{
"session_id": "20260524_120031_1cfb85",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-24T13:55:49.357315",
"last_updated": "2026-05-24T14:36:50.588247",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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": 106,
"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.\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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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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"role": "user",
"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"Ok, mach weiter\"\n\nKonkreter noch unerfüllter nächster Schritt aus dem Verlauf: `autoshorts/content/demo_pipeline.py` und `tests/test_content_demo_pipeline.py` bauen für den deterministischen End-to-End-Flow:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram Delivery Preview\n```\n\nz. B. als API:\n\n```python\nbuild_ai_life_systems_demo_delivery()\n```\n\nHinweis aus letztem Assistentenstatus: „Need README patch.“ — Es wurde offenbar gerade mit diesem nächsten Schritt begonnen bzw. sollte README danach aktualisiert werden.\n\n## Goal\nDer Nutzer will ein kontrolliertes AutoShorts-System aufbauen, das Content-Ideen strukturiert erarbeitet, daraus reviewbare Video-Kandidaten erzeugt, Quality Gates/Human Approval erzwingt und später Telegram-Review sowie Video-Erstellung/Optimierung unterstützt — zunächst ohne echte externe Side Effects, ohne echtes Telegram-Senden und ohne unkontrolliertes Publishing.\n\nGesamtpipeline-Ziel:\n\n```text\nContent-Pillar\n→ ContentBrief\n→ ScriptDraft\n→ CandidateDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram Delivery Preview\n→ später: ShotPlan / RenderBrief / Video-Erstellung testen und optimieren\n```\n\n## Constraints & Preferences\n- Sprache/Arbeitsstil: Deutsch, direkte knappe Statusupdates, gelegentlich humorvoll/sarkastisch.\n- Vorgehen: strikt TDD-orientiert:\n - zuerst RED-Test,\n - dann Implementierung,\n - dann GREEN,\n - dann Full Suite,\n - dann README,\n - dann Commit.\n- Keine echten Side Effects in frühen Stufen:\n - keine Telegram API Calls,\n - kein echtes Senden,\n - kein Publishing,\n - keine externen Video-/AI-APIs.\n- Human Approval bleibt mandatory.\n- Safety/Guardrails:\n - Review/Delivery darf keine `publish`-/`post`-Sprache enthalten.\n - Telegram-Nachrichtenlimit: max. `4096` Zeichen.\n - Content-Validierung gegen generische AI-Slop-Phrasen.\n - No-Go/Policy Notes werden durch die Pipeline weitergereicht.\n- Determinismus bevorzugt: Demo-Batches und Demo-Pipelines sollen reproduzierbar sein.\n- Ziel ist erst robuste Content-Erarbeitung/Review-Struktur, dann Video-Erstellung, damit GPU-Zeit nicht an schlechte Prompts verschwendet wird.\n- Niemals API Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen übernehmen; falls vorhanden: `[REDACTED]`.\n\n## Completed Actions\n1. CREATE `autoshorts/demo/__init__.py` — Demo-Package für Sample Review Batch angelegt [tool: editor/terminal].\n2. CREATE `autoshorts/demo/sample_batch.py` — deterministischen Demo-Batch implementiert mit `build_sample_ai_life_systems_batch()` und `render_sample_review_batch()` [tool: editor/terminal].\n3. CREATE `tests/test_sample_batch.py` — Tests für Sample Review Demo angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.demo'`, GREEN danach `2 passed` [tool: pytest].\n4. TEST Full Suite nach Sample Demo — Ergebnis: `43 passed in 0.09s` [tool: terminal].\n5. UPDATE `README.md` — Dokumentation ergänzt für `autoshorts.demo.sample_batch`, Sample Review Demo und Testabdeckung [tool: editor].\n6. COMMIT Sample Demo — Commit `30159ff feat: add sample review batch demo` auf Branch `feat/core-approval-quality-gates` [tool: git].\n7. CREATE `autoshorts/cli/__init__.py` — CLI-Package angelegt [tool: editor/terminal].\n8. CREATE `autoshorts/cli/demo_review_batch.py` — CLI Entry Point für `python -m autoshorts.cli.demo_review_batch` implementiert; druckt Sample Review Batch auf stdout [tool: editor/terminal].\n9. CREATE `tests/test_demo_review_batch_cli.py` — Test für Review-Batch-CLI angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.cli'`, GREEN danach `1 passed` [tool: pytest].\n10. TEST Full Suite nach Review-Batch-CLI — Ergebnis: `44 passed in 0.08s` [tool: terminal].\n11. VERIFY CLI manuell — `python -m autoshorts.cli.demo_review_batch`; zusätzlich Output nach `/tmp/autoshorts-demo-review.txt`, Datei hatte `39` Zeilen; erster Output:\n ```text\n ## Review Batch: sample-ai-life-systems\n Ready: 1\n Blocked: 1\n Invalid: 1\n ```\n [tool: terminal].\n12. UPDATE `README.md` — CLI-Ausführung und `autoshorts.cli.demo_review_batch` dokumentiert [tool: editor].\n13. COMMIT Review-Batch-CLI — Commit `dc1c59c feat: add demo review batch cli` [tool: git].\n14. CREATE `autoshorts/telegram/__init__.py` — Telegram-Package angelegt [tool: editor/terminal].\n15. CREATE `autoshorts/telegram/review_sender.py` — side-effect-free Telegram Delivery Preparation implementiert mit `prepare_review_delivery(review_text: str, *, topic: str = \"review\") -> ReviewDelivery` [tool: editor/terminal].\n16. CREATE `tests/test_telegram_review_sender.py` — Tests für Telegram Delivery Preparation angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.telegram'`, GREEN danach `4 passed` [tool: pytest].\n17. IMPLEMENT Guardrails in `autoshorts/telegram/review_sender.py` — validiert nicht-leeren Text, max. `4096` Zeichen, keine `publish`-/`post`-Sprache, `requires_human_approval=True`, `side_effects=()` [tool: editor].\n18. TEST Full Suite nach Telegram Delivery Preparation — Ergebnis: `48 passed in 0.09s` [tool: terminal].\n19. VERIFY Telegram Delivery manuell — Sample Review Batch durch `prepare_review_delivery()` geschickt; Output:\n ```text\n telegram_review\n review\n True\n ()\n 1000\n ```\n [tool: terminal].\n20. UPDATE `README.md` — `autoshorts.telegram.review_sender`, Delivery Preparation, Telegram-Limit, keine Side Effects, Human Approval dokumentiert [tool: editor].\n21. COMMIT Telegram Delivery Preparation — Commit `54863dd feat: add telegram review delivery preparation` [tool: git].\n22. CREATE `autoshorts/cli/demo_telegram_delivery.py` — CLI für Delivery Preview implementiert; ausführbar via `python -m autoshorts.cli.demo_telegram_delivery`; Pipeline: Sample Review Batch → `prepare_review_delivery()` → Metadata + Review Text auf stdout [tool: editor/terminal].\n23. CREATE `tests/test_demo_telegram_delivery_cli.py` — Test für Demo Telegram Delivery CLI angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.cli.demo_telegram_delivery'`, GREEN danach `1 passed` [tool: pytest].\n24. TEST Full Suite nach Demo Telegram Delivery CLI — Ergebnis: `49 passed in 0.08s` [tool: terminal].\n25. VERIFY Demo Telegram Delivery CLI manuell — Command:\n ```bash\n python -m autoshorts.cli.demo_telegram_delivery >/tmp/autoshorts-demo-delivery.txt\n ```\n Ergebnis:\n ```text\n 45 /tmp/autoshorts-demo-delivery.txt\n Delivery type: telegram_review\n Topic: review\n Requires human approval: yes\n Side effects: none\n Text length: 1000\n ```\n [tool: terminal].\n26. UPDATE `README.md` — `autoshorts.cli.demo_telegram_delivery`, Preview der vorbereiteten Telegram Delivery und „kein echtes Senden“ dokumentiert [tool: editor].\n27. COMMIT Demo Telegram Delivery CLI — Commit `53b45ca feat: add demo telegram delivery cli` [tool: git].\n28. CREATE `autoshorts/content/__init__.py` — Content-Package angelegt [tool: editor/terminal].\n29. CREATE `autoshorts/content/brief.py` — `ContentBrief`-Core implementiert inkl. `create_content_brief()` und `validate_content_brief()` [tool: editor/terminal].\n30. CREATE `tests/test_content_brief.py` — Tests für ContentBrief angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content'`, GREEN danach `4 passed` [tool: pytest].\n31. IMPLEMENT `create_content_brief()` — erzeugt strategischen Brief mit `brief_id`, Pillar, Topic, Audience Problem, Payoff, Positioning, Tone, Visual Rules, No-Go Topics, Zielplattformen und Ziel-Dauer [tool: editor].\n32. VERIFY ContentBrief Beispiel — Input:\n ```python\n create_content_brief(\n pillar_id=\"ai_life_systems\",\n topic=\"AI weekly review routine\",\n audience_problem=\"I use AI tools but still lose control of priorities.\",\n payoff=\"a 10-minute Sunday review that keeps AI as assistant, not boss\",\n )\n ```\n erzeugte verifizierte `brief_id`:\n ```text\n ai_life_systems-ai-weekly-review-routine\n ```\n [tool: terminal/pytest].\n33. IMPLEMENT `validate_content_brief()` Guardrails — rejects fehlendes Topic, fehlendes Audience Problem, fehlenden Payoff, unsupported Platform, Duration außerhalb `10..180` [tool: editor].\n34. CREATE `autoshorts/content/script_draft.py` — `ScriptDraft`-Core implementiert inkl. `create_script_draft()` und `validate_script_draft()` [tool: editor/terminal].\n35. CREATE `tests/test_script_draft.py` — Tests für ScriptDraft angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content.script_draft'`, GREEN danach `5 passed` [tool: pytest].\n36. IMPLEMENT `create_script_draft(brief)` — erzeugt Title, Hook, Script Outline, 3–5 Retention Beats, Visual Concept, Caption, CTA, Estimated Duration [tool: editor].\n37. VERIFY ScriptDraft Beispiel — Hook:\n ```text\n If AI runs your week, you have already lost control.\n ```\n Retention Beats: `4`; Validation: `True` [tool: terminal/pytest].\n38. IMPLEMENT `validate_script_draft()` Guardrails — rejects fehlenden Hook, fehlendes Script Outline, fehlendes Visual Concept, fehlende Caption/CTA, weniger als 3 oder mehr als 5 Retention Beats, Duration außerhalb `10..180`, AI-Slop-Phrasen:\n ```text\n in this video\n unlock your full potential\n game changer\n revolutionize your life\n boost productivity\n ```\n [tool: editor].