{
  "session_id": "20260515_152502_1d5472",
  "model": "gpt-5.5",
  "base_url": "https://chatgpt.com/backend-api/codex",
  "platform": "telegram",
  "session_start": "2026-05-15T15:25:02.176970",
  "last_updated": "2026-05-15T15:27:24.217886",
  "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. 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    - 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) [95% — 1,313/1,375 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 work preference: controlled phased execution with explicit safety gates; no mass imports without approval; aggregate-only reporting, never real financial quantities/values/balances/raw rows.\n\nConversation started: Friday, May 15, 2026 03:26 PM\nModel: gpt-5.5\nProvider: openai-codex",
  "tools": [
    {
      "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": "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": {
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              "description": "The action to perform."
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            "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."
            },
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            },
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              "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."
            },
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              "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."
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            "name"
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      "function": {
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        "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": {
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          "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."
            }
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          "required": [
            "name"
          ]
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    {
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      "function": {
        "name": "skills_list",
        "description": "List available skills (name + description). Use skill_view(name) to load full content.",
        "parameters": {
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          "properties": {
            "category": {
              "type": "string",
              "description": "Optional category filter to narrow results"
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      "role": "user",
      "content": "[M G] Danke. Der Crypto Price Update MVP ist akzeptiert.\n\nDie Umsetzung ist grundsätzlich sinnvoll:\n\n* lokaler Preis-Cache\n* kein Live-API-Call im Dashboard/Report\n* 429-Handling\n* Git-Safety sauber\n* Tests grün\n* Runtime-Daten außerhalb Git\n\nAllerdings bist du vom vorher vereinbarten Ablauf abgewichen. Das ist in diesem Fall nicht schlimm, weil der Preisupdate-Block nützlich ist. Bitte jetzt aber wieder kontrolliert auf die nächsten Prioritäten einschwenken.\n\nAktueller Stand:\n\n* Commit 3aa67d8 ist akzeptiert.\n* Preisupdate funktioniert technisch.\n* Aber nur 6 von 20 aktiven Crypto Assets hatten frische CHF-Preise.\n* 14 Assets hatten Warnings/Stale/Provider-Limit-Themen.\n* BTC-Test wurde noch nicht abgeschlossen.\n* Equity/ETF Manage UI wurde noch nicht begonnen.\n\nBitte jetzt wie folgt weiterfahren:\n\n## Teil 1: Crypto Price Update robuster machen\n\nBitte verbessere zuerst den Preisupdate-Mechanismus, bevor wir echte Reports mit Preisen ernsthaft nutzen.\n\nZiel:\nMöglichst alle vorhandenen Crypto Assets mit CoinGecko-ID sollen lokale CHF-Preise erhalten, ohne Dashboard-Live-API und ohne Rate-Limit-Crash.\n\nBitte implementiere:\n\n1. Batch-Abfrage prüfen/umsetzen\n\n* Wenn CoinGecko mehrere IDs in einem Request unterstützt, bitte nutzen.\n* Dadurch weniger API-Calls.\n* Kein Abruf pro Coin, falls Batch möglich ist.\n\n2. Konservatives Throttling\n\n* Pause zwischen Requests\n* konfigurierbar:\n\n  * --sleep-seconds\n  * --max-retries\n  * --initial-backoff\n  * --max-backoff\n\n3. CLI-Optionen ergänzen\n   Bitte vorbereiten/implementieren:\n\n* --only-missing\n* --only-stale\n* --only-symbol SYMBOL\n* --limit N\n* --dry-run\n* --currency CHF\n\n4. Bessere Summary\n   Bitte nach Update nur aggregiert ausgeben:\n\n* total assets\n* updated\n* cached\n* skipped\n* stale\n* warnings\n* errors\n* assets still missing local price\n* keine echten Mengen/Werte\n\n5. Data Quality\n\n* Für Assets ohne frischen Preis Data-Quality-Alert erzeugen oder aktualisieren.\n* Alert-Dedupe respektieren.\n* Keine Alert-Flut.\n\n6. Tests\n   Bitte Tests ergänzen für:\n\n* Batch-Request\n* --only-missing\n* --only-stale\n* --only-symbol\n* --limit\n* --dry-run\n* Rate-Limit mit Backoff\n* Alert-Dedupe bei fehlenden Preisen\n\nDanach bitte produktiv gegen Runtime-DB nochmals ausführen, aber vorsichtig und ohne echte Werte im Chat.\n\nBitte berichten:\n\n* Anzahl Assets total\n* Anzahl Assets mit frischem CHF-Preis\n* Anzahl weiterhin ohne frischen Preis\n* Anzahl Warnings/Errors\n* keine Preise/Werte/Mengen ausgeben\n\n## Teil 2: BTC-Test nachholen\n\nDanach bitte den BTC-Test wie zuletzt entschieden nachholen:\n\nOption A:\nBTC mit allen BTC-Wallet-Holdings aus Excel importieren.\n\nBedingungen:\n\n* Nur Bitcoin BTC\n* Coin-Name: Bitcoin\n* Symbol: BTC\n* CoinGecko-ID: bitcoin\n* Mapping-Status: selected\n* Snapshot-Datum: 31.12.2025\n* Alle BTC-Wallet-Holdings aus der echten Excel-Datei übernehmen\n* Pro BTC-Wallet mit Bestand genau eine initial_snapshot_addition bzw. initial_snapshot-Holding erzeugen\n* Mengen exakt als Decimal/Text übernehmen\n* Keine Float-Konvertierung\n* Keine echten Mengen/Werte im Chat\n* Keine anderen Coins importieren\n* Audit-Log Pflicht\n* Keine doppelte Holding für asset_id + wallet_id\n* Legacy-CHF-Werte ignorieren\n* Git-Safety vor/nachher prüfen\n\nBericht nur aggregiert:\n\n* BTC-Test durchgeführt: ja/nein\n* Anzahl BTC-Wallet-Holdings importiert\n* Asset-Anzahl erhöht: ja/nein\n* Holding-Anzahl erhöht: ja/nein\n* Audit-Logs erstellt: ja/nein\n* Doppelte Holdings: ja/nein\n* Fehler/Warnings\n* Git-Safety Ergebnis\n* Git Status\n\n## Teil 3: Danach Equity/ETF Manage UI\n\nErst wenn Teil 1 und Teil 2 sauber sind, bitte mit der Equity/ETF Manage UI beginnen.\n\nZiel:\nAktien und ETFs aus PostFinance, True Wealth und Raiffeisen später im Dashboard erfassen, korrigieren und als Initial Snapshot übernehmen können — auditierbar und ohne stilles Überschreiben.\n\nGrundlogik:\n\n* Für Aktien/ETFs ist ISIN die primäre Identifikation.\n* Ticker und Exchange sind Zusatzinformationen.\n* Positionen werden über initial_position_snapshot, buy, sell, dividend, fee oder manual_adjustment abgebildet.\n* Jede Änderung braucht\nAudit-Log.\n* Fremdwährungen USD/EUR brauchen FX-Kurs und CHF-Gegenwert.\n* Keine Scheingenauigkeit, wenn Kaufhistorie, Gebühren oder historische FX fehlen.\n* Erst mit synthetischen Daten und Dummy-Depots bauen.\n* Keine echten PostFinance-/TrueWealth-/Raiffeisen-Daten importieren.\n\nBitte vormerken für die Equity/ETF Manage UI:\n\n* Aktie/ETF als Initial Snapshot hinzufügen\n* Buy\n* Partial Sell\n* Full Sell\n* Dividend\n* ETF Distribution\n* Fee\n* Manual Adjustment\n* ISIN Pflicht oder stark empfohlen\n* ISIN/Ticker dürfen nicht beide leer sein\n* Währung Pflicht\n* Depot/Konto Pflicht\n* FX bei Fremdwährung\n* Audit-Log Pflicht\n* Notiz Pflicht bei manual_adjustment oder unvollständiger Historie\n\n## Weitere Funktionsmodule bitte vormerken, aber noch nicht bauen\n\nBitte für später als Backlog vormerken:\n\n1. Portfolio Health Seite\n\n* Gesamtwert CHF\n* Assetklassen\n* Plattformen\n* Datenqualität\n* Cashquote\n* fehlende Preise/FX\n\n2. Reconciliation / Abgleich-Seite\n\n* Dashboard-Bestand vs. letzter Snapshot\n* manuelle Prüfung „Bestand geprüft am Datum X“\n* Differenzen sichtbar machen\n\n3. Data Quality Center\n\n* fehlende Preise\n* stale prices\n* fehlende FX\n* fehlende ISIN\n* fehlende CoinGecko-ID\n* unvollständige Historie\n\n4. Manual Review Queue\n\n* ausgelassene Coins\n* unsichere Mappings\n* offene Alerts\n* offene Datenqualitätsprobleme\n\n5. Backup / Restore\n\n* Runtime-DB Backup\n* verschlüsseltes Backup optional\n* Restore-Test später\n\n6. Safe Mode / Read-only Mode\n\n* Dashboard im Lesemodus\n* Schreibfunktionen bewusst aktivieren\n* Schutz gegen versehentliche Änderungen\n\nBitte jetzt zuerst Teil 1 umsetzen, dann Teil 2, dann Teil 3. Keine echten Aktien-/ETF-Daten importieren."
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      "reasoning_content": "**Inspecting repository for finance skills**\n\nI need to act and inspect the repository related to finance-system-architecture. It's important to check for relevant skills that might be there, especially on GitHub. I want to get a clear understanding of the tools and structures involved in this project. Loading the repository should help me gather the details I need. Let's figure this out!",
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      "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: „Bitte jetzt keine weiteren Imports, keine Heatmaps, keine Scores, kein Rebalancing, kein Backtesting und keine Monte-Carlo-Module bauen.\n\nDer nächste Schritt ist:\n\n# Phase C3c — Assisted Public Instrument Lookup & Mapping Candidate Fill\n\nZiel:\nFür die 3 importierten True-Wealth-Instrumente sollen öffentliche Instrument-Metadaten recherchiert und als Mapping-Kandidaten im Runtime-Katalog gespeichert werden.\n\nWichtig:\n\n* Keine Portfolio-Mengen, Depotwerte, Kaufpreise, Kontostände oder persönlichen Daten an externe Dienste senden.\n* Erlaubt sind nur öffentliche Instrumentdaten:\n\n  * ISIN\n  * Instrumentname\n  * Ticker/Symbol\n  * Exchange\n  * Währung\n  * ETF-/Aktien-Metadaten\n* Keine automatischen finalen Mappings bei Unsicherheit.\n* Keine Preise abrufen.\n* Keine FX-Werte setzen.\n* Keine weiteren Portfolio-Imports.\n* Keine echten Werte/Mengen/Kontostände im Chat.\n* Keine echten Dateien ins Git.\n\n## 1. Vorprüfung missing_provider_symbol = 6\n\nBitte zuerst erklären oder bereinigen:\n\n* Warum gibt es missing_provider_symbol = 6 bei 3 True-Wealth-Instrumenten?\n* Sind das unterschiedliche Entity-Typen, z.B. Instrument Mapping und Catalog Entry?\n* Oder sind es doppelte Alerts?\n* Falls doppelt: bitte deduplizieren oder Rule/Entity/Fingerprint sauberer setzen.\n* Falls fachlich korrekt: im Data Quality Center verständlich anzeigen.\n\nBitte nur aggregiert berichten.\n\n## 2. Public Lookup Candidate Generator\n\nBitte für die 3 True-Wealth-Instrumente öffentliche Mapping-Kandidaten suchen.\n\nMögliche Quellen:\n\n* OpenFIGI\n* Financial Modeling Prep\n* Finnhub\n* Twelve Data\n* Stooq\n* Yahoo nur als Fallback\n* offizielle ETF-/Issuer-Seiten, falls über ISIN auffindbar\n\nWenn API-Key fehlt oder Provider nicht verfügbar:\n\n* kein Crash\n* provider_lookup_unavailable\n* manuelle Eingabe bleibt möglich\n\nBitte pro Instrument Runtime-intern Kandidaten speichern:\n\n* instrument_id\n* ISIN\n* candidate_provider\n* candidate_provider_symbol\n* candidate_exchange\n* candidate_currency\n* candidate_name\n* candidate_asset_class\n* candidate_is_hedged\n* candidate_hedged_to_currency\n* candidate_hedge_status\n* candidate_instrument_status\n* candidate_valuation_policy\n* evidence_source\n* evidence_note\n* confidence: high / medium / low\n* review_status: proposed / selected / rejected / needs_manual_review\n\nRegeln:\n\n* ISIN-Match ist stärker als Name/Ticker.\n* Name-only reicht nicht.\n* Ticker ohne Exchange bleibt review-pflichtig.\n* Mehrere Kandidaten bleiben needs_manual_review.\n* Kein Kandidat → missing_provider_symbol bleibt aktiv.\n* Hedge-Status nur setzen, wenn eindeutig erkennbar; sonst unknown.\n* Instrument-Status nur active, wenn belastbar; sonst unknown.\n\n## 3. Keine finale automatische Auswahl\n\nBitte keine Mapping-Kandidaten automatisch final bestätigen.\n\nErlaubt:\n\n* Kandidaten erzeugen\n* Confidence berechnen\n* Review Status proposed / needs_manual_review setzen\n* Data Quality aktualisieren\n* Dashboard/Review UI vorbereiten\n\nNicht erlaubt:\n\n* Provider-Symbol final auswählen\n* Hedge-Status final bestätigen\n* Instrument-Status final bestätigen\n* Preise abrufen\n* FX setzen\n* Positionen importieren\n\n## 4. Runtime-only Mapping Review Pack aktualisieren\n\nBitte das Mapping Review Pack für die 3 True-Wealth-Instrumente aktualisieren:\n\n* Kandidaten vorhanden ja/nein\n* Anzahl Kandidaten pro Instrument\n* Confidence-Verteilung\n* Hedge-Kandidat vorhanden ja/nein\n* Instrument-Status-Kandidat vorhanden ja/nein\n* manuelle Bestätigung nötig ja/nein\n\nIm Chat nur aggregiert berichten.\n\n## 5. Dashboard Review UI prüfen\n\nBitte sicherstellen, dass ich im Dashboard Kandidaten sehen, vergleichen, manuell auswählen/verwerfen und Review-Bedarf erkennen kann, ohne Live-API-Aufruf.\n\n## 8. Tests erweitern\n\nBitte Tests ergänzen für:\n\n* Provider-Lookup-Fehler crasht nicht\n* Kandidaten werden Runtime-intern gespeichert\n* keine finale automatische Auswahl\n* Mapping Template enthält Kandidatenfelder\n* Dashboard Review UI rendert Kandidaten ohne Live-API\n* Data Quality Center zeigt Kandidaten/Review-Bedarf\n* missing_provider_symbol wird nicht resolved, solange Kandidat nicht bestätigt ist\n* Git-Safety OK\n\n## 9. Produktiver vorsichtiger Runtime-Lauf\n\nBitte gegen Runtime-DB ausführen:\n\n* öffentliche Lookup-Kandidaten für 3 True-Wealth-Instrumente suchen, sofern Provider/Lookup verfügbar\n* Kandidaten speichern\n* keine finale Auswahl\n* keine Preise abrufen\n* keine FX-Werte setzen\n* keine weiteren Imports\n\nBericht nur aggregiert:\n\n* Instrumente total\n* Provider-Lookups versucht\n* Kandidaten gefunden\n* Kandidaten high/medium/low confidence\n* Instrumente ohne Kandidat\n* Instrumente mit mehreren Kandidaten\n* Hedge-Status weiterhin unknown\n* Instrument-Status weiterhin unknown\n* Provider-Lookup-Unavailable Count\n* Data-Quality-Alerts aggregiert\n* keine Preise/Werte/Mengen/Kontostände\n\n## 10. Nach Abschluss\n\nBitte ausführen:\n\n* Compile\n* pytest\n* Git-Safety\n* Git Status\n\nWenn Code geändert wurde:\n\n* Commit\n* Push\n\nBitte berichten:\n\n* implementierte Module\n* neue/geänderte Dateien\n* Tests\n* Testergebnis\n* Git-Safety\n* Commit Hash\n* Push Status\n* Mapping-Kandidaten aggregiert\n* Data-Quality-Status aggregiert\n* konkrete nächste Empfehlung\n\nKeine echten Mengen, Preise, Werte, Kontostände oder sensitiven Inhalte im Chat.“\n\n## Goal\nDas FinanceManager-System soll True-Wealth-Aktien-/ETF-Instrumente sicher, auditierbar und ohne Preis-/FX-/Portfolio-Datenabfluss verwalten: zuerst lokaler Instrument Catalog und manuelle Erfassung, jetzt Phase C3c mit unterstützter öffentlicher Instrument-Metadaten-Recherche und Runtime-only Mapping-Kandidaten, ohne automatische finale Mappings.\n\n## Constraints & Preferences\n- Sprache/Ansprache: Deutsch; Nutzer akzeptiert knappe, sachliche Statusberichte.\n- Keine echten Mengen, Preise, Werte, Depotwerte, Kaufpreise, Kontostände oder persönlichen Daten im Chat ausgeben.\n- Keine Portfolio-Mengen/-Werte/-Kontostände an externe Dienste senden.\n- Extern erlaubt nur öffentliche Instrumentdaten: ISIN, Instrumentname, Ticker/Symbol, Exchange, Währung, ETF-/Aktien-Metadaten.\n- Keine weiteren Portfolio-Imports.\n- Keine Preise abrufen.\n- Keine FX-Werte setzen.\n- Keine Heatmaps, Scores, Rebalancing, Backtesting oder Monte-Carlo-Module bauen.\n- Keine echten Dateien ins Git; Runtime-Daten dürfen nicht committed werden.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings im Chat oder Summary; vorhandene Credentials immer als `[REDACTED]`.\n- Git-Safety vor Commit/Push verwenden.\n- Nach Codeänderungen: Compile, pytest, Git-Safety, Git Status; dann Commit und Push.\n- Mapping-Regeln:\n  - ISIN-Match stärker als Name/Ticker.\n  - Name-only reicht nicht.\n  - Ticker ohne Exchange bleibt review-pflichtig.\n  - Mehrere Kandidaten bleiben `needs_manual_review`.\n  - Kein Kandidat → `missing_provider_symbol` bleibt aktiv.\n  - Hedge-Status nur setzen, wenn eindeutig erkennbar; sonst `unknown`.\n  - Instrument-Status nur `active`, wenn belastbar; sonst `unknown`.\n  - Keine automatische finale Auswahl von Provider-Symbol, Hedge-Status oder Instrument-Status.\n\n## Completed Actions\n1. RUN `PYTHONPATH=src python -m jarvis_finance.cli.main git-safety-scan .` in `/home/agent/.hermes/repos/FinanceManager` — initialer Lauf exit 1 mit 1 Zeile Output; danach Bereinigung durchgeführt [tool: terminal].\n2. RUN `git status --short && git diff --stat` in `/home/agent/.hermes/repos/FinanceManager` — exit 0, 1 Zeile Output [tool: terminal].\n3. CLEAN `.pytest_cache` und alle `__pycache__`-Verzeichnisse, dann RUN `PYTHONPATH=src python -m jarvis_finance.cli.main git-safety-scan . && git status --short` — exit 0, 1 Zeile Output [tool: terminal].\n4. RUN `git diff --check` — fand Trailing Whitespace in `src/jarvis_finance/equity/manage.py:621`: `def get_equity_management_options(conn: Connection) -> dict[str, list[dict[str, str]]]: ` [tool: terminal].\n5. READ `src/jarvis_finance/equity/manage.py` ab Zeile 615 — Kontext um Funktion `get_equity_management_options` geprüft [tool: read_file].\n6. PATCH `src/jarvis_finance/equity/manage.py` — trailing space am Ende der Funktionsdefinition `get_equity_management_options(...)` entfernt [tool: patch].\n7. RUN `git diff --check` — exit 0, keine Whitespace-Probleme mehr [tool: terminal].\n8. RUN `PYTHONPATH=src python -m compileall -q src tests && PYTHONPATH=src pytest -q` — exit 0; Tests erfolgreich, später berichtet als `201 passed` [tool: terminal].\n9. CLEAN `.pytest_cache` und `__pycache__`, RUN Git-Safety, dann staged relevante Dateien für Commit — exit 0 [tool: terminal].\n10. COMMIT `git commit -m \"Add instrument catalog manual add workflow\"` — erfolgreich; Commit `61709aa` [tool: terminal].\n11. PUSH via lokaler GitHub-Token-Datei unter `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv` — Credential-Wert nicht erhalten/ausgegeben; Push erfolgreich nach `https://github.com/Gamexgit/FinanceManager.git`, `6febde0..61709aa HEAD -> main` [tool: terminal].\n12. RUN `git status --short && git log -1 --oneline` — Ausgabe `61709aa Add instrument catalog manual add workflow`; Arbeitsbaum sauber impliziert durch fehlende Status-Zeilen [tool: terminal].\n13. RUN `PYTHONPATH=src python -m jarvis_finance.cli.main git-safety-scan .` — Ausgabe `GIT_SAFETY_OK`, exit 0 [tool: terminal].\n14. RUN Runtime-Aggregationsskript gegen FinanceManager-Runtime-DB — exit 0, aggregierte True-Wealth-/DQ-Werte ermittelt ohne sensitive Detailausgabe [tool: terminal].\n15. UPDATE Todo `ic-6` — „Compile, pytest, Git-Safety, Commit und Push“ auf completed gesetzt [tool: todo].\n16. REPORT an Nutzer — Phase „Instrument Catalog + Manual Add Workflow“ abgeschlossen, committed und gepusht; Nutzer hat diese Phase akzeptiert.\n\n## Active State\n- Working directory: `/home/agent/.hermes/repos/FinanceManager`.\n- Branch: `main`.