{
"session_id": "20260524_120031_1cfb85",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-24T09:21:06.836608",
"last_updated": "2026-05-24T12:29:44.734639",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\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\n\n\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\n\n\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\n\n\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\n\n\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\n\n\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\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\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\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 + ntn CLI: pages, databases, markdown, Workers.\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 - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\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 - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\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\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-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  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [98% — 2,167/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [98% — 1,360/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts/local media preference: free/local Retention Studio; photorealistic motion-led mechanism-reveal Shorts, quality > speed. Prefer keyframe→Wan2.2 I2V and Qwen-Image/ComfyUI where feasible; avoid static slideshows, generic avatars, reused clones, paid credits, flickering/dark/blurry artifacts; be blunt about quality/VRAM limits.\n\nConversation started: Sunday, May 24, 2026 12:00 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: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_doc_read",
"description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
"parameters": {
"type": "object",
"properties": {
"doc_token": {
"type": "string",
"description": "The document token (from the document URL or comment context)."
}
},
"required": [
"doc_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_add_comment",
"description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"content": {
"type": "string",
"description": "The comment text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comment_replies",
"description": "List all replies in a comment thread on a Feishu document.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to list replies for."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"page_size": {
"type": "integer",
"description": "Number of replies per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token",
"comment_id"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comments",
"description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"is_whole": {
"type": "boolean",
"description": "If true, only return whole-document comments.",
"default": false
},
"page_size": {
"type": "integer",
"description": "Number of comments per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_reply_comment",
"description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to reply to."
},
"content": {
"type": "string",
"description": "The reply text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"comment_id",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown  and the gateway will deliver it.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"aspect_ratio": {
"type": "string",
"enum": [
"landscape",
"square",
"portrait"
],
"description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
"default": "landscape"
}
},
"required": [
"prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
"default": "replace"
},
"path": {
"type": "string",
"description": "REQUIRED when mode='replace'. File path to edit."
},
"old_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit",
"close"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "send_message",
"description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"send",
"list"
],
"description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
},
"target": {
"type": "string",
"description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)"
},
"message": {
"type": "string",
"description": "The message text to send. To send an image or file, include MEDIA: (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=` 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 `. 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.), 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/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "video_analyze",
"description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
"parameters": {
"type": "object",
"properties": {
"video_url": {
"type": "string",
"description": "Video URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
}
},
"required": [
"video_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https), local file path, or data: URL to load."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
}
},
"required": [
"image_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "write_file",
"description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
},
"content": {
"type": "string",
"description": "Complete content to write to the file"
}
},
"required": [
"path",
"content"
]
}
}
}
],
"message_count": 121,
"messages": [
{
"role": "user",
"content": "[M G] co/Civitai/Qwen-Image-2512-GGUF \"Civitai/Qwen-Image-2512-GGUF · Hugging Face\"\nSollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?\n für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.\n\nMeine Rangliste für deinen Usecase:\n\n| Zweck | Empfehlung | Warum |\n| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Beste kostenlose GGUF-Bildgenerierung in ComfyUI | Qwen-Image-2512-GGUF | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |\n| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2]) |\n| Schnell / weniger VRAM / experimentell | Z-Image Turbo GGUF | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3]) |\n| Alternative zu Qwen, stark aber schwerer | FLUX.2-dev-GGUF | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4]) |\n| Aktuell stärkstes Open-Weights-Modell allgemein | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5]) |\n\nKurz gesagt:\nFür deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.\n\nDie passende Auswahl nach VRAM:\n\n| VRAM | Modell/Quantisierung |\n| ----------: | ------------------------------------------------------ |\n| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |\n| 16 GB | Q5 oder Q6 testen |\n| 24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität |\n| <8 GB | eher Z-Image Turbo GGUF statt Qwen |\n\nWichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])\n\nMeine konkrete Empfehlung:\nInstalliere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.\n\n[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF \"unsloth/Qwen-Image-2512-GGUF · Hugging Face\"\n[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF \"unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face\"\n[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF \"jayn7/Z-Image-Turbo-GGUF · Hugging Face\"\n[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF \"orabazes/FLUX.2-dev-GGUF · Hugging Face\"\n[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image \"Text to Image Leaderboard - Top AI Image Models\"\n[6]: https://huggingface."