\n39. TEST Full Suite nach ContentBrief + ScriptDraft — Ergebnis: `58 passed in 0.10s` [tool: terminal].\n40. UPDATE `README.md` — `autoshorts.content.brief`, `autoshorts.content.script_draft`, Content Development Flow und Validation Guardrails dokumentiert [tool: editor].\n41. COMMIT ContentBrief + ScriptDraft — Commit `9c125f9 feat: add content brief and script draft models` [tool: git].\n42. CREATE `autoshorts/content/to_candidate.py` — Bridge von `ContentBrief + ScriptDraft` zu `CandidateDraft`/`VideoCandidate` implementiert [tool: editor/terminal].\n43. CREATE `tests/test_content_to_candidate.py` — Tests für Content-to-Candidate-Bridge angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content.to_candidate'`, GREEN danach `4 passed` [tool: pytest].\n44. MODIFY `autoshorts/content/script_draft.py` — leere Overrides wie `hook=\"\"` werden nun wirklich als leer validiert statt durch Default-Hook ersetzt [tool: editor].\n45. IMPLEMENT `convert_script_draft_to_candidate_draft(brief, script)` — übernimmt Pillar, Title, Platforms, Duration, Hook, Script Outline, Visual Concept, Caption, CTA, Policy Notes aus Brief, Hypothesis und konservativen Initial Quality Score [tool: editor].\n46. IMPLEMENT Initial Quality Score für CandidateDraft — Werte:\n ```text\n hook_strength: 8\n novelty: 8\n clarity: 8\n retention: 8\n ai_slop_risk: 3\n policy_risk: low\n ```\n [tool: editor].\n47. IMPLEMENT `convert_script_draft_to_candidate(brief, script)` — nutzt bestehende Candidate Factory `create_candidate()` und erzeugt echten `VideoCandidate` mit stabiler Candidate ID, Default Hashtags, Duration Class, Pillar/Platform Validation und Candidate Validation [tool: editor].\n48. VERIFY End-to-End-Pfad manuell — Pfad:\n ```text\n ContentBrief\n → ScriptDraft\n → VideoCandidate\n → build_candidate_review_item()\n ```\n Output:\n ```text\n ai_life_systems-ai-weekly-review-routine-35s\n ('#AI', '#LifeSystems', '#Productivity')\n 8.0\n ready_for_review\n ```\n [tool: terminal].\n49. IMPLEMENT Bridge Guardrails — rejects invalid ScriptDrafts, z. B. leerer Hook; rejects mismatched Brief/Script Paare; nutzt Candidate Factory/Pillar Validation für weitere Fehler [tool: editor].\n50. ADD Policy Notes in Bridge — enthalten u. a.:\n ```text\n Source brief: ai_life_systems-ai-weekly-review-routine\n Human approval remains explicit\n No-go inherited: fake earnings\n ```\n [tool: editor].\n51. TEST Full Suite nach Content-to-Candidate-Bridge — Ergebnis: `62 passed in 0.09s` [tool: terminal].\n52. UPDATE `README.md` — `autoshorts.content.to_candidate`, ContentBrief + ScriptDraft → CandidateDraft und CandidateDraft → VideoCandidate via bestehender Factory dokumentiert [tool: editor].\n53. COMMIT Content-to-Candidate-Bridge — Commit `72dc887 feat: convert content drafts to video candidates` [tool: git].\n\n## Active State\n- Arbeitsverzeichnis: AutoShorts-Projekt; genauer Pfad nicht im kompaktierten Verlauf enthalten.\n- Branch:\n ```text\n feat/core-approval-quality-gates\n ```\n- Letzter bekannter Commit:\n ```text\n 72dc887 feat: convert content drafts to video candidates\n ```\n- Bekannter Commit-Verlauf auf Branch:\n ```text\n 72dc887 feat: convert content drafts to video candidates\n 9c125f9 feat: add content brief and script draft models\n 53b45ca feat: add demo telegram delivery cli\n 54863dd feat: add telegram review delivery preparation\n dc1c59c feat: add demo review batch cli\n 30159ff feat: add sample review batch demo\n b7b5270 feat: add review batch renderer\n 2155728 feat: add review workflow buckets\n deea24f feat: add candidate factory\n ae6a4f6 feat: add content pillar registry\n ```\n- Letzter bekannter Full-Suite-Status:\n ```text\n 62 passed in 0.09s\n ```\n- Keine laufenden Server oder Prozesse bekannt.\n- Keine echten externen Integrationen aktiv.\n- Keine Telegram API Calls.\n- Keine Video-/AI-API Calls.\n- Der letzte Assistenten-Zwischenstatus nach User „Ok, mach weiter“ war:\n ```text\n Need README patch.\n ```\n Das deutet darauf hin, dass der nächste Demo-Pipeline-Schritt begonnen werden sollte oder bereits Code teilweise erstellt wurde, README aber noch fehlt. Aktuellen Dateistatus per `git status` prüfen.\n\n## In Progress\n- Aufbau eines deterministischen Demo-End-to-End-Flows:\n ```text\n ContentBrief\n → ScriptDraft\n → VideoCandidate\n → TestBatch\n → ReviewBatch\n → Telegram Delivery Preview\n ```\n- Geplante Dateien:\n ```text\n autoshorts/content/demo_pipeline.py\n tests/test_content_demo_pipeline.py\n ```\n- Geplante API:\n ```python\n build_ai_life_systems_demo_delivery()\n ```\n- README-Patch steht wahrscheinlich noch aus oder wurde gerade als nötig erkannt.\n\n## Blocked\nKeine bekannten Blocker.\n\nBekannte frühere RED-Fehler wurden alle behoben:\n```text\nModuleNotFoundError: No module named 'autoshorts.demo'\nModuleNotFoundError: No module named 'autoshorts.cli'\nModuleNotFoundError: No module named 'autoshorts.telegram'\nModuleNotFoundError: No module named 'autoshorts.cli.demo_telegram_delivery'\nModuleNotFoundError: No module named 'autoshorts.content'\nModuleNotFoundError: No module named 'autoshorts.content.script_draft'\nModuleNotFoundError: No module named 'autoshorts.content.to_candidate'\n```\n\n## Key Decisions\n- Review/Telegram-Delivery bleibt zunächst vollständig side-effect-free, damit keine versehentliche Veröffentlichung oder Telegram-Auslieferung passiert.\n- Telegram Delivery Preparation erzeugt nur ein Delivery-Objekt mit Metadaten, `requires_human_approval=True` und `side_effects=()`.\n- Telegram-Limit von `4096` Zeichen wird bereits im Core validiert.\n- „publish“/„post“-Sprache ist in Review Deliveries verboten, um konzeptionell klar zwischen Review und Veröffentlichung zu trennen.\n- Content-Erarbeitung wurde bewusst vor Video-Erstellung eingeführt:\n - Erst Brief und Script strukturiert validieren.\n - Dann Candidate erzeugen.\n - Dann Review.\n - Erst später ShotPlan/RenderBrief/Video-Generation.\n- ContentBrief und ScriptDraft sind deterministisch modelliert, um Tests und Optimierung reproduzierbar zu machen.\n- AI-Slop-Phrasen werden hart rejectet, um generische Shorts-Sprache früh zu vermeiden.\n- `ScriptDraft` darf leere Overrides nicht stillschweigend durch Defaults reparieren; leere Eingaben müssen validierbar fehlschlagen.\n- Candidate Bridge nutzt bestehende Candidate Factory, damit ID, Hashtags, Duration Class und Validierungen konsistent bleiben.\n- Initialer Quality Score für aus Content erzeugte Kandidaten ist konservativ, aber reviewfähig:\n ```text\n hook_strength: 8\n novelty: 8\n clarity: 8\n retention: 8\n ai_slop_risk: 3\n policy_risk: low\n ```\n\n## Resolved Questions\n- Nutzer wollte nach Telegram Delivery Preview mit Content-Erarbeitung/Videos testen und optimieren beginnen.\n - Antwort/Umsetzung: Es wurde zuerst der kontrollierte Core für ContentBrief und ScriptDraft gebaut, noch ohne Video-Erzeugung oder externe APIs.\n- Nutzer wollte „mach weiter“ nach ContentBrief/ScriptDraft.\n - Antwort/Umsetzung: Es wurde die Bridge `ContentBrief + ScriptDraft → CandidateDraft → VideoCandidate` gebaut.\n- Nutzer wollte weiter nach der Bridge.\n - Geplanter nächster Schritt: Demo-Pipeline von Content-Erarbeitung bis Telegram Delivery Preview als ein deterministischer Pipeline-Call.\n\n## Pending User Asks\n- Noch nicht erfüllt: User sagte zuletzt „Ok, mach weiter“.\n- Daraus folgt noch ausstehend:\n - `autoshorts/content/demo_pipeline.py` erstellen.\n - `tests/test_content_demo_pipeline.py` erstellen.\n - API wie `build_ai_life_systems_demo_delivery()` implementieren.\n - Pipeline testen:\n ```text\n ContentBrief\n → ScriptDraft\n → VideoCandidate\n → TestBatch\n → ReviewBatch\n → Telegram Delivery Preview\n ```\n - README aktualisieren.\n - Full Suite laufen lassen.\n - Commit erstellen.\n\n## Relevant Files\n- `autoshorts/demo/__init__.py` — Demo-Package.\n- `autoshorts/demo/sample_batch.py` — deterministischer Sample Review Batch mit `build_sample_ai_life_systems_batch()` und `render_sample_review_batch()`.\n- `tests/test_sample_batch.py` — Tests für Sample Review Demo.\n- `autoshorts/cli/__init__.py` — CLI-Package.\n- `autoshorts/cli/demo_review_batch.py` — CLI für Sample Review Batch.\n- `tests/test_demo_review_batch_cli.py` — Tests für Review Batch CLI.\n- `autoshorts/telegram/__init__.py` — Telegram-Package.\n- `autoshorts/telegram/review_sender.py` — `ReviewDelivery` und `prepare_review_delivery()`.\n- `tests/test_telegram_review_sender.py` — Tests für Telegram Delivery Preparation.\n- `autoshorts/cli/demo_telegram_delivery.py` — CLI für Telegram Delivery Preview.\n- `tests/test_demo_telegram_delivery_cli.py` — Tests für Telegram Delivery CLI.\n- `autoshorts/content/__init__.py` — Content-Package.\n- `autoshorts/content/brief.py` — `ContentBrief`, `create_content_brief()`, `validate_content_brief()`.\n- `tests/test_content_brief.py` — Tests für ContentBrief.\n- `autoshorts/content/script_draft.py` — `ScriptDraft`, `create_script_draft()`, `validate_script_draft()`, inkl. Fix für leere Overrides.\n- `tests/test_script_draft.py` — Tests für ScriptDraft.\n- `autoshorts/content/to_candidate.py` — `convert_script_draft_to_candidate_draft()` und `convert_script_draft_to_candidate()`.\n- `tests/test_content_to_candidate.py` — Tests für Content-to-Candidate-Bridge.\n- `README.md` — mehrfach aktualisiert für Demo, CLI, Telegram Delivery, Content Flow und Candidate Bridge.\n- Geplant/noch zu prüfen:\n - `autoshorts/content/demo_pipeline.py`\n - `tests/test_content_demo_pipeline.py`\n\n## Remaining Work\n- Aktuellen Git-Status prüfen, da letzter Zwischenstatus „Need README patch.“ war.\n- Falls Demo-Pipeline-Dateien noch nicht existieren:\n 1. RED-Test für `autoshorts.content.demo_pipeline` schreiben.