\n- Letzter bekannter Commit: `61709aa Add instrument catalog manual add workflow`, gepusht nach `origin/main`.\n- Git Status: zuletzt clean nach Commit/Push.\n- Git-Safety: zuletzt `GIT_SAFETY_OK`.\n- Teststatus: `201 passed`.\n- Compile: `PYTHONPATH=src python -m compileall -q src tests` erfolgreich.\n- Keine laufenden Server/Prozesse bekannt.\n- Runtime-Zustand nach Phase C3b:\n  - 3 True-Wealth-Instrumente im Instrument Catalog vorbereitet.\n  - Mapping Review Pack erzeugt.\n  - Keine finale automatische Auswahl.\n  - Keine Preise abgerufen.\n  - Keine FX-Werte gesetzt.\n  - Keine weiteren Imports.\n  - Confirmed Provider-Mappings: `0`.\n  - Valuation ready: `0`.\n  - Preisupdate-fähig: nein.\n- Aggregierter Runtime-Status zuletzt berichtet:\n  - Instrumente total: `3`.\n  - Lokale Katalogeinträge erzeugt: `3`.\n  - Mehrere Listings erkannt: `0`.\n  - Provider-Kandidaten gefunden: `0`.\n  - Instrumente ohne Kandidat: `3`.\n  - Manuelle Auswahl erforderlich: ja.\n  - Hedge unknown: `3`.\n  - Instrument status unknown: `3`.\n  - Valuation ready count: `0`.\n- Aggregierte Data-Quality-Alerts zuletzt:\n  - `cost_basis_uncertain`: `3`.\n  - `hedge_status_unknown`: `3`.\n  - `instrument_status_unknown`: `3`.\n  - `manual_review_needed`: `3`.\n  - `missing_fx`: `3`.\n  - `missing_market_price`: `3`.\n  - `missing_provider_symbol`: `6`.\n  - `snapshot_only`: `3`.\n- Wichtig: `missing_provider_symbol = 6` bei nur 3 True-Wealth-Instrumenten ist noch ungeklärt und ist erster Prüfpunkt für Phase C3c.\n\n## In Progress\nKeine Codearbeit für Phase C3c wurde nach der Nutzeranfrage begonnen. Nächster Schritt ist Analyse/Implementierung der Vorprüfung `missing_provider_symbol = 6` und danach Public Lookup Candidate Generator.\n\n## Blocked\n- Kein aktueller technischer Blocker.\n- Potenzielle externe Provider-Blocker erwartet:\n  - Fehlende API-Keys für OpenFIGI/FMP/Finnhub/Twelve Data o.ä. dürfen nicht crashen.\n  - In dem Fall `provider_lookup_unavailable` setzen und manuelle Eingabe ermöglichen.\n- `missing_provider_symbol = 6` muss fachlich erklärt oder technisch dedupliziert werden, bevor weiter berichtet wird.\n\n## Key Decisions\n- Instrument Catalog trennt Instrument-Wahrheit von Market-Data-Provider-Mapping; Symbol allein ist keine eindeutige Wahrheit.\n- Mehrere Listings pro Instrument sind erlaubt; gleiche ISIN mit anderem Exchange/Währung kann parallel existieren.\n- Provider-Symbol bleibt Mapping-Kandidat/Market-Data-Mapping, nicht automatisch finale Instrument-Identität.\n- Externe Provider-Ergebnisse werden nur als Kandidaten behandelt; finale Auswahl immer manuell.\n- Decimal/Text statt Float für Mengen/Positionserfassung, um Rundungsfehler zu vermeiden.\n- Data-Quality-Alerts bleiben aktiv, bis echte Bestätigung/Behebung erfolgt; insbesondere `missing_provider_symbol` darf durch bloße Kandidaten nicht resolved werden.\n- Runtime-Daten und Review Packs bleiben runtime-only; keine echten Portfolio-/Runtime-Dateien ins Git.\n- Sicherer Workflow: erst lokale Katalog-/Review-Strukturen, dann Kandidaten, dann manuelle Bestätigung, erst danach Price Dry Run; produktive Preisupdates erst noch später.\n\n## Resolved Questions\n- Nutzer hat Phase „Instrument Catalog + Manual Add Workflow“ akzeptiert.\n- Commit- und Push-Status der vorigen Phase: Commit `61709aa`, Push nach `origin/main` erfolgreich.\n- Teststatus der vorigen Phase: `201 passed`.\n- Git-Safety der vorigen Phase: `GIT_SAFETY_OK`.\n- Es wurden in der vorigen Phase keine Preise abgerufen, keine FX-Werte gesetzt und keine weiteren Imports ausgeführt.\n- True-Wealth-Instrumente wurden nur vorbereitet; keine finale automatische Mapping-Auswahl.\n- `missing_provider_symbol=6` wurde bislang nur aggregiert berichtet; Erklärung/Bereinigung steht noch aus.\n\n## Pending User Asks\n- Phase C3c vollständig umsetzen:\n  1. `missing_provider_symbol = 6` bei 3 Instrumenten aggregiert erklären oder bereinigen/deduplizieren bzw. verständlich im Data Quality Center darstellen.\n  2. Public Lookup Candidate Generator für 3 True-Wealth-Instrumente implementieren/nutzen.\n  3. Kandidaten Runtime-intern speichern mit den geforderten Feldern.\n  4. Keine finalen Mappings automatisch bestätigen.\n  5. Runtime-only Mapping Review Pack aktualisieren.\n  6. Dashboard Review UI prüfen/erweitern, sodass Kandidaten ohne Live-API angezeigt und reviewt werden können.\n  7. Tests erweitern.\n  8. Vorsichtigen Runtime-Lauf ausführen.\n  9. Compile, pytest, Git-Safety, Git Status ausführen.\n  10. Falls Code geändert: Commit und Push.\n  11. Aggregiert berichten ohne echte Mengen/Preise/Werte/Kontostände/sensitive Inhalte.\n\n## Relevant Files\n- `src/jarvis_finance/market_data/catalog.py` — neu aus Phase C3b; lokaler Instrument Catalog, Suche, Provider-/Kandidatenlogik vorbereitet.\n- `src/jarvis_finance/cli/main.py` — geändert in Phase C3b; CLI-Erweiterungen inkl. Git-Safety/Instrument-Workflow.\n- `src/jarvis_finance/dashboard/data.py` — geändert; Dashboard-Datenzugriff für Equity/Instrument Catalog.\n- `src/jarvis_finance/dashboard/pages/page_13_equity_manage.py` — geändert; Dashboard-Komponente für Instrument Search / Add Position.\n- `src/jarvis_finance/equity/manage.py` — geändert; manuelle Equity-/ETF-Verwaltung, Catalog-Optionen; Whitespace-Fix bei Zeile 621 durchgeführt.\n- `src/jarvis_finance/storage/migrations.py` — geändert; DB-Schema/Migration für Instrument Catalog und zugehörige Strukturen.\n- `tests/unit/test_instrument_catalog_manual_add.py` — neu; Tests für lokale Suche, mehrere Listings, manuelle Auswahl/Audit, DQ-Hinweise, Dashboard-Render, Provider-Ausfall.\n- `tests/unit/test_schema.py` — geändert; Schema-/Migrationstests aktualisiert.\n- Runtime-Pfad mit GitHub-Token-Datei wurde benutzt: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; Inhalt ist `[REDACTED]` und darf nicht ausgegeben werden.\n\n## Remaining Work\n- Codebasis inspizieren, besonders:\n  - Data-Quality-Regeln/Fingerprints für `missing_provider_symbol`.\n  - Tabellen/Schema für Instrument Catalog, Mapping Review Pack, Alerts.\n  - Dashboard Data Quality Center und Equity Manage Review UI.\n- Entscheiden, ob `missing_provider_symbol=6` doppelte Alerts oder fachlich unterschiedliche Entities sind:\n  - Falls doppelt: Rule/Entity/Fingerprint deduplizieren.\n  - Falls fachlich korrekt: aggregierte Anzeige verständlich machen.\n- Datenmodell für Mapping-Kandidaten ergänzen, falls noch nicht vorhanden:\n  - `instrument_id`, `ISIN`, `candidate_provider`, `candidate_provider_symbol`, `candidate_exchange`, `candidate_currency`, `candidate_name`, `candidate_asset_class`, `candidate_is_hedged`, `candidate_hedged_to_currency`, `candidate_hedge_status`, `candidate_instrument_status`, `candidate_valuation_policy`, `evidence_source`, `evidence_note`, `confidence`, `review_status`.\n- Provider-/Lookup-Fassade implementieren:\n  - OpenFIGI, FMP, Finnhub, Twelve Data, Stooq, Yahoo-Fallback oder offizielle Issuer-Seiten soweit verfügbar.\n  - Fehlende Provider/API-Keys robust als `provider_lookup_unavailable`.\n- Runtime-Lauf nur mit öffentlichen Instrumentdaten; keine Portfolio- oder Wertdaten an externe Dienste.\n- Tests ergänzen und ausführen.\n- Git-Safety, Commit/Push nur nach sauberem Zustand.\n\n## Critical Context\n- Letzter gepushter Commit: `61709aa Add instrument catalog manual add workflow`.\n- Repository: `/home/agent/.hermes/repos/FinanceManager`.\n- Remote Push-Ziel zuletzt: `https://github.com/Gamexgit/FinanceManager.git`.\n- Credential-/Tokenwerte sind `[REDACTED]`.\n- Vor Phase C3c existieren 3 True-Wealth-Instrumente im Runtime Catalog, aber 0 bestätigte Provider-Mappings und 0 valuation-ready Instrumente.\n- `missing_provider_symbol=6` bei 3 Instrumenten ist ein zentraler offener Punkt; nicht vorschnell als Fehler behaupten, zuerst Entities/Fingerprints prüfen.\n- Nutzer verlangt ausdrücklich aggregierte Berichte und keine echten Mengen, Preise, Werte, Kontostände oder sensitiven Inhalte.\n- Nutzer verbietet aktuell: weitere Imports, Heatmaps, Scores, Rebalancing, Backtesting, Monte-Carlo-Module, Preisabrufe, FX-Setzung, automatische finale Mappings.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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      "reasoning_content": "**Troubleshooting search issues**\n\nI’m looking into why the search isn’t working. It could be a regex issue, or maybe I should try using terminal grep instead? But the instructions say to use search_files, which returned zero results, which seems impossible! I’m wondering if it’s a path problem. I think it’s a good idea to list the files to clarify what’s going on and see if I can spot the issue. Let's figure this out!",
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      "finish_reason": "tool_calls",
      "reasoning_content": "**Checking commit and coding needs**\n\nI'm considering whether I should commit my changes to git. I noticed something odd, maybe ripgrep is ignoring hidden files? Anyway, I think I need a patch using a Python script, which seems easier for me. Also, I'll create a template CSV writer using the csv library. I might not have to worry about a migration version since there's a schema change. The tests expect 13, so let’s figure out these patch candidates.",
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            "arguments": "{\"mode\":\"replace\",\"old_string\":\"class YahooSearchLookupProvider:\\n    \\\"\\\"\\\"Public metadata lookup via Yahoo Finance search endpoint; no prices or portfolio data.\\\"\\\"\\\"\",\"new_string\":\"class OpenFIGILookupProvider:\\n    \\\"\\\"\\\"Public ISIN-to-instrument metadata lookup via OpenFIGI; no prices or portfolio data.\\\"\\\"\\\"\\n\\n    name = \\\"openfigi\\\"\\n\\n    def __init__(self, *, timeout_seconds: float = 8.0):\\n        self.timeout_seconds = timeout_seconds\\n\\n    def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n        if not isin:\\n            return []\\n        token = os.environ.get(\\\"OPENFIGI_API_KEY\\\")\\n        headers = {\\\"Content-Type\\\": \\\"application/json\\\", \\\"User-Agent\\\": \\\"JarvisFinance/1.0 public-instrument-lookup\\\"}\\n        if token:\\n            headers[\\\"X-OPENFIGI-APIKEY\\\"] = token\\n        payload = json.dumps([{\\\"idType\\\": \\\"ID_ISIN\\\", \\\"idValue\\\": isin}]).encode(\\\"utf-8\\\")\\n        req = urllib.request.Request(\\\"https://api.openfigi.com/v3/mapping\\\", data=payload, headers=headers, method=\\\"POST\\\")\\n        try:\\n            with urllib.request.urlopen(req, timeout=self.timeout_seconds) as resp:\\n                data = json.loads(resp.read().decode(\\\"utf-8\\\"))\\n        except Exception as exc:  # pragma: no cover - network dependent\\n            raise LookupUnavailable(str(exc)) from exc\\n        out: list[ProviderCandidate] = []\\n        for item in (data[0].get(\\\"data\\\", []) if data else []):\\n            symbol = item.get(\\\"ticker\\\") or item.get(\\\"figi\\\")\\n            if not symbol:\\n                continue\\n            sec_type = \\\" \\\".join(str(item.get(k) or \\\"\\\") for k in (\\\"securityType\\\", \\\"securityType2\\\", \\\"marketSector\\\")).lower()\\n            asset_class = \\\"etf\\\" if \\\"fund\\\" in sec_type or \\\"etf\\\" in sec_type else \\\"equity\\\" if \\\"equity\\\" in sec_type else None\\n            out.append(ProviderCandidate(\\n                provider=self.name,\\n                provider_symbol=symbol,\\n                exchange=item.get(\\\"exchCode\\\") or item.get(\\\"marketSector\\\"),\\n                currency=item.get(\\\"currency\\\") or currency,\\n                name=item.get(\\\"name\\\"),\\n                isin=isin,\\n                asset_class=asset_class,\\n                hedge_status=\\\"unknown\\\",\\n                instrument_status=\\\"unknown\\\",\\n                valuation_policy=\\\"live_price\\\",\\n                evidence_source=\\\"openfigi_isin_mapping\\\",\\n                evidence_note=\\\"Public ISIN mapping metadata only; no prices, holdings, account data, or portfolio quantities sent.\\\",\\n            ))\\n        return out\\n\\n\\nclass YahooSearchLookupProvider:\\n    \\\"\\\"\\\"Public metadata lookup via Yahoo Finance search endpoint; no prices or portfolio data.\\\"\\\"\\\"\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -53,6 +53,52 @@\\n \\n     def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n         return []\\n+\\n+\\n+class OpenFIGILookupProvider:\\n+    \\\"\\\"\\\"Public ISIN-to-instrument metadata lookup via OpenFIGI; no prices or portfolio data.\\\"\\\"\\\"\\n+\\n+    name = \\\"openfigi\\\"\\n+\\n+    def __init__(self, *, timeout_seconds: float = 8.0):\\n+        self.timeout_seconds = timeout_seconds\\n+\\n+    def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n+        if not isin:\\n+            return []\\n+        token = os.environ.get(\\\"OPENFIGI_API_KEY\\\")\\n+        headers = {\\\"Content-Type\\\": \\\"application/json\\\", \\\"User-Agent\\\": \\\"JarvisFinance/1.0 public-instrument-lookup\\\"}\\n+        if token:\\n+            headers[\\\"X-OPENFIGI-APIKEY\\\"] = token\\n+        payload = json.dumps([{\\\"idType\\\": \\\"ID_ISIN\\\", \\\"idValue\\\": isin}]).encode(\\\"utf-8\\\")\\n+        req = urllib.request.Request(\\\"https://api.openfigi.com/v3/mapping\\\", data=payload, headers=headers, method=\\\"POST\\\")\\n+        try:\\n+            with urllib.request.urlopen(req, timeout=self.timeout_seconds) as resp:\\n+                data = json.loads(resp.read().decode(\\\"utf-8\\\"))\\n+        except Exception as exc:  # pragma: no cover - network dependent\\n+            raise LookupUnavailable(str(exc)) from exc\\n+        out: list[ProviderCandidate] = []\\n+        for item in (data[0].get(\\\"data\\\", []) if data else []):\\n+            symbol = item.get(\\\"ticker\\\") or item.get(\\\"figi\\\")\\n+            if not symbol:\\n+                continue\\n+            sec_type = \\\" \\\".join(str(item.get(k) or \\\"\\\") for k in (\\\"securityType\\\", \\\"securityType2\\\", \\\"marketSector\\\")).lower()\\n+            asset_class = \\\"etf\\\" if \\\"fund\\\" in sec_type or \\\"etf\\\" in sec_type else \\\"equity\\\" if \\\"equity\\\" in sec_type else None\\n+            out.append(ProviderCandidate(\\n+                provider=self.name,\\n+                provider_symbol=symbol,\\n+                exchange=item.get(\\\"exchCode\\\") or item.get(\\\"marketSector\\\"),\\n+                currency=item.get(\\\"currency\\\") or currency,\\n+                name=item.get(\\\"name\\\"),\\n+                isin=isin,\\n+                asset_class=asset_class,\\n+                hedge_status=\\\"unknown\\\",\\n+                instrument_status=\\\"unknown\\\",\\n+                valuation_policy=\\\"live_price\\\",\\n+                evidence_source=\\\"openfigi_isin_mapping\\\",\\n+                evidence_note=\\\"Public ISIN mapping metadata only; no prices, holdings, account data, or portfolio quantities sent.\\\",\\n+            ))\\n+        return out\\n \\n \\n class YahooSearchLookupProvider:\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_1L1egOJMKZSMc18qMRe9AuO5"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": null,
      "finish_reason": "tool_calls",
      "tool_calls": [
        {
          "id": "call_WYZdwtDkv3pgKzwiXP67RsmO",
          "call_id": "call_WYZdwtDkv3pgKzwiXP67RsmO",
          "response_item_id": "fc_0134d7700e125cf0016a071d86d5148191b351c68191d8da46",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"                    isin=None,\\n                    asset_class=asset_class,\",\"old_string\":\"                    isin=isin if isin and query == isin else None,\\n                    asset_class=asset_class,\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
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    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -136,7 +136,7 @@\\n                     exchange=exch,\\n                     currency=currency,\\n                     name=quote.get(\\\"longname\\\") or quote.get(\\\"shortname\\\"),\\n-                    isin=isin if isin and query == isin else None,\\n+                    isin=None,\\n                     asset_class=asset_class,\\n                     hedge_status=\\\"unknown\\\",\\n                     instrument_status=\\\"unknown\\\",\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_WYZdwtDkv3pgKzwiXP67RsmO"
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          "id": "call_zbPFub5SplrpbIEatkMOPQBb",
          "call_id": "call_zbPFub5SplrpbIEatkMOPQBb",
          "response_item_id": "fc_0134d7700e125cf0016a071d8bf2948191abf13feda2a16481",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"        self.providers = providers or [OpenFIGILookupProvider(), YahooSearchLookupProvider()]\",\"old_string\":\"        self.providers = providers or [YahooSearchLookupProvider()]\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -152,7 +152,7 @@\\n     name = \\\"public_composite\\\"\\n \\n     def __init__(self, providers: list[InstrumentLookupProvider] | None = None):\\n-        self.providers = providers or [YahooSearchLookupProvider()]\\n+        self.providers = providers or [OpenFIGILookupProvider(), YahooSearchLookupProvider()]\\n \\n     def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n         candidates: list[ProviderCandidate] = []\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_zbPFub5SplrpbIEatkMOPQBb"
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      "reasoning": "**Figuring out tests**\n\nI think I need to add some tests to compile things properly. It’s crucial to ensure everything works as it should, right? I wonder if there are specific tests I should focus on first. I could start with the most critical functions or elements and then work my way through the less essential ones. Testing might help catch any potential issues early, which would be super helpful! Let's get started on that!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Figuring out tests**\n\nI think I need to add some tests to compile things properly. It’s crucial to ensure everything works as it should, right? I wonder if there are specific tests I should focus on first. I could start with the most critical functions or elements and then work my way through the less essential ones. Testing might help catch any potential issues early, which would be super helpful! Let's get started on that!",
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          "id": "rs_0134d7700e125cf0016a071d90a3ec8191a3ac3dddfc66a4c9",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Figuring out tests**\n\nI think I need to add some tests to compile things properly. It’s crucial to ensure everything works as it should, right? I wonder if there are specific tests I should focus on first. I could start with the most critical functions or elements and then work my way through the less essential ones. Testing might help catch any potential issues early, which would be super helpful! Let's get started on that!"
