disabled with a reason. See `references/dashboard-functional-ux-stabilization.md` and `references/dashboard-usability-simplification.md`.\\n- Leaving an accepted sprint half-finished because the next feature is tempting. If cleanup, final tests, Git-safety, commit, and push are still missing, finish those in order before starting new work. Remove `.pytest_cache`/`__pycache__` before and after tests, then run Git-safety, `git diff --check`, verify clean status, commit, push, and fetch/update `origin/main` to prove sync. See `references/dashboard-usability-simplification.md`.\\n- Running Git-safety immediately after pytest without cleaning generated caches. Pytest can recreate `.pytest_cache`; remove `.pytest_cache` and `__pycache__` after the final test run, then run Git-safety before staging/commit/push.\\n- Relying on the user's prose description of a spreadsheet layout instead of detecting the actual workbook structure. In adjusted crypto XLSX files, CoinGecko IDs may appear as an `API ID` column in the same row rather than a preceding row; use the detected structure, validate IDs, and report the difference aggregate-only.\\n- Treating formula/scientific-notation source cells as automatic import blockers. Count them, but block only when exact Decimal/Text conversion fails; never use float arithmetic for crypto quantities.\\n- Printing raw DOCX/table rows while analyzing real broker files. Use masked structural flags and aggregate counts only; real position names/values/quantities stay out of chat/log summaries.\\n- Counting ticker-like tokens from DOCX as confirmed Equity/ETF mappings. Without ISIN plus exchange/provider confirmation, mappings are `probable` or `needs_manual_review`, not productive-import ready.\\n- Treating low-confidence Equity/ETF DOCX extraction as enough for import. If most positions lack ISIN, cost basis or transaction history, build/operate a staging resolver and Mapping Review Queue first. Ticker-only rows stay `needs_manual_review`; local Qwen may help extract candidates but must not decide mappings or write ledger rows. See `references/equity-docx-instrument-resolver-review-workflow.md`.\\n- Treating verifier/file-mutation warnings as either automatically true or automatically false. Check the exact commit with `git show --name-status --patch <commit> -- <file>`, then separately verify whether the test matrix actually covers the promised cases.\\n- Treating provider lookup smoke-test failures as simple `not reachable` / `no candidates` when runtime keys exist. Follow `references/provider-secret-auth-debugging.md`: confirm the runtime secret file is loaded without printing values, verify OpenFIGI uses `X-OPENFIGI-APIKEY` header and FMP uses `apikey`, split network reachability from provider auth, and classify HTTP 401/403 as `auth_failed`, 429 as `rate_limited`, empty 2xx as `no_results`, and DNS/timeout as `network_error`. 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      "tool_call_id": "call_Z7hiMWSLjqWCEvBaqY7Ae35r"
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    {
      "role": "assistant",
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      "reasoning": "**Updating provider integration**\n\nI need to make updates related to provider integration v2 and patch the main features. I should reference the skill_manage write_file and highlight common pitfalls. Maybe I also need to look into the GitHub PR workflow to ensure I push with the token correctly, which is something I've referenced before. This session had a push failure due to the username issue, as I used the token CSV and fetched updates from origin/main. I could also update the finance provider integration.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Updating provider integration**\n\nI need to make updates related to provider integration v2 and patch the main features. I should reference the skill_manage write_file and highlight common pitfalls. Maybe I also need to look into the GitHub PR workflow to ensure I push with the token correctly, which is something I've referenced before. This session had a push failure due to the username issue, as I used the token CSV and fetched updates from origin/main. I could also update the finance provider integration.",
      "codex_reasoning_items": [
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          "id": "rs_06c122bb065c7c6c016a0839bc101c81918d1a708beb2b1cbb",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Updating provider integration**\n\nI need to make updates related to provider integration v2 and patch the main features. I should reference the skill_manage write_file and highlight common pitfalls. Maybe I also need to look into the GitHub PR workflow to ensure I push with the token correctly, which is something I've referenced before. This session had a push failure due to the username issue, as I used the token CSV and fetched updates from origin/main. I could also update the finance provider integration."