\n 2. `build_ai_life_systems_demo_delivery()` implementieren.\n 3. Pipeline sollte deterministisch:\n ```text\n create_content_brief()\n → create_script_draft()\n → convert_script_draft_to_candidate()\n → TestBatch\n → build_review_batch()\n → render_review_batch_summary()\n → prepare_review_delivery()\n ```\n ausführen.\n 4. Tests für:\n - Delivery type `telegram_review`,\n - Topic `review`,\n - `requires_human_approval is True`,\n - `side_effects == ()`,\n - Review-Text enthält Batch-Header und Ready Count,\n - Candidate aus Content ist enthalten.\n 5. README dokumentieren.\n 6. Full Suite laufen lassen.\n 7. Commit erstellen.\n- Danach nächster größerer Block:\n ```text\n ScriptDraft\n → ShotPlan\n → RenderBrief\n → Visual/Video generation test\n ```\n um „Videos erstellen, testen und optimieren“ strukturiert anzugehen.\n\n## Critical Context\n- Sample Review Batch erzeugt exakt:\n ```text\n sample-ready → ready for review\n sample-blocked → valid, aber Quality Gate blockiert\n sample-invalid → invalid, weil Hook fehlt\n ```\n- Sample Review Output beginnt:\n ```text\n ## Review Batch: sample-ai-life-systems\n Ready: 1\n Blocked: 1\n Invalid: 1\n ```\n- Demo Telegram Delivery CLI Output beginnt:\n ```text\n Delivery type: telegram_review\n Topic: review\n Requires human approval: yes\n Side effects: none\n Text length: 1000\n\n ## Review Batch: sample-ai-life-systems\n Ready: 1\n ```\n- `prepare_review_delivery()` erzeugt:\n ```python\n ReviewDelivery(\n text=...,\n topic=\"review\",\n delivery_type=\"telegram_review\",\n requires_human_approval=True,\n side_effects=(),\n )\n ```\n- Telegram Delivery Guardrails:\n - Text nicht leer.\n - Max. `4096` Zeichen.\n - Keine `publish`-/`post`-Sprache.\n - Keine Side Effects.\n - Human Approval mandatory.\n- Beispiel-ContentBrief:\n ```python\n create_content_brief(\n pillar_id=\"ai_life_systems\",\n topic=\"AI weekly review routine\",\n audience_problem=\"I use AI tools but still lose control of priorities.\",\n payoff=\"a 10-minute Sunday review that keeps AI as assistant, not boss\",\n )\n ```\n erzeugt:\n ```text\n ai_life_systems-ai-weekly-review-routine\n ```\n- Beispiel-ScriptDraft-Hook:\n ```text\n If AI runs your week, you have already lost control.\n ```\n- Beispiel-ScriptDraft:\n - Retention Beats: `4`\n - Validation: `True`\n- AI-Slop-Blacklist:\n ```text\n in this video\n unlock your full potential\n game changer\n revolutionize your life\n boost productivity\n ```\n- Content-to-Candidate manuell verifizierter Output:\n ```text\n ai_life_systems-ai-weekly-review-routine-35s\n ('#AI', '#LifeSystems', '#Productivity')\n 8.0\n ready_for_review\n ```\n- Policy Notes aus Bridge enthalten:\n ```text\n Source brief: ai_life_systems-ai-weekly-review-routine\n Human approval remains explicit\n No-go inherited: fake earnings\n ```\n- Letzter sicherer Teststand:\n ```text\n 62 passed in 0.09s\n ```\n- Keine Secrets/Credentials im Summary erhalten; falls im entfernten Verlauf vorhanden, wurden sie nicht übernommen und wären als `[REDACTED]` zu behandeln.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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"content": "Erledigt, Sir.\n\n## Neu eingebaut: Content Demo Review Pipeline\n\nNeue Dateien:\n\n```text\nautoshorts/content/demo_pipeline.py\ntests/test_content_demo_pipeline.py\n```\n\n## Neuer End-to-End-Demo-Call\n\n```python\nbuild_ai_life_systems_demo_delivery()\n```\n\nDer läuft deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram ReviewDelivery\n```\n\nUnd gibt ein inspectable Result zurück:\n\n```python\nContentDemoPipelineResult(\n brief=...,\n script=...,\n candidate=...,\n testbatch=...,\n review_batch=...,\n delivery=...,\n)\n```\n\nAlso nicht nur finalen Text, sondern jeden Zwischenzustand. Sehr nützlich zum Debuggen und Optimieren, ohne in Rendering- oder Telegram-Nebenwirkungen zu rutschen.\n\n## Verifizierter Output\n\nManuell geprüft:\n\n```text\nai_life_systems-ai-weekly-review-routine\nai_life_systems-ai-weekly-review-routine-35s\n1 0 0\ntelegram_review review () 1294\n```\n\nBedeutung:\n\n- Brief ID: `ai_life_systems-ai-weekly-review-routine`\n- Candidate ID: `ai_life_systems-ai-weekly-review-routine-35s`\n- Review Buckets:\n - Ready: `1`\n - Blocked: `0`\n - Invalid: `0`\n- Delivery:\n - Type: `telegram_review`\n - Topic: `review`\n - Side effects: `()`\n - Text length: `1294`\n\n## Safety\n\nWeiterhin:\n\n- kein echtes Telegram-Senden\n- kein Publishing\n- keine externen API Calls\n- keine generierten Medien\n- keine Side Effects\n- Human approval bleibt Pflicht\n\nEine Pipeline, die tatsächlich nichts Gefährliches tut. Man möchte fast misstrauisch werden.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.content.demo_pipeline'\n```\n\nGREEN:\n\n```text\n2 passed\n```\n\nFull suite:\n\n```text\n64 passed in 0.10s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.content.demo_pipeline`\n- End-to-End-Flow\n- alle Zwischenartefakte\n- keine send/publish side effects\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n6e6569a feat: add content demo review pipeline\n```\n\nAktueller Verlauf:\n\n```text\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n9c125f9 feat: add content brief and script draft models\n53b45ca feat: add demo telegram delivery cli\n54863dd feat: add telegram review delivery preparation\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 können wir mit dem eigentlichen Video-Erstellungs-Vorlauf beginnen:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderBrief\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/rendering/shot_plan.py\ntests/test_shot_plan.py\n```\n\nZiel:\n\n- aus `ScriptDraft` einen 4–5 Scene ShotPlan erzeugen\n- pro Scene:\n - Start/End time\n - Voiceover beat\n - Visual action\n - Motion type\n - Caption beat\n - Asset mode, z. B.:\n - `mechanism_animation`\n - `screen_recording_simulation`\n - `cinematic_ai_video`\n - `kinetic_text_overlay`\n- harte Guardrails:\n - keine static slideshow\n - keine baked-in fake UI text\n - captions/labels renderer-owned\n - Szenenlängen plausibel\n - erster Frame hat Hook/Pattern Interrupt\n\nDas ist der notwendige Zwischenschritt, bevor wir Qwen-Image/Wan/ComfyUI sinnvoll anwerfen. Sonst generieren wir hübschen Nebel mit Stromrechnung."
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"content": "[M G] ok, mach das."
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"content": "Erledigt, Sir.\n\n## Neu eingebaut: Retention Shot Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/__init__.py\nautoshorts/rendering/shot_plan.py\ntests/test_shot_plan.py\n```\n\n## Neue API\n\n```python\ncreate_shot_plan(script: ScriptDraft) -> ShotPlan\n```\n\nErzeugt aus einem validierten `ScriptDraft` einen deterministischen 5-Scene ShotPlan:\n\n```text\nfirst_frame_hook\n→ pattern_interrupt\n→ core_explanation\n→ screen_or_card_visual\n→ cta\n```\n\n## ShotPlan-Struktur\n\n```python\nShotPlan(\n script_id=...,\n title=...,\n estimated_duration_seconds=...,\n scenes=(...)\n)\n```\n\nJede Szene enthält:\n\n```python\nShotScene(\n purpose=...,\n start_seconds=...,\n end_seconds=...,\n voiceover_beat=...,\n visual_action=...,\n asset_mode=...,\n caption_beat=...,\n retention_goal=...,\n labels_renderer_owned=True,\n)\n```\n\n## Asset Modes\n\nAktueller Default-Plan:\n\n```text\nfirst_frame_hook → kinetic_text_overlay\npattern_interrupt → mechanism_animation\ncore_explanation → screen_recording_simulation\nscreen_or_card_visual → mechanism_animation\ncta → kinetic_text_overlay\n```\n\nDamit vermeiden wir bewusst:\n\n```text\nstatic slideshow\n```\n\nWeil “AI slideshow mit Musik” nicht Content ist, sondern ein sehr langsamer Hilferuf.\n\n## Guardrails\n\n`validate_shot_plan()` rejected:\n\n- `static_slideshow`\n- baked-in fake UI text\n- generated/unreadable UI text\n- Labels/Captions, die nicht renderer-owned sind\n- schlechte Scene Timings\n- fehlende Pflichtfelder\n- Plan ohne `first_frame_hook` als erste Szene\n- ungültige Duration\n\n## Verifizierter Output\n\nManuell geprüft:\n\n```text\nai_life_systems-ai-weekly-review-routine\n5\n['first_frame_hook', 'pattern_interrupt', 'core_explanation', 'screen_or_card_visual', 'cta']\n['kinetic_text_overlay', 'mechanism_animation', 'screen_recording_simulation', 'mechanism_animation', 'kinetic_text_overlay']\nTrue\n```\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n69 passed in 0.10s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.shot_plan`\n- 5-Scene Retention Flow\n- Scene-Felder\n- Guardrails gegen Slideshow/Fake-UI/Baked Text\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\nd118a76 feat: add retention shot planning\n```\n\nAktueller Verlauf:\n\n```text\nd118a76 feat: add retention shot planning\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n9c125f9 feat: add content brief and script draft models\n53b45ca feat: add demo telegram delivery cli\n54863dd feat: add telegram review delivery preparation\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 kommt der nächste wichtige Zwischenlayer:\n\n```text\nShotPlan\n→ RenderManifest\n```\n\nIch würde bauen:\n\n```text\nautoshorts/rendering/manifest.py\ntests/test_render_manifest.py\n```\n\nZiel:\n\n- ShotPlan in maschinenlesbaren RenderManifest übersetzen\n- pro Timeline Item:\n - start/end\n - asset mode\n - motion instruction\n - caption layer\n - renderer-owned label layer\n - placeholder asset path\n- globale Render Settings:\n - vertical `1080x1920`\n - fps `30`\n - no generated text inside assets\n - captions renderer-owned\n - static_card_limit = `0`\n\nDanach können wir erst lokal einen Preview-Renderer bauen — und danach Qwen-Image/Wan/ComfyUI gezielt anbinden, statt blind GPU-Roulette zu spielen."