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_JMF04iH47rQo6rGpO8Tkox2n",
          "call_id": "call_JMF04iH47rQo6rGpO8Tkox2n",
          "response_item_id": "fc_0134d7700e125cf0016a071d91f7e08191b8a421c904629301",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src pytest -q tests/unit/test_true_wealth_mapping_candidates_c3.py tests/unit/test_schema.py\",\"timeout\":300,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
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    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"...................                                                      [100%]\\n19 passed in 0.39s\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_JMF04iH47rQo6rGpO8Tkox2n"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Testing and updating tasks**\n\nI need to run full tests later and update my to-do list. But first, I should start with a runtime run. It seems there’s a need for “no portfolio data to external.” The CLI public lookup provides ISIN, ticker, and name, which includes metadata from the DB, though not the quantity. I think I need to query before proceeding. Oh, and I should check the active counts for any generated alerts. I'll use terminal Python to aggregate that information.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Testing and updating tasks**\n\nI need to run full tests later and update my to-do list. But first, I should start with a runtime run. It seems there’s a need for “no portfolio data to external.” The CLI public lookup provides ISIN, ticker, and name, which includes metadata from the DB, though not the quantity. I think I need to query before proceeding. Oh, and I should check the active counts for any generated alerts. I'll use terminal Python to aggregate that information.",
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              "text": "**Testing and updating tasks**\n\nI need to run full tests later and update my to-do list. But first, I should start with a runtime run. It seems there’s a need for “no portfolio data to external.” The CLI public lookup provides ISIN, ticker, and name, which includes metadata from the DB, though not the quantity. I think I need to query before proceeding. Oh, and I should check the active counts for any generated alerts. I'll use terminal Python to aggregate that information."
            }
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        {
          "id": "call_VuCzDoJ41YVLig2RUVkUpsGX",
          "call_id": "call_VuCzDoJ41YVLig2RUVkUpsGX",
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            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom collections import Counter, defaultdict\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nsettings=load_settings(); conn=connect(settings.db_path); apply_migrations(conn)\\nrows=conn.execute(\\\"SELECT alert_id,entity_type,entity_id,fingerprint FROM alerts WHERE status='active' AND rule_id='missing_provider_symbol'\\\").fetchall()\\nprint({'active_missing_provider_symbol': len(rows), 'by_entity_type': dict(Counter(r['entity_type'] for r in rows)), 'duplicate_entities': sum(1 for k,v in Counter((r['entity_type'], r['entity_id']) for r in rows).items() if v>1)})\\nconn.close()\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
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    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'active_missing_provider_symbol': 6, 'by_entity_type': {'instrument': 6}, 'duplicate_entities': 3}\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_VuCzDoJ41YVLig2RUVkUpsGX"
    },
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      "role": "assistant",
      "content": "",
      "reasoning": "**Running CLI for backup**\n\nI need to run the CLI with the dedupe public lookup export. Before doing that, maybe I should back up first? That feels like a safe approach. Then, I could apply the necessary migration writes. I’m also contemplating running a backup of the runtime database since it’s crucial for real-time writing. Using the CLI backup seems like the right move to ensure everything is secure!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Running CLI for backup**\n\nI need to run the CLI with the dedupe public lookup export. Before doing that, maybe I should back up first? That feels like a safe approach. Then, I could apply the necessary migration writes. I’m also contemplating running a backup of the runtime database since it’s crucial for real-time writing. Using the CLI backup seems like the right move to ensure everything is secure!",
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            "arguments": "{\"command\":\"PYTHONPATH=src python -m jarvis_finance.cli.main backup-runtime-db\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
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          "response_item_id": "fc_0134d7700e125cf0016a071da02de88191ba842fae25c3bbe3",
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            "arguments": "{\"command\":\"PYTHONPATH=src python -m jarvis_finance.cli.main git-safety-scan .\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
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      "content": "{\"output\": \"RUNTIME_DB_BACKUP backup_file=/home/agent/jarvis_runtime/finance-system/backups/finance-runtime-db-20260515T132032Z.sqlite3 checksum_file=/home/agent/jarvis_runtime/finance-system/backups/finance-runtime-db-20260515T132032Z.sqlite3.sha256 sha256=6492d9ee6223adb7b574370f4a5804c748e8b16e9baf89996cba3258dd632b99 size_bytes=540672\", \"exit_code\": 0, \"error\": null}",
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    {
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      "name": "terminal",
      "content": "{\"output\": \".pytest_cache/CACHEDIR.TAG: runtime_cache_file_blocked\\n.pytest_cache/README.md: runtime_cache_file_blocked\\n.pytest_cache/.gitignore: runtime_cache_file_blocked\\n.pytest_cache/v/cache/nodeids: runtime_cache_file_blocked\", \"exit_code\": 1, \"error\": null}",
      "tool_call_id": "call_iTHqbQh3K0XC9H11yvnqeUh7"
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            "arguments": "{\"command\":\"rm -rf .pytest_cache && find . -type d -name __pycache__ -prune -exec rm -rf {} + && PYTHONPATH=src python -m jarvis_finance.cli.main git-safety-scan .\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
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      "name": "terminal",
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            "arguments": "{\"command\":\"PYTHONPATH=src python -m jarvis_finance.cli.main generate-mapping-candidates --source true_wealth --limit 3 --public-lookup --dedupe-missing-provider-alerts --export-template\",\"timeout\":300,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
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    {
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      "name": "terminal",
      "content": "{\"output\": \"MAPPING_CANDIDATES instruments_total=3 candidates_found=336 high=315 medium=18 low=3 instruments_without_candidate=0 hedge_unknown=3 instrument_status_unknown=3 provider_lookup_unavailable=0 ambiguous=3 deduped_missing_provider_alerts=3 template_updated=true template_instruments=3 template_candidates=329 dry_run=false\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_VUqaM4RAyZj0kBsBpzbVeCY2"
    },
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      "reasoning": "**Evaluating candidate limits**\n\nI'm noticing we have 336 candidates due to excessive OpenFIGI returns, which seems like too many. I wonder if we should limit the number of results per provider. Maybe capping OpenFIGI to a max of 20 results could help, but the current runtime has already exceeded that. How about implementing a cleanup before generating new candidates? An optional generator could be useful to prune prior candidates as well. Overall, we definitely need to manage this more effectively.\n**Planning cleanup and adjustments**\n\nI’m considering doing a manual cleanup before rerunning the process, while also setting a default cap for OpenFIGI results. I'm wondering if a max of 8 results is enough, but since one ISIN can relate to multiple exchanges, I think 12 would be better. After that, I could perform a runtime cleanup by deleting unconfirmed candidates for the three instruments and rerun the process. I should also run query counts after the cleanup since it seems like there's a high candidate distribution.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Evaluating candidate limits**\n\nI'm noticing we have 336 candidates due to excessive OpenFIGI returns, which seems like too many. I wonder if we should limit the number of results per provider. Maybe capping OpenFIGI to a max of 20 results could help, but the current runtime has already exceeded that. How about implementing a cleanup before generating new candidates? An optional generator could be useful to prune prior candidates as well. Overall, we definitely need to manage this more effectively.\n**Planning cleanup and adjustments**\n\nI’m considering doing a manual cleanup before rerunning the process, while also setting a default cap for OpenFIGI results. I'm wondering if a max of 8 results is enough, but since one ISIN can relate to multiple exchanges, I think 12 would be better. After that, I could perform a runtime cleanup by deleting unconfirmed candidates for the three instruments and rerun the process. I should also run query counts after the cleanup since it seems like there's a high candidate distribution.",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqBx2_ItDsGmS0O7bLqEJqyCf_bBEcO8RoaqtvEnuuVF6CzmfkkZatJPbMOoh9m_nsvCLj10pNfSOV_cijSAHXWxb4ZhfPtsGI9AGs55xdz-XLgFccxtrp3k3N4eLs9fbLnNy64mXDQf2ghKX_Q7cxyXwmMmvxPF3z13kc7ac47In6S8p3Qu-yWQ6ZohVSkw_n_xQAOYglhKOCI-7KtfUI9SkPb_hoES1_6d33vwxfU4SDo7ubkI8pMFd9qhfkyfGIFHxDuQP4QzAhmg_JbWGH137TYatT2FYE1ZkWQqIAUwfLLVnIzmjZZ4l9Evv-Gjb2aupOa5Fza4EScYKmeBZntS0xbwt0jtQFIR8M9cf2-dzAY-dOo5YaJHq658ueq_9rFrKvrD4OrJYpydbtuoyn99-TH1bHRzxr9G_m--SYybfx1IukJhezsx77lR9Hw0JM3dNZSipLrLHfI6hL5VAnu7NLTjbyk-O3CV_8KTxut-ccXqDj15nGEkaSCc4nkwftbOADkAFlFrr2VOrOfSQ0w-r1xMdMbSAwM9XLwo6Z_jndXi3i8ozdtcBNrJ_CEcarnBWfUf5c4Zrv7wnU508ETO_nD9YkzpMQu8yrf2eJam8uA9sY0tjE-LjnfK-Us1sJQYu00cVTY3B8tTmUL2Lzi34HfrrlFYVdQpNlUI-2t1svJrTv8YRJDFPolQB1oU9nyx0Nzu3rTpC4qygpV_JehFY-AY6EN5h9xepkiOrYQAOkLz-p-u8optYh1Wsko6IuUelSXwgF-V6kf8YKayQk5uXuHwN8LrcPXOeHMDKzptpTG0B0LLPIlnYUR-5WhUzcGyrq9XuR5kn7eTyf1LruscwueNOZv_iYUgTqQG5d2XlAcgo6vnmmKsnSX9HII03Ta1uq-O-jOEX6EWNCOU7gr_RIT7zkw5efz7low2rQsCAC9gIRjHFaWQq0p3rQXY-Nh6gTZ-aUFm2vlsWch8bnXMVzNcQobpBzlWIFns5iMlXZSFHxne-Us8UnJNc5FUrVx6ItZl35GQBoLwFmsoSxptvIpjCim2V4BF55Lo76bReOQLMmhBv-LmHHRCKvmmbDYXzwIhK-pxYz_UAqlbbGh9ahQZGhDSl2FCWjzRiSULaGfB3fOm6jng5oDnfuPZzJUhYdIcJAytvcQiwW4loQib6LVtg5-JSdru66ShZB12232ePyLe0Zac5AvrUuokK0_TzPqD0mzcy-G3lZEusKr1JS8k7AEAiSBbtyiVl7sCGk3nMR9QgQcVDTHO47YkYvT50muyaw1uRsJyDXZNzeNqlboXWePdkEyfxzprXz4qNpWkM46F5fLJOd_6NVx6Y7OHjVKDrDNTP_ZewCe8pFavxUQmgMcKUmztqzZRVDn743KbJFOlgEnqIIKyqVjFN6w21Q87lrSolLF3XdLj717Q3Mb9DWGi-k0c_fyPb1rYCW3zeD0gfujzRKZxIUhF4pSLDmeWXVhGQoUfAKeeyK5xUaOxSZ8IhZiuG0BHJWRWciVgrnT5KTGlRshqlBW6-fk7dwYMdukahJKQ-b4Ew5s20FvzHVwHrFNpeKY8R86BlWcaXVJCdKqfGEcFxXP-NlYwa6natiqomYUNFtYHDiMs54S10cCYp2nd-Gl4hc5IG7-RlDqNT7k5CijAQ7xBLiMFbxV7k-5TByhy7aVf7Ed2jg6-Hm1pnGEcV6vkJEY0wWYBit9CYRd5r6j1NRSvOGk55fNF-usJkYA6qiW70Ylr4-bXJpOlBJlaB7iK8yAgtlywuVKjJjUDLZIu3DXrhdXPy04T4vQdxak5rfkUSJJA7RNjgBzhpO-mmOYGT03K4UyroDcCZcEbO5IeIAlTHPB2jzGVBmnhcKxTzyzm5WMVucMkIq3u6X8kV_w3-0OJthNe_BCKSFBRvgzqFSdyz8fAtOieMNJNiP2pC4xPaMBgvhDG6FFbQWigvxTN8R8QUrAlwK4ymHpWBqs2IlJE9Mc4wxWHmgIZFjXJIhGH5zn_HyVy7I6svQzM2Kg9zdWjy20iFUhZ656vo_CrMMtjcGcQ3GjY6bN3l6AfyXM5hhIzY6KPknzpT_YnKeR8JYNXSyvBo2KOpLOZrML0uqSgFsNdVQoGGNRhX8ETep4z9uZBrvzdG8FVFwFfp_uNgYaybudWEG6IeRAm2H3v4uoMmd1qK5nZtZkClSI20FQ9T6VboSj5mdyJ4AcCIweiyuJahymAhYG9H_LQ-cLU9pNA9cT8qiLcE0ts1XY0zK-r_qGYFf7Xtmdu_W1F_anvR8sD15Ud8pyiDrFfHPhHWEpkdGc0mAZ-ohDXpnRPrZPd-kf9JDCGzv9hBW4AO-wCn6XOhMh5OmNHbkEbxT41hpsinM2THz1682QzuzATvhSRLRhKMLYLFbFqfX4dSGJratIGCJk5vaCFuJ1k6Ia_FdcSgV_s_2iDst9-5GxJZo_gxlr6KyDv_lZtoIQ1YCdXRroSSWkjqWyPepuy8AmUfBUAZD9WaNNI9raWWfDR2SlkrPdncU-YVQyx44lOEWFPUZjeb2vFqX2US5l-jrPnIC-CxIkwwqjyyjOxnsEKcb7n1V0hfNfiGfjlRbaCWzN1jiUEqIe9ua43pwXaGwZX-Y0I0_Azump1egWIf9gnftQGcO5fewgx8HANttJPn9Gmd5sDPndNbpKqawLu78QUUPDyzG_pkeCkKYOESx5yVsmbrwosphM93nL8W9cs_1Z6xBPfM4h5hHYtzLC8SZVdQI5WpWrl8r9EeO1DrkaGiaCJy3-SFXRIJvu3ZJYgpLu8msNjSRACCzBHQ0X-QksK5QZ-BW4BIUkEe6N1JRGx9fl5H_cI4b_eRUZAI3ASKJizO_5p8lYDPigP7zJt1nyr2h08VJKT0rrzU836gYRTqOD62mYzOwzuYhiDxBMD9PSF0THuMULlekjwtVz_WjCWQo687IHZjFzpZ1kTadNDpFA8XFqK2QFqpfCu00xbMUbIGhgV-RoppZNTfkcTLizb2-6fA7_PzOtiffN__VYmftlY_vlGESdY_yADSJ1Vzdc_h2jUDxly5kBlPEvH5E5-67Oqj6-3ULaHibA7ev4W-3-ceopgTUVryUs81OPk55xX-RsPrf6EEB3LSrOFnMwvNZI1kJmaXDtogEa11ClsAA00jGKNspDHPQMvVZqtZ68lncE4LTn42iS_duu8jMRWfmZkGtgtDkrpJ91syigHjTwzC7reetHbMmdfS_vPwzSBHu_q3t9cUPV7DCtFEPy6RwK7rjSBR5IEZwoNudYTNEGGLT4r3scflrefM00X8aaIYzNpU3OVw=",
          "id": "rs_0134d7700e125cf0016a071db5af0481918c08ccfd6850dc2e",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Evaluating candidate limits**\n\nI'm noticing we have 336 candidates due to excessive OpenFIGI returns, which seems like too many. I wonder if we should limit the number of results per provider. Maybe capping OpenFIGI to a max of 20 results could help, but the current runtime has already exceeded that. How about implementing a cleanup before generating new candidates? An optional generator could be useful to prune prior candidates as well. Overall, we definitely need to manage this more effectively."
            },
            {
              "type": "summary_text",
              "text": "**Planning cleanup and adjustments**\n\nI’m considering doing a manual cleanup before rerunning the process, while also setting a default cap for OpenFIGI results. I'm wondering if a max of 8 results is enough, but since one ISIN can relate to multiple exchanges, I think 12 would be better. After that, I could perform a runtime cleanup by deleting unconfirmed candidates for the three instruments and rerun the process. I should also run query counts after the cleanup since it seems like there's a high candidate distribution."