            }
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        {
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          "call_id": "call_q7AInSuxeNXz0qhfoM6GD9ym",
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            "arguments": "{\"action\":\"write_file\",\"name\":\"finance-system-architecture\",\"file_path\":\"references/provider-integration-sprint-v2.md\",\"file_content\":\"# Provider Integration Sprint v2 — Free/Fallback Market Data\\n\\nUse this reference when extending the FinanceManager Equity/ETF instrument search, price preview/update, FX update, and provider status flows with multiple external providers while preserving privacy and explicit-action semantics.\\n\\n## Hard constraints\\n\\n- Never commit API keys, `.env`, runtime DBs, real provider responses tied to real portfolio data, reports, or real financial values.\\n- Runtime secrets live outside Git, normally `~/jarvis_runtime/finance-system/secrets/.env`; verify mode `600` when practical, but never print values.\\n- Dashboard render must not call external providers. Provider calls only on explicit user clicks, CLI commands, or scheduled jobs.\\n- Do not send holdings, quantities, balances, cost basis, account IDs, wallet IDs, broker docs, private notes, or portfolio rows to providers. Only send explicit search terms, ISINs, tickers/symbols, exchanges, or FX pairs entered/confirmed for lookup.\\n- EODHD is low-volume fallback only; keep it out of default provider chains and require explicit opt-in such as `--provider eodhd --allow-low-volume`.\\n\\n## Secret aliases to support\\n\\n- OpenFIGI: `OPENFIGI_API_KEY`, `JARVIS_OPENFIGI_API_KEY`\\n- FMP: `FMP_API_KEY`, `JARVIS_FMP_API_KEY`\\n- Finnhub: `FINNHUB_API_KEY`, `JARVIS_FINNHUB_API_KEY`\\n- Twelve Data: `TWELVEDATA_API_KEY`, `TWELVE_DATA_API_KEY`, `JARVIS_TWELVEDATA_API_KEY`, `JARVIS_TWELVE_DATA_API_KEY`\\n- Massive: `MASSIVE_API_KEY`, `JARVIS_MASSIVE_API_KEY`\\n- EODHD: `EODHD_API_KEY`, `JARVIS_EODHD_API_KEY`\\n- Alpha Vantage status/optional fallback: `ALPHA_VANTAGE_API_KEY`, `JARVIS_ALPHA_VANTAGE_API_KEY`\\n\\n`TWELVEDATA_API_KEY` without the extra underscore is mandatory; do not only support `TWELVE_DATA_API_KEY`.\\n\\n## Provider role pattern\\n\\n### Instrument search / metadata\\n\\nPreferred explicit lookup order:\\n\\n1. Local catalog/cache first.\\n2. OpenFIGI for ISIN/Ticker mapping evidence.\\n3. FMP as primary public search/preview source where available.\\n4. Finnhub for symbol lookup/profile fallback.\\n5. Twelve Data for symbol search/quote fallback.\\n6. Massive for US ticker metadata/details fallback.\\n7. Manual creation with warnings.\\n\\nDo not include EODHD in default search because of daily call limits.\\n\\n### Equity/ETF prices\\n\\nImplement a composite provider for CLI/scheduled jobs, not dashboard render:\\n\\n1. FMP primary.\\n2. Twelve Data fallback.\\n3. Finnhub fallback.\\n4. Massive fallback.\\n5. EODHD only explicit low-volume opt-in.\\n\\nReturn sanitized quote objects with `provider`, `quality_status`, `error_message`, timestamp/currency, and `close=None` on failure instead of raising raw HTTP/provider exceptions into the UI.\\n\\n### FX rates\\n\\n- Frankfurter can be no-key primary for common FX updates.\\n- Twelve Data can be an optional keyed provider for pairs such as `USD/CHF` and `EUR/CHF`.\\n- Store rates locally; dashboard/report contexts read local rates only.\\n- `CHF/CHF = 1` is local, not a provider call.\\n\\n## Error classification\\n\\nMap provider failures into stable categories that are safe for UI/log/test output:\\n\\n- missing key: `*_api_key_missing` / status `missing_key`\\n- HTTP 401/403: `auth_failed`\\n- HTTP 402/paywall/tariff limit: `restricted_endpoint`\\n- HTTP 429: `rate_limited`\\n- DNS/timeout/connectivity: `network_error`\\n- empty successful response: `no_results`\\n- malformed or provider-side error JSON: `provider_error`\\n\\nSpecifics:\\n\\n- OpenFIGI uses header `X-OPENFIGI-APIKEY` plus `Content-Type: application/json`; a previous 401 must be retested after the user corrects the key and should not poison future status.\\n- FMP uses `apikey`; 402/tariff failures should be non-fatal `restricted_endpoint`, especially for endpoints not covered by the current plan.\\n- Massive may need Authorization-header first, with query-param fallback if the provider/client pattern requires it.\\n\\n## Status display pattern\\n\\nProvider status rows should be aggregate and secret-free. Use labels like present/missing/auth_failed/error/restricted/rate_limited, never key prefixes, key lengths, raw tokens, raw URLs containing credentials, or response bodies.\\n\\nWhen showing status in Streamlit/User Mode, use a non-probing mode (`probe=False`) so normal render only indicates configured/known/last explicit status. Live probes belong behind an explicit button or CLI smoke-test command.