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"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. 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. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. 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. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### 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\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Demo / Fixture Features\\n\\nWhen adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.\\n\\n## Side-Effect-Free Integration Boundaries\\n\\nWhen building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:\\n\\n1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.\\n2. RED should be a missing module/API or missing validation failure, not a real network/send failure.\\n3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.\\n4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.\\n5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.\\n6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.\\n\\nThis pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\\n- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`\\\"\\\"`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": {\"references\": [\"references/deterministic-demo-fixtures.md\", \"references/side-effect-free-delivery-boundaries.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Retention-led shot planning before any video rendering.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|from typing import Literal\\n 7|\\n 8|from autoshorts.content.script_draft import ScriptDraft, validate_script_draft\\n 9|from autoshorts.ideas.candidate import ValidationResult\\n 10|\\n 11|ShotPurpose = Literal[\\n 12| \\\"first_frame_hook\\\",\\n 13| \\\"pattern_interrupt\\\",\\n 14| \\\"core_explanation\\\",\\n 15| \\\"screen_or_card_visual\\\",\\n 16| \\\"cta\\\",\\n 17|]\\n 18|AssetMode = Literal[\\n 19| \\\"kinetic_text_overlay\\\",\\n 20| \\\"mechanism_animation\\\",\\n 21| \\\"screen_recording_simulation\\\",\\n 22| \\\"cinematic_ai_video\\\",\\n 23| \\\"static_slideshow\\\",\\n 24|]\\n 25|\\n 26|_ALLOWED_ASSET_MODES = {\\n 27| \\\"kinetic_text_overlay\\\",\\n 28| \\\"mechanism_animation\\\",\\n 29| \\\"screen_recording_simulation\\\",\\n 30| \\\"cinematic_ai_video\\\",\\n 31|}\\n 32|_BAKED_TEXT_MARKERS = (\\\"baked-in\\\", \\\"fake ui text\\\", \\\"generated text\\\", \\\"unreadable ui\\\")\\n 33|\\n 34|\\n 35|@dataclass(frozen=True)\\n 36|class ShotScene:\\n 37| purpose: ShotPurpose | str\\n 38| start_seconds: int\\n 39| end_seconds: int\\n 40| voiceover_beat: str\\n 41| visual_action: str\\n 42| asset_mode: AssetMode | str\\n 43| caption_beat: str\\n 44| retention_goal: str\\n 45| labels_renderer_owned: bool = True\\n 46|\\n 47|\\n 48|@dataclass(frozen=True)\\n 49|class ShotPlan:\\n 50| script_id: str\\n 51| title: str\\n 52| estimated_duration_seconds: int\\n 53| scenes: tuple[ShotScene, ...]\\n 54|\\n 55|\\n 56|def _is_blank(value: str) -> bool:\\n 57| return not (value or \\\"\\\").strip()\\n 58|\\n 59|\\n 60|def _scene_windows(duration_seconds: int) -> tuple[tuple[int, int], ...]:\\n 61| first_end = min(3, duration_seconds)\\n 62| second_end = min(8, duration_seconds)\\n 63| third_end = min(17, duration_seconds)\\n 64| fourth_end = min(max(duration_seconds - 5, third_end + 1), duration_seconds)\\n 65| return (\\n 66| (0, first_end),\\n 67| (first_end, second_end),\\n 68| (second_end, third_end),\\n 69| (third_end, fourth_end),\\n 70| (fourth_end, duration_seconds),\\n 71| )\\n 72|\\n 73|\\n 74|def create_shot_plan(script: ScriptDraft) -> ShotPlan:\\n 75| \\\"\\\"\\\"Create a deterministic five-scene retention shot plan from a script draft.\\\"\\\"\\\"\\n 76|\\n 77| validation = validate_script_draft(script)\\n 78| if not validation.is_valid:\\n 79| raise ValueError(\\\"; \\\".join(validation.errors))\\n 80|\\n 81| windows = _scene_windows(script.estimated_duration_seconds)\\n 82| beats = script.retention_beats\\n 83| scenes = (\\n 84| ShotScene(\\n 85| purpose=\\\"first_frame_hook\\\",\\n 86| start_seconds=windows[0][0],\\n 87| end_seconds=windows[0][1],\\n 88| voiceover_beat=script.hook,\\n 89| visual_action=\\\"Immediate motion punch-in on clean kinetic hook text; no generated UI lettering.\\\",\\n 90| asset_mode=\\\"kinetic_text_overlay\\\",\\n 91| caption_beat=script.hook,\\n 92| retention_goal=\\\"Stop the scroll with a first-frame hook and pattern interrupt.\\\",\\n 93| ),\\n 94| ShotScene(\\n 95| purpose=\\\"pattern_interrupt\\\",\\n 96| start_seconds=windows[1][0],\\n 97| end_seconds=windows[1][1],\\n 98| voiceover_beat=beats[0],\\n 99| visual_action=\\\"Rapid desk-to-phone change showing messy priorities becoming a controlled workflow.\\\",\\n 100| asset_mode=\\\"mechanism_animation\\\",\\n 101| caption_beat=\\\"AI suggests. Human approves.\\\",\\n 102| retention_goal=\\\"Create visible change before the viewer can classify it as a slideshow.\\\",\\n 103| ),\\n 104| ShotScene(\\n 105| purpose=\\\"core_explanation\\\",\\n 106| start_seconds=windows[2][0],\\n 107| end_seconds=windows[2][1],\\n 108| voiceover_beat=beats[1] if len(beats) > 1 else script.script_outline,\\n 109| visual_action=\\\"Simulated screen flow with renderer-owned boxes, cursor motion, and approval checkpoint.\\\",\\n 110| asset_mode=\\\"screen_recording_simulation\\\",\\n 111| caption_beat=\\\"The rule: AI drafts, you decide.\\\",\\n 112| retention_goal=\\\"Make the mechanism concrete without trusting generated text inside assets.\\\",\\n 113| ),\\n 114| ShotScene(\\n 115| purpose=\\\"screen_or_card_visual\\\",\\n 116| start_seconds=windows[3][0],\\n 117| end_seconds=windows[3][1],\\n 118| voiceover_beat=beats[2] if len(beats) > 2 else script.script_outline,\\n 119| visual_action=\\\"Animated checklist/card sequence showing the weekly review routine step by step.\\\",\\n 120| asset_mode=\\\"mechanism_animation\\\",\\n 121| caption_beat=\\\"10-minute review. One human checkpoint.\\\",\\n 122| retention_goal=\\\"Deliver the payoff visually before the CTA.\\\",\\n 123| ),\\n 124| ShotScene(\\n 125| purpose=\\\"cta\\\",\\n 126| start_seconds=windows[4][0],\\n 127| end_seconds=windows[4][1],\\n 128| voiceover_beat=script.cta,\\n 129| visual_action=\\\"Clean kinetic CTA overlay with renderer-owned caption and final checklist card motion.\\\",\\n 130| asset_mode=\\\"kinetic_text_overlay\\\",\\n 131| caption_beat=script.cta,\\n 132| retention_goal=\\\"Give a concrete comment action tied to the promised checklist.\\\",\\n 133| ),\\n 134| )\\n 135| return ShotPlan(\\n 136| script_id=script.brief_id,\\n 137| title=script.title,\\n 138| estimated_duration_seconds=script.estimated_duration_seconds,\\n 139| scenes=scenes,\\n 140| )\\n 141|\\n 142|\\n 143|def validate_shot_plan(plan: ShotPlan) -> ValidationResult:\\n 144| errors: list[str] = []\\n 145| if _is_blank(plan.script_id):\\n 146| errors.append(\\\"script_id is required\\\")\\n 147| if _is_blank(plan.title):\\n 148| errors.append(\\\"title is required\\\")\\n 149| if not 10 <= plan.estimated_duration_seconds <= 180:\\n 150| errors.append(\\\"duration_seconds must be between 10 and 180\\\")\\n 151| if not plan.scenes:\\n 152| errors.append(\\\"at least one scene is required\\\")\\n 153| elif plan.scenes[0].purpose != \\\"first_frame_hook\\\":\\n 154| errors.append(\\\"first scene must be first_frame_hook\\\")\\n 155|\\n 156| previous_end: int | None = None\\n 157| for index, scene in enumerate(plan.scenes, start=1):\\n 158| if scene.end_seconds <= scene.start_seconds:\\n 159| errors.append(f\\\"scene {index} end_seconds must be greater than start_seconds\\\")\\n 160| if previous_end is not None and scene.start_seconds < previous_end:\\n 161| errors.append(f\\\"scene {index} start_seconds overlaps previous scene\\\")\\n 162| previous_end = scene.end_seconds\\n 163|\\n 164| for field_name, value in {\\n 165| \\\"purpose\\\": scene.purpose,\\n 166| \\\"voiceover_beat\\\": scene.voiceover_beat,\\n 167| \\\"visual_action\\\": scene.visual_action,\\n 168| \\\"asset_mode\\\": scene.asset_mode,\\n 169| \\\"caption_beat\\\": scene.caption_beat,\\n 170| \\\"retention_goal\\\": scene.retention_goal,\\n 171| }.items():\\n 172| if _is_blank(value):\\n 173| errors.append(f\\\"scene {index} {field_name} is required\\\")\\n 174|\\n 175| if scene.asset_mode == \\\"static_slideshow\\\":\\n 176| errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n 177| elif scene.asset_mode not in _ALLOWED_ASSET_MODES:\\n 178| errors.append(f\\\"unsupported asset mode: {scene.asset_mode}\\\")\\n 179| if not scene.labels_renderer_owned:\\n 180| errors.append(\\\"labels and captions must be renderer-owned\\\")\\n 181| visual = scene.visual_action.casefold()\\n 182| if any(marker in visual for marker in _BAKED_TEXT_MARKERS):\\n 183| errors.append(\\\"baked-in fake UI text is not allowed\\\")\\n 184|\\n 185| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 186|\", \"total_lines\": 185, \"file_size\": 7007, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"encrypted_content": "gAAAAABqEudv4cH4nh30b9cEkDLvOGK4Uc7bg9XXHPdzabLx77NRElkMXXvm1jErEbBaSS0bMDOZRmYXrC051WDxvedJPAkWWZicOwD6k6NXlCDWBavlGZ7gvKONGPBGJQJxVQI5d7vM9MX6Evu0PrVkkeV909RLSzYs3emajZj7qu7EjUTzpFosHDv4UO-ts7zt9jD-aTWqDDpLvFfnWCleE_x2Tce-smfXOgKTCKSGrSw1I0gtg3q_gaPRtJ1-fHvfPlYjIG6ZoTm9Yqw2dvgnCzcacoIvj78dkfxP0Ze0x86GzMMHZcFPeaBtmkwiBq5XS5WgW9W6fF_tHuFnANCIXmOG72NI06eiRJAb_95LHz4KygSRo0L4hjgkQvYca8awr2YY-6FBE6T_D98Wo6iGE1ZDA5AQ1hoNFlt3yDCazt67X7hxr9u0uaN7wCMnkpnAs9tbgx5ZnhHv2erKo2Lw-BAu7Fb6PZEZMXxn5jOKyXqTkAIEF3ri4f4XQWrjxicSnoQYIZ54XIlnnyzsmIbHxP94YxI10xQH3S53prL6yP4tsGQ8gyvnJxVUzqPaAuwjOTJcanb2S1wBTCM2FsMYHUXjjU7_2kSge6yp5gD6PZTi7Zz2yZsATkAchDXeDwex4P6VPS4mDZxENDsDMj040B_NrB2VuVfc7bM9h-I0ejUoY7ewgJ1tQ33AvZ_RyP9o4N2tuK_6PmSlduGiznW1KqT9_7GpOxOVAGZQqqLfirAfFTXX7XcAMk0xbSWo07oHOBR5uTplmznzjDrA6ozHSoWa0uBFrEZwd1w0Xu91D0pKoI-pwk8uI0ZdvB6TvGglAANvl_gBOMoV2tZri4QKxho3fl82XNCCkS6M6GEtGmkyKA8vkIwB62tDvBfJNRu_63BeGiwFPh7Zt1JY-bl5EpRaSwxrJE_mZOyiHSYynBXYO8o-e6qIjU0sz2pURMk1_fHyj3XnHB3RSQ5sQmZruorcHrti4Pbx9OQjGAsf3Aew2pXyMHncW_-qkGpAweh3slbY0u-QIyrfe1kFeMVuiVmo8QPtvA==",
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"text": "**Considering implementation details**\n\nI need to implement the manifest. It’s crucial to inspect the test patterns for TestBatch and ReviewBatch to make sure everything is working correctly. I should check the README as well; it often contains valuable information that can guide me in this process. There’s a lot to juggle here, but I’m focusing on making sure I cover all the necessary parts effectively. Let's get started!"
