{
  "session_id": "20260514_154520_dde831",
  "model": "gpt-5.5",
  "base_url": "https://chatgpt.com/backend-api/codex",
  "platform": "telegram",
  "session_start": "2026-05-14T15:45:20.090789",
  "last_updated": "2026-05-14T15:48:40.673893",
  "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\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\nWhen the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n<tool_persistence>\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n</tool_persistence>\n\n<mandatory_tool_use>\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n</mandatory_tool_use>\n\n<act_dont_ask>\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n</act_dont_ask>\n\n<prerequisite_checks>\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n</prerequisite_checks>\n\n<verification>\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n</verification>\n\n<missing_context>\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n</missing_context>\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n<available_skills>\n  autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n    - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n    - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n    - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n    - opencode: Delegate coding to OpenCode CLI (features, PR review).\n  creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n    - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n    - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n    - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n    - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n    - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n    - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n    - comfyui: Generate images, video, and audio with ComfyUI — install,...\n    - design-md: Author/validate/export Google's DESIGN.md token spec files.\n    - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n    - humanizer: Humanize text: strip AI-isms and add real voice.\n    - ideation: Generate project ideas via creative constraints.\n    - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n    - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n    - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n    - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n    - pretext: Use when building creative browser demos with @chenglou/p...\n    - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n    - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n    - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n  cronjob:\n    - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n  data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n    - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n  devops:\n    - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n    - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n    - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n    - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n  dogfood:\n    - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n  edge-tts:\n    - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n  email: Skills for sending, receiving, searching, and managing email from the terminal.\n    - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n  gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n    - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n    - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n  github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n    - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n    - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n    - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n    - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n    - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n    - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n  health:\n    - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n  health-management:\n    - health-management: Health data management — lab values, symptom tracking, nu...\n  jarvis-routing-policy:\n    - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n  mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n    - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n  media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n    - gif-search: Search/download GIFs from Tenor via curl + jq.\n    - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n    - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n    - spotify: Spotify: play, search, queue, manage playlists and devices.\n    - youtube-content: YouTube transcripts to summaries, threads, blogs.\n  mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n    - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n  mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n    - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n    - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n  mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n    - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n    - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n    - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n    - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n  mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n    - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n    - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n  mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n    - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n  mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n    - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n    - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n    - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n  note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n    - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n    - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n  openclaw-imports:\n    - browser-act: Browser automation CLI for AI agents with anti-detection ...\n    - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n    - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n    - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n    - self-improvement: Captures learnings, errors, and corrections to enable con...\n    - sherpa-onnx-tts\n    - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n    - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n    - tavily: Tavily web search, content extraction, and research tools.\n    - tavily-extract: Extract clean markdown or text content from specific URLs...\n    - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n    - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n    - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n  productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n    - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n    - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n    - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n    - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n    - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n    - notion: Notion API via curl: pages, databases, blocks, search.\n    - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n    - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n    - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n  red-teaming:\n    - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n  research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n    - arxiv: Search arXiv papers by keyword, author, category, or ID.\n    - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n    - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n    - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n    - web-scraping: Techniques for scraping web content, handling bot detecti...\n  smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n    - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n  social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n    - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n  software-development:\n    - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n    - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n    - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n    - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n    - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n    - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n    - spike: Throwaway experiments to validate an idea before build.\n    - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n    - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n    - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n    - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n  yazio-integration:\n    - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n  yuanbao:\n    - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n</available_skills>\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-23-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.\n\nConversation started: Thursday, May 14, 2026 03:45 PM\nModel: gpt-5.5\nProvider: openai-codex",
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "browser_back",
        "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {}
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_click",
        "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "ref": {
              "type": "string",
              "description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
            }
          },
          "required": [
            "ref"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_console",
        "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
        "parameters": {
          "type": "object",
          "properties": {
            "clear": {
              "type": "boolean",
              "default": false,
              "description": "If true, clear the message buffers after reading"
            },
            "expression": {
              "type": "string",
              "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_get_images",
        "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {}
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_navigate",
        "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
        "parameters": {
          "type": "object",
          "properties": {
            "url": {
              "type": "string",
              "description": "The URL to navigate to (e.g., 'https://example.com')"
            }
          },
          "required": [
            "url"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_press",
        "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "key": {
              "type": "string",
              "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
            }
          },
          "required": [
            "key"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_scroll",
        "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "direction": {
              "type": "string",
              "enum": [
                "up",
                "down"
              ],
              "description": "Direction to scroll"
            }
          },
          "required": [
            "direction"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_snapshot",
        "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
        "parameters": {
          "type": "object",
          "properties": {
            "full": {
              "type": "boolean",
              "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
              "default": false
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_type",
        "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "ref": {
              "type": "string",
              "description": "The element reference from the snapshot (e.g., '@e3')"
            },
            "text": {
              "type": "string",
              "description": "The text to type into the field"
            }
          },
          "required": [
            "ref",
            "text"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "browser_vision",
        "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA:<screenshot_path> in your response. Requires browser_navigate to be called first.",
        "parameters": {
          "type": "object",
          "properties": {
            "question": {
              "type": "string",
              "description": "What you want to know about the page visually. Be specific about what you're looking for."
            },
            "annotate": {
              "type": "boolean",
              "default": false,
              "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
            }
          },
          "required": [
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "cronjob",
        "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "description": "One of: create, list, update, pause, resume, remove, run"
            },
            "job_id": {
              "type": "string",
              "description": "Required for update/pause/resume/remove/run"
            },
            "prompt": {
              "type": "string",
              "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
            },
            "schedule": {
              "type": "string",
              "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
            },
            "name": {
              "type": "string",
              "description": "Optional human-friendly name"
            },
            "repeat": {
              "type": "integer",
              "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
            },
            "deliver": {
              "type": "string",
              "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
            },
            "skills": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
            },
            "model": {
              "type": "object",
              "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
              "properties": {
                "provider": {
                  "type": "string",
                  "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
                },
                "model": {
                  "type": "string",
                  "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
                }
              },
              "required": [
                "model"
              ]
            },
            "script": {
              "type": "string",
              "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
            },
            "no_agent": {
              "type": "boolean",
              "default": false,
              "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
            },
            "context_from": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
            },
            "enabled_toolsets": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
            },
            "workdir": {
              "type": "string",
              "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
            }
          },
          "required": [
            "action"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "image_generate",
        "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.",
        "parameters": {
          "type": "object",
          "properties": {
            "prompt": {
              "type": "string",
              "description": "The text prompt describing the desired image. Be detailed and descriptive."
            },
            "aspect_ratio": {
              "type": "string",
              "enum": [
                "landscape",
                "square",
                "portrait"
              ],
              "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
              "default": "landscape"
            }
          },
          "required": [
            "prompt"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "patch",
        "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
        "parameters": {
          "type": "object",
          "properties": {
            "mode": {
              "type": "string",
              "enum": [
                "replace",
                "patch"
              ],
              "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
              "default": "replace"
            },
            "path": {
              "type": "string",
              "description": "REQUIRED when mode='replace'. File path to edit."
            },
            "old_string": {
              "type": "string",
              "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
            },
            "new_string": {
              "type": "string",
              "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
            },
            "replace_all": {
              "type": "boolean",
              "description": "Replace all occurrences instead of requiring a unique match (default: false)",
              "default": false
            },
            "patch": {
              "type": "string",
              "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
            }
          },
          "required": [
            "mode"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "process",
        "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "list",
                "poll",
                "log",
                "wait",
                "kill",
                "write",
                "submit",
                "close"
              ],
              "description": "Action to perform on background processes"
            },
            "session_id": {
              "type": "string",
              "description": "Process session ID (from terminal background output). Required for all actions except 'list'."
            },
            "data": {
              "type": "string",
              "description": "Text to send to process stdin (for 'write' and 'submit' actions)"
            },
            "timeout": {
              "type": "integer",
              "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
              "minimum": 1
            },
            "offset": {
              "type": "integer",
              "description": "Line offset for 'log' action (default: last 200 lines)"
            },
            "limit": {
              "type": "integer",
              "description": "Max lines to return for 'log' action",
              "minimum": 1
            }
          },
          "required": [
            "action"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "read_file",
        "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to read (absolute, relative, or ~/path)"
            },
            "offset": {
              "type": "integer",
              "description": "Line number to start reading from (1-indexed, default: 1)",
              "default": 1,
              "minimum": 1
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of lines to read (default: 500, max: 2000)",
              "default": 500,
              "maximum": 2000
            }
          },
          "required": [
            "path"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "search_files",
        "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
        "parameters": {
          "type": "object",
          "properties": {
            "pattern": {
              "type": "string",
              "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
            },
            "target": {
              "type": "string",
              "enum": [
                "content",
                "files"
              ],
              "description": "'content' searches inside file contents, 'files' searches for files by name",
              "default": "content"
            },
            "path": {
              "type": "string",
              "description": "Directory or file to search in (default: current working directory)",
              "default": "."
            },
            "file_glob": {
              "type": "string",
              "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of results to return (default: 50)",
              "default": 50
            },
            "offset": {
              "type": "integer",
              "description": "Skip first N results for pagination (default: 0)",
              "default": 0
            },
            "output_mode": {
              "type": "string",
              "enum": [
                "content",
                "files_only",
                "count"
              ],
              "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
              "default": "content"
            },
            "context": {
              "type": "integer",
              "description": "Number of context lines before and after each match (grep mode only)",
              "default": 0
            }
          },
          "required": [
            "pattern"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "send_message",
        "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "send",
                "list"
              ],
              "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
            },
            "target": {
              "type": "string",
              "description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:<account_id>' (DM), 'yuanbao:group:<group_code>' (group chat)"
            },
            "message": {
              "type": "string",
              "description": "The message text to send. To send an image or file, include MEDIA:<local_path> (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "session_search",
        "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
        "parameters": {
          "type": "object",
          "properties": {
            "query": {
              "type": "string",
              "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
            },
            "role_filter": {
              "type": "string",
              "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
            },
            "limit": {
              "type": "integer",
              "description": "Max sessions to summarize (default: 3, max: 5).",
              "default": 3
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skill_manage",
        "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=<umbrella>` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.",
        "parameters": {
          "type": "object",
          "properties": {
            "action": {
              "type": "string",
              "enum": [
                "create",
                "patch",
                "edit",
                "delete",
                "write_file",
                "remove_file"
              ],
              "description": "The action to perform."
