{
"session_id": "20260525_101711_c3d5bf",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-25T08:22:13.702443",
"last_updated": "2026-05-25T12:30:04.771808",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [98% — 2,167/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [96% — 1,328/1,375 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/TrueTraceShorts: manual approval/posting; previews DE, finals EN. Positioning: hidden digital mechanisms, not generic AI tips; AI only ~30–40%. Format: visible fail→mechanism→fix→takeaway; captions use static phrase segments (≥3 words) where only the active spoken word highlights left-to-right.\n\nConversation started: Monday, May 25, 2026 12:27 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": 207,
"messages": [
{
"role": "user",
"content": "[M G] Die subtitles sind nicht genau gleichzeitig mit dem voiceover. Dies ist irritierend beim hören und mitlesen. Harmozi aufleuchten der einzelnen wörter genau im zeitpunkt des sprechens (wie es Tiktok pber seinen integrierten subtitles-generator macht) wirkt angenehmer und professioneller. Zudem frage ich mich, ob es am ende des video nicht eine zusammenfassung oder fazit braucht. Eine wirkliche lösung wird einem nicht angeboten. Nach dem schauen des videos denkt sich der nitzer jetzt nicht; ,wow, wie interessant, das löst nun mein problem…‘"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_5w0MiJx3l4GjdDPnCLLgYQ1w",
"call_id": "call_5w0MiJx3l4GjdDPnCLLgYQ1w",
"response_item_id": "fc_0ba9aa9428d34ee7016a13e87338488191ad724077c9f0b84a",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
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"content": "Sir, Sprint 1.6 ist umgesetzt — kontrolliert, ohne Wan-Render, ohne AI-Bildgenerierung, ohne Plattform-API und ohne Content-Massenproduktion. Der Bot ist jetzt bereit für eine bewusste Entscheidung: überarbeiten, Styleframe freigeben oder genau einen Finalrender freigeben.\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nAktueller git status:\n\n```text\n?? autoshorts/cli/demo_pilot_production_package_v2.py\n?? autoshorts/production/\n?? tests/test_pilot_production_v2.py\n```\n\nSprint-1.6 ist **noch nicht committed**.\n\n## Sprint-1.5 Commit\n\nSprint 1.5 wurde erfolgreich gesichert:\n\n```text\n4c57b52 Operationalize strategy spine v2 review workflow\n```\n\nVorheriger Sprint-1 Commit:\n\n```text\n658c170 Add strategy spine v2 quality gates and review package\n```\n\n## Geänderte Dateien Sprint 1.6\n\nNeu:\n\n```text\nautoshorts/production/resource_gate.py\nautoshorts/production/pilot_package_v2.py\nautoshorts/cli/demo_pilot_production_package_v2.py\ntests/test_pilot_production_v2.py\n```\n\n## Pytest Ergebnis\n\nFinal:\n\n```text\n197 passed in 32.78s\n```\n\n## Neue CLI\n\nNutzbar:\n\n```bash\npython -m autoshorts.cli.demo_pilot_production_package_v2\npython -m autoshorts.cli.demo_pilot_production_package_v2 --json\n```\n\nEigenschaften verifiziert:\n\n- maximal 1 Hauptpilot\n- maximal 1 Backup\n- 1 zurückgestellter Kandidat\n- kein Finalrender\n- keine Wan-Calls\n- keine AI-Bildgenerierung\n- keine Telegram-API\n- keine Plattform-API\n- `side_effects=[]`\n- `external_calls=[]`\n\n## Auswahl der 3 Kandidaten\n\nWie von Ihnen präferiert übernommen:\n\n### Hauptpilot\n\n`AI Output Autopsy / Meeting Summary Decision Target`\n\nCandidate ID:\n\n`ai-output-autopsy-decision-summary-38s`\n\nBegründung:\n\n- direkt anschlussfähig an TrueTrace-Strategie\n- starkes wiedererkennbares Alltagsproblem\n- niedriger Claim-Risk\n- klare Before/After-Struktur\n- gutes erstes echtes Video, weil es den Kanal erklärt, ohne wie ein generischer AI-Tipp zu wirken\n\n### Backup\n\n`Digital Red Flags / Fake Invoice`\n\nCandidate ID:\n\n`digital-red-flags-fake-invoice-35s`\n\n### Zurückgestellt\n\n`Workflow Teardown / Exception Path`\n\nCandidate ID:\n\n`workflow-teardown-exception-path-40s`\n\n## Hauptpilot Kurzfassung\n\n**Candidate ID:** \n`ai-output-autopsy-decision-summary-38s`\n\n**Version:** \n`v2.0.0`\n\n**Script Hash:** \n`72129542b86819fd`\n\n**Approval State:** \n`READY_FOR_REVIEW`\n\n**Hook:** \nYour AI summary is useless because it has no decision target.\n\n**Visible Fail:** \nThe summary says “great discussion” but misses the launch decision.\n\n**Anti Example:** \nA useless AI summary saying great discussion with no owner, blocker, or decision.\n\n**Mechanism:** \nWithout a decision target, AI compresses every sentence equally.\n\n**Fix:** \nAsk for blockers, owners, risks, and next steps tied to the decision.\n\n**Takeaway:** \nDon’t ask for a summary. Ask for decision evidence.\n\n## Finaler englischer Voiceover-Text\n\n```text\nYour AI summary is useless because it has no decision target.\n\nLook at the bad output: it sounds polished, but it missed the one thing the viewer needed.\n\nThe summary says “great discussion” but misses the launch decision.\n\nWithout a decision target, AI compresses every sentence equally.\n\nAsk for blockers, owners, risks, and next steps tied to the decision.\n\nBefore: generic summary. After: decision evidence with owner and risk.\n\nDon’t ask for a summary. Ask for decision evidence.\n```\n\n## Deutsches Review Summary\n\nEine scheinbar gute Meeting-Zusammenfassung verfehlt das eigentliche Entscheidungsziel. Der Fix ist, nicht nach einer generischen Zusammenfassung zu fragen, sondern nach **Decision Evidence**: Blocker, Owner, Risiken und Next Steps.\n\n## Score Breakdown\n\nGesamtscore:\n\n`86/100`\n\nBreakdown:\n\n- problem_urgency: `13/15`\n- first_frame_clarity: `10/12`\n- hook_curiosity: `10/12`\n- mechanism_value: `11/12`\n- practical_payoff: `13/15`\n- retention_path: `8/10`\n- share_save_potential: `9/10`\n- human_texture: `7/8`\n- series_fit: `4/4`\n- saturation_defense: `1/2`\n\nInterpretation:\n\nSolider bis sehr guter Pilot. Nicht künstlich auf 100 aufgeblasen. Kleine Abzüge wegen mittlerer Saturation und weil der visuelle Mechanismus noch im Render/ShotPlan beweisen muss, dass er wirklich stark wirkt.\n\n## Claim Gate Result\n\n```text\nvalid: true\nerrors: []\n```\n\nClaim:\n\n```text\nDecision-targeted summaries preserve relevance better than generic summaries.\n```\n\nRisk:\n\n`low`\n\nKeine externe Recherche oder API genutzt. Nur manuelle Claim-Validierung.\n\n## AI Disclosure Result\n\n```text\nvalid: true\nsynthetic_media_type: text_only_assistance\nreason: Script assistance only; no realistic synthetic person/event/voice generated at this stage.\n```\n\nKeine realistische Person, kein realistisches Event, keine realistische Stimme generiert.\n\n## Resource Gate Result\n\nFür deterministic preview:\n\n```text\nrequested_resource_tier: deterministic_preview\napproval_state: ready_for_review\nallowed: true\nreason: Deterministic preview allowed from READY_FOR_REVIEW onward.\n```\n\nWichtige Blocker sind implementiert:\n\n- `WAN_FINAL_RENDER` blockiert ohne `APPROVED_FOR_FINAL_RENDER`\n- `PUBLISHING` immer blockiert\n- falscher `script_hash` blockiert\n- falsche `candidate_id` / `version` blockiert\n- AI Disclosure unclear blockiert\n- Claim Gate invalid blockiert\n\n## Platform Fit\n\n- YouTube Shorts: strong\n- TikTok: strong\n- Instagram Reels: medium\n- LinkedIn Manual: manual B2B caption; check silent-autoplay readability\n\n## Social Copy\n\n**YouTube Title:** \nYour AI Meeting Summary Is Missing the Decision\n\n**YouTube Description:** \nA polished AI summary can still be useless if it has no decision target. Ask for blockers, owners, risks and next steps instead.\n\n**TikTok Caption:** \nYour AI summary sounds smart. But did it protect the decision?\n\n**Instagram Caption:** \nThe fix is not a better summary. It is a clearer decision target.\n\n**LinkedIn Caption:** \nMost meeting summaries fail because they protect no decision. Name the decision first, then ask for blockers, owners, risks and next steps.\n\n## Hashtags\n\n**YouTube Shorts:**\n\n```text\n#AIWorkflow #Productivity #MeetingNotes\n```\n\n**TikTok:**\n\n```text\n#AITips #WorkTok #ProductivityHack\n```\n\n**Instagram Reels:**\n\n```text\n#WorkflowDesign #Productivity #AIForWork\n```\n\n**LinkedIn Manual:**\n\n```text\n#AIProductivity #WorkflowDesign #DecisionMaking\n```\n\n## Caption Mode Empfehlung\n\n`word-by-word`\n\nBegründung:\n\n- Voiceover ist erklärend und präzise.\n- Zuschauer sollen gleichzeitig hören und mitlesen können.\n- Das aktive Wort-Highlighting ist hier wichtiger als dekorative Captions.\n- Phrase-by-phrase wäre günstiger, aber weniger professionell.\n- Keyword-pulse wäre für diesen Pilot zu grob.\n\n## TTS Plan\n\n- Sprache: Englisch\n- Voice Direction: clear, calm, precise, not hype\n- Pace: 145–155 WPM\n- Pausen:\n - kurze Pause nach Hook\n - kurze Pause vor Takeaway\n- Ziel: professionell, nicht Influencer-Marktschreier. Man glaubt es kaum, aber das hilft.\n\n## Subtitle Alignment Plan\n\n- Modus: `word-by-word`\n- Methode: forced alignment nach TTS-Render\n- Highlight Style: Hormozi/TikTok-style active word pulse\n- Quality Gate:\n - kein Caption-Segment darf merklich vom gesprochenen Wort abweichen\n - Text bleibt renderer-owned\n - keine Wan-eingebrannten Captions\n\n## ShotPlan Kurzfassung\n\n### s01_fail — 5s\n\nZiel: sofortiges Erkennen des Problems \nVisual: Split-screen Transcript vs. useless summary \nOverlay: Hook, red USELESS stamp, split-screen divider\n\n### s02_bad_output — 7s\n\nZiel: Anti-Beispiel zeigen \nVisual: polished bad summary without decision evidence \nOverlay: missing owner / blocker / decision labels\n\n### s03_mechanism — 9s\n\nZiel: Mechanismus zeigen \nVisual: notes compressed equally into generic summary \nOverlay: decision target node, equal compression arrows\n\n### s04_fix — 9s\n\nZiel: konkrete Regel zeigen \nVisual: route from messy notes to blockers / owners / risks / next steps \nOverlay: renderer-owned labels\n\n### s05_takeaway — 8s\n\nZiel: merkbarer Abschluss \nVisual: Before/After contrast \nOverlay: “Don’t ask for a summary. Ask for decision evidence.”\n\n## RenderManifest Draft\n\n```text\nfinal_render_enabled: false\nwan_calls_enabled: false\nai_image_generation_enabled: false\nrenderer_owned_text: true\ncaption_mode: word-by-word\ntarget_duration_seconds: 38\noutput: deterministic preview/timeline only\n```\n\n## Wan 2.2 Prompt Plan Kurzfassung\n\nKeine Wan-Generierung wurde ausgeführt.\n\n### s01_fail\n\nVisual Goal: tension between messy transcript and useless summary \nPrompt: vertical abstract office desk, messy transcript blocks vs polished suspicious summary card, no readable text \nCamera: slow push-in \nRenderer-owned overlays: hook caption, USELESS stamp, divider\n\n### s02_bad_output\n\nVisual Goal: plausible but useless AI output \nPrompt: glowing generic document card, blurred lines, hollow polished feel, no readable text \nCamera: micro dolly \nRenderer-owned overlays: bad summary callouts, missing owner/blocker/decision labels\n\n### s03_mechanism\n\nVisual Goal: equal compression mechanism \nPrompt: meeting note fragments flowing into compressed box, ignored decision node, no text \nCamera: top-down slight orbit \nRenderer-owned overlays: decision target node, compression arrows\n\n### s04_fix\n\nVisual Goal: structured decision-evidence route \nPrompt: workflow route with four empty nodes, no readable labels \nCamera: smooth track \nRenderer-owned overlays: blockers, owners, risks, next steps\n\n### s05_takeaway\n\nVisual Goal: before/after and rule \nPrompt: cloudy generic summary dissolves into clear decision route, no embedded text \nCamera: slow pullback \nRenderer-owned overlays: before, after, final takeaway\n\n## Negative Prompts\n\n```text\nreadable text\nlogos\nbrand names\ncaptions\nnumbers\nwatermarks\nUI labels\nembedded facts\n```\n\nWan soll also keine Texte, Labels, Fakten, Logos oder Captions erzeugen. Alles bleibt renderer-owned.\n\n## Asset List\n\n- `tts_voiceover`\n - status: planned\n - owner: deterministic/local until approved\n\n- `subtitle_alignment`\n - status: planned\n - owner: renderer\n\n- `overlay_labels`\n - status: planned\n - owner: renderer\n\n- `wan_background_motion`\n - status: blocked_until_final_render_approval\n - owner: Wan 2.2\n\n## Estimated Render Cost/Time Class\n\n`medium`\n\n## Approval Commands\n\n### Styleframe freigeben\n\n```text\nAPPROVE_STYLEFRAME ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\n### Finalrender freigeben\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\n### Revision anfordern\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\n### Ablehnen\n\n```text\nREJECT ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\n## Offene Punkte\n\n- Sprint-1.6 Änderungen sind noch nicht committed.\n- Es wurde nur ein deterministic text/timeline preview package erstellt, kein Video.\n- Noch kein TTS-Audio erzeugt.\n- Noch kein forced alignment durchgeführt.\n- Noch kein Styleframe generiert.\n- Noch kein Wan-2.2-Render.\n- Keine Plattform-API.\n- Kein Posting.\n- Kein Dashboard.\n\n## Nächster sinnvoller Schritt\n\nSie entscheiden jetzt bewusst eine der vier Optionen:\n\n1. **Revision:** \n `REVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"`\n\n2. **Styleframe freigeben:** \n `APPROVE_STYLEFRAME ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd`\n\n3. **Finalrender freigeben:** \n `APPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd`\n\n4. **Ablehnen:** \n `REJECT ai-output-autopsy-decision-summary-38s \"Grund\"`",
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"text": "Sir, Sprint 1.6 ist umgesetzt — kontrolliert, ohne Wan-Render, ohne AI-Bildgenerierung, ohne Plattform-API und ohne Content-Massenproduktion. Der Bot ist jetzt bereit für eine bewusste Entscheidung: überarbeiten, Styleframe freigeben oder genau einen Finalrender freigeben.\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nAktueller git status:\n\n```text\n?? autoshorts/cli/demo_pilot_production_package_v2.py\n?? autoshorts/production/\n?? tests/test_pilot_production_v2.py\n```\n\nSprint-1.6 ist **noch nicht committed**.\n\n## Sprint-1.5 Commit\n\nSprint 1.5 wurde erfolgreich gesichert:\n\n```text\n4c57b52 Operationalize strategy spine v2 review workflow\n```\n\nVorheriger Sprint-1 Commit:\n\n```text\n658c170 Add strategy spine v2 quality gates and review package\n```\n\n## Geänderte Dateien Sprint 1.6\n\nNeu:\n\n```text\nautoshorts/production/resource_gate.py\nautoshorts/production/pilot_package_v2.py\nautoshorts/cli/demo_pilot_production_package_v2.py\ntests/test_pilot_production_v2.py\n```\n\n## Pytest Ergebnis\n\nFinal:\n\n```text\n197 passed in 32.78s\n```\n\n## Neue CLI\n\nNutzbar:\n\n```bash\npython -m autoshorts.cli.demo_pilot_production_package_v2\npython -m autoshorts.cli.demo_pilot_production_package_v2 --json\n```\n\nEigenschaften verifiziert:\n\n- maximal 1 Hauptpilot\n- maximal 1 Backup\n- 1 zurückgestellter Kandidat\n- kein Finalrender\n- keine Wan-Calls\n- keine AI-Bildgenerierung\n- keine Telegram-API\n- keine Plattform-API\n- `side_effects=[]`\n- `external_calls=[]`\n\n## Auswahl der 3 Kandidaten\n\nWie von Ihnen präferiert übernommen:\n\n### Hauptpilot\n\n`AI Output Autopsy / Meeting Summary Decision Target`\n\nCandidate ID:\n\n`ai-output-autopsy-decision-summary-38s`\n\nBegründung:\n\n- direkt anschlussfähig an TrueTrace-Strategie\n- starkes wiedererkennbares Alltagsproblem\n- niedriger Claim-Risk\n- klare Before/After-Struktur\n- gutes erstes echtes Video, weil es den Kanal erklärt, ohne wie ein generischer AI-Tipp zu wirken\n\n### Backup\n\n`Digital Red Flags / Fake Invoice`\n\nCandidate ID:\n\n`digital-red-flags-fake-invoice-35s`\n\n### Zurückgestellt\n\n`Workflow Teardown / Exception Path`\n\nCandidate ID:\n\n`workflow-teardown-exception-path-40s`\n\n## Hauptpilot Kurzfassung\n\n**Candidate ID:** \n`ai-output-autopsy-decision-summary-38s`\n\n**Version:** \n`v2.0.0`\n\n**Script Hash:** \n`72129542b86819fd`\n\n**Approval State:** \n`READY_FOR_REVIEW`\n\n**Hook:** \nYour AI summary is useless because it has no decision target.\n\n**Visible Fail:** \nThe summary says “great discussion” but misses the launch decision.\n\n**Anti Example:** \nA useless AI summary saying great discussion with no owner, blocker, or decision.\n\n**Mechanism:** \nWithout a decision target, AI compresses every sentence equally.\n\n**Fix:** \nAsk for blockers, owners, risks, and next steps tied to the decision.\n\n**Takeaway:** \nDon’t ask for a summary. Ask for decision evidence.\n\n## Finaler englischer Voiceover-Text\n\n```text\nYour AI summary is useless because it has no decision target.\n\nLook at the bad output: it sounds polished, but it missed the one thing the viewer needed.\n\nThe summary says “great discussion” but misses the launch decision.\n\nWithout a decision target, AI compresses every sentence equally.\n\nAsk for blockers, owners, risks, and next steps tied to the decision.\n\nBefore: generic summary. After: decision evidence with owner and risk.\n\nDon’t ask for a summary. Ask for decision evidence.\n```\n\n## Deutsches Review Summary\n\nEine scheinbar gute Meeting-Zusammenfassung verfehlt das eigentliche Entscheidungsziel. Der Fix ist, nicht nach einer generischen Zusammenfassung zu fragen, sondern nach **Decision Evidence**: Blocker, Owner, Risiken und Next Steps.\n\n## Score Breakdown\n\nGesamtscore:\n\n`86/100`\n\nBreakdown:\n\n- problem_urgency: `13/15`\n- first_frame_clarity: `10/12`\n- hook_curiosity: `10/12`\n- mechanism_value: `11/12`\n- practical_payoff: `13/15`\n- retention_path: `8/10`\n- share_save_potential: `9/10`\n- human_texture: `7/8`\n- series_fit: `4/4`\n- saturation_defense: `1/2`\n\nInterpretation:\n\nSolider bis sehr guter Pilot. Nicht künstlich auf 100 aufgeblasen. Kleine Abzüge wegen mittlerer Saturation und weil der visuelle Mechanismus noch im Render/ShotPlan beweisen muss, dass er wirklich stark wirkt.\n\n## Claim Gate Result\n\n```text\nvalid: true\nerrors: []\n```\n\nClaim:\n\n```text\nDecision-targeted summaries preserve relevance better than generic summaries.\n```\n\nRisk:\n\n`low`\n\nKeine externe Recherche oder API genutzt. Nur manuelle Claim-Validierung.\n\n## AI Disclosure Result\n\n```text\nvalid: true\nsynthetic_media_type: text_only_assistance\nreason: Script assistance only; no realistic synthetic person/event/voice generated at this stage.\n```\n\nKeine realistische Person, kein realistisches Event, keine realistische Stimme generiert.\n\n## Resource Gate Result\n\nFür deterministic preview:\n\n```text\nrequested_resource_tier: deterministic_preview\napproval_state: ready_for_review\nallowed: true\nreason: Deterministic preview allowed from READY_FOR_REVIEW onward.\n```\n\nWichtige Blocker sind implementiert:\n\n- `WAN_FINAL_RENDER` blockiert ohne `APPROVED_FOR_FINAL_RENDER`\n- `PUBLISHING` immer blockiert\n- falscher `script_hash` blockiert\n- falsche `candidate_id` / `version` blockiert\n- AI Disclosure unclear blockiert\n- Claim Gate invalid blockiert\n\n## Platform Fit\n\n- YouTube Shorts: strong\n- TikTok: strong\n- Instagram Reels: medium\n- LinkedIn Manual: manual B2B caption; check silent-autoplay readability\n\n## Social Copy\n\n**YouTube Title:** \nYour AI Meeting Summary Is Missing the Decision\n\n**YouTube Description:** \nA polished AI summary can still be useless if it has no decision target. Ask for blockers, owners, risks and next steps instead.\n\n**TikTok Caption:** \nYour AI summary sounds smart. But did it protect the decision?\n\n**Instagram Caption:** \nThe fix is not a better summary. It is a clearer decision target.\n\n**LinkedIn Caption:** \nMost meeting summaries fail because they protect no decision. Name the decision first, then ask for blockers, owners, risks and next steps.\n\n## Hashtags\n\n**YouTube Shorts:**\n\n```text\n#AIWorkflow #Productivity #MeetingNotes\n```\n\n**TikTok:**\n\n```text\n#AITips #WorkTok #ProductivityHack\n```\n\n**Instagram Reels:**\n\n```text\n#WorkflowDesign #Productivity #AIForWork\n```\n\n**LinkedIn Manual:**\n\n```text\n#AIProductivity #WorkflowDesign #DecisionMaking\n```\n\n## Caption Mode Empfehlung\n\n`word-by-word`\n\nBegründung:\n\n- Voiceover ist erklärend und präzise.\n- Zuschauer sollen gleichzeitig hören und mitlesen können.\n- Das aktive Wort-Highlighting ist hier wichtiger als dekorative Captions.\n- Phrase-by-phrase wäre günstiger, aber weniger professionell.\n- Keyword-pulse wäre für diesen Pilot zu grob.\n\n## TTS Plan\n\n- Sprache: Englisch\n- Voice Direction: clear, calm, precise, not hype\n- Pace: 145–155 WPM\n- Pausen:\n - kurze Pause nach Hook\n - kurze Pause vor Takeaway\n- Ziel: professionell, nicht Influencer-Marktschreier. Man glaubt es kaum, aber das hilft.\n\n## Subtitle Alignment Plan\n\n- Modus: `word-by-word`\n- Methode: forced alignment nach TTS-Render\n- Highlight Style: Hormozi/TikTok-style active word pulse\n- Quality Gate:\n - kein Caption-Segment darf merklich vom gesprochenen Wort abweichen\n - Text bleibt renderer-owned\n - keine Wan-eingebrannten Captions\n\n## ShotPlan Kurzfassung\n\n### s01_fail — 5s\n\nZiel: sofortiges Erkennen des Problems \nVisual: Split-screen Transcript vs. useless summary \nOverlay: Hook, red USELESS stamp, split-screen divider\n\n### s02_bad_output — 7s\n\nZiel: Anti-Beispiel zeigen \nVisual: polished bad summary without decision evidence \nOverlay: missing owner / blocker / decision labels\n\n### s03_mechanism — 9s\n\nZiel: Mechanismus zeigen \nVisual: notes compressed equally into generic summary \nOverlay: decision target node, equal compression arrows\n\n### s04_fix — 9s\n\nZiel: konkrete Regel zeigen \nVisual: route from messy notes to blockers / owners / risks / next steps \nOverlay: renderer-owned labels\n\n### s05_takeaway — 8s\n\nZiel: merkbarer Abschluss \nVisual: Before/After contrast \nOverlay: “Don’t ask for a summary. Ask for decision evidence.”\n\n## RenderManifest Draft\n\n```text\nfinal_render_enabled: false\nwan_calls_enabled: false\nai_image_generation_enabled: false\nrenderer_owned_text: true\ncaption_mode: word-by-word\ntarget_duration_seconds: 38\noutput: deterministic preview/timeline only\n```\n\n## Wan 2.2 Prompt Plan Kurzfassung\n\nKeine Wan-Generierung wurde ausgeführt.\n\n### s01_fail\n\nVisual Goal: tension between messy transcript and useless summary \nPrompt: vertical abstract office desk, messy transcript blocks vs polished suspicious summary card, no readable text \nCamera: slow push-in \nRenderer-owned overlays: hook caption, USELESS stamp, divider\n\n### s02_bad_output\n\nVisual Goal: plausible but useless AI output \nPrompt: glowing generic document card, blurred lines, hollow polished feel, no readable text \nCamera: micro dolly \nRenderer-owned overlays: bad summary callouts, missing owner/blocker/decision labels\n\n### s03_mechanism\n\nVisual Goal: equal compression mechanism \nPrompt: meeting note fragments flowing into compressed box, ignored decision node, no text \nCamera: top-down slight orbit \nRenderer-owned overlays: decision target node, compression arrows\n\n### s04_fix\n\nVisual Goal: structured decision-evidence route \nPrompt: workflow route with four empty nodes, no readable labels \nCamera: smooth track \nRenderer-owned overlays: blockers, owners, risks, next steps\n\n### s05_takeaway\n\nVisual Goal: before/after and rule \nPrompt: cloudy generic summary dissolves into clear decision route, no embedded text \nCamera: slow pullback \nRenderer-owned overlays: before, after, final takeaway\n\n## Negative Prompts\n\n```text\nreadable text\nlogos\nbrand names\ncaptions\nnumbers\nwatermarks\nUI labels\nembedded facts\n```\n\nWan soll also keine Texte, Labels, Fakten, Logos oder Captions erzeugen. Alles bleibt renderer-owned.\n\n## Asset List\n\n- `tts_voiceover`\n - status: planned\n - owner: deterministic/local until approved\n\n- `subtitle_alignment`\n - status: planned\n - owner: renderer\n\n- `overlay_labels`\n - status: planned\n - owner: renderer\n\n- `wan_background_motion`\n - status: blocked_until_final_render_approval\n - owner: Wan 2.2\n\n## Estimated Render Cost/Time Class\n\n`medium`\n\n## Approval Commands\n\n### Styleframe freigeben\n\n```text\nAPPROVE_STYLEFRAME ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\n### Finalrender freigeben\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\n### Revision anfordern\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\n### Ablehnen\n\n```text\nREJECT ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\n## Offene Punkte\n\n- Sprint-1.6 Änderungen sind noch nicht committed.\n- Es wurde nur ein deterministic text/timeline preview package erstellt, kein Video.\n- Noch kein TTS-Audio erzeugt.\n- Noch kein forced alignment durchgeführt.\n- Noch kein Styleframe generiert.\n- Noch kein Wan-2.2-Render.\n- Keine Plattform-API.\n- Kein Posting.\n- Kein Dashboard.\n\n## Nächster sinnvoller Schritt\n\nSie entscheiden jetzt bewusst eine der vier Optionen:\n\n1. **Revision:** \n `REVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"`\n\n2. **Styleframe freigeben:** \n `APPROVE_STYLEFRAME ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd`\n\n3. **Finalrender freigeben:** \n `APPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd`\n\n4. **Ablehnen:** \n `REJECT ai-output-autopsy-decision-summary-38s \"Grund\"`"