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"content": "{\"content\": \" 1|import pytest\\n 2|\\n 3|from autoshorts.content.brief import create_content_brief\\n 4|from autoshorts.content.script_draft import create_script_draft\\n 5|from autoshorts.rendering.shot_plan import (\\n 6| ShotPlan,\\n 7| ShotScene,\\n 8| create_shot_plan,\\n 9| validate_shot_plan,\\n 10|)\\n 11|\\n 12|\\n 13|def _script():\\n 14| brief = create_content_brief(\\n 15| pillar_id=\\\"ai_life_systems\\\",\\n 16| topic=\\\"AI weekly review routine\\\",\\n 17| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 18| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 19| )\\n 20| return create_script_draft(brief)\\n 21|\\n 22|\\n 23|def test_create_shot_plan_builds_five_retention_scenes_from_script_draft():\\n 24| script = _script()\\n 25|\\n 26| plan = create_shot_plan(script)\\n 27|\\n 28| assert isinstance(plan, ShotPlan)\\n 29| assert plan.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n 30| assert plan.title == \\\"AI weekly review routine\\\"\\n 31| assert plan.estimated_duration_seconds == 35\\n 32| assert [scene.purpose for scene in plan.scenes] == [\\n 33| \\\"first_frame_hook\\\",\\n 34| \\\"pattern_interrupt\\\",\\n 35| \\\"core_explanation\\\",\\n 36| \\\"screen_or_card_visual\\\",\\n 37| \\\"cta\\\",\\n 38| ]\\n 39| assert [scene.asset_mode for scene in plan.scenes] == [\\n 40| \\\"kinetic_text_overlay\\\",\\n 41| \\\"mechanism_animation\\\",\\n 42| \\\"screen_recording_simulation\\\",\\n 43| \\\"mechanism_animation\\\",\\n 44| \\\"kinetic_text_overlay\\\",\\n 45| ]\\n 46| assert plan.scenes[0].start_seconds == 0\\n 47| assert plan.scenes[-1].end_seconds == 35\\n 48| assert plan.scenes[0].caption_beat == script.hook\\n 49| assert \\\"pattern interrupt\\\" in plan.scenes[0].retention_goal.casefold()\\n 50| assert all(scene.labels_renderer_owned for scene in plan.scenes)\\n 51| assert all(\\\"fake UI text\\\" not in scene.visual_action for scene in plan.scenes)\\n 52|\\n 53|\\n 54|def test_validate_shot_plan_accepts_default_plan():\\n 55| result = validate_shot_plan(create_shot_plan(_script()))\\n 56|\\n 57| assert result.is_valid is True\\n 58| assert result.errors == ()\\n 59|\\n 60|\\n 61|def test_validate_shot_plan_rejects_static_slideshow_and_baked_text():\\n 62| scene = ShotScene(\\n 63| purpose=\\\"first_frame_hook\\\",\\n 64| start_seconds=0,\\n 65| end_seconds=3,\\n 66| voiceover_beat=\\\"Hook\\\",\\n 67| visual_action=\\\"Static slide with baked-in fake UI text\\\",\\n 68| asset_mode=\\\"static_slideshow\\\",\\n 69| caption_beat=\\\"Hook\\\",\\n 70| retention_goal=\\\"Stop scroll\\\",\\n 71| labels_renderer_owned=False,\\n 72| )\\n 73| plan = ShotPlan(\\n 74| script_id=\\\"script\\\",\\n 75| title=\\\"Title\\\",\\n 76| estimated_duration_seconds=3,\\n 77| scenes=(scene,),\\n 78| )\\n 79|\\n 80| result = validate_shot_plan(plan)\\n 81|\\n 82| assert result.is_valid is False\\n 83| assert \\\"static slideshow asset mode is not allowed\\\" in result.errors\\n 84| assert \\\"labels and captions must be renderer-owned\\\" in result.errors\\n 85| assert \\\"baked-in fake UI text is not allowed\\\" in result.errors\\n 86|\\n 87|\\n 88|def test_validate_shot_plan_rejects_bad_timing_and_missing_first_frame_hook():\\n 89| scene = ShotScene(\\n 90| purpose=\\\"pattern_interrupt\\\",\\n 91| start_seconds=2,\\n 92| end_seconds=1,\\n 93| voiceover_beat=\\\"Beat\\\",\\n 94| visual_action=\\\"Quick motion cut\\\",\\n 95| asset_mode=\\\"mechanism_animation\\\",\\n 96| caption_beat=\\\"Beat\\\",\\n 97| retention_goal=\\\"Interrupt\\\",\\n 98| )\\n 99| plan = ShotPlan(\\n 100| script_id=\\\"script\\\",\\n 101| title=\\\"Title\\\",\\n 102| estimated_duration_seconds=35,\\n 103| scenes=(scene,),\\n 104| )\\n 105|\\n 106| result = validate_shot_plan(plan)\\n 107|\\n 108| assert result.is_valid is False\\n 109| assert \\\"first scene must be first_frame_hook\\\" in result.errors\\n 110| assert \\\"scene 1 end_seconds must be greater than start_seconds\\\" in result.errors\\n 111|\\n 112|\\n 113|def test_create_shot_plan_rejects_invalid_script_draft():\\n 114| script = create_script_draft(_script_to_brief(), hook=\\\"\\\")\\n 115|\\n 116| with pytest.raises(ValueError, match=\\\"hook is required\\\"):\\n 117| create_shot_plan(script)\\n 118|\\n 119|\\n 120|def _script_to_brief():\\n 121| return create_content_brief(\\n 122| pillar_id=\\\"ai_life_systems\\\",\\n 123| topic=\\\"AI weekly review routine\\\",\\n 124| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 125| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 126| )\\n 127|\", \"total_lines\": 126, \"file_size\": 4049, \"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.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n 12|- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n 13|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 14|- `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n 15|- `autoshorts.content.demo_pipeline` — deterministic content-to-Telegram-review demo pipeline.\\n 16|- `autoshorts.content.script_draft` — structured script drafts with retention beats, visual concept, caption, CTA, and AI-slop validation.\\n 17|- `autoshorts.content.to_candidate` — converts content briefs and script drafts into candidate drafts and validated video candidates.\\n 18|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n 19|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 20|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 21|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 22|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 23|- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n 24|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 25|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 26|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 27|- `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n 28|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 29|- Tests for content briefs, content demo pipeline, content-to-candidate conversion, content pillars, candidate factory, idea candidates, script drafts, shot plans, testbatches, sample review CLI, sample Telegram delivery CLI, sample review demos, review workflow, review-batch rendering, approval parsing, review formatting, Telegram delivery preparation, and script quality gating.\\n 30|\\n 31|## Content development\\n 32|\\n 33|`autoshorts.content.brief.create_content_brief()` turns a pillar, topic, audience problem, and payoff into a deterministic `ContentBrief`. The brief carries positioning, tone, visual rules, no-go topics, target platforms, and target duration before any script or renderer exists.\\n 34|\\n 35|`validate_content_brief()` rejects missing strategy fields, unsupported platforms, and durations outside `10..180` seconds.\\n 36|\\n 37|`autoshorts.content.script_draft.create_script_draft()` turns a `ContentBrief` into a structured `ScriptDraft` with:\\n 38|\\n 39|- exact hook;\\n 40|- script outline;\\n 41|- 3–5 retention beats;\\n 42|- visual concept;\\n 43|- caption and CTA;\\n 44|- estimated duration.\\n 45|\\n 46|`validate_script_draft()` rejects missing core fields, invalid beat counts, invalid durations, and generic AI-slop phrases such as `in this video` or `unlock your full potential`.\\n 47|\\n 48|`autoshorts.content.to_candidate.convert_script_draft_to_candidate_draft()` turns a validated `ContentBrief` + `ScriptDraft` pair into the existing `CandidateDraft` type. It preserves platform strategy, hook, script outline, visual concept, caption, CTA, no-go policy notes, and an initial conservative quality score.\\n 49|\\n 50|`convert_script_draft_to_candidate()` then uses the existing candidate factory to create a validated `VideoCandidate`, including stable candidate id generation, default hashtags, duration classification, and pillar/platform validation.\\n 51|\\n 52|`autoshorts.content.demo_pipeline.build_ai_life_systems_demo_delivery()` runs the deterministic demo path end-to-end:\\n 53|\\n 54|```text\\n 55|ContentBrief\\n 56|→ ScriptDraft\\n 57|→ VideoCandidate\\n 58|→ TestBatch\\n 59|→ ReviewBatch\\n 60|→ Telegram ReviewDelivery\\n 61|```\\n 62|\\n 63|The result object exposes every intermediate artifact for inspection and still has no send/publish side effects.\\n 64|\\n 65|## Render planning\\n 66|\\n 67|`autoshorts.rendering.shot_plan.create_shot_plan()` turns a validated `ScriptDraft` into a deterministic five-scene retention plan:\\n 68|\\n 69|```text\\n 70|first_frame_hook\\n 71|→ pattern_interrupt\\n 72|→ core_explanation\\n 73|→ screen_or_card_visual\\n 74|→ cta\\n 75|```\\n 76|\\n 77|Each `ShotScene` carries timing, voiceover beat, visual action, asset mode, caption beat, retention goal, and whether labels/captions remain renderer-owned.\\n 78|\\n 79|`validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n 80|\\n 81|## Human approval commands\\n 82|\\n 83|Supported review responses:\\n 84|\\n 85|- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n 86|- `Freigabe YouTube`\\n 87|- `Freigabe TikTok`\\n 88|- `Freigabe Instagram`\\n 89|- `Ablehnen`\\n 90|- `Ändern: `\\n 91|\\n 92|Ambiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n 93|\\n 94|## Quality gate\\n 95|\\n 96|A script candidate only proceeds to rendering when:\\n 97|\\n 98|- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n 99|- AI-slop risk is not above `5`;\\n 100|- policy risk is not `high`.\\n 101|\\n 102|This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n 103|\\n 104|## Review package\\n 105|\\n 106|`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n 107|\\n 108|- candidate id, title, pillar, target platforms, and duration;\\n 109|- hook, script summary, visual concept, caption, and hashtags;\\n 110|- average quality score, AI-slop risk, policy notes, and render-gate status;\\n 111|- optional preview path;\\n 112|- only the relevant platform-specific approval commands.\\n 113|\\n 114|A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n 115|\\n 116|## Review workflow\\n 117|\\n 118|`build_candidate_review_item()` classifies one candidate as:\\n 119|\\n 120|- `ready_for_review` — valid and renderable; includes a review package;\\n 121|- `blocked` — valid but blocked by quality gate; still includes a review package with blocked gate status;\\n 122|- `invalid` — fails candidate validation; does not create a review package.\\n 123|\\n 124|`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 125|\\n 126|`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 127|\\n 128|## Sample review demo\\n 129|\\n 130|`autoshorts.demo.sample_batch.build_sample_ai_life_systems_batch()` creates a deterministic AI-life-systems sample with:\\n 131|\\n 132|- one ready candidate;\\n 133|- one blocked candidate that fails the quality gate;\\n 134|- one invalid candidate missing its hook.