{
"session_id": "20260523_202906_0cac4a",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-23T17:58:17.228542",
"last_updated": "2026-05-23T21:56:24.505923",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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) [99% — 2,191/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; use `github_pat_`/`ghp_` cell; never quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [97% — 1,336/1,375 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 preference: free/local Retention Studio; photorealistic motion-led mechanism-reveal Shorts, quality > speed. Prefer keyframe→Wan2.2 I2V over raw T2V; avoid static slideshows, generic avatars, reused clones, “Was wäre wenn” KI-Müll, paid credits, flickering/dark/blurry artifacts; be blunt about quality.\n\nConversation started: Saturday, May 23, 2026 09:54 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": 232,
"messages": [
{
"role": "user",
"content": "[M G] Ok, nächster schritt\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. 125 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 ---"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti."
},
{
"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 nächster schritt\"\n\n## Goal\nDer User möchte AutoShortsBot/Hermes von einer einfachen KI-Video-/Card-Rendering-Pipeline zu einem halbautomatischen **Retention-Studio für Shorts** weiterentwickeln. Ziel ist ein System, das datenbasierte, animierte Mechanismus-Erklärvideos produziert: starke Hooks in den ersten 1–2 Sekunden, visuelle Veränderung alle 1–2 Sekunden, keine statischen Slideshow-Videos, sondern „mechanism reveal / visual explanation / mini-investigation“-Content mit Fokus auf Hidden Money, AI Systems, Auto-/Finanzmechaniken und unsichtbare Systemeffekte.\n\n## Constraints & Preferences\n- Sprache/Kommunikation: Deutsch; User wird teilweise mit „Sir“ angesprochen.\n- Der User lehnt finale statische Textkarten-/KI-Bild-Slideshows ab; sie sind nur Preview/Storyboard, nicht Endprodukt.\n- Finale Videos sollen bewegte Sequenzen, AI-video Assets, Screen-Recording-Simulationen, Mechanismus-Animationen, kinetic captions, Sound/SFX und schnelle visuelle Wechsel enthalten.\n- Der aktuelle Card-/Preview-Renderer ist nur Timing-/Storyboard-Werkzeug.\n- `static_card_limit = 0` als Qualitätsprinzip für finale Inhalte.\n- Content-Richtung: kein generischer KI-Avatar-News-Kanal, kein „Was wäre wenn…“-KI-Müll, kein Reused-Content-Klon.\n- Avatare höchstens als Nebenfigur/Branding-Element, nicht als Hauptformat.\n- Bevorzugte Nische: **Hidden Money & AI Systems**.\n- Zielserie: **Unsichtbare Systeme in 30 Sekunden**.\n- Claim: „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“\n- Erfolgsmechanik: Hook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung.\n- Inspirationsmechaniken: Zack-D-Films-artige Mechanism Reveals, MrBeast/Jenny-Hoyos sofortiges Versprechen, TikTok Creative Code: Hook/Body/Close, dynamisches Editing, Bewegung, Text-Overlays, Sound/Voice/SFX.\n- User erlaubt, die untracked Telegram-Review-Dateien zu ignorieren, weil sie versehentlich durch einen falschen Telegram-Kanal entstanden sind.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen oder Artefakten bewahren; ggf. mit `[REDACTED]` ersetzen.\n\n## Completed Actions\n1. ANALYSED Preview-Renderer Typography Issue — festgestellt, dass Textgrößen im Preview uneinheitlich wirkten: Haupttext groß, Retention-/Erklärungstext unten deutlich kleiner; erklärt, dass diese Zwei-Ebenen-Logik technisch beabsichtigt, aber für Shorts visuell schwach ist [tool: conversation].\n2. UPDATED Preview-Renderer-Konzept — Renderer um adaptive Font-Fitting, konsistentere Text-Hierarchie, Mindestgrößen, maximale Zeilenanzahl und Typography-Metadaten erweitert; Ziel war weniger zu kleine/zu stark schwankende Textgrößen im Storyboard-Preview [tool: code/terminal claimed].\n3. ADDED Typography Test Rules für `540x960` Preview — Regeln dokumentiert/geprüft: `main_font_size >= 34`, `max(main_font_size) - min(main_font_size) <= 8`, `retention_font_size >= 22`, `main_line_count <= 4`, `retention_line_count <= 3` [tool: tests claimed].\n4. RENDERED Top-3 Previews neu — erzeugt unter `data/previews/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.mp4`, `ai-005-the-4-box-prompt-for-mental-overload.mp4`, `attention-002-the-algorithm-does-not-need-you-happy.mp4` [tool: terminal claimed].\n5. VERIFIED Preview `ai-004` — technische Werte: `width=540`, `height=960`, `avg_frame_rate=24/1`, `duration=43.041667`; Typography: `main_font_size: 34–42`, `retention_font_size: 25–26` [tool: ffprobe/tests claimed].\n6. TEST full suite nach Typography-Änderung — Ergebnis: `25 passed in 0.96s` [tool: pytest claimed].\n7. COMMITTED Typography-Fix — Commit `3cc201b fix: improve preview renderer text sizing` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n8. ANSWERED User-Frage zum Endergebnis — klargestellt: aktueller Preview-Renderer ist nicht finales Content-Format; Endprodukt soll Videosequenzen, Bewegung, AI-video Assets, Screen-Recordings, kinetic captions, Voiceover und Sounddesign nutzen, nicht statische Cards [tool: conversation].\n9. DEFINED bessere Zielrichtung für `ai-004` — vorgeschlagen: ca. 40% Screen Recording/UI Simulation, 30% cinematic human-at-desk/decision moments, 20% kinetic captions, 10% Cards nur für Prompt-Fragmente [tool: conversation].\n10. CREATED `autoshorts/asset_plan.py` — neue Asset-Plan-v2-Komponente für motion-led Asset Pipeline; entscheidet pro Szene zwischen `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, `kinetic_text_overlay` [tool: code claimed].\n11. CREATED `scripts/generate_asset_plans.py` — CLI zum Generieren von Asset-Plänen aus vorhandenen Render-/Storyboard-Daten [tool: code claimed].\n12. CREATED `tests/test_asset_plan.py` — Tests für Asset Plan Builder, u.a. Provider-Prompting und keine finalen statischen Cards [tool: code claimed].\n13. GENERATED Asset-Pläne Top-3 — Dateien erzeugt unter `data/asset_plans/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.json`, `ai-005-the-4-box-prompt-for-mental-overload.json`, `attention-002-the-algorithm-does-not-need-you-happy.json` [tool: terminal claimed].\n14. SET Asset Pipeline `static_card_limit = 0` — finale statische Textkarten explizit ausgeschlossen; Preview bleibt Storyboard/Timing-Werkzeug [tool: code claimed].\n15. BUILT Provider-ready Prompting für cinematic Szenen — Prompt-Felder mit photorealistischer vertikaler 9:16-Clip-Beschreibung, cinematic lighting, realistic motion, negative space for captions und „do not include readable text“ ergänzt [tool: code claimed].\n16. BUILT Negative Prompts für AI-video Provider — u.a. `no readable text`, `no subtitles`, `no logos`, `no watermarks`, `no fake UI text`, `no slideshow`, `no static image`, `no uncanny faces` [tool: code claimed].\n17. TEST full suite nach Asset Plan v2 — Ergebnis: `32 passed in 0.96s` [tool: pytest claimed].\n18. COMMITTED Motion-led Asset Planning — Commit `b60981b feat: add motion-led asset planning` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n19. IDENTIFIED untracked Telegram Review Package — nicht angefasst: `autoshorts/review_package.py`, `data/telegram_reviews/`, `scripts/generate_telegram_review_packages.py`, `tests/test_review_package.py` [tool: git status claimed].\n20. CREATED `autoshorts/retention_studio.py` — neue Strategiekomponente, die AutoShortsBot konzeptionell als `semi_automatic_retention_studio` statt KI-Video-Fabrik verankert [tool: code claimed].\n21. CREATED `scripts/generate_retention_studio.py` — CLI zum Generieren von Retention-Studio-Strategieartefakten [tool: code claimed].\n22. CREATED `tests/test_retention_studio.py` — Tests für Strategie, Dramaturgie, visuelle Bewegung, Sound Cues und Follow Trigger [tool: code claimed].\n23. GENERATED Retention-Studio-Artefakte — erzeugt unter `data/retention_studio/`: `strategy.json`, `pilot_ideas.json`, `mechanism_explainers.json` [tool: terminal claimed].\n24. SET Positionierung in Strategie — `Hidden Money & AI Systems`; Serie `Unsichtbare Systeme in 30 Sekunden`; Claim „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“ [tool: code claimed].\n25. EXCLUDED unerwünschte Content-Formate in Strategie — `ki_avatar_news`, `static_ai_slideshow`, `reused_content_clone`; Quality Gate `static_card_limit = 0` [tool: code claimed].\n26. IMPLEMENTED Mechanism-Explainer-Formel — Timing/Beats: `0.0–1.5s Pattern Interrupt`, `1.5–5s Problem`, `5–18s Mechanism Reveal`, `18–28s Twist`, `28–35s Follow Trigger` [tool: code claimed].\n27. ADDED Tests für Retention-Beats — Pattern Interrupt endet ≤ 1.5 Sekunden; jeder Beat braucht visuelle Bewegung und Sound Cue; kein generisches „Follow for more“; Follow Trigger als Serienversprechen [tool: pytest claimed].\n28. ADDED Format-Taxonomie — `mechanism_reveal`, `mistake_breakdown`, `hidden_cost`, `before_after`, `myth_vs_reality`, `mini_simulation`, `nobody_notices_this` [tool: code claimed].\n29. ADDED Content-Pillars — `hidden_money_systems`, `auto_trade_mechanics`, `ai_agents_explained`, `swiss_systems_explained`, `dangerous_thinking_errors`, plus `hidden_attention_systems` [tool: code claimed].\n30. GENERATED 10 Pilot-Ideen mit Scores — Top 3: Score 90 `hidden_attention_systems`/„Warum Shorts dich nicht wegen dem Inhalt halten“, Score 88 `hidden_money_systems`/„Warum ein billiges Auto teuer sein kann“, Score 86 `hidden_money_systems`/„Warum 0%-Finanzierung nicht kostenlos ist“ [tool: terminal claimed].\n31. SET Produktionsentscheidung — Empfehlung `produce_count = 5 von 10` Pilot-Ideen [tool: code claimed].\n32. BUILT Beispiel-Mechanism-Explainer für „Warum ein billiges Auto teuer sein kann“ — Beat-Struktur mit Occasion-Käufer, 3D-Car-X-Ray, Preis/Reparaturrisiko/Standtage/Händler-Marge/Liquidität, Twist „teuerster Fehler ist nicht Kaufpreis, sondern Zeitpunkt“ [tool: code claimed].\n33. TEST full suite nach Retention Studio — Ergebnis: `36 passed in 0.99s` [tool: pytest claimed].\n34. COMMITTED Retention-Studio-Strategie — Commit `a9fe897 feat: add retention studio strategy` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n35. UPDATED Skill-Notiz — Social-Shorts-/Retention-Studio-Skill um Retention Studio Strategy, Mechanism Explainer Shorts, Hidden Money & AI Systems und `static_card_limit = 0` erweitert [tool: file edit claimed].\n36. CREATED `autoshorts/mechanism_pipeline.py` — Adapter von Retention-Studio Mechanism Explainer zu renderer-nahem Mechanism Shot Plan [tool: code claimed].\n37. CREATED `scripts/generate_mechanism_shot_plans.py` — CLI zum Generieren von Mechanism Shot Plans aus Mechanism Explainern [tool: code claimed].\n38. CREATED `tests/test_mechanism_pipeline.py` — Tests für Adapter/Shot-Plan-Erzeugung, Timing, Asset Types, Bewegungs-/Sound-Metadaten und `static_card_limit = 0` [tool: code claimed].\n39. GENERATED Pilot-Shot-Plan — Datei `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` erzeugt [tool: terminal claimed].\n40. MAPPED Mechanism Beats zu Scenes — `pattern_interrupt`, `problem`, `mechanism`, `twist`, `follow_trigger` wurden in Szenen mit Timing, Asset Type, On-screen Text, Voiceover, Visual Direction, Visual Motion, Sound Cue, Retention Goal, Caption Beats und Final Render Intent übersetzt [tool: code claimed].\n41. CREATED Shot Plan für „Warum ein billiges Auto teuer sein kann“ — Scene 1 `0.0s–1.4s cinematic_ai_video`, Scene 2 `1.4s–5.0s cinematic_ai_video`, Scene 3 `5.0s–18.0s mechanism_animation`, Scene 4 `18.0s–28.0s mechanism_animation`, Scene 5 `28.0s–34.0s kinetic_text_overlay` [tool: terminal claimed].\n42. SET Shot-Plan-Constraints — `static_card_limit = 0`, `visual_change_every_seconds = 2`, `primary_style = mechanism_reveal_motion_explainer` [tool: code claimed].\n43. TEST full suite nach Mechanism Pipeline — Ergebnis: `40 passed in 0.97s` [tool: pytest claimed].\n44. COMMITTED Mechanism Explainer Adapter — Commit `5644c8b feat: adapt mechanism explainers to shot plans` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n45. UPDATED Retention-Studio-Skill-Notiz — Pipeline festgehalten: `strategy → scored ideas → mechanism explainer → mechanism shot plan → render manifest → asset plan → final renderer` [tool: file edit claimed].\n46. CONFIRMED Telegram Review Files ignored — User erklärte, dass die separaten Working-Tree-Dateien versehentlich durch falschen Telegram-Kanal entstanden sind; sie bleiben unberührt und uncommitted [tool: conversation].\n\n## Active State\n- Repository/Projekt: `/home/agent/jarvis_runtime/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Remote-Status zuletzt berichtet: `local == remote`.\n- Letzter bekannter Commit: `5644c8b feat: adapt mechanism explainers to shot plans`.\n- Vorherige relevante Commits:\n - `3cc201b fix: improve preview renderer text sizing`\n - `b60981b feat: add motion-led asset planning`\n - `a9fe897 feat: add retention studio strategy`\n- Letzter Teststatus: `40 passed in 0.97s`.\n- Aktuelles Pipeline-Level erreicht:\n - Retention Studio Strategie vorhanden.\n - Pilot-Ideen gescored.\n - Mechanism Explainer vorhanden.\n - Mechanism Shot Plan für Pilot „Warum ein billiges Auto teuer sein kann“ vorhanden.\n- Nächster logischer Schritt noch nicht erledigt:\n - `Mechanism Shot Plan → Render Manifest → Asset Plan` für den neuen Pilot, nicht mehr nur für alte AI-Life-Top-3.\n- Untracked/zu ignorieren:\n - `autoshorts/review_package.py`\n - `data/telegram_reviews/`\n - `scripts/generate_telegram_review_packages.py`\n - `tests/test_review_package.py`\n- Keine laufenden Server oder Prozesse erwähnt.\n- Keine Secrets/Credentials bewahren; falls vorhanden, als `[REDACTED]`.\n\n## In Progress\n- Der User hat „ok nächster schritt“ gesagt. Gemeinter nächster Arbeitsschritt laut vorheriger Antwort:\n - Mechanism Shot Plan für „Warum ein billiges Auto teuer sein kann“\n - → Render Manifest\n - → Asset Plan\n - Ziel: neue Retention-Studio-/Mechanism-Explainer-Pipeline an die bestehende Render-/Asset-Plan-Infrastruktur anbinden.\n- Noch nicht umgesetzt:\n - Adapter/CLI/Tests für `Mechanism Shot Plan → Render Manifest`.\n - Generierung eines Render Manifests für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.\n - Asset Plan für diesen neuen Pilot auf Basis des neuen Render Manifests.\n - Provider Job Specs für cinematic Szenen des Pilots.\n\n## Blocked\nKeine bekannten Blocker. \nKeine Fehlermeldungen offen. \nEinziger Hinweis: frühere 125 Messages konnten nicht zusammengefasst werden; daraus können ältere Details fehlen. Die aktuelle Richtung und letzten Artefakte sind aber in den neuen Turns beschrieben.\n\n## Key Decisions\n1. Der Preview-Renderer ist nicht das finale Produkt, sondern nur Storyboard-/Timing-Werkzeug. Grund: Statische Cards wirken langweilig und konkurrieren nicht mit modernen Shorts/Kling/Hailuo/Runway-Qualität.\n2. Finale Videos müssen motion-led sein: AI-video Clips, Screen-Recording-Simulationen, mechanism animations, kinetic captions, Voiceover, SFX und schnelle Schnitte.\n3. `static_card_limit = 0` ist ein strategisches Qualitätsgate für finale Videos. Grund: User lehnt statische KI-Bilder + Text/Voiceover als KI-Müll-Massenware ab.\n4. AutoShortsBot/Hermes wird als halbautomatisches Retention-Studio positioniert, nicht als KI-Video-Fabrik. Grund: Erfolg hängt von Hook, offenen Loops, visueller Stimulation und Payoff ab, nicht vom KI-Look.\n5. Gewählte Content-Nische: Hidden Money & AI Systems. Grund: verbindet Hermes, Autotrading, Finanzen, KI-Agenten, Politik/Systemverständnis und hat spätere Monetarisierungsmöglichkeiten über Newsletter, Tools, Beratung, Affiliate, Software oder Kurse.\n6. Kernformat: Mechanism Reveal / Visual Explanation / Mini-Investigation. Grund: zeigt unsichtbare Mechanismen visuell und bietet echten Erkenntniswert statt generischem Voiceover.\n7. Avatare nicht als Hauptformat. Grund: viele KI-Avatare wirken generisch, halten Retention schlecht, und es gibt Disclosure-/Trust-Risiken bei realistischen synthetischen Menschen.\n8. Re-Kuratierung erfolgreicher Videos ist strategisch erlaubt, aber nicht als Kopie/Neuvertontung. Grund: YouTube Reused Content vermeiden; Mechaniken adaptieren, nicht Inhalte klonen.\n9. Pilot-Fokus: „Warum ein billiges Auto teuer sein kann“. Grund: passt zu Money/Auto, klares visuelles Objekt, starke 3D-/X-Ray-/Layer-Animation möglich, hohe Monetarisierbarkeit.\n10. Untracked Telegram-Review-Dateien werden ignoriert. Grund: User schrieb versehentlich in falschen Telegram-Kanal; Änderungen sollen nicht mit dieser Pipeline vermischt werden.\n\n## Resolved Questions\n1. User fragte, warum Text oben groß und unten klein ist. Antwort: Der Preview-Renderer nutzt Haupttext plus kleineren Retention-/Erklärungstext; technisch beabsichtigt, aber visuell für Shorts ungeeignet. Finale Videos sollen weniger Text pro Frame und große rhythmische Captions nutzen.\n2. User fragte, ob Endergebnis Videosequenzen oder wieder statische Bilder sein sollen. Antwort: Nicht statische Bilder. Endziel sind Videosequenzen mit AI-video Assets, Screen Recordings, kinetic captions, Voiceover, Musik/SFX und fast cuts.\n3. User fragte, warum diese Videos besser sein sollen als bisheriger Content. Antwort: Nur wenn Scripting, visuelle Umsetzung und Retention-Struktur besser werden: starke Hooks, motion-led visuals, alle 1–2 Sekunden visueller Reiz, echter Mechanismus-Payoff.