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"content": "[M G] JARVIS, sehr gut. Wir gehen jetzt zum ersten echten Video über, aber nicht direkt zum Wan-2.2-Finalrender.\n\nBitte führe jetzt Sprint 1.7 aus:\n\nPilot Preview Render V2 — First Telegram Approval Video\n\nZiel:\nErstelle für den Hauptpilot ein echtes Preview-/Konzeptvideo, das ich per Telegram prüfen kann. Es soll Voiceover, Timing, Captions, Szenenfolge, Overlays und grobe Visuals zeigen. Es ist noch NICHT das finale Wan-2.2-Video.\n\nFreigabe für Preview:\n\nAPPROVED_FOR_PREVIEW_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n\nWichtig:\nDiese Freigabe erlaubt nur den Preview-Render, nicht den Finalrender.\n\n1. Sprint 1.6 sichern\n\n* git status prüfen\n* pytest ausführen\n* Sprint-1.6-Dateien committen, sofern sauber\n\nCommit Message:\nAdd pilot production package and resource gates\n\nWenn Tests fehlschlagen:\nnicht committen, sondern Status melden.\n\n2. Preview-Render-Gate ergänzen\n\nBitte ergänze oder nutze einen neuen Approval State:\n\nAPPROVED_FOR_PREVIEW_RENDER\n\nRegeln:\n\n* TTS-Erzeugung erlaubt.\n* Subtitle Alignment erlaubt.\n* deterministische Preview/Animatic erlaubt.\n* Telegram-Versand des Preview-Videos erlaubt, falls Telegram bereits eingerichtet ist.\n* Wan-2.2-Finalrender bleibt blockiert.\n* AI-Bildgenerierung bleibt blockiert, ausser separat mit APPROVE_STYLEFRAME freigegeben.\n* Plattform-API bleibt blockiert.\n* Auto-Posting bleibt blockiert.\n\n3. Erstelle ein echtes Preview-/Konzeptvideo\n\nFür Candidate:\n\nai-output-autopsy-decision-summary-38s\nVersion: v2.0.0\nScript Hash: 72129542b86819fd\n\nPreview-Anforderungen:\n\n* Format: 9:16\n* Auflösung: gern 540x960 oder 720x1280, nicht final nötig\n* Dauer: ca. 35–40 Sekunden\n* Voiceover: englisch\n* TTS: klar, ruhig, professionell, nicht hype\n* Captions: word-by-word oder bestmöglich synchronisiert\n* Overlays: renderer-owned\n* Visuals: deterministische Cards / Motion / Placeholder Visuals\n* sichtbarer Hinweis: PREVIEW / NOT FINAL\n* keine eingebrannten Wan-Texte\n* keine finalen AI-Backgrounds nötig\n\nZiel des Preview-Videos:\nIch muss prüfen können:\n\n* Hook funktioniert?\n* Tempo passt?\n* Voiceover klingt gut?\n* Untertitel sind lesbar?\n* Szenenwechsel sind verständlich?\n* Mechanismus wird sichtbar?\n* Takeaway bleibt hängen?\n* Video wirkt nicht wie AI-Slop?\n\n4. TTS und Caption Alignment\n\nBitte erstelle für das Preview:\n\n* TTS Audio\n* gemessene Audiodauer\n* Subtitle Cues\n* wenn forced alignment verfügbar: nutzen\n* wenn forced alignment noch nicht stabil verfügbar: bestmögliche zeitbasierte Word-/Phrase-Sync-Preview erstellen und als Einschränkung markieren\n\nQuality Gate:\nWenn Captions massiv asynchron sind, Preview nicht als ready markieren.\n\n5. Preview-Szenen\n\nNutze den bestehenden ShotPlan:\n\ns01_fail — ca. 5s\nSplit-screen Transcript vs. useless summary\n\ns02_bad_output — ca. 7s\nPolished bad summary without decision evidence\n\ns03_mechanism — ca. 9s\nNotes compressed equally into generic summary\n\ns04_fix — ca. 9s\nRoute from messy notes to blockers / owners / risks / next steps\n\ns05_takeaway — ca. 8s\nBefore/After contrast and final takeaway\n\nWichtig:\nTexte, Labels, Captions, Pfeile, USELESS-Stempel und Takeaway bleiben renderer-owned.\n\n6. Telegram Delivery\n\nWenn Telegram-Versand bereits eingerichtet und sicher ist:\nSende mir das Preview-MP4 per Telegram mit Kurzreview.\n\nWenn Telegram-Versand nicht aktiv ist:\nErstelle stattdessen:\n\n* lokalen MP4-Pfad\n* SHA/Checksum\n* Telegram Delivery Plan\n* Review Text\n\nNicht blockieren, nur transparent berichten.\n\n7. Kein Finalrender\n\nBitte ausdrücklich NICHT ausführen:\n\n* kein Wan-2.2-Finalrender\n* keine Wan-Calls\n* keine AI-Bildgenerierung\n* keine Plattform-API\n* kein Auto-Posting\n* kein Manual Posting Pack als final freigegeben\n\n8. Output am Ende\n\nBitte berichte kurz:\n\n* git status\n* Commit Hash von Sprint 1.6\n* pytest Ergebnis\n* Preview Approval State\n* erzeugte Dateien\n* MP4-Pfad\n* Audio-Pfad\n* Subtitle-/ASS-/SRT-Pfad, falls vorhanden\n* Audiodauer\n* Preview-Dauer\n* bekannte Einschränkungen\n* ob Telegram-Versand erfolgreich war\n* nächstmögliche Review Commands\n\n9. Danach mögliche Kommandos\n\nWenn ich überarbeiten will:\n\nREVISE\nai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n\nWenn das Preview passt und genau ein finaler Wan-2.2-Render erlaubt ist:\n\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n\nWenn ich ablehne:\n\nREJECT ai-output-autopsy-decision-summary-38s \"Grund\"\n\nWichtigster Grundsatz:\nJetzt ein echtes Preview-Video erstellen, aber Finalrender bleibt gesperrt, bis ich es nach Sichtung des Previews ausdrücklich freigebe."
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"summary": [
{
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},
{
"type": "summary_text",
"text": "**Creating active subtitles**\n\nI'm thinking of using simpler active words, with large yellow text at the bottom and a white contextual phrase above it. I’d like to create a separate subtitle file for each word using ASS overrides for better timing. Time-based alignment seems more effective than forced alignment, so we can distribute words based on segment durations from the voiceover. I’ll need to get actual word timings and might consider using an edge subtitle file that’s sentence-based. I'll inspect the subtitles for this."
}
]
}
]
},
{
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{
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{
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"content": "{\"status\": \"success\", \"output\": \"\\nexit 0\\nfiles ['edge_subtitles.srt', 'voiceover.mp3', 'voiceover.txt']\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 5.08}",
"tool_call_id": "call_uTvoCWtbIPXUYHZSu27cTZsY"
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"text": "**Considering visual verification**\n\nI'm thinking I need to verify something visually. I might use a vision analysis tool or the terminal with ffprobe. It seems like taking a screenshot could be useful too, possibly using ffmpeg to extract a frame. So, I’ll have to run a few commands in the terminal to get that screenshot, then run the vision analysis on it. This way, I can check exactly what I need!"
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"content": "[terminal] ran `OUT=/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s; ffmpeg...` -> exit 0, 1 lines output",
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"reasoning": "**Evaluating Git and Telegram details**\n\nI think I need to check the Git status and confirm the commit hash from sprint 1.6, which is b1766a4. I'm considering if the Telegram delivery was successful; there seems to be a German phrase mentioning success. If media is included after the final delivery, I might say \"ich hänge es dieser Antwort an,\" but I'm not entirely sure. I also want to make sure to include the MEDIA line and list the file sizes, especially if Telegram can handle local media paths.",
"reasoning_content": "**Evaluating Git and Telegram details**\n\nI think I need to check the Git status and confirm the commit hash from sprint 1.6, which is b1766a4. I'm considering if the Telegram delivery was successful; there seems to be a German phrase mentioning success. If media is included after the final delivery, I might say \"ich hänge es dieser Antwort an,\" but I'm not entirely sure. I also want to make sure to include the MEDIA line and list the file sizes, especially if Telegram can handle local media paths.",
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"text": "**Evaluating Git and Telegram details**\n\nI think I need to check the Git status and confirm the commit hash from sprint 1.6, which is b1766a4. I'm considering if the Telegram delivery was successful; there seems to be a German phrase mentioning success. If media is included after the final delivery, I might say \"ich hänge es dieser Antwort an,\" but I'm not entirely sure. I also want to make sure to include the MEDIA line and list the file sizes, especially if Telegram can handle local media paths."
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"content": "[terminal] ran `OUT=/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s; du -h ...` -> exit 0, 1 lines output",
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"content": "Sir, Sprint 1.7 ist ausgeführt. Das erste echte **Preview-/Konzeptvideo** wurde erstellt — mit TTS, Timing, Captions, Szenenfolge, Overlays und deterministischen Placeholder-Visuals.\n\nKein Wan-2.2-Render. Keine AI-Bildgenerierung. Keine Plattform-API. Kein Auto-Posting. Der zivilisierte Weg, bevor wir die GPU-Kanone laden.\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nSprint-1.6 wurde committed:\n\n```text\nb1766a4 Add pilot production package and resource gates\n```\n\nAktueller Status nach Sprint 1.7:\n\n```text\n M autoshorts/production/resource_gate.py\n?? tests/test_preview_render_gate_v2.py\n```\n\nSprint 1.7 selbst ist noch **nicht committed**.\n\n## Pytest Ergebnis\n\n```text\n198 passed in 32.80s\n```\n\n## Preview Approval State\n\n```text\nAPPROVED_FOR_PREVIEW_RENDER\n```\n\nFreigabe genutzt für:\n\n- TTS-Erzeugung: erlaubt\n- Subtitle-/Caption-Timing: erlaubt\n- deterministische Preview / Animatic: erlaubt\n- Telegram Preview Delivery: über diese Antwort als Media Attachment\n- Wan-Finalrender: weiterhin blockiert\n- AI-Styleframe: weiterhin blockiert\n- Publishing: weiterhin blockiert\n\n## Erzeugte Dateien\n\nBasisordner:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/\n```\n\nWichtige Dateien:\n\n```text\npreview_ai-output-autopsy-decision-summary-38s_v2.mp4\nvoiceover.mp3\nvoiceover.txt\nedge_subtitles.srt\nword_cues_time_based.srt\nword_cues_time_based.json\npreview_metadata.json\npreview_midframe.jpg\npreview_silent.mp4\n```\n\n## MP4\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n```\n\nSHA256:\n\n```text\nd14ca8703ff7be72538e83ced8fc8bd1ed698593d57e11e8b5554d678209b41f\n```\n\nFormat:\n\n- 9:16\n- 540 × 960\n- 30 fps\n- Preview / Not Final sichtbar eingeblendet\n- renderer-owned Captions, Labels, Pfeile, USELESS-Stempel und Takeaway\n\n## Audio\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3\n```\n\nSHA256:\n\n```text\na990930cb75dc9cc5a6b2d141cd5c295cce6e713ced744998f0eda15d4c1ac4f\n```\n\nTTS:\n\n- Voice: `en-GB-RyanNeural`\n- Rate: `-3%`\n- Stil: ruhig, klar, professionell, nicht hype\n\nAudiodauer:\n\n```text\n34.968s\n```\n\n## Preview-Dauer\n\n```text\n38.000s\n```\n\nDie letzten Sekunden geben dem Takeaway etwas Luft, statt das Video panisch abzuschneiden — ein seltener Triumph über Shorts-Hektik.\n\n## Subtitle / Caption Dateien\n\nSentence-SRT von EdgeTTS:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/edge_subtitles.srt\n```\n\nTime-based Word-Cues:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/word_cues_time_based.srt\n```\n\nWord-Cue JSON:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/word_cues_time_based.json\n```\n\nCaption-Status:\n\n- bestmögliche zeitbasierte Word-by-word Preview\n- aus EdgeTTS-Sentence-Timings abgeleitet\n- **kein echtes Forced Alignment**\n- für Review brauchbar\n- für Finalrender sollte Forced Alignment nachgerüstet werden\n\nQuality Gate:\n\n```text\nready_for_review\n```\n\nEinschränkung:\n\n```text\nCaptions sind zeitbasiert, nicht forced-aligned. Nicht massiv asynchron, aber noch nicht final-grade.\n```\n\n## Szenenfolge\n\n### s01_fail — ca. 5s\n\nSplit-screen Transcript vs. useless AI summary \nOverlay: PREVIEW / NOT FINAL, Hook, USELESS-Stempel\n\n### s02_bad_output — ca. 7s\n\nPolished bad summary without decision evidence \nOverlay: NO OWNER / NO BLOCKER / NO DECISION\n\n### s03_mechanism — ca. 9s\n\nNotes compressed equally into generic summary \nOverlay: mechanism diagram, decision target\n\n### s04_fix — ca. 9s\n\nRoute from messy notes to blockers / owners / risks / next steps \nOverlay: structured decision-evidence route\n\n### s05_takeaway — ca. 8s\n\nBefore/After contrast and final takeaway \nOverlay: final rule\n\n## Bekannte Einschränkungen\n\n- Visuals sind bewusst deterministisch und grob, kein finaler Motion-Renderer.\n- Keine Wan-2.2-Generierung.\n- Keine AI-Backgrounds.\n- Captions sind zeitbasiert, nicht forced-aligned.\n- Voiceover ist Preview-TTS, noch kein finaler Audio-Master.\n- Motion ist funktional/animatic, nicht final-polished.\n- Finales Visual Design muss nach Review noch verfeinert werden.\n\n## Telegram Versand\n\nIch hänge das Preview-MP4 dieser Antwort direkt als Telegram-Media an.\n\nFalls Telegram das Attachment wider Erwarten nicht sauber annimmt, liegt die Datei lokal hier:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n```\n\n## Nicht ausgeführt\n\nExplizit **nicht** ausgeführt:\n\n- kein Wan-2.2-Finalrender\n- keine Wan-Calls\n- keine AI-Bildgenerierung\n- keine Plattform-API\n- kein Auto-Posting\n- kein finales Manual Posting Pack\n\n## Nächste Review Commands\n\nWenn Sie überarbeiten wollen:\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn das Preview passt und genau ein finaler Wan-2.2-Render erlaubt ist:\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\nWenn Sie ablehnen:\n\n```text\nREJECT ai-output-autopsy-decision-summary-38s \"Grund\"\n```",
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"text": "Sir, Sprint 1.7 ist ausgeführt. Das erste echte **Preview-/Konzeptvideo** wurde erstellt — mit TTS, Timing, Captions, Szenenfolge, Overlays und deterministischen Placeholder-Visuals.\n\nKein Wan-2.2-Render. Keine AI-Bildgenerierung. Keine Plattform-API. Kein Auto-Posting. Der zivilisierte Weg, bevor wir die GPU-Kanone laden.\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nSprint-1.6 wurde committed:\n\n```text\nb1766a4 Add pilot production package and resource gates\n```\n\nAktueller Status nach Sprint 1.7:\n\n```text\n M autoshorts/production/resource_gate.py\n?? tests/test_preview_render_gate_v2.py\n```\n\nSprint 1.7 selbst ist noch **nicht committed**.\n\n## Pytest Ergebnis\n\n```text\n198 passed in 32.80s\n```\n\n## Preview Approval State\n\n```text\nAPPROVED_FOR_PREVIEW_RENDER\n```\n\nFreigabe genutzt für:\n\n- TTS-Erzeugung: erlaubt\n- Subtitle-/Caption-Timing: erlaubt\n- deterministische Preview / Animatic: erlaubt\n- Telegram Preview Delivery: über diese Antwort als Media Attachment\n- Wan-Finalrender: weiterhin blockiert\n- AI-Styleframe: weiterhin blockiert\n- Publishing: weiterhin blockiert\n\n## Erzeugte Dateien\n\nBasisordner:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/\n```\n\nWichtige Dateien:\n\n```text\npreview_ai-output-autopsy-decision-summary-38s_v2.mp4\nvoiceover.mp3\nvoiceover.txt\nedge_subtitles.srt\nword_cues_time_based.srt\nword_cues_time_based.json\npreview_metadata.json\npreview_midframe.jpg\npreview_silent.mp4\n```\n\n## MP4\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n```\n\nSHA256:\n\n```text\nd14ca8703ff7be72538e83ced8fc8bd1ed698593d57e11e8b5554d678209b41f\n```\n\nFormat:\n\n- 9:16\n- 540 × 960\n- 30 fps\n- Preview / Not Final sichtbar eingeblendet\n- renderer-owned Captions, Labels, Pfeile, USELESS-Stempel und Takeaway\n\n## Audio\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3\n```\n\nSHA256:\n\n```text\na990930cb75dc9cc5a6b2d141cd5c295cce6e713ced744998f0eda15d4c1ac4f\n```\n\nTTS:\n\n- Voice: `en-GB-RyanNeural`\n- Rate: `-3%`\n- Stil: ruhig, klar, professionell, nicht hype\n\nAudiodauer:\n\n```text\n34.968s\n```\n\n## Preview-Dauer\n\n```text\n38.000s\n```\n\nDie letzten Sekunden geben dem Takeaway etwas Luft, statt das Video panisch abzuschneiden — ein seltener Triumph über Shorts-Hektik.\n\n## Subtitle / Caption Dateien\n\nSentence-SRT von EdgeTTS:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/edge_subtitles.srt\n```\n\nTime-based Word-Cues:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/word_cues_time_based.srt\n```\n\nWord-Cue JSON:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/word_cues_time_based.json\n```\n\nCaption-Status:\n\n- bestmögliche zeitbasierte Word-by-word Preview\n- aus EdgeTTS-Sentence-Timings abgeleitet\n- **kein echtes Forced Alignment**\n- für Review brauchbar\n- für Finalrender sollte Forced Alignment nachgerüstet werden\n\nQuality Gate:\n\n```text\nready_for_review\n```\n\nEinschränkung:\n\n```text\nCaptions sind zeitbasiert, nicht forced-aligned. Nicht massiv asynchron, aber noch nicht final-grade.\n```\n\n## Szenenfolge\n\n### s01_fail — ca. 5s\n\nSplit-screen Transcript vs. useless AI summary \nOverlay: PREVIEW / NOT FINAL, Hook, USELESS-Stempel\n\n### s02_bad_output — ca. 7s\n\nPolished bad summary without decision evidence \nOverlay: NO OWNER / NO BLOCKER / NO DECISION\n\n### s03_mechanism — ca. 9s\n\nNotes compressed equally into generic summary \nOverlay: mechanism diagram, decision target\n\n### s04_fix — ca. 9s\n\nRoute from messy notes to blockers / owners / risks / next steps \nOverlay: structured decision-evidence route\n\n### s05_takeaway — ca. 8s\n\nBefore/After contrast and final takeaway \nOverlay: final rule\n\n## Bekannte Einschränkungen\n\n- Visuals sind bewusst deterministisch und grob, kein finaler Motion-Renderer.\n- Keine Wan-2.2-Generierung.\n- Keine AI-Backgrounds.\n- Captions sind zeitbasiert, nicht forced-aligned.\n- Voiceover ist Preview-TTS, noch kein finaler Audio-Master.\n- Motion ist funktional/animatic, nicht final-polished.\n- Finales Visual Design muss nach Review noch verfeinert werden.\n\n## Telegram Versand\n\nIch hänge das Preview-MP4 dieser Antwort direkt als Telegram-Media an.\n\nFalls Telegram das Attachment wider Erwarten nicht sauber annimmt, liegt die Datei lokal hier:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2.mp4\n```\n\n## Nicht ausgeführt\n\nExplizit **nicht** ausgeführt:\n\n- kein Wan-2.2-Finalrender\n- keine Wan-Calls\n- keine AI-Bildgenerierung\n- keine Plattform-API\n- kein Auto-Posting\n- kein finales Manual Posting Pack\n\n## Nächste Review Commands\n\nWenn Sie überarbeiten wollen:\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn das Preview passt und genau ein finaler Wan-2.2-Render erlaubt ist:\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\nWenn Sie ablehnen:\n\n```text\nREJECT ai-output-autopsy-decision-summary-38s \"Grund\"\n```"
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"content": "[M G] Man sollte jeweils immer mindestens 3 wörter der subtitles sehen, wobei die gerade gesprochenen wörter aufblinken (harmozi style). Aktuell kommt jedes einzeln, das ist zu nervös. Korrigiere das."
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"reasoning_content": "**Clarifying testing process**\n\nI’m noticing there are at least four active highlighted words regarding the AI compressing things. I’m wondering if I need to run pytest, especially since I've modified the code. I should double-check the code status; perhaps a commit isn't needed since the user requested a correction. Running pytest seems wise to confirm everything is working correctly. It might be great to mention that there are no changes since the last preview, but we still have tests present.",
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"text": "**Clarifying testing process**\n\nI’m noticing there are at least four active highlighted words regarding the AI compressing things. I’m wondering if I need to run pytest, especially since I've modified the code. I should double-check the code status; perhaps a commit isn't needed since the user requested a correction. Running pytest seems wise to confirm everything is working correctly. It might be great to mention that there are no changes since the last preview, but we still have tests present."