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"content": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen.",
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"text": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen."
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Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. 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They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. 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"content": "{\"content\": \" 1|from autoshorts.ideas.candidate import VideoCandidate\\n 2|from autoshorts.ideas.testbatch import TestBatch\\n 3|from autoshorts.review.render import render_review_batch_summary\\n 4|from autoshorts.review.workflow import build_review_batch\\n 5|from autoshorts.scripting.quality import ScriptQualityScore\\n 6|\\n 7|\\n 8|def _good_score() -> ScriptQualityScore:\\n 9| return ScriptQualityScore(\\n 10| hook_strength=9,\\n 11| novelty=8,\\n 12| clarity=9,\\n 13| retention=8,\\n 14| ai_slop_risk=2,\\n 15| policy_risk=\\\"low\\\",\\n 16| )\\n 17|\\n 18|\\n 19|def _weak_score() -> ScriptQualityScore:\\n 20| return ScriptQualityScore(\\n 21| hook_strength=5,\\n 22| novelty=5,\\n 23| clarity=6,\\n 24| retention=5,\\n 25| ai_slop_risk=8,\\n 26| policy_risk=\\\"low\\\",\\n 27| )\\n 28|\\n 29|\\n 30|def _candidate(**overrides) -> VideoCandidate:\\n 31| data = {\\n 32| \\\"candidate_id\\\": \\\"vid-001\\\",\\n 33| \\\"pillar\\\": \\\"ai_life_systems\\\",\\n 34| \\\"title\\\": \\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n 35| \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n 36| \\\"duration_seconds\\\": 34,\\n 37| \\\"hook\\\": \\\"AI sollte dir dienen, nicht dich führen.\\\",\\n 38| \\\"script_outline\\\": \\\"Show AI chaos, then a controlled human-approved workflow.\\\",\\n 39| \\\"visual_concept\\\": \\\"Calm desk setup, explicit approval checkpoint, no fake dashboards.\\\",\\n 40| \\\"caption\\\": \\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n 41| \\\"cta\\\": \\\"Follow for practical AI life systems.\\\",\\n 42| \\\"hashtags\\\": (\\\"#AI\\\", \\\"#LifeSystems\\\"),\\n 43| \\\"quality_score\\\": _good_score(),\\n 44| \\\"policy_notes\\\": (\\\"No fake earnings claims\\\",),\\n 45| \\\"hypothesis\\\": \\\"Values-first AI positioning gets more saves than hype content.\\\",\\n 46| }\\n 47| data.update(overrides)\\n 48| return VideoCandidate(**data)\\n 49|\\n 50|\\n 51|def test_render_review_batch_summary_contains_counts_and_sections():\\n 52| batch = TestBatch(\\n 53| batch_id=\\\"batch-001\\\",\\n 54| candidates=(\\n 55| _candidate(candidate_id=\\\"ready\\\", title=\\\"Ready title\\\"),\\n 56| _candidate(candidate_id=\\\"blocked\\\", title=\\\"Blocked title\\\", quality_score=_weak_score()),\\n 57| _candidate(candidate_id=\\\"invalid\\\", title=\\\"Invalid title\\\", hook=\\\"\\\"),\\n 58| ),\\n 59| )\\n 60| review_batch = build_review_batch(\\n 61| batch,\\n 62| preview_paths={\\\"ready\\\": \\\"data/local_motion_renders/ready.mp4\\\"},\\n 63| )\\n 64|\\n 65| text = render_review_batch_summary(review_batch)\\n 66|\\n 67| assert \\\"## Review Batch: batch-001\\\" in text\\n 68| assert \\\"Ready: 1\\\" in text\\n 69| assert \\\"Blocked: 1\\\" in text\\n 70| assert \\\"Invalid: 1\\\" in text\\n 71| assert \\\"## Ready for review\\\" in text\\n 72| assert \\\"Review: ready\\\" in text\\n 73| assert \\\"Preview: data/local_motion_renders/ready.mp4\\\" in text\\n 74| assert \\\"## Blocked\\\" in text\\n 75| assert \\\"blocked — Blocked title\\\" in text\\n 76| assert \\\"quality gate blocked candidate\\\" in text\\n 77| assert \\\"## Invalid\\\" in text\\n 78| assert \\\"invalid — Invalid title\\\" in text\\n 79| assert \\\"hook is required\\\" in text\\n 80|\\n 81|\\n 82|def test_render_review_batch_summary_omits_empty_sections():\\n 83| batch = TestBatch(batch_id=\\\"only-ready\\\", candidates=(_candidate(candidate_id=\\\"ready\\\"),))\\n 84| review_batch = build_review_batch(batch)\\n 85|\\n 86| text = render_review_batch_summary(review_batch)\\n 87|\\n 88| assert \\\"Ready: 1\\\" in text\\n 89| assert \\\"Blocked: 0\\\" in text\\n 90| assert \\\"Invalid: 0\\\" in text\\n 91| assert \\\"## Ready for review\\\" in text\\n 92| assert \\\"## Blocked\\\" not in text\\n 93| assert \\\"## Invalid\\\" not in text\\n 94|\\n 95|\\n 96|def test_render_review_batch_summary_has_no_publish_language():\\n 97| batch = TestBatch(batch_id=\\\"safe\\\", candidates=(_candidate(candidate_id=\\\"ready\\\"),))\\n 98| review_batch = build_review_batch(batch)\\n 99|\\n 100| text = render_review_batch_summary(review_batch)\\n 101|\\n 102| assert \\\"publish\\\" not in text.casefold()\\n 103| assert \\\"post\\\" not in text.casefold()\\n 104|\", \"total_lines\": 103, \"file_size\": 3582, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n 4|\\n 5|The project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n 6|\\n 7|## Current scope\\n 8|\\n 9|Implemented foundation:\\n 10|\\n 11|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 12|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 13|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 14|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 15|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 16|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 17|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 18|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 19|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 20|- Tests for content pillars, candidate factory, idea candidates, testbatches, review workflow, review-batch rendering, approval parsing, review formatting, and script quality gating.\\n 21|\\n 22|## Human approval commands\\n 23|\\n 24|Supported review responses:\\n 25|\\n 26|- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n 27|- `Freigabe YouTube`\\n 28|- `Freigabe TikTok`\\n 29|- `Freigabe Instagram`\\n 30|- `Ablehnen`\\n 31|- `Ändern: `\\n 32|\\n 33|Ambiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n 34|\\n 35|## Quality gate\\n 36|\\n 37|A script candidate only proceeds to rendering when:\\n 38|\\n 39|- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n 40|- AI-slop risk is not above `5`;\\n 41|- policy risk is not `high`.