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_xgvSi0mKBIsh1MVclazxqiWv",
          "call_id": "call_xgvSi0mKBIsh1MVclazxqiWv",
          "response_item_id": "fc_0134d7700e125cf0016a071dbf1a8481919cb611d1c5e488d9",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"    def __init__(self, *, timeout_seconds: float = 8.0, max_results: int = 12):\\n        self.timeout_seconds = timeout_seconds\\n        self.max_results = max_results\",\"old_string\":\"    def __init__(self, *, timeout_seconds: float = 8.0):\\n        self.timeout_seconds = timeout_seconds\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -60,8 +60,9 @@\\n \\n     name = \\\"openfigi\\\"\\n \\n-    def __init__(self, *, timeout_seconds: float = 8.0):\\n+    def __init__(self, *, timeout_seconds: float = 8.0, max_results: int = 12):\\n         self.timeout_seconds = timeout_seconds\\n+        self.max_results = max_results\\n \\n     def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n         if not isin:\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_xgvSi0mKBIsh1MVclazxqiWv"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": null,
      "finish_reason": "tool_calls",
      "tool_calls": [
        {
          "id": "call_fkEKo5ZPYeSE0G2YjzUZ5Z7Q",
          "call_id": "call_fkEKo5ZPYeSE0G2YjzUZ5Z7Q",
          "response_item_id": "fc_0134d7700e125cf0016a071dc30c688191a7a09895161cb58d",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"        for item in (data[0].get(\\\"data\\\", []) if data else [])[: self.max_results]:\",\"old_string\":\"        for item in (data[0].get(\\\"data\\\", []) if data else []):\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -79,7 +79,7 @@\\n         except Exception as exc:  # pragma: no cover - network dependent\\n             raise LookupUnavailable(str(exc)) from exc\\n         out: list[ProviderCandidate] = []\\n-        for item in (data[0].get(\\\"data\\\", []) if data else []):\\n+        for item in (data[0].get(\\\"data\\\", []) if data else [])[: self.max_results]:\\n             symbol = item.get(\\\"ticker\\\") or item.get(\\\"figi\\\")\\n             if not symbol:\\n                 continue\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_fkEKo5ZPYeSE0G2YjzUZ5Z7Q"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Cleaning up data**\n\nI need to run a cleanup and maybe rerun an operation. I'll use SQL to delete unselected candidates, and I'm considering whether to create alerts for ambiguous duplicates. It might be good to ensure that the deduplication process increments correctly while avoiding duplicates entirely. Overall, I plan to execute the cleanup along with the command line interface (CLI) execution. Just need to double-check that everything aligns properly!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Cleaning up data**\n\nI need to run a cleanup and maybe rerun an operation. I'll use SQL to delete unselected candidates, and I'm considering whether to create alerts for ambiguous duplicates. It might be good to ensure that the deduplication process increments correctly while avoiding duplicates entirely. Overall, I plan to execute the cleanup along with the command line interface (CLI) execution. Just need to double-check that everything aligns properly!",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqBx3MNDxjyjaWDd5MCRgK17Sop2qEu-ntGjuGy1CE5C4oWSj3yIuFqQ5KZhZu7gjUgw8fqqnGHc6LySxDX3x_jkoIfJikkQsZ3llBPGInxLcnxV5HU4yIr0mSKIjPs1dl9K0ISA3eTloOUsyT1MdHEOB3O6GdKlRI1klRDdOcgFYkorJ4DCTqeUeKxqjL9Dh7semGISHz8QDqhyYPdVpdv89hD0C2w1q3q_Xa37JDxdsMgmjhRw2znUROuXvVCdq_ojSTZ0eJ_8GWmXoLPrg1mUp1HgF0vihhF863Yw2PEr23dlriupXzmUtR5gS3HzojJStVQviPiHig1tlbrw4rGVa_sCxgpXL_d5yB5zd7sJwiVlI42x2-WRCmJfUEVikri3-YhRHREqZMVNgFYmga9xq1isvbCgCVv58RiSEqtGMUHn5-dRYAa7M3Tf4utXCe1QJ9oSjvaIjLps0PeNTnF0gXO5UsXDygShjz4bHGHmSRXhNq_-BBOHA3N9091FJVOsI_8i5iESPOyq99tguTc1HYKeZPc5HRzoET3kTM9WNfq-q4MKvJeElzUJUmBR4IvYDV8I-eD3pjnQ33_z76jfKCEO8iEk3G2mg_C-oFsXFsKu3gGiTRasIRLSMmeZVdXHJ4zJq_hWmZyDyOyHcTdm97otZehDHmEhFb-0fR2qZdA1QrPHgcD48UeMaUPuVMa3oMtiejszAeyaF0iTLDP5mNRv3W7FgWVrXTKx2Gwn81RYGe_0oT4a5vicpAuy2yolwYakyZA-Xo2Aa1VFyRaKvKNrE0RaJ_mkXMIJB-TmoMD3_nIrvSJGudoUncHOEGNHOZ2v4jlWb_No3vdWf4P7d6rOupgk9J2RlEQgvpMitRtxl_4tSWj7RpDGngcETmykjAobxO8G0H5yiSD6ILMb-axfPI_LSQZOXXje4Bn0Ik5ugM6d3eckYuT_V0YVdNq7wpXJSqr5NcNgpmAvX-Y9Zy3v6_JOjB-OZnjdngvGgrUpmwDpIcN-9CbNVAEwaBjTymSEB2ZzHYcpBLda8A_lvr6IYxrJkxTU_uBYU4lok-6SYIjuceSc6ZtvENC93BOWWM2YlQVJ9viECXdVVBX7bf3aQQgmDSlPReq3XBKVbRW9x9cX9da5-RRGaUHJhP7Q9T",
          "id": "rs_0134d7700e125cf0016a071dca737481919206879de8ebff2d",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Cleaning up data**\n\nI need to run a cleanup and maybe rerun an operation. I'll use SQL to delete unselected candidates, and I'm considering whether to create alerts for ambiguous duplicates. It might be good to ensure that the deduplication process increments correctly while avoiding duplicates entirely. Overall, I plan to execute the cleanup along with the command line interface (CLI) execution. Just need to double-check that everything aligns properly!"
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_8947RS06kGPLrPMxqvJm01Y0",
          "call_id": "call_8947RS06kGPLrPMxqvJm01Y0",
          "response_item_id": "fc_0134d7700e125cf0016a071dcc5ab881919a0aa22e5ded4877",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nsettings=load_settings(); conn=connect(settings.db_path); apply_migrations(conn)\\nids=[r['instrument_id'] for r in conn.execute(\\\"SELECT DISTINCT instrument_id FROM transactions WHERE transaction_type='initial_position_snapshot' AND source_type='broker_import_reviewed_snapshot' AND COALESCE(is_voided,0)=0 AND instrument_id IS NOT NULL ORDER BY instrument_id LIMIT 3\\\").fetchall()]\\nif ids:\\n    q=','.join('?' for _ in ids)\\n    conn.execute(f\\\"DELETE FROM instrument_price_mapping_candidates WHERE instrument_id IN ({q}) AND review_status IN ('proposed','needs_manual_review')\\\", ids)\\nconn.commit(); print({'cleaned_instruments': len(ids)})\\nconn.close()\\nPY\\nPYTHONPATH=src python -m jarvis_finance.cli.main generate-mapping-candidates --source true_wealth --limit 3 --public-lookup --dedupe-missing-provider-alerts --export-template\",\"timeout\":300,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'cleaned_instruments': 3}\\nMAPPING_CANDIDATES instruments_total=3 candidates_found=57 high=36 medium=18 low=3 instruments_without_candidate=0 hedge_unknown=3 instrument_status_unknown=3 provider_lookup_unavailable=0 ambiguous=3 deduped_missing_provider_alerts=0 template_updated=true template_instruments=3 template_candidates=57 dry_run=false\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_8947RS06kGPLrPMxqvJm01Y0"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Clarifying numbers and queries**\n\nI’m looking at the number 57 and wondering if that's manageable—maybe it is with 3 instruments and an average of 19. Yet, I think I need to clarify what \"Kandidaten pro Instrument\" means in the context of aggregating multiple entries. I should also query DQ about it. There’s also a question regarding the status of candidates for hedges, which seems to be unknown at the moment, so I need counts on that too.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Clarifying numbers and queries**\n\nI’m looking at the number 57 and wondering if that's manageable—maybe it is with 3 instruments and an average of 19. Yet, I think I need to clarify what \"Kandidaten pro Instrument\" means in the context of aggregating multiple entries. I should also query DQ about it. There’s also a question regarding the status of candidates for hedges, which seems to be unknown at the moment, so I need counts on that too.",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqBx3Zo4Oa9MQqlge5JLVc2_SSBboLDXySG6u8Wf6Oban-ZMuD2P4zdQ_2XbtqPWdEObJ0pqO7yEHL7CtXKn9_ew-pJCvJ9K6Q8FSoVCPNCdQoCMQReuQQ3nOIVffhQbq2ekMBEsgVZTc9Ws24xycAvhTTctDv2kxGoyaO2MBiYIU54pgxwRMJ1ZlHrix2DpBtnm1JlfEiXiwty0B89ztWV6gus2Ws_LCuyBu4OuAZ__nZgFZOEgytN_fOZyXPd5Od3HJq2amiQWIBQ8I9aCUparC1Hn9vsz8xkIu0w5ihBAp0ql_VB7876_RYzPnJHIarTbj4xHpfbh6fb39qfKny9N-6mYP7ld85KbLKI6wstTnvnKaYaNpSsvVqr8nHZjBR2oziqSEp1EgRmJS6kzTknjwflFqV7Hwj4vNRZhNLMgMVpsPvLO-nNxjKiNNeulAsYJMnenK7E4KxeHtefsdrfKhyUG0fzidsuBjVA_lHWvde0GvbZ8uidtQMlFhztpWYg-v_DI5asAFMZjTywqQAT2dgxGq6IMJBRXmIjKFzf0LyJ6GjtCkPR4phpP447C-frIhatJRsTSedYPaYivin4gM6hikL5uuzAsohgUT3tLbO-NZn37zeA7V0tAOYlyriJc1jmuFOz7H9NUgzwjeleGdI9yOeuqa5aOGZcQ6Py0-OgHF0Gb4KyuyhZaHETsVZQJaoA4owgb2FA0rglu5UFqk5q9OBWqsfb_eGTVH51lXqEyaJ25Yh-bY6aJS7OlBpyn9ICyDcueHldqFKiDKWFM9nqduYfxBfxecKlOi-gn6-0KzayuSkiN8vwn3carh0qhZfKFouJtI_OGSQXI5NcucVP4HPovJxwj4kU5SZrN1rzhtXEzyku6s89NXi4RHatVg6Tsee5x7_jlTk5AGDRJ4KmmIY-_yjcVF6KOCLnenoqDjjTITd8JumDRV6QgO42fkdpFoHX3FvxipO3YFRX6352lErOj6drRngWkOdxOnTYJ6hsGtRQZFLaJ99Q5vEG4rUCaf7eLcPetNAm0Pz4OrC9MFbXzRf53mMCJ38fo6ddRcNVXK20FNYHUClKVALcF89NE5a3_7M9nGEFlijfzTIAY08Af4TttXppN0T2xk2bfQf6WjEMKohCBHiHm3zXRDnuYSZc1fxrx6ICHVTl96to9MtrYDigy_gk8jKuMpiD2E8Car6ezEf05XRZwM6wZu1xLB-nvJL3TGIaoRVdLKui3T_sm9cMqixEnHz2og4p18=",
          "id": "rs_0134d7700e125cf0016a071dd5d9e48191b2416e0f037b88fc",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Clarifying numbers and queries**\n\nI’m looking at the number 57 and wondering if that's manageable—maybe it is with 3 instruments and an average of 19. Yet, I think I need to clarify what \"Kandidaten pro Instrument\" means in the context of aggregating multiple entries. I should also query DQ about it. There’s also a question regarding the status of candidates for hedges, which seems to be unknown at the moment, so I need counts on that too."
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_kytQ3PTrZGzle6nZ6zU26wsY",
          "call_id": "call_kytQ3PTrZGzle6nZ6zU26wsY",
          "response_item_id": "fc_0134d7700e125cf0016a071dd9682c8191b7e38bd48f64c3c3",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom collections import Counter\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nsettings=load_settings(); conn=connect(settings.db_path); apply_migrations(conn)\\ninst=[r['instrument_id'] for r in conn.execute(\\\"SELECT DISTINCT instrument_id FROM transactions WHERE transaction_type='initial_position_snapshot' AND source_type='broker_import_reviewed_snapshot' AND COALESCE(is_voided,0)=0 AND instrument_id IS NOT NULL\\\").fetchall()]\\nq=','.join('?' for _ in inst) if inst else \\\"''\\\"\\nrows=conn.execute(f\\\"SELECT confidence,review_status,candidate_hedge_status,candidate_instrument_status,instrument_id FROM instrument_price_mapping_candidates WHERE instrument_id IN ({q})\\\", inst).fetchall() if inst else []\\nalerts=conn.execute(\\\"SELECT rule_id, COUNT(*) AS c FROM alerts WHERE status='active' GROUP BY rule_id ORDER BY rule_id\\\").fetchall()\\nper_inst=Counter(r['instrument_id'] for r in rows)\\nprint({\\n 'instruments_total': len(inst),\\n 'candidates_total': len(rows),\\n 'confidence': dict(Counter(r['confidence'] for r in rows)),\\n 'review_status': dict(Counter(r['review_status'] for r in rows)),\\n 'without_candidate': sum(1 for iid in inst if per_inst[iid]==0),\\n 'with_multiple_candidates': sum(1 for iid in inst if per_inst[iid]>1),\\n 'hedge_candidate_known': sum(1 for r in rows if r['candidate_hedge_status']!='unknown'),\\n 'instrument_status_candidate_known': sum(1 for r in rows if r['candidate_instrument_status']!='unknown'),\\n 'instrument_hedge_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(hedge_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'instrument_status_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(instrument_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'active_alerts_by_rule': {r['rule_id']: r['c'] for r in alerts},\\n 'template_exists': str(settings.runtime_paths.exports_dir/'true_wealth_mapping_review_template.csv')\\n})\\nconn.close()\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'instruments_total': 3, 'candidates_total': 57, 'confidence': {'high': 36, 'medium': 18, 'low': 3}, 'review_status': {'needs_manual_review': 57}, 'without_candidate': 0, 'with_multiple_candidates': 3, 'hedge_candidate_known': 0, 'instrument_status_candidate_known': 0, 'instrument_hedge_unknown': 3, 'instrument_status_unknown': 3, 'active_alerts_by_rule': {'ambiguous_provider_mapping': 6, 'cost_basis_uncertain': 3, 'hedge_status_unknown': 3, 'instrument_status_unknown': 3, 'manual_review_needed': 3, 'missing_fx': 3, 'missing_market_price': 3, 'missing_provider_symbol': 3, 'snapshot_only': 3}, 'template_exists': '/home/agent/jarvis_runtime/finance-system/exports/true_wealth_mapping_review_template.csv'}\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_kytQ3PTrZGzle6nZ6zU26wsY"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Evaluating deduplication strategies**\n\nI'm assessing how to handle six ambiguous duplicates. It seems I might need to create a deduplication alert key using fingerprint and entity to reduced down to three. My previous run had a candidate count of 100 compared to 19 created, which might have changed the fingerprint. I’m considering whether I need to deduplicate ambiguous entries too. Maybe an active alerts deduplication function per entity is a good idea? And possibly include a SQL approach? I’ll need to patch the deduplication functionality for both missing and ambiguous alerts.\n**Finalizing deduplication process**\n\nI need to ensure that the final count of active ambiguous alerts is three. I’m considering using manual SQL for that and modifying the dedupe function for missing provider alerts to also handle various rules for each entity. This will return a total count. The naming might not be perfect, but it's acceptable for now. I'll check if the test passes and then rerun the CLI dedupe without cleanup. It should upsert candidates while resolving duplicates from different fingerprints. I’ve observed a decrease in duplicates after the dedupe. Time to patch the function!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Evaluating deduplication strategies**\n\nI'm assessing how to handle six ambiguous duplicates. It seems I might need to create a deduplication alert key using fingerprint and entity to reduced down to three. My previous run had a candidate count of 100 compared to 19 created, which might have changed the fingerprint. I’m considering whether I need to deduplicate ambiguous entries too. Maybe an active alerts deduplication function per entity is a good idea? And possibly include a SQL approach? I’ll need to patch the deduplication functionality for both missing and ambiguous alerts.\n**Finalizing deduplication process**\n\nI need to ensure that the final count of active ambiguous alerts is three. I’m considering using manual SQL for that and modifying the dedupe function for missing provider alerts to also handle various rules for each entity. This will return a total count. The naming might not be perfect, but it's acceptable for now. I'll check if the test passes and then rerun the CLI dedupe without cleanup. It should upsert candidates while resolving duplicates from different fingerprints. I’ve observed a decrease in duplicates after the dedupe. Time to patch the function!",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqBx3vMXSRooKhNEOHL_doeQTiLXIE44LdFUv8gjnjogJWj6NP_UJBufa_IA1RWU3QDSvmMV4OeAFdfTU2TvpL577xeXLSUyz2cdLGs-gL8V3oNKkkI1iXwM_svMN7dIN2K0naGsu8-lig7HMdEO2j6VQPdlkfykEeDXVME8qg21d423WZRk9tj2GFutsMVC9ZtRk4QUIyh8faKaSHSeWkpZ2lxbWADux63UWOCmYgML0SZREipdWXzoWX7bYyXZHH6ftGUi9C5Ce-9XwUG9tuAqcNflL2wvMGFeUYUrXGjMDNEucDzqcRV3pWNQoyZxpBUEPsb8h1PXlUW6iJTZQll-gd36BDi4vvP8yoY1btzcndWkgE7IEpanmIj7-Mk9NuweoEId6SKHpIZUg8kMypU0gkXYj7EUXEM08NMJRk2WBkuf-IfLXUXJAHyOaDLhM4M488q0bTKQ0mXH6AnXo3jm3ebxPcg_n4N166Kr61SsolYDI3DMDRX4yeSK8xy-KTU2jgpwvoEKPO7yyY1kelCXrjIVu3nYLwPhhmG_a5A_bYeyP11ar5YP1eIANnlrWP_A77mLUHswEs2P_qY2Lebs0PJiZDLALuyVgIPTbWRGP8Mbyyyb3mmYzfSBw-MGsrXaEcbRb8y33rTx6n8OIFvklf_ufl0oCMRpfffh5cX8ETehIfZMOKbdj8NgFNq8gLremUp5_sTUnKj1GJxenOpCBh3tRIdtkoJpuMn_ZdY1Xaoe1OuY7Z9Ou_vqfPCohkyEzQZLuYYi3uCIckyJ3II1UwyBFhrCLKUV2OwanDW1xVC6_4EGUU91DXOCYYIdoEmjstfqfpTJyVDpSRymexXLDk591u3t64hXtBMo3eLji2myEZ12tX_BjbKawh4RJ36JTEXIyjGk1AeFz-eBQGccB6AWH2wzsAUskywZ6HkJsuUMESR3hdue1lH3QAA4s5-YKC4Gjoq22CXMKY3gpXJ0IuNAfAEBktD1R7vq40D380aCx44tFI38IZUNZm3hwBobvsYtcREtJPIcU-lalFvMJFd-snb0Zqq2QZUN_YhHyLvVgDBg0gFea035yyiumjP12BKCyBOzVJBLKtDGf5QD5nOZPty2SOLZKBlRBdKZnvkjmIDkaU9n_BHh4SgV_6Kuk4mxYpNwdAHHHudBv8kNcEOehfPR4NQHwarS8JXrNgNV7HQxBCCQPOE6lPZdoVore1LvN5A9j9GSUGaZxpWS2BKcQizDGbLcYn2LqiTaVjLxGuIb_8V0ZOM7Dn9ZjFXFSw9p0t4Wvf1dgCx8pfzfQOlcu9Xa_Ch6zvA6nEtZ1gnJpj9lGMBi-8dwuBTSk-KNnGwryKbEv7Rr2Y-WTouaYwwHoSPakZQQRRI6bh0xngCRjksMhIc6YctcgmC73_kMktF54k6XdSBjHQ95VNwYuRoOW9KLqLgo1OOsywXi3KuhFmpb-Liqw3Lz235kKvBtf1C4K8iWlViAgSHSrSpNm4jDWwwsF0uzY9XtalnJMo8vARZb-petsH1t2PPhxc7IP5rtBGoLdMnRD1ZjhJxDt1ea7_cR5qDwro2wIpdncx8GeChV7w973wyVx0GmSbJytlm_OPzADlJ6AA6irMdOrzSHBCAbfTADna60KZFrS5-8PBEZ1ikwzkwrFr2N70tjLJ-dxN3UAMI3UYG6jO6cASjgVBk-O-UOCojKSCZ0pws5qAhqqJkjz7rMOH9WpoE44xY4R7dHxOg6kAtHkKCuHWwjbdSROO_TONwvl88b8Na1zNBQq3jV_VxKhD7Nr713UmQxtnR8C2Na88P9aEVvWW_4WUIxN1XkYBcQQdGZGlSx67Nutxs8eWjZ9_gTTHJKddMdZRK7hPvDNogSaY7ZXboR6yOwZxTbZOJgpyNUnEdqvpS4OulH3Km_TT6BIU1YpdL0cfu0A97D-N_cg_5Sh6R4bALRrTi4svlkosdpPOLIX-G5BlrkMNvtSQeroVryYMRlA7324Lniej63UApreqJhaFwhODh9vIM5RTYAgYs1ZUPq1lTsWWlqb0FEMQQhvqANvHHxjSMF9FHNPOfMvtglzjtl4pMbc4YqQ-rncwCeiTmycgA78MMPylGU0722a0MfhgiZYy0CWGHFQ6WLMvu8SeOFsCgvumLjb16VdiPQO-MemFfRb-brrua58sfgJbdSLICo_KcE_YKIOnFeXMDdoywAs3OBSfMW9R8PCSCjGwdv2cutvh15bNkIBqGHcRBggX4ub9AJEQpjzVB7unpmFaU9RdfE8wz8du-JmJHCwaAS5n3To5sS6n6Om45mdNusU1QKLsN8YOwkFazo-WeTagXPcv9U0-GibVyMmZ6mBp08Nv7GwTJ3Aq4QqLsb4HE6CbSfwc1D6LayF4vSUYb6JN9goD7-RKuC0grzzNz1LqrV_Ada2mT1aqV-ksE3EV6xahHh3CTsIYxhYrWio_FkAfV82L_ncOTkwsE0gU_7gbSu0ckCtJ4QCuOzQ57hbp_Dg6-KqCccYJzU5rqDK2QZZVveEQVOGLAmVwxP2VNc12IBH4_xCEqNe4efNIvgVUsN-bTDA1RTixr2gJty19bEvadMcoyJZnywf06qEV70A1MgQrvISKkVFx15aRl1nrATTe_BLYfGLW4R0LRwyqQO5kNR1xhHz16h0vwQc3yHEMxpzpT1SlBfEkkLKGmM9qllK0y3SP7Di8d5G-kcMJhFtMz5GJ-Wol8HiJEZ1qrmTdOtH_oO6CMVig0vAJai1en6Tyyx73JRUGbf6Azrf0ZPA6kU7XQHzHB-EGwn76GYA_11m7NZxMyGYXJR2KXXhRB2G9BnNBEjLzGe55kZzIVdSrozWkSmuwRT29_lYdIqMxmT8VJASbQrxPN2Fl4seZhVIWHpPZ_vvz077B7eHKf8YJKkee4Vo-JUEQ14ub_-wbV_DizNmGjRBKNwkdaytgzqkZj9v3gd922c8Q3aZ5DGF8KXmlbaHsMTLVMN4UwoFvYyimGJsCCcrqz3BH-vIo5UVuEcl5jrPhuXfisGWokIuIuQuO6UGkpA-xtyZH5DUlkCVAvE_4D2vL7GX4451eAWaboF88wY2_g0g7KOQaogcSnkD7qwcAEg2csQ9jQsP6IZ7TwmR0wUISa8VFTPa9kHxx6ovBhkUa1MhVEte6HBj5Glqa0lQUZxkfGE9QEtRI58aEWzvfjhsscQrtR2Fm_jzC-ZXnbCU_LWSyrAWY1A==",
          "id": "rs_0134d7700e125cf0016a071de4c4bc8191876692541ffd0740",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Evaluating deduplication strategies**\n\nI'm assessing how to handle six ambiguous duplicates. It seems I might need to create a deduplication alert key using fingerprint and entity to reduced down to three. My previous run had a candidate count of 100 compared to 19 created, which might have changed the fingerprint. I’m considering whether I need to deduplicate ambiguous entries too. Maybe an active alerts deduplication function per entity is a good idea? And possibly include a SQL approach? I’ll need to patch the deduplication functionality for both missing and ambiguous alerts."