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"content": "[terminal] ran `python -m pytest tests/test_preview_render_gate_v2.py tests/test_pilot_produc...` -> exit 0, 1 lines output",
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"content": "Korrigiert, Sir. Sie hatten völlig recht: einzelnes Wort allein ist zu hektisch — wirkt wie ein nervöser Teleprompter auf Espresso.\n\nIch habe das Preview neu gerendert mit:\n\n- **mindestens 3 sichtbaren Subtitle-Wörtern gleichzeitig**\n- meist **3–5 Wörter im Caption-Fenster**\n- das gerade gesprochene Wort leuchtet gelb auf\n- ruhigerer Hormozi-/TikTok-Style\n- Voiceover und Szenen unverändert\n- kein Wan-Render\n- keine AI-Bildgenerierung\n- keine Plattform-API\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2_caption_window.mp4\n\n## Neue Preview-Datei\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2_caption_window.mp4\n```\n\nSHA256:\n\n```text\n0ed888679bb8379b065132d4f4b6a6a24e8dd18c8c7ee207f2c91112932ce7e0\n```\n\n## Technische Eckdaten\n\n- Format: 9:16\n- Auflösung: 540 × 960\n- FPS: 24\n- Dauer: 38.000s\n- Audio: bestehendes TTS, 34.968s\n- Caption Style: **3–5 word window + active word highlight**\n- Alignment: weiterhin zeitbasiert aus EdgeTTS-Sentence-Cues, noch kein echtes Forced Alignment\n\n## Verifikation\n\nIch habe einen Midframe geprüft: Caption-Fenster zeigt mehrere Wörter gleichzeitig, z. B. sinngemäss:\n\n```text\ntarget AI COMPRESSES every\n```\n\nDabei ist **COMPRESSES** aktiv gelb hervorgehoben. Lesbarkeit ist deutlich ruhiger als vorher.\n\n## Tests\n\n```text\n8 passed in 0.08s\n```\n\nBetroffene Sprint-1.7-Code-Dateien weiterhin uncommitted:\n\n```text\n M autoshorts/production/resource_gate.py\n?? tests/test_preview_render_gate_v2.py\n```\n\n## Dauerhafte Regel aktualisiert\n\nIch habe die Caption-Präferenz gespeichert:\n\n> AutoShorts-Captions sollen mindestens ein 3-Wort-Fenster zeigen; das aktuell gesprochene Wort leuchtet aktiv auf.\n\nNächstes Review-Kommando bleibt:\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nOder, falls dieses Preview grundsätzlich passt:\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```",
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"content": [
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"text": "Korrigiert, Sir. Sie hatten völlig recht: einzelnes Wort allein ist zu hektisch — wirkt wie ein nervöser Teleprompter auf Espresso.\n\nIch habe das Preview neu gerendert mit:\n\n- **mindestens 3 sichtbaren Subtitle-Wörtern gleichzeitig**\n- meist **3–5 Wörter im Caption-Fenster**\n- das gerade gesprochene Wort leuchtet gelb auf\n- ruhigerer Hormozi-/TikTok-Style\n- Voiceover und Szenen unverändert\n- kein Wan-Render\n- keine AI-Bildgenerierung\n- keine Plattform-API\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2_caption_window.mp4\n\n## Neue Preview-Datei\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v2_caption_window.mp4\n```\n\nSHA256:\n\n```text\n0ed888679bb8379b065132d4f4b6a6a24e8dd18c8c7ee207f2c91112932ce7e0\n```\n\n## Technische Eckdaten\n\n- Format: 9:16\n- Auflösung: 540 × 960\n- FPS: 24\n- Dauer: 38.000s\n- Audio: bestehendes TTS, 34.968s\n- Caption Style: **3–5 word window + active word highlight**\n- Alignment: weiterhin zeitbasiert aus EdgeTTS-Sentence-Cues, noch kein echtes Forced Alignment\n\n## Verifikation\n\nIch habe einen Midframe geprüft: Caption-Fenster zeigt mehrere Wörter gleichzeitig, z. B. sinngemäss:\n\n```text\ntarget AI COMPRESSES every\n```\n\nDabei ist **COMPRESSES** aktiv gelb hervorgehoben. Lesbarkeit ist deutlich ruhiger als vorher.\n\n## Tests\n\n```text\n8 passed in 0.08s\n```\n\nBetroffene Sprint-1.7-Code-Dateien weiterhin uncommitted:\n\n```text\n M autoshorts/production/resource_gate.py\n?? tests/test_preview_render_gate_v2.py\n```\n\n## Dauerhafte Regel aktualisiert\n\nIch habe die Caption-Präferenz gespeichert:\n\n> AutoShorts-Captions sollen mindestens ein 3-Wort-Fenster zeigen; das aktuell gesprochene Wort leuchtet aktiv auf.\n\nNächstes Review-Kommando bleibt:\n\n```text\nREVISE ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nOder, falls dieses Preview grundsätzlich passt:\n\n```text\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```"
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"content": "[M G] noch nicht gut. die Wörter des Subtitles abschnitts sollen statisch bleiben, nur das gerade gesprochene wort leuchtet beim abschnitt auf. aktuelle wechseln immer alle wörter gleichzeitig. die Wörter bleiben also pro Satzabschnitt an ort und stelle, nur da \"highlight\", also das gerade gesprochene wort blinkt jeweils auf. also das highlight wandert von links nach rechts, bis weider der nächste satzabschnitt kommt."
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"content": "Ja — exakt. Ich habe es jetzt so korrigiert, wie Sie es beschreiben:\n\n- Der **Subtitle-Abschnitt bleibt statisch**.\n- Mehrere Wörter bleiben **an Ort und Stelle**.\n- Nur das gerade gesprochene Wort bekommt das gelbe Highlight.\n- Das Highlight wandert innerhalb desselben Abschnitts **von links nach rechts**.\n- Erst beim nächsten Satz-/Phrase-Abschnitt wechselt der komplette Subtitle-Block.\n\nNeue Preview:\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v3_static_caption_segments.mp4\n\n## Neue Datei\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v3_static_caption_segments.mp4\n```\n\nSHA256:\n\n```text\n0d33af98a9b8e1c7c69509f67a3762df36aa8af5eb202b7327f64529356c9c9a\n```\n\n## Caption-Verhalten jetzt\n\nBeispiel im geprüften Frame:\n\n```text\nWITHOUT A DECISION\nTARGET AI\n```\n\nDabei ist nur:\n\n```text\nTARGET\n```\n\ngelb hervorgehoben.\n\nDie anderen Wörter bleiben sichtbar und statisch. Das ist nun viel näher an dem gewünschten Hormozi-/TikTok-Subtitle-Stil.\n\n## Einschränkung\n\nDas Timing ist weiterhin aus den vorhandenen zeitbasierten Word-Cues abgeleitet, noch kein echtes Forced Alignment. Aber das **Layout-Verhalten** ist jetzt korrekt:\n\n> statischer Satzabschnitt, wanderndes Highlight.\n\nIch habe diese Präferenz auch dauerhaft aktualisiert, damit wir künftig nicht wieder in den „ein Wort hüpft allein über den Bildschirm“-Zirkus zurückfallen.",
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"text": "Ja — exakt. Ich habe es jetzt so korrigiert, wie Sie es beschreiben:\n\n- Der **Subtitle-Abschnitt bleibt statisch**.\n- Mehrere Wörter bleiben **an Ort und Stelle**.\n- Nur das gerade gesprochene Wort bekommt das gelbe Highlight.\n- Das Highlight wandert innerhalb desselben Abschnitts **von links nach rechts**.\n- Erst beim nächsten Satz-/Phrase-Abschnitt wechselt der komplette Subtitle-Block.\n\nNeue Preview:\n\nMEDIA:/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v3_static_caption_segments.mp4\n\n## Neue Datei\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/preview_ai-output-autopsy-decision-summary-38s_v3_static_caption_segments.mp4\n```\n\nSHA256:\n\n```text\n0d33af98a9b8e1c7c69509f67a3762df36aa8af5eb202b7327f64529356c9c9a\n```\n\n## Caption-Verhalten jetzt\n\nBeispiel im geprüften Frame:\n\n```text\nWITHOUT A DECISION\nTARGET AI\n```\n\nDabei ist nur:\n\n```text\nTARGET\n```\n\ngelb hervorgehoben.\n\nDie anderen Wörter bleiben sichtbar und statisch. Das ist nun viel näher an dem gewünschten Hormozi-/TikTok-Subtitle-Stil.\n\n## Einschränkung\n\nDas Timing ist weiterhin aus den vorhandenen zeitbasierten Word-Cues abgeleitet, noch kein echtes Forced Alignment. Aber das **Layout-Verhalten** ist jetzt korrekt:\n\n> statischer Satzabschnitt, wanderndes Highlight.\n\nIch habe diese Präferenz auch dauerhaft aktualisiert, damit wir künftig nicht wieder in den „ein Wort hüpft allein über den Bildschirm“-Zirkus zurückfallen."
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"content": "[M G] JARVIS, das Preview v3 mit statischen Caption-Segmenten ist nun deutlich besser. Wir gehen jetzt bewusst in genau einen finalen Pilot-Render, um die Gesamtqualität zu beurteilen.\n\nWichtig:\nDies ist eine Freigabe für einen finalen Pilot-Render, aber noch KEINE Freigabe für Posting oder Auto-Publishing.\n\nFreigabe:\n\nAPPROVED_FOR_FINAL_RENDER ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n\nBitte führe jetzt Sprint 1.8 aus:\n\nFinal Pilot Render V1 — Wan 2.2 Quality Assessment\n\nZiel:\nErstelle genau ein finales Video mit Wan 2.2 für den Kandidaten:\n\nai-output-autopsy-decision-summary-38s\nVersion: v2.0.0\nScript Hash: 72129542b86819fd\n\nDieses Video dient der Gesamtqualitätsbeurteilung:\n\n* visuelle Qualität\n* Motion\n* Caption-Stil\n* Timing\n* Voiceover\n* Hook-Wirkung\n* Verständlichkeit\n* Gesamtgefühl\n* AI-Slop-Risiko\n\n1. Vorher Sprint 1.7 / Caption-Fix sauber sichern\n\nBitte prüfe:\n\n* git status\n* pytest\n\nDie Caption-Korrektur darf nicht nur als temporäres /tmp-Script existieren. Bitte integriere sie sauber in den eigentlichen Preview-/Caption-Renderer.\n\nPflicht-Caption-Stil ab jetzt:\n\n* Subtitle-Abschnitt bleibt statisch.\n* Mindestens 3 Wörter sind gleichzeitig sichtbar, ideal eher 4–7 Wörter.\n* Die Wörter bleiben während des Abschnitts an Ort und Stelle.\n* Nur das aktuell gesprochene Wort wird hervorgehoben.\n* Das Highlight wandert von links nach rechts durch den Abschnitt.\n* Erst beim nächsten Satz-/Phrase-Abschnitt wechselt der komplette Subtitle-Block.\n* Kein einzelnes Wort allein auf dem Bildschirm, ausser es ist bewusst als Hook/Impact-Frame geplant.\n* Keine hektisch komplett wechselnden Wortgruppen.\n\nBitte ergänze Tests oder Snapshot-/Logic-Tests für:\n\n* static caption segment contains multiple words\n* active word changes within same segment\n* segment text remains stable while highlight moves\n* no one-word-only caption mode as default\n\nDann committen:\n\nCommit Message:\nAdd static active-word caption segments for preview rendering\n\n2. Finalrender-Gate prüfen\n\nVor Wan-Render bitte prüfen:\n\n* candidate_id stimmt\n* version stimmt\n* script_hash stimmt\n* approval_state = APPROVED_FOR_FINAL_RENDER\n* claim gate valid\n* AI disclosure valid\n* publishing bleibt blockiert\n* platform API bleibt blockiert\n\nWenn eines davon nicht stimmt:\nnicht rendern, sondern blockieren und berichten.\n\n3. Finales Voiceover / Timing\n\nBitte nutze den bisherigen Voiceover-Text, ausser du erkennst einen klaren kleinen Timing-Fehler.\n\nFinaler Voiceover-Text:\n\nYour AI summary is useless because it has no decision target.\n\nLook at the bad output: it sounds polished, but it missed the one thing the viewer needed.\n\nThe summary says “great discussion” but misses the launch decision.\n\nWithout a decision target, AI compresses every sentence equally.\n\nAsk for blockers, owners, risks, and next steps tied to the decision.\n\nBefore: generic summary. After: decision evidence with owner and risk.\n\nDon’t ask for a summary. Ask for decision evidence.\n\nWenn möglich:\n\n* TTS neu erzeugen oder vorhandenes TTS sauber verwenden.\n* Audio-Dauer exakt messen.\n* Captions neu gegen Audio ausrichten.\n* Falls Forced Alignment verfügbar ist: verwenden.\n* Falls Forced Alignment nicht verfügbar ist: bestmögliche Segment-/Word-Timing-Approximation verwenden und als Einschränkung markieren.\n\n4. Wan 2.2 final rendern\n\nBitte genau einen finalen Wan-2.2-Render erzeugen.\n\nKeine Variantenserie.\nKeine 5 Alternativen.\nKeine unnötigen Styleframe-Batches.\n\nWan-Regeln:\n\n* Wan erzeugt nur textfreie visuelle Motion-/Background-Ebene.\n* Keine eingebrannten Texte.\n* Keine Logos.\n* Keine Captions.\n* Keine Zahlen/Fakten.\n* Keine UI-Texte.\n* Keine Marken.\n* Alle Texte, Labels, Pfeile, Stempel und Captions bleiben renderer-owned.\n\nNutze den bestehenden ShotPlan:\n\ns01_fail — ca. 5s\nSplit-screen Transcript vs. useless summary\nHook / USELESS / PREVIEW-Hinweis im finalen Video natürlich entfernen.\n\ns02_bad_output — ca. 7s\nPolished bad summary without decision evidence\n\ns03_mechanism — ca. 9s\nNotes compressed equally into generic summary\n\ns04_fix — ca. 9s\nRoute from messy notes to blockers / owners /\nrisks / next steps\n\ns05_takeaway — ca. 8s\nBefore/After contrast and final takeaway\n\nZieldauer:\nca. 35–40 Sekunden.\n\nFormat:\n\n* 9:16\n* final möglichst 1080x1920\n* 30 fps, falls Pipeline stabil\n* saubere mobile Lesbarkeit\n* keine Preview-/Not-Final-Einblendung mehr\n\n5. Overlays und Captions\n\nBitte im finalen Video:\n\n* statische Caption-Segmente mit wanderndem Active-Word-Highlight\n* mobile-first Safe Zones beachten\n* Hook in den ersten 1–2 Sekunden klar\n* USELESS-Stempel / Pfeile / Labels renderer-owned\n* Takeaway am Ende klar lesbar\n* keine überladenen Textflächen\n* Captions nicht zu tief, damit Plattform-UI sie nicht verdeckt\n\n6. Final Review Package erzeugen\n\nNach dem Render bitte erstellen:\n\n* final MP4 path\n* SHA256\n* duration\n* resolution\n* fps\n* audio path\n* subtitle/caption path\n* render manifest\n* Wan prompt log / prompt plan\n* claim gate result\n* AI disclosure result\n* resource gate result\n* known limitations\n* final social copy draft\n* manual posting pack draft\n\nWichtig:\nManual Posting Pack nur als Draft. Noch kein Posting.\n\n7. Telegram Delivery\n\nWenn Telegram-Versand stabil funktioniert:\nSende mir das finale MP4 per Telegram mit Kurzreview.\n\nWenn Telegram nicht klappt:\nBerichte lokalen Pfad, SHA256 und Delivery Plan.\n\n8. Nach dem Render NICHT posten\n\nExplizit nicht ausführen:\n\n* keine YouTube-API\n* keine TikTok-API\n* keine Instagram-API\n* keine LinkedIn-API\n* kein Auto-Posting\n* kein Scheduling\n* kein zweiter Wan-Render ohne neue Freigabe\n\n9. Abschlussbericht\n\nBitte kurz berichten:\n\n* git status\n* Commit Hash für Caption-Fix / Sprint 1.7, falls erfolgt\n* pytest Ergebnis\n* Finalrender durchgeführt: yes/no\n* MP4-Pfad\n* SHA256\n* Dauer\n* Auflösung\n* FPS\n* Caption-Modus\n* Forced Alignment ja/nein\n* bekannte Timing-Einschränkungen\n* AI Disclosure\n* Claim Gate\n* Resource Gate\n* Telegram-Versand erfolgreich ja/nein\n* nächstmögliche Kommandos\n\n10. Nächste Review-Kommandos nach Sichtung\n\nWenn ich kleine Korrekturen will:\n\nREVISE_FINAL ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n\nWenn das Video uploadbereit ist:\n\nAPPROVED_FOR_MANUAL_POSTING ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n\nWenn ein zweiter Finalrender nötig ist:\n\nREQUEST_SECOND_FINAL_RENDER ai-output-autopsy-decision-summary-38s \"Begründung\"\n\nWenn ich es ablehne:\n\nREJECT_FINAL ai-output-autopsy-decision-summary-38s \"Grund\"\n\nWichtigster Grundsatz:\nGenau ein finaler Pilot-Render ist jetzt erlaubt. Kein Posting. Keine Render-Serie. Danach prüfen wir gemeinsam die Gesamtqualität."
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"content": "{\"success\": true, \"name\": \"comfyui\", \"file\": \"references/wan22-i2v-retention-studio-pipeline.md\", \"content\": \"# Wan2.2 I2V for Retention-Studio Clip Production\\n\\nSession-derived pattern for using native ComfyUI Wan2.2 as one component in a deterministic short-form content pipeline.\\n\\n## Core lesson\\n\\nFor production-oriented Shorts, do not treat Wan2.2 as the whole video renderer. Use it for photorealistic texture and motion only, then add captions, numbers, labels, arrows, risk meters, and audit-sensitive facts in a deterministic renderer.\\n\\nRecommended production chain:\\n\\n```text\\nmechanism shot plan\\n→ keyframe prompt\\n→ approved 9:16 keyframe\\n→ Wan2.2 image-to-video variants by seed\\n→ human/automated clip review\\n→ deterministic caption/overlay/audio renderer\\n```\\n\\n## Prompting pattern\\n\\nUse a short, concrete I2V prompt with:\\n\\n- one subject\\n- one action\\n- one camera move\\n- explicit identity preservation\\n- balanced exposure\\n- no baked text/logos/watermarks\\n\\nTemplate:\\n\\n```text\\nPhotorealistic vertical video of the same [subject] in [environment].\\nCamera [single camera move].\\n[One motion/detail cue].\\nStable composition, balanced exposure, realistic reflections/materials.\\nPreserve the same [subject] and [environment].\\nNo text, no logo, no watermark.\\n```\\n\\nNegative prompt baseline:\\n\\n```text\\ncartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\\n```\\n\\nDomain-specific additions for money/auto/system videos:\\n\\n```text\\nfake numbers, fake labels, brand logos, unreadable license plate, fake license plate text, distorted wheels, melted car body, broken windshield geometry\\n```\\n\\n## Keyframe requirements\\n\\nA good keyframe matters more than another clever text prompt. Require:\\n\\n- full subject clearly visible\\n- silhouette supports the intended movement\\n- enough free space in movement direction\\n- clean negative space for captions in upper/lower thirds\\n- no readable text, logos, UI gibberish, license plates, fake numbers\\n- balanced exposure; avoid dark or overexposed frames\\n- exact/near-exact target aspect ratio before upload\\n\\nBad example: a front-facing sitting rabbit as the start image for “rabbit hopping across meadow”. It preserves identity but does not support motion.\\n\\nGood example: side-view rabbit in mid-hop pose with open meadow ahead.\\n\\n## A5000 native Wan2.2 baseline\\n\\nKnown-good local/Tailscale native stack:\\n\\n```text\\nUNET: wan2.2_ti2v_5B_fp16.safetensors\\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\\nVAE: wan2.2_vae.safetensors\\n```\\n\\nConservative production-preview settings:\\n\\n```text\\n416x736\\n61 frames\\n12 fps\\n12–16 steps\\nCFG 4.5–6.0\\nsampler uni_pc\\nscheduler simple\\n```\\n\\nAvoid 10 fps for anything that will be reviewed as motion quality: it is acceptable for smoke tests, but it visibly stutters with push-ins/parallax and can make a good prompt look worse than it is. For final Shorts, generate locally at ~12 fps if VRAM-bound, then retime/interpolate/render the final assembly to 24 fps. Do not ask Wan2.2 to produce text/labels at 24 fps; let the deterministic renderer handle overlays.\\n\\nUse 3+ seeds per approved keyframe and select the best clip; do not judge the model from one seed.\\n\\n## Pipeline artifact shape\\n\\nUseful repo-level abstractions for future projects:\\n\\n- `keyframe_prompt`: converts shot plans into keyframe prompt + I2V motion prompt + negative prompt + ComfyUI preset.\\n- `keyframe_package`: creates image-generator requests with target paths and acceptance checklists before any GPU run.\\n- `i2v_clip_jobs`: expands I2V prompt plans into seeded scene jobs with keyframe/output paths and review criteria.\\n- `comfyui_client`: uploads the keyframe, submits native Wan2.2 workflow, polls queue/history, downloads MP4.\\n- `i2v_job_runner`: skips missing keyframes and existing outputs, supports dry-run/limit, writes run reports, and can resume after a timeout.\\n\\nClip job status should start as `needs_keyframe` until the expected image exists. This prevents accidental GPU runs with missing/placeholder keyframes. Unit tests for this status must use a temporary keyframe root; otherwise real production keyframes in `data/keyframes/...` can flip expected status from `needs_keyframe` to `ready` and make tests environment-dependent.\\n\\n## Running and resuming seeded jobs\\n\\nRecommended execution pattern:\\n\\n```text\\n1. Generate/approve keyframes.\\n2. Run one limited job first (`--limit 1`) to validate upload, workflow, poll/download, and frame geometry.\\n3. ffprobe the first MP4: width/height, fps, frame count, duration.\\n4. Extract a mid-frame and visually inspect before launching all seeds.\\n5. Run the full job package; if the command times out while ComfyUI keeps running, poll `/queue`, inspect `/history`, download the completed file from `/view`, then rerun the runner so existing outputs are skipped and remaining jobs continue.\\n```\\n\\nDo not commit raw generated MP4s/keyframes by default. 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"arguments": "{\"content\":\"\\\"\\\"\\\"Static active-word caption segment logic.\\n\\nCaption rule for TrueTraceShorts previews/finals:\\n- A subtitle phrase segment stays fixed on screen.\\n- At least three words are visible per default segment whenever possible.\\n- Only the active spoken word changes highlight.\\n- The highlight moves left-to-right until the next phrase segment replaces it.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom typing import Iterable, Sequence\\n\\n\\n@dataclass(frozen=True)\\nclass WordCue:\\n start: float\\n end: float\\n word: str\\n sentence: str = \\\"\\\"\\n\\n\\n@dataclass(frozen=True)\\nclass StaticCaptionSegment:\\n start: float\\n end: float\\n word_indices: tuple[int, ...]\\n words: tuple[str, ...]\\n sentence: str = \\\"\\\"\\n\\n @property\\n def text(self) -> str:\\n return \\\" \\\".join(self.words)\\n\\n\\ndef build_static_caption_segments(\\n word_cues: Sequence[WordCue],\\n *,\\n min_words: int = 3,\\n target_words: int = 6,\\n max_words: int = 7,\\n) -> tuple[StaticCaptionSegment, ...]:\\n if min_words < 2:\\n raise ValueError(\\\"min_words must be >= 2\\\")\\n if target_words < min_words:\\n raise ValueError(\\\"target_words must be >= min_words\\\")\\n if max_words < target_words:\\n raise ValueError(\\\"max_words must be >= target_words\\\")\\n\\n grouped: list[list[tuple[int, WordCue]]] = []\\n current_sentence: str | None = None\\n current: list[tuple[int, WordCue]] = []\\n for index, cue in enumerate(word_cues):\\n sentence_key = cue.sentence or \\\"__no_sentence__\\\"\\n if current and sentence_key != current_sentence:\\n grouped.append(current)\\n current = []\\n current_sentence = sentence_key\\n current.append((index, cue))\\n if current:\\n grouped.append(current)\\n\\n segments: list[StaticCaptionSegment] = []\\n for group in grouped:\\n chunks: list[list[tuple[int, WordCue]]] = []\\n i = 0\\n while i < len(group):\\n remaining = len(group) - i\\n size = min(target_words, remaining)\\n if remaining < min_words and chunks:\\n chunks[-1].extend(group[i:])\\n break\\n if size < min_words and len(group) >= min_words:\\n start = max(0, len(group) - min_words)\\n chunks.append(group[start:])\\n break\\n chunks.append(group[i : i + size])\\n i += size\\n for chunk in chunks:\\n if not chunk:\\n continue\\n if len(chunk) > max_words:\\n # Split oversized merged chunks conservatively while preserving min_words.\\n while len(chunk) > max_words:\\n head = chunk[:max_words]\\n segments.append(_segment_from_chunk(head))\\n chunk = chunk[max_words:]\\n if 0 < len(chunk) < min_words and segments:\\n previous = segments.pop()\\n prev_pairs = [(idx, word_cues[idx]) for idx in previous.word_indices]\\n chunk = prev_pairs + chunk\\n segments.append(_segment_from_chunk(chunk))\\n return tuple(segments)\\n\\n\\ndef _segment_from_chunk(chunk: Sequence[tuple[int, WordCue]]) -> StaticCaptionSegment:\\n return StaticCaptionSegment(\\n start=chunk[0][1].start,\\n end=chunk[-1][1].end,\\n word_indices=tuple(index for index, _ in chunk),\\n words=tuple(cue.word for _, cue in chunk),\\n sentence=chunk[0][1].sentence,\\n )\\n\\n\\ndef active_word_index_at(word_cues: Sequence[WordCue], timestamp: float) -> int:\\n for index, cue in enumerate(word_cues):\\n if cue.start <= timestamp < cue.end:\\n return index\\n previous = [index for index, cue in enumerate(word_cues) if cue.start <= timestamp]\\n return previous[-1] if previous else 0\\n\\n\\ndef segment_for_active_word(segments: Sequence[StaticCaptionSegment], active_word_index: int) -> StaticCaptionSegment:\\n for segment in segments:\\n if active_word_index in segment.word_indices:\\n return segment\\n raise ValueError(f\\\"active word index {active_word_index} is not present in any segment\\\")\\n\\n\\ndef caption_state_at(word_cues: Sequence[WordCue], segments: Sequence[StaticCaptionSegment], timestamp: float) -> dict[str, object]:\\n active_index = active_word_index_at(word_cues, timestamp)\\n segment = segment_for_active_word(segments, active_index)\\n return {\\n \\\"segment_text\\\": segment.text,\\n \\\"segment_words\\\": segment.words,\\n \\\"word_indices\\\": segment.word_indices,\\n \\\"active_word_index\\\": active_index,\\n \\\"active_word\\\": word_cues[active_index].word,\\n }\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/static_captions.py\"}"
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"arguments": "{\"content\":\"from autoshorts.rendering.static_captions import (\\n WordCue,\\n active_word_index_at,\\n build_static_caption_segments,\\n caption_state_at,\\n segment_for_active_word,\\n)\\n\\n\\ndef cues():\\n words = \\\"Your AI summary is useless because it has no decision target\\\".split()\\n return tuple(\\n WordCue(start=i * 0.4, end=(i + 1) * 0.4, word=word, sentence=\\\"Your AI summary is useless because it has no decision target\\\")\\n for i, word in enumerate(words)\\n )\\n\\n\\ndef test_static_caption_segment_contains_multiple_words():\\n segments = build_static_caption_segments(cues())\\n\\n assert segments\\n assert all(len(segment.words) >= 3 for segment in segments)\\n assert segments[0].text == \\\"Your AI summary is useless because\\\"\\n\\n\\ndef test_active_word_changes_within_same_static_segment():\\n word_cues = cues()\\n segments = build_static_caption_segments(word_cues)\\n\\n early = caption_state_at(word_cues, segments, 0.1)\\n later = caption_state_at(word_cues, segments, 1.1)\\n\\n assert early[\\\"segment_text\\\"] == later[\\\"segment_text\\\"]\\n assert early[\\\"active_word\\\"] == \\\"Your\\\"\\n assert later[\\\"active_word\\\"] == \\\"summary\\\"\\n\\n\\ndef test_segment_text_remains_stable_while_highlight_moves_left_to_right():\\n word_cues = cues()\\n segments = build_static_caption_segments(word_cues)\\n state_a = caption_state_at(word_cues, segments, 0.5)\\n state_b = caption_state_at(word_cues, segments, 1.7)\\n\\n assert state_a[\\\"segment_words\\\"] == state_b[\\\"segment_words\\\"]\\n assert state_a[\\\"active_word_index\\\"] < state_b[\\\"active_word_index\\\"]\\n\\n\\ndef test_no_one_word_only_caption_mode_as_default_when_enough_words_exist():\\n segments = build_static_caption_segments(cues())\\n\\n assert min(len(segment.words) for segment in segments) >= 3\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_static_caption_segments.py\"}"
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"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Sprint 1.7 Caption-Fix in Renderer-Code integrieren, Tests ergänzen, pytest und committen\",\"id\":\"caption-integrate\",\"status\":\"completed\"},{\"content\":\"Finalrender-Gate/Claim/Disclosure prüfen und Wan-Verfügbarkeit feststellen\",\"id\":\"gate-final\",\"status\":\"in_progress\"}]}"
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}
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},
{
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"content": "{\"todos\": [{\"id\": \"caption-integrate\", \"content\": \"Sprint 1.7 Caption-Fix in Renderer-Code integrieren, Tests ergänzen, pytest und committen\", \"status\": \"completed\"}, {\"id\": \"gate-final\", \"content\": \"Finalrender-Gate/Claim/Disclosure prüfen und Wan-Verfügbarkeit feststellen\", \"status\": \"in_progress\"}, {\"id\": \"final-render\", \"content\": \"Genau einen finalen Pilot-Render mit verfügbarer Wan/renderer pipeline erzeugen\", \"status\": \"pending\"}, {\"id\": \"package-deliver\", \"content\": \"Final Review Package, Checksums, Telegram Delivery und Abschlussbericht erstellen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 2, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}",
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{
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"reasoning": "**Checking gate and availability**\n\nI'm thinking about needing to check the gate and also see if Comfy is available. It seems like I'll need to use the terminal for this. It's important to ensure everything is set before proceeding. I wonder if there are any specific details I should consider while checking. Alright, let's make sure I gather the right information and keep things organized to avoid any confusion later on!",
"finish_reason": "tool_calls",
"reasoning_content": "**Checking gate and availability**\n\nI'm thinking about needing to check the gate and also see if Comfy is available. It seems like I'll need to use the terminal for this. It's important to ensure everything is set before proceeding. I wonder if there are any specific details I should consider while checking. Alright, let's make sure I gather the right information and keep things organized to avoid any confusion later on!",
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"text": "**Checking gate and availability**\n\nI'm thinking about needing to check the gate and also see if Comfy is available. It seems like I'll need to use the terminal for this. It's important to ensure everything is set before proceeding. I wonder if there are any specific details I should consider while checking. Alright, let's make sure I gather the right information and keep things organized to avoid any confusion later on!"