\\n 135|\\n 136|`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 137|\\n 138|Run the same fixture locally with:\\n 139|\\n 140|```bash\\n 141|python -m autoshorts.cli.demo_review_batch\\n 142|```\\n 143|\\n 144|## Telegram review delivery preparation\\n 145|\\n 146|`autoshorts.telegram.review_sender.prepare_review_delivery()` wraps rendered review text in a side-effect-free `ReviewDelivery` object. It does not call Telegram. It validates that:\\n 147|\\n 148|- review text is not blank;\\n 149|- text fits Telegram's 4096-character message limit;\\n 150|- review deliveries do not contain publish/post language;\\n 151|- the delivery still requires human approval.\\n 152|\\n 153|Actual Telegram I/O remains a later explicit boundary.\\n 154|\\n 155|Preview the prepared delivery locally with:\\n 156|\\n 157|```bash\\n 158|python -m autoshorts.cli.demo_telegram_delivery\\n 159|```\\n 160|\\n 161|It prints delivery metadata first, then the review text. It still sends nothing.\\n 162|\\n 163|## Idea candidates and testbatches\\n 164|\\n 165|`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n 166|\\n 167|- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n 168|- hook, script outline, visual concept, caption, CTA, and hashtags;\\n 169|- quality score and policy notes.\\n 170|\\n 171|`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n 172|\\n 173|`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 174|\\n 175|## Candidate factory\\n 176|\\n 177|`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n 178|\\n 179|`create_candidate()` turns a draft into a candidate by:\\n 180|\\n 181|- generating a stable id from pillar, title, and duration;\\n 182|- adding default pillar hashtags when no hashtags are supplied;\\n 183|- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n 184|- appending the duration class to policy notes;\\n 185|- enforcing candidate validation and pillar/platform fit validation.\\n 186|\\n 187|Duration classes:\\n 188|\\n 189|- `growth_short`: 20–45 seconds;\\n 190|- `monetization_candidate`: 60–75 seconds;\\n 191|- `experiment`: all other valid durations.\\n 192|\\n 193|Missing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n 194|\\n 195|## Content pillars\\n 196|\\n 197|`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n 198|\\n 199|- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n 200|\\n 201|A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n 202|\\n 203|`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 204|\\n 205|## Development\\n 206|\\n 207|Run tests:\\n 208|\\n 209|```bash\\n 210|python -m pytest tests -q\\n 211|```\\n 212|\\n 213|## Planned architecture\\n 214|\\n 215|```text\\n 216|autoshorts/\\n 217| config/ # niches, platforms, workflow settings\\n 218| ideas/ # idea generation and scoring\\n 219| scripting/ # script drafts, quality gates, retention checks\\n 220| rendering/ # local/AI-assisted preview rendering\\n 221| review/ # Telegram review package and approval parser\\n 222| publishing/ # platform-specific publishing metadata and later APIs\\n 223| analytics/ # performance imports and feedback loop\\n 224|```\\n 225|\\n 226|## Non-negotiables\\n 227|\\n 228|- No credentials in git.\\n 229|- No generated media in git unless explicitly intended.\\n 230|- No automatic publishing without explicit approval.\\n 231|- Quality over speed; avoid generic AI-slop.\\n 232|\", \"total_lines\": 231, \"file_size\": 10992, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Deterministic content-to-review demo pipeline.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.content.brief import ContentBrief, create_content_brief\\n 8|from autoshorts.content.script_draft import ScriptDraft, create_script_draft\\n 9|from autoshorts.content.to_candidate import convert_script_draft_to_candidate\\n 10|from autoshorts.ideas.candidate import VideoCandidate\\n 11|from autoshorts.ideas.testbatch import TestBatch\\n 12|from autoshorts.review.render import render_review_batch_summary\\n 13|from autoshorts.review.workflow import ReviewBatch, build_review_batch\\n 14|from autoshorts.telegram.review_sender import ReviewDelivery, prepare_review_delivery\\n 15|\\n 16|\\n 17|@dataclass(frozen=True)\\n 18|class ContentDemoPipelineResult:\\n 19| brief: ContentBrief\\n 20| script: ScriptDraft\\n 21| candidate: VideoCandidate\\n 22| testbatch: TestBatch\\n 23| review_batch: ReviewBatch\\n 24| delivery: ReviewDelivery\\n 25|\\n 26|\\n 27|def build_ai_life_systems_demo_delivery() -> ContentDemoPipelineResult:\\n 28| \\\"\\\"\\\"Run the deterministic AI-life-systems content demo through review prep.\\\"\\\"\\\"\\n 29|\\n 30| brief = create_content_brief(\\n 31| pillar_id=\\\"ai_life_systems\\\",\\n 32| topic=\\\"AI weekly review routine\\\",\\n 33| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 34| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 35| )\\n 36| script = create_script_draft(brief)\\n 37| candidate = convert_script_draft_to_candidate(brief, script)\\n 38| testbatch = TestBatch(\\n 39| batch_id=\\\"content-demo-ai-life-systems\\\",\\n 40| candidates=(candidate,),\\n 41| )\\n 42| review_batch = build_review_batch(testbatch)\\n 43| review_text = render_review_batch_summary(review_batch)\\n 44| delivery = prepare_review_delivery(review_text, topic=\\\"review\\\")\\n 45|\\n 46| return ContentDemoPipelineResult(\\n 47| brief=brief,\\n 48| script=script,\\n 49| candidate=candidate,\\n 50| testbatch=testbatch,\\n 51| review_batch=review_batch,\\n 52| delivery=delivery,\\n 53| )\\n 54|\", \"total_lines\": 53, \"file_size\": 1942, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_manifest.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.manifest import (\\n RenderManifest,\\n RenderSettings,\\n build_render_manifest,\\n validate_render_manifest,\\n)\\nfrom autoshorts.rendering.shot_plan import ShotPlan, ShotScene, create_shot_plan\\n\\n\\ndef _shot_plan():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n return create_shot_plan(script)\\n\\n\\ndef test_build_render_manifest_turns_shot_plan_into_vertical_timeline():\\n plan = _shot_plan()\\n\\n manifest = build_render_manifest(plan)\\n\\n assert isinstance(manifest, RenderManifest)\\n assert manifest.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n assert manifest.title == \\\"AI weekly review routine\\\"\\n assert manifest.settings == RenderSettings(\\n width=1080,\\n height=1920,\\n fps=30,\\n no_generated_text_inside_assets=True,\\n captions_renderer_owned=True,\\n static_card_limit=0,\\n )\\n assert len(manifest.timeline) == len(plan.scenes)\\n assert [item.scene_purpose for item in manifest.timeline] == [scene.purpose for scene in plan.scenes]\\n assert manifest.timeline[0].start_seconds == 0\\n assert manifest.timeline[-1].end_seconds == 35\\n assert manifest.timeline[0].placeholder_asset_path == (\\n \\\"placeholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\\\"\\n )\\n assert all(item.caption_layer.renderer_owned for item in manifest.timeline)\\n assert all(item.label_layer.renderer_owned for item in manifest.timeline)\\n assert all(\\\"static\\\" not in item.motion_instruction.casefold() for item in manifest.timeline)\\n\\n\\ndef test_validate_render_manifest_accepts_default_manifest():\\n result = validate_render_manifest(build_render_manifest(_shot_plan()))\\n\\n assert result.is_valid is True\\n assert result.errors == ()\\n\\n\\ndef test_build_render_manifest_rejects_invalid_shot_plan():\\n invalid_plan = ShotPlan(\\n script_id=\\\"script\\\",\\n title=\\\"Title\\\",\\n estimated_duration_seconds=35,\\n scenes=(\\n ShotScene(\\n purpose=\\\"first_frame_hook\\\",\\n start_seconds=0,\\n end_seconds=3,\\n voiceover_beat=\\\"Hook\\\",\\n visual_action=\\\"Static card\\\",\\n asset_mode=\\\"static_slideshow\\\",\\n caption_beat=\\\"Hook\\\",\\n retention_goal=\\\"Stop scroll\\\",\\n ),\\n ),\\n )\\n\\n with pytest.raises(ValueError, match=\\\"static slideshow asset mode is not allowed\\\"):\\n build_render_manifest(invalid_plan)\\n\\n\\ndef test_validate_render_manifest_rejects_unsafe_render_settings():\\n manifest = build_render_manifest(_shot_plan())\\n unsafe = RenderManifest(\\n script_id=manifest.script_id,\\n title=manifest.title,\\n settings=RenderSettings(\\n width=1920,\\n height=1080,\\n fps=24,\\n no_generated_text_inside_assets=False,\\n captions_renderer_owned=False,\\n static_card_limit=1,\\n ),\\n timeline=manifest.timeline,\\n )\\n\\n result = validate_render_manifest(unsafe)\\n\\n assert result.is_valid is False\\n assert \\\"render size must be vertical 1080x1920\\\" in result.errors\\n assert \\\"fps must be 30\\\" in result.errors\\n assert \\\"generated text inside assets is not allowed\\\" in result.errors\\n assert \\\"captions must be renderer-owned\\\" in result.errors\\n assert \\\"static_card_limit must be 0\\\" in result.errors\\n\\n\\ndef test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers():\\n manifest = build_render_manifest(_shot_plan())\\n first = manifest.timeline[0]\\n bad_item = type(first)(\\n scene_purpose=first.scene_purpose,\\n start_seconds=first.start_seconds,\\n end_seconds=first.end_seconds,\\n asset_mode=first.asset_mode,\\n motion_instruction=first.motion_instruction,\\n placeholder_asset_path=first.placeholder_asset_path,\\n caption_layer=type(first.caption_layer)(text=first.caption_layer.text, renderer_owned=False),\\n label_layer=type(first.label_layer)(text=first.label_layer.text, renderer_owned=False),\\n )\\n unsafe = RenderManifest(\\n script_id=manifest.script_id,\\n title=manifest.title,\\n settings=manifest.settings,\\n timeline=(bad_item,) + manifest.timeline[1:],\\n )\\n\\n result = validate_render_manifest(unsafe)\\n\\n assert result.is_valid is False\\n assert \\\"timeline item 1 caption layer must be renderer-owned\\\" in result.errors\\n assert \\\"timeline item 1 label layer must be renderer-owned\\\" in result.errors\\n\"}"