            },
            "name": {
              "type": "string",
              "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
            },
            "content": {
              "type": "string",
              "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
            },
            "old_string": {
              "type": "string",
              "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
            },
            "new_string": {
              "type": "string",
              "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
            },
            "replace_all": {
              "type": "boolean",
              "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
            },
            "category": {
              "type": "string",
              "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
            },
            "file_path": {
              "type": "string",
              "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
            },
            "file_content": {
              "type": "string",
              "description": "Content for the file. Required for 'write_file'."
            },
            "absorbed_into": {
              "type": "string",
              "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."
            }
          },
          "required": [
            "action",
            "name"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skill_view",
        "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
        "parameters": {
          "type": "object",
          "properties": {
            "name": {
              "type": "string",
              "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')."
            },
            "file_path": {
              "type": "string",
              "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
            }
          },
          "required": [
            "name"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "skills_list",
        "description": "List available skills (name + description). Use skill_view(name) to load full content.",
        "parameters": {
          "type": "object",
          "properties": {
            "category": {
              "type": "string",
              "description": "Optional category filter to narrow results"
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "terminal",
        "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n  (1) Long-lived processes that never exit (servers, watchers).\n  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
        "parameters": {
          "type": "object",
          "properties": {
            "command": {
              "type": "string",
              "description": "The command to execute on the VM"
            },
            "background": {
              "type": "boolean",
              "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
              "default": false
            },
            "timeout": {
              "type": "integer",
              "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
              "minimum": 1
            },
            "workdir": {
              "type": "string",
              "description": "Working directory for this command (absolute path). Defaults to the session working directory."
            },
            "pty": {
              "type": "boolean",
              "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
              "default": false
            },
            "notify_on_complete": {
              "type": "boolean",
              "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
              "default": false
            },
            "watch_patterns": {
              "type": "array",
              "items": {
                "type": "string"
              },
              "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
            }
          },
          "required": [
            "command"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "text_to_speech",
        "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.<name>), not model-selected.",
        "parameters": {
          "type": "object",
          "properties": {
            "text": {
              "type": "string",
              "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."
            },
            "output_path": {
              "type": "string",
              "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3"
            }
          },
          "required": [
            "text"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "todo",
        "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
        "parameters": {
          "type": "object",
          "properties": {
            "todos": {
              "type": "array",
              "description": "Task items to write. Omit to read current list.",
              "items": {
                "type": "object",
                "properties": {
                  "id": {
                    "type": "string",
                    "description": "Unique item identifier"
                  },
                  "content": {
                    "type": "string",
                    "description": "Task description"
                  },
                  "status": {
                    "type": "string",
                    "enum": [
                      "pending",
                      "in_progress",
                      "completed",
                      "cancelled"
                    ],
                    "description": "Current status"
                  }
                },
                "required": [
                  "id",
                  "content",
                  "status"
                ]
              }
            },
            "merge": {
              "type": "boolean",
              "description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
              "default": false
            }
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "vision_analyze",
        "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
        "parameters": {
          "type": "object",
          "properties": {
            "image_url": {
              "type": "string",
              "description": "Image URL (http/https), local file path, or data: URL to load."
            },
            "question": {
              "type": "string",
              "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
            }
          },
          "required": [
            "image_url",
            "question"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "write_file",
        "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
            },
            "content": {
              "type": "string",
              "description": "Complete content to write to the file"
            }
          },
          "required": [
            "path",
            "content"
          ]
        }
      }
    }
  ],
  "message_count": 8,
  "messages": [
    {
      "role": "user",
      "content": "Draft a structured German Anforderungsdokument v0.1 for an automated finance management and decision-support system using the user's requirements. Include architecture, functional/non-functional requirements, data model, dashboard UX, transaction accounting, watchlist pipeline, reports, tax/income, total return, risk, macro overlay, stress tests, liquidity ladder, bias detection, alert priorities, data quality, MVP roadmap. No implementation code."
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Considering document creation**\n\nI need to produce a document, and I’m wondering if I should use the planning tools. The task is about writing a requirements document, but I’m unsure if the \"writing-plans\" skill is relevant. There’s no implementation aspect, so maybe a software plan wouldn’t apply. I think I might need to create a file since the user asked for a draft. Let’s load the writing-plans skill to see if that helps!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Considering document creation**\n\nI need to produce a document, and I’m wondering if I should use the planning tools. The task is about writing a requirements document, but I’m unsure if the \"writing-plans\" skill is relevant. There’s no implementation aspect, so maybe a software plan wouldn’t apply. I think I might need to create a file since the user asked for a draft. Let’s load the writing-plans skill to see if that helps!",
      "codex_reasoning_items": [
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          "id": "rs_08794dd723558222016a05d1f1a4b8819192c1309d39366cb5",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Considering document creation**\n\nI need to produce a document, and I’m wondering if I should use the planning tools. The task is about writing a requirements document, but I’m unsure if the \"writing-plans\" skill is relevant. There’s no implementation aspect, so maybe a software plan wouldn’t apply. I think I might need to create a file since the user asked for a draft. Let’s load the writing-plans skill to see if that helps!"