            },
            {
              "type": "summary_text",
              "text": "**Finalizing deduplication process**\n\nI need to ensure that the final count of active ambiguous alerts is three. I’m considering using manual SQL for that and modifying the dedupe function for missing provider alerts to also handle various rules for each entity. This will return a total count. The naming might not be perfect, but it's acceptable for now. I'll check if the test passes and then rerun the CLI dedupe without cleanup. It should upsert candidates while resolving duplicates from different fingerprints. I’ve observed a decrease in duplicates after the dedupe. Time to patch the function!"
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_QiUc3EoUIvQ2QYXjRFMza9PP",
          "call_id": "call_QiUc3EoUIvQ2QYXjRFMza9PP",
          "response_item_id": "fc_0134d7700e125cf0016a071def31f08191af32d77a86242a6a",
          "type": "function",
          "function": {
            "name": "patch",
            "arguments": "{\"mode\":\"replace\",\"new_string\":\"def dedupe_missing_provider_symbol_alerts(conn: Connection) -> int:\\n    \\\"\\\"\\\"Keep one active mapping-quality alert per rule/instrument; resolve duplicate active rows.\\\"\\\"\\\"\\n    now = utc_now()\\n    resolved = 0\\n    rows = conn.execute(\\n        \\\"\\\"\\\"\\n        SELECT rule_id, entity_id, GROUP_CONCAT(alert_id) AS ids, COUNT(*) AS c\\n        FROM alerts\\n        WHERE status='active'\\n          AND rule_id IN ('missing_provider_symbol','ambiguous_provider_mapping','provider_lookup_unavailable')\\n          AND entity_type='instrument'\\n        GROUP BY rule_id, entity_id\\n        HAVING COUNT(*) > 1\\n        \\\"\\\"\\\"\\n    ).fetchall()\\n    for row in rows:\\n        ids = [x for x in (row[\\\"ids\\\"] or \\\"\\\").split(\\\",\\\") if x]\\n        for alert_id in ids[1:]:\\n            conn.execute(\\\"UPDATE alerts SET status='resolved', resolved_at=?, last_seen_at=? WHERE alert_id=?\\\", (now, now, alert_id))\\n            resolved += 1\\n    conn.commit()\\n    return resolved\",\"old_string\":\"def dedupe_missing_provider_symbol_alerts(conn: Connection) -> int:\\n    \\\"\\\"\\\"Keep one active missing-provider-symbol alert per instrument; resolve duplicates.\\\"\\\"\\\"\\n    now = utc_now()\\n    resolved = 0\\n    rows = conn.execute(\\n        \\\"\\\"\\\"\\n        SELECT entity_id, GROUP_CONCAT(alert_id) AS ids, COUNT(*) AS c\\n        FROM alerts\\n        WHERE status='active' AND rule_id='missing_provider_symbol' AND entity_type='instrument'\\n        GROUP BY entity_id\\n        HAVING COUNT(*) > 1\\n        \\\"\\\"\\\"\\n    ).fetchall()\\n    for row in rows:\\n        ids = [x for x in (row[\\\"ids\\\"] or \\\"\\\").split(\\\",\\\") if x]\\n        for alert_id in ids[1:]:\\n            conn.execute(\\\"UPDATE alerts SET status='resolved', resolved_at=?, last_seen_at=? WHERE alert_id=?\\\", (now, now, alert_id))\\n            resolved += 1\\n    conn.commit()\\n    return resolved\",\"path\":\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "patch",
      "content": "{\"success\": true, \"diff\": \"--- a//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n+++ b//home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\\n@@ -341,15 +341,17 @@\\n \\n \\n def dedupe_missing_provider_symbol_alerts(conn: Connection) -> int:\\n-    \\\"\\\"\\\"Keep one active missing-provider-symbol alert per instrument; resolve duplicates.\\\"\\\"\\\"\\n+    \\\"\\\"\\\"Keep one active mapping-quality alert per rule/instrument; resolve duplicate active rows.\\\"\\\"\\\"\\n     now = utc_now()\\n     resolved = 0\\n     rows = conn.execute(\\n         \\\"\\\"\\\"\\n-        SELECT entity_id, GROUP_CONCAT(alert_id) AS ids, COUNT(*) AS c\\n+        SELECT rule_id, entity_id, GROUP_CONCAT(alert_id) AS ids, COUNT(*) AS c\\n         FROM alerts\\n-        WHERE status='active' AND rule_id='missing_provider_symbol' AND entity_type='instrument'\\n-        GROUP BY entity_id\\n+        WHERE status='active'\\n+          AND rule_id IN ('missing_provider_symbol','ambiguous_provider_mapping','provider_lookup_unavailable')\\n+          AND entity_type='instrument'\\n+        GROUP BY rule_id, entity_id\\n         HAVING COUNT(*) > 1\\n         \\\"\\\"\\\"\\n     ).fetchall()\\n\", \"files_modified\": [\"/home/agent/.hermes/repos/FinanceManager/src/jarvis_finance/market_data/candidates.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
      "tool_call_id": "call_QiUc3EoUIvQ2QYXjRFMza9PP"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": null,
      "finish_reason": "tool_calls",
      "tool_calls": [
        {
          "id": "call_sP92BTkYnKKTwBd7dZWQluTT",
          "call_id": "call_sP92BTkYnKKTwBd7dZWQluTT",
          "response_item_id": "fc_0134d7700e125cf0016a071df85a848191b0c860fe826669f4",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python -m jarvis_finance.cli.main generate-mapping-candidates --source true_wealth --limit 3 --public-lookup --dedupe-missing-provider-alerts --export-template\",\"timeout\":300,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"MAPPING_CANDIDATES instruments_total=3 candidates_found=57 high=36 medium=18 low=3 instruments_without_candidate=0 hedge_unknown=3 instrument_status_unknown=3 provider_lookup_unavailable=0 ambiguous=3 deduped_missing_provider_alerts=3 template_updated=true template_instruments=3 template_candidates=59 dry_run=false\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_sP92BTkYnKKTwBd7dZWQluTT"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Reviewing template candidates**\n\nI’m looking at template candidates and noticing we have 59, which seems odd since we only deleted three from the first limit by instrument_id. Wait, is it possible there are actually 57 active candidates? I need to query all to check that. It might be I’m dealing with selected or rejected old ones. I should clean all unselected candidates for True Wealth before the rerun. And I should also check the counts by review status for clarity.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Reviewing template candidates**\n\nI’m looking at template candidates and noticing we have 59, which seems odd since we only deleted three from the first limit by instrument_id. Wait, is it possible there are actually 57 active candidates? I need to query all to check that. It might be I’m dealing with selected or rejected old ones. I should clean all unselected candidates for True Wealth before the rerun. And I should also check the counts by review status for clarity.",
      "codex_reasoning_items": [
        {
          "type": "reasoning",
          "encrypted_content": "gAAAAABqBx4F33SNxGQff0ndQvnJb0AAelH9KRl_SEuHN8G-WrR8tqYAEYNaQ6Rr5AKp0TdG2g_1zGrFMq8s1-UlAc0n88WxfNzcFxxPSum6FV3VdlbssiGHO0Dyg5S1irs7pUUloPJAqd8c15SzjHBOLvXt1UntQT2BQMDUfD9gP2BVeR2BEDOF-sz3hcYGKxx9h88jOkTFdSzfHyNvYfWkXCTA2NxbXmZfJNVQJNj-aUY155tZ9FlQCfxlBkdMHIE_TgSZEyST-kho6y47ETXGYede1MRvfuLkpniUYi2BZ3UBN50Lv8QA-OEhhkwJjIt7NjHssAKfhMjkoY5aNLY_Jak7oipNqtjpJQv06abWu50BRSm4RjW9yypWYn1apooLf9rPKZccgYN4e2ngiRtvwxuy4EOPyffWQMnC4WWvLe7JXCdZTu3kdmg0noejPlBn086jBV4TRXOzXb_Oa_qQnfl42c8YRaEb86TbWEdNY_BBUV_L1LWHKpwC5v7Jy_IYSIEqny6en_4X5SwituaaZsGP5h7iFZf1rZTf3DwW9UzJnMHtu-ko3-kr8ytvFy5t9yQIF4rrkWV2opvZQ-9hSpugX4dd4rO1vOzYCDInVl36cpvlumWT9zNpSwrc5SxZrdLZhzmdwc92yzT7pjH2Ra225org7YPsBccUZu84Kv1-EgoJu6ChbS7O9T8gQx9QoHgOfYhTF_rp_h3ukG4XUiF4ZlP-vsRJRCysnqurNVD3hq-DtTAiALujhhLGKSEjg2bZOU1NUPOLw9z9RfDFDfF--1nN4hK9rEQhUws7lztxZubaS3jJhNQ7hqlGID5FX2s6WX7MBjjNY7sJeBUCyq8rIIEAy4rXXmUymGAxkOxk4IQD-NZD6jnhdUDwWahwoHwJWZZVpblhEOus30Nm76Sc8j0nDU2qxWd2JZj6udEcpqAQKa0qHihlxrr0WazeZfI4YbooeGqgzGF-XeQbQjOb-qyjc-sCwC66Va6hetOxG87g37Ct1-MgTIA-kZuqrzHsB2qxGgxrQQDdPk7VwR2XcfY563RT9a15tx2V1ESQZIxtKvAc0Mqziw04XEwPm5mi5uggWIX6aEZeB_WCNGx2KJaYKUG8kJk34685QWLGQIe9Q0HfZqP_SHq5P5btAYu9bF8yVveoL4C9xNMiBQnX8nRTdm-xTlREWQOSKq79TYaR91WdZwKqo6U7_TlJxofXOPXWy6CXPSQY1_EH6nIrFUDjWFtCO45rVbNOTbC7yUI7PfnJ5cgjNKl30_8m9uYKDrrPJflyV-xImmgFNSplq6PsQbzFimSjCZMVG1-GqdZ402HEWj4Rrb5AakGx8ylIDyAFBqcLbzc7Id3fKceQhGQRD_ZaIFjs2IghPK_c1d8h0RBWSS_AHIfVcko50R_dI-SCNMNf9ksgmAf_V68__aYrPy5MbxzxSGdcN2v3x7jii-ib0MxNiTq8YZmYYvTDIst0",
          "id": "rs_0134d7700e125cf0016a071e00b48c8191970438519d8cb174",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Reviewing template candidates**\n\nI’m looking at template candidates and noticing we have 59, which seems odd since we only deleted three from the first limit by instrument_id. Wait, is it possible there are actually 57 active candidates? I need to query all to check that. It might be I’m dealing with selected or rejected old ones. I should clean all unselected candidates for True Wealth before the rerun. And I should also check the counts by review status for clarity."
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_smxaogJdx2tFZls16uyhbd2R",
          "call_id": "call_smxaogJdx2tFZls16uyhbd2R",
          "response_item_id": "fc_0134d7700e125cf0016a071e0562008191ac88e84dfd94d66a",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom collections import Counter\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nconn=connect(load_settings().db_path); apply_migrations(conn)\\nrows=conn.execute(\\\"SELECT review_status, COUNT(*) c FROM instrument_price_mapping_candidates GROUP BY review_status\\\").fetchall()\\nprint({r['review_status']:r['c'] for r in rows})\\nprint(conn.execute(\\\"SELECT COUNT(*) c FROM instrument_price_mapping_candidates\\\").fetchone()['c'])\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'needs_manual_review': 59}\\n59\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_smxaogJdx2tFZls16uyhbd2R"
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      "content": "",
      "reasoning": "**Cleaning up instrument data**\n\nI’m trying to figure out why there are 57 generated items but 59 total when including instruments that weren't in the initial count. It's possible there are more than 3 True Wealth instances. The user mentioned importing 3 TWs, so I wonder if the IDs for those three are all valid, but sorting might be an issue. It looks like I need to clean up the candidates for all imported snapshots and run the process again.",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Cleaning up instrument data**\n\nI’m trying to figure out why there are 57 generated items but 59 total when including instruments that weren't in the initial count. It's possible there are more than 3 True Wealth instances. The user mentioned importing 3 TWs, so I wonder if the IDs for those three are all valid, but sorting might be an issue. It looks like I need to clean up the candidates for all imported snapshots and run the process again.",
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            {
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              "text": "**Cleaning up instrument data**\n\nI’m trying to figure out why there are 57 generated items but 59 total when including instruments that weren't in the initial count. It's possible there are more than 3 True Wealth instances. The user mentioned importing 3 TWs, so I wonder if the IDs for those three are all valid, but sorting might be an issue. It looks like I need to clean up the candidates for all imported snapshots and run the process again."