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_render_manifest.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_manifest.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_render_manifest.py:5: in \\n from autoshorts.rendering.manifest import (\\nE ModuleNotFoundError: No module named 'autoshorts.rendering.manifest'\\n=========================== short test summary info ============================\\nERROR tests/test_render_manifest.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py\",\"content\":\"\\\"\\\"\\\"Side-effect-free render manifest generation from shot plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.shot_plan import ShotPlan, validate_shot_plan\\n\\n\\n@dataclass(frozen=True)\\nclass RenderSettings:\\n width: int = 1080\\n height: int = 1920\\n fps: int = 30\\n no_generated_text_inside_assets: bool = True\\n captions_renderer_owned: bool = True\\n static_card_limit: int = 0\\n\\n\\n@dataclass(frozen=True)\\nclass RenderLayer:\\n text: str\\n renderer_owned: bool = True\\n\\n\\n@dataclass(frozen=True)\\nclass RenderTimelineItem:\\n scene_purpose: str\\n start_seconds: int\\n end_seconds: int\\n asset_mode: str\\n motion_instruction: str\\n placeholder_asset_path: str\\n caption_layer: RenderLayer\\n label_layer: RenderLayer\\n\\n\\n@dataclass(frozen=True)\\nclass RenderManifest:\\n script_id: str\\n title: str\\n settings: RenderSettings\\n timeline: tuple[RenderTimelineItem, ...]\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef _placeholder_path(script_id: str, index: int, purpose: str) -> str:\\n return f\\\"placeholders/{script_id}/{index:02d}-{purpose}.png\\\"\\n\\n\\ndef build_render_manifest(plan: ShotPlan) -> RenderManifest:\\n \\\"\\\"\\\"Turn a validated shot plan into a machine-readable render manifest.\\\"\\\"\\\"\\n\\n validation = validate_shot_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n timeline = tuple(\\n RenderTimelineItem(\\n scene_purpose=scene.purpose,\\n start_seconds=scene.start_seconds,\\n end_seconds=scene.end_seconds,\\n asset_mode=scene.asset_mode,\\n motion_instruction=scene.visual_action,\\n placeholder_asset_path=_placeholder_path(plan.script_id, index, scene.purpose),\\n caption_layer=RenderLayer(text=scene.caption_beat, renderer_owned=True),\\n label_layer=RenderLayer(text=scene.purpose.replace(\\\"_\\\", \\\" \\\"), renderer_owned=True),\\n )\\n for index, scene in enumerate(plan.scenes, start=1)\\n )\\n return RenderManifest(\\n script_id=plan.script_id,\\n title=plan.title,\\n settings=RenderSettings(),\\n timeline=timeline,\\n )\\n\\n\\ndef validate_render_manifest(manifest: RenderManifest) -> ValidationResult:\\n errors: list[str] = []\\n if _is_blank(manifest.script_id):\\n errors.append(\\\"script_id is required\\\")\\n if _is_blank(manifest.title):\\n errors.append(\\\"title is required\\\")\\n\\n settings = manifest.settings\\n if (settings.width, settings.height) != (1080, 1920):\\n errors.append(\\\"render size must be vertical 1080x1920\\\")\\n if settings.fps != 30:\\n errors.append(\\\"fps must be 30\\\")\\n if not settings.no_generated_text_inside_assets:\\n errors.append(\\\"generated text inside assets is not allowed\\\")\\n if not settings.captions_renderer_owned:\\n errors.append(\\\"captions must be renderer-owned\\\")\\n if settings.static_card_limit != 0:\\n errors.append(\\\"static_card_limit must be 0\\\")\\n\\n if not manifest.timeline:\\n errors.append(\\\"at least one timeline item is required\\\")\\n\\n previous_end: int | None = None\\n for index, item in enumerate(manifest.timeline, start=1):\\n if _is_blank(item.scene_purpose):\\n errors.append(f\\\"timeline item {index} scene_purpose is required\\\")\\n if item.end_seconds <= item.start_seconds:\\n errors.append(f\\\"timeline item {index} end_seconds must be greater than start_seconds\\\")\\n if previous_end is not None and item.start_seconds < previous_end:\\n errors.append(f\\\"timeline item {index} start_seconds overlaps previous item\\\")\\n previous_end = item.end_seconds\\n\\n for field_name, value in {\\n \\\"asset_mode\\\": item.asset_mode,\\n \\\"motion_instruction\\\": item.motion_instruction,\\n \\\"placeholder_asset_path\\\": item.placeholder_asset_path,\\n \\\"caption_layer text\\\": item.caption_layer.text,\\n \\\"label_layer text\\\": item.label_layer.text,\\n }.items():\\n if _is_blank(value):\\n errors.append(f\\\"timeline item {index} {field_name} is required\\\")\\n\\n if item.asset_mode == \\\"static_slideshow\\\":\\n errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n if \\\"baked-in\\\" in item.motion_instruction.casefold():\\n errors.append(\\\"baked-in text in render assets is not allowed\\\")\\n if not item.caption_layer.renderer_owned:\\n errors.append(f\\\"timeline item {index} caption layer must be renderer-owned\\\")\\n if not item.label_layer.renderer_owned:\\n errors.append(f\\\"timeline item {index} label layer must be renderer-owned\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\"}"
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"text": "Erledigt, Sir.\n\n## Neu eingebaut: Render Manifest Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/manifest.py\ntests/test_render_manifest.py\n```\n\n## Neue API\n\n```python\nbuild_render_manifest(plan: ShotPlan) -> RenderManifest\nvalidate_render_manifest(manifest: RenderManifest) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderManifest\n```\n\n## RenderManifest-Struktur\n\n```python\nRenderManifest(\n script_id=...,\n title=...,\n settings=RenderSettings(...),\n timeline=(...)\n)\n```\n\n### Render Settings\n\nAktuell hart und bewusst konservativ:\n\n```python\nRenderSettings(\n width=1080,\n height=1920,\n fps=30,\n no_generated_text_inside_assets=True,\n captions_renderer_owned=True,\n static_card_limit=0,\n)\n```\n\nAlso vertikal, Shorts-tauglich, captions/labels im Renderer — nicht in generierten Assets eingebrannt. Genau richtig, weil AI-Modelle bei Text gern aussehen, als hätte ein Drucker Dämonenbeschwörung gelernt.\n\n## Timeline Items\n\nPro ShotScene entsteht ein `RenderTimelineItem` mit:\n\n```text\nscene_purpose\nstart_seconds\nend_seconds\nasset_mode\nmotion_instruction\nplaceholder_asset_path\ncaption_layer\nlabel_layer\n```\n\nBeispiel verifiziert:\n\n```text\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\n```\n\n## Guardrails\n\n`validate_render_manifest()` rejected:\n\n- falsches Format statt `1080x1920`\n- falsche FPS statt `30`\n- generated text inside assets\n- nicht renderer-owned captions\n- `static_card_limit != 0`\n- leere Timeline\n- schlechte Timeline-Timings\n- `static_slideshow`\n- nicht renderer-owned caption/label layers\n- fehlende Pflichtfelder\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.manifest'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n74 passed in 0.11s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n(1080, 1920, 30)\n5\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\nTrue\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.manifest`\n- RenderManifest\n- Timeline Items\n- Vertical Settings\n- Safety Constraints\n- keine Render-/API-Side-Effects\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\ne258635 feat: add render manifest planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir einen lokalen Preview-Renderer bauen — weiterhin ohne ComfyUI/Wan/Qwen/API:\n\n```text\nRenderManifest\n→ Local Preview Frames / HTML Preview\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/rendering/preview.py\ntests/test_render_preview.py\n```\n\nZiel:\n\n- aus `RenderManifest` eine side-effect-free oder temp-dir-basierte Preview erzeugen\n- erstmal als HTML/SVG Timeline oder simple frame manifest\n- keine echten AI Assets\n- keine Videos\n- keine externen Calls\n- aber sichtbar genug, um Timing, Captions, Scene Flow und Motion Instructions zu prüfen\n\nDanach erst Qwen-Image/Wan/ComfyUI anbinden. Erst die Regie, dann die GPU. Sonst macht die Maschine teure Kunst mit null dramaturgischem Rückgrat."
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"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. 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. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. 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. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### 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\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Demo / Fixture Features\\n\\nWhen adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.\\n\\n## Side-Effect-Free Integration Boundaries\\n\\nWhen building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:\\n\\n1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.\\n2. RED should be a missing module/API or missing validation failure, not a real network/send failure.\\n3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.\\n4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.\\n5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.\\n6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.\\n\\nThis pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.\\n\\n### Media / Rendering Pipeline Boundaries\\n\\nWhen building toward image/video generation or renderer integration, add deterministic planning artifacts before any GPU/API work:\\n\\n1. Write tests for the wished-for pure API first, e.g. `create_shot_plan(script)`, `build_render_manifest(plan)`, or `prepare_preview(manifest)`.\\n2. RED should be a missing module/API or missing validation behavior — never a failed ComfyUI/Wan/Qwen/API invocation.\\n3. Keep each stage inspectable and side-effect-free until the explicit adapter layer: `ScriptDraft -> ShotPlan -> RenderManifest -> PreviewPlan/PreviewArtifact -> renderer adapter`.\\n4. Assert stable IDs/order, scene counts, timing continuity, vertical settings (`1080x1920`, `30fps` when building Shorts/Reels/TikTok), and placeholder paths before generating media.\\n5. Add guardrail tests that reject static slideshows, baked-in/generated text inside assets, non-renderer-owned captions/labels, unsafe render settings, missing fields, and bad timings.\\n6. Only after the manifest/preview boundary is green should a later adapter call local GPU tools or external services. Planning first; GPU roulette later.\\n\\nThis prevents wasting render time on unclear direction and keeps expensive media generation behind a small, audited boundary.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\\n- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`\\\"\\\"`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": {\"references\": [\"references/deterministic-demo-fixtures.md\", \"references/side-effect-free-delivery-boundaries.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Side-effect-free render manifest generation from shot plans.\\\"\\\"\\\"\\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\\n 8|from autoshorts.rendering.shot_plan import ShotPlan, validate_shot_plan\\n 9|\\n 10|\\n 11|@dataclass(frozen=True)\\n 12|class RenderSettings:\\n 13| width: int = 1080\\n 14| height: int = 1920\\n 15| fps: int = 30\\n 16| no_generated_text_inside_assets: bool = True\\n 17| captions_renderer_owned: bool = True\\n 18| static_card_limit: int = 0\\n 19|\\n 20|\\n 21|@dataclass(frozen=True)\\n 22|class RenderLayer:\\n 23| text: str\\n 24| renderer_owned: bool = True\\n 25|\\n 26|\\n 27|@dataclass(frozen=True)\\n 28|class RenderTimelineItem:\\n 29| scene_purpose: str\\n 30| start_seconds: int\\n 31| end_seconds: int\\n 32| asset_mode: str\\n 33| motion_instruction: str\\n 34| placeholder_asset_path: str\\n 35| caption_layer: RenderLayer\\n 36| label_layer: RenderLayer\\n 37|\\n 38|\\n 39|@dataclass(frozen=True)\\n 40|class RenderManifest:\\n 41| script_id: str\\n 42| title: str\\n 43| settings: RenderSettings\\n 44| timeline: tuple[RenderTimelineItem, ...]