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_mVWj6LntqfKgv7ActGOOgOXd",
          "call_id": "call_mVWj6LntqfKgv7ActGOOgOXd",
          "response_item_id": "fc_08794dd723558222016a05d1f51a4c8191a64aa87f6329a6a1",
          "type": "function",
          "function": {
            "name": "skill_view",
            "arguments": "{\"name\":\"writing-plans\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "name": "skill_view",
      "content": "{\"success\": true, \"name\": \"writing-plans\", \"description\": \"Write implementation plans: bite-sized tasks, paths, code.\", \"tags\": [\"planning\", \"design\", \"implementation\", \"workflow\", \"documentation\"], \"related_skills\": [\"subagent-driven-development\", \"test-driven-development\", \"requesting-code-review\"], \"content\": \"---\\nname: writing-plans\\ndescription: \\\"Write implementation plans: bite-sized tasks, paths, code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n  hermes:\\n    tags: [planning, design, implementation, workflow, documentation]\\n    related_skills: [subagent-driven-development, test-driven-development, requesting-code-review]\\n---\\n\\n# Writing Implementation Plans\\n\\n## Overview\\n\\nWrite comprehensive implementation plans assuming the implementer has zero context for the codebase and questionable taste. Document everything they need: which files to touch, complete code, testing commands, docs to check, how to verify. Give them bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.\\n\\nAssume the implementer is a skilled developer but knows almost nothing about the toolset or problem domain. Assume they don't know good test design very well.\\n\\n**Core principle:** A good plan makes implementation obvious. If someone has to guess, the plan is incomplete.\\n\\n## When to Use\\n\\n**Always use before:**\\n- Implementing multi-step features\\n- Breaking down complex requirements\\n- Delegating to subagents via subagent-driven-development\\n\\n**Don't skip when:**\\n- Feature seems simple (assumptions cause bugs)\\n- You plan to implement it yourself (future you needs guidance)\\n- Working alone (documentation matters)\\n\\n## Bite-Sized Task Granularity\\n\\n**Each task = 2-5 minutes of focused work.**\\n\\nEvery step is one action:\\n- \\\"Write the failing test\\\" — step\\n- \\\"Run it to make sure it fails\\\" — step\\n- \\\"Implement the minimal code to make the test pass\\\" — step\\n- \\\"Run the tests and make sure they pass\\\" — step\\n- \\\"Commit\\\" — step\\n\\n**Too big:**\\n```markdown\\n### Task 1: Build authentication system\\n[50 lines of code across 5 files]\\n```\\n\\n**Right size:**\\n```markdown\\n### Task 1: Create User model with email field\\n[10 lines, 1 file]\\n\\n### Task 2: Add password hash field to User\\n[8 lines, 1 file]\\n\\n### Task 3: Create password hashing utility\\n[15 lines, 1 file]\\n```\\n\\n## Plan Document Structure\\n\\n### Header (Required)\\n\\nEvery plan MUST start with:\\n\\n```markdown\\n# [Feature Name] Implementation Plan\\n\\n> **For Hermes:** Use subagent-driven-development skill to implement this plan task-by-task.\\n\\n**Goal:** [One sentence describing what this builds]\\n\\n**Architecture:** [2-3 sentences about approach]\\n\\n**Tech Stack:** [Key technologies/libraries]\\n\\n---\\n```\\n\\n### Task Structure\\n\\nEach task follows this format:\\n\\n````markdown\\n### Task N: [Descriptive Name]\\n\\n**Objective:** What this task accomplishes (one sentence)\\n\\n**Files:**\\n- Create: `exact/path/to/new_file.py`\\n- Modify: `exact/path/to/existing.py:45-67` (line numbers if known)\\n- Test: `tests/path/to/test_file.py`\\n\\n**Step 1: Write failing test**\\n\\n```python\\ndef test_specific_behavior():\\n    result = function(input)\\n    assert result == expected\\n```\\n\\n**Step 2: Run test to verify failure**\\n\\nRun: `pytest tests/path/test.py::test_specific_behavior -v`\\nExpected: FAIL — \\\"function not defined\\\"\\n\\n**Step 3: Write minimal implementation**\\n\\n```python\\ndef function(input):\\n    return expected\\n```\\n\\n**Step 4: Run test to verify pass**\\n\\nRun: `pytest tests/path/test.py::test_specific_behavior -v`\\nExpected: PASS\\n\\n**Step 5: Commit**\\n\\n```bash\\ngit add tests/path/test.py src/path/file.py\\ngit commit -m \\\"feat: add specific feature\\\"\\n```\\n````\\n\\n## Writing Process\\n\\n### Step 1: Understand Requirements\\n\\nRead and understand:\\n- Feature requirements\\n- Design documents or user description\\n- Acceptance criteria\\n- Constraints\\n\\n### Step 2: Explore the Codebase\\n\\nUse Hermes tools to understand the project:\\n\\n```python\\n# Understand project structure\\nsearch_files(\\\"*.py\\\", target=\\\"files\\\", path=\\\"src/\\\")\\n\\n# Look at similar features\\nsearch_files(\\\"similar_pattern\\\", path=\\\"src/\\\", file_glob=\\\"*.py\\\")\\n\\n# Check existing tests\\nsearch_files(\\\"*.py\\\", target=\\\"files\\\", path=\\\"tests/\\\")\\n\\n# Read key files\\nread_file(\\\"src/app.py\\\")\\n```\\n\\n### Step 3: Design Approach\\n\\nDecide:\\n- Architecture pattern\\n- File organization\\n- Dependencies needed\\n- Testing strategy\\n\\n### Step 4: Write Tasks\\n\\nCreate tasks in order:\\n1. Setup/infrastructure\\n2. Core functionality (TDD for each)\\n3. Edge cases\\n4. Integration\\n5. Cleanup/documentation\\n\\n### Step 5: Add Complete Details\\n\\nFor each task, include:\\n- **Exact file paths** (not \\\"the config file\\\" but `src/config/settings.py`)\\n- **Complete code examples** (not \\\"add validation\\\" but the actual code)\\n- **Exact commands** with expected output\\n- **Verification steps** that prove the task works\\n\\n### Step 6: Review the Plan\\n\\nCheck:\\n- [ ] Tasks are sequential and logical\\n- [ ] Each task is bite-sized (2-5 min)\\n- [ ] File paths are exact\\n- [ ] Code examples are complete (copy-pasteable)\\n- [ ] Commands are exact with expected output\\n- [ ] No missing context\\n- [ ] DRY, YAGNI, TDD principles applied\\n\\n### Step 7: Save the Plan\\n\\n```bash\\nmkdir -p docs/plans\\n# Save plan to docs/plans/YYYY-MM-DD-feature-name.md\\ngit add docs/plans/\\ngit commit -m \\\"docs: add implementation plan for [feature]\\\"\\n```\\n\\n## Principles\\n\\n### DRY (Don't Repeat Yourself)\\n\\n**Bad:** Copy-paste validation in 3 places\\n**Good:** Extract validation function, use everywhere\\n\\n### YAGNI (You Aren't Gonna Need It)\\n\\n**Bad:** Add \\\"flexibility\\\" for future requirements\\n**Good:** Implement only what's needed now\\n\\n```python\\n# Bad — YAGNI violation\\nclass User:\\n    def __init__(self, name, email):\\n        self.name = name\\n        self.email = email\\n        self.preferences = {}  # Not needed yet!\\n        self.metadata = {}     # Not needed yet!\\n\\n# Good — YAGNI\\nclass User:\\n    def __init__(self, name, email):\\n        self.name = name\\n        self.email = email\\n```\\n\\n### TDD (Test-Driven Development)\\n\\nEvery task that produces code should include the full TDD cycle:\\n1. Write failing test\\n2. Run to verify failure\\n3. Write minimal code\\n4. Run to verify pass\\n\\nSee `test-driven-development` skill for details.\\n\\n### Frequent Commits\\n\\nCommit after every task:\\n```bash\\ngit add [files]\\ngit commit -m \\\"type: description\\\"\\n```\\n\\n## Common Mistakes\\n\\n### Vague Tasks\\n\\n**Bad:** \\\"Add authentication\\\"\\n**Good:** \\\"Create User model with email and password_hash fields\\\"\\n\\n### Incomplete Code\\n\\n**Bad:** \\\"Step 1: Add validation function\\\"\\n**Good:** \\\"Step 1: Add validation function\\\" followed by the complete function code\\n\\n### Missing Verification\\n\\n**Bad:** \\\"Step 3: Test it works\\\"\\n**Good:** \\\"Step 3: Run `pytest tests/test_auth.py -v`, expected: 3 passed\\\"\\n\\n### Missing File Paths\\n\\n**Bad:** \\\"Create the model file\\\"\\n**Good:** \\\"Create: `src/models/user.py`\\\"\\n\\n## Execution Handoff\\n\\nAfter saving the plan, offer the execution approach:\\n\\n**\\\"Plan complete and saved. Ready to execute using subagent-driven-development — I'll dispatch a fresh subagent per task with two-stage review (spec compliance then code quality). Shall I proceed?\\\"**\\n\\nWhen executing, use the `subagent-driven-development` skill:\\n- Fresh `delegate_task` per task with full context\\n- Spec compliance review after each task\\n- Code quality review after spec passes\\n- Proceed only when both reviews approve\\n\\n## Remember\\n\\n```\\nBite-sized tasks (2-5 min each)\\nExact file paths\\nComplete code (copy-pasteable)\\nExact commands with expected output\\nVerification steps\\nDRY, YAGNI, TDD\\nFrequent commits\\n```\\n\\n**A good plan makes implementation obvious.**\\n\", \"path\": \"software-development/writing-plans/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/writing-plans\", \"linked_files\": null, \"usage_hint\": null, \"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\": [\"planning\", \"design\", \"implementation\", \"workflow\", \"documentation\"], \"related_skills\": [\"subagent-driven-development\", \"test-driven-development\", \"requesting-code-review\"]}}}",