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_hLRHqXQN7AWlpJnFHaGcki9t",
          "call_id": "call_hLRHqXQN7AWlpJnFHaGcki9t",
          "response_item_id": "fc_0134d7700e125cf0016a071e0c6e388191bf9082a2f15b676b",
          "type": "function",
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            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nconn=connect(load_settings().db_path); apply_migrations(conn)\\nids=[r['instrument_id'] for r in conn.execute(\\\"SELECT DISTINCT instrument_id FROM transactions WHERE transaction_type='initial_position_snapshot' AND source_type='broker_import_reviewed_snapshot' AND COALESCE(is_voided,0)=0 AND instrument_id IS NOT NULL\\\").fetchall()]\\nq=','.join('?' for _ in ids)\\nconn.execute(f\\\"DELETE FROM instrument_price_mapping_candidates WHERE instrument_id IN ({q}) AND review_status IN ('proposed','needs_manual_review')\\\", ids)\\nconn.commit(); print({'cleaned_instruments': len(ids), 'remaining_candidates': conn.execute('SELECT COUNT(*) c FROM instrument_price_mapping_candidates').fetchone()['c']})\\nconn.close()\\nPY\\nPYTHONPATH=src python -m jarvis_finance.cli.main generate-mapping-candidates --source true_wealth --limit 3 --public-lookup --dedupe-missing-provider-alerts --export-template\",\"timeout\":300,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'cleaned_instruments': 3, 'remaining_candidates': 0}\\nMAPPING_CANDIDATES instruments_total=3 candidates_found=57 high=36 medium=18 low=3 instruments_without_candidate=0 hedge_unknown=3 instrument_status_unknown=3 provider_lookup_unavailable=0 ambiguous=3 deduped_missing_provider_alerts=0 template_updated=true template_instruments=3 template_candidates=57 dry_run=false\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_hLRHqXQN7AWlpJnFHaGcki9t"
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      "content": "",
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        }
      ],
      "tool_calls": [
        {
          "id": "call_8wTKCmdUy9e90qpVVzFurEnR",
          "call_id": "call_8wTKCmdUy9e90qpVVzFurEnR",
          "response_item_id": "fc_0134d7700e125cf0016a071e185d5c8191a1d994c8af51909d",
          "type": "function",
          "function": {
            "name": "terminal",
            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom collections import Counter\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nsettings=load_settings(); conn=connect(settings.db_path); apply_migrations(conn)\\ninst=[r['instrument_id'] for r in conn.execute(\\\"SELECT DISTINCT instrument_id FROM transactions WHERE transaction_type='initial_position_snapshot' AND source_type='broker_import_reviewed_snapshot' AND COALESCE(is_voided,0)=0 AND instrument_id IS NOT NULL\\\").fetchall()]\\nq=','.join('?' for _ in inst) if inst else \\\"''\\\"\\nrows=conn.execute(f\\\"SELECT confidence,review_status,candidate_hedge_status,candidate_instrument_status,instrument_id FROM instrument_price_mapping_candidates WHERE instrument_id IN ({q})\\\", inst).fetchall() if inst else []\\nalerts=conn.execute(\\\"SELECT rule_id, COUNT(*) AS c FROM alerts WHERE status='active' GROUP BY rule_id ORDER BY rule_id\\\").fetchall()\\nper_inst=Counter(r['instrument_id'] for r in rows)\\nprint({\\n 'instruments_total': len(inst),\\n 'provider_lookups_attempted': len(inst),\\n 'candidates_total': len(rows),\\n 'confidence': dict(Counter(r['confidence'] for r in rows)),\\n 'review_status': dict(Counter(r['review_status'] for r in rows)),\\n 'without_candidate': sum(1 for iid in inst if per_inst[iid]==0),\\n 'with_multiple_candidates': sum(1 for iid in inst if per_inst[iid]>1),\\n 'hedge_candidate_known': sum(1 for r in rows if r['candidate_hedge_status']!='unknown'),\\n 'instrument_status_candidate_known': sum(1 for r in rows if r['candidate_instrument_status']!='unknown'),\\n 'instrument_hedge_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(hedge_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'instrument_status_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(instrument_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'active_alerts_by_rule': {r['rule_id']: r['c'] for r in alerts},\\n 'template_candidates': conn.execute('SELECT COUNT(*) AS c FROM instrument_price_mapping_candidates').fetchone()['c']\\n})\\nconn.close()\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "terminal",
      "content": "{\"output\": \"{'instruments_total': 3, 'provider_lookups_attempted': 3, 'candidates_total': 57, 'confidence': {'high': 36, 'medium': 18, 'low': 3}, 'review_status': {'needs_manual_review': 57}, 'without_candidate': 0, 'with_multiple_candidates': 3, 'hedge_candidate_known': 0, 'instrument_status_candidate_known': 0, 'instrument_hedge_unknown': 3, 'instrument_status_unknown': 3, 'active_alerts_by_rule': {'ambiguous_provider_mapping': 3, 'cost_basis_uncertain': 3, 'hedge_status_unknown': 3, 'instrument_status_unknown': 3, 'manual_review_needed': 3, 'missing_fx': 3, 'missing_market_price': 3, 'missing_provider_symbol': 3, 'snapshot_only': 3}, 'template_candidates': 57}\", \"exit_code\": 0, \"error\": null}",
      "tool_call_id": "call_8wTKCmdUy9e90qpVVzFurEnR"
    },
    {
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      "content": "",
      "reasoning": null,
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      "reasoning": "**Ensuring cache safety**\n\nI need to handle my cache in Git carefully to maintain safety. It's important to clean it up, as unnecessary files could cause confusion or errors. I'm considering what the best method is for this task. I should look into the recommended practices for cleaning up the cache effectively without losing any important data. It's crucial to follow the right steps to make sure everything remains intact and secure in my repository!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Ensuring cache safety**\n\nI need to handle my cache in Git carefully to maintain safety. It's important to clean it up, as unnecessary files could cause confusion or errors. I'm considering what the best method is for this task. I should look into the recommended practices for cleaning up the cache effectively without losing any important data. It's crucial to follow the right steps to make sure everything remains intact and secure in my repository!",
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      "content": "{\"output\": \"diff --git a/src/jarvis_finance/cli/main.py b/src/jarvis_finance/cli/main.py\\nindex ff2a92a..f63e3d6 100644\\n--- a/src/jarvis_finance/cli/main.py\\n+++ b/src/jarvis_finance/cli/main.py\\n@@ -8,7 +8,7 @@ from jarvis_finance.config.settings import ensure_runtime_dirs, load_settings\\n from jarvis_finance.fx.providers import MockFxProvider\\n from jarvis_finance.fx.rates import recheck_transaction_fx_status, update_fx_rates\\n from jarvis_finance.market.providers import CoinGeckoClient, refresh_crypto_prices\\n-from jarvis_finance.market_data.candidates import EmptyLookupProvider, generate_mapping_candidates\\n+from jarvis_finance.market_data.candidates import CompositePublicLookupProvider, EmptyLookupProvider, dedupe_missing_provider_symbol_alerts, export_mapping_review_template, generate_mapping_candidates\\n from jarvis_finance.market_data.catalog import prepare_catalog_from_instruments\\n from jarvis_finance.market_data.prices import MockEquityPriceProvider, refresh_market_prices\\n from jarvis_finance.quality.data_quality import check_crypto_data_quality\\n@@ -68,6 +68,10 @@ def main(argv: list[str] | None = None) -> int:\\n     candidates.add_argument(\\\"--source\\\", default=\\\"true_wealth\\\")\\n     candidates.add_argument(\\\"--limit\\\", type=int)\\n     candidates.add_argument(\\\"--dry-run\\\", action=\\\"store_true\\\")\\n+    candidates.add_argument(\\\"--public-lookup\\\", action=\\\"store_true\\\")\\n+    candidates.add_argument(\\\"--export-template\\\", action=\\\"store_true\\\")\\n+    candidates.add_argument(\\\"--template-path\\\")\\n+    candidates.add_argument(\\\"--dedupe-missing-provider-alerts\\\", action=\\\"store_true\\\")\\n     sub.add_parser(\\\"backup-runtime-db\\\")\\n     restore = sub.add_parser(\\\"restore-runtime-db\\\")\\n     restore.add_argument(\\\"--from\\\", dest=\\\"backup_file\\\", required=True)\\n@@ -229,7 +233,16 @@ def main(argv: list[str] | None = None) -> int:\\n         conn = connect(settings.db_path)\\n         try:\\n             apply_migrations(conn)\\n-            result = generate_mapping_candidates(conn, provider=EmptyLookupProvider(), source=args.source, limit=args.limit, dry_run=args.dry_run)\\n+            if args.dedupe_missing_provider_alerts:\\n+                resolved = dedupe_missing_provider_symbol_alerts(conn)\\n+            else:\\n+                resolved = 0\\n+            provider = CompositePublicLookupProvider() if args.public_lookup else EmptyLookupProvider()\\n+            result = generate_mapping_candidates(conn, provider=provider, source=args.source, limit=args.limit, dry_run=args.dry_run)\\n+            template = None\\n+            if args.export_template:\\n+                template_path = args.template_path or str(settings.runtime_paths.exports_dir / \\\"true_wealth_mapping_review_template.csv\\\")\\n+                template = export_mapping_review_template(conn, output_path=template_path)\\n             print(\\n                 \\\"MAPPING_CANDIDATES \\\"\\n                 f\\\"instruments_total={result.instruments_total} candidates_found={result.candidates_found} \\\"\\n@@ -237,6 +250,8 @@ def main(argv: list[str] | None = None) -> int:\\n                 f\\\"instruments_without_candidate={result.instruments_without_candidate} \\\"\\n                 f\\\"hedge_unknown={result.instruments_with_hedge_unknown} instrument_status_unknown={result.instruments_with_status_unknown} \\\"\\n                 f\\\"provider_lookup_unavailable={result.provider_lookup_unavailable} ambiguous={result.ambiguous_provider_mapping} \\\"\\n+                f\\\"deduped_missing_provider_alerts={resolved} \\\"\\n+                f\\\"template_updated={str(bool(template)).lower()} template_instruments={template['instrument_count'] if template else 0} template_candidates={template['candidate_rows'] if template else 0} \\\"\\n                 f\\\"dry_run={str(result.dry_run).lower()}\\\"\\n             )\\n             return 0\\ndiff --git a/src/jarvis_finance/market_data/candidates.py b/src/jarvis_finance/market_data/candidates.py\\nindex 10abd1b..c7ed397 100644\\n--- a/src/jarvis_finance/market_data/candidates.py\\n+++ b/src/jarvis_finance/market_data/candidates.py\\n@@ -1,6 +1,12 @@\\n from __future__ import annotations\\n \\n+import csv\\n+import json\\n+import os\\n+import urllib.parse\\n+import urllib.request\\n from dataclasses import dataclass, field\\n+from pathlib import Path\\n from sqlite3 import Connection\\n from typing import Protocol\\n \\n@@ -21,9 +27,12 @@ class ProviderCandidate:\\n     currency: str | None = None\\n     name: str | None = None\\n     isin: str | None = None\\n+    asset_class: str | None = None\\n     is_hedged: bool | None = None\\n     hedged_to_currency: str | None = None\\n+    hedge_status: str = \\\"unknown\\\"\\n     instrument_status: str = \\\"unknown\\\"\\n+    valuation_policy: str = \\\"live_price\\\"\\n     evidence_source: str = \\\"provider_lookup\\\"\\n     evidence_note: str = \\\"\\\"\\n \\n@@ -46,6 +55,119 @@ class EmptyLookupProvider:\\n         return []\\n \\n \\n+class OpenFIGILookupProvider:\\n+    \\\"\\\"\\\"Public ISIN-to-instrument metadata lookup via OpenFIGI; no prices or portfolio data.\\\"\\\"\\\"\\n+\\n+    name = \\\"openfigi\\\"\\n+\\n+    def __init__(self, *, timeout_seconds: float = 8.0, max_results: int = 12):\\n+        self.timeout_seconds = timeout_seconds\\n+        self.max_results = max_results\\n+\\n+    def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n+        if not isin:\\n+            return []\\n+        token = os.environ.get(\\\"OPENFIGI_API_KEY\\\")\\n+        headers = {\\\"Content-Type\\\": \\\"application/json\\\", \\\"User-Agent\\\": \\\"JarvisFinance/1.0 public-instrument-lookup\\\"}\\n+        if token:\\n+            headers[\\\"X-OPENFIGI-APIKEY\\\"] = token\\n+        payload = json.dumps([{\\\"idType\\\": \\\"ID_ISIN\\\", \\\"idValue\\\": isin}]).encode(\\\"utf-8\\\")\\n+        req = urllib.request.Request(\\\"https://api.openfigi.com/v3/mapping\\\", data=payload, headers=headers, method=\\\"POST\\\")\\n+        try:\\n+            with urllib.request.urlopen(req, timeout=self.timeout_seconds) as resp:\\n+                data = json.loads(resp.read().decode(\\\"utf-8\\\"))\\n+        except Exception as exc:  # pragma: no cover - network dependent\\n+            raise LookupUnavailable(str(exc)) from exc\\n+        out: list[ProviderCandidate] = []\\n+        for item in (data[0].get(\\\"data\\\", []) if data else [])[: self.max_results]:\\n+            symbol = item.get(\\\"ticker\\\") or item.get(\\\"figi\\\")\\n+            if not symbol:\\n+                continue\\n+            sec_type = \\\" \\\".join(str(item.get(k) or \\\"\\\") for k in (\\\"securityType\\\", \\\"securityType2\\\", \\\"marketSector\\\")).lower()\\n+            asset_class = \\\"etf\\\" if \\\"fund\\\" in sec_type or \\\"etf\\\" in sec_type else \\\"equity\\\" if \\\"equity\\\" in sec_type else None\\n+            out.append(ProviderCandidate(\\n+                provider=self.name,\\n+                provider_symbol=symbol,\\n+                exchange=item.get(\\\"exchCode\\\") or item.get(\\\"marketSector\\\"),\\n+                currency=item.get(\\\"currency\\\") or currency,\\n+                name=item.get(\\\"name\\\"),\\n+                isin=isin,\\n+                asset_class=asset_class,\\n+                hedge_status=\\\"unknown\\\",\\n+                instrument_status=\\\"unknown\\\",\\n+                valuation_policy=\\\"live_price\\\",\\n+                evidence_source=\\\"openfigi_isin_mapping\\\",\\n+                evidence_note=\\\"Public ISIN mapping metadata only; no prices, holdings, account data, or portfolio quantities sent.\\\",\\n+            ))\\n+        return out\\n+\\n+\\n+class YahooSearchLookupProvider:\\n+    \\\"\\\"\\\"Public metadata lookup via Yahoo Finance search endpoint; no prices or portfolio data.\\\"\\\"\\\"\\n+\\n+    name = \\\"yahoo_fallback\\\"\\n+\\n+    def __init__(self, *, timeout_seconds: float = 8.0, max_results: int = 8):\\n+        self.timeout_seconds = timeout_seconds\\n+        self.max_results = max_results\\n+\\n+    def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n+        queries = [q for q in [isin, ticker, name] if q]\\n+        if not queries:\\n+            return []\\n+        found: dict[tuple[str, str | None], ProviderCandidate] = {}\\n+        for query in queries[:2]:\\n+            url = \\\"https://query2.finance.yahoo.com/v1/finance/search?\\\" + urllib.parse.urlencode({\\\"q\\\": query, \\\"quotesCount\\\": self.max_results, \\\"newsCount\\\": 0})\\n+            try:\\n+                req = urllib.request.Request(url, headers={\\\"User-Agent\\\": \\\"JarvisFinance/1.0 public-instrument-lookup\\\"})\\n+                with urllib.request.urlopen(req, timeout=self.timeout_seconds) as resp:\\n+                    payload = json.loads(resp.read().decode(\\\"utf-8\\\"))\\n+            except Exception as exc:  # pragma: no cover - network dependent\\n+                raise LookupUnavailable(str(exc)) from exc\\n+            for quote in payload.get(\\\"quotes\\\", [])[: self.max_results]:\\n+                symbol = quote.get(\\\"symbol\\\")\\n+                if not symbol:\\n+                    continue\\n+                qtype = (quote.get(\\\"quoteType\\\") or \\\"\\\").lower()\\n+                asset_class = \\\"etf\\\" if qtype == \\\"etf\\\" else \\\"equity\\\" if qtype in {\\\"equity\\\", \\\"stock\\\"} else None\\n+                exch = quote.get(\\\"exchDisp\\\") or quote.get(\\\"exchange\\\")\\n+                cand = ProviderCandidate(\\n+                    provider=self.name,\\n+                    provider_symbol=symbol,\\n+                    exchange=exch,\\n+                    currency=currency,\\n+                    name=quote.get(\\\"longname\\\") or quote.get(\\\"shortname\\\"),\\n+                    isin=None,\\n+                    asset_class=asset_class,\\n+                    hedge_status=\\\"unknown\\\",\\n+                    instrument_status=\\\"unknown\\\",\\n+                    valuation_policy=\\\"live_price\\\",\\n+                    evidence_source=\\\"yahoo_finance_search\\\",\\n+                    evidence_note=\\\"Public symbol/search metadata only; no prices, holdings, account data, or portfolio quantities sent.\\\",\\n+                )\\n+                found[(symbol, exch)] = cand\\n+        return list(found.values())\\n+\\n+\\n+class CompositePublicLookupProvider:\\n+    name = \\\"public_composite\\\"\\n+\\n+    def __init__(self, providers: list[InstrumentLookupProvider] | None = None):\\n+        self.providers = providers or [OpenFIGILookupProvider(), YahooSearchLookupProvider()]\\n+\\n+    def lookup(self, *, isin: str | None, name: str | None, ticker: str | None, exchange: str | None, currency: str | None) -> list[ProviderCandidate]:\\n+        candidates: list[ProviderCandidate] = []\\n+        errors = 0\\n+        for provider in self.providers:\\n+            try:\\n+                candidates.extend(provider.lookup(isin=isin, name=name, ticker=ticker, exchange=exchange, currency=currency))\\n+            except Exception:\\n+                errors += 1\\n+        if errors and not candidates:\\n+            raise LookupUnavailable(\\\"all public lookup providers unavailable\\\")\\n+        return candidates\\n+\\n+\\n @dataclass\\n class CandidateGenerationResult:\\n     instruments_total: int = 0\\n@@ -120,23 +242,32 @@ def upsert_candidate(conn: Connection, *, instrument_id: str, candidate: Provide\\n     if inst is None:\\n         raise ValueError(f\\\"instrument not found: {instrument_id}\\\")\\n     hedged, hedged_to = _infer_hedge(inst[\\\"name\\\"], candidate)\\n+    hedge_status = candidate.hedge_status if candidate.hedge_status in {\\\"hedged\\\", \\\"unhedged\\\", \\\"unknown\\\"} else \\\"unknown\\\"\\n+    if hedge_status == \\\"unknown\\\" and hedged is True:\\n+        hedge_status = \\\"hedged\\\"\\n+    elif hedge_status == \\\"unknown\\\" and hedged is False:\\n+        hedge_status = \\\"unhedged\\\"\\n     candidate_id = stable_id(\\\"ipmc\\\", instrument_id, candidate.provider, candidate.provider_symbol, candidate.exchange or \\\"\\\", candidate.currency or \\\"\\\")\\n     now = utc_now()\\n     conn.execute(\\n         \\\"\\\"\\\"\\n         INSERT INTO instrument_price_mapping_candidates(\\n             candidate_id, instrument_id, isin, candidate_provider, candidate_provider_symbol,\\n-            candidate_exchange, candidate_currency, candidate_name, candidate_is_hedged,\\n-            candidate_hedged_to_currency, candidate_instrument_status, confidence,\\n+            candidate_exchange, candidate_currency, candidate_name, candidate_asset_class,\\n+            candidate_is_hedged, candidate_hedged_to_currency, candidate_hedge_status,\\n+            candidate_instrument_status, candidate_valuation_policy, confidence,\\n             evidence_source, evidence_note, review_status, created_at, updated_at\\n-        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\\n+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\\n         ON CONFLICT(candidate_id) DO UPDATE SET\\n             candidate_exchange=excluded.candidate_exchange,\\n             candidate_currency=excluded.candidate_currency,\\n             candidate_name=excluded.candidate_name,\\n+            candidate_asset_class=excluded.candidate_asset_class,\\n             candidate_is_hedged=excluded.candidate_is_hedged,\\n             candidate_hedged_to_currency=excluded.candidate_hedged_to_currency,\\n+            candidate_hedge_status=excluded.candidate_hedge_status,\\n             candidate_instrument_status=excluded.candidate_instrument_status,\\n+            candidate_valuation_policy=excluded.candidate_valuation_policy,\\n             confidence=excluded.confidence,\\n             evidence_source=excluded.evidence_source,\\n             evidence_note=excluded.evidence_note,\\n@@ -145,8 +276,9 @@ def upsert_candidate(conn: Connection, *, instrument_id: str, candidate: Provide\\n         \\\"\\\"\\\",\\n         (\\n             candidate_id, instrument_id, inst[\\\"isin\\\"], candidate.provider, candidate.provider_symbol,\\n-            candidate.exchange, candidate.currency or inst[\\\"currency\\\"], candidate.name, 1 if hedged else 0 if hedged is False else None,\\n-            hedged_to, candidate.instrument_status or \\\"unknown\\\", confidence, candidate.evidence_source,\\n+            candidate.exchange, candidate.currency or inst[\\\"currency\\\"], candidate.name, candidate.asset_class or (inst[\\\"asset_class\\\"] or \\\"\\\").lower(),\\n+            1 if hedged else 0 if hedged is False else None, hedged_to, hedge_status,\\n+            candidate.instrument_status or \\\"unknown\\\", candidate.valuation_policy or \\\"live_price\\\", confidence, candidate.evidence_source,\\n             evidence_note or candidate.evidence_note, review_status, now, now,\\n         ),\\n     )\\n@@ -208,6 +340,75 @@ def generate_mapping_candidates(\\n     return result\\n \\n \\n+def dedupe_missing_provider_symbol_alerts(conn: Connection) -> int:\\n+    \\\"\\\"\\\"Keep one active mapping-quality alert per rule/instrument; resolve duplicate active rows.\\\"\\\"\\\"\\n+    now = utc_now()\\n+    resolved = 0\\n+    rows = conn.execute(\\n+        \\\"\\\"\\\"\\n+        SELECT rule_id, entity_id, GROUP_CONCAT(alert_id) AS ids, COUNT(*) AS c\", \"exit_code\": 0, \"error\": null}",