\n4. User fragte implizit, ob statische Cards reichen. Antwort: Nein, statische Cards sind nur Preview/Storyboard und als finales Format strategisch zu schwach.\n5. User brachte „Chloe vs History“, Kling, Hailuo, photorealistische KI-Videos als Qualitätsreferenz ein. Antwort: Diese Richtung wurde aufgenommen; finale Pipeline soll provider-ready prompts für cinematic AI-video Szenen erzeugen, aber nicht in generischem KI-Look enden.\n6. User schlug Retention-Studio statt KI-Video-Content vor. Antwort: Konzept wurde als `semi_automatic_retention_studio` umgesetzt und in Strategieartefakten verankert.\n7. User schlug Zack-D-Films-artige Mechanism Reveals vor. Antwort: Mechanism-Explainer-Formel, Beat-Struktur und Pilot „Warum ein billiges Auto teuer sein kann“ wurden umgesetzt.\n8. User erklärte, der separate Working Tree könne ignoriert werden. Antwort: Telegram-Review-Dateien wurden nicht angefasst und nicht committet.\n\n## Pending User Asks\n- „ok nächster schritt“ ist noch offen. Gemeint ist nach aktuellem Stand der nächste Build-Schritt:\n - Mechanism Shot Plan → Render Manifest → Asset Plan für den Pilot „Warum ein billiges Auto teuer sein kann“.\n\n## Relevant Files\n- `autoshorts/asset_plan.py` — Asset-Plan-v2-Komponente für motion-led finale Asset-Auswahl pro Szene.\n- `scripts/generate_asset_plans.py` — CLI zur Asset-Plan-Generierung.\n- `tests/test_asset_plan.py` — Tests für Asset-Plan-Logik.\n- `data/asset_plans/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `autoshorts/retention_studio.py` — Strategie-/Pilot-/Mechanism-Explainer-Komponente für Retention Studio.\n- `scripts/generate_retention_studio.py` — CLI zur Generierung von Retention-Studio-Artefakten.\n- `tests/test_retention_studio.py` — Tests für Strategie, Beats, visuelle Bewegung, Sound Cues, Follow Trigger.\n- `data/retention_studio/strategy.json` — Retention-Studio-Strategie.\n- `data/retention_studio/pilot_ideas.json` — gescorte Pilot-Ideen.\n- `data/retention_studio/mechanism_explainers.json` — Mechanism-Explainer-Daten.\n- `autoshorts/mechanism_pipeline.py` — Adapter von Mechanism Explainer zu Mechanism Shot Plan.\n- `scripts/generate_mechanism_shot_plans.py` — CLI zur Shot-Plan-Generierung.\n- `tests/test_mechanism_pipeline.py` — Tests für Mechanism Shot Plan Adapter.\n- `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` — generierter Pilot-Shot-Plan.\n- `data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4` — neu gerenderter Preview.\n- `autoshorts/review_package.py` — untracked/zu ignorieren.\n- `data/telegram_reviews/` — untracked/zu ignorieren.\n- `scripts/generate_telegram_review_packages.py` — untracked/zu ignorieren.\n- `tests/test_review_package.py` — untracked/zu ignorieren.\n\n## Remaining Work\n- Neuen Adapter bauen: `Mechanism Shot Plan → Render Manifest`.\n- Render Manifest für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` erzeugen.\n- Asset Plan v2 auf diesen neuen Mechanism-Render-Manifest anwenden.\n- Provider Job Specs für cinematic AI-video Szenen exportieren, z.B. Kling/Hailuo/Runway-kompatible Prompts.\n- Mechanism Animation Specs definieren für 3D-Car-X-Ray, Preis-/Risiko-/Marge-/Timing-Layer.\n- Final Renderer erweitern, damit er echte Clip-Dateien, Screen-Recording-Simulationen, mechanism animations und kinetic captions zusammensetzt.\n- Optional: Preview-Storyboard weiter nutzen, aber klar als nicht-final markieren.\n- Später: Analysepipeline für Retention nach 1s/3s, durchschnittliche Wiedergabedauer, Replays, Kommentare pro 1’000 Views, Follows pro 1’000 Views.\n\n## Critical Context\n- Frühere Kontextzusammenfassung war nicht verfügbar: „Summary generation was unavailable. 125 message(s) were removed…“. Frühere Details vor den neuen Turns können fehlen.\n- Branch: `feat/initial-growth-engine`.\n- PR-Link bleibt: `https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine`.\n- Letzter bekannter Teststatus: `40 passed in 0.97s`.\n- Wichtigster aktueller Pilot: `Warum ein billiges Auto teuer sein kann`.\n- Pilot-Shot-Plan-Szenen:\n - Scene 1 — `pattern_interrupt`, `0.0s–1.4s`, `cinematic_ai_video`, Visual: 3D car x-ray with price tag layers and risk meters freezes mid-action, Motion: hard zoom/freeze-frame/red signal pulse, Sound: bass hit + camera snap.\n - Scene 2 — `problem`, `1.4s–5.0s`, `cinematic_ai_video`, Visual: surface layer peels away to reveal hidden system, Motion: layer peel + parallax push.\n - Scene 3 — `mechanism_reveal`, `5.0s–18.0s`, `mechanism_animation`, Visual: 3D car x-ray with price tag layers and risk meters, Motion: sequential layer reveal every 1–2 seconds.\n - Scene 4 — `twist`, `18.0s–28.0s`, `mechanism_animation`, Visual: wrong cause fades out, real cause appears, Motion: snap zoom + wrong answer collapses.\n - Scene 5 — `follow_trigger`, `28.0s–34.0s`, `kinetic_text_overlay`, Visual: animated system map, Motion: map draws itself, final node glows.\n- Strategy constraints to preserve:\n - `static_card_limit = 0`\n - `visual_change_every_seconds = 2`\n - `primary_style = mechanism_reveal_motion_explainer`\n- Provider negative prompt concepts:\n - no readable text\n - no subtitles\n - no logos\n - no watermarks\n - no fake UI text\n - no slideshow\n - no static image\n - no uncanny faces\n- Do not include or preserve any credentials/secrets; replace with `[REDACTED]` if encountered.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
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"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 109 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."
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"content": "[M G] jetzt haben wir aber auch modelle und bestandteile runtergeladen, welche ic wieder löschen kann, oder? ich habe nämlich nicht mehr viel speicherplatz..."
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"content": "Ja, Sir. Da liegt inzwischen einiges herum, was wir **gefahrlos aufräumen** können — besonders die Wrapper-/Test-Modelle, wenn wir jetzt auf den **offiziellen nativen Wan2.2-Workflow** wechseln.\n\nWichtig: Ich kann den Speicher auf deinem GPU-Host nicht direkt per Shell sehen, aber anhand unserer Downloads kann ich dir sehr klar sagen, was weg kann.\n\n---\n\n# Kurzfassung\n\n## Behalten für native Wan2.2 Qualitätstests\n\nDiese drei bitte behalten:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\n~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist der offizielle native ComfyUI-Wan2.2-Stack.\n\n---\n\n## Kannst du löschen, wenn wir Kijai/WanVideoWrapper nicht weiter testen\n\nDiese Dateien sind für unsere nächsten Schritte nicht mehr nötig:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nOptional auch:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nAber diese drei würde ich nur löschen, wenn du wirklich Platz brauchst. Sie sind unser schneller Wan2.1-Fallback.\n\n---\n\n# Erst prüfen: Was ist groß?\n\nBitte auf dem GPU-Host ausführen:\n\n```bash\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\n```\n\nUnd für alle Safetensors:\n\n```bash\nfind ~/ai/ComfyUI/models -type f -name \"*.safetensors\" -exec ls -lh {} \\; | sort -k5 -h\n```\n\nFalls du HuggingFace-Cache prüfen willst:\n\n```bash\ndu -h -d 2 ~/.cache/huggingface | sort -h\n```\n\nDer Cache kann riesig sein, weil `hf download` Dateien teils zusätzlich im Cache hält. Charmant, wie immer: einmal Modell laden, zweimal Speicher verlieren. Fortschritt.\n\n---\n\n# Sichere Löschliste: Wrapper-Wan2.2 entfernen\n\nWenn wir jetzt offiziell native Wan2.2 nutzen, kannst du löschen:\n\n```bash\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nDas spart ungefähr:\n\n```text\n~4.9 GB + ~1.3 GB = ~6.2 GB\n```\n\nFalls der Ordner danach leer ist:\n\n```bash\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n```\n\n---\n\n# Temporäre Download-Ordner löschen\n\nWir haben native Modelle nach `/tmp/wan22_native` geladen und dann kopiert. Den temporären Ordner kannst du löschen:\n\n```bash\nrm -rf /tmp/wan22_native\nrm -rf /tmp/wan22_text_encoder\n```\n\nJe nachdem, was noch dort liegt, spart das mehrere GB.\n\nPrüfen vorher:\n\n```bash\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\n```\n\nDann löschen:\n\n```bash\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n```\n\n---\n\n# HuggingFace Cache bereinigen\n\nDas kann viel bringen.\n\n## Cache-Größe anzeigen\n\n```bash\ndu -sh ~/.cache/huggingface 2>/dev/null\n```\n\n## Variante A — interaktiv aufräumen\n\n```bash\nhf cache scan\nhf cache prune\n```\n\n## Variante B — wenn du aggressiv Platz brauchst\n\nNur machen, wenn alle benötigten Modelle bereits in `~/ai/ComfyUI/models/...` liegen:\n\n```bash\nrm -rf ~/.cache/huggingface/hub\n```\n\nDanach sind Downloads nicht weg aus ComfyUI, sofern du sie kopiert hast. Nur der Download-Cache ist weg.\n\nVorher prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nWenn die drei existieren: Cache darf weg.\n\n---\n\n# Optional: alte Testvideos löschen\n\nComfyUI-Outputs können ebenfalls wachsen:\n\n```bash\ndu -sh ~/ai/ComfyUI/output\n```\n\nWenn du die Testvideos nicht mehr brauchst:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -print\n```\n\nDann löschen:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -delete\n```\n\nFalls du wirklich alle Outputs löschen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Optional: Wrapper Custom Node behalten oder löschen?\n\nIch würde den Code erstmal behalten:\n\n```text\n~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nDer Ordner selbst ist wahrscheinlich viel kleiner als die Modelle. Nutzen wir vielleicht später für Spezialfeatures.\n\nNur wenn du radikal aufräumen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nAber: Dann funktionieren die Kijai-Wrapper-Workflows nicht mehr. Für native ComfyUI-Wan2.2 ist das okay.\n\nMeine Empfehlung:\n\n```text\nCustom node behalten\nWrapper-Modelle löschen\n```\n\n---\n\n# Minimaler sauberer Bestand für unseren nächsten Test\n\nWenn du wirklich maximal aufräumen willst und nur native Wan2.2 behalten möchtest, sollte am Ende mindestens das hier existieren:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nOptional behalten für Wan2.1-Fallback:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n# Meine empfohlene Aufräum-Reihenfolge\n\nBitte so:\n\n```bash\n# 1. Größen ansehen\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\ndu -sh ~/.cache/huggingface 2>/dev/null\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\ndu -sh ~/ai/ComfyUI/output 2>/dev/null\n\n# 2. Temporäre Download-Ordner weg\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n\n# 3. Wrapper-Wan2.2-Modelle weg\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n\n# 4. HF Cache leeren, falls Modelle in ComfyUI/models vorhanden sind\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n\nrm -rf ~/.cache/huggingface/hub\n\n# 5. Optional Outputs löschen\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Nicht löschen\n\nDiese bitte **nicht** löschen, wenn wir gleich native Wan2.2 testen:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\nwan2.2_vae.safetensors\n```\n\n---\n\n# Geschätztes Einsparpotenzial\n\nJe nachdem, wie `hf download` gecached hat:\n\n```text\nWrapper-Wan2.2 löschen: ~6 GB\n/tmp Downloads löschen: ~10–17 GB möglich\nHF Cache löschen: ~10–25+ GB möglich\nComfyUI outputs löschen: variabel\n```\n\nRealistisch kannst du wahrscheinlich:\n\n```text\n20–40 GB\n```\n\nzurückholen.\n\nKurz: **Ja, wir können einiges löschen. Behalte nur den offiziellen nativen Wan2.2-Stack; die Kijai-Wan2.2-Testmodelle und Download-Caches können weg.**",
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"text": "Ja, Sir. Da liegt inzwischen einiges herum, was wir **gefahrlos aufräumen** können — besonders die Wrapper-/Test-Modelle, wenn wir jetzt auf den **offiziellen nativen Wan2.2-Workflow** wechseln.\n\nWichtig: Ich kann den Speicher auf deinem GPU-Host nicht direkt per Shell sehen, aber anhand unserer Downloads kann ich dir sehr klar sagen, was weg kann.\n\n---\n\n# Kurzfassung\n\n## Behalten für native Wan2.2 Qualitätstests\n\nDiese drei bitte behalten:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\n~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist der offizielle native ComfyUI-Wan2.2-Stack.\n\n---\n\n## Kannst du löschen, wenn wir Kijai/WanVideoWrapper nicht weiter testen\n\nDiese Dateien sind für unsere nächsten Schritte nicht mehr nötig:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nOptional auch:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nAber diese drei würde ich nur löschen, wenn du wirklich Platz brauchst. Sie sind unser schneller Wan2.1-Fallback.\n\n---\n\n# Erst prüfen: Was ist groß?\n\nBitte auf dem GPU-Host ausführen:\n\n```bash\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\n```\n\nUnd für alle Safetensors:\n\n```bash\nfind ~/ai/ComfyUI/models -type f -name \"*.safetensors\" -exec ls -lh {} \\; | sort -k5 -h\n```\n\nFalls du HuggingFace-Cache prüfen willst:\n\n```bash\ndu -h -d 2 ~/.cache/huggingface | sort -h\n```\n\nDer Cache kann riesig sein, weil `hf download` Dateien teils zusätzlich im Cache hält. Charmant, wie immer: einmal Modell laden, zweimal Speicher verlieren. Fortschritt.\n\n---\n\n# Sichere Löschliste: Wrapper-Wan2.2 entfernen\n\nWenn wir jetzt offiziell native Wan2.2 nutzen, kannst du löschen:\n\n```bash\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nDas spart ungefähr:\n\n```text\n~4.9 GB + ~1.3 GB = ~6.2 GB\n```\n\nFalls der Ordner danach leer ist:\n\n```bash\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n```\n\n---\n\n# Temporäre Download-Ordner löschen\n\nWir haben native Modelle nach `/tmp/wan22_native` geladen und dann kopiert. Den temporären Ordner kannst du löschen:\n\n```bash\nrm -rf /tmp/wan22_native\nrm -rf /tmp/wan22_text_encoder\n```\n\nJe nachdem, was noch dort liegt, spart das mehrere GB.\n\nPrüfen vorher:\n\n```bash\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\n```\n\nDann löschen:\n\n```bash\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n```\n\n---\n\n# HuggingFace Cache bereinigen\n\nDas kann viel bringen.\n\n## Cache-Größe anzeigen\n\n```bash\ndu -sh ~/.cache/huggingface 2>/dev/null\n```\n\n## Variante A — interaktiv aufräumen\n\n```bash\nhf cache scan\nhf cache prune\n```\n\n## Variante B — wenn du aggressiv Platz brauchst\n\nNur machen, wenn alle benötigten Modelle bereits in `~/ai/ComfyUI/models/...` liegen:\n\n```bash\nrm -rf ~/.cache/huggingface/hub\n```\n\nDanach sind Downloads nicht weg aus ComfyUI, sofern du sie kopiert hast. Nur der Download-Cache ist weg.\n\nVorher prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nWenn die drei existieren: Cache darf weg.\n\n---\n\n# Optional: alte Testvideos löschen\n\nComfyUI-Outputs können ebenfalls wachsen:\n\n```bash\ndu -sh ~/ai/ComfyUI/output\n```\n\nWenn du die Testvideos nicht mehr brauchst:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -print\n```\n\nDann löschen:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -delete\n```\n\nFalls du wirklich alle Outputs löschen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Optional: Wrapper Custom Node behalten oder löschen?\n\nIch würde den Code erstmal behalten:\n\n```text\n~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nDer Ordner selbst ist wahrscheinlich viel kleiner als die Modelle. Nutzen wir vielleicht später für Spezialfeatures.\n\nNur wenn du radikal aufräumen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nAber: Dann funktionieren die Kijai-Wrapper-Workflows nicht mehr. Für native ComfyUI-Wan2.2 ist das okay.\n\nMeine Empfehlung:\n\n```text\nCustom node behalten\nWrapper-Modelle löschen\n```\n\n---\n\n# Minimaler sauberer Bestand für unseren nächsten Test\n\nWenn du wirklich maximal aufräumen willst und nur native Wan2.2 behalten möchtest, sollte am Ende mindestens das hier existieren:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nOptional behalten für Wan2.1-Fallback:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n# Meine empfohlene Aufräum-Reihenfolge\n\nBitte so:\n\n```bash\n# 1. Größen ansehen\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\ndu -sh ~/.cache/huggingface 2>/dev/null\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\ndu -sh ~/ai/ComfyUI/output 2>/dev/null\n\n# 2. Temporäre Download-Ordner weg\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n\n# 3. Wrapper-Wan2.2-Modelle weg\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n\n# 4. HF Cache leeren, falls Modelle in ComfyUI/models vorhanden sind\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n\nrm -rf ~/.cache/huggingface/hub\n\n# 5. Optional Outputs löschen\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Nicht löschen\n\nDiese bitte **nicht** löschen, wenn wir gleich native Wan2.2 testen:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\nwan2.2_vae.safetensors\n```\n\n---\n\n# Geschätztes Einsparpotenzial\n\nJe nachdem, wie `hf download` gecached hat:\n\n```text\nWrapper-Wan2.2 löschen: ~6 GB\n/tmp Downloads löschen: ~10–17 GB möglich\nHF Cache löschen: ~10–25+ GB möglich\nComfyUI outputs löschen: variabel\n```\n\nRealistisch kannst du wahrscheinlich:\n\n```text\n20–40 GB\n```\n\nzurückholen.\n\nKurz: **Ja, wir können einiges löschen. Behalte nur den offiziellen nativen Wan2.2-Stack; die Kijai-Wan2.2-Testmodelle und Download-Caches können weg.**"
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"content": "[M G] ok, erstelle nun nochmals ein Testvideo. Ein Hause hüpft über eine Wiese. 5sekunden lang."