      "tool_call_id": "call_mVWj6LntqfKgv7ActGOOgOXd"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "**Considering file creation**\n\nI think I need to create a file in the /home/agent directory since the task specifies the workspace. A markdown document might work, and I don’t think I need any code for it. It seems like I should draft this in German. I probably need to use the tool to write the file, so I’ll create /home/agent/anforderungsdokument. I’ll make sure it’s focused on writing rather than coding!",
      "finish_reason": "tool_calls",
      "reasoning_content": "**Considering file creation**\n\nI think I need to create a file in the /home/agent directory since the task specifies the workspace. A markdown document might work, and I don’t think I need any code for it. It seems like I should draft this in German. I probably need to use the tool to write the file, so I’ll create /home/agent/anforderungsdokument. I’ll make sure it’s focused on writing rather than coding!",
      "codex_reasoning_items": [
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          "id": "rs_010ae75d53983414016a05d1f6950881918dba5ff79dd6c3f9",
          "summary": [
            {
              "type": "summary_text",
              "text": "**Considering file creation**\n\nI think I need to create a file in the /home/agent directory since the task specifies the workspace. A markdown document might work, and I don’t think I need any code for it. It seems like I should draft this in German. I probably need to use the tool to write the file, so I’ll create /home/agent/anforderungsdokument. I’ll make sure it’s focused on writing rather than coding!"
            }
          ]
        }
      ],
      "tool_calls": [
        {
          "id": "call_Ixi1kh0qtJFkv348rNGOm4WL",
          "call_id": "call_Ixi1kh0qtJFkv348rNGOm4WL",
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            "arguments": "{\"path\":\"/home/agent/anforderungsdokument_finance_decision_support_v0.1.md\",\"content\":\"# Anforderungsdokument v0.1\\n## Automatisiertes Finance Management & Decision-Support-System\\n\\n**Version:** 0.1  \\n**Status:** Draft / Requirements Baseline  \\n**Sprache:** Deutsch mit English Technical Terms  \\n**Scope:** Technische Systemarchitektur, Datenmodell, fachliche Anforderungen und MVP-Roadmap  \\n**Nicht-Ziel:** Keine Investmentstrategie, keine Anlageberatung, kein automatisiertes Trading\\n\\n---\\n\\n## 1. Zweck und Leitprinzipien\\n\\nDas System soll private oder professionelle Finanzdaten automatisiert konsolidieren, strukturieren, analysieren und für menschliche Entscheidungen aufbereiten. Es unterstützt Portfolio-Controlling, Cashflow-Planung, Risikoüberwachung, Steuer-/Income-Übersicht, Watchlist-Monitoring, Reporting und Entscheidungsdokumentation.\\n\\n### 1.1 Kernprinzipien\\n\\n- **Human Decision Authority:** Jede Investment-, Rebalancing-, Verkaufs-, Kauf- oder Steuerentscheidung bleibt beim Menschen.\\n- **No Automated Trading:** Das System darf keine Orders platzieren, keine Broker-Transaktionen auslösen und keine automatisierten Trade-Ausführungen durchführen.\\n- **Rule-based First:** Berechnungen, Klassifikationen, Alerts und Scoring müssen primär regelbasiert, nachvollziehbar und auditierbar sein.\\n- **Minimal LLM Usage:** LLMs dürfen nur unterstützend für nicht-deterministische Aufgaben eingesetzt werden, z. B. Textzusammenfassung, Belegklassifikationsvorschläge oder Report-Formulierungen. Kritische Berechnungen und Entscheidungen dürfen nicht LLM-basiert sein.\\n- **Explainability:** Jede Kennzahl, jeder Alert und jeder Score muss eine nachvollziehbare Quelle, Regel und Berechnungslogik haben.\\n- **Data Quality over Automation:** Fehlende, doppelte oder inkonsistente Daten müssen sichtbar gemacht werden, statt stillschweigend korrigiert zu werden.\\n\\n---\\n\\n## 2. Systemgrenzen\\n\\n### 2.1 In Scope\\n\\n- Import und Normalisierung von Konto-, Broker-, Depot-, Transaktions-, Preis-, FX-, Dividenden-, Steuer-, Watchlist- und Makrodaten.\\n- Portfolio Accounting inklusive Cash, Securities, Fees, Taxes, Dividends, Interest, Splits und Corporate Actions.\\n- Total Return, Income, Risk, Liquidity und Exposure Analytics.\\n- Watchlist Pipeline mit regelbasiertem Monitoring und Signal-Priorisierung.\\n- Dashboard UX für Vermögen, Risiken, Cashflows, Alerts und Reports.\\n- Entscheidungsunterstützung durch Szenarien, Stress Tests, Bias Detection und Audit Trail.\\n- Exportierbare Reports für persönliche Review, Steuerunterlagen und Dokumentation.\\n\\n### 2.2 Out of Scope\\n\\n- Automatisierte Orderausführung.\\n- Portfolio-Optimierung mit automatischer Umsetzung.\\n- Black-box Investmentempfehlungen.\\n- Steuerberatung oder rechtlich verbindliche Steuerberechnung.\\n- Garantie für Marktdatenvollständigkeit oder Performance.\\n\\n---\\n\\n## 3. Zielnutzer und Rollen\\n\\n### 3.1 Rollen\\n\\n- **Owner / Investor:** Prüft Vermögen, Risiken, Cashflows, Reports und entscheidet manuell.\\n- **Analyst / Controller:** Pflegt Regeln, prüft Datenqualität, erstellt Reports und Szenarien.\\n- **Admin:** Konfiguriert Integrationen, Berechtigungen, Datenquellen und Systemparameter.\\n- **Read-only Reviewer:** Sieht Reports, Audit Trail und Dashboards ohne Änderungsrechte.\\n\\n### 3.2 Rollenrechte\\n\\n- Owner: Vollzugriff auf Dashboards, Regeln, Reports und manuelle Korrekturen.\\n- Analyst: Zugriff auf Datenpflege, Analysen, Reports, Watchlists; keine Sicherheits- oder Integrationsadministration.\\n- Admin: Systemkonfiguration und User Management; nicht automatisch fachlich entscheidungsberechtigt.\\n- Reviewer: Nur Lesen, Export und Kommentierung.\\n\\n---\\n\\n## 4. Zielarchitektur\\n\\n### 4.1 Architekturüberblick\\n\\nDas System soll modular aufgebaut sein und eine klare Trennung zwischen Data Ingestion, Accounting Engine, Analytics Layer, Rule Engine, Reporting Layer und Dashboard Frontend besitzen.\\n\\n**Zielbild:**\\n\\n1. **Data Sources**\\n   - Banken, Broker, CSV/Excel, APIs, PDF/Statements, Market Data Provider, FX Provider, Macro Data Provider.\\n\\n2. **Ingestion Layer**\\n   - Connectoren, File Uploads, Parser, API Jobs.\\n   - Validierung von Format, Pflichtfeldern, Währung, Datum und Duplikaten.\\n\\n3. **Normalization & Staging**\\n   - Mapping auf internes Canonical Data Model.\\n   - Quarantäne für unsichere Daten.\\n   - Source Lineage und Import Audit.\\n\\n4. **Accounting Engine**\\n   - Positionsführung, Cash Ledger, Transaction Ledger, Cost Basis, Realized/Unrealized P&L, Fees, Taxes, Corporate Actions.\\n\\n5. **Reference Data & Market Data Store**\\n   - Instrumente, ISIN/Ticker, Preise, FX Rates, Benchmarks, Sektoren, Länder, Asset Classes.\\n\\n6. **Analytics & Rule Engine**\\n   - Total Return, Risk, Exposure, Stress Tests, Liquidity Ladder, Alerts, Watchlist Scoring, Bias Detection.\\n\\n7. **Reporting & Export Layer**\\n   - Periodische Reports, Steuer-/Income-Exports, PDF/CSV/Excel, Audit Snapshots.\\n\\n8. **Dashboard UX**\\n   - Portfolio Overview, Transactions, Watchlists, Alerts, Risk, Income, Tax, Macro Overlay, Reports.\\n\\n9. **Governance & Security Layer**\\n   - RBAC, Audit Log, Data Quality Monitoring, Configuration Versioning, Backups.\\n\\n### 4.2 Architekturprinzipien\\n\\n- **Event-sourced Ledger where useful:** Transaktionen werden unverändert als Events gespeichert; abgeleitete Positionen sind reproduzierbar.\\n- **Immutable Raw Data:** Rohimporte dürfen nicht überschrieben werden.\\n- **Canonical Model:** Alle Quellen werden auf ein einheitliches internes Modell gemappt.\\n- **Separation of Concerns:** Accounting, Analytics, Rules, UI und Integrationen bleiben entkoppelt.\\n- **Idempotent Imports:** Wiederholte Imports derselben Datei/API-Daten dürfen keine Duplikate erzeugen.\\n- **Configurable Rules:** Schwellenwerte, Kategorien, Alert Rules und Watchlist-Kriterien sind konfigurierbar.\\n- **Explainable Outputs:** Jede abgeleitete Kennzahl referenziert Inputdaten, Regelversion und Berechnungszeitpunkt.\\n\\n---\\n\\n## 5. Funktionale Anforderungen\\n\\n## 5.1 Datenimport und Normalisierung\\n\\n### FR-001: Multi-Source Import\\nDas System muss Daten aus mehreren Quellen importieren können:\\n\\n- Bankkonten.\\n- Broker/Depotkonten.\\n- CSV/Excel-Dateien.\\n- PDF-Statements, sofern technisch möglich.\\n- Marktdaten-APIs.\\n- FX-Rates.\\n- Makrodaten.\\n- Manuelle Eingaben.\\n\\n### FR-002: Canonical Transaction Mapping\\nAlle importierten Bewegungen müssen auf ein internes Transaktionsschema gemappt werden:\\n\\n- Datum / Buchungsdatum / Valutadatum.\\n- Konto oder Depot.\\n- Instrument oder Cash Asset.\\n- Transaktionstyp.\\n- Menge.\\n- Preis.\\n- Brutto-/Nettowert.\\n- Gebühren.