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            "arguments": "{\"command\":\"PYTHONPATH=src python - <<'PY'\\nfrom collections import Counter\\nfrom jarvis_finance.config.settings import load_settings\\nfrom jarvis_finance.storage.database import connect\\nfrom jarvis_finance.storage.migrations import apply_migrations\\nsettings=load_settings(); conn=connect(settings.db_path); apply_migrations(conn)\\ninst=[r['instrument_id'] for r in conn.execute(\\\"SELECT DISTINCT instrument_id FROM transactions WHERE transaction_type='initial_position_snapshot' AND source_type='broker_import_reviewed_snapshot' AND COALESCE(is_voided,0)=0 AND instrument_id IS NOT NULL\\\").fetchall()]\\nq=','.join('?' for _ in inst) if inst else \\\"''\\\"\\nrows=conn.execute(f\\\"SELECT confidence,review_status,candidate_hedge_status,candidate_instrument_status,instrument_id FROM instrument_price_mapping_candidates WHERE instrument_id IN ({q})\\\", inst).fetchall() if inst else []\\nalerts=conn.execute(\\\"SELECT rule_id, COUNT(*) AS c FROM alerts WHERE status='active' GROUP BY rule_id ORDER BY rule_id\\\").fetchall()\\nper_inst=Counter(r['instrument_id'] for r in rows)\\nprint({\\n 'instruments_total': len(inst),\\n 'provider_lookups_attempted': len(inst),\\n 'candidates_total': len(rows),\\n 'confidence': dict(Counter(r['confidence'] for r in rows)),\\n 'review_status': dict(Counter(r['review_status'] for r in rows)),\\n 'without_candidate': sum(1 for iid in inst if per_inst[iid]==0),\\n 'with_multiple_candidates': sum(1 for iid in inst if per_inst[iid]>1),\\n 'hedge_candidate_known': sum(1 for r in rows if r['candidate_hedge_status']!='unknown'),\\n 'instrument_status_candidate_known': sum(1 for r in rows if r['candidate_instrument_status']!='unknown'),\\n 'instrument_hedge_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(hedge_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'instrument_status_unknown': conn.execute(f\\\"SELECT COUNT(*) AS c FROM instruments WHERE instrument_id IN ({q}) AND COALESCE(instrument_status,'unknown')='unknown'\\\", inst).fetchone()['c'] if inst else 0,\\n 'active_alerts_by_rule': {r['rule_id']: r['c'] for r in alerts},\\n})\\nconn.close()\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/.hermes/repos/FinanceManager\"}"
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    {
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      "content": "{\"output\": \"{'instruments_total': 3, 'provider_lookups_attempted': 3, 'candidates_total': 57, 'confidence': {'high': 36, 'medium': 18, 'low': 3}, 'review_status': {'needs_manual_review': 57}, 'without_candidate': 0, 'with_multiple_candidates': 3, 'hedge_candidate_known': 0, 'instrument_status_candidate_known': 0, 'instrument_hedge_unknown': 3, 'instrument_status_unknown': 3, 'active_alerts_by_rule': {'ambiguous_provider_mapping': 3, 'cost_basis_uncertain': 3, 'hedge_status_unknown': 3, 'instrument_status_unknown': 3, 'manual_review_needed': 3, 'missing_fx': 3, 'missing_market_price': 3, 'missing_provider_symbol': 3, 'snapshot_only': 3}}\", \"exit_code\": 0, \"error\": null}",
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      "name": "todo",
      "content": "{\"todos\": [{\"id\": \"c3c-0\", \"content\": \"Vorprüfung missing_provider_symbol=6 aggregiert analysieren\", \"status\": \"completed\"}, {\"id\": \"c3c-1\", \"content\": \"Public Lookup Candidate Generator + Runtime Template implementieren\", \"status\": \"completed\"}, {\"id\": \"c3c-2\", \"content\": \"Dashboard Review UI/Data Quality Center für Kandidaten prüfen/erweitern\", \"status\": \"completed\"}, {\"id\": \"c3c-3\", \"content\": \"Tests für Mock Public Lookup, DQ und Template ergänzen\", \"status\": \"completed\"}, {\"id\": \"c3c-4\", \"content\": \"Produktiven vorsichtigen Runtime-Lauf aggregiert ausführen\", \"status\": \"completed\"}, {\"id\": \"c3c-5\", \"content\": \"Compile, pytest, Git-Safety, Commit und Push\", \"status\": \"completed\"}], \"summary\": {\"total\": 6, \"pending\": 0, \"in_progress\": 0, \"completed\": 6, \"cancelled\": 0}}",
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    {
      "role": "assistant",
      "content": "Sir, Phase **C3c — Assisted Public Instrument Lookup & Mapping Candidate Fill** ist abgeschlossen, committed und gepusht.\n\n## Ergebnis\n\n- **Commit:** `8e6e8f7` — `Add assisted public instrument lookup candidates`\n- **Push:** erfolgreich nach `origin/main`\n- **Compile:** OK\n- **Tests:** `205 passed`\n- **Git-Safety:** `GIT_SAFETY_OK`\n- **Git Status:** clean\n- **Runtime-Backup vor produktivem Lauf:** erstellt\n- **Keine Preise abgerufen**\n- **Keine FX-Werte gesetzt**\n- **Keine Portfolio-Imports**\n- **Keine finalen Mappings automatisch bestätigt**\n- **Keine echten Mengen, Werte, Kontostände oder Preise im Chat**\n\n## 1. Vorprüfung `missing_provider_symbol = 6`\n\nErgebnis der Vorprüfung:\n\n- `missing_provider_symbol` aktiv vor Bereinigung: `6`\n- Entity-Type: ausschließlich `instrument`\n- Doppelte Entity-Gruppen: `3`\n\nInterpretation:\n\n- Es waren **keine fachlich unterschiedlichen Entity-Typen**.\n- Es waren **doppelte aktive Alerts pro Instrument**.\n- Ursache: wiederholte Mapping-/Catalog-Läufe mit teils unterschiedlichem Fingerprint-Kontext.\n- Bereinigung wurde implementiert und produktiv ausgeführt.\n\nNach Bereinigung:\n\n- aktive `missing_provider_symbol`: `3`\n- entspricht: ein aktiver Alert pro True-Wealth-Instrument\n- keine Alert-Flut mehr\n\nZusätzlich wurde die Dedupe-Logik auch für mapping-nahe Alerts gehärtet, damit `ambiguous_provider_mapping` und `provider_lookup_unavailable` ebenfalls nicht mehrfach aktiv pro Instrument stehen bleiben. Sehr zivilisiert. Fast untypisch für Data Quality.\n\n## 2. Implementierte Module\n\nNeu/erweitert:\n\n- **Public Lookup Candidate Generator**\n  - OpenFIGI ISIN-Mapping Provider\n  - Yahoo Search Fallback\n  - Composite Provider\n  - Provider-Ausfall ohne Crash\n  - nur öffentliche Instrumentdaten\n  - keine Preise\n  - keine FX\n  - keine Portfolio-Daten an externe Dienste\n\n- **Candidate Storage erweitert**\n  - `candidate_asset_class`\n  - `candidate_hedge_status`\n  - `candidate_valuation_policy`\n  - `candidate_instrument_status`\n  - Evidence Source / Note\n  - Review Status\n\n- **Mapping Candidate Rules**\n  - ISIN-Match stärker als Name/Ticker\n  - Name-only bleibt low confidence\n  - Ticker ohne Exchange bleibt review-pflichtig\n  - mehrere Kandidaten → `needs_manual_review`\n  - kein Kandidat → `missing_provider_symbol` bleibt aktiv\n  - Kandidaten lösen `missing_provider_symbol` nicht automatisch\n  - finale Auswahl bleibt manuell und auditierbar\n\n- **CLI erweitert**\n  - `generate-mapping-candidates --public-lookup`\n  - `--export-template`\n  - `--template-path`\n  - `--dedupe-missing-provider-alerts`\n\n- **Runtime-only Mapping Review Template**\n  - Export nur unter Runtime-Verzeichnis\n  - zusätzliche Kandidatenfelder enthalten\n  - keine Git-Ablage\n\n- **Dashboard Review UI erweitert**\n  - Kandidatenliste rendert ohne Live-API\n  - Provider\n  - Provider-Symbol\n  - Exchange\n  - Handelswährung\n  - Hedge-Status\n  - Hedged-to Currency\n  - Instrument-Status\n  - Valuation Policy\n  - Evidence Source / Note\n  - Confidence\n  - Review Status\n  - Button: Kandidat bestätigen\n  - Button: Kandidat ablehnen\n  - manuelle Erfassung bleibt über Instrument Search / Add Position möglich\n  - Bestätigung/Ablehnung schreibt Audit-Log\n  - keine Portfolio-Transaktion wird durch Review geändert\n\n- **Data Quality Center erweitert**\n  - unbestätigte Mapping-Kandidaten\n  - Instrumente mit mehreren Mapping-Kandidaten\n  - `missing_provider_symbol`\n  - `provider_lookup_unavailable`\n  - `ambiguous_provider_mapping`\n  - `hedge_status_unknown`\n  - `instrument_status_unknown`\n  - `valuation_not_ready`\n  - `manual_review_needed`\n\n## 3. Neue/geänderte Dateien\n\nGeändert:\n\n- `src/jarvis_finance/cli/main.py`\n- `src/jarvis_finance/dashboard/data.py`\n- `src/jarvis_finance/dashboard/pages/page_13_equity_manage.py`\n- `src/jarvis_finance/market_data/candidates.py`\n- `src/jarvis_finance/storage/migrations.py`\n- `tests/unit/test_schema.py`\n- `tests/unit/test_true_wealth_mapping_candidates_c3.py`\n\nKeine echten Dateien, Exporte, DBs oder Runtime-Daten wurden ins Git aufgenommen.\n\n## 4. Tests ergänzt\n\nAbgedeckt:\n\n- Public Lookup mit Mock Provider erzeugt Kandidaten\n- ISIN-Match erzeugt high-confidence Kandidat\n- mehrere Kandidaten erzeugen `needs_manual_review`\n- kein Kandidat lässt `missing_provider_symbol` aktiv\n- Provider-Lookup-Ausfall crasht nicht\n- Kandidaten werden Runtime-intern gespeichert\n- keine finale automatische Auswahl\n- Mapping Template enthält Kandidatenfelder\n- Dashboard Review UI rendert Kandidaten ohne Live-API\n- Data Quality Center zeigt Kandidaten-/Review-Bedarf\n- `missing_provider_symbol` wird nicht resolved, solange Kandidat nicht bestätigt ist\n- Alert-Dedupe pro Instrument\n- Git-Safety OK\n\n## 5. Produktiver vorsichtiger Runtime-Lauf\n\nAusgeführt gegen Runtime-DB:\n\n- öffentliche Lookup-Kandidaten für die 3 True-Wealth-Instrumente gesucht\n- Kandidaten gespeichert\n- Mapping Review Pack aktualisiert\n- Runtime-only Template aktualisiert\n- Alerts dedupliziert\n- keine finale Auswahl\n- keine Preise\n- keine FX-Werte\n- keine weiteren Imports\n\nAggregierter Stand:\n\n- Instrumente total: `3`\n- Provider-Lookups versucht: `3`\n- Kandidaten gefunden: `57`\n- Confidence high: `36`\n- Confidence medium: `18`\n- Confidence low: `3`\n- Instrumente ohne Kandidat: `0`\n- Instrumente mit mehreren Kandidaten: `3`\n- Review Status: alle `needs_manual_review`\n- Hedge-Status weiterhin unknown: `3`\n- Instrument-Status weiterhin unknown: `3`\n- Provider-Lookup-Unavailable Count: `0`\n\nWichtig: Trotz vieler Kandidaten wurde **nichts final bestätigt**. Alle 3 Instrumente bleiben manuell review-pflichtig.\n\n## 6. Runtime Mapping Template\n\n- Template aktualisiert: ja\n- Anzahl Instrumente: `3`\n- Anzahl Kandidaten: `57`\n- Speicherort: Runtime-Export-Verzeichnis\n- Git-Ablage: nein\n\nEnthaltene neue Felder:\n\n- `candidate_provider`\n- `candidate_provider_symbol`\n- `candidate_exchange`\n- `candidate_currency`\n- `confidence`\n- `hedge_status_candidate`\n- `instrument_status_candidate`\n- `evidence_source`\n- `reviewer_decision`\n- `reviewer_note`\n\n## 7. Data-Quality-Status aggregiert\n\nAktive Alerts nach Bereinigung:\n\n- `ambiguous_provider_mapping`: `3`\n- `cost_basis_uncertain`: `3`\n- `hedge_status_unknown`: `3`\n- `instrument_status_unknown`: `3`\n- `manual_review_needed`: `3`\n- `missing_fx`: `3`\n- `missing_market_price`: `3`\n- `missing_provider_symbol`: `3`\n- `snapshot_only`: `3`\n\nInterpretation:\n\n- Kandidaten sind vorhanden, aber nicht bestätigt.\n- Deshalb bleibt `missing_provider_symbol` korrekt aktiv.\n- Zusätzlich zeigt `ambiguous_provider_mapping`, dass manuelle Auswahl nötig ist.\n- `hedge_status_unknown` und `instrument_status_unknown` bleiben ebenfalls korrekt aktiv.\n- Valuation bleibt nicht ready.\n\n## 8. Abschlussprüfung\n\n- Compile: OK\n- pytest: `205 passed`\n- Git-Safety: `GIT_SAFETY_OK`\n- Git Status: clean\n- Commit: `8e6e8f7`\n- Push: erfolgreich\n\n## Konkrete nächste Empfehlung\n\nAls nächstes im Dashboard:\n\n1. **Equity/ETF Manage → Mapping Candidate Review** öffnen.\n2. Pro True-Wealth-Instrument Kandidatenliste prüfen.\n3. Pro Instrument genau einen Kandidaten manuell bestätigen — nur wenn Exchange, Handelswährung und Provider-Symbol plausibel sind.\n4. Kandidaten mit falscher Börse/Währung ablehnen.\n5. Hedge-Status und Instrument-Status nicht raten; nur bestätigen, wenn Evidence eindeutig ist.\n6. Danach erst:\n   - `missing_provider_symbol` auflösen\n   - `ambiguous_provider_mapping` auflösen\n   - optional Market-Price Dry Run gegen bestätigte Mappings\n\nNoch nicht empfehlenswert:\n\n- Equity/ETF Preisabruf produktiv\n- FX-Setzung\n- Performance-/P&L-Auswertung\n- Rebalancing, Scores, Heatmaps, Monte Carlo oder sonstige Kaffeesatz-Orakel mit UI.",
      "reasoning": null,
      "finish_reason": "stop",
      "codex_reasoning_items": [
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          "type": "message",
          "role": "assistant",
          "status": "completed",
          "content": [
            {
              "type": "output_text",
              "text": "Sir, Phase **C3c — Assisted Public Instrument Lookup & Mapping Candidate Fill** ist abgeschlossen, committed und gepusht.\n\n## Ergebnis\n\n- **Commit:** `8e6e8f7` — `Add assisted public instrument lookup candidates`\n- **Push:** erfolgreich nach `origin/main`\n- **Compile:** OK\n- **Tests:** `205 passed`\n- **Git-Safety:** `GIT_SAFETY_OK`\n- **Git Status:** clean\n- **Runtime-Backup vor produktivem Lauf:** erstellt\n- **Keine Preise abgerufen**\n- **Keine FX-Werte gesetzt**\n- **Keine Portfolio-Imports**\n- **Keine finalen Mappings automatisch bestätigt**\n- **Keine echten Mengen, Werte, Kontostände oder Preise im Chat**\n\n## 1. Vorprüfung `missing_provider_symbol = 6`\n\nErgebnis der Vorprüfung:\n\n- `missing_provider_symbol` aktiv vor Bereinigung: `6`\n- Entity-Type: ausschließlich `instrument`\n- Doppelte Entity-Gruppen: `3`\n\nInterpretation:\n\n- Es waren **keine fachlich unterschiedlichen Entity-Typen**.\n- Es waren **doppelte aktive Alerts pro Instrument**.\n- Ursache: wiederholte Mapping-/Catalog-Läufe mit teils unterschiedlichem Fingerprint-Kontext.\n- Bereinigung wurde implementiert und produktiv ausgeführt.\n\nNach Bereinigung:\n\n- aktive `missing_provider_symbol`: `3`\n- entspricht: ein aktiver Alert pro True-Wealth-Instrument\n- keine Alert-Flut mehr\n\nZusätzlich wurde die Dedupe-Logik auch für mapping-nahe Alerts gehärtet, damit `ambiguous_provider_mapping` und `provider_lookup_unavailable` ebenfalls nicht mehrfach aktiv pro Instrument stehen bleiben. Sehr zivilisiert. Fast untypisch für Data Quality.\n\n## 2. Implementierte Module\n\nNeu/erweitert:\n\n- **Public Lookup Candidate Generator**\n  - OpenFIGI ISIN-Mapping Provider\n  - Yahoo Search Fallback\n  - Composite Provider\n  - Provider-Ausfall ohne Crash\n  - nur öffentliche Instrumentdaten\n  - keine Preise\n  - keine FX\n  - keine Portfolio-Daten an externe Dienste\n\n- **Candidate Storage erweitert**\n  - `candidate_asset_class`\n  - `candidate_hedge_status`\n  - `candidate_valuation_policy`\n  - `candidate_instrument_status`\n  - Evidence Source / Note\n  - Review Status\n\n- **Mapping Candidate Rules**\n  - ISIN-Match stärker als Name/Ticker\n  - Name-only bleibt low confidence\n  - Ticker ohne Exchange bleibt review-pflichtig\n  - mehrere Kandidaten → `needs_manual_review`\n  - kein Kandidat → `missing_provider_symbol` bleibt aktiv\n  - Kandidaten lösen `missing_provider_symbol` nicht automatisch\n  - finale Auswahl bleibt manuell und auditierbar\n\n- **CLI erweitert**\n  - `generate-mapping-candidates --public-lookup`\n  - `--export-template`\n  - `--template-path`\n  - `--dedupe-missing-provider-alerts`\n\n- **Runtime-only Mapping Review Template**\n  - Export nur unter Runtime-Verzeichnis\n  - zusätzliche Kandidatenfelder enthalten\n  - keine Git-Ablage\n\n- **Dashboard Review UI erweitert**\n  - Kandidatenliste rendert ohne Live-API\n  - Provider\n  - Provider-Symbol\n  - Exchange\n  - Handelswährung\n  - Hedge-Status\n  - Hedged-to Currency\n  - Instrument-Status\n  - Valuation Policy\n  - Evidence Source / Note\n  - Confidence\n  - Review Status\n  - Button: Kandidat bestätigen\n  - Button: Kandidat ablehnen\n  - manuelle Erfassung bleibt über Instrument Search / Add Position möglich\n  - Bestätigung/Ablehnung schreibt Audit-Log\n  - keine Portfolio-Transaktion wird durch Review geändert\n\n- **Data Quality Center erweitert**\n  - unbestätigte Mapping-Kandidaten\n  - Instrumente mit mehreren Mapping-Kandidaten\n  - `missing_provider_symbol`\n  - `provider_lookup_unavailable`\n  - `ambiguous_provider_mapping`\n  - `hedge_status_unknown`\n  - `instrument_status_unknown`\n  - `valuation_not_ready`\n  - `manual_review_needed`\n\n## 3. Neue/geänderte Dateien\n\nGeändert:\n\n- `src/jarvis_finance/cli/main.py`\n- `src/jarvis_finance/dashboard/data.py`\n- `src/jarvis_finance/dashboard/pages/page_13_equity_manage.py`\n- `src/jarvis_finance/market_data/candidates.py`\n- `src/jarvis_finance/storage/migrations.py`\n- `tests/unit/test_schema.py`\n- `tests/unit/test_true_wealth_mapping_candidates_c3.py`\n\nKeine echten Dateien, Exporte, DBs oder Runtime-Daten wurden ins Git aufgenommen.\n\n## 4. Tests ergänzt\n\nAbgedeckt:\n\n- Public Lookup mit Mock Provider erzeugt Kandidaten\n- ISIN-Match erzeugt high-confidence Kandidat\n- mehrere Kandidaten erzeugen `needs_manual_review`\n- kein Kandidat lässt `missing_provider_symbol` aktiv\n- Provider-Lookup-Ausfall crasht nicht\n- Kandidaten werden Runtime-intern gespeichert\n- keine finale automatische Auswahl\n- Mapping Template enthält Kandidatenfelder\n- Dashboard Review UI rendert Kandidaten ohne Live-API\n- Data Quality Center zeigt Kandidaten-/Review-Bedarf\n- `missing_provider_symbol` wird nicht resolved, solange Kandidat nicht bestätigt ist\n- Alert-Dedupe pro Instrument\n- Git-Safety OK\n\n## 5. Produktiver vorsichtiger Runtime-Lauf\n\nAusgeführt gegen Runtime-DB:\n\n- öffentliche Lookup-Kandidaten für die 3 True-Wealth-Instrumente gesucht\n- Kandidaten gespeichert\n- Mapping Review Pack aktualisiert\n- Runtime-only Template aktualisiert\n- Alerts dedupliziert\n- keine finale Auswahl\n- keine Preise\n- keine FX-Werte\n- keine weiteren Imports\n\nAggregierter Stand:\n\n- Instrumente total: `3`\n- Provider-Lookups versucht: `3`\n- Kandidaten gefunden: `57`\n- Confidence high: `36`\n- Confidence medium: `18`\n- Confidence low: `3`\n- Instrumente ohne Kandidat: `0`\n- Instrumente mit mehreren Kandidaten: `3`\n- Review Status: alle `needs_manual_review`\n- Hedge-Status weiterhin unknown: `3`\n- Instrument-Status weiterhin unknown: `3`\n- Provider-Lookup-Unavailable Count: `0`\n\nWichtig: Trotz vieler Kandidaten wurde **nichts final bestätigt**. Alle 3 Instrumente bleiben manuell review-pflichtig.\n\n## 6. Runtime Mapping Template\n\n- Template aktualisiert: ja\n- Anzahl Instrumente: `3`\n- Anzahl Kandidaten: `57`\n- Speicherort: Runtime-Export-Verzeichnis\n- Git-Ablage: nein\n\nEnthaltene neue Felder:\n\n- `candidate_provider`\n- `candidate_provider_symbol`\n- `candidate_exchange`\n- `candidate_currency`\n- `confidence`\n- `hedge_status_candidate`\n- `instrument_status_candidate`\n- `evidence_source`\n- `reviewer_decision`\n- `reviewer_note`\n\n## 7. Data-Quality-Status aggregiert\n\nAktive Alerts nach Bereinigung:\n\n- `ambiguous_provider_mapping`: `3`\n- `cost_basis_uncertain`: `3`\n- `hedge_status_unknown`: `3`\n- `instrument_status_unknown`: `3`\n- `manual_review_needed`: `3`\n- `missing_fx`: `3`\n- `missing_market_price`: `3`\n- `missing_provider_symbol`: `3`\n- `snapshot_only`: `3`\n\nInterpretation:\n\n- Kandidaten sind vorhanden, aber nicht bestätigt.\n- Deshalb bleibt `missing_provider_symbol` korrekt aktiv.\n- Zusätzlich zeigt `ambiguous_provider_mapping`, dass manuelle Auswahl nötig ist.\n- `hedge_status_unknown` und `instrument_status_unknown` bleiben ebenfalls korrekt aktiv.\n- Valuation bleibt nicht ready.\n\n## 8. Abschlussprüfung\n\n- Compile: OK\n- pytest: `205 passed`\n- Git-Safety: `GIT_SAFETY_OK`\n- Git Status: clean\n- Commit: `8e6e8f7`\n- Push: erfolgreich\n\n## Konkrete nächste Empfehlung\n\nAls nächstes im Dashboard:\n\n1. **Equity/ETF Manage → Mapping Candidate Review** öffnen.\n2. Pro True-Wealth-Instrument Kandidatenliste prüfen.\n3. Pro Instrument genau einen Kandidaten manuell bestätigen — nur wenn Exchange, Handelswährung und Provider-Symbol plausibel sind.\n4. Kandidaten mit falscher Börse/Währung ablehnen.\n5. Hedge-Status und Instrument-Status nicht raten; nur bestätigen, wenn Evidence eindeutig ist.\n6. Danach erst:\n   - `missing_provider_symbol` auflösen\n   - `ambiguous_provider_mapping` auflösen\n   - optional Market-Price Dry Run gegen bestätigte Mappings\n\nNoch nicht empfehlenswert:\n\n- Equity/ETF Preisabruf produktiv\n- FX-Setzung\n- Performance-/P&L-Auswertung\n- Rebalancing, Scores, Heatmaps, Monte Carlo oder sonstige Kaffeesatz-Orakel mit UI."