\\n 42|\\n 43|This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n 44|\\n 45|## Review package\\n 46|\\n 47|`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n 48|\\n 49|- candidate id, title, pillar, target platforms, and duration;\\n 50|- hook, script summary, visual concept, caption, and hashtags;\\n 51|- average quality score, AI-slop risk, policy notes, and render-gate status;\\n 52|- optional preview path;\\n 53|- only the relevant platform-specific approval commands.\\n 54|\\n 55|A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n 56|\\n 57|## Review workflow\\n 58|\\n 59|`build_candidate_review_item()` classifies one candidate as:\\n 60|\\n 61|- `ready_for_review` — valid and renderable; includes a review package;\\n 62|- `blocked` — valid but blocked by quality gate; still includes a review package with blocked gate status;\\n 63|- `invalid` — fails candidate validation; does not create a review package.\\n 64|\\n 65|`build_review_batch()` processes a `TestBatch` into `ready_for_review`, `blocked`, and `invalid` buckets while preserving input order inside each bucket. Optional preview paths are attached by candidate id.\\n 66|\\n 67|`render_review_batch_summary()` turns the complete `ReviewBatch` into one Telegram-safe text block with summary counts, full ready-for-review packages, and compact blocked/invalid reason lists. It deliberately avoids publish/post language; approval and publishing remain separate later stages.\\n 68|\\n 69|## Idea candidates and testbatches\\n 70|\\n 71|`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n 72|\\n 73|- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n 74|- hook, script outline, visual concept, caption, CTA, and hashtags;\\n 75|- quality score and policy notes.\\n 76|\\n 77|`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n 78|\\n 79|`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n 80|\\n 81|## Candidate factory\\n 82|\\n 83|`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n 84|\\n 85|`create_candidate()` turns a draft into a candidate by:\\n 86|\\n 87|- generating a stable id from pillar, title, and duration;\\n 88|- adding default pillar hashtags when no hashtags are supplied;\\n 89|- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n 90|- appending the duration class to policy notes;\\n 91|- enforcing candidate validation and pillar/platform fit validation.\\n 92|\\n 93|Duration classes:\\n 94|\\n 95|- `growth_short`: 20–45 seconds;\\n 96|- `monetization_candidate`: 60–75 seconds;\\n 97|- `experiment`: all other valid durations.\\n 98|\\n 99|Missing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n 100|\\n 101|## Content pillars\\n 102|\\n 103|`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n 104|\\n 105|- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n 106|\\n 107|A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n 108|\\n 109|`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n 110|\\n 111|## Development\\n 112|\\n 113|Run tests:\\n 114|\\n 115|```bash\\n 116|python -m pytest tests -q\\n 117|```\\n 118|\\n 119|## Planned architecture\\n 120|\\n 121|```text\\n 122|autoshorts/\\n 123| config/ # niches, platforms, workflow settings\\n 124| ideas/ # idea generation and scoring\\n 125| scripting/ # script drafts, quality gates, retention checks\\n 126| rendering/ # local/AI-assisted preview rendering\\n 127| review/ # Telegram review package and approval parser\\n 128| publishing/ # platform-specific publishing metadata and later APIs\\n 129| analytics/ # performance imports and feedback loop\\n 130|```\\n 131|\\n 132|## Non-negotiables\\n 133|\\n 134|- No credentials in git.\\n 135|- No generated media in git unless explicitly intended.\\n 136|- No automatic publishing without explicit approval.\\n 137|- Quality over speed; avoid generic AI-slop.\\n 138|\", \"total_lines\": 137, \"file_size\": 6099, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Factory helpers for turning drafts into validated video candidates.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|import re\\n 7|import unicodedata\\n 8|from typing import Literal\\n 9|\\n 10|from autoshorts.config.pillars import (\\n 11| DEFAULT_PILLARS,\\n 12| ContentPillar,\\n 13| validate_candidate_against_pillars,\\n 14|)\\n 15|from autoshorts.ideas.candidate import VideoCandidate, validate_candidate\\n 16|from autoshorts.scripting.quality import ScriptQualityScore\\n 17|\\n 18|DurationClass = Literal[\\\"growth_short\\\", \\\"monetization_candidate\\\", \\\"experiment\\\"]\\n 19|\\n 20|_DEFAULT_HASHTAGS_BY_PILLAR = {\\n 21| \\\"ai_life_systems\\\": (\\\"#AI\\\", \\\"#LifeSystems\\\", \\\"#Productivity\\\"),\\n 22|}\\n 23|\\n 24|\\n 25|@dataclass(frozen=True)\\n 26|class CandidateDraft:\\n 27| pillar: str\\n 28| title: str\\n 29| platforms: tuple[str, ...]\\n 30| duration_seconds: int\\n 31| hook: str\\n 32| script_outline: str\\n 33| visual_concept: str\\n 34| caption: str\\n 35| cta: str\\n 36| quality_score: ScriptQualityScore\\n 37| policy_notes: tuple[str, ...]\\n 38| hypothesis: str\\n 39| hashtags: tuple[str, ...] = ()\\n 40|\\n 41|\\n 42|def classify_duration(duration_seconds: int) -> DurationClass:\\n 43| if 20 <= duration_seconds <= 45:\\n 44| return \\\"growth_short\\\"\\n 45| if 60 <= duration_seconds <= 75:\\n 46| return \\\"monetization_candidate\\\"\\n 47| return \\\"experiment\\\"\\n 48|\\n 49|\\n 50|def _slugify(text: str, *, max_words: int = 5) -> str:\\n 51| normalized = unicodedata.normalize(\\\"NFKD\\\", text or \\\"\\\")\\n 52| ascii_text = normalized.encode(\\\"ascii\\\", \\\"ignore\\\").decode(\\\"ascii\\\")\\n 53| tokens = re.findall(r\\\"[a-z0-9]+\\\", ascii_text.casefold())\\n 54| return \\\"-\\\".join(tokens[:max_words]) or \\\"untitled\\\"\\n 55|\\n 56|\\n 57|def create_candidate_id(pillar: str, title: str, duration_seconds: int) -> str:\\n 58| return f\\\"{pillar}-{_slugify(title)}-{duration_seconds}s\\\"\\n 59|\\n 60|\\n 61|def _default_hashtags_for_pillar(pillar: str) -> tuple[str, ...]:\\n 62| return _DEFAULT_HASHTAGS_BY_PILLAR.get(pillar, ())\\n 63|\\n 64|\\n 65|def create_candidate(\\n 66| draft: CandidateDraft,\\n 67| *,\\n 68| registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n 69|) -> VideoCandidate:\\n 70| if not (draft.hypothesis or \\\"\\\").strip():\\n 71| raise ValueError(\\\"hypothesis is required\\\")\\n 72|\\n 73| duration_class = classify_duration(draft.duration_seconds)\\n 74| policy_notes = tuple(draft.policy_notes) + (f\\\"Duration class: {duration_class}\\\",)\\n 75| hashtags = draft.hashtags or _default_hashtags_for_pillar(draft.pillar)\\n 76|\\n 77| candidate = VideoCandidate(\\n 78| candidate_id=create_candidate_id(\\n 79| pillar=draft.pillar,\\n 80| title=draft.title,\\n 81| duration_seconds=draft.duration_seconds,\\n 82| ),\\n 83| pillar=draft.pillar,\\n 84| title=draft.title,\\n 85| platforms=draft.platforms,\\n 86| duration_seconds=draft.duration_seconds,\\n 87| hook=draft.hook,\\n 88| script_outline=draft.script_outline,\\n 89| visual_concept=draft.visual_concept,\\n 90| caption=draft.caption,\\n 91| cta=draft.cta,\\n 92| hashtags=hashtags,\\n 93| quality_score=draft.quality_score,\\n 94| policy_notes=policy_notes,\\n 95| hypothesis=draft.hypothesis,\\n 96| )\\n 97|\\n 98| candidate_result = validate_candidate(candidate)\\n 99| pillar_result = validate_candidate_against_pillars(candidate, registry)\\n 100| errors = candidate_result.errors + pillar_result.errors\\n 101| if errors:\\n 102| raise ValueError(\\\"; \\\".join(errors))\\n 103| return candidate\\n 104|\", \"total_lines\": 103, \"file_size\": 3184, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_ioeOk3Ocdhz0j9CIyBk7nMo8"