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"reasoning": "**Deciding on video creation**\n\nI need to figure out the best approach for creating a test video with ComfyUI. I'll have to check if we have a native model available and consider using the current server setup. It looks like I might need to work with the Kijai wrapper for this, especially for the house jumping scene. Since the video is short (5 seconds at 12 fps, giving 61 frames), I also need to ensure the frame size fits well. Maybe vertical will work best, given the context!\n**Considering video parameters**\n\nI could use a resolution of 416x736 with 61 frames and 8 steps. If that doesn't work, I might try a fallback of 49 frames at 10 fps, which gives about 4.9 seconds. Since the user requested 5 seconds, the 49 frames would be close enough. I'll check if the Wan2.2 5B wrapper is suitable. I need to inspect the native nodes for any models that could fit into this specific workflow, like those for loading diffusion or CLIP processes. Let’s see what options I have!",
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"arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/house_jump_mid.jpg\",\"question\":\"Beurteile den Frame knapp: ist ein Haus auf/über einer Wiese erkennbar? Wirkt es qualitativ brauchbar oder noch unbrauchbar? Nenne sichtbare Probleme.\"}"
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"content": "[vision_analyze] 'Beurteile den Frame knapp: ist ein Haus auf/über e' (281 chars)",
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"content": "Erledigt, Sir.\n\nIch habe jetzt **nochmals ein Testvideo mit dem nativen Wan2.2-Workflow** erzeugt — nicht mehr über den Kijai Wrapper.\n\n## Testvideo\n\nPrompt:\n\n```text\nEin kleines gemütliches Haus hüpft fröhlich über eine helle grüne Wiese.\n```\n\nTechnik:\n\n```text\nModell: native Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nAuflösung: 416x736\nFrames: 49\nFPS: 10\nDauer: 4.9s\nSteps: 12\n```\n\nWarum 4.9s statt exakt 5.0s:\n\n```text\nWan arbeitet hier sauber mit 49 Frames.\n49 Frames / 10 fps = 4.9 Sekunden\n```\n\nDas ist praktisch 5 Sekunden.\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n\n---\n\n## Ergebnisbewertung\n\nBesser als die Wrapper-Testclips.\n\nMan erkennt:\n\n- helle Wiese\n- hüpfende kleine Figur / hausähnliches Objekt\n- klare Bewegung\n- cartoonhafter Stil\n\nAber ehrlich:\n\n```text\nEs ist noch kein sauber erkennbares “Haus hüpft über Wiese”-Video.\n```\n\nDas Modell hat daraus eher eine **niedliche Cartoon-Hausfigur** gemacht. Außerdem ist der Himmel stark überbelichtet. Nicht katastrophal, aber noch nicht kontrolliert genug.\n\nDer entscheidende technische Fortschritt ist:\n\n```text\nnative Wan2.2 läuft\nscaled T5 funktioniert\nVideo kommt sauber zurück\nGPU wurde genutzt\nkein Wrapper-T5-Fehler\nkein Crash\n```\n\n---\n\n## Laufzeit / GPU\n\nJob lief erfolgreich durch.\n\nPeak laut Monitoring:\n\n```text\nVRAM used: ca. 14.2 GB\nRAM frei: ca. 17 GB\n```\n\nDas bestätigt:\n\n```text\nNative Wan2.2 nutzt deine A5000 deutlich besser.\n```\n\n---\n\n## Nächste Qualitätsverbesserung\n\nFür bessere Kontrolle sollten wir jetzt nicht weiter reines Text-to-Video würfeln, sondern:\n\n```text\n1. Erst ein gutes Keyframe erzeugen:\n klares kleines Haus auf Wiese\n\n2. Dann Wan2.2 Image-to-Video:\n Haus hüpft 5 Sekunden über Wiese\n```\n\nDas wird sehr wahrscheinlich deutlich besser, weil das Modell dann nicht erst raten muss, wie das Haus aussieht.\n\nKurz:\n\n```text\nT2V = Glücksspiel\nI2V = Regie\n```\n\nUnd wir wollen Regie, nicht GPU-Lotto.",
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"text": "Erledigt, Sir.\n\nIch habe jetzt **nochmals ein Testvideo mit dem nativen Wan2.2-Workflow** erzeugt — nicht mehr über den Kijai Wrapper.\n\n## Testvideo\n\nPrompt:\n\n```text\nEin kleines gemütliches Haus hüpft fröhlich über eine helle grüne Wiese.\n```\n\nTechnik:\n\n```text\nModell: native Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nAuflösung: 416x736\nFrames: 49\nFPS: 10\nDauer: 4.9s\nSteps: 12\n```\n\nWarum 4.9s statt exakt 5.0s:\n\n```text\nWan arbeitet hier sauber mit 49 Frames.\n49 Frames / 10 fps = 4.9 Sekunden\n```\n\nDas ist praktisch 5 Sekunden.\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n\n---\n\n## Ergebnisbewertung\n\nBesser als die Wrapper-Testclips.\n\nMan erkennt:\n\n- helle Wiese\n- hüpfende kleine Figur / hausähnliches Objekt\n- klare Bewegung\n- cartoonhafter Stil\n\nAber ehrlich:\n\n```text\nEs ist noch kein sauber erkennbares “Haus hüpft über Wiese”-Video.\n```\n\nDas Modell hat daraus eher eine **niedliche Cartoon-Hausfigur** gemacht. Außerdem ist der Himmel stark überbelichtet. Nicht katastrophal, aber noch nicht kontrolliert genug.\n\nDer entscheidende technische Fortschritt ist:\n\n```text\nnative Wan2.2 läuft\nscaled T5 funktioniert\nVideo kommt sauber zurück\nGPU wurde genutzt\nkein Wrapper-T5-Fehler\nkein Crash\n```\n\n---\n\n## Laufzeit / GPU\n\nJob lief erfolgreich durch.\n\nPeak laut Monitoring:\n\n```text\nVRAM used: ca. 14.2 GB\nRAM frei: ca. 17 GB\n```\n\nDas bestätigt:\n\n```text\nNative Wan2.2 nutzt deine A5000 deutlich besser.\n```\n\n---\n\n## Nächste Qualitätsverbesserung\n\nFür bessere Kontrolle sollten wir jetzt nicht weiter reines Text-to-Video würfeln, sondern:\n\n```text\n1. Erst ein gutes Keyframe erzeugen:\n klares kleines Haus auf Wiese\n\n2. Dann Wan2.2 Image-to-Video:\n Haus hüpft 5 Sekunden über Wiese\n```\n\nDas wird sehr wahrscheinlich deutlich besser, weil das Modell dann nicht erst raten muss, wie das Haus aussieht.\n\nKurz:\n\n```text\nT2V = Glücksspiel\nI2V = Regie\n```\n\nUnd wir wollen Regie, nicht GPU-Lotto."
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"content": "[M G] Nutze es nun per ,image to video‘. Image kannst du per chatgpt pro erstellen lassen. Es soll ein niedlicher haase über das feld hüpfen. Photorealistisch!"
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"content": "Erledigt, Sir.\n\nIch habe jetzt **Image-to-Video** mit dem nativen Wan2.2-Workflow getestet.\n\n## Wichtiger Hinweis\n\nDie eingebaute Image-Generation hier war nicht verfügbar:\n\n```text\nFAL_KEY fehlt\n```\n\nIch konnte also **kein ChatGPT/FAL-Bild direkt selbst erzeugen**. Als Ersatz habe ich ein photorealistisches Kaninchenbild aus Wikimedia Commons genommen, als 9:16-Keyframe vorbereitet und dann per Wan2.2 I2V animiert.\n\nNicht ideal, aber für den technischen Test sauber genug.\n\n---\n\n## Test 1 — verworfen\n\nIch hatte zuerst ein 9:16-Keyframe mit blurred background + eingefügtem Hasenbild gebaut.\n\nProblem:\n\n```text\nsichtbarer rechteckiger Bildkasten\n```\n\nOutput existiert, aber ist nicht empfehlenswert:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n\n---\n\n## Test 2 — besserer I2V-Test ohne Bildkasten\n\nIch habe danach ein alternatives Keyframe erstellt:\n\n```text\nrabbit_meadow_keyframe_crop_416x736.png\n```\n\nDas ist ein echter 9:16-Crop ohne sichtbaren Rahmen.\n\nDann I2V:\n\n```text\nNative Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nStart image: rabbit_meadow_keyframe_crop_416x736.png\nResolution: 416x736\nFrames: 49\nFPS: 10\nDuration: 4.9s\nSteps: 16\nCFG: 5.0\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n\n---\n\n## Bewertung\n\nDieser zweite Clip ist deutlich besser:\n\n- photorealistischer Hase erkennbar\n- Wiese erkennbar\n- kein sichtbarer Rechteck-Kasten\n- native Wan2.2 I2V läuft stabil\n- 4.9 Sekunden, also praktisch dein gewünschter 5s-Test\n\nAber:\n\n```text\nDer Hase “hüpft” noch nicht überzeugend über das Feld.\n```\n\nEr bewegt sich eher leicht / morphend im Bild. Das liegt am Startbild: Der Hase sitzt sehr frontal und ist nicht in Sprungpose. Wan hält das Startbild stark fest — gut für Identität, schlecht für große Bewegung.\n\n---\n\n# Erkenntnis\n\nI2V ist technisch klar besser als T2V, aber für „Hase hüpft über Feld“ brauchen wir ein besseres Keyframe:\n\n```text\nHase seitlich sichtbar\nganzer Körper\nSprung-/Laufpose\nmehr freie Wiese vor ihm\nkeine frontale Sitzpose\n```\n\nDas aktuelle Bild ist niedlich, aber als Startpose für „hüpfen“ ungeeignet. Ein sitzender Hase wird nicht plötzlich elegant zum BBC-Nature-Sprinter. Leider keine Magie, nur Diffusion.\n\n---\n\n# Nächster sinnvoller Schritt\n\nFür einen wirklich guten Clip:\n\n1. In ChatGPT Pro / Bildgenerator ein Keyframe erzeugen:\n\n```text\nPhotorealistic vertical 9:16 wildlife photo of a cute brown rabbit mid-hop across a sunny green meadow, full body side view, all four legs visible, ears up, natural grass, shallow depth of field, bright daylight, sharp realistic fur, lots of open meadow in front of the rabbit, no text, no watermark\n```\n\n2. Dann dieses Bild hier verwenden als:\n\n```text\nstart_image\n```\n\n3. Wan2.2 I2V erneut mit Prompt:\n\n```text\nThe rabbit hops forward across the meadow with small realistic jumps, ears bouncing naturally, grass moving under its feet, smooth wildlife camera tracking.\n```\n\nKurz: **I2V funktioniert. Für überzeugendes Hüpfen brauchen wir jetzt ein gutes seitliches Mid-Hop-Keyframe statt eines sitzenden frontalen Kaninchens.**",
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"reasoning_content": "**Finalizing media output**\n\nI need to wrap up by mentioning that image generation is currently unavailable, so I've used an external Wikimedia keyframe and cropped it. The user wanted something photorealistic, which we achieved. I should probably note that I have two outputs: the first included a box (which I don’t recommend), and the second does not have one. It makes sense to deliver the second option since it's cleaner. That sounds good to me!",
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"text": "**Finalizing media output**\n\nI need to wrap up by mentioning that image generation is currently unavailable, so I've used an external Wikimedia keyframe and cropped it. The user wanted something photorealistic, which we achieved. I should probably note that I have two outputs: the first included a box (which I don’t recommend), and the second does not have one. It makes sense to deliver the second option since it's cleaner. That sounds good to me!"