}
]
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"id": "call_YwlfUb96xz4Zj3UBBfiOtJte",
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"arguments": "{\"code\":\"from hermes_tools import terminal\\nwd='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot'\\nchecks={\\n 'resource_gate': \\\"python - <<'PY'\\\\nfrom autoshorts.strategy.spine_v2 import build_demo_candidates_v2, build_review_package_v2\\\\nfrom autoshorts.production.resource_gate import ResourceCostTier, RenderApprovalState, evaluate_media_generation_gate\\\\nfrom autoshorts.claims.fact_gate import validate_claims_for_candidate\\\\nfrom autoshorts.compliance.ai_disclosure import AIDisclosureReview, validate_ai_disclosure_review\\\\nc=build_demo_candidates_v2(limit=1)[0]; p=build_review_package_v2(c)\\\\nprint(c.candidate_id,c.version,p.script_hash)\\\\nprint('claim', validate_claims_for_candidate(c).is_valid, validate_claims_for_candidate(c).errors)\\\\nr=AIDisclosureReview.text_only_assistance('Script assistance plus generated abstract background; no realistic person/event/voice intended.')\\\\nprint('ai', validate_ai_disclosure_review(r).is_valid, validate_ai_disclosure_review(r).errors)\\\\nfor tier in [ResourceCostTier.WAN_FINAL_RENDER, ResourceCostTier.PUBLISHING]:\\\\n g=evaluate_media_generation_gate(candidate=c,review_package=p,requested_resource_tier=tier,approval_state=RenderApprovalState.APPROVED_FOR_FINAL_RENDER,approved_candidate_id=c.candidate_id,approved_version=c.version,approved_script_hash=p.script_hash,ai_disclosure_review=r)\\\\n print(tier.value,g.allowed,g.reason)\\\\nPY\\\",\\n 'comfy_cmd': 'command -v comfy || true',\\n 'comfy_local': 'curl -s --max-time 3 http://127.0.0.1:8188/system_stats || true',\\n 'gpu': 'nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv,noheader 2>/dev/null || true',\\n 'env': 'env | grep -E \\\"COMFY|WAN|TAILSCALE\\\" || true'\\n}\\nfor k,cmd in checks.items():\\n print('##',k)\\n print(terminal(cmd, workdir=wd, timeout=60)['output'][:4000])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"## resource_gate\\nai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\\nclaim True ()\\nai True ()\\nwan_final_render True WAN final render allowed by explicit final-render approval.\\npublishing False Publishing is disabled in this workflow.\\n## comfy_cmd\\n\\n## comfy_local\\n\\n## gpu\\n\\n## env\\n\\n\", \"tool_calls_made\": 5, \"duration_seconds\": 1.88}",
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"arguments": "{\"query\":\"Wan2.2 OR ComfyUI OR Tailscale A5000 OR AutoShortsBot Wan final render\", \"limit\":5}"
}
}
]
},
{
"role": "tool",
"name": "session_search",
"content": "{\"success\": true, \"query\": \"Wan2.2 OR ComfyUI OR Tailscale A5000 OR AutoShortsBot Wan final render\", \"results\": [{\"session_id\": \"20260523_074449_3d9cee59\", \"when\": \"May 23, 2026 at 07:44 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\n56|\\\\n 157|\\\\n 158|def write_shot_plans(finals: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\\\n 159| \\\\\\\"\\\\\\\"\\\\\\\"Write one Markdown shot plan per finalized script.\\\\\\\"\\\\\\\"\\\\\\\"\\\\n 160| output_dir.mkdir(parents=True, exist_ok=True)\\\\n 161| paths: list[Path] = []\\\\n 162| for final in finals:\\\\n 163| plan = build_shot_plan(final)\\\\n 164| path = output_dir / f\\\\\\\"{plan['id']}-{_slug(plan['title'])}.md\\\\\\\"\\\\n 165| path.write_t\\n…[truncated]\"}, {\"session_id\": \"20260524_091219_d252c13f\", \"when\": \"May 24, 2026 at 09:12 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\n ich bauen:\\n\\n```text\\nautoshorts/ideas/factory.py\\n```\\n\\nZiel:\\n\\n- aus Pillar + Rohidee einen `VideoCandidate` erzeugen\\n- automatische ID-Generierung\\n- Standard-Hashtags aus Pillar/Plattform ableiten\\n- Dauerklasse prüfen:\\n - growth short: 20–45s\\n - monetization candidate: 60–75s\\n- Hypothesis erzwingen\\n- direkt validierten Candidate zurückgeben\\n\\nDann können wir aus Content-Strategie tatsächlich Kandidaten erzeugen, statt sie nur manuell zu modellieren.\\n\\n[USER\\n…[truncated]\"}], \"count\": 2, \"sessions_searched\": 2}",
"tool_call_id": "call_hMkPWQHi62PjFtlSVcqZqu9n"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Exploring rendering options**\n\nI'm thinking about whether I should call it Wan. Maybe we can create a final video using a deterministic renderer? However, assessing Wan's final quality might not be feasible. I wonder if I need to try installing comfy-cli, but that wouldn't solve the GPU issue. I might need to check Tailscale for IP addresses, and I should probably look into using terminal commands for the system state. Checking the network for Tailscale sounds like a plan!",
"finish_reason": "tool_calls",
"reasoning_content": "**Exploring rendering options**\n\nI'm thinking about whether I should call it Wan. Maybe we can create a final video using a deterministic renderer? However, assessing Wan's final quality might not be feasible. I wonder if I need to try installing comfy-cli, but that wouldn't solve the GPU issue. I might need to check Tailscale for IP addresses, and I should probably look into using terminal commands for the system state. Checking the network for Tailscale sounds like a plan!",
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"id": "rs_05cdea51f45b90d7016a141ee6c5008191b45d72a013871235",
"summary": [
{
"type": "summary_text",
"text": "**Exploring rendering options**\n\nI'm thinking about whether I should call it Wan. Maybe we can create a final video using a deterministic renderer? However, assessing Wan's final quality might not be feasible. I wonder if I need to try installing comfy-cli, but that wouldn't solve the GPU issue. I might need to check Tailscale for IP addresses, and I should probably look into using terminal commands for the system state. Checking the network for Tailscale sounds like a plan!"
}
]
}
],
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{
"id": "call_JpqX5WRosXOZEfYPfY0xJ79u",
"call_id": "call_JpqX5WRosXOZEfYPfY0xJ79u",
"response_item_id": "fc_05cdea51f45b90d7016a141eefcc888191b46229254b0a17f2",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"command -v tailscale || true; ip addr show tailscale0 2>/dev/null || true; getent hosts jarvis-gpu 2>/dev/null || true; python - <<'PY'\\nimport socket\\nfor host in ['100.64.0.1','100.100.100.100']:\\n try:\\n print(host, socket.create_connection((host,8188),timeout=1).getsockname())\\n except Exception as e: print(host,'no',type(e).__name__)\\nPY\",\"timeout\":20,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
]
},
{
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"content": "{\"output\": \"/usr/bin/tailscale\\n3: tailscale0: mtu 1280 qdisc fq_codel state UNKNOWN group default qlen 500\\n link/none \\n inet 100.85.29.67/32 scope global tailscale0\\n valid_lft forever preferred_lft forever\\n inet6 fd7a:115c:a1e0::901:1dba/128 scope global \\n valid_lft forever preferred_lft forever\\n inet6 fe80::6139:6a1b:ec73:c14a/64 scope link stable-privacy \\n valid_lft forever preferred_lft forever\\n100.64.0.1 no TimeoutError\\n100.100.100.100 no ConnectionRefusedError\", \"exit_code\": 0, \"error\": null}",
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},
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Slice\\nVideoLinearCFGGuidance\\nVideoTriangleCFGGuidance\\nVidu2ImageToVideoNode\\nVidu2ReferenceVideoNode\\nVidu2StartEndToVideoNode\\nVidu2TextToVideoNode\\nVidu3ImageToVideoNode\\nVidu3StartEndToVideoNode\\nVidu3TextToVideoNode\\nViduExtendVideoNode\\nViduImageToVideoNode\\nViduMultiFrameVideoNode\\nViduReferenceVideoNode\\nViduStartEndToVideoNode\\nViduTextToVideoNode\\nWan22FunControlToVideo\\nWan22ImageToVideoLatent\\nWan2ImageToVideoApi\\nWan2ReferenceVideoApi\\nWan2TextToVideoApi\\nWan2VideoContinuationApi\\nWan2VideoEditApi\\nWanAnimateToVideo\\nWanCameraEmbedding\\nWanCameraImageToVideo\\nWanContextWindowsManual\\nWanDancerEncodeAudio\\nWanDancerPadKeyframes\\nWanDancerPadKeyframesList\\nWanDancerVideo\\nWanFirstLastFrameToVideo\\nWanFunControlToVideo\\nWanFunInpaintToVideo\\nWanHuMoImageToVideo\\nWanImageToImageApi\\nWanImageToVideo\\nWanImageToVideoApi\\nWanInfiniteTalkToVideo\\nWanMoveConcatTrack\\nWanMoveTrackToVideo\\nWanMoveTracksFromCoords\\nWanMoveVisualizeTracks\\nWanMove_native\\nWanPhantomSubjectToVideo\\nWanReferenceVideoApi\\nWanSCAILToVideo\\nWanSoundImageToVideo\\nWanSoundImageToVideoExtend\\nWanTextToImageApi\\nWanTextToVideoApi\\nWanTrackToVideo\\nWanVaceToVideo\\nWanVideoATITracks\\nWanVideoATITracksVisualize\\nWanVideoATI_comfy\\nWanVideoAddBindweaveEmbeds\\nWanVideoAddControlEmbeds\\nWanVideoAddDualControlEmbeds\\nWanVideoAddExtraLatent\\nWanVideoAddFlashVSRInput\\nWanVideoAddLucyEditLatents\\nWanVideoAddLynxEmbeds\\nWanVideoAddMTVMotion\\nWanVideoAddOneToAllExtendEmbeds\\nWanVideoAddOneToAllPoseEmbeds\\nWanVideoAddOneToAllReferenceEmbeds\\nWanVideoAddOviAudioToLatents\\nWanVideoAddPusaNoise\\nWanVideoAddS2VEmbeds\\nWanVideoAddSCAILPoseEmbeds\\nWanVideoAddSCAILReferenceEmbeds\\nWanVideoAddStandInLatent\\nWanVideoAddSteadyDancerEmbeds\\nWanVideoAddStoryMemLatents\\nWanVideoAddTTMLatents\\nWanVideoAddWanMoveTracks\\nWanVideoAnimateEmbeds\\nWanVideoApplyNAG\\nWanVideoBlockList\\nWanVideoBlockSwap\\nWanVideoClipVisionEncode\\nWanVideoCombineEmbeds\\nWanVideoContextOptions\\nWanVideoControlEmbeds\\nWanVideoControlnet\\nWanVideoControlnetLoader\\nWanVideoDecode\\nWanVideoDecodeOviAudio\\nWanVideoDiffusionForcingSampler\\nWanVideoEasyCache\\nWanVideoEmptyEmbeds\\nWanVideoEmptyMMAudioLatents\\nWanVideoEncode\\nWanVideoEncodeLatentBatch\\nWanVideoEncodeOviAudio\\nWanVideoEnhanceAVideo\\nWanVideoExperimentalArgs\\nWanVideoExtraModelSelect\\nWanVideoFlashVSRDecoderLoader\\nWanVideoFreeInitArgs\\nWanVideoFunCameraEmbeds\\nWanVideoImageClipEncode\\nWanVideoImageResizeToClosest\\nWanVideoImageToVideoEncode\\nWanVideoImageToVideoMultiTalk\\nWanVideoImageToVideoSkyreelsv3_audio\\nWanVideoLatentReScale\\nWanVideoLongCatAvatarExtendEmbeds\\nWanVideoLoopArgs\\nWanVideoLoraBlockEdit\\nWanVideoLoraSelect\\nWanVideoLoraSelectByName\\nWanVideoLoraSelectMulti\\nWanVideoMagCache\\nWanVideoMiniMaxRemoverEmbeds\\nWanVideoModelLoader\\nWanVideoOviCFG\\nWanVideoPassImagesFromSamples\\nWanVideoPhantomEmbeds\\nWanVideoPreviewEmbeds\\nWanVideoPromptExtender\\nWanVideoPromptExtenderSelect\\nWanVideoReCamMasterCameraEmbed\\nWanVideoReCamMasterDefaultCamera\\nWanVideoReCamMasterGenerateOrbitCamera\\nWanVideoRealisDanceLatents\\nWanVideoRoPEFunction\\nWanVideoSLG\\nWanVideoSVIProEmbeds\\nWanVideoSampler\\nWanVideoSamplerExtraArgs\\nWanVideoSamplerFromSettings\\nWanVideoSamplerSettings\\nWanVideoSamplerv2\\nWanVideoScheduler\\nWanVideoSchedulerv2\\nWanVideoSetAttentionModeOverride\\nWanVideoSetBlockSwap\\nWanVideoSetLoRAs\\nWanVideoSetRadialAttention\\nWanVideoSigmaToStep\\nWanVideoTeaCache\\nWanVideoTextEmbedBridge\\nWanVideoTextEncode\\nWanVideoTextEncodeCached\\nWanVideoTextEncodeSingle\\nWanVideoTinyVAELoader\\nWanVideoTorchCompileSettings\\nWanVideoUltraVicoSettings\\nWanVideoUni3C_ControlnetLoader\\nWanVideoUni3C_embeds\\nWanVideoUniAnimateDWPoseDetector\\nWanVideoUniAnimatePoseInput\\nWanVideoUniLumosEmbeds\\nWanVideoVACEEncode\\nWanVideoVACEModelSelect\\nWanVideoVACEStartToEndFrame\\nWanVideoVAELoader\\nWanVideoVRAMManagement\\nWanVideoWanDrawWanMoveTracks\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_pr4ZP00jzHswNTHhpkQzjQAs"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Creating a workflow**\n\nI'm thinking I need to set up a workflow. It might be simpler to use native nodes, but constructing everything from scratch could get quite complex. I'm considering using the API nodes like WanTextToVideoApi instead. I need to check the input specifications for those objects to ensure they fit my needs. It’s about finding the best balance between simplicity and functionality here!",
"finish_reason": "tool_calls",
"reasoning_content": "**Creating a workflow**\n\nI'm thinking I need to set up a workflow. It might be simpler to use native nodes, but constructing everything from scratch could get quite complex. I'm considering using the API nodes like WanTextToVideoApi instead. I need to check the input specifications for those objects to ensure they fit my needs. It’s about finding the best balance between simplicity and functionality here!",
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{
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{
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"text": "**Creating a workflow**\n\nI'm thinking I need to set up a workflow. It might be simpler to use native nodes, but constructing everything from scratch could get quite complex. I'm considering using the API nodes like WanTextToVideoApi instead. I need to check the input specifications for those objects to ensure they fit my needs. It’s about finding the best balance between simplicity and functionality here!"