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Video candidate domain model and validation.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.review.package import ReviewPackage\\n 8|from autoshorts.scripting.quality import ScriptQualityScore\\n 9|\\n 10|SUPPORTED_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n 11|\\n 12|\\n 13|@dataclass(frozen=True)\\n 14|class ValidationResult:\\n 15| is_valid: bool\\n 16| errors: tuple[str, ...] = ()\\n 17|\\n 18|\\n 19|@dataclass(frozen=True)\\n 20|class VideoCandidate:\\n 21| candidate_id: str\\n 22| pillar: str\\n 23| title: str\\n 24| platforms: tuple[str, ...]\\n 25| duration_seconds: int\\n 26| hook: str\\n 27| script_outline: str\\n 28| visual_concept: str\\n 29| caption: str\\n 30| cta: str\\n 31| hashtags: tuple[str, ...]\\n 32| quality_score: ScriptQualityScore\\n 33| policy_notes: tuple[str, ...]\\n 34| hypothesis: str\\n 35|\\n 36| def to_review_package(self, preview_path: str | None = None) -> ReviewPackage:\\n 37| return ReviewPackage(\\n 38| candidate_id=self.candidate_id,\\n 39| title=self.title,\\n 40| pillar=self.pillar,\\n 41| duration_seconds=self.duration_seconds,\\n 42| hook=self.hook,\\n 43| script_summary=self.script_outline,\\n 44| visual_concept=self.visual_concept,\\n 45| caption=self.caption,\\n 46| hashtags=self.hashtags,\\n 47| platforms=self.platforms,\\n 48| quality_score=self.quality_score,\\n 49| policy_notes=self.policy_notes,\\n 50| preview_path=preview_path,\\n 51| )\\n 52|\\n 53|\\n 54|def _is_blank(value: str) -> bool:\\n 55| return not (value or \\\"\\\").strip()\\n 56|\\n 57|\\n 58|def validate_candidate(candidate: VideoCandidate) -> ValidationResult:\\n 59| errors: list[str] = []\\n 60|\\n 61| required_fields = {\\n 62| \\\"candidate_id\\\": candidate.candidate_id,\\n 63| \\\"pillar\\\": candidate.pillar,\\n 64| \\\"title\\\": candidate.title,\\n 65| \\\"hook\\\": candidate.hook,\\n 66| \\\"script_outline\\\": candidate.script_outline,\\n 67| \\\"visual_concept\\\": candidate.visual_concept,\\n 68| \\\"caption\\\": candidate.caption,\\n 69| \\\"cta\\\": candidate.cta,\\n 70| \\\"hypothesis\\\": candidate.hypothesis,\\n 71| }\\n 72| for field_name, value in required_fields.items():\\n 73| if _is_blank(value):\\n 74| errors.append(f\\\"{field_name} is required\\\")\\n 75|\\n 76| if not 10 <= candidate.duration_seconds <= 180:\\n 77| errors.append(\\\"duration_seconds must be between 10 and 180\\\")\\n 78|\\n 79| if not candidate.platforms:\\n 80| errors.append(\\\"at least one platform is required\\\")\\n 81| for platform in candidate.platforms:\\n 82| if platform not in SUPPORTED_PLATFORMS:\\n 83| errors.append(f\\\"unsupported platform: {platform}\\\")\\n 84|\\n 85| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 86|\", \"total_lines\": 85, \"file_size\": 2557, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_sz6GWJxEpHQDKQWNq25cOhxq"
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Review workflow orchestration before rendering or publishing.\\n 2|\\n 3|This module classifies candidates into deterministic buckets so later Telegram,\\n 4|rendering, and publishing code can stay boring. Boring is a feature here.\\n 5|\\\"\\\"\\\"\\n 6|\\n 7|from __future__ import annotations\\n 8|\\n 9|from dataclasses import dataclass\\n 10|from typing import Literal\\n 11|\\n 12|from autoshorts.ideas.candidate import VideoCandidate, validate_candidate\\n 13|from autoshorts.ideas.testbatch import TestBatch\\n 14|from autoshorts.review.package import ReviewPackage\\n 15|from autoshorts.scripting.quality import should_render\\n 16|\\n 17|ReviewStatus = Literal[\\\"ready_for_review\\\", \\\"blocked\\\", \\\"invalid\\\"]\\n 18|\\n 19|\\n 20|@dataclass(frozen=True)\\n 21|class CandidateReviewItem:\\n 22| candidate: VideoCandidate\\n 23| status: ReviewStatus\\n 24| package: ReviewPackage | None\\n 25| errors: tuple[str, ...] = ()\\n 26|\\n 27|\\n 28|@dataclass(frozen=True)\\n 29|class ReviewBatch:\\n 30| batch_id: str\\n 31| ready_for_review: tuple[CandidateReviewItem, ...]\\n 32| blocked: tuple[CandidateReviewItem, ...]\\n 33| invalid: tuple[CandidateReviewItem, ...]\\n 34|\\n 35| # Prevent pytest collection warnings if imported into tests.\\n 36| __test__ = False\\n 37|\\n 38|\\n 39|def build_candidate_review_item(\\n 40| candidate: VideoCandidate,\\n 41| *,\\n 42| preview_path: str | None = None,\\n 43|) -> CandidateReviewItem:\\n 44| validation = validate_candidate(candidate)\\n 45| if not validation.is_valid:\\n 46| return CandidateReviewItem(\\n 47| candidate=candidate,\\n 48| status=\\\"invalid\\\",\\n 49| package=None,\\n 50| errors=validation.errors,\\n 51| )\\n 52|\\n 53| package = candidate.to_review_package(preview_path=preview_path)\\n 54| if not should_render(candidate.quality_score):\\n 55| return CandidateReviewItem(\\n 56| candidate=candidate,\\n 57| status=\\\"blocked\\\",\\n 58| package=package,\\n 59| errors=(\\\"quality gate blocked candidate\\\",),\\n 60| )\\n 61|\\n 62| return CandidateReviewItem(\\n 63| candidate=candidate,\\n 64| status=\\\"ready_for_review\\\",\\n 65| package=package,\\n 66| errors=(),\\n 67| )\\n 68|\\n 69|\\n 70|def build_review_batch(\\n 71| batch: TestBatch,\\n 72| *,\\n 73| preview_paths: dict[str, str] | None = None,\\n 74|) -> ReviewBatch:\\n 75| preview_paths = preview_paths or {}\\n 76| ready: list[CandidateReviewItem] = []\\n 77| blocked: list[CandidateReviewItem] = []\\n 78| invalid: list[CandidateReviewItem] = []\\n 79|\\n 80| for candidate in batch.candidates:\\n 81| item = build_candidate_review_item(\\n 82| candidate,\\n 83| preview_path=preview_paths.get(candidate.candidate_id),\\n 84| )\\n 85| if item.status == \\\"ready_for_review\\\":\\n 86| ready.append(item)\\n 87| elif item.status == \\\"blocked\\\":\\n 88| blocked.append(item)\\n 89| else:\\n 90| invalid.append(item)\\n 91|\\n 92| return ReviewBatch(\\n 93| batch_id=batch.batch_id,\\n 94| ready_for_review=tuple(ready),\\n 95| blocked=tuple(blocked),\\n 96| invalid=tuple(invalid),\\n 97| )\\n 98|\", \"total_lines\": 97, \"file_size\": 2778, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Telegram-safe rendering for complete review batches.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from autoshorts.review.package import render_telegram_review_package\\n 6|from autoshorts.review.workflow import CandidateReviewItem, ReviewBatch\\n 7|\\n 8|\\n 9|def _render_error_item(item: CandidateReviewItem) -> str:\\n 10| errors = \\\"; \\\".join(item.errors) if item.errors else \\\"-\\\"\\n 11| return f\\\"- {item.candidate.candidate_id} — {item.candidate.title}\\\\n Reasons: {errors}\\\"\\n 12|\\n 13|\\n 14|def render_review_batch_summary(review_batch: ReviewBatch) -> str:\\n 15| \\\"\\\"\\\"Render a full review batch as one Telegram-friendly text block.