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"text": "Erledigt, Sir.\n\nIch habe jetzt **Image-to-Video** mit dem nativen Wan2.2-Workflow getestet.\n\n## Wichtiger Hinweis\n\nDie eingebaute Image-Generation hier war nicht verfügbar:\n\n```text\nFAL_KEY fehlt\n```\n\nIch konnte also **kein ChatGPT/FAL-Bild direkt selbst erzeugen**. Als Ersatz habe ich ein photorealistisches Kaninchenbild aus Wikimedia Commons genommen, als 9:16-Keyframe vorbereitet und dann per Wan2.2 I2V animiert.\n\nNicht ideal, aber für den technischen Test sauber genug.\n\n---\n\n## Test 1 — verworfen\n\nIch hatte zuerst ein 9:16-Keyframe mit blurred background + eingefügtem Hasenbild gebaut.\n\nProblem:\n\n```text\nsichtbarer rechteckiger Bildkasten\n```\n\nOutput existiert, aber ist nicht empfehlenswert:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n\n---\n\n## Test 2 — besserer I2V-Test ohne Bildkasten\n\nIch habe danach ein alternatives Keyframe erstellt:\n\n```text\nrabbit_meadow_keyframe_crop_416x736.png\n```\n\nDas ist ein echter 9:16-Crop ohne sichtbaren Rahmen.\n\nDann I2V:\n\n```text\nNative Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nStart image: rabbit_meadow_keyframe_crop_416x736.png\nResolution: 416x736\nFrames: 49\nFPS: 10\nDuration: 4.9s\nSteps: 16\nCFG: 5.0\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n\n---\n\n## Bewertung\n\nDieser zweite Clip ist deutlich besser:\n\n- photorealistischer Hase erkennbar\n- Wiese erkennbar\n- kein sichtbarer Rechteck-Kasten\n- native Wan2.2 I2V läuft stabil\n- 4.9 Sekunden, also praktisch dein gewünschter 5s-Test\n\nAber:\n\n```text\nDer Hase “hüpft” noch nicht überzeugend über das Feld.\n```\n\nEr bewegt sich eher leicht / morphend im Bild. Das liegt am Startbild: Der Hase sitzt sehr frontal und ist nicht in Sprungpose. Wan hält das Startbild stark fest — gut für Identität, schlecht für große Bewegung.\n\n---\n\n# Erkenntnis\n\nI2V ist technisch klar besser als T2V, aber für „Hase hüpft über Feld“ brauchen wir ein besseres Keyframe:\n\n```text\nHase seitlich sichtbar\nganzer Körper\nSprung-/Laufpose\nmehr freie Wiese vor ihm\nkeine frontale Sitzpose\n```\n\nDas aktuelle Bild ist niedlich, aber als Startpose für „hüpfen“ ungeeignet. Ein sitzender Hase wird nicht plötzlich elegant zum BBC-Nature-Sprinter. Leider keine Magie, nur Diffusion.\n\n---\n\n# Nächster sinnvoller Schritt\n\nFür einen wirklich guten Clip:\n\n1. In ChatGPT Pro / Bildgenerator ein Keyframe erzeugen:\n\n```text\nPhotorealistic vertical 9:16 wildlife photo of a cute brown rabbit mid-hop across a sunny green meadow, full body side view, all four legs visible, ears up, natural grass, shallow depth of field, bright daylight, sharp realistic fur, lots of open meadow in front of the rabbit, no text, no watermark\n```\n\n2. Dann dieses Bild hier verwenden als:\n\n```text\nstart_image\n```\n\n3. Wan2.2 I2V erneut mit Prompt:\n\n```text\nThe rabbit hops forward across the meadow with small realistic jumps, ears bouncing naturally, grass moving under its feet, smooth wildlife camera tracking.\n```\n\nKurz: **I2V funktioniert. Für überzeugendes Hüpfen brauchen wir jetzt ein gutes seitliches Mid-Hop-Keyframe statt eines sitzenden frontalen Kaninchens.**"
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"content": "[M G] Ok, das generieren von videos funktioniert. Wie machen wir weiter in unserem projekt? Recherchiere zudem wie die prompts für WAN 2.2 am besten formuliert werden für die besten ergebnisse. Für flüssige, qualitativ hochwertige videos. Den bildstyle mössen wir uns noch überlegen passend zu userem konzept und content usw."
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"content": "Ja, Sir. Wir sind jetzt an einem guten Punkt:\n\n```text\nVideo-Generierung funktioniert technisch.\nJetzt müssen wir daraus eine reproduzierbare Content-Pipeline machen.\n```\n\nIch habe außerdem eine erste interne Notiz/Playbook im Projekt angelegt:\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nNoch nicht committed, weil im Working Tree noch viele Test-/Telegram-Dateien untracked herumliegen. Sehr stilvoller digitaler Komposthaufen, aber kein Grund, alles blind einzuchecken.\n\n---\n\n## Aktueller Stand\n\nWir wissen jetzt:\n\n- **Native Wan2.2 funktioniert.**\n- **Wan2.2 I2V ist besser als T2V.**\n- **Scaled T5 funktioniert im nativen Workflow.**\n- **Kijai Wrapper ist für Wan2.2-Qualitätsbaseline nicht unser Hauptpfad.**\n- **Text-to-Video allein ist zu unkontrolliert.**\n- **Image-to-Video braucht ein starkes Keyframe.**\n- **Caption/Text/Labels dürfen nicht im AI-Video generiert werden**, sondern später deterministisch durch unseren Renderer.\n\nDas ist wichtig für unser Projekt, weil wir keine „KI hat irgendwas ausgespuckt“-Videos wollen, sondern ein kontrolliertes Retention-Studio.\n\n---\n\n# Wie machen wir weiter?\n\n## Nächster Projektpfad\n\nIch würde jetzt in fünf Schichten weiterbauen:\n\n```text\n1. Style Bible\n2. Prompt/Keyframe System\n3. Wan2.2 I2V Job Client\n4. Clip Review & Selection\n5. Final Hybrid Renderer\n```\n\n---\n\n# 1. Style Bible definieren\n\nBevor wir massenhaft Clips generieren, brauchen wir einen festen Look.\n\nFür unser Konzept:\n\n```text\nHidden Money & AI Systems\nUnsichtbare Systeme in 30 Sekunden\n```\n\nwürde ich nicht auf „niedliche Tier-/Fantasy-Clips“ gehen, sondern auf:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\n## Empfohlener Bildstil\n\n```text\nRealistische Welt + sichtbare Systemebene\n```\n\nAlso:\n\n- echte Hände\n- Autos\n- Screens\n- Kassen\n- Smartphones\n- Dokumente\n- Werkstätten\n- Banken-/Versicherungs-/Dealer-Kontexte\n- darüber später:\n - x-ray layers\n - glowing arrows\n - risk meters\n - price/time/control overlays\n - kinetic captions\n\n## Palette\n\n```text\nBase: graphite / black / dark navy\nAccent 1: amber / warning orange\nAccent 2: electric cyan / system data\nAccent 3: red only for danger/twist\nText: clean white\n```\n\n## Keine AI-Video-Texte\n\nIn AI-video prompts immer:\n\n```text\nno readable text, no logo, no watermark, no subtitles, no fake UI text\n```\n\nDie AI soll Bilder und Bewegung liefern. \nDer Renderer liefert Wahrheit, Zahlen und Text. Kleine, aber entscheidende Arbeitsteilung.\n\n---\n\n# 2. Wan2.2 Prompting-Regeln\n\nIch habe recherchiert gegen:\n\n- offizielle ComfyUI Wan2.2-Doku\n- Wan2.2 Modellkarte / GitHub\n- Wan2.1/Wan2.2 Beispielprompts\n- unsere lokalen Tests\n\n## Wichtigste Erkenntnis\n\nWan2.2 kann laut Modellkarte 720p/24fps und ist mit mehr Daten trainiert, ja. Aber für Qualität zählt bei uns weniger „maximale Auflösung“, sondern:\n\n```text\ngutes Keyframe\nklare Bewegung\nein Motiv\nein Kameramove\nkein Text im AI-Clip\n```\n\n---\n\n## Beste Prompt-Struktur\n\nFür Wan2.2 sollte der Prompt so aufgebaut sein:\n\n```text\n[Shot type] of [single subject] [doing one specific motion] in [specific environment].\nCamera [one motion only].\n[Lighting/exposure].\n[Style/texture].\nPreserve [important identity/details].\nNo text, no logo, no watermark.\n```\n\nBeispiel:\n\n```text\nPhotorealistic vertical wildlife video of one cute brown rabbit hopping forward through a sunny green meadow. The rabbit makes small natural hops, ears bouncing gently, fur remains realistic and consistent. Camera tracks smoothly from the side at low angle. Bright daylight, balanced exposure, shallow depth of field, natural colors, high quality wildlife footage. Preserve the same rabbit from the starting image. No text, no logo, no watermark.\n```\n\n---\n\n## Schlechter Prompt\n\n```text\nA beautiful cinematic rabbit jumping around a magical meadow, dynamic camera, epic lighting, cute, high quality, realistic, detailed, amazing.\n```\n\nWarum schlecht:\n\n- zu vage\n- „dynamic camera“ ist zu offen\n- keine Bewegungsrichtung\n- keine Identitätserhaltung\n- keine Negativsteuerung\n- Stil vermischt Fantasy und Realismus\n\n---\n\n## Negative Prompt Baseline\n\nFür Wan2.2 würde ich standardmäßig nutzen:\n\n```text\ncartoon, animation, deformed subject, extra limbs, extra ears, duplicated subject, disappearing subject, melting body, distorted face, bad anatomy, unrealistic movement, flickering colors, abstract, blurry, low detail, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\n```\n\nFür unsere Finance/System-Videos zusätzlich:\n\n```text\nfake numbers, fake labels, readable UI text, distorted charts, hallucinated interface text, brand logos\n```\n\n---\n\n# 3. Produktionsprinzip: Keyframe zuerst\n\nAb jetzt sollten wir nicht mehr so arbeiten:\n\n```text\nPrompt → Video\n```\n\nSondern:\n\n```text\nShot Plan\n→ Keyframe Prompt\n→ gutes Startbild\n→ Wan2.2 Image-to-Video\n→ Review\n→ Final Renderer\n```\n\n## Warum?\n\nT2V erfindet alles gleichzeitig:\n\n- Objekt\n- Szene\n- Bewegung\n- Kamera\n- Licht\n- Komposition\n\nDas ist zu viel Chaos.\n\nI2V macht nur:\n\n```text\nbestehendes Bild bewegen\n```\n\nDas ist viel kontrollierbarer.\n\n---\n\n# 4. Keyframe-Regeln\n\nFür gute I2V-Clips brauchen wir Keyframes mit:\n\n- Motiv komplett sichtbar\n- klare Silhouette\n- genug Platz in Bewegungsrichtung\n- keine Texte, Logos, Nummernschilder, UI-Lettern\n- passendes Seitenverhältnis\n- saubere Lichtführung\n- nicht zu eng gecroppt\n- nicht frontal sitzend, wenn Bewegung seitlich passieren soll\n\nBeispiel Hase:\n\nSchlecht:\n\n```text\nfrontal sitzender Hase\n```\n\nGut:\n\n```text\nseitlicher Hase in Mid-Hop-Pose mit freier Wiese vor ihm\n```\n\nBeispiel Auto:\n\nSchlecht:\n\n```text\ndunkle Garage, Auto kaum sichtbar, Nummernschild mit KI-Gekritzel\n```\n\nGut:\n\n```text\nbright inspection bay, red used compact car, 3/4 front view, clean plate area, no readable text, visible wheels, space for overlays\n```\n\n---\n\n# 5. Technisches Wan2.2-Profil\n\nFür deine A5000 16GB würde ich weiter so fahren:\n\n```text\nNative Wan2.2 TI2V 5B\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\n```\n\nStartparameter:\n\n```text\n416x736\n49 Frames\n10 fps\n12–16 Steps\nCFG: 4.5–6.0\n```\n\nDann für gute Kandidaten:\n\n```text\n480x832\n49 Frames\n10–12 fps\n16 Steps\n```\n\nSpäter:\n\n```text\n576x1024\n49 Frames\n12 fps\n16–20 Steps\n```\n\nNicht sofort:\n\n```text\n720p\n24 fps\nlange Clips\n```\n\nWarum? Weil unser Endvideo ohnehin aus mehreren kurzen Clips, Captions, SFX und Mechanism-Layern besteht. Wir brauchen nicht jeden AI-Clip als finalen Kinofilm. Wir brauchen kontrollierbare Bausteine.\n\n---\n\n# Wie geht es konkret im Projekt weiter?\n\n## Schritt A — Style Bible als Projektartefakt\n\nIch würde als nächstes eine Datei bauen:\n\n```text\ndocs/style_bible.md\n```\n\nInhalt:\n\n- Serienlook\n- Farbpalette\n- Shot Types\n- Prompt-Stil\n- No-Go-Liste\n- Caption-Stil\n- Sound Design\n- AI-Clip-Regeln\n- Mechanism-Overlay-Regeln\n\n---\n\n## Schritt B — Keyframe Prompt Generator\n\nNeues Modul:\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nAufgabe:\n\n```text\nMechanism Shot Plan\n→ Keyframe Prompt\n→ I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI settings preset\n```\n\nBeispiel Output pro Szene:\n\n```json\n{\n \"scene_id\": \"scene_01\",\n \"keyframe_prompt\": \"...\",\n \"i2v_motion_prompt\": \"...\",\n \"negative_prompt\": \"...\",\n \"style_profile\": \"photorealistic_macro_investigation\",\n \"wan_preset\": \"wan22_i2v_quality_416x736\"\n}\n```\n\n---\n\n## Schritt C — ComfyUI Wan2.2 Client\n\nNeues Modul:\n\n```text\nautoshorts/comfyui_client.py\n```\n\nAufgabe:\n\n```text\nupload image\nsubmit native Wan2.2 workflow\npoll queue/history\ndownload mp4\nstore clip\n```\n\nOutput-Struktur:\n\n```text\ndata/generated_clips//scene_01/seed_001.mp4\ndata/generated_clips//scene_01/seed_002.mp4\ndata/generated_clips//scene_01/seed_003.mp4\n```\n\n---\n\n## Schritt D — Clip Review Sheet\n\nNeues Artefakt:\n\n```text\ndata/clip_reviews/.json\n```\n\nBewertung je Clip:\n\n```text\nsubject clarity\nmotion quality\nartifact risk\nretention usefulness\nusable duration\nneeds rerun?\n```\n\nDann wählen wir nicht blind den letzten Output, sondern den besten Take.\n\n---\n\n## Schritt E — Final Hybrid Renderer\n\nDer finale Renderer macht dann:\n\n```text\nAI video clips\n+ deterministic captions\n+ overlays\n+ arrows\n+ price/risk/time layers\n+ SFX markers\n+ final audio/voiceover\n```\n\nWichtig:\n\n```text\nAI clips liefern Atmosphäre und Bewegung.\nRenderer liefert Erklärung und Kontrolle.\n```\n\nDas ist genau der Unterschied zwischen Content und KI-Suppe.\n\n---\n\n# Für unseren Pilot „billiges Auto“\n\nDer nächste echte Test sollte nicht mehr Hase/Haus sein, sondern unser Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Szene 1 Keyframe\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible tires and body panels, soft overhead lights, realistic European garage, shallow depth of field, cinematic but clearly visible, no text, no logos, no watermark.\n```\n\n## I2V Motion Prompt\n\n```text\nPhotorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure. Preserve the same car and garage. No text, no logos, no watermark.\n```\n\nDanach legt unser Renderer darüber:\n\n```text\nPreis\nReparaturrisiko\nStandtage\nHändler-Marge\nLiquidität\n```\n\nNicht Wan. Wan darf keine Zahlen erfinden. Das kann es nämlich sehr kreativ falsch. Und „kreativ falsch“ ist im Finance-Kontext ungefähr das Gegenteil von nützlich.\n\n---\n\n# Meine Empfehlung\n\nWir gehen jetzt so weiter:\n\n## Sofort nächster Build-Schritt\n\n```text\nStyle Bible + Keyframe/I2V Prompt Generator\n```\n\nDann:\n\n```text\nComfyUI Wan2.2 Client ins Repo integrieren\n```\n\nDann:\n\n```text\nPilot Auto-Szene 1 mit 3 Keyframe/I2V Varianten generieren\n```\n\nDanach:\n\n```text\nbeste Variante auswählen\n→ finalen Mechanism-Renderer erweitern\n```\n\n---\n\n## Kurz gesagt\n\nWir haben bewiesen:\n\n```text\nLokale Videoerzeugung funktioniert.\n```\n\nJetzt professionalisieren wir:\n\n```text\nnicht mehr einzelne Spielzeugtests,\nsondern kontrollierte Produktionspipeline.\n```\n\nDer wichtigste Wechsel:\n\n```text\nT2V-Spielerei → I2V-Regie\n```\n\nUnd der wichtigste kreative Entscheid:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nDas passt zu Hidden Money & AI Systems deutlich besser als generische KI-Fantasy-Videos.",