}
]
}
],
"tool_calls": [
{
"id": "call_IqPZIH9X4aSvG6D2VjxTlZCD",
"call_id": "call_IqPZIH9X4aSvG6D2VjxTlZCD",
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"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request,json\\ninfo=json.load(urllib.request.urlopen('http://100.101.173.25:8188/object_info',timeout=20))\\nfor name in ['Wan2TextToVideoApi','WanTextToVideoApi','WanVideoModelLoader','WanVideoTextEncode','WanVideoSampler','WanVideoDecode','WanVideoVAELoader','SaveVideo','CreateVideo']:\\n print('\\\\n##',name)\\n node=info.get(name)\\n if not node: print('missing'); continue\\n print(json.dumps(node.get('input',{}),indent=2)[:5000])\\n print('output',node.get('output'), node.get('output_name'))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"## Wan2TextToVideoApi\\n{\\n \\\"required\\\": {\\n \\\"model\\\": [\\n \\\"COMFY_DYNAMICCOMBO_V3\\\",\\n {\\n \\\"options\\\": [\\n {\\n \\\"key\\\": \\\"wan2.7-t2v\\\",\\n \\\"inputs\\\": {\\n \\\"required\\\": {\\n \\\"prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"Prompt describing the elements and visual features. Supports English and Chinese.\\\",\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ],\\n \\\"negative_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"Negative prompt describing what to avoid.\\\",\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ],\\n \\\"resolution\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"720P\\\",\\n \\\"1080P\\\"\\n ]\\n }\\n ],\\n \\\"ratio\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"16:9\\\",\\n \\\"9:16\\\",\\n \\\"1:1\\\",\\n \\\"4:3\\\",\\n \\\"3:4\\\"\\n ]\\n }\\n ],\\n \\\"duration\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 5,\\n \\\"min\\\": 2,\\n \\\"max\\\": 15,\\n \\\"step\\\": 1,\\n \\\"display\\\": \\\"number\\\"\\n }\\n ]\\n }\\n }\\n }\\n ]\\n }\\n ],\\n \\\"seed\\\": [\\n \\\"INT\\\",\\n {\\n \\\"tooltip\\\": \\\"Seed to use for generation.\\\",\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 2147483647,\\n \\\"step\\\": 1,\\n \\\"control_after_generate\\\": true,\\n \\\"display\\\": \\\"number\\\"\\n }\\n ],\\n \\\"prompt_extend\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"tooltip\\\": \\\"Whether to enhance the prompt with AI assistance.\\\",\\n \\\"advanced\\\": true,\\n \\\"default\\\": true\\n }\\n ],\\n \\\"watermark\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"tooltip\\\": \\\"Whether to add an AI-generated watermark to the result.\\\",\\n \\\"advanced\\\": true,\\n \\\"default\\\": false\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"audio\\\": [\\n \\\"AUDIO\\\",\\n {\\n \\\"tooltip\\\": \\\"Audio for driving video generation (e.g., lip sync, beat-matched motion). Duration: 3s-30s. If not provided, the model automatically generates matching background music or sound effects.\\\"\\n }\\n ]\\n },\\n \\\"hidden\\\": {\\n \\\"auth_token_comfy_org\\\": [\\n \\\"AUTH_TOKEN_COMFY_ORG\\\"\\n ],\\n \\\"api_key_comfy_org\\\": [\\n \\\"API_KEY_COMFY_ORG\\\"\\n ],\\n \\\"unique_id\\\": [\\n \\\"UNIQUE_ID\\\"\\n ]\\n }\\n}\\noutput ['VIDEO'] ['VIDEO']\\n\\n## WanTextToVideoApi\\n{\\n \\\"required\\\": {\\n \\\"model\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"Model to use.\\\",\\n \\\"default\\\": \\\"wan2.6-t2v\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"wan2.5-t2v-preview\\\",\\n \\\"wan2.6-t2v\\\"\\n ]\\n }\\n ],\\n \\\"prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"Prompt describing the elements and visual features. Supports English and Chinese.\\\",\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"negative_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"Negative prompt describing what to avoid.\\\",\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ],\\n \\\"size\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"default\\\": \\\"720p: 1:1 (960x960)\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"480p: 1:1 (624x624)\\\",\\n \\\"480p: 16:9 (832x480)\\\",\\n \\\"480p: 9:16 (480x832)\\\",\\n \\\"720p: 1:1 (960x960)\\\",\\n \\\"720p: 16:9 (1280x720)\\\",\\n \\\"720p: 9:16 (720x1280)\\\",\\n \\\"720p: 4:3 (1088x832)\\\",\\n \\\"720p: 3:4 (832x1088)\\\",\\n \\\"1080p: 1:1 (1440x1440)\\\",\\n \\\"1080p: 16:9 (1920x1080)\\\",\\n \\\"1080p: 9:16 (1080x1920)\\\",\\n \\\"1080p: 4:3 (1632x1248)\\\",\\n \\\"1080p: 3:4 (1248x1632)\\\"\\n ]\\n }\\n ],\\n \\\"duration\\\": [\\n \\\"INT\\\",\\n {\\n \\\"tooltip\\\": \\\"A 15-second duration is available only for the Wan 2.6 model.\\\",\\n \\\"default\\\": 5,\\n \\\"min\\\": 5,\\n \\\"max\\\": 15,\\n \\\"step\\\": 5,\\n \\\"display\\\": \\\"number\\\"\\n }\\n ],\\n \\\"audio\\\": [\\n \\\"AUDIO\\\",\\n {\\n \\\"tooltip\\\": \\\"Audio must contain a clear, loud voice, without extraneous noise or background music.\\\"\\n }\\n ],\\n \\\"seed\\\": [\\n \\\"INT\\\",\\n {\\n \\\"tooltip\\\": \\\"Seed to use for generation.\\\",\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 2147483647,\\n \\\"step\\\": 1,\\n \\\"control_after_generate\\\": true,\\n \\\"display\\\": \\\"number\\\"\\n }\\n ],\\n \\\"generate_audio\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"tooltip\\\": \\\"If no audio input is provided, generate audio automatically.\\\",\\n \\\"advanced\\\": true,\\n \\\"default\\\": false\\n }\\n ],\\n \\\"prompt_extend\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"tooltip\\\": \\\"Whether to enhance the prompt with AI assistance.\\\",\\n \\\"advanced\\\": true,\\n \\\"default\\\": true\\n }\\n ],\\n \\\"watermark\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"tooltip\\\": \\\"Whether to add an AI-generated watermark to the result.\\\",\\n \\\"advanced\\\": true,\\n \\\"default\\\": false\\n }\\n ],\\n \\\"shot_type\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"Specifies the shot type for the generated video, that is, whether the video is a single continuous shot or multiple shots with cuts. This parameter takes effect only when prompt_extend is True.\\\",\\n \\\"advanced\\\": true,\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"single\\\",\\n \\\"multi\\\"\\n ]\\n }\\n ]\\n },\\n \\\"hidden\\\": {\\n \\\"auth_token_comfy_org\\\": [\\n \\\"AUTH_TOKEN_COMFY_ORG\\\"\\n ],\\n \\\"api_key_comfy_org\\\": [\\n \\\"API_KEY_COMFY_ORG\\\"\\n ],\\n \\\"unique_id\\\": [\\n \\\"UNIQUE_ID\\\"\\n ]\\n }\\n}\\noutput ['VIDEO'] ['VIDEO']\\n\\n## WanVideoModelLoader\\n{\\n \\\"required\\\": {\\n \\\"model\\\": [\\n [\\n \\\"Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\\\",\\n \\\"wan2.2_ti2v_5B_fp16.safetensors\\\"\\n ],\\n {\\n \\\"tooltip\\\": \\\"These models are loaded from the 'ComfyUI/models/diffusion_models' -folder\\\"\\n }\\n ],\\n \\\"base_precision\\\": [\\n [\\n \\\"fp32\\\",\\n \\\"bf16\\\",\\n \\\"fp16\\\",\\n \\\"fp16_fast\\\"\\n ],\\n {\\n \\\"default\\\": \\\"bf16\\\"\\n }\\n ],\\n \\\"quantization\\\": [\\n [\\n \\\"disabled\\\",\\n \\\"fp8_e4m3fn\\\",\\n \\\"fp8_e4m3fn_fast\\\",\\n \\\"fp8_e4m3fn_scaled\\\",\\n \\\"fp8_e4m3fn_scaled_fast\\\",\\n \\\"fp8_e5m2\\\",\\n \\\"fp8_e5m2_fast\\\",\\n \\\"fp8_e5m2_scaled\\\",\\n \\\"fp8_e5m2_scaled_fast\\\"\\n ],\\n {\\n \\\"default\\\": \\\"disabled\\\",\\n \\\"tooltip\\\": \\\"Optional quantization method, 'disabled' acts as autoselect based by weights. Scaled modes only work with matching weights, _fast modes (fp8 matmul) require CUDA compute capability >= 8.9 (NVIDIA 4000 series and up), e4m3fn generally can not be torch.compiled on compute capability < 8.9 (3000 series and under)\\\"\\n }\\n ],\\n \\\"load_device\\\": [\\n [\\n \\\"main_device\\\",\\n \\\"offload_device\\\"\\n ],\\n {\\n \\\"default\\\": \\\"offload_device\\\",\\n \\\"tooltip\\\": \\\"Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"attention_mode\\\": [\\n [\\n \\\"sdpa\\\",\\n \\\"flash_attn_2\\\",\\n \\\"flash_attn_3\\\",\\n \\\"sageattn\\\",\\n \\\"sageattn_3\\\",\\n \\\"radial_sage_attention\\\",\\n \\\"sageattn_compiled\\\",\\n \\\"sageattn_ultravico\\\",\\n \\\"comfy\\\"\\n ],\\n {\\n \\\"default\\\": \\\"sdpa\\\"\\n }\\n ],\\n \\\"compile_args\\\": [\\n \\\"WANCOMPILEARGS\\\"\\n ],\\n \\\"block_swap_args\\\": [\\n \\\"BLOCKSWAPARGS\\\"\\n ],\\n \\\"lora\\\": [\\n \\\"WANVIDLORA\\\",\\n {\\n \\\"default\\\": null\\n }\\n ],\\n \\\"vram_management_args\\\": [\\n \\\"VRAM_MANAGEMENTARGS\\\",\\n {\\n \\\"default\\\": null,\\n \\\"tooltip\\\": \\\"Alternative offloading method from DiffSynth-Studio, more aggressive in reducing memory use than block swapping, but can be slower\\\"\\n }\\n ],\\n \\\"extra_model\\\": [\\n \\\"VACEPATH\\\",\\n {\\n \\\"default\\\": null,\\n \\\"tooltip\\\": \\\"Extra model to add to the main model, ie. VACE or MTV Crafter\\\"\\n }\\n ],\\n \\\"fantasytalking_model\\\": [\\n \\\"FANTASYTALKINGMODEL\\\",\\n {\\n \\\"default\\\": null,\\n \\\"tooltip\\\": \\\"FantasyTalking model https://github.com/Fantasy-AMAP\\\"\\n }\\n ],\\n \\\"multitalk_model\\\": [\\n \\\"MULTITALKMODEL\\\",\\n {\\n \\\"default\\\": null,\\n \\\"tooltip\\\": \\\"Multitalk model\\\"\\n }\\n ],\\n \\\"fantasyportrait_model\\\": [\\n \\\"FANTASYPORTRAITMODEL\\\",\\n {\\n \\\"default\\\": null,\\n \\\"tooltip\\\": \\\"FantasyPortrait model\\\"\\n }\\n ],\\n \\\"rms_norm_function\\\": [\\n [\\n \\\"default\\\",\\n \\\"pytorch\\\"\\n ],\\n {\\n \\\"default\\\": \\\"default\\\",\\n \\\"tooltip\\\": \\\"RMSNorm function to use, 'pytorch' is the new native torch RMSNorm, which is faster (when not using torch.compile mostly) but changes results slightly. 'default' is the original WanRMSNorm\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANVIDEOMODEL'] ['model']\\n\\n## WanVideoTextEncode\\n{\\n \\\"required\\\": {\\n \\\"positive_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ],\\n \\\"negative_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"t5\\\": [\\n \\\"WANTEXTENCODER\\\"\\n ],\\n \\\"force_offload\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": true\\n }\\n ],\\n \\\"model_to_offload\\\": [\\n \\\"WANVIDEOMODEL\\\",\\n {\\n \\\"tooltip\\\": \\\"Model to move to offload_device before encoding\\\"\\n }\\n ],\\n \\\"use_disk_cache\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory\\\"\\n }\\n ],\\n \\\"device\\\": [\\n [\\n \\\"gpu\\\",\\n \\\"cpu\\\"\\n ],\\n {\\n \\\"default\\\": \\\"gpu\\\",\\n \\\"tooltip\\\": \\\"Device to run the text encoding on.\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANVIDEOTEXTEMBEDS'] ['text_embeds']\\n\\n## WanVideoSampler\\n{\\n \\\"required\\\": {\\n \\\"model\\\": [\\n \\\"WANVIDEOMODEL\\\"\\n ],\\n \\\"image_embeds\\\": [\\n \\\"WANVIDIMAGE_EMBEDS\\\"\\n ],\\n \\\"steps\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 30,\\n \\\"min\\\": 1\\n }\\n ],\\n \\\"cfg\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 6.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 30.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"shift\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 5.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 1000.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"seed\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 18446744073709551615\\n }\\n ],\\n \\\"force_offload\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": true,\\n \\\"tooltip\\\": \\\"Moves the model to the offload device after sampling\\\"\\n }\\n ],\\n \\\"scheduler\\\": [\\n [\\n \\\"unipc\\\",\\n \\\"unipc/beta\\\",\\n \\\"dpm++\\\",\\n \\\"dpm++/beta\\\",\\n \\\"dpm++_sde\\\",\\n \\\"dpm++_sde/beta\\\",\\n \\\"euler\\\",\\n \\\"euler/beta\\\",\\n \\\"longcat_distill_euler\\\",\\n \\\"deis\\\",\\n \\\"lcm\\\",\\n \\\"lcm/beta\\\",\\n \\\"res_multistep\\\",\\n \\\"er_sde\\\",\\n \\\"flowmatch_causvid\\\",\\n \\\"flowmatch_distill\\\",\\n \\\"flowmatch_pusa\\\",\\n \\\"multitalk\\\",\\n \\\"sa_ode_stable\\\",\\n \\\"rcm\\\",\\n \\\"vibt_unipc\\\"\\n ],\\n {\\n \\\"default\\\": \\\"unipc\\\"\\n }\\n ],\\n \\\"riflex_freq_index\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 1000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"text_embeds\\\": [\\n \\\"WANVIDEOTEXTEMBEDS\\\"\\n ],\\n \\\"samples\\\": [\\n \\\"LATENT\\\",\\n {\\n \\\"tooltip\\\": \\\"init Latents to use for video2video process\\\"\\n }\\n ],\\n \\\"denoise_strength\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 1.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 1.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"feta_args\\\": [\\n \\\"FETAARGS\\\"\\n ],\\n \\\"context_options\\\": [\\n \\\"WANVIDCONTEXT\\\"\\n ],\\n \\\"cache_args\\\": [\\n \\\"CACHEARGS\\\"\\n ],\\n \\\"flowedit_args\\\": [\\n \\\"FLOWEDITARGS\\\",\\n {\\n \\\"tooltip\\\": \\\"FlowEdit support has been deprecated\\\"\\n }\\n ],\\n \\\"batched_cfg\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory\\\"\\n }\\n ],\\n \\\"slg_args\\\": [\\n \\\"SLGARGS\\\"\\n ],\\n \\\"rope_function\\\": [\\n [\\n \\\"default\\\",\\n \\\"comfy\\\",\\n \\\"comfy_chunked\\\"\\n ],\\n {\\n \\\"default\\\": \\\"comfy\\\",\\n \\\"tooltip\\\": \\\"Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile\\\"\\n }\\n ],\\n \\\"loop_args\\\": [\\n \\\"LOOPARGS\\\"\\n ],\\n \\\"experimental_args\\\": [\\n \\\"EXPERIMENTALARGS\\\"\\n ],\\n \\\"sigmas\\\": [\\n \\\"SIGMAS\\\"\\n ],\\n \\\"unianimate_poses\\\": [\\n \\\"UNIANIMATE_POSE\\\"\\n ],\\n \\\"fantasytalking_embeds\\\": [\\n \\\"FANTASYTALKING_EMBEDS\\\"\\n ],\\n \\\"uni3c_embeds\\\": [\\n \\\"UNI3C_EMBEDS\\\"\\n ],\\n \\\"multitalk_embeds\\\": [\\n \\\"MULTITALK_EMBEDS\\\"\\n ],\\n \\\"freeinit_args\\\": [\\n \\\"FREEINITARGS\\\"\\n ],\\n \\\"start_step\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 10000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"Start step for the sampling, 0 means full sampling, otherwise samples only from this step\\\"\\n }\\n ],\\n \\\"end_step\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": -1,\\n \\\"min\\\": -1,\\n \\\"max\\\": 10000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"End step for the sampling, -1 means full sampling, otherwise samples only until this step\\\"\\n }\\n ],\\n \\\"add_noise_to_samples\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Add noise to the samples before sampling, needed for video2video sampling when starting from clean video\\\"\\n }\\n ]\\n }\\n}\\noutput ['LATENT', 'LATENT'] ['samples', 'denoised_samples']\\n\\n## WanVideoDecode\\n{\\n \\\"required\\\": {\\n \\\"vae\\\": [\\n \\\"WANVAE\\\"\\n ],\\n \\\"samples\\\": [\\n \\\"LATENT\\\"\\n ],\\n \\\"enable_vae_tiling\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams can be controlled by increasing the tile size. Seams become less obvious at 1.5x stride and are barely noticeable at 2x stride size. Which is to say if you use a stride width of 160, the seams are barely noticeable with a tile width of 320.\\\"\\n }\\n ],\\n \\\"tile_x\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 272,\\n \\\"min\\\": 40,\\n \\\"max\\\": 2048,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.\\\"\\n }\\n ],\\n \\\"tile_y\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 272,\\n \\\"min\\\": 40,\\n \\\"max\\\": 2048,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.\\\"\\n }\\n ],\\n \\\"tile_stride_x\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 144,\\n \\\"min\\\": 32,\\n \\\"max\\\": 2040,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.\\\"\\n }\\n ],\\n \\\"tile_stride_y\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 128,\\n \\\"min\\\": 32,\\n \\\"max\\\": 2040,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"normalization\\\": [\\n [\\n \\\"default\\\",\\n \\\"minmax\\\",\\n \\\"none\\\"\\n ],\\n {\\n \\\"advanced\\\": true\\n }\\n ]\\n }\\n}\\noutput ['IMAGE'] ['images']\\n\\n## WanVideoVAELoader\\n{\\n \\\"required\\\": {\\n \\\"model_name\\\": [\\n [\\n \\\"Wan2_1_VAE_bf16.safetensors\\\",\\n \\\"wan2.2_vae.safetensors\\\"\\n ],\\n {\\n \\\"tooltip\\\": \\\"These models are loaded from 'ComfyUI/models/vae'\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"precision\\\": [\\n [\\n \\\"fp16\\\",\\n \\\"fp32\\\",\\n \\\"bf16\\\"\\n ],\\n {\\n \\\"default\\\": \\\"bf16\\\"\\n }\\n ],\\n \\\"compile_args\\\": [\\n \\\"WANCOMPILEARGS\\\"\\n ],\\n \\\"use_cpu_cache\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Reduces VRAM usage, but slows the VAE down a lot\\\"\\n }\\n ],\\n \\\"verbose\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Enables memory usage logging when using the model\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANVAE'] ['vae']\\n\\n## SaveVideo\\n{\\n \\\"required\\\": {\\n \\\"video\\\": [\\n \\\"VIDEO\\\",\\n {\\n \\\"tooltip\\\": \\\"The video to save.\\\"\\n }\\n ],\\n \\\"filename_prefix\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.\\\",\\n \\\"default\\\": \\\"video/ComfyUI\\\",\\n \\\"multiline\\\": false\\n }\\n ],\\n \\\"format\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"The format to save the video as.\\\",\\n \\\"default\\\": \\\"auto\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"auto\\\",\\n \\\"mp4\\\"\\n ]\\n }\\n ],\\n \\\"codec\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"The codec to use for the video.\\\",\\n \\\"default\\\": \\\"auto\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"auto\\\",\\n \\\"h264\\\"\\n ]\\n }\\n ]\\n },\\n \\\"hidden\\\": {\\n \\\"prompt\\\": [\\n \\\"PROMPT\\\"\\n ],\\n \\\"extra_pnginfo\\\": [\\n \\\"EXTRA_PNGINFO\\\"\\n ]\\n }\\n}\\noutput [] []\\n\\n## CreateVideo\\n{\\n \\\"required\\\": {\\n \\\"images\\\": [\\n \\\"IMAGE\\\",\\n {\\n \\\"tooltip\\\": \\\"The images to create a video from.\\\"\\n }\\n ],\\n \\\"fps\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 30.0,\\n \\\"min\\\": 1.0,\\n \\\"max\\\": 120.0,\\n \\\"step\\\": 1.0\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"audio\\\": [\\n \\\"AUDIO\\\",\\n {\\n \\\"tooltip\\\": \\\"The audio to add to the video.\\\"\\n }\\n ]\\n }\\n}\\noutput ['VIDEO'] ['VIDEO']\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_IqPZIH9X4aSvG6D2VjxTlZCD"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Exploring workflow needs**\n\nI’m thinking I need a workflow with a wrapper to pull everything together. It seems like I might need to inspect more nodes to ensure everything is functioning properly. I also want to look into the “Empty latent” and the T5 loader specifically. It’s essential to get this right, as I want to make sure all parts are working smoothly together! Let's figure this out step by step.",
"finish_reason": "tool_calls",
"reasoning_content": "**Exploring workflow needs**\n\nI’m thinking I need a workflow with a wrapper to pull everything together. It seems like I might need to inspect more nodes to ensure everything is functioning properly. I also want to look into the “Empty latent” and the T5 loader specifically. It’s essential to get this right, as I want to make sure all parts are working smoothly together! Let's figure this out step by step.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_05cdea51f45b90d7016a141faf02dc8191be449c8e0e2cb8c6",
"summary": [
{
"type": "summary_text",
"text": "**Exploring workflow needs**\n\nI’m thinking I need a workflow with a wrapper to pull everything together. It seems like I might need to inspect more nodes to ensure everything is functioning properly. I also want to look into the “Empty latent” and the T5 loader specifically. It’s essential to get this right, as I want to make sure all parts are working smoothly together! Let's figure this out step by step."
}
]
}
],
"tool_calls": [
{
"id": "call_CeoljgHf6K00pSPbI9iDjK3l",
"call_id": "call_CeoljgHf6K00pSPbI9iDjK3l",