\\n- Steuern.\\n- Währung.\\n- FX Rate.\\n- Quelle und Import-ID.\\n\\n### FR-003: Import Audit Trail\\nJeder Import muss dokumentieren:\\n\\n- Quelle.\\n- Zeitpunkt.\\n- Importmethode.\\n- Anzahl gelesener Zeilen.\\n- Anzahl akzeptierter Zeilen.\\n- Anzahl abgelehnter oder quarantined Zeilen.\\n- Mapping-Version.\\n- Prüfsumme oder eindeutige Source-ID.\\n\\n### FR-004: Data Quarantine\\nFehlerhafte oder unsichere Datensätze müssen in eine Quarantine Queue verschoben werden, z. B. bei:\\n\\n- fehlendem Datum.\\n- unbekannter Währung.\\n- unklarer Instrument-ID.\\n- doppelverdächtiger Transaktion.\\n- nicht plausibler Menge oder Preis.\\n- fehlender FX Rate.\\n\\n---\\n\\n## 5.2 Transaction Accounting\\n\\n### FR-010: Transaction Ledger\\nDas System muss ein vollständiges, unveränderbares Transaction Ledger führen.\\n\\nPflichttypen:\\n\\n- Buy.\\n- Sell.\\n- Dividend.\\n- Interest.\\n- Deposit.\\n- Withdrawal.\\n- Fee.\\n- Tax.\\n- Tax Refund.\\n- FX Conversion.\\n- Split.\\n- Merger.\\n- Spin-off.\\n- Transfer In.\\n- Transfer Out.\\n- Cash Adjustment.\\n- Manual Correction.\\n\\n### FR-011: Cash Ledger\\nDas System muss Cash je Konto und Währung führen:\\n\\n- Anfangsbestand.\\n- Einzahlungen.\\n- Auszahlungen.\\n- Käufe/Verkäufe.\\n- Dividenden/Zinsen.\\n- Gebühren/Steuern.\\n- FX Conversions.\\n- Endbestand.\\n\\n### FR-012: Position Accounting\\nDas System muss Positionen je Instrument, Konto und Tax Lot berechnen:\\n\\n- Menge.\\n- Average Cost oder FIFO/LIFO je Konfiguration.\\n- Cost Basis.\\n- Marktwert.\\n- Unrealized P&L.\\n- Realized P&L.\\n- Gebührenanteile.\\n- Steueranteile.\\n\\n### FR-013: Corporate Actions\\nDas System muss Corporate Actions abbilden:\\n\\n- Splits und Reverse Splits.\\n- Dividenden in Cash oder Stock.\\n- Mergers.\\n- Spin-offs.\\n- Symbol-/ISIN-Wechsel.\\n- Delistings.\\n\\n### FR-014: Reconciliation\\nDas System muss Bestände gegen Broker-/Bank-Snapshots abgleichen:\\n\\n- Cash Reconciliation.\\n- Position Reconciliation.\\n- Market Value Reconciliation.\\n- Difference Report.\\n- manuelle Resolution mit Audit Trail.\\n\\n---\\n\\n## 5.3 Watchlist Pipeline\\n\\n### FR-020: Watchlist Entities\\nDas System muss Watchlists für Assets, Instrumente, Themen oder Makroindikatoren unterstützen.\\n\\nEine Watchlist Item muss enthalten:\\n\\n- Name.\\n- Identifier, z. B. ISIN, ticker, symbol, macro series ID.\\n- Asset Class.\\n- Region.\\n- Sector / Theme.\\n- Status, z. B. active, paused, archived.\\n- Beobachtungsgrund.\\n- Regeln und Schwellenwerte.\\n- Notizen und Decision Log.\\n\\n### FR-021: Rule-based Watchlist Scoring\\nWatchlist Scores müssen regelbasiert sein und z. B. folgende Komponenten unterstützen:\\n\\n- Valuation Trigger.\\n- Momentum Trigger.\\n- Volatility Trigger.\\n- Drawdown Trigger.\\n- Earnings / Dividend Trigger.\\n- Macro Sensitivity Trigger.\\n- Liquidity Trigger.\\n- News/Sentiment Summary nur als optionales, nicht entscheidendes Signal.\\n\\n### FR-022: Watchlist Pipeline Steps\\nDie Pipeline muss folgende Schritte unterstützen:\\n\\n1. Data Refresh.\\n2. Data Quality Check.\\n3. Rule Evaluation.\\n4. Score Calculation.\\n5. Alert Prioritization.\\n6. Human Review Queue.\\n7. Decision Logging.\\n8. Rule Feedback / Rule Adjustment.\\n\\n### FR-023: No Auto-Execution\\nEin Watchlist Signal darf ausschließlich eine Review-Aufgabe oder einen Alert erzeugen, niemals eine Order.\\n\\n---\\n\\n## 5.4 Dashboard UX\\n\\n### FR-030: Dashboard Overview\\nDas Hauptdashboard muss eine Executive Summary zeigen:\\n\\n- Net Worth.\\n- Portfolio Market Value.\\n- Cash Balance.\\n- Daily / Monthly / YTD P&L.\\n- Total Return.\\n- Income YTD.\\n- Realized Gains/Losses.\\n- Asset Allocation.\\n- Top Risks.\\n- High Priority Alerts.\\n- Data Quality Status.\\n\\n### FR-031: Portfolio View\\nDie Portfolio View muss unterstützen:\\n\\n- Gruppierung nach Konto, Asset Class, Region, Sector, Currency, Strategy Tag.\\n- Drill-down von Portfolio zu Position zu Tax Lot zu Transaktion.\\n- Performance Attribution.\\n- Exposure Analyse.\\n- Vergleich gegen Benchmark.\\n\\n### FR-032: Transaction View\\nDie Transaction View muss bieten:\\n\\n- Filter nach Datum, Konto, Instrument, Typ, Währung, Quelle, Status.\\n- Split View: Raw Import vs. Normalized Transaction.\\n- Manual Correction Workflow.\\n- Duplicate Detection Hinweise.\\n- Export.\\n\\n### FR-033: Alert Center\\nDas Alert Center muss Alerts nach Priorität, Kategorie und Status anzeigen:\\n\\n- Critical.\\n- High.\\n- Medium.\\n- Low.\\n- Info.\\n\\nStatus:\\n\\n- New.\\n- Acknowledged.\\n- In Review.\\n- Deferred.\\n- Resolved.\\n- False Positive.\\n\\n### FR-034: Decision Journal\\nDas System muss ein Decision Journal bereitstellen:\\n\\n- Anlass / Trigger.\\n- relevante Kennzahlen.\\n- betrachtete Alternativen.\\n- menschliche Entscheidung.\\n- Begründung.\\n- Zeitstempel.\\n- beteiligte Nutzer.\\n- Folgeaufgaben.\\n\\n---\\n\\n## 5.5 Reports\\n\\n### FR-040: Standard Reports\\nDas System muss folgende Reports unterstützen:\\n\\n- Monthly Portfolio Report.\\n- Quarterly Review Report.\\n- Annual Performance Report.\\n- Income Report.\\n- Tax Preparation Report.\\n- Risk Report.\\n- Liquidity Report.\\n- Data Quality Report.\\n- Watchlist Review Report.\\n- Decision Audit Report.\\n\\n### FR-041: Report Inhalte\\nReports müssen enthalten können:\\n\\n- Zeitraum.\\n- Start-/Endvermögen.\\n- Cashflows.\\n- Time-weighted Return.\\n- Money-weighted Return, optional.\\n- Income.\\n- Fees.\\n- Taxes.\\n- Realized/Unrealized P&L.\\n- Allocation.\\n- Top Contributors/Detractors.\\n- Risk Kennzahlen.\\n- Alerts und Entscheidungen.\\n- Data Quality Hinweise.\\n\\n### FR-042: Exportformate\\nDas System muss mindestens folgende Exportformate unterstützen:\\n\\n- PDF.\\n- CSV.\\n- Excel.\\n- JSON für maschinenlesbare Exporte.\\n\\n---\\n\\n## 5.6 Tax und Income\\n\\n### FR-050: Income Tracking\\nDas System muss Income separat tracken:\\n\\n- Dividenden.\\n- Zinsen.\\n- Ausschüttungen.\\n- Quellensteuer.\\n- Steuererstattungen.\\n- Fees bezogen auf Income.\\n- Währung und FX Rate zum Zahlungszeitpunkt.\\n\\n### FR-051: Tax Buckets\\nDas System muss steuerrelevante Buckets führen:\\n\\n- Realized short-term gains/losses, sofern relevant.\\n- Realized long-term gains/losses, sofern relevant.\\n- Dividend income.\\n- Interest income.\\n- Withholding tax.\\n- Transaction taxes.\\n- Deductible fees, sofern relevant.\\n- Carry-forward losses, manuell/konfigurierbar.\\n\\n### FR-052: Tax Disclaimer\\nAlle Steuerreports müssen einen Hinweis enthalten, dass die Ausgabe keine Steuerberatung ersetzt und durch Nutzer oder Steuerberater geprüft werden muss.\\n\\n### FR-053: Jurisdiction Configuration\\nSteuerlogik muss nach Jurisdiction konfigurierbar sein, z. B. Deutschland, USA, Schweiz, Österreich. MVP darf mit generischem Tax Categorization Model starten.\\n\\n---\\n\\n## 5.7 Total Return und Performance\\n\\n### FR-060: Performance Metrics\\nDas System muss berechnen:\\n\\n- Absolute Return.\\n- Total Return inklusive Dividenden und Zinsen.\\n- Time-weighted Return.\\n- Money-weighted Return / IRR, optional nach MVP.\\n- Realized P&L.\\n- Unrealized P&L.\\n- Fees Impact.\\n- Tax Impact.\\n- FX Impact.\\n\\n### FR-061: Benchmarking\\nDas System muss Portfolio- und Segmentperformance gegen Benchmarks darstellen können:\\n\\n- ein Benchmark pro Portfolio.\\n- optionale Benchmarks pro Asset Class.\\n- Abweichung absolut und relativ.\\n- Tracking Difference.\\n\\n### FR-062: Performance Attribution\\nDas System sollte Performance nach folgenden Dimensionen zerlegen:\\n\\n- Asset Class.\\n- Region.\\n- Sector.\\n- Currency.\\n- Instrument.\\n- Income vs. Price Return.\\n- FX Contribution.\\n\\n---\\n\\n## 5.8 Risk Management\\n\\n### FR-070: Risk Metrics\\nDas System muss Risiko-Kennzahlen berechnen oder darstellen:\\n\\n- Volatility.\\n- Maximum Drawdown.\\n- Value at Risk, optional und mit Methodikhinweis.\\n- Concentration Risk.\\n- Currency Exposure.\\n- Sector Exposure.\\n- Region Exposure.\\n- Counterparty/Broker Exposure.\\n- Liquidity Risk.\\n- Correlation Matrix.\\n\\n### FR-071: Risk Limits\\nDas System muss regelbasierte Risk Limits unterstützen:\\n\\n- maximale Positionsgröße.\\n- maximale Asset-Class-Quote.\\n- maximale Währungsquote.\\n- maximale Broker-Exposure.\\n- maximale Drawdown-Schwelle.\\n- minimale Cash Reserve.\\n- maximale illiquide Quote.\\n\\n### FR-072: Risk Breach Workflow\\nBei Limit-Verletzung muss ein Alert erzeugt werden mit:\\n\\n- verletzter Regel.\\n- Ist-Wert.\\n- Grenzwert.\\n- betroffene Assets.\\n- Schweregrad.\\n- vorgeschlagene Review-Aktion, keine automatische Handlung.\\n\\n---\\n\\n## 5.9 Macro Overlay\\n\\n### FR-080: Macro Data Integration\\nDas System soll Makrodaten integrieren können:\\n\\n- Zinskurven.\\n- Inflation.\\n- Arbeitsmarktdaten.\\n- GDP / PMI.\\n- Credit Spreads.\\n- Volatility Indices.\\n- FX Rates.\\n- Commodity Prices.\\n\\n### FR-081: Macro Regime Tags\\nDas System soll regelbasierte Macro Regime Tags erzeugen:\\n\\n- rising rates.