\\n 45|\\n 46|\\n 47|def _is_blank(value: str) -> bool:\\n 48| return not (value or \\\"\\\").strip()\\n 49|\\n 50|\\n 51|def _placeholder_path(script_id: str, index: int, purpose: str) -> str:\\n 52| return f\\\"placeholders/{script_id}/{index:02d}-{purpose}.png\\\"\\n 53|\\n 54|\\n 55|def build_render_manifest(plan: ShotPlan) -> RenderManifest:\\n 56| \\\"\\\"\\\"Turn a validated shot plan into a machine-readable render manifest.\\\"\\\"\\\"\\n 57|\\n 58| validation = validate_shot_plan(plan)\\n 59| if not validation.is_valid:\\n 60| raise ValueError(\\\"; \\\".join(validation.errors))\\n 61|\\n 62| timeline = tuple(\\n 63| RenderTimelineItem(\\n 64| scene_purpose=scene.purpose,\\n 65| start_seconds=scene.start_seconds,\\n 66| end_seconds=scene.end_seconds,\\n 67| asset_mode=scene.asset_mode,\\n 68| motion_instruction=scene.visual_action,\\n 69| placeholder_asset_path=_placeholder_path(plan.script_id, index, scene.purpose),\\n 70| caption_layer=RenderLayer(text=scene.caption_beat, renderer_owned=True),\\n 71| label_layer=RenderLayer(text=scene.purpose.replace(\\\"_\\\", \\\" \\\"), renderer_owned=True),\\n 72| )\\n 73| for index, scene in enumerate(plan.scenes, start=1)\\n 74| )\\n 75| return RenderManifest(\\n 76| script_id=plan.script_id,\\n 77| title=plan.title,\\n 78| settings=RenderSettings(),\\n 79| timeline=timeline,\\n 80| )\\n 81|\\n 82|\\n 83|def validate_render_manifest(manifest: RenderManifest) -> ValidationResult:\\n 84| errors: list[str] = []\\n 85| if _is_blank(manifest.script_id):\\n 86| errors.append(\\\"script_id is required\\\")\\n 87| if _is_blank(manifest.title):\\n 88| errors.append(\\\"title is required\\\")\\n 89|\\n 90| settings = manifest.settings\\n 91| if (settings.width, settings.height) != (1080, 1920):\\n 92| errors.append(\\\"render size must be vertical 1080x1920\\\")\\n 93| if settings.fps != 30:\\n 94| errors.append(\\\"fps must be 30\\\")\\n 95| if not settings.no_generated_text_inside_assets:\\n 96| errors.append(\\\"generated text inside assets is not allowed\\\")\\n 97| if not settings.captions_renderer_owned:\\n 98| errors.append(\\\"captions must be renderer-owned\\\")\\n 99| if settings.static_card_limit != 0:\\n 100| errors.append(\\\"static_card_limit must be 0\\\")\\n 101|\\n 102| if not manifest.timeline:\\n 103| errors.append(\\\"at least one timeline item is required\\\")\\n 104|\\n 105| previous_end: int | None = None\\n 106| for index, item in enumerate(manifest.timeline, start=1):\\n 107| if _is_blank(item.scene_purpose):\\n 108| errors.append(f\\\"timeline item {index} scene_purpose is required\\\")\\n 109| if item.end_seconds <= item.start_seconds:\\n 110| errors.append(f\\\"timeline item {index} end_seconds must be greater than start_seconds\\\")\\n 111| if previous_end is not None and item.start_seconds < previous_end:\\n 112| errors.append(f\\\"timeline item {index} start_seconds overlaps previous item\\\")\\n 113| previous_end = item.end_seconds\\n 114|\\n 115| for field_name, value in {\\n 116| \\\"asset_mode\\\": item.asset_mode,\\n 117| \\\"motion_instruction\\\": item.motion_instruction,\\n 118| \\\"placeholder_asset_path\\\": item.placeholder_asset_path,\\n 119| \\\"caption_layer text\\\": item.caption_layer.text,\\n 120| \\\"label_layer text\\\": item.label_layer.text,\\n 121| }.items():\\n 122| if _is_blank(value):\\n 123| errors.append(f\\\"timeline item {index} {field_name} is required\\\")\\n 124|\\n 125| if item.asset_mode == \\\"static_slideshow\\\":\\n 126| errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n 127| if \\\"baked-in\\\" in item.motion_instruction.casefold():\\n 128| errors.append(\\\"baked-in text in render assets is not allowed\\\")\\n 129| if not item.caption_layer.renderer_owned:\\n 130| errors.append(f\\\"timeline item {index} caption layer must be renderer-owned\\\")\\n 131| if not item.label_layer.renderer_owned:\\n 132| errors.append(f\\\"timeline item {index} label layer must be renderer-owned\\\")\\n 133|\\n 134| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 135|\", \"total_lines\": 134, \"file_size\": 4800, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|import pytest\\n 2|\\n 3|from autoshorts.content.brief import create_content_brief\\n 4|from autoshorts.content.script_draft import create_script_draft\\n 5|from autoshorts.rendering.manifest import (\\n 6| RenderManifest,\\n 7| RenderSettings,\\n 8| build_render_manifest,\\n 9| validate_render_manifest,\\n 10|)\\n 11|from autoshorts.rendering.shot_plan import ShotPlan, ShotScene, create_shot_plan\\n 12|\\n 13|\\n 14|def _shot_plan():\\n 15| brief = create_content_brief(\\n 16| pillar_id=\\\"ai_life_systems\\\",\\n 17| topic=\\\"AI weekly review routine\\\",\\n 18| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 19| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 20| )\\n 21| script = create_script_draft(brief)\\n 22| return create_shot_plan(script)\\n 23|\\n 24|\\n 25|def test_build_render_manifest_turns_shot_plan_into_vertical_timeline():\\n 26| plan = _shot_plan()\\n 27|\\n 28| manifest = build_render_manifest(plan)\\n 29|\\n 30| assert isinstance(manifest, RenderManifest)\\n 31| assert manifest.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n 32| assert manifest.title == \\\"AI weekly review routine\\\"\\n 33| assert manifest.settings == RenderSettings(\\n 34| width=1080,\\n 35| height=1920,\\n 36| fps=30,\\n 37| no_generated_text_inside_assets=True,\\n 38| captions_renderer_owned=True,\\n 39| static_card_limit=0,\\n 40| )\\n 41| assert len(manifest.timeline) == len(plan.scenes)\\n 42| assert [item.scene_purpose for item in manifest.timeline] == [scene.purpose for scene in plan.scenes]\\n 43| assert manifest.timeline[0].start_seconds == 0\\n 44| assert manifest.timeline[-1].end_seconds == 35\\n 45| assert manifest.timeline[0].placeholder_asset_path == (\\n 46| \\\"placeholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\\\"\\n 47| )\\n 48| assert all(item.caption_layer.renderer_owned for item in manifest.timeline)\\n 49| assert all(item.label_layer.renderer_owned for item in manifest.timeline)\\n 50| assert all(\\\"static\\\" not in item.motion_instruction.casefold() for item in manifest.timeline)\\n 51|\\n 52|\\n 53|def test_validate_render_manifest_accepts_default_manifest():\\n 54| result = validate_render_manifest(build_render_manifest(_shot_plan()))\\n 55|\\n 56| assert result.is_valid is True\\n 57| assert result.errors == ()\\n 58|\\n 59|\\n 60|def test_build_render_manifest_rejects_invalid_shot_plan():\\n 61| invalid_plan = ShotPlan(\\n 62| script_id=\\\"script\\\",\\n 63| title=\\\"Title\\\",\\n 64| estimated_duration_seconds=35,\\n 65| scenes=(\\n 66| ShotScene(\\n 67| purpose=\\\"first_frame_hook\\\",\\n 68| start_seconds=0,\\n 69| end_seconds=3,\\n 70| voiceover_beat=\\\"Hook\\\",\\n 71| visual_action=\\\"Static card\\\",\\n 72| asset_mode=\\\"static_slideshow\\\",\\n 73| caption_beat=\\\"Hook\\\",\\n 74| retention_goal=\\\"Stop scroll\\\",\\n 75| ),\\n 76| ),\\n 77| )\\n 78|\\n 79| with pytest.raises(ValueError, match=\\\"static slideshow asset mode is not allowed\\\"):\\n 80| build_render_manifest(invalid_plan)\\n 81|\\n 82|\\n 83|def test_validate_render_manifest_rejects_unsafe_render_settings():\\n 84| manifest = build_render_manifest(_shot_plan())\\n 85| unsafe = RenderManifest(\\n 86| script_id=manifest.script_id,\\n 87| title=manifest.title,\\n 88| settings=RenderSettings(\\n 89| width=1920,\\n 90| height=1080,\\n 91| fps=24,\\n 92| no_generated_text_inside_assets=False,\\n 93| captions_renderer_owned=False,\\n 94| static_card_limit=1,\\n 95| ),\\n 96| timeline=manifest.timeline,\\n 97| )\\n 98|\\n 99| result = validate_render_manifest(unsafe)\\n 100|\\n 101| assert result.is_valid is False\\n 102| assert \\\"render size must be vertical 1080x1920\\\" in result.errors\\n 103| assert \\\"fps must be 30\\\" in result.errors\\n 104| assert \\\"generated text inside assets is not allowed\\\" in result.errors\\n 105| assert \\\"captions must be renderer-owned\\\" in result.errors\\n 106| assert \\\"static_card_limit must be 0\\\" in result.errors\\n 107|\\n 108|\\n 109|def test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers():\\n 110| manifest = build_render_manifest(_shot_plan())\\n 111| first = manifest.timeline[0]\\n 112| bad_item = type(first)(\\n 113| scene_purpose=first.scene_purpose,\\n 114| start_seconds=first.start_seconds,\\n 115| end_seconds=first.end_seconds,\\n 116| asset_mode=first.asset_mode,\\n 117| motion_instruction=first.motion_instruction,\\n 118| placeholder_asset_path=first.placeholder_asset_path,\\n 119| caption_layer=type(first.caption_layer)(text=first.caption_layer.text, renderer_owned=False),\\n 120| label_layer=type(first.label_layer)(text=first.label_layer.text, renderer_owned=False),\\n 121| )\\n 122| unsafe = RenderManifest(\\n 123| script_id=manifest.script_id,\\n 124| title=manifest.title,\\n 125| settings=manifest.settings,\\n 126| timeline=(bad_item,) + manifest.timeline[1:],\\n 127| )\\n 128|\\n 129| result = validate_render_manifest(unsafe)\\n 130|\\n 131| assert result.is_valid is False\\n 132| assert \\\"timeline item 1 caption layer must be renderer-owned\\\" in result.errors\\n 133| assert \\\"timeline item 1 label layer must be renderer-owned\\\" in result.errors\\n 134|\", \"total_lines\": 133, \"file_size\": 4888, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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{
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"tool_calls": [
{
"id": "call_VeIvamIVIAdQjD024US8zKv0",
"call_id": "call_VeIvamIVIAdQjD024US8zKv0",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_preview.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.manifest import RenderManifest, RenderSettings, build_render_manifest\\nfrom autoshorts.rendering.preview import RenderPreview, build_html_preview, validate_render_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _manifest():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n return build_render_manifest(create_shot_plan(script))\\n\\n\\ndef test_build_html_preview_creates_side_effect_free_timeline_preview():\\n manifest = _manifest()\\n\\n preview = build_html_preview(manifest)\\n\\n assert isinstance(preview, RenderPreview)\\n assert preview.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n assert preview.preview_type == \\\"html_timeline\\\"\\n assert preview.side_effects == ()\\n assert preview.external_calls == ()\\n assert \\\"alert('x')\\\",\\n placeholder_asset_path=first.placeholder_asset_path,\\n caption_layer=type(first.caption_layer)(text=\\\"Human < approval\\\", renderer_owned=True),\\n label_layer=first.label_layer,\\n )\\n dangerous = RenderManifest(\\n script_id=manifest.script_id,\\n title=manifest.title,\\n settings=manifest.settings,\\n timeline=(dangerous_item,) + manifest.timeline[1:],\\n )\\n\\n preview = build_html_preview(dangerous)\\n\\n assert \\\"