            }
          ],
          "id": "msg_0134d7700e125cf0016a071e8a7be481918c16e309730dc9ca",
          "phase": "final_answer"
        }
      ]
    },
    {
      "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."
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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, and `references/instrument-catalog-manual-add-workflow.md` for the general Instrument Catalog + dashboard manual-add workflow that must underpin future Equity/ETF/Crypto adds. 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. 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.\\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.\\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.\\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.\\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- 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\\nBefore 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.\\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.\\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. See `references/instrument-catalog-manual-add-workflow.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- 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- Adding broad Git-safety tests that only assert `len(findings) >= N`; prefer explicit assertions for token filename, PAT patterns, private keys, CSV/JSON path policy, runtime/data directories, allowed synthetic paths, `config.example/`, and normal `src/` false-positive protection.\\n\", \"path\": \"finance/finance-system-architecture/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/finance/finance-system-architecture\", \"linked_files\": {\"references\": [\"references/crypto-single-coin-runtime-test.md\", \"references/guided-manual-review-pilot.md\", \"references/multi-wallet-crypto-and-equity-manage-ui.md\", \"references/mvp-crypto-report-export.md\", \"references/broker-review-queue-hardening.md\", \"references/mvp-import-pipeline-tasks-9-13.md\", \"references/broker-mini-import-completion-and-dq-consolidation.md\", \"references/broker-parser-dry-run-pipeline.md\", \"references/crypto-xlsx-initial-snapshot-dry-run.md\", \"references/crypto-price-refresh-and-idempotent-btc-runtime.md\", \"references/spec-v0.3-decisions.md\", \"references/runtime-backup-and-broker-mapping.md\", \"references/controlled-real-broker-dry-run.md\", \"references/broker-mini-import-regression-and-corrections.md\", \"references/equity-fx-market-data-phase-c.md\", \"references/true-wealth-valuation-pilot-c2.md\", \"references/mvp-market-import-safety-hardening.md\", \"references/spec-v0.4-implementation-blueprint.md\", \"references/import-wizard-mapping-layer.md\", \"references/crypto-runtime-db-consistency-verification.md\", \"references/github-token-push-from-drive.md\", \"references/instrument-catalog-manual-add-workflow.md\", \"references/broker-import-execution-plan-mini-import.md\", \"references/mvp-streamlit-dashboard.md\", \"references/spec-v0.4.1-corrections.md\", \"references/mvp-crypto-wallet-ledger.md\", \"references/true-wealth-mapping-candidate-review-c3.md\", \"references/mvp-safety-gate-implementation.md\", \"references/crypto-xlsx-initial-snapshot-production-import.md\", \"references/spec-v0.5-repo-blueprint.md\", \"references/spec-v0.2-decisions.md\", \"references/mvp-crypto-manual-management-ui.md\", \"references/mvp-ledger-cash-wac-positions.md\", \"references/git-safety-verification-coverage.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"finance\", \"portfolio\", \"accounting\", \"dashboard\", \"csv\", \"fx\", \"tax\", \"privacy\"]}}}",
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      "content": "{\"success\": true, \"name\": \"finance-system-architecture\", \"file\": \"references/true-wealth-mapping-candidate-review-c3.md\", \"content\": \"# True-Wealth Mapping Candidate Review (Phase C3)\\n\\nUse this reference when hardening Equity/ETF market-data mappings after small True-Wealth initial-position imports.\\n\\n## Scope and guardrails\\n\\n- Do not import additional portfolio positions while mapping candidates are being reviewed.\\n- Do not expose real quantities, prices, balances, account values, raw rows, or source files in chat.\\n- Do not commit real files or runtime DB artifacts to Git.\\n- Dashboard/review pages must read local DB only; no live provider/API calls during render.\\n- Candidate creation is allowed in runtime DB; final provider-symbol selection remains manual unless the user explicitly approves and the match is unambiguous.\\n- No heatmaps, scores, rebalancing, backtesting, Monte-Carlo, or analytics pivots during this phase.\\n\\n## Data model pattern\\n\\nCreate/extend a runtime table such as `instrument_price_mapping_candidates` with at least:\\n\\n- `candidate_id` primary key\\n- `instrument_id`, `isin`\\n- `candidate_provider`, `candidate_provider_symbol`, `candidate_exchange`, `candidate_currency`, `candidate_name`\\n- `candidate_is_hedged`, `candidate_hedged_to_currency`\\n- `candidate_instrument_status`\\n- `confidence`: `high`, `medium`, `low`\\n- `evidence_source`, `evidence_note`\\n- `review_status`: `proposed`, `selected`, `rejected`, `needs_manual_review`\\n- `created_at`\\n\\nRecommended uniqueness: `(instrument_id, candidate_provider, candidate_provider_symbol, candidate_exchange)` to keep candidate generation idempotent.\\n\\n## Candidate generation rules\\n\\n- ISIN match is strongest evidence and may be `high` confidence when provider symbol/currency/exchange are coherent.\\n- Name-only candidates are insufficient; mark `low` and `needs_manual_review`.\\n- Ticker without exchange remains review-required.\\n- Multiple candidates for the same instrument => all candidates `needs_manual_review` and create/keep a deduplicated `ambiguous_provider_mapping` alert.\\n- No candidate => keep/create deduplicated `missing_provider_symbol` alert.\\n- Provider lookup/API failure must not crash; create/keep `provider_lookup_unavailable` and continue.\\n- Hedge status may only be proposed automatically when obvious (`CHF hedged`, `hedged CHF`, `currency hedged`, etc.); the instrument itself remains `hedge_status=unknown` until user confirmation.\\n- Instrument status may be proposed `active` only with reliable provider evidence; otherwise keep `unknown`. Lookup failure is not enough to mark `delisted`.\\n\\n## Review UI/service requirements\\n\\nInstrument Price Mapping Review should allow:\\n\\n- list instruments and mapping status\\n- show candidates and confidence/evidence\\n- select provider/provider-symbol/exchange/currency\\n- confirm hedge status: `hedged`, `unhedged`, `unknown`\\n- set hedged-to currency if hedged\\n- confirm instrument status: `active`, `suspended`, `delisted`, `merged`, `inactive`, `unknown`\\n- set valuation policy: `live_price`, `last_known_price`, `manual_value`, `exclude_from_auto_price_update`\\n- save selection with mandatory note/audit log\\n- resolve `missing_provider_symbol` and `ambiguous_provider_mapping` only after a confirmed selection\\n\\nThe selection service should write a normal provider mapping row separately from the candidate row and should never mutate positions/ledger rows.\\n\\n## FX override review pattern\\n\\nFor foreign-currency snapshot positions:\\n\\n- aggregate required FX pairs/dates only; do not print actual rates/values unless explicitly approved.\\n- keep `missing_fx`, `manual_override_required`, and/or `historical_fx_unavailable` active until a local historical rate or approved manual override exists.\\n- manual FX override requires pair, date, Decimal/Text rate, source/note, audit log, and resolves matching `missing_fx` alerts.\\n- do not set manual FX rates without explicit user approval.\\n\\n## Testing checklist\\n\\nAdd tests for:\\n\\n- ISIN-match candidate generation.\\n- Name-only candidate stays review-required.\\n- Multiple candidates become `needs_manual_review` and alert.\\n- No candidate keeps `missing_provider_symbol` active.\\n- Hedge unknown creates alert.\\n- CHF-hedged candidate remains `proposed` until confirmed.\\n- Instrument status unknown creates alert.\\n- Provider lookup failures do not crash.\\n- Mapping selection writes audit and resolves `missing_provider_symbol`.\\n- FX override requires note and resolves `missing_fx`.\\n- Market-price dry run without confirmed provider symbols calls no provider.\\n- Dashboard review UI renders without live API.\\n- Git-safety after tests, with generated caches removed.\\n\\n## Runtime reporting shape\\n\\nReport only aggregate counts:\\n\\n- instruments total\\n- candidates found\\n- high/medium/low candidates\\n- instruments without candidate\\n- instruments with hedge unknown\\n- instruments with instrument-status unknown\\n- mapping actions performed: candidate creation only vs confirmed selections\\n- warnings/errors and active data-quality counts\\n\\nNever report real quantities, prices, balances, account values, raw rows, or source filenames from real portfolio data.\\n\", \"file_type\": \".md\"}",
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      "tool_calls": [
        {
          "id": "call_g7bGJBcRxLs4vUxfkMKxwm6S",
          "call_id": "call_g7bGJBcRxLs4vUxfkMKxwm6S",
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            "arguments": "{\"action\":\"write_file\",\"name\":\"finance-system-architecture\",\"file_path\":\"references/public-instrument-lookup-candidate-generator.md\",\"file_content\":\"# Public Instrument Lookup Candidate Generator\\n\\nUse this reference when generating Equity/ETF provider-symbol mapping candidates from public instrument metadata after instruments already exist in the runtime catalog.\\n\\n## Scope and secrecy\\n\\n- Allowed outbound lookup fields only: ISIN, public instrument name, public ticker/symbol, exchange, currency, ETF/equity metadata.\\n- Never send portfolio quantities, account/wallet IDs, balances, cost basis, private notes, source files, row hashes, or personal portfolio context to providers.\\n- Never fetch prices or FX during candidate generation.\\n- Store results in the runtime DB/export directory only; do not commit generated review templates, DBs, or real files.\\n- Chat/reporting must be aggregate-only.\\n\\n## Provider pattern\\n\\n- Prefer ISIN-aware public metadata sources first (for example OpenFIGI ISIN mapping), then symbol/name search fallbacks (for example Yahoo search) only as lower-confidence evidence.\\n- Treat provider failures as data-quality events, not crashes: increment `provider_lookup_unavailable`, keep manual entry possible, and continue other instruments/providers.\\n- Cap provider result sets (for example first 8-12 rows per provider/query). ISIN mapping endpoints can return many listings for the same instrument; unbounded writes create noisy review packs.\\n- Do not treat Yahoo/search results as confirmed ISIN matches unless the provider response actually returns ISIN evidence. If the search query was an ISIN but the response only returns symbols/names, leave candidate `isin` evidence empty and keep confidence/review conservative.\\n\\n## Candidate storage fields\\n\\nCandidate rows should include at least:\\n\\n- `instrument_id`, `isin`\\n- `candidate_provider`, `candidate_provider_symbol`\\n- `candidate_exchange`, `candidate_currency`, `candidate_name`, `candidate_asset_class`\\n- `candidate_is_hedged`, `candidate_hedged_to_currency`, `candidate_hedge_status`\\n- `candidate_instrument_status`, `candidate_valuation_policy`\\n- `evidence_source`, `evidence_note`\\n- `confidence`: `high`, `medium`, `low`\\n- `review_status`: `proposed`, `selected`, `rejected`, `needs_manual_review`\\n\\nUse deterministic candidate IDs/upserts based on instrument/provider/symbol/exchange/currency so repeated lookup runs are idempotent. If a productive re-run should replace noisy proposed rows, delete only `proposed`/`needs_manual_review` candidates for the targeted instruments before regenerating; never delete `selected` or `rejected` review history unless explicitly approved.\\n\\n## Data-quality lifecycle\\n\\n- Before/after candidate generation, dedupe active mapping-quality alerts by `(rule_id, entity_type, entity_id)` for at least `missing_provider_symbol`, `ambiguous_provider_mapping`, and `provider_lookup_unavailable`.\\n- A generated candidate does **not** resolve `missing_provider_symbol`; resolution requires a confirmed provider mapping.\\n- Multiple candidates for an instrument should create/keep one active `ambiguous_provider_mapping` alert per instrument and set candidates to `needs_manual_review`.\\n- No candidates should keep one active `missing_provider_symbol` alert per instrument.\\n- Hedge status and instrument status remain unknown until confirmed; candidate evidence may be displayed but must not silently mutate the instrument.\\n\\n## Runtime review pack and UI\\n\\n- Export review packs under the runtime export directory only.\\n- Include candidate counts, confidence, provider, symbol, exchange, currency, asset class, hedge/status candidate fields, evidence source/note, reviewer decision, and reviewer note.\\n- Dashboard review pages must render candidates from the local DB only; no live lookup/API calls during page render.\\n- The UI may allow confirm/reject actions, but both require audit logs and must not alter positions, cash, quantities, or ledger rows.\\n\\n## Tests and verification\\n\\nTest at minimum:\\n\\n- provider lookup failure does not crash and records aggregate unavailable status\\n- candidates are stored runtime-internally with the extended fields\\n- no final provider mapping is auto-selected\\n- multiple candidates become `needs_manual_review`\\n- no candidate leaves `missing_provider_symbol` active\\n- generated candidates do not resolve `missing_provider_symbol`\\n- alert dedupe for duplicate active `missing_provider_symbol`/`ambiguous_provider_mapping`\\n- review template contains candidate fields\\n- dashboard review UI renders candidates without live API\\n- Data Quality Center shows candidate/review needs\\n- market-price dry runs call no provider when confirmed mappings are zero\\n\\nBefore commit/push: compile, full pytest, remove `.pytest_cache`/`__pycache__`, run Git-safety, then check Git status.\\n\"}"
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            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"old_string\":\"and `references/instrument-catalog-manual-add-workflow.md` for the general Instrument Catalog + dashboard manual-add workflow that must underpin future Equity/ETF/Crypto adds. Key decisions:\",\"new_string\":\"`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. Key decisions:\"}"
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    {
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      "name": "skill_manage",
      "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'finance-system-architecture' (1 replacement).\"}",
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            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"new_string\":\"- 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`.\",\"old_string\":\"- 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.\"}"
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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_u83Tip8Giyv2EaFGWjtJ1Qsv"
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          "call_id": "call_XTTivcmTQyF9aoC76kQCRtTV",
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            "arguments": "{\"action\":\"patch\",\"name\":\"finance-system-architecture\",\"new_string\":\"- 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.\",\"old_string\":\"- 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.\"}"
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      "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'finance-system-architecture' (1 replacement).\"}",
      "tool_call_id": "call_XTTivcmTQyF9aoC76kQCRtTV"
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      "role": "assistant",
      "content": "Skill library updated.\n\n- Patched existing loaded skill: `finance-system-architecture`\n- Added reference file:\n  - `references/public-instrument-lookup-candidate-generator.md`\n- Added SKILL.md pointers to the new reference.\n- Expanded the relevant market-data/pitfall guidance with:\n  - runtime-only public metadata lookup rules\n  - OpenFIGI/Yahoo fallback evidence handling\n  - provider result caps\n  - no auto-selection of mappings\n  - alert dedupe by rule/entity\n  - review-pack/UI/test requirements\n  - reminder to avoid treating search fallback as ISIN-confirmed evidence\n\nNo new narrow one-off skill was created; the learning was folded into the existing class-level finance-system umbrella.",
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              "text": "Skill library updated.\n\n- Patched existing loaded skill: `finance-system-architecture`\n- Added reference file:\n  - `references/public-instrument-lookup-candidate-generator.md`\n- Added SKILL.md pointers to the new reference.\n- Expanded the relevant market-data/pitfall guidance with:\n  - runtime-only public metadata lookup rules\n  - OpenFIGI/Yahoo fallback evidence handling\n  - provider result caps\n  - no auto-selection of mappings\n  - alert dedupe by rule/entity\n  - review-pack/UI/test requirements\n  - reminder to avoid treating search fallback as ISIN-confirmed evidence\n\nNo new narrow one-off skill was created; the learning was folded into the existing class-level finance-system umbrella."
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}