\\n- falling rates.\\n- high inflation.\\n- disinflation.\\n- risk-on.\\n- risk-off.\\n- recession watch.\\n- liquidity tightening.\\n\\n### FR-082: Macro-to-Portfolio Mapping\\nDas System soll Portfolio-Exposures gegen Makrofaktoren mappen:\\n\\n- Duration Sensitivity.\\n- Equity Beta.\\n- Credit Sensitivity.\\n- Inflation Sensitivity.\\n- FX Sensitivity.\\n- Commodity Sensitivity.\\n\\n### FR-083: Macro Overlay Disclaimer\\nMacro Overlay darf keine Investmentanweisung erzeugen, sondern nur Kontext und Risikoindikatoren anzeigen.\\n\\n---\\n\\n## 5.10 Stress Tests und Szenarien\\n\\n### FR-090: Scenario Library\\nDas System muss eine Szenario-Bibliothek unterstützen:\\n\\n- Equity Crash.\\n- Rate Shock.\\n- Inflation Shock.\\n- FX Shock.\\n- Credit Spread Widening.\\n- Commodity Shock.\\n- Liquidity Freeze.\\n- Custom Scenario.\\n\\n### FR-091: Portfolio Stress Test\\nFür jedes Szenario muss das System berechnen:\\n\\n- geschätzter Portfolio Impact.\\n- Impact je Asset Class.\\n- Impact je Position.\\n- Cash Impact.\\n- Risk Limit Breaches.\\n- betroffene Watchlist Items.\\n\\n### FR-092: Assumption Transparency\\nJeder Stress Test muss Annahmen anzeigen:\\n\\n- verwendete Shocks.\\n- Mapping-Regeln.\\n- Sensitivitäten.\\n- Datenstand.\\n- Limitations.\\n\\n---\\n\\n## 5.11 Liquidity Ladder\\n\\n### FR-100: Liquidity Classification\\nDas System muss Assets nach Liquiditätsklassen einordnen:\\n\\n- sofort verfügbar, z. B. Cash.\\n- T+1/T+2 liquide Wertpapiere.\\n- kurzfristig liquidierbar.\\n- eingeschränkt liquide.\\n- illiquide.\\n- unbekannt.\\n\\n### FR-101: Liquidity Ladder View\\nDas System muss eine Liquidity Ladder anzeigen:\\n\\n- verfügbare Liquidität heute.\\n- verfügbare Liquidität innerhalb 2 Tage.\\n- innerhalb 1 Woche.\\n- innerhalb 1 Monat.\\n- innerhalb 3 Monate.\\n- länger als 3 Monate.\\n\\n### FR-102: Cash Need Matching\\nDas System soll geplante Cash Needs gegen verfügbare Liquidität mappen:\\n\\n- regelmäßige Ausgaben.\\n- Steuerzahlungen.\\n- geplante Investitionen.\\n- Notfallreserve.\\n- Ausschüttungsbedarf.\\n\\n---\\n\\n## 5.12 Bias Detection\\n\\n### FR-110: Behavioral Bias Indicators\\nDas System soll regelbasierte Bias-Indikatoren erkennen:\\n\\n- Home Bias.\\n- Concentration Bias.\\n- Recency Bias.\\n- Overtrading.\\n- Loss Aversion / Disposition Effect.\\n- Anchoring an Kaufkursen.\\n- Confirmation Bias über Watchlist/Decision Notes.\\n- Performance Chasing.\\n\\n### FR-111: Bias Evidence\\nJeder Bias Hinweis muss Evidenz anzeigen:\\n\\n- betroffene Datenpunkte.\\n- Regeldefinition.\\n- Zeitraum.\\n- Vergleichswert.\\n- mögliche Reflexionsfrage.\\n\\n### FR-112: No Psychometric Claims\\nBias Detection darf keine psychologische Diagnose darstellen, sondern nur Datenmuster für Selbstreflexion anzeigen.\\n\\n---\\n\\n## 5.13 Alert Priorities\\n\\n### FR-120: Alert Kategorien\\nAlerts müssen kategorisiert werden:\\n\\n- Data Quality.\\n- Accounting/Reconciliation.\\n- Risk Limit.\\n- Liquidity.\\n- Tax/Income.\\n- Watchlist.\\n- Macro.\\n- Bias.\\n- Report Due.\\n- Security/System.\\n\\n### FR-121: Prioritätslogik\\nPriorität muss regelbasiert abgeleitet werden aus:\\n\\n- Impact.\\n- Urgency.\\n- Confidence.\\n- Data Quality.\\n- User-defined criticality.\\n- Wiederholungshäufigkeit.\\n\\n### FR-122: Alert Escalation\\nAlerts müssen eskalieren können:\\n\\n- Critical: sofortige Review erforderlich.\\n- High: Review innerhalb kurzer Frist.\\n- Medium: Review im nächsten Routinezyklus.\\n- Low: Information / Watch.\\n- Info: rein dokumentarisch.\\n\\n### FR-123: Alert Fatigue Prevention\\nDas System muss Mechanismen zur Alert Fatigue Prevention unterstützen:\\n\\n- Deduplizierung.\\n- Snooze.\\n- Aggregation ähnlicher Alerts.\\n- Prioritätsdämpfung bei bekannten False Positives.\\n- periodische Alert Quality Review.\\n\\n---\\n\\n## 5.14 Data Quality\\n\\n### FR-130: Data Quality Dimensions\\nDas System muss Data Quality anhand folgender Dimensionen bewerten:\\n\\n- Completeness.\\n- Validity.\\n- Consistency.\\n- Timeliness.\\n- Uniqueness.\\n- Accuracy.\\n- Lineage Coverage.\\n\\n### FR-131: Data Quality Score\\nDas System muss einen Data Quality Score pro Datenbereich anzeigen:\\n\\n- Transactions.\\n- Prices.\\n- FX Rates.\\n- Instruments.\\n- Corporate Actions.\\n- Tax Data.\\n- Watchlist Data.\\n- Macro Data.\\n\\n### FR-132: Blocking Rules\\nBestimmte Analysen müssen blockiert oder markiert werden, wenn Datenqualität unzureichend ist, z. B.:\\n\\n- Performance Report ohne vollständige Preise.\\n- Tax Report ohne vollständige Steuerbuchungen.\\n- FX Exposure ohne aktuelle FX Rates.\\n- Reconciliation ohne Broker Snapshot.\\n\\n---\\n\\n## 6. Datenmodell v0.1\\n\\n### 6.1 Core Entities\\n\\n#### User\\n- user_id.\\n- name.\\n- email.\\n- role.\\n- status.\\n- preferences.\\n\\n#### Account\\n- account_id.\\n- institution_id.\\n- account_type.\\n- base_currency.\\n- owner.\\n- status.\\n\\n#### Institution\\n- institution_id.\\n- name.\\n- type, z. B. bank, broker, custodian.\\n- country.\\n\\n#### Instrument\\n- instrument_id.\\n- name.\\n- identifiers, z. B. ISIN, WKN, ticker, CUSIP.\\n- asset_class.\\n- currency.\\n- exchange.\\n- country.\\n- sector.\\n- issuer.\\n- status.\\n\\n#### Transaction\\n- transaction_id.\\n- source_id.\\n- account_id.\\n- instrument_id, optional bei Cash.\\n- transaction_type.\\n- trade_date.\\n- settlement_date.\\n- quantity.\\n- price.\\n- gross_amount.\\n- net_amount.\\n- fees.\\n- taxes.\\n- currency.\\n- fx_rate.\\n- metadata.\\n- import_batch_id.\\n- status.\\n\\n#### Position\\n- position_id.\\n- account_id.\\n- instrument_id.\\n- quantity.\\n- cost_basis.\\n- market_value.\\n- unrealized_pnl.\\n- currency.\\n- valuation_date.\\n\\n#### TaxLot\\n- lot_id.\\n- position_id.\\n- acquisition_date.\\n- quantity_open.\\n- acquisition_price.\\n- fees_allocated.\\n- cost_basis.\\n- realized_status.\\n\\n#### Price\\n- price_id.\\n- instrument_id.\\n- price_date.\\n- close_price.\\n- currency.\\n- source.\\n- quality_flag.\\n\\n#### FXRate\\n- fx_rate_id.\\n- base_currency.\\n- quote_currency.\\n- rate_date.\\n- rate.\\n- source.\\n\\n#### CorporateAction\\n- action_id.\\n- instrument_id.\\n- action_type.\\n- effective_date.\\n- terms.\\n- source.\\n- status.\\n\\n#### WatchlistItem\\n- watchlist_item_id.\\n- instrument_id oder macro_series_id.\\n- list_name.\\n- reason.\\n- status.\\n- rules.\\n- tags.\\n- owner.\\n\\n#### Alert\\n- alert_id.\\n- category.\\n- priority.\\n- status.\\n- entity_ref.\\n- rule_id.\\n- message.\\n- evidence.\\n- created_at.\\n- resolved_at.\\n\\n#### DecisionLog\\n- decision_id.\\n- trigger_ref.\\n- user_id.\\n- decision_type.\\n- rationale.\\n- considered_options.\\n- timestamp.\\n- attachments.\\n\\n#### Report\\n- report_id.\\n- report_type.\\n- period_start.\\n- period_end.\\n- generated_at.\\n- data_quality_status.\\n- file_refs.\\n\\n#### Rule\\n- rule_id.\\n- rule_type.\\n- name.\\n- definition.\\n- threshold.\\n- priority_mapping.\\n- version.\\n- active_status.\\n\\n#### DataQualityIssue\\n- issue_id.\\n- entity_type.\\n- entity_id.\\n- dimension.\\n- severity.\\n- description.\\n- status.\\n- detected_at.\\n\\n### 6.2 Relationships\\n\\n- Ein User kann mehrere Accounts besitzen oder verwalten.\\n- Ein Account gehört zu einer Institution.\\n- Eine Transaction referenziert Account und optional Instrument.\\n- Transactions erzeugen Positions- und Cash-Ledger-Effekte.\\n- Eine Position besteht aus einem oder mehreren TaxLots.\\n- Prices und FXRates bewerten Positions und Returns.\\n- CorporateActions transformieren Instruments, Positions und TaxLots.\\n- WatchlistItems referenzieren Instruments oder Macro Series.\\n- Rules erzeugen Alerts.\\n- Alerts können DecisionLogs auslösen.\\n- Reports referenzieren aggregierte Daten, Perioden und Data Quality Status.\\n\\n---\\n\\n## 7. Nicht-funktionale Anforderungen\\n\\n### NFR-001: Security\\n\\n- Verschlüsselung ruhender Daten, soweit sensibel.\\n- TLS für Datenübertragung.\\n- Role-based Access Control.\\n- Secret Management für API Keys.\\n- Audit Log für kritische Aktionen.\\n\\n### NFR-002: Privacy\\n\\n- Datenminimierung.\\n- klare Trennung von Rohdaten, normalisierten Daten und Reports.\\n- Export und Löschkonzept.\\n- keine Weitergabe an LLMs ohne explizite Freigabe und Redaction.\\n\\n### NFR-003: Auditability\\n\\n- Jeder Wert im Dashboard muss auf Rohdaten oder definierte Regeln zurückführbar sein.\\n- Jede manuelle Korrektur muss versioniert und begründet werden.\\n- Reports müssen Datenstand, Regelversion und Generierungszeitpunkt enthalten.\\n\\n### NFR-004: Reliability\\n\\n- Idempotente Importjobs.\\n- Wiederanlauf nach Fehlern.\\n- Backups.\\n- Monitoring der Datenpipelines.\\n- definierte Recovery Procedures.\\n\\n### NFR-005: Performance\\n\\n- Dashboard Overview für normale Portfoliogrößen innerhalb weniger Sekunden.\\n- Batch-Import großer CSV-Dateien ohne UI-Blockade.\\n- Analytics Jobs asynchron, wenn Laufzeit hoch ist.