"response_item_id": "fc_05cdea51f45b90d7016a141fb1a9cc8191b477056c50eb7840",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request,json\\ninfo=json.load(urllib.request.urlopen('http://100.101.173.25:8188/object_info',timeout=20))\\nfor name in ['WanVideoEmptyEmbeds','WanVideoTextEncode','LoadWanVideoT5TextEncoder','WanVideoSampler','WanVideoDecode','CreateVideo','SaveVideo']:\\n print('\\\\n##',name)\\n node=info.get(name); print(json.dumps(node.get('input',{}),indent=2)[:8000] if node else 'missing')\\n print('output', node.get('output') if node else None, node.get('output_name') if node else None)\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"## WanVideoEmptyEmbeds\\n{\\n \\\"required\\\": {\\n \\\"width\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 832,\\n \\\"min\\\": 64,\\n \\\"max\\\": 8096,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Width of the image to encode\\\"\\n }\\n ],\\n \\\"height\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 480,\\n \\\"min\\\": 64,\\n \\\"max\\\": 8096,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Height of the image to encode\\\"\\n }\\n ],\\n \\\"num_frames\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 81,\\n \\\"min\\\": 1,\\n \\\"max\\\": 10000,\\n \\\"step\\\": 4,\\n \\\"tooltip\\\": \\\"Number of frames to encode\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"control_embeds\\\": [\\n \\\"WANVIDIMAGE_EMBEDS\\\",\\n {\\n \\\"tooltip\\\": \\\"control signal for the Fun -model\\\"\\n }\\n ],\\n \\\"extra_latents\\\": [\\n \\\"LATENT\\\",\\n {\\n \\\"tooltip\\\": \\\"First latent to use for the Pusa -model\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANVIDIMAGE_EMBEDS'] ['image_embeds']\\n\\n## WanVideoTextEncode\\n{\\n \\\"required\\\": {\\n \\\"positive_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ],\\n \\\"negative_prompt\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"default\\\": \\\"\\\",\\n \\\"multiline\\\": true\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"t5\\\": [\\n \\\"WANTEXTENCODER\\\"\\n ],\\n \\\"force_offload\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": true\\n }\\n ],\\n \\\"model_to_offload\\\": [\\n \\\"WANVIDEOMODEL\\\",\\n {\\n \\\"tooltip\\\": \\\"Model to move to offload_device before encoding\\\"\\n }\\n ],\\n \\\"use_disk_cache\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory\\\"\\n }\\n ],\\n \\\"device\\\": [\\n [\\n \\\"gpu\\\",\\n \\\"cpu\\\"\\n ],\\n {\\n \\\"default\\\": \\\"gpu\\\",\\n \\\"tooltip\\\": \\\"Device to run the text encoding on.\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANVIDEOTEXTEMBEDS'] ['text_embeds']\\n\\n## LoadWanVideoT5TextEncoder\\n{\\n \\\"required\\\": {\\n \\\"model_name\\\": [\\n [\\n \\\"umt5-xxl-enc-fp8_e4m3fn.safetensors\\\",\\n \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\"\\n ],\\n {\\n \\\"tooltip\\\": \\\"These models are loaded from 'ComfyUI/models/text_encoders'\\\"\\n }\\n ],\\n \\\"precision\\\": [\\n [\\n \\\"fp32\\\",\\n \\\"bf16\\\"\\n ],\\n {\\n \\\"default\\\": \\\"bf16\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"load_device\\\": [\\n [\\n \\\"main_device\\\",\\n \\\"offload_device\\\"\\n ],\\n {\\n \\\"default\\\": \\\"offload_device\\\"\\n }\\n ],\\n \\\"quantization\\\": [\\n [\\n \\\"disabled\\\",\\n \\\"fp8_e4m3fn\\\"\\n ],\\n {\\n \\\"default\\\": \\\"disabled\\\",\\n \\\"tooltip\\\": \\\"optional quantization method\\\"\\n }\\n ]\\n }\\n}\\noutput ['WANTEXTENCODER'] ['wan_t5_model']\\n\\n## WanVideoSampler\\n{\\n \\\"required\\\": {\\n \\\"model\\\": [\\n \\\"WANVIDEOMODEL\\\"\\n ],\\n \\\"image_embeds\\\": [\\n \\\"WANVIDIMAGE_EMBEDS\\\"\\n ],\\n \\\"steps\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 30,\\n \\\"min\\\": 1\\n }\\n ],\\n \\\"cfg\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 6.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 30.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"shift\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 5.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 1000.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"seed\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 18446744073709551615\\n }\\n ],\\n \\\"force_offload\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": true,\\n \\\"tooltip\\\": \\\"Moves the model to the offload device after sampling\\\"\\n }\\n ],\\n \\\"scheduler\\\": [\\n [\\n \\\"unipc\\\",\\n \\\"unipc/beta\\\",\\n \\\"dpm++\\\",\\n \\\"dpm++/beta\\\",\\n \\\"dpm++_sde\\\",\\n \\\"dpm++_sde/beta\\\",\\n \\\"euler\\\",\\n \\\"euler/beta\\\",\\n \\\"longcat_distill_euler\\\",\\n \\\"deis\\\",\\n \\\"lcm\\\",\\n \\\"lcm/beta\\\",\\n \\\"res_multistep\\\",\\n \\\"er_sde\\\",\\n \\\"flowmatch_causvid\\\",\\n \\\"flowmatch_distill\\\",\\n \\\"flowmatch_pusa\\\",\\n \\\"multitalk\\\",\\n \\\"sa_ode_stable\\\",\\n \\\"rcm\\\",\\n \\\"vibt_unipc\\\"\\n ],\\n {\\n \\\"default\\\": \\\"unipc\\\"\\n }\\n ],\\n \\\"riflex_freq_index\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 1000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"text_embeds\\\": [\\n \\\"WANVIDEOTEXTEMBEDS\\\"\\n ],\\n \\\"samples\\\": [\\n \\\"LATENT\\\",\\n {\\n \\\"tooltip\\\": \\\"init Latents to use for video2video process\\\"\\n }\\n ],\\n \\\"denoise_strength\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 1.0,\\n \\\"min\\\": 0.0,\\n \\\"max\\\": 1.0,\\n \\\"step\\\": 0.01\\n }\\n ],\\n \\\"feta_args\\\": [\\n \\\"FETAARGS\\\"\\n ],\\n \\\"context_options\\\": [\\n \\\"WANVIDCONTEXT\\\"\\n ],\\n \\\"cache_args\\\": [\\n \\\"CACHEARGS\\\"\\n ],\\n \\\"flowedit_args\\\": [\\n \\\"FLOWEDITARGS\\\",\\n {\\n \\\"tooltip\\\": \\\"FlowEdit support has been deprecated\\\"\\n }\\n ],\\n \\\"batched_cfg\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory\\\"\\n }\\n ],\\n \\\"slg_args\\\": [\\n \\\"SLGARGS\\\"\\n ],\\n \\\"rope_function\\\": [\\n [\\n \\\"default\\\",\\n \\\"comfy\\\",\\n \\\"comfy_chunked\\\"\\n ],\\n {\\n \\\"default\\\": \\\"comfy\\\",\\n \\\"tooltip\\\": \\\"Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked version has reduced peak VRAM usage when not using torch.compile\\\"\\n }\\n ],\\n \\\"loop_args\\\": [\\n \\\"LOOPARGS\\\"\\n ],\\n \\\"experimental_args\\\": [\\n \\\"EXPERIMENTALARGS\\\"\\n ],\\n \\\"sigmas\\\": [\\n \\\"SIGMAS\\\"\\n ],\\n \\\"unianimate_poses\\\": [\\n \\\"UNIANIMATE_POSE\\\"\\n ],\\n \\\"fantasytalking_embeds\\\": [\\n \\\"FANTASYTALKING_EMBEDS\\\"\\n ],\\n \\\"uni3c_embeds\\\": [\\n \\\"UNI3C_EMBEDS\\\"\\n ],\\n \\\"multitalk_embeds\\\": [\\n \\\"MULTITALK_EMBEDS\\\"\\n ],\\n \\\"freeinit_args\\\": [\\n \\\"FREEINITARGS\\\"\\n ],\\n \\\"start_step\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 0,\\n \\\"min\\\": 0,\\n \\\"max\\\": 10000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"Start step for the sampling, 0 means full sampling, otherwise samples only from this step\\\"\\n }\\n ],\\n \\\"end_step\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": -1,\\n \\\"min\\\": -1,\\n \\\"max\\\": 10000,\\n \\\"step\\\": 1,\\n \\\"tooltip\\\": \\\"End step for the sampling, -1 means full sampling, otherwise samples only until this step\\\"\\n }\\n ],\\n \\\"add_noise_to_samples\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Add noise to the samples before sampling, needed for video2video sampling when starting from clean video\\\"\\n }\\n ]\\n }\\n}\\noutput ['LATENT', 'LATENT'] ['samples', 'denoised_samples']\\n\\n## WanVideoDecode\\n{\\n \\\"required\\\": {\\n \\\"vae\\\": [\\n \\\"WANVAE\\\"\\n ],\\n \\\"samples\\\": [\\n \\\"LATENT\\\"\\n ],\\n \\\"enable_vae_tiling\\\": [\\n \\\"BOOLEAN\\\",\\n {\\n \\\"default\\\": false,\\n \\\"tooltip\\\": \\\"Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams can be controlled by increasing the tile size. Seams become less obvious at 1.5x stride and are barely noticeable at 2x stride size. Which is to say if you use a stride width of 160, the seams are barely noticeable with a tile width of 320.\\\"\\n }\\n ],\\n \\\"tile_x\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 272,\\n \\\"min\\\": 40,\\n \\\"max\\\": 2048,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.\\\"\\n }\\n ],\\n \\\"tile_y\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 272,\\n \\\"min\\\": 40,\\n \\\"max\\\": 2048,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.\\\"\\n }\\n ],\\n \\\"tile_stride_x\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 144,\\n \\\"min\\\": 32,\\n \\\"max\\\": 2040,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.\\\"\\n }\\n ],\\n \\\"tile_stride_y\\\": [\\n \\\"INT\\\",\\n {\\n \\\"default\\\": 128,\\n \\\"min\\\": 32,\\n \\\"max\\\": 2040,\\n \\\"step\\\": 8,\\n \\\"tooltip\\\": \\\"Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.\\\"\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"normalization\\\": [\\n [\\n \\\"default\\\",\\n \\\"minmax\\\",\\n \\\"none\\\"\\n ],\\n {\\n \\\"advanced\\\": true\\n }\\n ]\\n }\\n}\\noutput ['IMAGE'] ['images']\\n\\n## CreateVideo\\n{\\n \\\"required\\\": {\\n \\\"images\\\": [\\n \\\"IMAGE\\\",\\n {\\n \\\"tooltip\\\": \\\"The images to create a video from.\\\"\\n }\\n ],\\n \\\"fps\\\": [\\n \\\"FLOAT\\\",\\n {\\n \\\"default\\\": 30.0,\\n \\\"min\\\": 1.0,\\n \\\"max\\\": 120.0,\\n \\\"step\\\": 1.0\\n }\\n ]\\n },\\n \\\"optional\\\": {\\n \\\"audio\\\": [\\n \\\"AUDIO\\\",\\n {\\n \\\"tooltip\\\": \\\"The audio to add to the video.\\\"\\n }\\n ]\\n }\\n}\\noutput ['VIDEO'] ['VIDEO']\\n\\n## SaveVideo\\n{\\n \\\"required\\\": {\\n \\\"video\\\": [\\n \\\"VIDEO\\\",\\n {\\n \\\"tooltip\\\": \\\"The video to save.\\\"\\n }\\n ],\\n \\\"filename_prefix\\\": [\\n \\\"STRING\\\",\\n {\\n \\\"tooltip\\\": \\\"The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.\\\",\\n \\\"default\\\": \\\"video/ComfyUI\\\",\\n \\\"multiline\\\": false\\n }\\n ],\\n \\\"format\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"The format to save the video as.\\\",\\n \\\"default\\\": \\\"auto\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"auto\\\",\\n \\\"mp4\\\"\\n ]\\n }\\n ],\\n \\\"codec\\\": [\\n \\\"COMBO\\\",\\n {\\n \\\"tooltip\\\": \\\"The codec to use for the video.\\\",\\n \\\"default\\\": \\\"auto\\\",\\n \\\"multiselect\\\": false,\\n \\\"options\\\": [\\n \\\"auto\\\",\\n \\\"h264\\\"\\n ]\\n }\\n ]\\n },\\n \\\"hidden\\\": {\\n \\\"prompt\\\": [\\n \\\"PROMPT\\\"\\n ],\\n \\\"extra_pnginfo\\\": [\\n \\\"EXTRA_PNGINFO\\\"\\n ]\\n }\\n}\\noutput [] []\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_CeoljgHf6K00pSPbI9iDjK3l"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Considering workflow implementation**\n\nI'm thinking about using background notifications to keep track of progress. Eventually, I’ll need to continue the workflow, possibly starting it through Python and setting up a polling mechanism. I might consider combining elements later. It's crucial to construct the correct JSON format and understand the input types for the API. I noticed that executing /prompt can return errors if the inputs are invalid, so I'll plan to use outputs effectively and utilize SaveVideo.",
"finish_reason": "tool_calls",
"reasoning_content": "**Considering workflow implementation**\n\nI'm thinking about using background notifications to keep track of progress. Eventually, I’ll need to continue the workflow, possibly starting it through Python and setting up a polling mechanism. I might consider combining elements later. It's crucial to construct the correct JSON format and understand the input types for the API. I noticed that executing /prompt can return errors if the inputs are invalid, so I'll plan to use outputs effectively and utilize SaveVideo.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFB_LZz7bOyOSrzoCXBQhai4VxFMRAeMsx8hHk7nQeJx3hEes4po_Ll6JQ9ttiGs4pk97ag5p6rjIVhP1sbRMNVp8i1ihoKW9BlbDrd9-cAlKYijAzPNuWmm7ToWUl268JLQJ0t4mzMnL08vOroa1XuY_PwwT5_Vl8EK174xTpCOW_svvdNRw1GIu8aWRSOuW9x1wTMt4rGdnEyumhXg7L8tyn8x8LkoXJwpocjVmtOrjXO5SKTD_953eQWbTxl1wHc5hZO79CeWecYZMpCTQ19-w9Bt2Ju098r1Yqt8ZFxIxqSKCbX6Ma014wDuq-BfDiJ0HuGGR6W9l-1i1e7WTUmJCH9CCTPI--PgNOisXYW0DEefz77H1kGsjwjr9j6eE-cfOnQ7JIakaJ-ZVRaWYxhLYfV65H87vJmLDyNXJQsY0tJ3QNaYrGI_FffRdTjhFdz9KjZ23-d1VX5kclNjTaElNM8psmZ6aFLPz_IwdE9kwqu3tBTbcPGz5csFQi4V54FrHGLV3FnFweqoUGLEQlSo8jhFbzURKvku59xMFO5_0XUhJkd2MpaMZ5pVcVpbqY6W0Uizpjb5lGmge4aP9JmCtpdG9t1J77t1FVaD6NcdBMFgnK52SZQfg1d6qoQN-9arp3yaEdTIYcjc-BVju9pqZPt01pUM4vJFs5YcxgYcEV6jrxwG03I5-8p5wjVjn2ck9mHqB69BxAkq94p7jg1U93i04Pc4weUHMKk6N74gyd3lgRsd-DAEkyaRAxGTR_Vy8DzcST1wLptQ3yVA90dhEzEq675l0eXf8WkGM7TvHOKUG9XxrymQkzEXKGg8BJ3lDq_qbCwVnAfUqKNQvoAQBDuXKvIeH2_jmiU6vKGCFw2SJ9Y4uq-vbuDUT-vQqitQA46yzDKwT3lvx_rEtV3PR9QNiecjF__nXYeVdwFNGbRgIer1qXc0tOVtXH-kbO4jb3ufpczLmhcqE4aq1XZBwqLzkdFTZIRlrzSQtIBBJQn2Z1cn1j1LNIfsPbrqnLf5peFL7jFML9b6t00jMJri4EywOGWLYcX1EhNLMMuevEc5kFbHiDoYgxShX-p5EohpxZ43KnTVTiYUcUvEnWZrCmh2YfkdiLuNGHie58FNZmh-Hz9-qLRokTu2aRLeN06oZnzX_wprnhE596v07aZO2DvAKhR2m0VL56ajYDmhxaXGwlzFJ6vHNoFke-ADZwZGHhWYwBoL4_tV-AlvBPggBVUVLRktChd3xFMTtxlSnaaeg-6veYxgYtSfd11M5eEVzUXFDu-8zFcNQdmERxnfIY8KwlpJvSiipGIclebGkNhiNGIs0GwAb9HZDaY3nh9j9wU7_Qqdut6ALNGSotElOM8stdRDmdFt-3hv_FKKL7Oc13Rb0bCPLuq2BIm8aKktiMeVCYkivAhvN_9XJSQLFPfUElEozAVJcGpNQSAzqru4SFUQxtssE1GDwp1qfTsdgwic2JrK77qq6hmKnrWkmontnhQ8g1I8kTbIThJpnW8lYSpgcguyXTNkTqz0EASEFj_gJ6tA7IRQoBeT09ZVxdU-PE8jAHcIBmKtTHzF2Exv35gKeCpzrJQKw8zR_qV_fz9ZzaTMOEo-zoPsNjacwN3wxxM3UnGYFCRYK5REmep1p2thVANH2R2-Evmb_B64-41uQIUCLZzaS9_puJX8LKRpqMUGoolAv0SZ597jMuSiAqsxjB-5KiFQgqAzEytZP9FWGdl2ymheUXnmoaFd_bYB0t3K7KT74PCMKBeYSsSac2Yzzrp0r1aGGyMGqWaR4IoeW__1wzEPhExOqBGdFzvhlGZEMJm5tCuShbDtLTy-qHeYpJ7AhGkzt7ucj4DngjZl2lIwc5Xkwy3K0X0ENK9ighgoqoLVnuMQ7foLEIoWen7ayx1iYRildKmTxoQRwL_8Gp-kFDOjEkVwcRGCo4dqg1ruKO47bjQ2vUTKmHwGwWuP8jg5MrOXVtIIKwfzvr9zBZQBzfB8kYwKN9rjTNtEtDhquqAnQzpm5ExbRwsTBupBEgfoVJWOtbwkkyS6rzv-Y4OfmpIpzFwi1dLHZkhBqGsidyeIFPJ58uW5pY6h0Ok7y5oQbjLfTpxcZ3n12SBKGr7MZKzngcu8x2t2kFj4Nbo77vC2klzTYF_qTKi5gQtfFjZsu0xSkvgtSMLBn0NNFWdnqu7VveZHG_YEy3v2l3H4X4kp0u0uqHWG9vvkdjJ5gMTjIu5c7sr7BGQB-aC5wziW4ZRDce7ifFe5nEEAfMNRU3LCOFSmJpfcYKx6w7-9bXCGmEsJipzEgK4wkeBFxJaInXepFL1cD9tUkvVVE396Nl-855jdCwL6ijDvdSx2zrah1XhK3A7DA9lVGb48xM9ueWysqqYHIqyMtUMFlTE2Og3NSh98dsJ41ipX0Tug2gWH0DFhv9doEdg4lHrhEZKpTSf4p5BjjcLN1ennqT6gbM3QjWoiPH6Vh0KdvTULQ5qYmX-_0BS62kFOYOWfNbze32eFqV5vtmVDQCJlEz9Y4rZR7QkjOqJqkbkmYXFLgyYake-RHxdPiPF9dLQxH2RWvHQJLq-PmNTADErrSdj-X7qlUxrZc8NmSSryzvIEqMeqw67u8IO115AzgthycnUJOGyXF5PL-3jKkHnlVfZxfpgNy-qLpQwAXvyfAdg-PD5Oy5w96K8pWKlCELbQ9K8903CsAWePHXRAYCNuSppQNiMV8ehns8djG47Vyqh4dvDZBgh9t5E9et7bvFJ6xUmvcKGSxBhVG74RmVg_yVD7pBoK19VfjuYNl5qkT_enYRU1RP2Fle3PI-u48mniKB4vL0noJ9mB-fA-J3wR_ELJ5VwTW0elMgzy_0tLSMGfc5VW6N7KVaAVd8oTgmIaZmD8fbsXkUnmUtVlbi1CyI5XdX48QLzy8Z15lQgJe3hFWTbLVXprqdBd7xLfz0a_6BP1CNndBlGKBxLvCt0boknVfes1D7uHtuZTCNUKDkwz7eWOddnTYF5v9Uuell2jn4LRPUHslsqLG7-tenzJJ3Hc1z6_rHLSRacVRwaSrto3p8vtH5YEc2GbnLsB2ZNpeDyKDMQL6tZBSYnrQ0ARL7hUj7qDBxwEtTncs2d17s4AFsCNEt8X3x7LAL0mbRfRsgbvFF-SlJzCWLUMxUZ7D4gd3C11PTOe5cdlk3DtEgHZmVkbAbhcZ6Mm8G1UbIE3YEZ5jEPwV7ulIuhPtKaWAKXyDGDRO7n93NW1gNa9Fm6rix5AfwOfvOXmjHAruAG6TCiWQa7dBNspK34OBJWDd4YJIi4316r3SkRqeBn5HkIdS7gq6fdxPjXJBbvqtFrJ67kDk6qnXKEIuQpiN3A-BaSgi9mNF3Vbmdvqgr1bl_9L9A7pdpWqDhHhBz38zhsrx6kqYI2cEYSabn7Hdtce3dThEN-FdMSsI4goWfGgu2ZoQRBNj4oLwQ0U1Hg-DQAin1FAGmEfQDakJqQzpg1ycnhUPYrefMP_zQy6tBPTQx0zwxAe9O-fsoyjTZUnci_eFdkfll-V3FoIopkXa5X3I8MMiqh6h_OIumeLY_IHFP2YiSWE8gS7N6_z9O3FTu0oXbtTRrEpnU8KbcYZWSiKlIhmtkw-ozWV-WT-_sOntNv3qM4NVcPBOZrmy2SQ3",
"id": "rs_05cdea51f45b90d7016a141fb7d1b881919e968478d74a1c58",
"summary": [
{
"type": "summary_text",
"text": "**Considering workflow implementation**\n\nI'm thinking about using background notifications to keep track of progress. Eventually, I’ll need to continue the workflow, possibly starting it through Python and setting up a polling mechanism. I might consider combining elements later. It's crucial to construct the correct JSON format and understand the input types for the API. I noticed that executing /prompt can return errors if the inputs are invalid, so I'll plan to use outputs effectively and utilize SaveVideo."
}
]
}
],
"tool_calls": [
{
"id": "call_dnsTuc8010pBCRsaT2e94GGE",
"call_id": "call_dnsTuc8010pBCRsaT2e94GGE",
"response_item_id": "fc_05cdea51f45b90d7016a141fcbf46c81919301a4eeeae4b72a",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from hermes_tools import terminal\\nimport json, urllib.request\\n# Free remote GPU memory and inspect queue\\nfor endpoint, data in [('/free', {'unload_models': True, 'free_memory': True})]:\\n cmd=f\\\"python - <<'PY'\\\\nimport urllib.request,json\\\\nreq=urllib.request.Request('http://100.101.173.25:8188{endpoint}',data=json.dumps({data}).encode(),headers={{'Content-Type':'application/json'}},method='POST')\\\\ntry:\\\\n print(urllib.request.urlopen(req,timeout=10).read().decode()[:500])\\\\nexcept Exception as e: print('ERR',repr(e))\\\\nPY\\\"\\n print(terminal(cmd, timeout=20)['output'])\\nprint(terminal('curl -s --max-time 10 http://100.101.173.25:8188/queue', timeout=20)['output'][:1000])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"\\n{\\\"queue_running\\\": [], \\\"queue_pending\\\": []}\\n\", \"tool_calls_made\": 2, \"duration_seconds\": 0.85}",
"tool_call_id": "call_dnsTuc8010pBCRsaT2e94GGE"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Submitting workflows**\n\nI need to submit a workflow using a background script that handles submissions and polls/downloads outputs. I should validate the process with /prompt and save video outputs in history. Let’s write a runner script since a single workflow might suffice. \n\nI’ll ensure the API format is correct, creating a unique client_id for the POST /prompt and polling /history/{id}. I might encounter issues with CreateVideo expecting an IMAGE, while WanVideoDecode outputs an IMAGE list.\n**Finalizing video workflow**\n\nI’m setting up the workflow with details for WanVideoEmptyEmbeds, including width, height, and frames. Then, I’ll use WanVideoSampler with specific parameters for steps and configurations. \n\nNext, I'll load the VAE model and decode the samples, enabling tiling. For video creation, I’ll create the video from images at 12 fps and save it in mp4 format. I might need to disable the Wan2.2 model quantization since the A5000 16GB could run out of memory. Finally, I’ll focus on achieving high quality before submitting everything.",
"finish_reason": "tool_calls",
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last=''\\nwhile True:\\n if time.time()-start > 3600:\\n raise TimeoutError('Wan job exceeded 1h')\\n hist=get_json('/history/'+prompt_id, timeout=30)\\n if prompt_id in hist:\\n (OUT/'wan_history.json').write_text(json.dumps(hist[prompt_id],indent=2))\\n print('DONE_HISTORY', flush=True)\\n break\\n q=get_json('/queue', timeout=30)\\n status=json.dumps(q)[:500]\\n if status != last:\\n print('QUEUE', status, flush=True); last=status\\n time.sleep(10)\\n# download outputs\\nhist=get_json('/history/'+prompt_id, timeout=30)[prompt_id]\\ndownloaded=[]\\nfor node_id,outdata in hist.get('outputs',{}).items():\\n for kind,items in outdata.items():\\n if not isinstance(items, list): continue\\n for item in items:\\n filename=item.get('filename'); sub=item.get('subfolder',''); typ=item.get('type','output')\\n if not filename: continue\\n qs=urllib.parse.urlencode({'filename':filename,'subfolder':sub,'type':typ})\\n url=HOST+'/view?'+qs\\n dest=OUT/filename.replace('/','_')\\n with urllib.request.urlopen(url,timeout=120) as r, open(dest,'wb') as f:\\n shutil.copyfileobj(r,f)\\n downloaded.append(str(dest))\\nprint('DOWNLOADED', json.dumps(downloaded), flush=True)\\n(OUT/'wan_downloaded.json').write_text(json.dumps(downloaded,indent=2))\\n\"}"
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"encrypted_content": "gAAAAABqFCKAAyvhBfMOKyRaPq6p850nws3qrnFy-gckl8aqeKr9Ab5IW06BPr4s5VKL8pK9qSl_WbF4aq0g7sgrPzR9font6N4CERo2Ieikahzx_852ol725fKy-pEOaObEh6fh05SiiFrykw7nhgf9P62W8ITSpbCjCxeD8dBYQzfxwytLnNMaD1oeuXyQ1v0KUKCLVYzalFyASfzsUq3QhXtuOKHyA5Ov0cm5MUWsucBWtW6p2r2K-8qnjD1GMAvAqlUz1ZnWSD5JgavebjEkhF8G8wxbnxbXbQZoMiT7bzPY4p1xOf0PmO3WU6dKeIaAb7H48GKTsgDqrXyFtunMr86SnhLljq3Eyr5zcfBCPe71VLq7IDq4OWemCQAkpBXGZ9XgJuJ1GpComHOjWrJWLYyB7p86rLamcIzuTcvq_V2PDOBGEDxzyFlUlyC2qVkEpWT-uqgbQnES94z0mdrk2Bil8x_-dNhDLMLCjxbRXLinc7LT2q3PckDo6wH-pv2Et2wbRGWNKXnC20wJQcyvMQwrQfvzGqCPjzaEu-cr5oOKwPq8o5TSX39RhropThgvL1u-EdalT-j-Qg3sGkd6f5p2yJac0vyPVrSrBqW_KHAVwfBtQPVH1zarJlxdtoCCcJLsLECvkAFB_P8bfTCg1O6MNNbeuP003rCsKo7Wd8pk49HyEknQWrn6vDhAU3ESzAQxLDITkufiDPVZcoJOl_2Mx-6QOqO2sW30hzOE3yVib4HJMGxNZuR9NgUW9iSmcDnTIeUIfRM31w_AbkKrl3gdBS3A_fHv5nJAvm7ddPqX4Lb4expJnMtGxn0E613XiOM3VRFb3oC_-d3B7_gcXlTIO59O1JCIYcVY2FKQB8CX2wEdEvdcVDt5gnkjj2h4YgRzJh7DiF_UaONgORp_KtOupt7B_xR9mjNkt9zEP108QAzhrhH1I4tLi2FnYx8LxkmFuafUyncnOZBNUzVD1USpXceWYOWdGC-B_HX_TLhuus7WxKIEy59JSatRdPlCUs6UtxH9Tc7xGVpHJxZI21jULA_hIVCnZSNm3KZMGWysH48lYO2gOv5ocZEh1f2Tsh1x8VdIryjHufXRIkudaheJPhgRug==",
"id": "rs_05cdea51f45b90d7016a14227d5fe0819197b14520f6658386",
"summary": []
}
],
"tool_calls": [
{
"id": "call_z1oHmHUYgPw5LMTSKD4TC8MG",
"call_id": "call_z1oHmHUYgPw5LMTSKD4TC8MG",
"response_item_id": "fc_05cdea51f45b90d7016a14228009008191acd178df0362fc7e",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from pathlib import Path\\nimport urllib.request, urllib.parse, shutil, json, hashlib\\nfrom hermes_tools import terminal\\nHOST='http://100.101.173.25:8188'\\nOUT=Path('/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s')\\nqs=urllib.parse.urlencode({'filename':'final_pilot_wan_bg_ai_output_autopsy_00001_.mp4','subfolder':'autoshorts','type':'output'})\\ndest=OUT/'wan22_background_final_pilot_00001.mp4'\\nwith urllib.request.urlopen(HOST+'/view?'+qs,timeout=180) as r, open(dest,'wb') as f:\\n shutil.copyfileobj(r,f)\\nprint(dest, dest.stat().st_size, hashlib.sha256(dest.read_bytes()).hexdigest())\\nprint(terminal(f\\\"ffprobe -v error -select_streams v:0 -show_entries stream=width,height,r_frame_rate -show_entries format=duration -of default=noprint_wrappers=1 {dest}\\\")['output'])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan22_background_final_pilot_00001.mp4 125549 ab4efcd509cfc472ad6b03c6e493d433daf687c624136eb5e8266724604e9aac\\nwidth=416\\nheight=736\\nr_frame_rate=12/1\\nduration=5.083333\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.46}",
"tool_call_id": "call_z1oHmHUYgPw5LMTSKD4TC8MG"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Creating final video composition**\n\nI need to finalize a video with a looped Wan background at 1080x1920, 30fps, likely using ffmpeg for scaling. I’ll overlay captions using PIL and read background frames with cv2. The existing TTS will be integrated. I should generate a final captions JSON file, ensuring it’s not heavy, possibly using a static caption module. We'll maintain a resolution of 1080, rendering with ffmpeg while employing cv2 for the frames and drawing overlays. A Python script will help streamline this.",
"finish_reason": "tool_calls",
"reasoning_content": "**Creating final video composition**\n\nI need to finalize a video with a looped Wan background at 1080x1920, 30fps, likely using ffmpeg for scaling. I’ll overlay captions using PIL and read background frames with cv2. The existing TTS will be integrated. I should generate a final captions JSON file, ensuring it’s not heavy, possibly using a static caption module. We'll maintain a resolution of 1080, rendering with ffmpeg while employing cv2 for the frames and drawing overlays. A Python script will help streamline this.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFCKgvqQFsSwlRc4p6FkcIgW10ZH-TwEb88m5Hx0DCtIJ99bB6V9WUe-Z-P21qlAsCEPU2UKjWo7-1ITj9lMtfoH_sF-Ne9itmsJlQWpSZkB5bXN67wEcxBNz1IVfDOIKuSAC2DmntVZ0QeuR9CL0NvLWYSweMX_vWN4NDbtfNUWSTlbOqf1HXp6s1kTKPImdGf53ULttNCG2DpAe5qtUA080MFVSZlDIMv67ZEjvZXiyDMalL9pGUw393aQNBS0GelYPvL5GO95NtdJJsEyVBRQLDpT7dpvj46gvOg0QBYDEnsDL8xWEGOHq_2wvCDe7zkWj5bQ9xxqu9PUipoKlphgoS46h5Xb-iOhPNWxFG-EjA9nCqCFoiAj8hQUxn9JpwmDOarcYfqT71fTGfQ2TdYzeuZUFqWAH3-wAEYA7hQqrCXcjq3HfP_tLDYY5NtPIqY025E3NwO1FZSfYauSWBKw_d7Jhb0mBOhOhc-3Tk-t3x0CE4ANqRgapJ3q9DByr3P4OfZgd-fqvazkjFMAMI4XwiYsVnphL9pjdh4qG8dFE8m57ODgggKJKzCkvaUrbiVcHLWbvqrd6r6SqmJDbQ9T8FN9TeyRxAgmjpSLESLMIGALYVcxu7VePW7auRRbpeTReAH8faO5gxd1fC1U3LMWfEJ3G5TSpnpPoyrffXiMQypl4mYKB1aqTrASIBAe2Hd3u_InqRtExhXydbl6nT3HO1D4mSOXbZtcs3FElEOpLVpxp5C6pNPXIkG5rFcpP9Q2edqegrZpq7L8gSnKW4gg9gW7K0lDrd3-c_XsCSF4-61BOYpF7U60AhHfQeueyKkzlL6G_r6mWQx_F77-T7BLSInz1qFl2gjMal22CpusbjqpIrQ4WZ-WLf7FsXVAlpuc-2mlYchFJrNxOZ8thtWZ4yfePgMKEuw_VSnwziujlE4WZXsAgjituMYEMnQWlRYBbJcZhyIVv0ml8uWrtfYXrHb_4VameA5gcEJKgw5R9NrgF_aAylZRnJP9KMFMETNjR8R6tptnGgPbL69Vo_kYcoFLxqmOovg9al4-E411pDSme-KP1KXOeA3Bq52qw7bWAuZQeLccyqjWc8ef0IxTA6jn0dN2bR_8OCSpcqx5iAZR9u94pzTebaGV4uUm76hqo1hVZCdwy_Jr9IjPuB-qydkCl2HSZ2-PvlhLLBb646R_7Pq8_XbIxWn0JOpWGI-5CUJeXj9JUANnXaEN8p8a9vVb4ufDU41zfH-VP4Q2d1Sb4H25YTPxzORttEn8_owGJO6uSuGMsldh-2WDx1KZ6kHfpYSazXdcEPQgLE_mqpqt-_n-KG1yVSCYHuX5iBOURkNmfRKOQf652MazFK80OkE7MyrjUMA6p2CeejU_YIeriuAPDScqo7T50qQfXhBOxY7SKCKxYpo7t7BsdzlSBzrTbPqJ3XwoiSvd7ardPEOLsKJthhXnonr3K0CmxyFw-MDKDNXV4-65p2itNWi06kbfWHnC__22tQViuKnKI5rzpLsGenoTu_MZeH6_C-22DdC_SzlnWTa7DIXSqnGW9ShhlGnWWorfLQhgWje-ojg6LVIkjSnMFqTnXQ1sJlz15F0XMr7_o4OGs4Q2FF_sraWLNeNFWN1nrRUJmC4HrMyzmUkPUh_guHPkZAlppj7wrSLmePK4-DS_zBscCbNWfeXqsl9MGzXww0SVdidG2fnAMB0SK1EH_EHTqlMuPNmEh4w9nkcD0ER5sRt_-IRcTOGPqSV6Tex3bAJrc14o0zz8iFlylOhKp0sAAFFznEdBe85h5WO1Stoxm9REyAoSoX0UkNBdJwB18Yv7sTopFcTG4LfD0w863gqIpxCYvYpz1c09b1_au63dfhVC5WcxdyuhEhl5Dc5fS9L_ckEmy_zd4qDAXT6bRQMkQEpMJHYZ1iE080vxS_AtxQ61vRbnaXRCwA_XcoUr_JQFppJ0eAplQzg2Etw1bHp85cZ7Kl9wK-Oype4xLdYSijeYED_ijf5yx1q99bBELGg1N1e9olsanUlMTcm5X6T_yPqKr4_jPH44mr4ovGtVpERJO4oARU79CaWUFbusbMBhUOFbKKpkukq9wJCaAbM6p8sqbi2c7",
"id": "rs_05cdea51f45b90d7016a14229263b481919698091280df3f2a",
"summary": [
{
"type": "summary_text",
"text": "**Creating final video composition**\n\nI need to finalize a video with a looped Wan background at 1080x1920, 30fps, likely using ffmpeg for scaling. I’ll overlay captions using PIL and read background frames with cv2. The existing TTS will be integrated. I should generate a final captions JSON file, ensuring it’s not heavy, possibly using a static caption module. We'll maintain a resolution of 1080, rendering with ffmpeg while employing cv2 for the frames and drawing overlays. A Python script will help streamline this."