\\n 16|\\n 17| The output deliberately avoids language that implies publication. It is a\\n 18| review document, not a launch button. Civilization, barely.\\n 19| \\\"\\\"\\\"\\n 20|\\n 21| lines: list[str] = [\\n 22| f\\\"## Review Batch: {review_batch.batch_id}\\\",\\n 23| f\\\"Ready: {len(review_batch.ready_for_review)}\\\",\\n 24| f\\\"Blocked: {len(review_batch.blocked)}\\\",\\n 25| f\\\"Invalid: {len(review_batch.invalid)}\\\",\\n 26| ]\\n 27|\\n 28| if review_batch.ready_for_review:\\n 29| lines.extend([\\\"\\\", \\\"## Ready for review\\\"])\\n 30| for item in review_batch.ready_for_review:\\n 31| if item.package is not None:\\n 32| lines.append(render_telegram_review_package(item.package))\\n 33|\\n 34| if review_batch.blocked:\\n 35| lines.extend([\\\"\\\", \\\"## Blocked\\\"])\\n 36| lines.extend(_render_error_item(item) for item in review_batch.blocked)\\n 37|\\n 38| if review_batch.invalid:\\n 39| lines.extend([\\\"\\\", \\\"## Invalid\\\"])\\n 40| lines.extend(_render_error_item(item) for item in review_batch.invalid)\\n 41|\\n 42| return \\\"\\\\n\\\".join(lines)\\n 43|\", \"total_lines\": 42, \"file_size\": 1568, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_CV0MS4skTfr167pJouen2tao"
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\",\"limit\":220}"
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}
],
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}
]
},
{
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Testbatch model and deterministic filtering helpers.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult, VideoCandidate, validate_candidate\\n 8|from autoshorts.scripting.quality import should_render\\n 9|\\n 10|\\n 11|@dataclass(frozen=True)\\n 12|class TestBatch:\\n 13| batch_id: str\\n 14| candidates: tuple[VideoCandidate, ...]\\n 15|\\n 16| # Prevent pytest from trying to collect this domain model as a test class.\\n 17| __test__ = False\\n 18|\\n 19|\\n 20|def validate_testbatch(batch: TestBatch) -> ValidationResult:\\n 21| errors: list[str] = []\\n 22|\\n 23| if not (batch.batch_id or \\\"\\\").strip():\\n 24| errors.append(\\\"batch_id is required\\\")\\n 25| if not batch.candidates:\\n 26| errors.append(\\\"at least one candidate is required\\\")\\n 27|\\n 28| ids = [candidate.candidate_id for candidate in batch.candidates]\\n 29| if len(ids) != len(set(ids)):\\n 30| errors.append(\\\"candidate ids must be unique\\\")\\n 31|\\n 32| for candidate in batch.candidates:\\n 33| result = validate_candidate(candidate)\\n 34| errors.extend(f\\\"{candidate.candidate_id}: {error}\\\" for error in result.errors)\\n 35|\\n 36| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 37|\\n 38|\\n 39|def renderable_candidates(batch: TestBatch) -> tuple[VideoCandidate, ...]:\\n 40| \\\"\\\"\\\"Return valid candidates that pass the quality/render gate.\\\"\\\"\\\"\\n 41|\\n 42| selected: list[VideoCandidate] = []\\n 43| for candidate in batch.candidates:\\n 44| if validate_candidate(candidate).is_valid and should_render(candidate.quality_score):\\n 45| selected.append(candidate)\\n 46| return tuple(selected)\\n 47|\", \"total_lines\": 46, \"file_size\": 1543, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_PMacGwe04FdaYMeiKq3xMTSz"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_57TDRZDlq3jRnH5X2fSpUFJD",
"call_id": "call_57TDRZDlq3jRnH5X2fSpUFJD",
"response_item_id": "fc_0d99e9da68392f44016a12cc8a18f88191bc5d22337076d580",
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"function": {
"name": "write_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_sample_batch.py\",\"content\":\"from autoshorts.demo.sample_batch import (\\n build_sample_ai_life_systems_batch,\\n render_sample_review_batch,\\n)\\nfrom autoshorts.review.workflow import build_review_batch\\n\\n\\ndef test_sample_ai_life_systems_batch_has_ready_blocked_and_invalid_candidate():\\n batch = build_sample_ai_life_systems_batch()\\n\\n assert batch.batch_id == \\\"sample-ai-life-systems\\\"\\n assert [candidate.candidate_id for candidate in batch.candidates] == (\\n \\\"sample-ready\\\",\\n \\\"sample-blocked\\\",\\n \\\"sample-invalid\\\",\\n )\\n\\n review_batch = build_review_batch(batch)\\n\\n assert [item.candidate.candidate_id for item in review_batch.ready_for_review] == [\\\"sample-ready\\\"]\\n assert [item.candidate.candidate_id for item in review_batch.blocked] == [\\\"sample-blocked\\\"]\\n assert [item.candidate.candidate_id for item in review_batch.invalid] == [\\\"sample-invalid\\\"]\\n\\n\\ndef test_render_sample_review_batch_returns_telegram_safe_summary():\\n text = render_sample_review_batch()\\n\\n assert \\\"## Review Batch: sample-ai-life-systems\\\" in text\\n assert \\\"Ready: 1\\\" in text\\n assert \\\"Blocked: 1\\\" in text\\n assert \\\"Invalid: 1\\\" in text\\n assert \\\"Review: sample-ready\\\" in text\\n assert \\\"sample-blocked —\\\" in text\\n assert \\\"quality gate blocked candidate\\\" in text\\n assert \\\"sample-invalid —\\\" in text\\n assert \\\"hook is required\\\" in text\\n assert \\\"publish\\\" not in text.casefold()\\n assert \\\"post\\\" not in text.casefold()\\n\"}"
}
}
],
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"reasoning": "**Troubleshooting video candidate**\n\nI’m considering whether to use a factory, but it seems like that’s not valid. Instead, maybe I’ll look into using a helper base called VideoCandidate. I need to write tests for it and ensure I’m following Test-Driven Development (TDD) principles, which means my tests should initially fail. It looks like I’m missing an import for autoshorts.demo. First, I’ll create the test and then run it to see how it performs!",
"reasoning_content": "**Troubleshooting video candidate**\n\nI’m considering whether to use a factory, but it seems like that’s not valid. Instead, maybe I’ll look into using a helper base called VideoCandidate. I need to write tests for it and ensure I’m following Test-Driven Development (TDD) principles, which means my tests should initially fail. It looks like I’m missing an import for autoshorts.demo. First, I’ll create the test and then run it to see how it performs!",
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"text": "**Troubleshooting video candidate**\n\nI’m considering whether to use a factory, but it seems like that’s not valid. Instead, maybe I’ll look into using a helper base called VideoCandidate. I need to write tests for it and ensure I’m following Test-Driven Development (TDD) principles, which means my tests should initially fail. It looks like I’m missing an import for autoshorts.demo. First, I’ll create the test and then run it to see how it performs!"