\\n\\n### NFR-006: Maintainability\\n\\n- Modularer Aufbau.\\n- klare Schnittstellen.\\n- dokumentierte Regeln.\\n- Versionierung von Mapping und Rule Configs.\\n\\n### NFR-007: Explainability\\n\\n- Rule Evidence muss pro Alert sichtbar sein.\\n- Berechnungsformeln müssen dokumentiert sein.\\n- LLM-generierte Texte müssen als solche markiert werden.\\n\\n### NFR-008: Compliance-by-Design\\n\\n- Keine automatisierte Anlageberatung.\\n- Keine automatische Orderausführung.\\n- Steuerberichte mit Disclaimer.\\n- Trennung von Analyse und Entscheidung.\\n\\n---\\n\\n## 8. LLM Policy\\n\\n### 8.1 Erlaubte LLM Use Cases\\n\\n- Zusammenfassung langer News- oder Reporttexte.\\n- Vorschläge zur Kategorisierung von Transaktionsbeschreibungen, mit menschlicher Bestätigung.\\n- Formulierung von Report Narratives aus deterministisch berechneten Kennzahlen.\\n- Extraktion unstrukturierter PDF-Informationen als Vorschlag mit Confidence Score.\\n\\n### 8.2 Nicht erlaubte LLM Use Cases\\n\\n- Berechnung von Returns, Taxes, Risk Metrics oder Accounting-Werten.\\n- finale Klassifikation ohne Regel oder Human Review.\\n- Kauf-/Verkaufsempfehlungen.\\n- Orderauslösung.\\n- Änderung von Regeln ohne menschliche Freigabe.\\n\\n### 8.3 LLM Governance\\n\\n- Prompt und Response Logging, sofern datenschutzkonform.\\n- Redaction sensibler Daten.\\n- Human-in-the-loop Review.\\n- Kennzeichnung LLM-generierter Inhalte.\\n- Fallback ohne LLM muss möglich sein.\\n\\n---\\n\\n## 9. Dashboard UX Struktur\\n\\n### 9.1 Navigation\\n\\n- Overview.\\n- Portfolio.\\n- Transactions.\\n- Cash & Liquidity.\\n- Income & Tax.\\n- Risk.\\n- Watchlist.\\n- Macro.\\n- Stress Tests.\\n- Alerts.\\n- Reports.\\n- Data Quality.\\n- Decision Journal.\\n- Settings.\\n\\n### 9.2 UX Prinzipien\\n\\n- Jede Kennzahl mit Drill-down.\\n- Ampelstatus nur mit erklärender Detailansicht.\\n- Keine versteckten Auto-Entscheidungen.\\n- Alerts priorisiert statt chronologisch überladen.\\n- Data Quality sichtbar auf jeder Analyse.\\n- Unterschied zwischen Ist-Daten, Simulation und Annahme klar markieren.\\n\\n### 9.3 Critical Screens MVP\\n\\n1. Portfolio Overview.\\n2. Transaction Ledger.\\n3. Data Quality Queue.\\n4. Alert Center.\\n5. Income & Tax Summary.\\n6. Risk Exposure View.\\n7. Watchlist Review.\\n8. Monthly Report Export.\\n\\n---\\n\\n## 10. Accounting- und Berechnungsregeln\\n\\n### 10.1 Grundsatz\\n\\nAlle Accounting-Berechnungen müssen deterministisch, reproduzierbar und testbar sein.\\n\\n### 10.2 Cost Basis\\n\\n- MVP: Average Cost oder FIFO konfigurierbar.\\n- Erweiterung: Jurisdiction-specific tax lot rules.\\n- Gebühren müssen optional in Cost Basis einbezogen werden können.\\n\\n### 10.3 FX Behandlung\\n\\n- Jede Transaktion in Fremdwährung benötigt FX Rate zum relevanten Datum.\\n- Reporting Currency muss konfigurierbar sein.\\n- FX Impact soll separat ausgewiesen werden.\\n\\n### 10.4 Fees und Taxes\\n\\n- Fees getrennt von Taxes speichern.\\n- Fees nach Transaktion, Position, Zeitraum und Instrument aggregieren.\\n- Taxes nach Art und Jurisdiction klassifizieren.\\n\\n### 10.5 Cashflows\\n\\n- External Cashflows von internal Cashflows trennen.\\n- Deposits/Withdrawals dürfen Performance nicht verzerren.\\n- Dividenden/Zinsen zählen als Income und Total Return Komponente.\\n\\n---\\n\\n## 11. Risk, Stress und Liquidity Methodik\\n\\n### 11.1 Risk Methodik\\n\\n- Historical Volatility auf Basis historischer Preise.\\n- Drawdown auf Portfolio- und Instrumentebene.\\n- Exposure Limits über aktuelle Marktwerte.\\n- Correlation auf Basis verfügbarer Return-Zeitreihen.\\n- VaR nur optional und mit Methodikhinweis.\\n\\n### 11.2 Stress Methodik\\n\\n- Szenarien müssen simple, nachvollziehbare Shocks verwenden.\\n- Mapping von Shocks auf Asset Classes muss konfigurierbar sein.\\n- Ergebnisse sind Schätzungen, keine Prognosen.\\n\\n### 11.3 Liquidity Methodik\\n\\n- Cash ist sofort verfügbar.\\n- Listed Securities nach Settlement Cycle und Handelsliquidität klassifizieren.\\n- Illiquide Assets benötigen manuelle Klassifikation.\\n- Liquidity Ladder muss Unsicherheiten markieren.\\n\\n---\\n\\n## 12. Data Quality Konzept\\n\\n### 12.1 Quality Gates\\n\\n- Import Gate: Format, Pflichtfelder, Typvalidierung.\\n- Mapping Gate: Instrument, Währung, Konto, Transaktionstyp.\\n- Accounting Gate: Ledger Balance, Cash Consistency, Position Consistency.\\n- Analytics Gate: Preise, FX Rates, Benchmarks, Macro Data Freshness.\\n- Report Gate: Vollständigkeit für Zeitraum und Berichtstyp.\\n\\n### 12.2 Issue Workflow\\n\\n1. Issue Detection.\\n2. Severity Assignment.\\n3. Quarantine oder Warning.\\n4. Human Resolution.\\n5. Re-run affected calculations.\\n6. Audit Log.\\n\\n### 12.3 Data Quality Severity\\n\\n- Critical: Report oder Accounting unbrauchbar.\\n- High: wichtige Kennzahl potenziell falsch.\\n- Medium: eingeschränkte Aussagekraft.\\n- Low: kosmetisch oder wenig Auswirkung.\\n- Info: Hinweis ohne unmittelbare Auswirkung.\\n\\n---\\n\\n## 13. Alert Priority Matrix\\n\\n### 13.1 Priority Inputs\\n\\n- Financial Impact.\\n- Time Sensitivity.\\n- Confidence.\\n- Data Quality Confidence.\\n- User Criticality.\\n- Regulatory/Tax Relevance.\\n\\n### 13.2 Beispiele\\n\\n- Critical: Cash Ledger stimmt nicht mit Broker Snapshot überein und Report wird heute erzeugt.\\n- Critical: Steuerreport basiert auf unvollständigen Transaktionen.\\n- High: Risk Limit für Einzelposition überschritten.\\n- High: Watchlist Item erreicht mehrere harte Trigger.\\n- Medium: Macro Regime wechselt auf risk-off.\\n- Medium: Home Bias Schwelle überschritten.\\n- Low: einzelne Preisquelle verspätet, alternative Quelle verfügbar.\\n- Info: Monthly Report ist bereit.\\n\\n---\\n\\n## 14. MVP Roadmap\\n\\n## Phase 0: Discovery und Baseline\\n\\nZiele:\\n\\n- Datenquellen inventarisieren.\\n- Ziel-Reporting Currency festlegen.\\n- Transaktionstypen finalisieren.\\n- MVP-Broker/Bank-Formate priorisieren.\\n- Steuer-Jurisdiction für MVP bestimmen.\\n\\nDeliverables:\\n\\n- Source Inventory.\\n- Canonical Data Model v0.1.\\n- Rule Catalogue v0.1.\\n- Dashboard Wireframe v0.1.\\n\\n## Phase 1: Core Ledger MVP\\n\\nZiele:\\n\\n- CSV/Excel Import.\\n- Transaction Ledger.\\n- Cash Ledger.\\n- Position Accounting.\\n- Basic Instrument Master Data.\\n- Manual Correction Workflow.\\n- Import Audit Trail.\\n\\nAkzeptanzkriterien:\\n\\n- Wiederholter Import erzeugt keine Duplikate.\\n- Cash und Positionen lassen sich aus Transaktionen reproduzieren.\\n- Unklare Daten landen in Quarantine.\\n\\n## Phase 2: Portfolio Dashboard und Reporting\\n\\nZiele:\\n\\n- Portfolio Overview.\\n- Transaction View.\\n- Market Value und P&L.\\n- Monthly Report.\\n- Data Quality Dashboard.\\n- Basic PDF/CSV Export.\\n\\nAkzeptanzkriterien:\\n\\n- Dashboard zeigt Datenstand und Quality Status.\\n- Monthly Report enthält Start-/Endwerte, Cashflows, P&L, Income und Fees.\\n\\n## Phase 3: Income, Tax und Total Return\\n\\nZiele:\\n\\n- Dividend/Interest Tracking.\\n- Tax Buckets.\\n- Total Return inkl. Income.\\n- Realized/Unrealized P&L.\\n- FX Impact.\\n\\nAkzeptanzkriterien:\\n\\n- Income Report ist nach Zeitraum, Instrument, Konto und Währung filterbar.\\n- Tax Export enthält Disclaimer und prüfbare Quelltransaktionen.\\n\\n## Phase 4: Watchlist und Alerts\\n\\nZiele:\\n\\n- Watchlist Management.\\n- Rule-based Watchlist Scoring.\\n- Alert Center.\\n- Decision Journal.\\n- Alert Priority Matrix.\\n\\nAkzeptanzkriterien:\\n\\n- Watchlist Trigger erzeugen Review Alerts, keine Orders.\\n- Jede Entscheidung wird mit Kontext dokumentiert.\\n\\n## Phase 5: Risk, Stress und Liquidity\\n\\nZiele:\\n\\n- Exposure Limits.\\n- Volatility/Drawdown.\\n- Concentration Risk.\\n- Liquidity Ladder.\\n- Scenario Stress Tests.\\n\\nAkzeptanzkriterien:\\n\\n- Risk Breaches erzeugen nachvollziehbare Alerts.\\n- Stress Tests zeigen Annahmen und Limitations.\\n\\n## Phase 6: Macro Overlay und Bias Detection\\n\\nZiele:\\n\\n- Macro Data Integration.\\n- Macro Regime Tags.\\n- Portfolio Macro Mapping.\\n- Behavioral Bias Indicators.\\n\\nAkzeptanzkriterien:\\n\\n- Macro Overlay ist klar als Kontext markiert.\\n- Bias Detection zeigt Evidenz und Reflexionsfragen, keine Diagnose.\\n\\n---\\n\\n## 15. Abnahmekriterien v0.1\\n\\nDas System erfüllt v0.1, wenn:\\n\\n- Datenimport, Ledger und Positionen reproduzierbar funktionieren.\\n- Dashboard die Kernwerte mit Data Quality Status anzeigt.\\n- Reports exportierbar und auditierbar sind.\\n- Alerts regelbasiert und priorisiert erzeugt werden.\\n- Watchlist Signale ausschließlich Review-Aufgaben erzeugen.\\n- Steuer-/Income-Daten transparent und mit Disclaimer dargestellt werden.\\n- Risk, Liquidity und Stress Ergebnisse ihre Annahmen offenlegen.\\n- LLM-Nutzung optional, markiert und nicht entscheidungsführend ist.\\n- keine Funktion automatisierte Trades auslösen kann.\\n\\n---\\n\\n## 16. Offene Fragen für v0.2\\n\\n- Welche konkreten Broker- und Bankformate haben Priorität?