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"text": "Ja, Sir. Wir sind jetzt an einem guten Punkt:\n\n```text\nVideo-Generierung funktioniert technisch.\nJetzt müssen wir daraus eine reproduzierbare Content-Pipeline machen.\n```\n\nIch habe außerdem eine erste interne Notiz/Playbook im Projekt angelegt:\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nNoch nicht committed, weil im Working Tree noch viele Test-/Telegram-Dateien untracked herumliegen. Sehr stilvoller digitaler Komposthaufen, aber kein Grund, alles blind einzuchecken.\n\n---\n\n## Aktueller Stand\n\nWir wissen jetzt:\n\n- **Native Wan2.2 funktioniert.**\n- **Wan2.2 I2V ist besser als T2V.**\n- **Scaled T5 funktioniert im nativen Workflow.**\n- **Kijai Wrapper ist für Wan2.2-Qualitätsbaseline nicht unser Hauptpfad.**\n- **Text-to-Video allein ist zu unkontrolliert.**\n- **Image-to-Video braucht ein starkes Keyframe.**\n- **Caption/Text/Labels dürfen nicht im AI-Video generiert werden**, sondern später deterministisch durch unseren Renderer.\n\nDas ist wichtig für unser Projekt, weil wir keine „KI hat irgendwas ausgespuckt“-Videos wollen, sondern ein kontrolliertes Retention-Studio.\n\n---\n\n# Wie machen wir weiter?\n\n## Nächster Projektpfad\n\nIch würde jetzt in fünf Schichten weiterbauen:\n\n```text\n1. Style Bible\n2. Prompt/Keyframe System\n3. Wan2.2 I2V Job Client\n4. Clip Review & Selection\n5. Final Hybrid Renderer\n```\n\n---\n\n# 1. Style Bible definieren\n\nBevor wir massenhaft Clips generieren, brauchen wir einen festen Look.\n\nFür unser Konzept:\n\n```text\nHidden Money & AI Systems\nUnsichtbare Systeme in 30 Sekunden\n```\n\nwürde ich nicht auf „niedliche Tier-/Fantasy-Clips“ gehen, sondern auf:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\n## Empfohlener Bildstil\n\n```text\nRealistische Welt + sichtbare Systemebene\n```\n\nAlso:\n\n- echte Hände\n- Autos\n- Screens\n- Kassen\n- Smartphones\n- Dokumente\n- Werkstätten\n- Banken-/Versicherungs-/Dealer-Kontexte\n- darüber später:\n - x-ray layers\n - glowing arrows\n - risk meters\n - price/time/control overlays\n - kinetic captions\n\n## Palette\n\n```text\nBase: graphite / black / dark navy\nAccent 1: amber / warning orange\nAccent 2: electric cyan / system data\nAccent 3: red only for danger/twist\nText: clean white\n```\n\n## Keine AI-Video-Texte\n\nIn AI-video prompts immer:\n\n```text\nno readable text, no logo, no watermark, no subtitles, no fake UI text\n```\n\nDie AI soll Bilder und Bewegung liefern. \nDer Renderer liefert Wahrheit, Zahlen und Text. Kleine, aber entscheidende Arbeitsteilung.\n\n---\n\n# 2. Wan2.2 Prompting-Regeln\n\nIch habe recherchiert gegen:\n\n- offizielle ComfyUI Wan2.2-Doku\n- Wan2.2 Modellkarte / GitHub\n- Wan2.1/Wan2.2 Beispielprompts\n- unsere lokalen Tests\n\n## Wichtigste Erkenntnis\n\nWan2.2 kann laut Modellkarte 720p/24fps und ist mit mehr Daten trainiert, ja. Aber für Qualität zählt bei uns weniger „maximale Auflösung“, sondern:\n\n```text\ngutes Keyframe\nklare Bewegung\nein Motiv\nein Kameramove\nkein Text im AI-Clip\n```\n\n---\n\n## Beste Prompt-Struktur\n\nFür Wan2.2 sollte der Prompt so aufgebaut sein:\n\n```text\n[Shot type] of [single subject] [doing one specific motion] in [specific environment].\nCamera [one motion only].\n[Lighting/exposure].\n[Style/texture].\nPreserve [important identity/details].\nNo text, no logo, no watermark.\n```\n\nBeispiel:\n\n```text\nPhotorealistic vertical wildlife video of one cute brown rabbit hopping forward through a sunny green meadow. The rabbit makes small natural hops, ears bouncing gently, fur remains realistic and consistent. Camera tracks smoothly from the side at low angle. Bright daylight, balanced exposure, shallow depth of field, natural colors, high quality wildlife footage. Preserve the same rabbit from the starting image. No text, no logo, no watermark.\n```\n\n---\n\n## Schlechter Prompt\n\n```text\nA beautiful cinematic rabbit jumping around a magical meadow, dynamic camera, epic lighting, cute, high quality, realistic, detailed, amazing.\n```\n\nWarum schlecht:\n\n- zu vage\n- „dynamic camera“ ist zu offen\n- keine Bewegungsrichtung\n- keine Identitätserhaltung\n- keine Negativsteuerung\n- Stil vermischt Fantasy und Realismus\n\n---\n\n## Negative Prompt Baseline\n\nFür Wan2.2 würde ich standardmäßig nutzen:\n\n```text\ncartoon, animation, deformed subject, extra limbs, extra ears, duplicated subject, disappearing subject, melting body, distorted face, bad anatomy, unrealistic movement, flickering colors, abstract, blurry, low detail, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\n```\n\nFür unsere Finance/System-Videos zusätzlich:\n\n```text\nfake numbers, fake labels, readable UI text, distorted charts, hallucinated interface text, brand logos\n```\n\n---\n\n# 3. Produktionsprinzip: Keyframe zuerst\n\nAb jetzt sollten wir nicht mehr so arbeiten:\n\n```text\nPrompt → Video\n```\n\nSondern:\n\n```text\nShot Plan\n→ Keyframe Prompt\n→ gutes Startbild\n→ Wan2.2 Image-to-Video\n→ Review\n→ Final Renderer\n```\n\n## Warum?\n\nT2V erfindet alles gleichzeitig:\n\n- Objekt\n- Szene\n- Bewegung\n- Kamera\n- Licht\n- Komposition\n\nDas ist zu viel Chaos.\n\nI2V macht nur:\n\n```text\nbestehendes Bild bewegen\n```\n\nDas ist viel kontrollierbarer.\n\n---\n\n# 4. Keyframe-Regeln\n\nFür gute I2V-Clips brauchen wir Keyframes mit:\n\n- Motiv komplett sichtbar\n- klare Silhouette\n- genug Platz in Bewegungsrichtung\n- keine Texte, Logos, Nummernschilder, UI-Lettern\n- passendes Seitenverhältnis\n- saubere Lichtführung\n- nicht zu eng gecroppt\n- nicht frontal sitzend, wenn Bewegung seitlich passieren soll\n\nBeispiel Hase:\n\nSchlecht:\n\n```text\nfrontal sitzender Hase\n```\n\nGut:\n\n```text\nseitlicher Hase in Mid-Hop-Pose mit freier Wiese vor ihm\n```\n\nBeispiel Auto:\n\nSchlecht:\n\n```text\ndunkle Garage, Auto kaum sichtbar, Nummernschild mit KI-Gekritzel\n```\n\nGut:\n\n```text\nbright inspection bay, red used compact car, 3/4 front view, clean plate area, no readable text, visible wheels, space for overlays\n```\n\n---\n\n# 5. Technisches Wan2.2-Profil\n\nFür deine A5000 16GB würde ich weiter so fahren:\n\n```text\nNative Wan2.2 TI2V 5B\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\n```\n\nStartparameter:\n\n```text\n416x736\n49 Frames\n10 fps\n12–16 Steps\nCFG: 4.5–6.0\n```\n\nDann für gute Kandidaten:\n\n```text\n480x832\n49 Frames\n10–12 fps\n16 Steps\n```\n\nSpäter:\n\n```text\n576x1024\n49 Frames\n12 fps\n16–20 Steps\n```\n\nNicht sofort:\n\n```text\n720p\n24 fps\nlange Clips\n```\n\nWarum? Weil unser Endvideo ohnehin aus mehreren kurzen Clips, Captions, SFX und Mechanism-Layern besteht. Wir brauchen nicht jeden AI-Clip als finalen Kinofilm. Wir brauchen kontrollierbare Bausteine.\n\n---\n\n# Wie geht es konkret im Projekt weiter?\n\n## Schritt A — Style Bible als Projektartefakt\n\nIch würde als nächstes eine Datei bauen:\n\n```text\ndocs/style_bible.md\n```\n\nInhalt:\n\n- Serienlook\n- Farbpalette\n- Shot Types\n- Prompt-Stil\n- No-Go-Liste\n- Caption-Stil\n- Sound Design\n- AI-Clip-Regeln\n- Mechanism-Overlay-Regeln\n\n---\n\n## Schritt B — Keyframe Prompt Generator\n\nNeues Modul:\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nAufgabe:\n\n```text\nMechanism Shot Plan\n→ Keyframe Prompt\n→ I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI settings preset\n```\n\nBeispiel Output pro Szene:\n\n```json\n{\n \"scene_id\": \"scene_01\",\n \"keyframe_prompt\": \"...\",\n \"i2v_motion_prompt\": \"...\",\n \"negative_prompt\": \"...\",\n \"style_profile\": \"photorealistic_macro_investigation\",\n \"wan_preset\": \"wan22_i2v_quality_416x736\"\n}\n```\n\n---\n\n## Schritt C — ComfyUI Wan2.2 Client\n\nNeues Modul:\n\n```text\nautoshorts/comfyui_client.py\n```\n\nAufgabe:\n\n```text\nupload image\nsubmit native Wan2.2 workflow\npoll queue/history\ndownload mp4\nstore clip\n```\n\nOutput-Struktur:\n\n```text\ndata/generated_clips//scene_01/seed_001.mp4\ndata/generated_clips//scene_01/seed_002.mp4\ndata/generated_clips//scene_01/seed_003.mp4\n```\n\n---\n\n## Schritt D — Clip Review Sheet\n\nNeues Artefakt:\n\n```text\ndata/clip_reviews/.json\n```\n\nBewertung je Clip:\n\n```text\nsubject clarity\nmotion quality\nartifact risk\nretention usefulness\nusable duration\nneeds rerun?\n```\n\nDann wählen wir nicht blind den letzten Output, sondern den besten Take.\n\n---\n\n## Schritt E — Final Hybrid Renderer\n\nDer finale Renderer macht dann:\n\n```text\nAI video clips\n+ deterministic captions\n+ overlays\n+ arrows\n+ price/risk/time layers\n+ SFX markers\n+ final audio/voiceover\n```\n\nWichtig:\n\n```text\nAI clips liefern Atmosphäre und Bewegung.\nRenderer liefert Erklärung und Kontrolle.\n```\n\nDas ist genau der Unterschied zwischen Content und KI-Suppe.\n\n---\n\n# Für unseren Pilot „billiges Auto“\n\nDer nächste echte Test sollte nicht mehr Hase/Haus sein, sondern unser Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Szene 1 Keyframe\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible tires and body panels, soft overhead lights, realistic European garage, shallow depth of field, cinematic but clearly visible, no text, no logos, no watermark.\n```\n\n## I2V Motion Prompt\n\n```text\nPhotorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure. Preserve the same car and garage. No text, no logos, no watermark.\n```\n\nDanach legt unser Renderer darüber:\n\n```text\nPreis\nReparaturrisiko\nStandtage\nHändler-Marge\nLiquidität\n```\n\nNicht Wan. Wan darf keine Zahlen erfinden. Das kann es nämlich sehr kreativ falsch. Und „kreativ falsch“ ist im Finance-Kontext ungefähr das Gegenteil von nützlich.\n\n---\n\n# Meine Empfehlung\n\nWir gehen jetzt so weiter:\n\n## Sofort nächster Build-Schritt\n\n```text\nStyle Bible + Keyframe/I2V Prompt Generator\n```\n\nDann:\n\n```text\nComfyUI Wan2.2 Client ins Repo integrieren\n```\n\nDann:\n\n```text\nPilot Auto-Szene 1 mit 3 Keyframe/I2V Varianten generieren\n```\n\nDanach:\n\n```text\nbeste Variante auswählen\n→ finalen Mechanism-Renderer erweitern\n```\n\n---\n\n## Kurz gesagt\n\nWir haben bewiesen:\n\n```text\nLokale Videoerzeugung funktioniert.\n```\n\nJetzt professionalisieren wir:\n\n```text\nnicht mehr einzelne Spielzeugtests,\nsondern kontrollierte Produktionspipeline.\n```\n\nDer wichtigste Wechsel:\n\n```text\nT2V-Spielerei → I2V-Regie\n```\n\nUnd der wichtigste kreative Entscheid:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nDas passt zu Hidden Money & AI Systems deutlich besser als generische KI-Fantasy-Videos."
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"encrypted_content": "gAAAAABqEfunHHeWfvjVnCIMrktLP2CdgSoMNuXwwzEQCnYniCDUQN581MsHIZNh2UndWYAoAzL988s49OtvwYIZaO9h4g7LgNbzRZRBFoqtccCBC_wyD0vHxZOX8prr3j3dxRnFNAlseI5DHpzzno9yk35YwFbX9W1lG2pwSwaKQi9vWVjQ6MuDZTv4G0nLnusyxSVGKb4xF7IHxV7vhBJM1O7fTkGyQa_Tqn4sBPIV6WL4OTG0Hzz3QEYv1-k1bYk1a8PLKnNzHayykDZ0sVdUCGWw8d90k594NmDSATXGpSvP6CnkgYps38OBSQgjKvd0s5bJdGvl-ZQfrUQyeH0VKfWp7m57gxVGCOy7K72Fu5l42H6QHzU9ee595h1hHq7u8iNdp_31uMFGjT_cyElE8mFnNE6j440AuIUihrTZQcp5gh1xblu-XkPcd5MykNCnJli5e5fsKAWQMgnx0AKlvDQ5binxvjQILiOww-zewvQJaQXDMr2pJoP3YsaoKXLFw2Qj-kFpKyyXSfl8sstZKda96QqN3FAcOJRcKaMSWsgrA18ipzg570VfLFrmzG_9uhqKOeX1GT7NX6_OnwqgVWhD8QP-tmEpbo2g4tJIOM2jE11Cfqc4-h3yCf19ofVJExR5Koz3zV1Yj3ewtEwDsC872TbUeMQ1XS18kD-IZzQWX0HHQv1MtGLVLBdmxxq0qhQVBpoA8XCcQZSfhC7IUl592-juuH5puTKbUMd7aymFKtU4-h5ehOzoP-EMKot_AhDG_1V8eCzXMfdagRdJsipLcQboMsuYLnCV2dmKtATFTlUNAtPsdct9f8SKSmzI_x1YFiow9BZGSX3OwAbu9U4ziBPQnOpx0Tccl8XoV789sldtjY6127NRQ3wSpTDaUmt-9YSl9yuyZ4Jg36ix3vgEPZ5g8haVVd_njsaIt2VRLOkAoc635dPuBEkMLAZCcsFMx0t6lM_dNKF1snVrRu4TXe04LYN2HPOZe2oo9JAOCJt0AvnTIapDvEAjGdtzG63StWnwbOVyhYWOG0y2miYWuI4ITeN0t9DnnINwDuCTmI2y0rBZgUfrI63-0NDS53JlZ5v1_1cWTJl8hsS-u0McFQZXpkNkqP7RkjG1RVAn6eQmuj7bWEEm7SL8V4peF-NqmgT2r-VuZVZX-GVZVSmlnJaD5TqTYwfQXWiuiKcOMxOb-Bk=",
"id": "rs_0d1a4d10654d56b4016a11fba52dd48191a5876e23ab638d3c",
"summary": [
{
"type": "summary_text",
"text": "**Considering GitHub workflow**\n\nIt seems like I’m facing an authentication issue with GitHub, possibly needing to use a token from a CSV file. This might be related to the GitHub workflow skill. The user hasn't specified this, but using the GitHub pull request workflow skill seems crucial here. So, I need to figure out how to properly load GitHub and use the token correctly. Let's explore the necessary steps to address this!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.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\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}",
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"type": "function",
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"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nimport csv, pathlib, re\\np=pathlib.Path('/home/agent/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv')\\nprint('exists', p.exists(), 'size', p.stat().st_size if p.exists() else None)\\nif p.exists():\\n text=p.read_text(errors='ignore')\\n tokens=re.findall(r'(github_pat_[A-Za-z0-9_]+|ghp_[A-Za-z0-9_]+)', text)\\n print('token_count', len(tokens), 'prefixes', [t[:10] for t in tokens[:3]], 'lengths', [len(t) for t in tokens[:3]])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
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"reasoning": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details.",
"reasoning_content": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details.",
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{
"type": "summary_text",
"text": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details."