}
]
}
],
"tool_calls": [
{
"id": "call_9dSHSxehTximoeBGZ6SNaCn4",
"call_id": "call_9dSHSxehTximoeBGZ6SNaCn4",
"response_item_id": "fc_05cdea51f45b90d7016a1422a0bbb88191bb426fd40036e7ee",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"from pathlib import Path\\nimport json, hashlib, subprocess, math\\nfrom PIL import Image, ImageDraw, ImageFont\\nimport cv2\\nimport numpy as np\\nfrom autoshorts.rendering.static_captions import WordCue, build_static_caption_segments, caption_state_at\\n\\nROOT=Path('/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s')\\nPREVIEW=Path('/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s')\\nAUDIO=PREVIEW/'voiceover.mp3'\\nWORD_JSON=PREVIEW/'word_cues_time_based.json'\\nWAN_BG=ROOT/'wan22_background_final_pilot_00001.mp4'\\nSILENT=ROOT/'final_pilot_silent_overlay.mp4'\\nFINAL=ROOT/'final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4'\\nCAPTION_JSON=ROOT/'final_caption_static_segments.json'\\nMANIFEST=ROOT/'final_render_manifest.json'\\nW,H,FPS=1080,1920,30\\nDURATION=38.0\\nROOT.mkdir(parents=True, exist_ok=True)\\nword_cues_raw=json.loads(WORD_JSON.read_text())\\nword_cues=tuple(WordCue(start=c['start'], end=c['end'], word=c['word'], sentence=c.get('sentence','')) for c in word_cues_raw)\\nsegments=build_static_caption_segments(word_cues, min_words=3, target_words=6, max_words=7)\\nCAPTION_JSON.write_text(json.dumps([{'start':s.start,'end':s.end,'words':s.words,'word_indices':s.word_indices,'sentence':s.sentence} for s in segments], indent=2))\\n\\nfont=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', 44)\\nfont_b=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', 66)\\nfont_big=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', 82)\\nfont_small=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', 34)\\nfont_tiny=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', 26)\\nfont_word=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', 58)\\nfont_word_active=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf', 62)\\n\\nscenes=[\\n {'id':'s01_fail','start':0,'end':5,'title':'Your AI summary is useless','kicker':'because it has no decision target','label':'FAIL','desc':'A polished summary can still miss the decision.'},\\n {'id':'s02_bad_output','start':5,'end':12,'title':'The bad output sounds helpful','kicker':'but it misses owner, blocker and decision','label':'ANTI-EXAMPLE','desc':'“Great discussion” is not decision evidence.'},\\n {'id':'s03_mechanism','start':12,'end':21,'title':'The hidden mechanism','kicker':'equal compression without a target','label':'MECHANISM','desc':'Every sentence gets treated as equally important.'},\\n {'id':'s04_fix','start':21,'end':30,'title':'The fix','kicker':'ask for decision evidence','label':'RULE','desc':'Blockers → Owners → Risks → Next steps'},\\n {'id':'s05_takeaway','start':30,'end':38,'title':'Don’t ask for a summary','kicker':'Ask for decision evidence.','label':'TAKEAWAY','desc':'A sharper question makes the output useful.'},\\n]\\n\\ndef ffprobe_duration(path):\\n return float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=noprint_wrappers=1:nokey=1',str(path)], text=True).strip())\\n\\ndef scene_at(t):\\n for s in scenes:\\n if s['start'] <= t < s['end']: return s\\n return scenes[-1]\\n\\ndef rounded(draw, box, fill, outline=None, width=3, radius=30):\\n draw.rounded_rectangle(box, radius=radius, fill=fill, outline=outline, width=width)\\n\\ndef wrap(draw,text,font,max_width):\\n words=text.split(); lines=[]; cur=''\\n for w in words:\\n test=(cur+' '+w).strip()\\n if draw.textbbox((0,0),test,font=font)[2]<=max_width: cur=test\\n else:\\n if cur: lines.append(cur)\\n cur=w\\n if cur: lines.append(cur)\\n return lines\\n\\ndef centered(draw,y,text,font,fill,max_width=W-120,gap=10):\\n lines=wrap(draw,text,font,max_width)\\n hs=[draw.textbbox((0,0),l,font=font)[3]-draw.textbbox((0,0),l,font=font)[1] for l in lines]\\n yy=y-(sum(hs)+gap*(len(lines)-1))/2\\n for l,h in zip(lines,hs):\\n bb=draw.textbbox((0,0),l,font=font); tw=bb[2]-bb[0]\\n draw.text(((W-tw)/2,yy),l,font=font,fill=fill)\\n yy+=h+gap\\n\\ndef draw_scene_overlays(draw,t,s):\\n # top hook safe zone\\n rounded(draw,(54,84,1026,250),(8,12,22,210),(90,120,170),3,34)\\n centered(draw,145,s['title'],font_b,(245,248,255),900)\\n centered(draw,218,s['kicker'],font_small,(245,196,55),900)\\n draw.text((72,276),s['label'],font=font_small,fill=(245,196,55))\\n # deterministic mechanism overlays, renderer-owned\\n if s['id']=='s01_fail':\\n rounded(draw,(80,420,470,1030),(20,28,46,215),(95,125,180),3,28)\\n rounded(draw,(610,420,1000,1030),(35,31,40,215),(210,85,90),3,28)\\n draw.text((132,465),'TRANSCRIPT',font=font_small,fill=(170,205,255))\\n draw.text((675,465),'AI SUMMARY',font=font_small,fill=(255,195,195))\\n for i in range(12): draw.line((130,540+i*34,390-(i%3)*35,540+i*34),fill=(110,132,175),width=8)\\n for i in range(5): draw.line((675,560+i*58,920-(i%2)*70,560+i*58),fill=(145,145,155),width=10)\\n rounded(draw,(670,875,935,970),(135,30,38,235),(255,105,105),5,22); draw.text((725,910),'USELESS',font=font_b,fill=(255,235,235))\\n draw.line((540,460,540,1010),fill=(245,196,55),width=6)\\n elif s['id']=='s02_bad_output':\\n rounded(draw,(120,420,960,1000),(22,28,42,220),(105,125,170),3,40)\\n draw.text((185,470),'POLISHED OUTPUT',font=font_small,fill=(200,215,245))\\n for i in range(4):\\n y=565+i*75; rounded(draw,(180,y,900,y+40),(58,64,82,230),None,2,14); draw.line((210,y+21,720+i*30,y+21),fill=(130,140,165),width=9)\\n for label,y in [('NO OWNER',815),('NO BLOCKER',875),('NO DECISION',935)]:\\n draw.text((190,y),label,font=font_small,fill=(255,105,105)); draw.line((410,y+18,875,y+18),fill=(255,88,88),width=5)\\n elif s['id']=='s03_mechanism':\\n cx=540; box_y=785\\n for i in range(9):\\n x=110+(i%3)*290; y=420+(i//3)*140\\n rounded(draw,(x,y,x+190,y+70),(34,46,72,220),(100,125,180),3,18)\\n draw.line((x+95,y+70,cx,box_y),fill=(105,125,165),width=4)\\n rounded(draw,(360,760,720,930),(38,42,54,235),(245,196,55),5,34); centered(draw,850,'GENERIC SUMMARY',font_small,(245,245,245),330)\\n rounded(draw,(390,1030,690,1120),(38,72,55,230),(105,230,130),4,24); centered(draw,1076,'DECISION TARGET',font_small,(210,255,220),270)\\n draw.line((540,1030,540,935),fill=(255,85,85),width=8)\\n elif s['id']=='s04_fix':\\n nodes=[('BLOCKERS',110),('OWNERS',330),('RISKS',550),('NEXT STEPS',770)]\\n for i,(label,x) in enumerate(nodes):\\n rounded(draw,(x,620,x+190,730),(38,70,67,230),(105,230,200),4,24); centered(draw,678,label,font_tiny,(220,255,248),170)\\n if i>0: draw.line((nodes[i-1][1]+190,675,x,675),fill=(245,196,55),width=8)\\n rounded(draw,(220,875,860,1015),(28,62,46,235),(110,235,130),5,34); centered(draw,945,'DECISION EVIDENCE',font_b,(220,255,225),580)\\n else:\\n rounded(draw,(90,430,500,980),(56,44,50,220),(175,95,95),4,30); rounded(draw,(580,430,990,980),(32,62,50,220),(95,220,135),4,30)\\n centered(draw,500,'BEFORE',font_b,(255,195,195),350); centered(draw,500,'AFTER',font_b,(190,255,205),350)\\n for i in range(5): draw.line((155,610+i*62,410,610+i*62),fill=(135,118,128),width=11)\\n for i,label in enumerate(['owner','risk','next step']):\\n y=620+i*86; rounded(draw,(650,y,910,y+48),(58,82,64,230),None,2,16); centered(draw,y+26,label,font_small,(205,255,215),230)\\n draw.line((510,720,570,720),fill=(245,196,55),width=10); draw.polygon([(570,720),(545,702),(545,738)],fill=(245,196,55))\\n\\ndef draw_captions(draw,t):\\n state=caption_state_at(word_cues,segments,t)\\n words=list(state['segment_words']); indices=list(state['word_indices']); active=int(state['active_word_index'])\\n rounded(draw,(70,1430,1010,1725),(10,14,24,235),(76,96,145),3,34)\\n # layout fixed by segment words, not active word\\n tokens=[]\\n for idx,w in zip(indices,words):\\n disp=w.upper(); bb=draw.textbbox((0,0),disp,font=font_word); tokens.append((idx,disp,bb[2]-bb[0],bb[3]-bb[1]))\\n rows=[[]]; cur=0\\n for tok in tokens:\\n add=tok[2]+30\\n if cur+add > 850 and rows[-1]: rows.append([]); cur=0\\n rows[-1].append(tok); cur+=add\\n y=1510 if len(rows)==1 else 1482\\n positions=[]\\n for row in rows[:2]:\\n total=sum(tok[2] for tok in row)+30*(len(row)-1); x=(W-total)/2; maxh=max(tok[3] for tok in row)\\n for tok in row:\\n positions.append((tok,x,y,maxh)); x+=tok[2]+30\\n y+=maxh+50\\n for (idx,disp,tw,th),x,y,maxh in positions:\\n if idx!=active: draw.text((x,y),disp,font=font_word,fill=(225,232,248))\\n for (idx,disp,tw,th),x,y,maxh in positions:\\n if idx==active:\\n pulse=0.72+0.28*math.sin(t*18)\\n rounded(draw,(x-18,y-12,x+tw+18,y+th+22),(245,int(185+50*pulse),45,255),(255,238,130),3,22)\\n draw.text((x,y-2),disp,font=font_word_active,fill=(12,15,22))\\n\\n# Load Wan frames\\ncap=cv2.VideoCapture(str(WAN_BG)); bg_frames=[]\\nwhile True:\\n ok,frame=cap.read()\\n if not ok: break\\n bg_frames.append(frame)\\ncap.release()\\nif not bg_frames: raise SystemExit('No Wan background frames')\\n\\nwriter=cv2.VideoWriter(str(SILENT), cv2.VideoWriter_fourcc(*'mp4v'), FPS, (W,H))\\nfor frame_no in range(int(DURATION*FPS)):\\n t=frame_no/FPS\\n bg=bg_frames[frame_no % len(bg_frames)]\\n bg=cv2.resize(bg,(W,H),interpolation=cv2.INTER_CUBIC)\\n # darken for overlays\\n bg=(bg.astype(np.float32)*0.55).clip(0,255).astype(np.uint8)\\n img=Image.fromarray(cv2.cvtColor(bg,cv2.COLOR_BGR2RGB)).convert('RGBA')\\n overlay=Image.new('RGBA',(W,H),(0,0,0,0)); draw=ImageDraw.Draw(overlay)\\n s=scene_at(t); draw_scene_overlays(draw,t,s); draw_captions(draw,t)\\n # progress rule very subtle\\n draw.rounded_rectangle((110,1815,970,1828),radius=7,fill=(60,65,78,180))\\n draw.rounded_rectangle((110,1815,110+int(860*t/DURATION),1828),radius=7,fill=(245,196,55,220))\\n img=Image.alpha_composite(img,overlay).convert('RGB')\\n writer.write(cv2.cvtColor(np.array(img),cv2.COLOR_RGB2BGR))\\nwriter.release()\\nsubprocess.check_call(['ffmpeg','-y','-i',str(SILENT),'-i',str(AUDIO),'-map','0:v:0','-map','1:a:0','-c:v','libx264','-pix_fmt','yuv420p','-preset','veryfast','-crf','22','-c:a','aac','-b:a','160k','-t',str(DURATION),'-movflags','+faststart',str(FINAL)], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\\nmanifest={\\n 'candidate_id':'ai-output-autopsy-decision-summary-38s','version':'v2.0.0','script_hash':'72129542b86819fd','final_render':True,\\n 'wan_background_path':str(WAN_BG),'wan_model':'wan2.2_ti2v_5B_fp16.safetensors','wan_seed':72129542,'wan_resolution':'416x736','wan_frames':61,'wan_fps':12,'wan_steps':8,\\n 'final_mp4_path':str(FINAL),'silent_overlay_path':str(SILENT),'audio_path':str(AUDIO),'caption_segments_path':str(CAPTION_JSON),\\n 'duration_seconds':ffprobe_duration(FINAL),'audio_duration_seconds':ffprobe_duration(AUDIO),'sha256':hashlib.sha256(FINAL.read_bytes()).hexdigest(),\\n 'caption_mode':'static phrase segments with active-word highlight','forced_alignment':False,\\n 'wan_calls':1,'ai_image_generation':False,'platform_api':False,'posting':False\\n}\\nMANIFEST.write_text(json.dumps(manifest,indent=2))\\nprint(json.dumps(manifest,indent=2))\\n\",\"path\":\"/tmp/compose_final_pilot_v1.py\"}"
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}
}
],
"tool_call_id": "call_1LQ1cegkSdp91tG5bUgtqhVt"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Preparing final package**\n\nI need to ensure the vision looks good and create the final package draft file along with some social copy. It seems like I should make a \"final_review_package\" JSON with all the results. I also have to run \"git status\" and \"pytest\" as part of the process. There’s a mention of final delivery including media, and it seems I might need to send a message since the final can include the MEDIA path. So, I'll get started on that!",
"finish_reason": "tool_calls",
"reasoning_content": "**Preparing final package**\n\nI need to ensure the vision looks good and create the final package draft file along with some social copy. It seems like I should make a \"final_review_package\" JSON with all the results. I also have to run \"git status\" and \"pytest\" as part of the process. There’s a mention of final delivery including media, and it seems I might need to send a message since the final can include the MEDIA path. So, I'll get started on that!",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_05cdea51f45b90d7016a142367c31c81918fe38cd36036b5f1",
"summary": [
{
"type": "summary_text",
"text": "**Preparing final package**\n\nI need to ensure the vision looks good and create the final package draft file along with some social copy. It seems like I should make a \"final_review_package\" JSON with all the results. I also have to run \"git status\" and \"pytest\" as part of the process. There’s a mention of final delivery including media, and it seems I might need to send a message since the final can include the MEDIA path. So, I'll get started on that!"
}
]
}
],
"tool_calls": [
{
"id": "call_vmzgO8vbqlSLyN6D37dCzkl4",
"call_id": "call_vmzgO8vbqlSLyN6D37dCzkl4",
"response_item_id": "fc_05cdea51f45b90d7016a14236af40c8191846cb5b91aa33082",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from pathlib import Path\\nimport json, hashlib, subprocess\\nfrom hermes_tools import terminal\\nROOT=Path('/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s')\\nFINAL=ROOT/'final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4'\\nAUDIO=Path('/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3')\\nCAP=ROOT/'final_caption_static_segments.json'\\nMAN=ROOT/'final_render_manifest.json'\\n\\ndef ffprobe_json(path):\\n out=terminal(f\\\"ffprobe -v error -print_format json -show_format -show_streams {path}\\\")['output']\\n return json.loads(out)\\nprobe=ffprobe_json(str(FINAL))\\nv=next(s for s in probe['streams'] if s['codec_type']=='video')\\nmanifest=json.loads(MAN.read_text())\\nreview={\\n 'candidate_id':'ai-output-autopsy-decision-summary-38s','version':'v2.0.0','script_hash':'72129542b86819fd',\\n 'final_mp4_path':str(FINAL),'sha256':hashlib.sha256(FINAL.read_bytes()).hexdigest(),\\n 'duration_seconds':float(probe['format']['duration']),'resolution':f\\\"{v['width']}x{v['height']}\\\",'fps':v['r_frame_rate'],\\n 'audio_path':str(AUDIO),'subtitle_caption_path':str(CAP),'render_manifest_path':str(MAN),'wan_prompt_log_path':str(ROOT/'wan_prompt_plan.json'),'wan_history_path':str(ROOT/'wan_history.json'),\\n 'claim_gate_result':{'valid':True,'errors':[],'risk':'low'},\\n 'ai_disclosure_result':{'valid':True,'synthetic_media_type':'text_only_assistance + abstract_background','realistic_person_event_voice':False,'manual_review_required':False},\\n 'resource_gate_result':{'allowed':True,'tier':'wan_final_render','approval_state':'approved_for_final_render','publishing_blocked':True,'platform_api_blocked':True},\\n 'caption_mode':'static phrase segments with active-word highlight','forced_alignment':False,\\n 'known_limitations':['Wan background is one 5.08s 416x736 Wan2.2 clip looped/upscaled under deterministic overlays to respect exactly one Wan render.','Caption timing is time-based from EdgeTTS sentence cues, not forced alignment.','Wan generated only background/motion layer; all text/labels/captions are renderer-owned.'],\\n 'final_social_copy_draft':{\\n 'youtube_title':'Your AI Meeting Summary Is Missing the Decision',\\n 'youtube_description':'A polished AI summary can still be useless if it has no decision target. Ask for blockers, owners, risks and next steps instead.',\\n 'tiktok_caption':'Your AI summary sounds smart. But did it protect the decision?',\\n 'instagram_caption':'The fix is not a better summary. It is a clearer decision target.',\\n 'linkedin_caption':'Most meeting summaries fail because they protect no decision. Name the decision first, then ask for blockers, owners, risks and next steps.'},\\n 'manual_posting_pack_draft':{'status':'draft_only_not_approved','posting_enabled':False,'platforms':['youtube_shorts','tiktok','instagram_reels','linkedin_manual']},\\n 'no_posting_performed':True,'no_second_wan_render_performed':True,\\n}\\n(ROOT/'final_review_package.json').write_text(json.dumps(review,indent=2))\\nprint(json.dumps(review,indent=2)[:5000])\\nprint('GIT')\\nprint(terminal('git status --short', workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'])\\nprint('LOG')\\nprint(terminal('git log --oneline -3', workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"candidate_id\\\": \\\"ai-output-autopsy-decision-summary-38s\\\",\\n \\\"version\\\": \\\"v2.0.0\\\",\\n \\\"script_hash\\\": \\\"72129542b86819fd\\\",\\n \\\"final_mp4_path\\\": \\\"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4\\\",\\n \\\"sha256\\\": \\\"9b34940ff7ce9a122fbd81f45caea106e9b38607814de604bc1d3ca2cd4b45f4\\\",\\n \\\"duration_seconds\\\": 38.0,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"30/1\\\",\\n \\\"audio_path\\\": \\\"/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3\\\",\\n \\\"subtitle_caption_path\\\": \\\"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_caption_static_segments.json\\\",\\n \\\"render_manifest_path\\\": \\\"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_render_manifest.json\\\",\\n \\\"wan_prompt_log_path\\\": \\\"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_prompt_plan.json\\\",\\n \\\"wan_history_path\\\": \\\"/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_history.json\\\",\\n \\\"claim_gate_result\\\": {\\n \\\"valid\\\": true,\\n \\\"errors\\\": [],\\n \\\"risk\\\": \\\"low\\\"\\n },\\n \\\"ai_disclosure_result\\\": {\\n \\\"valid\\\": true,\\n \\\"synthetic_media_type\\\": \\\"text_only_assistance + abstract_background\\\",\\n \\\"realistic_person_event_voice\\\": false,\\n \\\"manual_review_required\\\": false\\n },\\n \\\"resource_gate_result\\\": {\\n \\\"allowed\\\": true,\\n \\\"tier\\\": \\\"wan_final_render\\\",\\n \\\"approval_state\\\": \\\"approved_for_final_render\\\",\\n \\\"publishing_blocked\\\": true,\\n \\\"platform_api_blocked\\\": true\\n },\\n \\\"caption_mode\\\": \\\"static phrase segments with active-word highlight\\\",\\n \\\"forced_alignment\\\": false,\\n \\\"known_limitations\\\": [\\n \\\"Wan background is one 5.08s 416x736 Wan2.2 clip looped/upscaled under deterministic overlays to respect exactly one Wan render.\\\",\\n \\\"Caption timing is time-based from EdgeTTS sentence cues, not forced alignment.\\\",\\n \\\"Wan generated only background/motion layer; all text/labels/captions are renderer-owned.\\\"\\n ],\\n \\\"final_social_copy_draft\\\": {\\n \\\"youtube_title\\\": \\\"Your AI Meeting Summary Is Missing the Decision\\\",\\n \\\"youtube_description\\\": \\\"A polished AI summary can still be useless if it has no decision target. Ask for blockers, owners, risks and next steps instead.\\\",\\n \\\"tiktok_caption\\\": \\\"Your AI summary sounds smart. But did it protect the decision?\\\",\\n \\\"instagram_caption\\\": \\\"The fix is not a better summary. It is a clearer decision target.\\\",\\n \\\"linkedin_caption\\\": \\\"Most meeting summaries fail because they protect no decision. Name the decision first, then ask for blockers, owners, risks and next steps.\\\"\\n },\\n \\\"manual_posting_pack_draft\\\": {\\n \\\"status\\\": \\\"draft_only_not_approved\\\",\\n \\\"posting_enabled\\\": false,\\n \\\"platforms\\\": [\\n \\\"youtube_shorts\\\",\\n \\\"tiktok\\\",\\n \\\"instagram_reels\\\",\\n \\\"linkedin_manual\\\"\\n ]\\n },\\n \\\"no_posting_performed\\\": true,\\n \\\"no_second_wan_render_performed\\\": true\\n}\\nGIT\\n\\nLOG\\nd485b47 Add static active-word caption segments for preview rendering\\nb1766a4 Add pilot production package and resource gates\\n4c57b52 Operationalize strategy spine v2 review workflow\\n\", \"tool_calls_made\": 3, \"duration_seconds\": 1.06}",
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"content": "Sir, Sprint 1.8 ist abgeschlossen: **genau ein finaler Pilot-Render** wurde erzeugt und als Telegram-MP4 angehängt.\n\nKein Posting. Keine Plattform-API. Keine Render-Serie. Kein zweiter Wan-Render. Der Gentleman bleibt diszipliniert.\n\nMEDIA:/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nGit Status:\n\n```text\nclean\n```\n\n## Caption-Fix / Sprint 1.7 Commit\n\nCaption-Fix wurde sauber in den Renderer-Code integriert und committed:\n\n```text\nd485b47 Add static active-word caption segments for preview rendering\n```\n\nEnthalten:\n\n- `autoshorts/rendering/static_captions.py`\n- Tests für:\n - static caption segment contains multiple words\n - active word changes within same segment\n - segment text remains stable while highlight moves\n - no one-word-only caption mode as default\n\n## Pytest Ergebnis\n\n```text\n202 passed in 34.38s\n```\n\n## Finalrender durchgeführt\n\n```text\nyes\n```\n\n## Final MP4\n\nPfad:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4\n```\n\nSHA256:\n\n```text\n9b34940ff7ce9a122fbd81f45caea106e9b38607814de604bc1d3ca2cd4b45f4\n```\n\nDauer:\n\n```text\n38.000s\n```\n\nAuflösung:\n\n```text\n1080x1920\n```\n\nFPS:\n\n```text\n30 fps\n```\n\nDateigrösse:\n\n```text\nca. 3.5 MB\n```\n\n## Audio\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3\n```\n\nAudiodauer:\n\n```text\n34.968s\n```\n\nVoice:\n\n```text\nen-GB-RyanNeural\n```\n\n## Caption-Modus\n\n```text\nstatic phrase segments with active-word highlight\n```\n\nUmgesetzt:\n\n- Subtitle-Abschnitt bleibt statisch.\n- Mehrere Wörter bleiben an Ort und Stelle.\n- Nur das aktuell gesprochene Wort wird hervorgehoben.\n- Highlight wandert links nach rechts.\n- Erst beim nächsten Segment wechselt der komplette Subtitle-Block.\n- Kein nervöses Einzelwort-Gehüpfe mehr.\n\nCaption-Daten:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_caption_static_segments.json\n```\n\nForced Alignment:\n\n```text\nnein\n```\n\nTiming-Einschränkung:\n\n```text\nCaptions basieren weiterhin auf zeitbasierten Word-Cues aus EdgeTTS-Sentence-Cues. Für spätere Production-Grade-Version sollte Forced Alignment ergänzt werden.\n```\n\n## Wan 2.2 Render\n\nDurchgeführt:\n\n```text\nyes — genau 1 Wan-Render\n```\n\nWan-Background:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan22_background_final_pilot_00001.mp4\n```\n\nWan Settings:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nseed: 72129542\nresolution: 416x736\nframes: 61\nfps: 12\nsteps: 8\n```\n\nWichtige Einschränkung:\n\nDer Wan-Render wurde als **textfreie Motion-/Background-Ebene** erzeugt und dann unter deterministic renderer-owned Overlays/Captions in ein 1080x1920 Finalvideo komponiert.\n\nDas bedeutet:\n\n- Wan erzeugt keine Texte.\n- Wan erzeugt keine Labels.\n- Wan erzeugt keine Captions.\n- Wan erzeugt keine Logos.\n- Alle Fakten, Pfeile, Labels, USELESS-Stempel und Captions bleiben renderer-owned.\n\n## Render Manifest / Logs\n\nRender Manifest:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_render_manifest.json\n```\n\nWan Prompt Plan:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_prompt_plan.json\n```\n\nWan History:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_history.json\n```\n\nFinal Review Package:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_review_package.json\n```\n\n## Gate Results\n\n### Resource Gate\n\n```text\nallowed: true\ntier: wan_final_render\napproval_state: approved_for_final_render\npublishing_blocked: true\nplatform_api_blocked: true\n```\n\n### Claim Gate\n\n```text\nvalid: true\nrisk: low\nerrors: []\n```\n\n### AI Disclosure\n\n```text\nvalid: true\nsynthetic_media_type: text_only_assistance + abstract_background\nrealistic_person_event_voice: false\nmanual_review_required: false\n```\n\n## Telegram Delivery\n\nTelegram Delivery:\n\n```text\nyes\n```\n\nDas finale MP4 ist dieser Antwort als Media-Datei angehängt.\n\n## Nicht ausgeführt\n\nExplizit nicht ausgeführt:\n\n- kein YouTube Upload\n- keine TikTok API\n- keine Instagram API\n- keine LinkedIn API\n- kein Auto-Posting\n- kein Scheduling\n- kein zweiter Wan-Render\n- keine Render-Variantenserie\n\n## Bekannte Einschränkungen\n\n- Wan-Background ist ein einzelner kurzer Wan2.2-Clip, der unter deterministic Overlays genutzt wurde.\n- Captions sind noch nicht forced-aligned.\n- Finalvideo ist ein erster Pilot zur Qualitätsbeurteilung, kein freigegebenes Publishing Asset.\n- Kein Manual Posting Pack wurde final freigegeben; nur Draft-Daten wurden erzeugt.\n\n## Nächste Review-Kommandos\n\nWenn Sie kleine Korrekturen wollen:\n\n```text\nREVISE_FINAL ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn das Video uploadbereit ist:\n\n```text\nAPPROVED_FOR_MANUAL_POSTING ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\nWenn ein zweiter Finalrender nötig ist:\n\n```text\nREQUEST_SECOND_FINAL_RENDER ai-output-autopsy-decision-summary-38s \"Begründung\"\n```\n\nWenn Sie es ablehnen:\n\n```text\nREJECT_FINAL ai-output-autopsy-decision-summary-38s \"Grund\"\n```",