\\n- Welche Steuer-Jurisdiction ist für den ersten produktiven Einsatz maßgeblich?\\n- Welche Market Data Provider sollen genutzt werden?\\n- Welche Reporting Currency ist Default?\\n- Welche Benchmarks sollen initial unterstützt werden?\\n- Welche Asset Classes sind im MVP zwingend?\\n- Welche Genauigkeit wird für historische FX Rates benötigt?\\n- Welche Rollen und Berechtigungen werden organisatorisch benötigt?\\n- Welche Retention Policy gilt für Rohdaten und Reports?\\n- Welche Integrationen sind langfristig geplant, z. B. Steuerberater, Notion, Obsidian, Google Sheets?\\n\\n---\\n\\n## 17. Risiken und Annahmen\\n\\n### 17.1 Annahmen\\n\\n- Nutzer akzeptieren manuelle Review bei unsicheren Daten.\\n- Initiale Datenquellen können per CSV/Excel bereitgestellt werden.\\n- Marktdatenqualität ist für MVP ausreichend, wenn Datenlücken sichtbar sind.\\n- Steuerlogik startet generisch und wird später jurisdiction-specific erweitert.\\n\\n### 17.2 Projektrisiken\\n\\n- Brokerdaten sind uneinheitlich und schwer zu normalisieren.\\n- Corporate Actions können Accounting-Komplexität stark erhöhen.\\n- Steuerregeln sind länderspezifisch und fehleranfällig.\\n- Zu viele Alerts können Nutzer überfordern.\\n- LLM-Nutzung kann Datenschutz- und Erklärbarkeitsrisiken erzeugen.\\n\\n### 17.3 Gegenmaßnahmen\\n\\n- Quarantine und Review Queue statt stiller Autokorrektur.\\n- MVP mit begrenzten Datenquellen starten.\\n- Regelversionierung und Audit Trail.\\n- Alert Priority Matrix und Deduplizierung.\\n- LLM optional, redacted und nicht entscheidungsführend.\\n\\n---\\n\\n## 18. Glossar\\n\\n- **Accounting Engine:** deterministische Komponente zur Ableitung von Cash, Positions, P&L und Cost Basis aus Transactions.\\n- **Canonical Data Model:** einheitliches internes Datenmodell über alle Quellen hinweg.\\n- **Data Quarantine:** Bereich für Datensätze, die nicht automatisch verarbeitet werden dürfen.\\n- **Decision Journal:** dokumentierte menschliche Entscheidung mit Kontext und Begründung.\\n- **Human Decision Authority:** finale Entscheidungsgewalt bleibt beim Menschen.\\n- **Liquidity Ladder:** Darstellung, wann welche Mittel voraussichtlich verfügbar sind.\\n- **Macro Overlay:** Kontextschicht mit makroökonomischen Indikatoren und Regime Tags.\\n- **Rule Engine:** deterministische Auswertung von Regeln und Schwellenwerten.\\n- **Tax Lot:** steuerlich relevante Teilposition mit eigener Anschaffungshistorie.\\n- **Total Return:** Gesamtrendite inklusive Kursänderung, Dividenden, Zinsen und relevanter Effekte.\\n- **Watchlist Pipeline:** Prozess zur Überwachung interessanter Assets oder Makroindikatoren anhand definierter Regeln.\\n\\n---\\n\\n## 19. Zusammenfassung\\n\\nDieses Anforderungsdokument v0.1 definiert ein rule-based Finance Management und Decision-Support-System mit klarer menschlicher Entscheidungsautorität. Der Fokus liegt auf Datenqualität, auditierbarem Transaction Accounting, transparenten Reports, risikobewusstem Dashboarding, Watchlist Monitoring und nachvollziehbaren Alerts. Automatisiertes Trading und black-box Entscheidungen sind explizit ausgeschlossen.\\n\"}"
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      "content": "{\"content\": \"     1|# Anforderungsdokument v0.1\\n     2|## Automatisiertes Finance Management & Decision-Support-System\\n     3|\\n     4|**Version:** 0.1  \\n     5|**Status:** Draft / Requirements Baseline  \\n     6|**Sprache:** Deutsch mit English Technical Terms  \\n     7|**Scope:** Technische Systemarchitektur, Datenmodell, fachliche Anforderungen und MVP-Roadmap  \\n     8|**Nicht-Ziel:** Keine Investmentstrategie, keine Anlageberatung, kein automatisiertes Trading\\n     9|\\n    10|---\\n    11|\\n    12|## 1. Zweck und Leitprinzipien\\n    13|\\n    14|Das System soll private oder professionelle Finanzdaten automatisiert konsolidieren, strukturieren, analysieren und für menschliche Entscheidungen aufbereiten. Es unterstützt Portfolio-Controlling, Cashflow-Planung, Risikoüberwachung, Steuer-/Income-Übersicht, Watchlist-Monitoring, Reporting und Entscheidungsdokumentation.\\n    15|\\n    16|### 1.1 Kernprinzipien\\n    17|\\n    18|- **Human Decision Authority:** Jede Investment-, Rebalancing-, Verkaufs-, Kauf- oder Steuerentscheidung bleibt beim Menschen.\\n    19|- **No Automated Trading:** Das System darf keine Orders platzieren, keine Broker-Transaktionen auslösen und keine automatisierten Trade-Ausführungen durchführen.\\n    20|- **Rule-based First:** Berechnungen, Klassifikationen, Alerts und Scoring müssen primär regelbasiert, nachvollziehbar und auditierbar sein.\\n    21|- **Minimal LLM Usage:** LLMs dürfen nur unterstützend für nicht-deterministische Aufgaben eingesetzt werden, z. B. Textzusammenfassung, Belegklassifikationsvorschläge oder Report-Formulierungen. Kritische Berechnungen und Entscheidungen dürfen nicht LLM-basiert sein.\\n    22|- **Explainability:** Jede Kennzahl, jeder Alert und jeder Score muss eine nachvollziehbare Quelle, Regel und Berechnungslogik haben.\\n    23|- **Data Quality over Automation:** Fehlende, doppelte oder inkonsistente Daten müssen sichtbar gemacht werden, statt stillschweigend korrigiert zu werden.\\n    24|\\n    25|---\\n    26|\\n    27|## 2. Systemgrenzen\\n    28|\\n    29|### 2.1 In Scope\\n    30|\\n    31|- Import und Normalisierung von Konto-, Broker-, Depot-, Transaktions-, Preis-, FX-, Dividenden-, Steuer-, Watchlist- und Makrodaten.\\n    32|- Portfolio Accounting inklusive Cash, Securities, Fees, Taxes, Dividends, Interest, Splits und Corporate Actions.\\n    33|- Total Return, Income, Risk, Liquidity und Exposure Analytics.\\n    34|- Watchlist Pipeline mit regelbasiertem Monitoring und Signal-Priorisierung.\\n    35|- Dashboard UX für Vermögen, Risiken, Cashflows, Alerts und Reports.\\n    36|- Entscheidungsunterstützung durch Szenarien, Stress Tests, Bias Detection und Audit Trail.\\n    37|- Exportierbare Reports für persönliche Review, Steuerunterlagen und Dokumentation.\\n    38|\\n    39|### 2.2 Out of Scope\\n    40|\\n    41|\", \"total_lines\": 1335, \"file_size\": 34275, \"truncated\": true, \"hint\": \"Use offset=41 to continue reading (showing 1-40 of 1335 lines)\", \"is_binary\": false, \"is_image\": false}",
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      "content": "Erledigt.\n\n**Was ich gemacht habe**\n- Ein strukturiertes deutsches Anforderungsdokument v0.1 für ein automatisiertes Finance Management & Decision-Support-System erstellt.\n- Die Anforderungen konsequent als technische Systemarchitektur formuliert, nicht als Investmentstrategie.\n- Die Leitplanken berücksichtigt:\n  - rule-based first\n  - minimal LLM usage\n  - no automated trading\n  - human decision authority\n  - auditierbare und erklärbare Outputs\n  - keine Implementierungscode-Beispiele\n\n**Was enthalten ist**\n- Zielbild und Systemgrenzen\n- Zielarchitektur mit Ingestion, Normalization, Accounting Engine, Analytics/Rule Engine, Reporting und Dashboard\n- Funktionale Anforderungen\n- Nicht-funktionale Anforderungen\n- Datenmodell v0.1\n- Dashboard UX\n- Transaction Accounting\n- Watchlist Pipeline\n- Reports\n- Tax/Income\n- Total Return\n- Risk\n- Macro Overlay\n- Stress Tests\n- Liquidity Ladder\n- Bias Detection\n- Alert Priorities\n- Data Quality Konzept\n- MVP Roadmap\n- Abnahmekriterien\n- offene Fragen für v0.2\n- Risiken, Annahmen und Glossar\n\n**Erstellte Datei**\n- `/home/agent/anforderungsdokument_finance_decision_support_v0.1.md`\n\n**Verifikation**\n- Datei wurde erfolgreich geschrieben und per `read_file` geprüft.\n- Keine Issues encountered.",
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              "text": "Erledigt.\n\n**Was ich gemacht habe**\n- Ein strukturiertes deutsches Anforderungsdokument v0.1 für ein automatisiertes Finance Management & Decision-Support-System erstellt.\n- Die Anforderungen konsequent als technische Systemarchitektur formuliert, nicht als Investmentstrategie.\n- Die Leitplanken berücksichtigt:\n  - rule-based first\n  - minimal LLM usage\n  - no automated trading\n  - human decision authority\n  - auditierbare und erklärbare Outputs\n  - keine Implementierungscode-Beispiele\n\n**Was enthalten ist**\n- Zielbild und Systemgrenzen\n- Zielarchitektur mit Ingestion, Normalization, Accounting Engine, Analytics/Rule Engine, Reporting und Dashboard\n- Funktionale Anforderungen\n- Nicht-funktionale Anforderungen\n- Datenmodell v0.1\n- Dashboard UX\n- Transaction Accounting\n- Watchlist Pipeline\n- Reports\n- Tax/Income\n- Total Return\n- Risk\n- Macro Overlay\n- Stress Tests\n- Liquidity Ladder\n- Bias Detection\n- Alert Priorities\n- Data Quality Konzept\n- MVP Roadmap\n- Abnahmekriterien\n- offene Fragen für v0.2\n- Risiken, Annahmen und Glossar\n\n**Erstellte Datei**\n- `/home/agent/anforderungsdokument_finance_decision_support_v0.1.md`\n\n**Verifikation**\n- Datei wurde erfolgreich geschrieben und per `read_file` geprüft.\n- Keine Issues encountered."
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