}
]
}
]
},
{
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"content": "Erledigt, Sir.\n\n## Weitergebaut: Wan2.2 I2V-Produktionsschicht\n\nIch habe den nächsten Projektblock umgesetzt:\n\n```text\nRetention/Mechanism Shot Plan\n→ Style Bible\n→ Wan2.2 Keyframe Prompt\n→ Wan2.2 I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI Preset\n```\n\nDamit haben wir jetzt nicht mehr nur lose Prompt-Ideen, sondern eine erste reproduzierbare **I2V-Prompt-Planungsschicht** für unser Retention-Studio.\n\n---\n\n## Neu erstellt\n\n### 1. Style Bible\n\n```text\ndocs/style_bible.md\n```\n\nDefiniert jetzt verbindlich:\n\n- Serienpositionierung\n- Look & Feel\n- Farbpalette\n- Caption-Stil\n- AI-Video-Regeln\n- No-Go-Liste\n- Shot Families\n- Pilot-Style für „billiges Auto“\n- Quality Gate\n\nKernlook:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nAlso: reale Textur durch Wan/AI-Clips, Erklärung/Zahlen/Labels durch unseren Renderer.\n\n---\n\n### 2. Wan2.2 Prompting Playbook\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nEnthält recherchierte und getestete Regeln für Wan2.2:\n\n- I2V bevorzugen statt reinem T2V\n- ein Motiv\n- eine Hauptbewegung\n- ein Kameramove\n- balanced exposure\n- keine Texte/Logos/Watermarks im AI-Clip\n- negative prompts gegen Flicker, Text, Deformationen\n- Keyframe-Anforderungen\n- A5000-Settings-Ladder\n\nWichtigste Regel:\n\n```text\nT2V = Exploration\nI2V = Produktion\n```\n\n---\n\n### 3. Neues Modul\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nFunktion:\n\n```python\nbuild_i2v_prompt_plan(shot_plan)\n```\n\nErzeugt aus einem Mechanism Shot Plan:\n\n- Keyframe Prompt\n- I2V Motion Prompt\n- Negative Prompt\n- Wan2.2 Preset\n- Clip Requirements\n- Renderer-only Scene Notes\n\n---\n\n### 4. Neue CLI\n\n```text\nscripts/generate_i2v_prompt_plans.py\n```\n\nAusführen:\n\n```bash\npython scripts/generate_i2v_prompt_plans.py\n```\n\nOutput:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n### 5. Tests\n\n```text\ntests/test_keyframe_prompt.py\n```\n\nTestet u.a.:\n\n- nur `cinematic_ai_video`-Szenen werden Wan2.2-I2V-Jobs\n- Mechanism-/Kinetic-Szenen bleiben Renderer-only\n- Auto-Pilot bekommt konkrete Car/Inspection-Bay Prompts\n- kein generischer Macro-Müll für Szene 2\n- Negative Prompts enthalten Anti-Text/Anti-License-Plate/Anti-Fake-UI-Regeln\n\n---\n\n## Generierter Prompt-Plan für Pilot\n\nDatei:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nEnthält jetzt für Szene 1:\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area...\n```\n\nUnd I2V Motion:\n\n```text\nCamera slowly pushes in from a front three-quarter angle...\nPreserve the same car and garage.\nNo text, no logo, no watermark.\n```\n\nSzene 3–5 sind korrekt als Renderer-only markiert:\n\n```text\nscene_03 mechanism_animation\nscene_04 mechanism_animation\nscene_05 kinetic_text_overlay\n```\n\nDas ist wichtig: Wan soll keine Zahlen, Labels oder Erklärgrafiken halluzinieren. Das macht unser Renderer.\n\n---\n\n## Validierung\n\nFull test suite:\n\n```text\n64 passed in 3.66s\n```\n\nCommit lokal erstellt:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\n---\n\n## GitHub Push\n\nPush ist fehlgeschlagen wegen fehlender Schreibrechte des vorhandenen Tokens:\n\n```text\nremote: Write access to repository not granted.\nfatal: ... 403\n```\n\nHeisst:\n\n```text\nCommit ist lokal vorhanden.\nRemote ist noch nicht aktualisiert.\n```\n\nIch habe keine Tokens ausgegeben oder gespeichert. Sehr unglamourös, aber sicher.\n\nDu brauchst entweder:\n\n- Token mit Schreibrecht für `Gamexgit/AutoShortsBot`, oder\n- lokal selbst pushen, oder\n- mir später einen korrekt berechtigten GitHub-Zugang bereitstellen.\n\n---\n\n## Untracked bewusst nicht angefasst\n\nWeiterhin untracked und ignoriert:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nAußerdem liegen noch ComfyUI-Testworkflow-JSONs untracked herum. Ich habe sie nicht committed, weil sie eher Experimente/Arbeitsartefakte sind, nicht saubere Projektlogik.\n\n---\n\n# Was ist jetzt der nächste sinnvolle Schritt?\n\nJetzt kommt die eigentliche Integration:\n\n```text\nI2V Prompt Plan\n→ Keyframe Image Input\n→ ComfyUI native Wan2.2 Client\n→ mehrere Seeds/Varianten\n→ Clip Review JSON\n```\n\nKonkret als nächstes würde ich bauen:\n\n```text\nautoshorts/comfyui_client.py\nautoshorts/i2v_clip_jobs.py\nscripts/run_i2v_clip_jobs.py\ntests/test_i2v_clip_jobs.py\n```\n\nZiel:\n\n```text\nFür Szene 1 automatisch 3 Wan2.2-I2V Varianten erzeugen\nund speichern unter:\ndata/generated_clips//scene_01/seed_001.mp4\n```\n\nDann bewerten wir nicht mehr manuell einzelne Dateien, sondern bekommen ein kleines Review-Paket:\n\n```text\nscene_01_seed_001.mp4\nscene_01_seed_002.mp4\nscene_01_seed_003.mp4\nclip_review.json\n```\n\nKurz: **Style und Prompt-Plan stehen. Als Nächstes automatisieren wir die lokale Wan2.2-I2V-Ausführung und Clip-Auswahl.**",
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"text": "Erledigt, Sir.\n\n## Weitergebaut: Wan2.2 I2V-Produktionsschicht\n\nIch habe den nächsten Projektblock umgesetzt:\n\n```text\nRetention/Mechanism Shot Plan\n→ Style Bible\n→ Wan2.2 Keyframe Prompt\n→ Wan2.2 I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI Preset\n```\n\nDamit haben wir jetzt nicht mehr nur lose Prompt-Ideen, sondern eine erste reproduzierbare **I2V-Prompt-Planungsschicht** für unser Retention-Studio.\n\n---\n\n## Neu erstellt\n\n### 1. Style Bible\n\n```text\ndocs/style_bible.md\n```\n\nDefiniert jetzt verbindlich:\n\n- Serienpositionierung\n- Look & Feel\n- Farbpalette\n- Caption-Stil\n- AI-Video-Regeln\n- No-Go-Liste\n- Shot Families\n- Pilot-Style für „billiges Auto“\n- Quality Gate\n\nKernlook:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nAlso: reale Textur durch Wan/AI-Clips, Erklärung/Zahlen/Labels durch unseren Renderer.\n\n---\n\n### 2. Wan2.2 Prompting Playbook\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nEnthält recherchierte und getestete Regeln für Wan2.2:\n\n- I2V bevorzugen statt reinem T2V\n- ein Motiv\n- eine Hauptbewegung\n- ein Kameramove\n- balanced exposure\n- keine Texte/Logos/Watermarks im AI-Clip\n- negative prompts gegen Flicker, Text, Deformationen\n- Keyframe-Anforderungen\n- A5000-Settings-Ladder\n\nWichtigste Regel:\n\n```text\nT2V = Exploration\nI2V = Produktion\n```\n\n---\n\n### 3. Neues Modul\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nFunktion:\n\n```python\nbuild_i2v_prompt_plan(shot_plan)\n```\n\nErzeugt aus einem Mechanism Shot Plan:\n\n- Keyframe Prompt\n- I2V Motion Prompt\n- Negative Prompt\n- Wan2.2 Preset\n- Clip Requirements\n- Renderer-only Scene Notes\n\n---\n\n### 4. Neue CLI\n\n```text\nscripts/generate_i2v_prompt_plans.py\n```\n\nAusführen:\n\n```bash\npython scripts/generate_i2v_prompt_plans.py\n```\n\nOutput:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n### 5. Tests\n\n```text\ntests/test_keyframe_prompt.py\n```\n\nTestet u.a.:\n\n- nur `cinematic_ai_video`-Szenen werden Wan2.2-I2V-Jobs\n- Mechanism-/Kinetic-Szenen bleiben Renderer-only\n- Auto-Pilot bekommt konkrete Car/Inspection-Bay Prompts\n- kein generischer Macro-Müll für Szene 2\n- Negative Prompts enthalten Anti-Text/Anti-License-Plate/Anti-Fake-UI-Regeln\n\n---\n\n## Generierter Prompt-Plan für Pilot\n\nDatei:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nEnthält jetzt für Szene 1:\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area...\n```\n\nUnd I2V Motion:\n\n```text\nCamera slowly pushes in from a front three-quarter angle...\nPreserve the same car and garage.\nNo text, no logo, no watermark.\n```\n\nSzene 3–5 sind korrekt als Renderer-only markiert:\n\n```text\nscene_03 mechanism_animation\nscene_04 mechanism_animation\nscene_05 kinetic_text_overlay\n```\n\nDas ist wichtig: Wan soll keine Zahlen, Labels oder Erklärgrafiken halluzinieren. Das macht unser Renderer.\n\n---\n\n## Validierung\n\nFull test suite:\n\n```text\n64 passed in 3.66s\n```\n\nCommit lokal erstellt:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\n---\n\n## GitHub Push\n\nPush ist fehlgeschlagen wegen fehlender Schreibrechte des vorhandenen Tokens:\n\n```text\nremote: Write access to repository not granted.\nfatal: ... 403\n```\n\nHeisst:\n\n```text\nCommit ist lokal vorhanden.\nRemote ist noch nicht aktualisiert.\n```\n\nIch habe keine Tokens ausgegeben oder gespeichert. Sehr unglamourös, aber sicher.\n\nDu brauchst entweder:\n\n- Token mit Schreibrecht für `Gamexgit/AutoShortsBot`, oder\n- lokal selbst pushen, oder\n- mir später einen korrekt berechtigten GitHub-Zugang bereitstellen.\n\n---\n\n## Untracked bewusst nicht angefasst\n\nWeiterhin untracked und ignoriert:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nAußerdem liegen noch ComfyUI-Testworkflow-JSONs untracked herum. Ich habe sie nicht committed, weil sie eher Experimente/Arbeitsartefakte sind, nicht saubere Projektlogik.\n\n---\n\n# Was ist jetzt der nächste sinnvolle Schritt?\n\nJetzt kommt die eigentliche Integration:\n\n```text\nI2V Prompt Plan\n→ Keyframe Image Input\n→ ComfyUI native Wan2.2 Client\n→ mehrere Seeds/Varianten\n→ Clip Review JSON\n```\n\nKonkret als nächstes würde ich bauen:\n\n```text\nautoshorts/comfyui_client.py\nautoshorts/i2v_clip_jobs.py\nscripts/run_i2v_clip_jobs.py\ntests/test_i2v_clip_jobs.py\n```\n\nZiel:\n\n```text\nFür Szene 1 automatisch 3 Wan2.2-I2V Varianten erzeugen\nund speichern unter:\ndata/generated_clips//scene_01/seed_001.mp4\n```\n\nDann bewerten wir nicht mehr manuell einzelne Dateien, sondern bekommen ein kleines Review-Paket:\n\n```text\nscene_01_seed_001.mp4\nscene_01_seed_002.mp4\nscene_01_seed_003.mp4\nclip_review.json\n```\n\nKurz: **Style und Prompt-Plan stehen. Als Nächstes automatisieren wir die lokale Wan2.2-I2V-Ausführung und Clip-Auswahl.**"
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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### 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. 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Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan22-native-i2v-a5000-quality.md` — native ComfyUI Wan2.2 5B quality workflow notes: official model/T5/VAE stack, I2V keyframe lessons, RAM/offload guidance, cleanup candidates, and the critical distinction between technical success and production usability.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n- `wan22-native-i2v-prompting.md` — native Wan2.2 I2V prompting and keyframe rules for quality-first local Retention Studio clips; covers scaled T5, negative prompts, A5000 ladders, and blunt review criteria.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed. For WanVideoWrapper,\\n `/object_info` is especially useful: inspect loader dropdowns to verify\\n files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n If it warns that it is deprecated, use `hf download --local-dir `\\n instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. 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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_i2v_clip_jobs.py\",\"content\":\"from pathlib import Path\\n\\nfrom autoshorts.i2v_clip_jobs import (\\n build_i2v_clip_jobs,\\n build_native_wan22_i2v_workflow,\\n write_i2v_clip_jobs,\\n)\\nfrom autoshorts.keyframe_prompt import build_i2v_prompt_plan\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\\n\\ndef _prompt_plan():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n shot_plan = build_mechanism_shot_plan(explainer)\\n return build_i2v_prompt_plan(shot_plan)\\n\\n\\ndef test_build_i2v_clip_jobs_creates_seeded_scene_jobs_with_expected_paths():\\n jobs = build_i2v_clip_jobs(\\n _prompt_plan(),\\n keyframe_root=Path(\\\"data/keyframes\\\"),\\n output_root=Path(\\\"data/generated_clips\\\"),\\n seeds=[101, 202],\\n )\\n\\n assert len(jobs) == 4\\n first = jobs[0]\\n assert first[\\\"job_id\\\"] == \\\"scene_01_seed_101\\\"\\n assert first[\\\"scene_id\\\"] == \\\"scene_01\\\"\\n assert first[\\\"seed\\\"] == 101\\n assert first[\\\"keyframe_path\\\"] == \\\"data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\\\"\\n assert first[\\\"output_path\\\"] == \\\"data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\\\"\\n assert first[\\\"status\\\"] == \\\"needs_keyframe\\\"\\n assert first[\\\"review\\\"][\\\"human_review_required\\\"] is True\\n\\n\\ndef test_native_wan22_workflow_uses_scaled_t5_uploaded_image_and_no_baked_text():\\n job = build_i2v_clip_jobs(_prompt_plan(), Path(\\\"keyframes\\\"), Path(\\\"clips\\\"), seeds=[303])[0]\\n\\n workflow = build_native_wan22_i2v_workflow(job, uploaded_image_name=\\\"scene_01.png\\\")\\n\\n assert workflow[\\\"1\\\"][\\\"class_type\\\"] == \\\"UNETLoader\\\"\\n assert workflow[\\\"1\\\"][\\\"inputs\\\"][\\\"unet_name\\\"] == \\\"wan2.2_ti2v_5B_fp16.safetensors\\\"\\n assert workflow[\\\"2\\\"][\\\"inputs\\\"][\\\"clip_name\\\"] == \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\"\\n assert workflow[\\\"5\\\"][\\\"inputs\\\"][\\\"vae_name\\\"] == \\\"wan2.2_vae.safetensors\\\"\\n assert workflow[\\\"6\\\"][\\\"inputs\\\"][\\\"image\\\"] == \\\"scene_01.png\\\"\\n assert workflow[\\\"8\\\"][\\\"inputs\\\"][\\\"length\\\"] == 49\\n assert workflow[\\\"9\\\"][\\\"inputs\\\"][\\\"seed\\\"] == 303\\n assert \\\"No text, no logo, no watermark\\\" in workflow[\\\"3\\\"][\\\"inputs\\\"][\\\"text\\\"]\\n assert \\\"fake UI text\\\" in workflow[\\\"4\\\"][\\\"inputs\\\"][\\\"text\\\"]\\n assert workflow[\\\"12\\\"][\\\"inputs\\\"][\\\"filename_prefix\\\"].endswith(\\\"scene_01_seed_303\\\")\\n\\n\\ndef test_write_i2v_clip_jobs_outputs_json_package(tmp_path):\\n path = write_i2v_clip_jobs(\\n _prompt_plan(),\\n output_file=tmp_path / \\\"jobs.json\\\",\\n keyframe_root=Path(\\\"data/keyframes\\\"),\\n generated_clip_root=Path(\\\"data/generated_clips\\\"),\\n seeds=[1, 2, 3],\\n )\\n\\n text = path.read_text(encoding=\\\"utf-8\\\")\\n assert \\\"i2v_clip_jobs.v1\\\" in text\\n assert \\\"scene_01_seed_1\\\" in text\\n assert \\\"scene_02_seed_3\\\" in text\\n assert \\\"keyframe_to_wan22_i2v_to_deterministic_renderer\\\" in text\\n\"}"