\\n\\n## Smoke-test pattern\\n\\nFor explicit provider test runs, report only aggregate booleans/counts and categories:\\n\\n- key found yes/no\\n- reachable yes/no\\n- auth OK yes/no\\n- candidate/price/FX found yes/no\\n- error category\\n- rate-limit headers read yes/no when applicable\\n- no position saved\\n- no portfolio data used\\n\\nTypical safe instruments/pairs:\\n\\n- OpenFIGI mapping ISIN `US9229087690`\\n- OpenFIGI search query `Vanguard Total Stock`, `exchCode=US`\\n- OpenFIGI mapping ticker `VTI`, `exchCode=US`\\n- FMP/Finnhub/Twelve/Massive test ticker `VTI` or `AAPL`\\n- Twelve Data FX `USD/CHF` or `EUR/CHF`\\n- Avoid EODHD live smoke unless explicitly necessary; at most one call.\\n\\n## Tests and verification\\n\\n- Mock provider HTTP responses for unit tests; do not require real keys in test suite.\\n- Add tests proving missing keys skip provider calls and sanitize errors.\\n- Add tests proving dashboard/price preview reads local market prices and does not call APIs on render.\\n- Add tests for provider aliases, especially `TWELVEDATA_API_KEY`.\\n- After `pytest`, remove `.pytest_cache` and `__pycache__` before `git-safety-scan`; pytest recreates caches.\\n- Run: compileall, pytest, Git-safety scan, `git diff --check`, `git status`, commit, push, then fetch/update `origin/main` or otherwise verify the remote tracking state.\\n\"}"
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      "name": "skill_manage",
      "content": "{\"success\": true, \"message\": \"File 'references/provider-integration-sprint-v2.md' written to skill 'finance-system-architecture'.\", \"path\": \"/home/agent/.hermes/skills/finance/finance-system-architecture/references/provider-integration-sprint-v2.md\"}",
      "tool_call_id": "call_q7AInSuxeNXz0qhfoM6GD9ym"
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      "tool_calls": [
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          "call_id": "call_pHOBcaKpTfpoEpkMFNWVcI1j",
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            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"old_string\":\"`references/equity-etf-provider-integration-openfigi-fmp.md` for the concrete OpenFIGI-first/FMP-second integration pattern, provider merge rules, local-price preview semantics, and `update-equity-prices` workflow, and `references/provider-secret-auth-debugging.md` for targeted runtime-secret/provider-auth debugging when keys are present but OpenFIGI/FMP smoke tests fail. Key decisions:\",\"new_string\":\"`references/equity-etf-provider-integration-openfigi-fmp.md` for the concrete OpenFIGI-first/FMP-second integration pattern, provider merge rules, local-price preview semantics, and `update-equity-prices` workflow, `references/provider-integration-sprint-v2.md` for the multi-provider Free/Fallback pattern covering OpenFIGI/FMP/Finnhub/Twelve Data/Massive/EODHD, explicit-action dashboard semantics, sanitized status/error categories, and low-volume EODHD handling, and `references/provider-secret-auth-debugging.md` for targeted runtime-secret/provider-auth debugging when keys are present but OpenFIGI/FMP smoke tests fail. Key decisions:\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "skill_manage",
      "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'finance-system-architecture' (1 replacement).\"}",
      "tool_call_id": "call_pHOBcaKpTfpoEpkMFNWVcI1j"
    },
    {
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      "tool_calls": [
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          "call_id": "call_UQpL5XXHwOG8IQXZIdbkFwyT",
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            "name": "skill_manage",
            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"old_string\":\"For the concrete Dashboard/User-Mode implementation pattern, provider order (local/OpenFIGI/FMP/Finnhub/manual), runtime `.env` secret lookup, provider-result caching, and final compile/test/Git-safety/commit/push discipline, also follow `references/manual-position-entry-provider-ux.md` and `references/equity-etf-provider-integration-openfigi-fmp.md`. If provider smoke tests report missing candidates or unreachable providers despite keys being stored, first follow `references/provider-secret-auth-debugging.md`: verify runtime secret loading without printing values, classify 401/403 as `auth_failed` rather than generic reachability failure, and improve status diagnostics before adding features.\",\"new_string\":\"For the concrete Dashboard/User-Mode implementation pattern, provider order (local/OpenFIGI/FMP/Finnhub/manual), runtime `.env` secret lookup, provider-result caching, and final compile/test/Git-safety/commit/push discipline, also follow `references/manual-position-entry-provider-ux.md` and `references/equity-etf-provider-integration-openfigi-fmp.md`. For multi-provider hardening across OpenFIGI/FMP/Finnhub/Twelve Data/Massive/EODHD, follow `references/provider-integration-sprint-v2.md`: support all documented secret aliases including `TWELVEDATA_API_KEY`, keep FMP primary, use Twelve/Finnhub/Massive as fallback, keep EODHD explicit low-volume only, sanitize status/error output, and ensure dashboard status uses non-probing render mode. If provider smoke tests report missing candidates or unreachable providers despite keys being stored, first follow `references/provider-secret-auth-debugging.md`: verify runtime secret loading without printing values, classify 401/403 as `auth_failed` rather than generic reachability failure, and improve status diagnostics before adding features.\"}"