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"content": "{\"bytes_written\": 1425, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.demo.sample_batch\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}",
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_sample_batch.py __________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_sample_batch.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_sample_batch.py:1: in \\n from autoshorts.demo.sample_batch import (\\nE ModuleNotFoundError: No module named 'autoshorts.demo'\\n=========================== short test summary info ============================\\nERROR tests/test_sample_batch.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.12s\", \"exit_code\": 2, \"error\": null}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Telegram review-package formatting for content candidates.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.scripting.quality import ScriptQualityScore, should_render\\n 8|\\n 9|_PLATFORM_LABELS = {\\n 10| \\\"youtube\\\": \\\"YouTube\\\",\\n 11| \\\"tiktok\\\": \\\"TikTok\\\",\\n 12| \\\"instagram\\\": \\\"Instagram\\\",\\n 13|}\\n 14|\\n 15|\\n 16|@dataclass(frozen=True)\\n 17|class ReviewPackage:\\n 18| \\\"\\\"\\\"Everything Sir needs to approve, reject, or request changes.\\n 19|\\n 20| This structure is deterministic by design: strategy/rendering code can fill\\n 21| it, Telegram code can send the rendered text, and publishing code can wait\\n 22| for an explicit parsed approval decision.\\n 23| \\\"\\\"\\\"\\n 24|\\n 25| candidate_id: str\\n 26| title: str\\n 27| pillar: str\\n 28| duration_seconds: int\\n 29| hook: str\\n 30| script_summary: str\\n 31| visual_concept: str\\n 32| caption: str\\n 33| hashtags: tuple[str, ...]\\n 34| platforms: tuple[str, ...]\\n 35| quality_score: ScriptQualityScore\\n 36| policy_notes: tuple[str, ...] = ()\\n 37| preview_path: str | None = None\\n 38|\\n 39|\\n 40|def _join_or_dash(items: tuple[str, ...]) -> str:\\n 41| return \\\"; \\\".join(items) if items else \\\"-\\\"\\n 42|\\n 43|\\n 44|def _platform_names(platforms: tuple[str, ...]) -> str:\\n 45| labels = [_PLATFORM_LABELS.get(platform, platform) for platform in platforms]\\n 46| return \\\", \\\".join(labels) if labels else \\\"-\\\"\\n 47|\\n 48|\\n 49|def _approval_commands(platforms: tuple[str, ...]) -> list[str]:\\n 50| commands = [\\\"Freigabe alle\\\"]\\n 51| for platform in platforms:\\n 52| label = _PLATFORM_LABELS.get(platform, platform.title())\\n 53| commands.append(f\\\"Freigabe {label}\\\")\\n 54| commands.extend([\\\"Ablehnen\\\", \\\"Ändern: \\\"])\\n 55| return commands\\n 56|\\n 57|\\n 58|def render_telegram_review_package(package: ReviewPackage) -> str:\\n 59| \\\"\\\"\\\"Render a concise Telegram-friendly review message.\\\"\\\"\\\"\\n 60|\\n 61| score = package.quality_score\\n 62| render_allowed = should_render(score)\\n 63| gate_line = \\\"Render gate: READY\\\" if render_allowed else \\\"Render gate: BLOCKED — nicht automatisch rendern\\\"\\n 64| hashtags = \\\" \\\".join(package.hashtags) if package.hashtags else \\\"-\\\"\\n 65| preview = package.preview_path or \\\"-\\\"\\n 66| commands = \\\"\\\\n\\\".join(f\\\"- `{command}`\\\" for command in _approval_commands(package.platforms))\\n 67|\\n 68| return \\\"\\\\n\\\".join(\\n 69| [\\n 70| f\\\"## Review: {package.candidate_id}\\\",\\n 71| f\\\"Title: {package.title}\\\",\\n 72| f\\\"Pillar: {package.pillar}\\\",\\n 73| f\\\"Platforms: {_platform_names(package.platforms)}\\\",\\n 74| f\\\"Duration: {package.duration_seconds}s\\\",\\n 75| \\\"\\\",\\n 76| f\\\"Hook: {package.hook}\\\",\\n 77| f\\\"Script: {package.script_summary}\\\",\\n 78| f\\\"Visual: {package.visual_concept}\\\",\\n 79| f\\\"Caption: {package.caption}\\\",\\n 80| f\\\"Hashtags: {hashtags}\\\",\\n 81| \\\"\\\",\\n 82| f\\\"Quality: {score.average_quality:.1f}/10\\\",\\n 83| f\\\"AI-slop risk: {score.ai_slop_risk}/10\\\",\\n 84| f\\\"Policy: {_join_or_dash(package.policy_notes)}\\\",\\n 85| gate_line,\\n 86| f\\\"Preview: {preview}\\\",\\n 87| \\\"\\\",\\n 88| \\\"Approval commands:\\\",\\n 89| commands,\\n 90| ]\\n 91| )\\n 92|\", \"total_lines\": 91, \"file_size\": 2964, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "Erledigt, Sir.\n\n## Neu eingebaut\n\nSample Review Demo:\n\n```text\nautoshorts/demo/__init__.py\nautoshorts/demo/sample_batch.py\ntests/test_sample_batch.py\n```\n\n## Funktionalität\n\nEs gibt jetzt einen deterministischen Demo-Batch:\n\n```python\nbuild_sample_ai_life_systems_batch()\n```\n\nEr erzeugt exakt:\n\n- `sample-ready` → ready for review\n- `sample-blocked` → valid, aber Quality Gate blockiert\n- `sample-invalid` → invalid, weil Hook fehlt\n\nUnd dazu:\n\n```python\nrender_sample_review_batch()\n```\n\nDas baut den Sample-Batch, jagt ihn durch:\n\n```text\nTestBatch\n→ build_review_batch()\n→ render_review_batch_summary()\n```\n\nund liefert direkt einen Telegram-safe Review-Text.\n\n## Verifizierter Output\n\nDer lokale Demo-Render erzeugt:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n...\n```\n\nMit vollständigem Review Package für `sample-ready` und kompakten Fehlerlisten für blocked/invalid.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.demo'\n```\n\nGREEN:\n\n```text\n2 passed\n```\n\nFull suite:\n\n```text\n43 passed in 0.09s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- neues `autoshorts.demo.sample_batch`\n- Sample Review Demo\n- Testabdeckung für Sample Review Demos\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n30159ff feat: add sample review batch demo\n```\n\nAktueller Stand sauber:\n\n```text\n## feat/core-approval-quality-gates\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir einen kleinen CLI-Entry bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\nZiel:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\nOutput: genau dieser Review-Text.\n\nDanach kann Telegram-Anbindung schlicht diesen Text nehmen und senden — ohne schon irgendeine gefährliche Automation anzufassen. Eine wohltuende Seltenheit.",
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"text": "Erledigt, Sir.\n\n## Neu eingebaut\n\nSample Review Demo:\n\n```text\nautoshorts/demo/__init__.py\nautoshorts/demo/sample_batch.py\ntests/test_sample_batch.py\n```\n\n## Funktionalität\n\nEs gibt jetzt einen deterministischen Demo-Batch:\n\n```python\nbuild_sample_ai_life_systems_batch()\n```\n\nEr erzeugt exakt:\n\n- `sample-ready` → ready for review\n- `sample-blocked` → valid, aber Quality Gate blockiert\n- `sample-invalid` → invalid, weil Hook fehlt\n\nUnd dazu:\n\n```python\nrender_sample_review_batch()\n```\n\nDas baut den Sample-Batch, jagt ihn durch:\n\n```text\nTestBatch\n→ build_review_batch()\n→ render_review_batch_summary()\n```\n\nund liefert direkt einen Telegram-safe Review-Text.\n\n## Verifizierter Output\n\nDer lokale Demo-Render erzeugt:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n...\n```\n\nMit vollständigem Review Package für `sample-ready` und kompakten Fehlerlisten für blocked/invalid.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.demo'\n```\n\nGREEN:\n\n```text\n2 passed\n```\n\nFull suite:\n\n```text\n43 passed in 0.09s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- neues `autoshorts.demo.sample_batch`\n- Sample Review Demo\n- Testabdeckung für Sample Review Demos\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n30159ff feat: add sample review batch demo\n```\n\nAktueller Stand sauber:\n\n```text\n## feat/core-approval-quality-gates\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir einen kleinen CLI-Entry bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\nZiel:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\nOutput: genau dieser Review-Text.\n\nDanach kann Telegram-Anbindung schlicht diesen Text nehmen und senden — ohne schon irgendeine gefährliche Automation anzufassen. Eine wohltuende Seltenheit."