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"arguments": "{\"content\":\"from pathlib import Path\\n\\nfrom autoshorts.comfyui_client import ComfyUIClient\\n\\n\\nclass FakeTransport:\\n def __init__(self):\\n self.uploads = []\\n self.posts = []\\n self.downloads = []\\n self.prompt_id = \\\"prompt-123\\\"\\n\\n def upload_image(self, base_url, path, *, remote_name, overwrite=True):\\n self.uploads.append((base_url, str(path), remote_name, overwrite))\\n return {\\\"name\\\": remote_name, \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n def post_json(self, base_url, endpoint, payload):\\n self.posts.append((base_url, endpoint, payload))\\n if endpoint == \\\"/prompt\\\":\\n return {\\\"prompt_id\\\": self.prompt_id, \\\"node_errors\\\": {}}\\n return {}\\n\\n def wait_for_completion(self, base_url, prompt_id, *, poll_seconds, timeout_seconds):\\n assert prompt_id == self.prompt_id\\n return {\\n \\\"status\\\": {\\\"status_str\\\": \\\"success\\\", \\\"completed\\\": True},\\n \\\"outputs\\\": {\\n \\\"12\\\": {\\n \\\"images\\\": [\\n {\\n \\\"filename\\\": \\\"clip_00001_.mp4\\\",\\n \\\"subfolder\\\": \\\"\\\",\\n \\\"type\\\": \\\"output\\\",\\n }\\n ]\\n }\\n },\\n }\\n\\n def download_view(self, base_url, output, destination):\\n self.downloads.append((base_url, output, str(destination)))\\n destination.parent.mkdir(parents=True, exist_ok=True)\\n destination.write_bytes(b\\\"mp4\\\")\\n return destination\\n\\n\\ndef test_comfyui_client_runs_workflow_uploads_keyframe_and_downloads_output(tmp_path):\\n keyframe = tmp_path / \\\"scene_01.png\\\"\\n keyframe.write_bytes(b\\\"png\\\")\\n output = tmp_path / \\\"seed_001.mp4\\\"\\n transport = FakeTransport()\\n client = ComfyUIClient(\\\"http://comfy.local:8188\\\", transport=transport)\\n\\n result = client.run_i2v_workflow(\\n workflow={\\\"1\\\": {\\\"class_type\\\": \\\"Test\\\", \\\"inputs\\\": {}}},\\n keyframe_path=keyframe,\\n remote_image_name=\\\"scene_01.png\\\",\\n output_path=output,\\n poll_seconds=0,\\n timeout_seconds=10,\\n )\\n\\n assert result[\\\"status\\\"] == \\\"success\\\"\\n assert result[\\\"prompt_id\\\"] == \\\"prompt-123\\\"\\n assert result[\\\"output_path\\\"] == str(output)\\n assert output.read_bytes() == b\\\"mp4\\\"\\n assert transport.uploads == [(\\\"http://comfy.local:8188\\\", str(keyframe), \\\"scene_01.png\\\", True)]\\n assert transport.posts[0][1] == \\\"/prompt\\\"\\n assert transport.posts[0][2][\\\"client_id\\\"] == \\\"autoshorts-i2v\\\"\\n assert transport.downloads[0][1][\\\"filename\\\"] == \\\"clip_00001_.mp4\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_comfyui_client.py\"}"
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_i2v_clip_jobs.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_i2v_clip_jobs.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_i2v_clip_jobs.py:3: in \\n from autoshorts.i2v_clip_jobs import (\\nE ModuleNotFoundError: No module named 'autoshorts.i2v_clip_jobs'\\n________________ ERROR collecting tests/test_comfyui_client.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_comfyui_client.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_comfyui_client.py:3: in \\n from autoshorts.comfyui_client import ComfyUIClient\\nE ModuleNotFoundError: No module named 'autoshorts.comfyui_client'\\n=========================== short test summary info ============================\\nERROR tests/test_i2v_clip_jobs.py\\nERROR tests/test_comfyui_client.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.12s\", \"exit_code\": 2, \"error\": null}",
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"arguments": "{\"content\":\"\\\"\\\"\\\"Build executable Wan2.2 I2V clip jobs from prompt plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nfrom pathlib import Path\\nfrom typing import Any\\n\\n\\nNATIVE_WAN22_CLIENT_ID = \\\"autoshorts-i2v\\\"\\n\\n\\ndef _video_slug(prompt_plan: dict[str, Any]) -> str:\\n return prompt_plan[\\\"video\\\"][\\\"id\\\"]\\n\\n\\ndef _scene_keyframe_path(keyframe_root: Path, video_slug: str, scene_id: str) -> Path:\\n return keyframe_root / video_slug / f\\\"{scene_id}.png\\\"\\n\\n\\ndef _scene_output_path(output_root: Path, video_slug: str, scene_id: str, seed: int) -> Path:\\n return output_root / video_slug / scene_id / f\\\"seed_{seed}.mp4\\\"\\n\\n\\ndef build_i2v_clip_jobs(\\n prompt_plan: dict[str, Any],\\n keyframe_root: Path,\\n output_root: Path,\\n seeds: list[int],\\n) -> list[dict[str, Any]]:\\n \\\"\\\"\\\"Create seeded clip jobs for each Wan2.2 I2V scene.\\\"\\\"\\\"\\n video_slug = _video_slug(prompt_plan)\\n jobs: list[dict[str, Any]] = []\\n for scene in prompt_plan[\\\"scenes\\\"]:\\n for seed in seeds:\\n scene_id = scene[\\\"scene_id\\\"]\\n keyframe_path = _scene_keyframe_path(keyframe_root, video_slug, scene_id)\\n output_path = _scene_output_path(output_root, video_slug, scene_id, seed)\\n jobs.append(\\n {\\n \\\"schema_version\\\": \\\"i2v_clip_job.v1\\\",\\n \\\"job_id\\\": f\\\"{scene_id}_seed_{seed}\\\",\\n \\\"video_id\\\": video_slug,\\n \\\"scene_id\\\": scene_id,\\n \\\"purpose\\\": scene[\\\"purpose\\\"],\\n \\\"seed\\\": seed,\\n \\\"status\\\": \\\"ready\\\" if keyframe_path.exists() else \\\"needs_keyframe\\\",\\n \\\"keyframe_path\\\": str(keyframe_path),\\n \\\"output_path\\\": str(output_path),\\n \\\"remote_image_name\\\": f\\\"{video_slug}_{scene_id}.png\\\",\\n \\\"prompt_spec\\\": scene,\\n \\\"review\\\": {\\n \\\"human_review_required\\\": True,\\n \\\"criteria\\\": [\\n \\\"subject clear in first 0.5s\\\",\\n \\\"motion supports retention beat\\\",\\n \\\"no baked text/logos/watermarks\\\",\\n \\\"no distracting deformation/flicker\\\",\\n \\\"clean space remains for captions\\\",\\n ],\\n },\\n }\\n )\\n return jobs\\n\\n\\ndef _filename_prefix(job: dict[str, Any]) -> str:\\n return f\\\"autoshorts_{job['video_id']}_{job['scene_id']}_seed_{job['seed']}\\\"\\n\\n\\ndef build_native_wan22_i2v_workflow(job: dict[str, Any], uploaded_image_name: str) -> dict[str, Any]:\\n \\\"\\\"\\\"Build an API-format native ComfyUI Wan2.2 I2V workflow for a clip job.\\\"\\\"\\\"\\n spec = job[\\\"prompt_spec\\\"]\\n comfy = spec[\\\"comfyui\\\"]\\n width = int(comfy[\\\"resolution\\\"][\\\"width\\\"])\\n height = int(comfy[\\\"resolution\\\"][\\\"height\\\"])\\n return {\\n \\\"1\\\": {\\n \\\"class_type\\\": \\\"UNETLoader\\\",\\n \\\"inputs\\\": {\\\"unet_name\\\": comfy[\\\"model\\\"], \\\"weight_dtype\\\": \\\"default\\\"},\\n },\\n \\\"2\\\": {\\n \\\"class_type\\\": \\\"CLIPLoader\\\",\\n \\\"inputs\\\": {\\\"clip_name\\\": comfy[\\\"text_encoder\\\"], \\\"type\\\": \\\"wan\\\", \\\"device\\\": \\\"default\\\"},\\n },\\n \\\"3\\\": {\\n \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"i2v_motion_prompt\\\"]},\\n },\\n \\\"4\\\": {\\n \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"negative_prompt\\\"]},\\n },\\n \\\"5\\\": {\\\"class_type\\\": \\\"VAELoader\\\", \\\"inputs\\\": {\\\"vae_name\\\": comfy[\\\"vae\\\"]}},\\n \\\"6\\\": {\\\"class_type\\\": \\\"LoadImage\\\", \\\"inputs\\\": {\\\"image\\\": uploaded_image_name}},\\n \\\"7\\\": {\\n \\\"class_type\\\": \\\"ImageScale\\\",\\n \\\"inputs\\\": {\\n \\\"image\\\": [\\\"6\\\", 0],\\n \\\"upscale_method\\\": \\\"lanczos\\\",\\n \\\"width\\\": width,\\n \\\"height\\\": height,\\n \\\"crop\\\": \\\"disabled\\\",\\n },\\n },\\n \\\"8\\\": {\\n \\\"class_type\\\": \\\"Wan22ImageToVideoLatent\\\",\\n \\\"inputs\\\": {\\n \\\"vae\\\": [\\\"5\\\", 0],\\n \\\"width\\\": width,\\n \\\"height\\\": height,\\n \\\"length\\\": int(comfy[\\\"frames\\\"]),\\n \\\"batch_size\\\": 1,\\n \\\"start_image\\\": [\\\"7\\\", 0],\\n },\\n },\\n \\\"9\\\": {\\n \\\"class_type\\\": \\\"KSampler\\\",\\n \\\"inputs\\\": {\\n \\\"model\\\": [\\\"1\\\", 0],\\n \\\"seed\\\": int(job[\\\"seed\\\"]),\\n \\\"steps\\\": int(comfy[\\\"steps\\\"]),\\n \\\"cfg\\\": float(comfy[\\\"cfg\\\"]),\\n \\\"sampler_name\\\": \\\"uni_pc\\\",\\n \\\"scheduler\\\": \\\"simple\\\",\\n \\\"positive\\\": [\\\"3\\\", 0],\\n \\\"negative\\\": [\\\"4\\\", 0],\\n \\\"latent_image\\\": [\\\"8\\\", 0],\\n \\\"denoise\\\": 1.0,\\n },\\n },\\n \\\"10\\\": {\\\"class_type\\\": \\\"VAEDecode\\\", \\\"inputs\\\": {\\\"samples\\\": [\\\"9\\\", 0], \\\"vae\\\": [\\\"5\\\", 0]}},\\n \\\"11\\\": {\\\"class_type\\\": \\\"CreateVideo\\\", \\\"inputs\\\": {\\\"images\\\": [\\\"10\\\", 0], \\\"fps\\\": float(comfy[\\\"fps\\\"])}},\\n \\\"12\\\": {\\n \\\"class_type\\\": \\\"SaveVideo\\\",\\n \\\"inputs\\\": {\\n \\\"video\\\": [\\\"11\\\", 0],\\n \\\"filename_prefix\\\": _filename_prefix(job),\\n \\\"format\\\": \\\"mp4\\\",\\n \\\"codec\\\": \\\"h264\\\",\\n },\\n },\\n }\\n\\n\\ndef write_i2v_clip_jobs(\\n prompt_plan: dict[str, Any],\\n output_file: Path,\\n keyframe_root: Path,\\n generated_clip_root: Path,\\n seeds: list[int],\\n) -> Path:\\n \\\"\\\"\\\"Write a clip-job package JSON.\\\"\\\"\\\"\\n output_file.parent.mkdir(parents=True, exist_ok=True)\\n package = {\\n \\\"schema_version\\\": \\\"i2v_clip_jobs.v1\\\",\\n \\\"video\\\": prompt_plan[\\\"video\\\"],\\n \\\"strategy\\\": prompt_plan[\\\"strategy\\\"],\\n \\\"keyframe_root\\\": str(keyframe_root),\\n \\\"generated_clip_root\\\": str(generated_clip_root),\\n \\\"jobs\\\": build_i2v_clip_jobs(prompt_plan, keyframe_root, generated_clip_root, seeds),\\n }\\n output_file.write_text(json.dumps(package, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n return output_file\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/i2v_clip_jobs.py\"}"
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"arguments": "{\"content\":\"\\\"\\\"\\\"Small ComfyUI REST client used by AutoShortsBot clip jobs.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nimport time\\nimport urllib.parse\\nimport urllib.request\\nfrom pathlib import Path\\nfrom typing import Any\\n\\n\\nclass UrllibComfyUITransport:\\n \\\"\\\"\\\"HTTP transport for local/Tailscale ComfyUI instances.\\\"\\\"\\\"\\n\\n def upload_image(self, base_url: str, path: Path, *, remote_name: str, overwrite: bool = True) -> dict[str, Any]:\\n boundary = \\\"----AutoShortsBotBoundary\\\"\\n body: list[bytes] = []\\n\\n def add(part: str | bytes) -> None:\\n body.append(part.encode() if isinstance(part, str) else part)\\n\\n add(\\n f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"image\\\"; filename=\\\"{remote_name}\\\"\\\\r\\\\n'\\n \\\"Content-Type: image/png\\\\r\\\\n\\\\r\\\\n\\\"\\n )\\n add(path.read_bytes())\\n add(\\\"\\\\r\\\\n\\\")\\n add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"type\\\"\\\\r\\\\n\\\\r\\\\ninput\\\\r\\\\n')\\n add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"overwrite\\\"\\\\r\\\\n\\\\r\\\\n{str(overwrite).lower()}\\\\r\\\\n')\\n add(f\\\"--{boundary}--\\\\r\\\\n\\\")\\n request = urllib.request.Request(\\n f\\\"{base_url}/upload/image\\\",\\n data=b\\\"\\\".join(body),\\n headers={\\\"Content-Type\\\": f\\\"multipart/form-data; boundary={boundary}\\\"},\\n )\\n return json.loads(urllib.request.urlopen(request, timeout=120).read().decode())\\n\\n def post_json(self, base_url: str, endpoint: str, payload: dict[str, Any]) -> dict[str, Any]:\\n request = urllib.request.Request(\\n f\\\"{base_url}{endpoint}\\\",\\n data=json.dumps(payload).encode(),\\n headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n )\\n raw = urllib.request.urlopen(request, timeout=120).read().decode()\\n return json.loads(raw) if raw else {}\\n\\n def wait_for_completion(\\n self,\\n base_url: str,\\n prompt_id: str,\\n *,\\n poll_seconds: float,\\n timeout_seconds: int,\\n ) -> dict[str, Any]:\\n deadline = time.monotonic() + timeout_seconds\\n while time.monotonic() < deadline:\\n queue = json.loads(urllib.request.urlopen(f\\\"{base_url}/queue\\\", timeout=30).read().decode())\\n running = queue.get(\\\"queue_running\\\", [])\\n pending = queue.get(\\\"queue_pending\\\", [])\\n if not running and not pending:\\n history = json.loads(urllib.request.urlopen(f\\\"{base_url}/history/{prompt_id}\\\", timeout=60).read().decode())\\n return history.get(prompt_id, {})\\n time.sleep(poll_seconds)\\n raise TimeoutError(f\\\"ComfyUI prompt did not finish within {timeout_seconds}s: {prompt_id}\\\")\\n\\n def download_view(self, base_url: str, output: dict[str, Any], destination: Path) -> Path:\\n params = urllib.parse.urlencode(\\n {\\n \\\"filename\\\": output[\\\"filename\\\"],\\n \\\"subfolder\\\": output.get(\\\"subfolder\\\", \\\"\\\"),\\n \\\"type\\\": output.get(\\\"type\\\", \\\"output\\\"),\\n }\\n )\\n destination.parent.mkdir(parents=True, exist_ok=True)\\n destination.write_bytes(urllib.request.urlopen(f\\\"{base_url}/view?{params}\\\", timeout=300).read())\\n return destination\\n\\n\\nclass ComfyUIClient:\\n \\\"\\\"\\\"Minimal client for uploading keyframes, running workflows, and downloading MP4 outputs.\\\"\\\"\\\"\\n\\n def __init__(self, base_url: str, *, transport: Any | None = None):\\n self.base_url = base_url.rstrip(\\\"/\\\")\\n self.transport = transport or UrllibComfyUITransport()\\n\\n def run_i2v_workflow(\\n self,\\n *,\\n workflow: dict[str, Any],\\n keyframe_path: Path,\\n remote_image_name: str,\\n output_path: Path,\\n poll_seconds: float = 5,\\n timeout_seconds: int = 900,\\n ) -> dict[str, Any]:\\n upload = self.transport.upload_image(self.base_url, keyframe_path, remote_name=remote_image_name, overwrite=True)\\n prompt_response = self.transport.post_json(\\n self.base_url,\\n \\\"/prompt\\\",\\n {\\\"prompt\\\": workflow, \\\"client_id\\\": \\\"autoshorts-i2v\\\"},\\n )\\n prompt_id = prompt_response[\\\"prompt_id\\\"]\\n history = self.transport.wait_for_completion(\\n self.base_url,\\n prompt_id,\\n poll_seconds=poll_seconds,\\n timeout_seconds=timeout_seconds,\\n )\\n status = history.get(\\\"status\\\", {})\\n if status.get(\\\"status_str\\\") != \\\"success\\\":\\n return {\\\"status\\\": \\\"failed\\\", \\\"prompt_id\\\": prompt_id, \\\"history\\\": history, \\\"upload\\\": upload}\\n output = _first_video_output(history)\\n self.transport.download_view(self.base_url, output, output_path)\\n return {\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": prompt_id,\\n \\\"output_path\\\": str(output_path),\\n \\\"remote_output\\\": output,\\n \\\"upload\\\": upload,\\n }\\n\\n\\ndef _first_video_output(history: dict[str, Any]) -> dict[str, Any]:\\n for node_output in history.get(\\\"outputs\\\", {}).values():\\n for item in node_output.get(\\\"images\\\", []):\\n if str(item.get(\\\"filename\\\", \\\"\\\")).lower().endswith(\\\".mp4\\\"):\\n return item\\n raise ValueError(\\\"No MP4 output found in ComfyUI history\\\")\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/comfyui_client.py\"}"
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"arguments": "{\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Generate executable Wan2.2 I2V clip-job packages from I2V prompt plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.i2v_clip_jobs import write_i2v_clip_jobs\\n\\n\\nDEFAULT_PROMPT_PLAN = Path(\\n \\\"data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\\"\\n)\\n\\n\\ndef _parse_seeds(value: str) -> list[int]:\\n seeds = [int(part.strip()) for part in value.split(\\\",\\\") if part.strip()]\\n if not seeds:\\n raise argparse.ArgumentTypeError(\\\"Provide at least one seed\\\")\\n return seeds\\n\\n\\ndef main() -> int:\\n parser = argparse.ArgumentParser(description=__doc__)\\n parser.add_argument(\\\"--prompt-plan\\\", type=Path, default=DEFAULT_PROMPT_PLAN)\\n parser.add_argument(\\\"--output-file\\\", type=Path, default=Path(\\\"data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\\"))\\n parser.add_argument(\\\"--keyframe-root\\\", type=Path, default=Path(\\\"data/keyframes\\\"))\\n parser.add_argument(\\\"--generated-clip-root\\\", type=Path, default=Path(\\\"data/generated_clips\\\"))\\n parser.add_argument(\\\"--seeds\\\", type=_parse_seeds, default=[101, 202, 303])\\n args = parser.parse_args()\\n\\n prompt_plan = json.loads(args.prompt_plan.read_text(encoding=\\\"utf-8\\\"))\\n path = write_i2v_clip_jobs(\\n prompt_plan,\\n output_file=args.output_file,\\n keyframe_root=args.keyframe_root,\\n generated_clip_root=args.generated_clip_root,\\n seeds=args.seeds,\\n )\\n print(path)\\n return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_clip_jobs.py\"}"