          }
        }
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    },
    {
      "role": "tool",
      "name": "skill_manage",
      "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'finance-system-architecture' (1 replacement).\"}",
      "tool_call_id": "call_UQpL5XXHwOG8IQXZIdbkFwyT"
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      "role": "assistant",
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      "tool_calls": [
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          "id": "call_OWVsdrhfDIhnHRnws3JkZzne",
          "call_id": "call_OWVsdrhfDIhnHRnws3JkZzne",
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          "type": "function",
          "function": {
            "name": "skill_manage",
            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"new_string\":\"- Treating provider lookup smoke-test failures as simple `not reachable` / `no candidates` when runtime keys exist. Follow `references/provider-secret-auth-debugging.md`: confirm the runtime secret file is loaded without printing values, verify OpenFIGI uses `X-OPENFIGI-APIKEY` header and FMP uses `apikey`, split network reachability from provider auth, and classify HTTP 401/403 as `auth_failed`, 429 as `rate_limited`, empty 2xx as `no_results`, and DNS/timeout as `network_error`. Do not start a new feature block until the secret/auth diagnosis is resolved.\\n- Letting multi-provider market-data integration become implicit dashboard behavior or an API-call spray. Follow `references/provider-integration-sprint-v2.md`: normal dashboard render must use local DB/non-probing status only; provider calls require explicit click/CLI/schedule; FMP remains primary for search/price preview; Twelve Data/Finnhub/Massive are fallbacks; EODHD is never default and requires explicit low-volume opt-in; provider statuses and errors must be sanitized categories, not raw responses or secret-bearing URLs.\\n\",\"old_string\":\"- Treating provider lookup smoke-test failures as simple `not reachable` / `no candidates` when runtime keys exist. Follow `references/provider-secret-auth-debugging.md`: confirm the runtime secret file is loaded without printing values, verify OpenFIGI uses `X-OPENFIGI-APIKEY` header and FMP uses `apikey`, split network reachability from provider auth, and classify HTTP 401/403 as `auth_failed`, 429 as `rate_limited`, empty 2xx as `no_results`, and DNS/timeout as `network_error`. Do not start a new feature block until the secret/auth diagnosis is resolved.\\n\"}"
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    {
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      "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'finance-system-architecture' (1 replacement).\"}",
      "tool_call_id": "call_OWVsdrhfDIhnHRnws3JkZzne"
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    {
      "role": "assistant",
      "content": "Skill library updated.\n\n- Added `references/provider-integration-sprint-v2.md` under `finance-system-architecture`.\n- Patched `finance-system-architecture` to point future FinanceManager provider work to that reference.\n- Added a pitfall covering multi-provider market-data integration:\n  - no dashboard live calls on render\n  - FMP primary\n  - Twelve/Finnhub/Massive fallback\n  - EODHD explicit low-volume only\n  - sanitized provider status/error categories\n  - no raw keys/responses/secret-bearing URLs\n\nI kept this under the existing class-level Finance System skill rather than creating a narrow one-session skill.",
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      "codex_message_items": [
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            {
              "type": "output_text",
              "text": "Skill library updated.\n\n- Added `references/provider-integration-sprint-v2.md` under `finance-system-architecture`.\n- Patched `finance-system-architecture` to point future FinanceManager provider work to that reference.\n- Added a pitfall covering multi-provider market-data integration:\n  - no dashboard live calls on render\n  - FMP primary\n  - Twelve/Finnhub/Massive fallback\n  - EODHD explicit low-volume only\n  - sanitized provider status/error categories\n  - no raw keys/responses/secret-bearing URLs\n\nI kept this under the existing class-level Finance System skill rather than creating a narrow one-session skill."
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