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"content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n 4|\\n 5|The project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n 6|\\n 7|## Current scope\\n 8|\\n 9|Implemented foundation:\\n 10|\\n 11|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 12|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n 13|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 14|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 15|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 16|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 17|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 18|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 19|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 20|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 21|- Tests for content pillars, candidate factory, idea candidates, testbatches, sample review demos, review workflow, review-batch rendering, approval parsing, review formatting, and script quality gating.\\n 22|\\n 23|## Human approval commands\\n 24|\\n 25|Supported review responses:\\n 26|\\n 27|- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n 28|- `Freigabe YouTube`\\n 29|- `Freigabe TikTok`\\n 30|- `Freigabe Instagram`\\n 31|- `Ablehnen`\\n 32|- `Ändern: `\\n 33|\\n 34|Ambiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n 35|\\n 36|## Quality gate\\n 37|\\n 38|A script candidate only proceeds to rendering when:\\n 39|\\n 40|- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n 41|- AI-slop risk is not above `5`;\\n 42|- policy risk is not `high`.\\n 43|\\n 44|This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n 45|\\n 46|## Review package\\n 47|\\n 48|`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n 49|\\n 50|- candidate id, title, pillar, target platforms, and duration;\\n 51|- hook, script summary, visual concept, caption, and hashtags;\\n 52|- average quality score, AI-slop risk, policy notes, and render-gate status;\\n 53|- optional preview path;\\n 54|- only the relevant platform-specific approval commands.\\n 55|\\n 56|A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n 57|\\n 58|## Review workflow\\n 59|\\n 60|`build_candidate_review_item()` classifies one candidate as:\\n 61|\\n 62|- `ready_for_review` — valid and renderable; includes a review package;\\n 63|- `blocked` — valid but blocked by quality gate; still includes a review package with blocked gate status;\\n 64|- `invalid` — fails candidate validation; does not create a review package.\\n 65|\\n 66|`build_review_batch()` processes a `TestBatch` into `ready_for_review`, `blocked`, and `invalid` buckets while preserving input order inside each bucket. Optional preview paths are attached by candidate id.\\n 67|\\n 68|`render_review_batch_summary()` turns the complete `ReviewBatch` into one Telegram-safe text block with summary counts, full ready-for-review packages, and compact blocked/invalid reason lists. It deliberately avoids publish/post language; approval and publishing remain separate later stages.\\n 69|\\n 70|## Sample review demo\\n 71|\\n 72|`autoshorts.demo.sample_batch.build_sample_ai_life_systems_batch()` creates a deterministic AI-life-systems sample with:\\n 73|\\n 74|- one ready candidate;\\n 75|- one blocked candidate that fails the quality gate;\\n 76|- one invalid candidate missing its hook.\\n 77|\\n 78|`render_sample_review_batch()` builds that sample, runs it through the review workflow, and returns the Telegram-safe batch summary. This gives the later Telegram sender one boring, repeatable fixture to wire against.\\n 79|\\n 80|## Idea candidates and testbatches\\n 81|\\n 82|`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n 83|\\n 84|- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n 85|- hook, script outline, visual concept, caption, CTA, and hashtags;\\n 86|- quality score and policy notes.\\n 87|\\n 88|`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n 89|\\n 90|`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n 91|\\n 92|## Candidate factory\\n 93|\\n 94|`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n 95|\\n 96|`create_candidate()` turns a draft into a candidate by:\\n 97|\\n 98|- generating a stable id from pillar, title, and duration;\\n 99|- adding default pillar hashtags when no hashtags are supplied;\\n 100|- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n 101|- appending the duration class to policy notes;\\n 102|- enforcing candidate validation and pillar/platform fit validation.\\n 103|\\n 104|Duration classes:\\n 105|\\n 106|- `growth_short`: 20–45 seconds;\\n 107|- `monetization_candidate`: 60–75 seconds;\\n 108|- `experiment`: all other valid durations.\\n 109|\\n 110|Missing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n 111|\\n 112|## Content pillars\\n 113|\\n 114|`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n 115|\\n 116|- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n 117|\\n 118|A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n 119|\\n 120|`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n 121|\\n 122|## Development\\n 123|\\n 124|Run tests:\\n 125|\\n 126|```bash\\n 127|python -m pytest tests -q\\n 128|```\\n 129|\\n 130|## Planned architecture\\n 131|\\n 132|```text\\n 133|autoshorts/\\n 134| config/ # niches, platforms, workflow settings\\n 135| ideas/ # idea generation and scoring\\n 136| scripting/ # script drafts, quality gates, retention checks\\n 137| rendering/ # local/AI-assisted preview rendering\\n 138| review/ # Telegram review package and approval parser\\n 139| publishing/ # platform-specific publishing metadata and later APIs\\n 140| analytics/ # performance imports and feedback loop\\n 141|```\\n 142|\\n 143|## Non-negotiables\\n 144|\\n 145|- No credentials in git.\\n 146|- No generated media in git unless explicitly intended.\\n 147|- No automatic publishing without explicit approval.\\n 148|- Quality over speed; avoid generic AI-slop.\\n 149|\", \"total_lines\": 148, \"file_size\": 6727, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "Erledigt, Sir.\n\n## Neu eingebaut\n\nCLI Entry Point:\n\n```text\nautoshorts/cli/__init__.py\nautoshorts/cli/demo_review_batch.py\ntests/test_demo_review_batch_cli.py\n```\n\nJetzt ausführbar mit:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\n## Was der Command macht\n\nEr druckt den deterministischen Sample Review Batch direkt auf stdout:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n```\n\nAlso genau der Text, den später ein Telegram-Sender übernehmen kann.