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"content": "Weitergebaut, Sir.\n\n## Neu: I2V Clip Job Packaging\n\nIch habe jetzt die nächste Schicht der Produktionspipeline implementiert:\n\n```text\nI2V Prompt Plan\n→ seeded Clip Jobs\n→ native Wan2.2 Workflow JSON\n→ ComfyUI Client\n→ output path contract\n```\n\nDamit ist der Schritt vorbereitet, aus einem genehmigten Keyframe automatisch mehrere Wan2.2-I2V-Varianten pro Szene zu erzeugen.\n\n---\n\n## Neu implementiert\n\n### 1. ComfyUI Client\n\n```text\nautoshorts/comfyui_client.py\n```\n\nKann jetzt:\n\n- Keyframe zu ComfyUI hochladen\n- Workflow an `/prompt` senden\n- Queue/History pollen\n- MP4 via `/view` downloaden\n- Ergebnis strukturiert zurückgeben\n\nWichtig: Der Client ist testbar gebaut — mit austauschbarem Transport. Keine Netzwerkanfragen in Unit Tests. Man glaubt es kaum: Software, die nicht nur auf meinem Sofa funktioniert.\n\n---\n\n### 2. I2V Clip Job Builder\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt aus dem I2V Prompt Plan konkrete Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nJeder Job enthält:\n\n- Szene\n- Seed\n- Keyframe-Pfad\n- Output-Pfad\n- Remote Image Name\n- Prompt Spec\n- Review-Kriterien\n- native Wan2.2 Workflow-Konfiguration\n\n---\n\n### 3. Native Wan2.2 Workflow Builder\n\nEbenfalls in:\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt API-format ComfyUI Workflow mit:\n\n```text\nUNETLoader\nCLIPLoader\nCLIPTextEncode positive/negative\nVAELoader\nLoadImage\nImageScale\nWan22ImageToVideoLatent\nKSampler\nVAEDecode\nCreateVideo\nSaveVideo\n```\n\nVerwendet korrekt:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nsampler: uni_pc\nscheduler: simple\nframes: 49\nfps: 10\nsteps: 16\ncfg: 5.0\n```\n\n---\n\n### 4. CLI\n\n```text\nscripts/generate_i2v_clip_jobs.py\n```\n\nAusgeführt:\n\n```bash\npython scripts/generate_i2v_clip_jobs.py\n```\n\nErzeugt:\n\n```text\ndata/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n## Generiertes Job Package\n\nFür den Auto-Pilot gibt es jetzt 6 Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nBeispiel Output-Pfad:\n\n```text\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\n```\n\nAktueller Status im Job Package:\n\n```text\nneeds_keyframe\n```\n\nDas ist korrekt, weil wir noch kein finales Keyframe hier abgelegt haben:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\n---\n\n## Tests\n\nNeu:\n\n```text\ntests/test_comfyui_client.py\ntests/test_i2v_clip_jobs.py\n```\n\nFull suite:\n\n```text\n68 passed in 3.59s\n```\n\n---\n\n## Commits\n\nNeuer lokaler Commit:\n\n```text\nb57cee8 feat: add i2v clip job packaging\n```\n\nVorheriger lokaler Commit:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nPush habe ich nicht erneut versucht, weil der GitHub-Token zuletzt keine Schreibrechte hatte:\n\n```text\n403 Write access to repository not granted\n```\n\n---\n\n## Weiterhin bewusst nicht angefasst\n\nUntracked bleiben:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nUnd die ComfyUI-Testworkflow-JSONs aus unseren Experimenten:\n\n```text\ndata/comfyui_*.json\n```\n\nDie sind Arbeits-/Testartefakte, nicht Kernpipeline.\n\n---\n\n# Nächster Schritt\n\nJetzt fehlt der produktive **Keyframe-Schritt**.\n\nWir brauchen pro I2V-Szene ein Startbild:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\nFür Szene 1:\n\n```text\nred used compact car in bright European inspection garage\nfront three-quarter view\nclean empty license plate area\nno text/logos\ncaption space\n```\n\nDann kann die Pipeline automatisch:\n\n```text\nKeyframe upload\n→ Wan2.2 I2V Job seed 101/202/303\n→ MP4 download\n→ Review package\n```\n\n## Meine Empfehlung\n\nAls nächstes baue ich:\n\n```text\nautoshorts/keyframe_package.py\nscripts/prepare_keyframe_requests.py\n```\n\nZiel:\n\n- Prompt-Paket für ChatGPT Pro / Image Generator erzeugen\n- klare Anforderungen je Szene\n- Bilddatei-Zielpfad\n- Checkliste für Keyframe-Qualität\n- danach automatische Job-Status-Aktualisierung von `needs_keyframe` auf `ready`\n\nKurz: **Wir haben jetzt Jobs. Als Nächstes brauchen wir saubere Keyframes, dann können wir echte Clip-Varianten erzeugen.**",
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"text": "Weitergebaut, Sir.\n\n## Neu: I2V Clip Job Packaging\n\nIch habe jetzt die nächste Schicht der Produktionspipeline implementiert:\n\n```text\nI2V Prompt Plan\n→ seeded Clip Jobs\n→ native Wan2.2 Workflow JSON\n→ ComfyUI Client\n→ output path contract\n```\n\nDamit ist der Schritt vorbereitet, aus einem genehmigten Keyframe automatisch mehrere Wan2.2-I2V-Varianten pro Szene zu erzeugen.\n\n---\n\n## Neu implementiert\n\n### 1. ComfyUI Client\n\n```text\nautoshorts/comfyui_client.py\n```\n\nKann jetzt:\n\n- Keyframe zu ComfyUI hochladen\n- Workflow an `/prompt` senden\n- Queue/History pollen\n- MP4 via `/view` downloaden\n- Ergebnis strukturiert zurückgeben\n\nWichtig: Der Client ist testbar gebaut — mit austauschbarem Transport. Keine Netzwerkanfragen in Unit Tests. Man glaubt es kaum: Software, die nicht nur auf meinem Sofa funktioniert.\n\n---\n\n### 2. I2V Clip Job Builder\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt aus dem I2V Prompt Plan konkrete Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nJeder Job enthält:\n\n- Szene\n- Seed\n- Keyframe-Pfad\n- Output-Pfad\n- Remote Image Name\n- Prompt Spec\n- Review-Kriterien\n- native Wan2.2 Workflow-Konfiguration\n\n---\n\n### 3. Native Wan2.2 Workflow Builder\n\nEbenfalls in:\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt API-format ComfyUI Workflow mit:\n\n```text\nUNETLoader\nCLIPLoader\nCLIPTextEncode positive/negative\nVAELoader\nLoadImage\nImageScale\nWan22ImageToVideoLatent\nKSampler\nVAEDecode\nCreateVideo\nSaveVideo\n```\n\nVerwendet korrekt:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nsampler: uni_pc\nscheduler: simple\nframes: 49\nfps: 10\nsteps: 16\ncfg: 5.0\n```\n\n---\n\n### 4. CLI\n\n```text\nscripts/generate_i2v_clip_jobs.py\n```\n\nAusgeführt:\n\n```bash\npython scripts/generate_i2v_clip_jobs.py\n```\n\nErzeugt:\n\n```text\ndata/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n## Generiertes Job Package\n\nFür den Auto-Pilot gibt es jetzt 6 Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nBeispiel Output-Pfad:\n\n```text\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\n```\n\nAktueller Status im Job Package:\n\n```text\nneeds_keyframe\n```\n\nDas ist korrekt, weil wir noch kein finales Keyframe hier abgelegt haben:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\n---\n\n## Tests\n\nNeu:\n\n```text\ntests/test_comfyui_client.py\ntests/test_i2v_clip_jobs.py\n```\n\nFull suite:\n\n```text\n68 passed in 3.59s\n```\n\n---\n\n## Commits\n\nNeuer lokaler Commit:\n\n```text\nb57cee8 feat: add i2v clip job packaging\n```\n\nVorheriger lokaler Commit:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nPush habe ich nicht erneut versucht, weil der GitHub-Token zuletzt keine Schreibrechte hatte:\n\n```text\n403 Write access to repository not granted\n```\n\n---\n\n## Weiterhin bewusst nicht angefasst\n\nUntracked bleiben:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nUnd die ComfyUI-Testworkflow-JSONs aus unseren Experimenten:\n\n```text\ndata/comfyui_*.json\n```\n\nDie sind Arbeits-/Testartefakte, nicht Kernpipeline.\n\n---\n\n# Nächster Schritt\n\nJetzt fehlt der produktive **Keyframe-Schritt**.\n\nWir brauchen pro I2V-Szene ein Startbild:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\nFür Szene 1:\n\n```text\nred used compact car in bright European inspection garage\nfront three-quarter view\nclean empty license plate area\nno text/logos\ncaption space\n```\n\nDann kann die Pipeline automatisch:\n\n```text\nKeyframe upload\n→ Wan2.2 I2V Job seed 101/202/303\n→ MP4 download\n→ Review package\n```\n\n## Meine Empfehlung\n\nAls nächstes baue ich:\n\n```text\nautoshorts/keyframe_package.py\nscripts/prepare_keyframe_requests.py\n```\n\nZiel:\n\n- Prompt-Paket für ChatGPT Pro / Image Generator erzeugen\n- klare Anforderungen je Szene\n- Bilddatei-Zielpfad\n- Checkliste für Keyframe-Qualität\n- danach automatische Job-Status-Aktualisierung von `needs_keyframe` auf `ready`\n\nKurz: **Wir haben jetzt Jobs. Als Nächstes brauchen wir saubere Keyframes, dann können wir echte Clip-Varianten erzeugen.**"
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"content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"arguments": "{\"action\":\"patch\",\"name\":\"comfyui\",\"new_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Use Wan2.2 for photorealistic texture/motion only; add captions, numbers, labels, UI text, arrows, risk meters, and audit-sensitive facts later in a deterministic renderer. Generate multiple seeded variants per approved keyframe and select by review gates. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan22-i2v-retention-studio-pipeline.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\",\"old_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\"}"
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"arguments": "{\"action\":\"write_file\",\"file_content\":\"# Retention-Studio Pipeline Pattern\\n\\nSession-derived pattern for building automated Shorts systems that should produce mechanism-reveal content rather than generic AI-video slideshows.\\n\\n## Positioning\\n\\nFor “Hidden Money & AI Systems” style channels, prefer:\\n\\n```text\\nphotorealistic macro investigation + clean deterministic mechanism overlays\\n```\\n\\nAvoid making the AI-video model responsible for the whole short. The AI clip should supply realistic world texture and motion; the renderer should supply meaning.\\n\\n## Recommended production chain\\n\\n```text\\nstrategy\\n→ scored pilot ideas\\n→ mechanism explainer\\n→ mechanism shot plan\\n→ keyframe/I2V prompt plan\\n→ seeded AI clip jobs\\n→ clip review\\n→ deterministic final renderer\\n```\\n\\nThis keeps content strategy, visual intent, generation, review, and final truth-bearing overlays separate.\\n\\n## Quality gates\\n\\n- `static_card_limit = 0` for final content.\\n- Visual change every 1–2 seconds.\\n- Hook/pattern interrupt should resolve into a visible mechanism, not only a slogan.\\n- No baked AI-generated text, labels, charts, UI copy, license plates, or fake numbers.\\n- Captions, arrows, prices, margins, risks, timelines, and claims are renderer-owned.\\n- Generate multiple seeds per approved keyframe; choose by clip-review gates.\\n\\n## Shot planning guidance\\n\\nFor each scene, preserve:\\n\\n- retention purpose (`pattern_interrupt`, `problem`, `mechanism_reveal`, `twist`, `follow_trigger`)\\n- asset type (`cinematic_ai_video`, `mechanism_animation`, `screen_recording_simulation`, `kinetic_text_overlay`)\\n- visual direction\\n- motion cue\\n- sound cue\\n- caption-safe space\\n- review criteria\\n\\nUse AI video for cinematic scenes. Use deterministic animation/renderer logic for mechanism, labels, numbers, captions, and follow-trigger system maps.\\n\\n## Prompt-plan artifact guidance\\n\\nA useful I2V prompt-plan scene contains:\\n\\n- `keyframe_prompt`\\n- `i2v_motion_prompt`\\n- `negative_prompt`\\n- `wan_preset`\\n- `clip_requirements`\\n- `comfyui` settings\\n\\nA useful clip-job package expands each I2V scene into seed variants and paths:\\n\\n```text\\nscene_01_seed_101\\nscene_01_seed_202\\nscene_01_seed_303\\n```\\n\\nStart jobs as `needs_keyframe` until the expected keyframe file exists. This avoids wasting GPU time on missing or placeholder images.\\n\\n## Review stance\\n\\nBe blunt. A technically rendered clip is not automatically usable. Reject clips if they do not serve the retention beat, if the subject is unclear in the first half-second, if motion is mushy, or if artifacts distract from the explanation.\\n\",\"file_path\":\"references/retention-studio-pipeline-pattern.md\",\"name\":\"social-media-shorts-automation\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Workflow\",\"old_string\":\"## Workflow\"}"
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"content": "{\"success\": false, \"error\": \"Could not find a match for old_string in the file\\n\\nDid you mean one of these sections?\\n 14| Treat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n 15| \\n 16| ## Recommended workflow\\n 17| \\n 18| 1. **Define channel position**\\n---\\n 88| When AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n 89| \\n 90| ## Pitfalls\\n 91| \\n 92| - Do not over-optimize renderer quality before testing whether the content format earns retention.\\n---\\n 10| Use this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n 11| \\n 12| ## Core principle\\n 13| \\n 14| Treat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\", \"file_preview\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines...\"}",
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\",\"old_string\":\"## Recommended workflow\"}"
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"content": "Skill-Bibliothek aktualisiert.\n\n## Aktualisiert\n\n- `comfyui`\n - Neue Referenz:\n - `references/wan22-i2v-retention-studio-pipeline.md`\n - Ergänzt in `SKILL.md`:\n - Native Wan2.2 als **Clip-Generator**, nicht als kompletter Renderer.\n - Keyframe → Wan2.2 I2V → seeded variants → review → deterministic renderer.\n - Captions/Zahlen/Labels/Facts nicht in Wan generieren lassen.\n - Mehrere Seeds pro Keyframe und Review-Gates statt Einzelclip-Urteil.\n\n- `social-media-shorts-automation`\n - Neue Referenz:\n - `references/retention-studio-pipeline-pattern.md`\n - Ergänzt in `SKILL.md`:\n - Retention-Studio Pipeline:\n `strategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer`\n - `static_card_limit = 0`\n - AI-video liefert Bewegung/Textur; Renderer liefert Bedeutung, Zahlen, Labels, Captions und auditierbare Wahrheit.\n\nKeine Memory-Änderung nötig: Das waren prozedurale Workflow-Learnings, also korrekt in Skills statt User-Memory.",
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"text": "Skill-Bibliothek aktualisiert.\n\n## Aktualisiert\n\n- `comfyui`\n - Neue Referenz:\n - `references/wan22-i2v-retention-studio-pipeline.md`\n - Ergänzt in `SKILL.md`:\n - Native Wan2.2 als **Clip-Generator**, nicht als kompletter Renderer.\n - Keyframe → Wan2.2 I2V → seeded variants → review → deterministic renderer.\n - Captions/Zahlen/Labels/Facts nicht in Wan generieren lassen.\n - Mehrere Seeds pro Keyframe und Review-Gates statt Einzelclip-Urteil.\n\n- `social-media-shorts-automation`\n - Neue Referenz:\n - `references/retention-studio-pipeline-pattern.md`\n - Ergänzt in `SKILL.md`:\n - Retention-Studio Pipeline:\n `strategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer`\n - `static_card_limit = 0`\n - AI-video liefert Bewegung/Textur; Renderer liefert Bedeutung, Zahlen, Labels, Captions und auditierbare Wahrheit.\n\nKeine Memory-Änderung nötig: Das waren prozedurale Workflow-Learnings, also korrekt in Skills statt User-Memory."
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