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.cli'\n```\n\nGREEN:\n\n```text\n1 passed\n```\n\nFull suite:\n\n```text\n44 passed in 0.08s\n```\n\n## Manuelle CLI-Verifikation\n\nCommand geprüft:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\nZusätzlich in Datei gerendert:\n\n```text\n39 /tmp/autoshorts-demo-review.txt\n```\n\nErster Output:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.cli.demo_review_batch`\n- CLI-Ausführung\n- Testabdeckung für Sample Review CLI\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\ndc1c59c feat: add demo review batch cli\n```\n\nAktueller Verlauf:\n\n```text\ndc1c59c feat: add demo review batch cli\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt ist der sauberste nächste Schritt:\n\n```text\nautoshorts/telegram/review_sender.py\n```\n\nAber noch ohne echte Telegram API.\n\nErst testbarer Core:\n\n```python\nbuild_review_message()\n```\n\noder:\n\n```python\nprepare_review_delivery()\n```\n\nZiel:\n\n- nimmt Review-Text\n- validiert Telegram-Sicherheitsregeln\n- gibt ein Delivery-Objekt zurück\n- keine Side Effects\n- kein echtes Senden\n- keine Publishing-Sprache\n\nDanach kann die tatsächliche Hermes/Telegram-Auslieferung drangehängt werden, ohne dass wir versehentlich einen Content-Katapult bauen.",
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"text": "Erledigt, Sir.\n\n## Neu eingebaut\n\nCLI Entry Point:\n\n```text\nautoshorts/cli/__init__.py\nautoshorts/cli/demo_review_batch.py\ntests/test_demo_review_batch_cli.py\n```\n\nJetzt ausführbar mit:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\n## Was der Command macht\n\nEr druckt den deterministischen Sample Review Batch direkt auf stdout:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n```\n\nAlso genau der Text, den später ein Telegram-Sender übernehmen kann.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.cli'\n```\n\nGREEN:\n\n```text\n1 passed\n```\n\nFull suite:\n\n```text\n44 passed in 0.08s\n```\n\n## Manuelle CLI-Verifikation\n\nCommand geprüft:\n\n```bash\npython -m autoshorts.cli.demo_review_batch\n```\n\nZusätzlich in Datei gerendert:\n\n```text\n39 /tmp/autoshorts-demo-review.txt\n```\n\nErster Output:\n\n```text\n## Review Batch: sample-ai-life-systems\nReady: 1\nBlocked: 1\nInvalid: 1\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.cli.demo_review_batch`\n- CLI-Ausführung\n- Testabdeckung für Sample Review CLI\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\ndc1c59c feat: add demo review batch cli\n```\n\nAktueller Verlauf:\n\n```text\ndc1c59c feat: add demo review batch cli\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt ist der sauberste nächste Schritt:\n\n```text\nautoshorts/telegram/review_sender.py\n```\n\nAber noch ohne echte Telegram API.\n\nErst testbarer Core:\n\n```python\nbuild_review_message()\n```\n\noder:\n\n```python\nprepare_review_delivery()\n```\n\nZiel:\n\n- nimmt Review-Text\n- validiert Telegram-Sicherheitsregeln\n- gibt ein Delivery-Objekt zurück\n- keine Side Effects\n- kein echtes Senden\n- keine Publishing-Sprache\n\nDanach kann die tatsächliche Hermes/Telegram-Auslieferung drangehängt werden, ohne dass wir versehentlich einen Content-Katapult bauen."
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Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"arguments": "{\"action\":\"patch\",\"name\":\"test-driven-development\",\"old_string\":\"### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\",\"new_string\":\"### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### CLI Entry Points\\n\\nFor new `python -m package.cli.command` style entry points, keep the TDD loop boring and side-effect free:\\n\\n1. Write a test that imports `main` from the missing CLI module and calls `main([])`.\\n2. Capture stdout/stderr with the project test framework (`capsys` in pytest) or inject a `stdout` file-like object if supported.\\n3. RED should usually be an import failure for the missing CLI module/package, not a vague end-to-end shell failure.\\n4. Implement `main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int` so tests can call it without spawning a subprocess.\\n5. Add the `if __name__ == \\\"__main__\\\": raise SystemExit(main())` guard only after the callable path is green.\\n6. Verify both the unit test and the real module invocation, e.g. `python -m package.cli.command`, before committing.\\n\\nThis catches command wiring while avoiding accidental network calls, publishing actions, or other theatrical nonsense masquerading as a CLI test.\\n\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'test-driven-development' (1 replacement).\"}",
"tool_call_id": "call_6fMMvZ0f9ucFL5nAbFkhQrW2"
},
{
"role": "assistant",
"content": "Skill library updated.\n\nPatched loaded skill:\n\n```text\ntest-driven-development\n```\n\nAdded a new subsection:\n\n```text\nCLI Entry Points\n```\n\nCaptured the reusable pattern from this session:\n\n- test CLI `main([])` directly before shelling out;\n- use `capsys` or injectable `stdout`;\n- expect RED as missing CLI import/module where appropriate;\n- implement `main(argv=None, stdout=None) -> int`;\n- only then add `if __name__ == \"__main__\": raise SystemExit(main())`;\n- verify both unit test and real `python -m package.cli.command`.\n\nThis belongs in the TDD umbrella skill rather than a narrow AutoShorts-specific skill.",
"reasoning": null,
"finish_reason": "stop",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Skill library updated.\n\nPatched loaded skill:\n\n```text\ntest-driven-development\n```\n\nAdded a new subsection:\n\n```text\nCLI Entry Points\n```\n\nCaptured the reusable pattern from this session:\n\n- test CLI `main([])` directly before shelling out;\n- use `capsys` or injectable `stdout`;\n- expect RED as missing CLI import/module where appropriate;\n- implement `main(argv=None, stdout=None) -> int`;\n- only then add `if __name__ == \"__main__\": raise SystemExit(main())`;\n- verify both unit test and real `python -m package.cli.command`.\n\nThis belongs in the TDD umbrella skill rather than a narrow AutoShorts-specific skill."
}
],
"id": "msg_0d99e9da68392f44016a12d31578248191bbb21545294b9691",
"phase": "final_answer"
}
]
}
]
}