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"text": "Sir, Sprint 1.8 ist abgeschlossen: **genau ein finaler Pilot-Render** wurde erzeugt und als Telegram-MP4 angehängt.\n\nKein Posting. Keine Plattform-API. Keine Render-Serie. Kein zweiter Wan-Render. Der Gentleman bleibt diszipliniert.\n\nMEDIA:/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4\n\n## Git Status\n\nAktueller Branch:\n\n`strategy-refactor-v2`\n\nGit Status:\n\n```text\nclean\n```\n\n## Caption-Fix / Sprint 1.7 Commit\n\nCaption-Fix wurde sauber in den Renderer-Code integriert und committed:\n\n```text\nd485b47 Add static active-word caption segments for preview rendering\n```\n\nEnthalten:\n\n- `autoshorts/rendering/static_captions.py`\n- Tests für:\n - static caption segment contains multiple words\n - active word changes within same segment\n - segment text remains stable while highlight moves\n - no one-word-only caption mode as default\n\n## Pytest Ergebnis\n\n```text\n202 passed in 34.38s\n```\n\n## Finalrender durchgeführt\n\n```text\nyes\n```\n\n## Final MP4\n\nPfad:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_pilot_ai-output-autopsy-decision-summary-38s_v1.mp4\n```\n\nSHA256:\n\n```text\n9b34940ff7ce9a122fbd81f45caea106e9b38607814de604bc1d3ca2cd4b45f4\n```\n\nDauer:\n\n```text\n38.000s\n```\n\nAuflösung:\n\n```text\n1080x1920\n```\n\nFPS:\n\n```text\n30 fps\n```\n\nDateigrösse:\n\n```text\nca. 3.5 MB\n```\n\n## Audio\n\nPfad:\n\n```text\n/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s/voiceover.mp3\n```\n\nAudiodauer:\n\n```text\n34.968s\n```\n\nVoice:\n\n```text\nen-GB-RyanNeural\n```\n\n## Caption-Modus\n\n```text\nstatic phrase segments with active-word highlight\n```\n\nUmgesetzt:\n\n- Subtitle-Abschnitt bleibt statisch.\n- Mehrere Wörter bleiben an Ort und Stelle.\n- Nur das aktuell gesprochene Wort wird hervorgehoben.\n- Highlight wandert links nach rechts.\n- Erst beim nächsten Segment wechselt der komplette Subtitle-Block.\n- Kein nervöses Einzelwort-Gehüpfe mehr.\n\nCaption-Daten:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_caption_static_segments.json\n```\n\nForced Alignment:\n\n```text\nnein\n```\n\nTiming-Einschränkung:\n\n```text\nCaptions basieren weiterhin auf zeitbasierten Word-Cues aus EdgeTTS-Sentence-Cues. Für spätere Production-Grade-Version sollte Forced Alignment ergänzt werden.\n```\n\n## Wan 2.2 Render\n\nDurchgeführt:\n\n```text\nyes — genau 1 Wan-Render\n```\n\nWan-Background:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan22_background_final_pilot_00001.mp4\n```\n\nWan Settings:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nseed: 72129542\nresolution: 416x736\nframes: 61\nfps: 12\nsteps: 8\n```\n\nWichtige Einschränkung:\n\nDer Wan-Render wurde als **textfreie Motion-/Background-Ebene** erzeugt und dann unter deterministic renderer-owned Overlays/Captions in ein 1080x1920 Finalvideo komponiert.\n\nDas bedeutet:\n\n- Wan erzeugt keine Texte.\n- Wan erzeugt keine Labels.\n- Wan erzeugt keine Captions.\n- Wan erzeugt keine Logos.\n- Alle Fakten, Pfeile, Labels, USELESS-Stempel und Captions bleiben renderer-owned.\n\n## Render Manifest / Logs\n\nRender Manifest:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_render_manifest.json\n```\n\nWan Prompt Plan:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_prompt_plan.json\n```\n\nWan History:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/wan_history.json\n```\n\nFinal Review Package:\n\n```text\n/tmp/autoshorts_final_v1/ai-output-autopsy-decision-summary-38s/final_review_package.json\n```\n\n## Gate Results\n\n### Resource Gate\n\n```text\nallowed: true\ntier: wan_final_render\napproval_state: approved_for_final_render\npublishing_blocked: true\nplatform_api_blocked: true\n```\n\n### Claim Gate\n\n```text\nvalid: true\nrisk: low\nerrors: []\n```\n\n### AI Disclosure\n\n```text\nvalid: true\nsynthetic_media_type: text_only_assistance + abstract_background\nrealistic_person_event_voice: false\nmanual_review_required: false\n```\n\n## Telegram Delivery\n\nTelegram Delivery:\n\n```text\nyes\n```\n\nDas finale MP4 ist dieser Antwort als Media-Datei angehängt.\n\n## Nicht ausgeführt\n\nExplizit nicht ausgeführt:\n\n- kein YouTube Upload\n- keine TikTok API\n- keine Instagram API\n- keine LinkedIn API\n- kein Auto-Posting\n- kein Scheduling\n- kein zweiter Wan-Render\n- keine Render-Variantenserie\n\n## Bekannte Einschränkungen\n\n- Wan-Background ist ein einzelner kurzer Wan2.2-Clip, der unter deterministic Overlays genutzt wurde.\n- Captions sind noch nicht forced-aligned.\n- Finalvideo ist ein erster Pilot zur Qualitätsbeurteilung, kein freigegebenes Publishing Asset.\n- Kein Manual Posting Pack wurde final freigegeben; nur Draft-Daten wurden erzeugt.\n\n## Nächste Review-Kommandos\n\nWenn Sie kleine Korrekturen wollen:\n\n```text\nREVISE_FINAL ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn das Video uploadbereit ist:\n\n```text\nAPPROVED_FOR_MANUAL_POSTING ai-output-autopsy-decision-summary-38s v2.0.0 72129542b86819fd\n```\n\nWenn ein zweiter Finalrender nötig ist:\n\n```text\nREQUEST_SECOND_FINAL_RENDER ai-output-autopsy-decision-summary-38s \"Begründung\"\n```\n\nWenn Sie es ablehnen:\n\n```text\nREJECT_FINAL ai-output-autopsy-decision-summary-38s \"Grund\"\n```"
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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.\n\n[Your active task list was preserved across context compression]\n- [>] gate-final. Finalrender-Gate/Claim/Disclosure prüfen und Wan-Verfügbarkeit feststellen (in_progress)\n- [ ] final-render. Genau einen finalen Pilot-Render mit verfügbarer Wan/renderer pipeline erzeugen (pending)\n- [ ] package-deliver. Final Review Package, Checksums, Telegram Delivery und Abschlussbericht erstellen (pending)"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\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\\nReference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects.\\n\\nUse 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\\n## Core principle\\n\\nTreat 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\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\nFor the user's TrueTraceShorts / AutoShortsBot strategy, do **not** default to a narrow AI/prompt-tips channel. The current strategic direction is broader: explain hidden digital everyday mechanisms — AI, work, attention, decisions, scams, productivity, workflows, social search — and end each video with one small system rule that fixes the outcome. Use the frame `Trace the hidden system. Fix the outcome.` AI should remain an important pillar, but normally only about 30–40% of the topic mix, not the whole product. See `references/truetrace-strategic-neuausrichtung.md`.\\n\\nWhen the user asks to start implementation work on this strategy, especially “Sprint 1: Strategic Spine Refactor”, follow `references/truetrace-strategic-spine-refactor-v2.md`: implement a side-effect-free V2 spine in parallel to existing V1 models, with mandatory strategic fields, hard quality gates, anti-AI-slop blockers, hash/version-bound ReviewPackageV2, and tests before integration with renderers or platform workflows.\\n\\nWhen the user asks for “Sprint 1.5” or to make the V2 spine operational without production, follow `references/truetrace-v2-operational-bridge.md`: first secure/commit the Sprint-1 baseline, then add only minimal side-effect-free bridges (`to_candidate_v2`, `workflow_v2`, `demo_review_batch_v2`), deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated non-generous scoring, anti-example/human-texture gates, minimal manual claim/disclosure stubs, LinkedIn manual metadata, and a Remotion-as-later-spike note. Keep it capped at 3 demo candidates and avoid rendering, platform APIs, dashboards, auto-posting, or 30-day batches.\\n\\n## 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\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\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\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\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\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - For TrueTraceShorts, prefer the structure `visible fail → identification → mechanism → fix → before/after → memorable takeaway` over generic AI-tip narration. Every video must answer: what goes wrong, why it happens, what rule fixes it, what sentence sticks, why viewers save/share it, and why they follow the series.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n - first-frame clarity\\n - mechanism value\\n - practical payoff\\n - save/share/follow reason\\n - trust/human texture\\n - series fit and saturation defense\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n For TrueTraceShorts, use the 100-point scoring and hard blockers in `references/truetrace-strategic-neuausrichtung.md`: block generic AI hooks, pure text first frames, unsourced claims, missing before/after, missing human texture, unclear AI disclosure, and candidates too similar to recent videos.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or simplify captions before sending another final-near clip. Maintain three caption modes: word-by-word for fast hooks, phrase-by-phrase for complex explanations, and keyword-pulse for a calmer premium look.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/truetrace-strategic-spine-refactor-v2.md` for implementing the new TrueTraceShorts strategy in code: V2 strategic models, hard gates, anti-AI-slop blockers, SeriesRegistry, 100-point scoring, hash-bound ReviewPackageV2, and deterministic five-candidate examples without rendering or platform side effects. See `references/truetrace-v2-operational-bridge.md` for the next implementation step: secure the Sprint-1 commit, add minimal side-effect-free bridge/workflow/CLI modules, deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated scoring, anti-example/human-texture gates, manual claim/disclosure stubs, LinkedIn manual metadata, and Remotion-as-later-spike documentation without production expansion.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked.\\n\\nSee `references/final-motion-hybrid-rendering.md` for the final-near hybrid renderer pattern: ChatGPT Image/Codex or other AI tools provide text-free background/keyframe texture while deterministic renderer overlays own text, arrows, labels, captions, and final assembly. For quick prototypes, prefer ChatGPT Image/Codex backgrounds before local Qwen/ComfyUI unless privacy/cost/local-only constraints dominate. After a 1080 export, create a side-effect-free final approval package with media path, SHA256, platform metadata, social copy, quality notes, and approval commands before any delivery or platform step.\\n\\nSee `references/hybrid-scene-backgrounds-codex.md` for the concrete ChatGPT Image/Codex scene-background workflow: side-effect-free `SceneBackgroundPlan`, per-scene text-free backgrounds, renderer-owned overlays, and visual QA before review delivery. Prefer this over local Qwen/ComfyUI when the user allows Codex image generation and local GPU should be reserved for video/model work.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen 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\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not assume local Qwen/ComfyUI is the best first image step for every final-near short. 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"content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. 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- `wan22-i2v-retention-studio-pipeline.md` — session-proven production pattern for using native Wan2.2 as a clip generator inside a deterministic Retention-Studio pipeline: keyframe prompts, seeded I2V jobs, ComfyUI client shape, and review gates.\\n- `qwen-image-gguf-local.md` — Qwen-Image-2512-GGUF model choice, VRAM/quantization ladder, and orchestration guidance for using Qwen keyframe generation alongside Wan/Wan2.2 video without assuming concurrent GPU residency.\\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- User asks about local GGUF image models in ComfyUI, especially Qwen-Image/Qwen-Image-Edit, model quantization choice, or whether image and video models can share one GPU concurrently.\\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. 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`.\\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\\n14. **Qwen-Image GGUF and Wan video are separate heavy GPU residents** — for local GGUF image generation, prefer Qwen-Image-2512-GGUF as the practical ComfyUI-GGUF choice; use Qwen-Image-Edit-2511-GGUF for edit/reference workflows. If ChatGPT Image/Codex image generation is available, prefer it for text-free keyframes/background textures before spending local GPU time on Qwen Image, especially when the same GPU is needed for Wan/Wan2.2 video. Start Q4 on 8–12 GB VRAM, Q5/Q6 on 16 GB, and Q6/Q8 on 24 GB only after a smoke test. Do not assume Qwen-Image can run in parallel with Wan/Wan2.2 on a 16 GB class GPU: generate keyframes/images first, call `/free` with `unload_models` and `free_memory`, then run video. See `references/qwen-image-gguf-local.md`.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/wan22-native-i2v-a5000-quality.md\", \"references/rest-api.md\", \"references/wan22-i2v-retention-studio-pipeline.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/wan22-native-i2v-prompting.md\", \"references/qwen-image-gguf-local.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"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\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}",
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"arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/static-active-word-captions-and-final-pilot-render.md\",\"file_content\":\"# Static active-word captions + controlled final pilot render\\n\\nSession learning from the first TrueTraceShorts pilot (`AI Output Autopsy / Meeting Summary Decision Target`). Use this for final-near preview/final render work where the user reviews voiceover, subtitle feel, Wan/background quality, and anti-AI-slop risk.\\n\\n## Caption style correction\\n\\nThe user explicitly rejected two subtitle behaviours:\\n\\n1. **One word shown alone** — too nervous / teleprompter-like.\\n2. **Sliding word window where all visible words change at once** — still too jumpy.\\n\\nRequired default style for this user's AutoShorts/TrueTraceShorts videos:\\n\\n```text\\nstatic phrase segment + active-word highlight\\n```\\n\\nRules:\\n\\n- Keep the subtitle phrase segment static on screen.\\n- Show at least 3 words at once; ideal is 4–7 words when the phrase allows it.\\n- Words stay in the same positions during the segment.\\n- Only the currently spoken word highlights / pulses.\\n- The highlight moves left-to-right through the fixed phrase.\\n- Replace the whole phrase only at the next sentence/phrase boundary.\\n- Do not default to one-word-only captions except for a deliberate hook/impact frame.\\n- Do not use hectic windows where all words change at once every word.\\n\\nImplementation pattern:\\n\\n```text\\nword cues → phrase segments of 3–7 words → fixed layout per segment → active_word_index selects highlight only\\n```\\n\\nLogic tests to add for any renderer implementation:\\n\\n- static caption segment contains multiple words\\n- active word changes within the same segment\\n- segment text remains stable while highlight moves\\n- no one-word-only caption mode as default when enough words exist\\n\\n## Preview/final render gate sequence\\n\\nUse approval states as resource gates, not as vague comments:\\n\\n- `APPROVED_FOR_PREVIEW_RENDER` permits TTS, subtitle alignment, deterministic animatic/preview MP4, and Telegram delivery of that preview only.\\n- `APPROVED_FOR_FINAL_RENDER` permits exactly the approved final render resource tier for the hash/version-bound candidate.\\n- Publishing/platform APIs remain blocked until a separate manual-posting approval.\\n- AI image/keyframe generation remains blocked unless separately approved.\\n\\nBefore final render, verify:\\n\\n- candidate_id matches\\n- version matches\\n- script_hash matches\\n- claim gate valid\\n- AI disclosure valid\\n- resource gate allows only the intended render tier\\n- publishing and platform API tiers still block\\n\\n## Wan/background policy for this class\\n\\nFor a controlled pilot final render, Wan may generate only a **text-free visual motion/background layer**. Deterministic renderer must own:\\n\\n- captions\\n- labels\\n- arrows\\n- stamps such as `USELESS`\\n- mechanism diagrams\\n- factual claims / UI-like text\\n- final takeaway text\\n\\nWan negative prompt must include variants of:\\n\\n```text\\nreadable text, words, subtitles, captions, numbers, logos, watermark, brand names, UI text, fake letters\\n```\\n\\nIf constrained to exactly one Wan render, it is acceptable to create one short text-free Wan background clip and loop/upscale/compose it under deterministic overlays, but label that limitation clearly in the review package. Do not silently imply that every scene was separately generated.\\n\\n## Review package checklist\\n\\nAfter final pilot render, produce:\\n\\n- final MP4 path\\n- SHA256\\n- duration, resolution, fps\\n- audio path and measured duration\\n- subtitle/caption path\\n- render manifest\\n- Wan prompt plan/history/log\\n- claim gate result\\n- AI disclosure result\\n- resource gate result\\n- known limitations, especially forced-alignment status and any looped background layer\\n- draft social copy and manual posting pack marked draft-only\\n- explicit note: no posting, no platform API, no second final render without approval\\n\\n## Pitfall\\n\\nDo not treat a caption-style correction as a one-off `/tmp` script patch. Integrate the behaviour into the actual renderer/caption library and commit tests so the next render starts from the corrected style.\\n\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"Reference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects.\",\"new_string\":\"Reference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected caption style and controlled final pilot render pattern: static 3–7 word phrase segments with only the active spoken word highlighted, hash-bound final render gates, one Wan text-free background layer, deterministic overlays, and draft-only manual posting packages.\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\" - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or simplify captions before sending another final-near clip. Maintain three caption modes: word-by-word for fast hooks, phrase-by-phrase for complex explanations, and keyword-pulse for a calmer premium look.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\",\"new_string\":\" - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or simplify captions before sending another final-near clip. For this user's AutoShorts/TrueTraceShorts default, do **not** show one word alone and do **not** slide a changing word-window every word. Use static phrase segments: keep 3–7 words fixed on screen while only the currently spoken word highlights/pulses left-to-right; replace the full segment only at a sentence/phrase boundary. See `references/static-active-word-captions-and-final-pilot-render.md`.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"See `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected Hormozi-style caption behaviour and the one-render final pilot review-package checklist.\",\"old_string\":\"See `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked.\"}"
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"arguments": "{\"action\":\"write_file\",\"file_content\":\"# Wan2.2 one-render pilot background pattern\\n\\nUse this when the user explicitly allows exactly one final pilot render and wants a quality-assessment video, not a variant batch or platform upload.\\n\\n## Pattern\\n\\nWan2.2 can be used as a **single text-free background/motion layer** under a deterministic renderer when:\\n\\n- the approval is for exactly one final pilot render\\n- no styleframe/keyframe batch is approved\\n- platform APIs/posting remain blocked\\n- all captions/labels/arrows/factual text must remain renderer-owned\\n\\nThis is not the ideal multi-scene Wan pipeline, but it is a controlled way to evaluate overall video feel without creating a render series.\\n\\n## Gate checklist before submitting `/prompt`\\n\\n- Candidate ID, version, and script hash match the explicit approval.\\n- Claim gate valid.\\n- AI disclosure valid.\\n- Resource gate allows `WAN_FINAL_RENDER` only.\\n- Publishing tier still blocks.\\n- Platform API tier still blocks.\\n- Queue is checked; optionally call `/free` with `unload_models` and `free_memory`.\\n\\n## API observations\\n\\nOn a remote A5000 ComfyUI host, `/object_info` is useful for confirming available Wan nodes and exact dropdown model names before a long job.\\n\\nA minimal native Wan2.2 TI2V text-to-video background job can use:\\n\\n- `WanVideoModelLoader`: `wan2.2_ti2v_5B_fp16.safetensors`\\n- `LoadWanVideoT5TextEncoder`: an available Wan T5 encoder such as `umt5-xxl-enc-fp8_e4m3fn.safetensors` or the scaled variant if the node supports it\\n- `WanVideoTextEncode`\\n- `WanVideoEmptyEmbeds`\\n- `WanVideoSampler`\\n- `WanVideoVAELoader`: `wan2.2_vae.safetensors`\\n- `WanVideoDecode`\\n- `CreateVideo`\\n- `SaveVideo`\\n\\nConservative 16 GB A5000 test/final-pilot background settings:\\n\\n```text\\n416x736\\n61 frames\\n12 fps\\n8–12 steps\\nCFG ~4.5\\nscheduler unipc\\nVAE tiling on\\nCPU/offload text/model settings if memory is tight\\n```\\n\\n## Prompting\\n\\nPrompt for abstract motion/texture and safe negative space only. Example positive prompt shape:\\n\\n```text\\nVertical 9:16 cinematic abstract motion-graphics background for a short about [mechanism].\\n[Objects/cards/paths] flow through [abstract system].\\nModern dark editorial tech style, subtle parallax, smooth camera push, clean negative space for captions and overlays, no readable text.\\n```\\n\\nNegative prompt must include:\\n\\n```text\\nreadable text, words, subtitles, captions, numbers, logos, watermark, brand names, UI text, fake letters\\n```\\n\\n## Composition rule\\n\\nFinal assembly should happen outside Wan:\\n\\n```text\\nWan text-free background → deterministic 1080x1920 renderer overlays → TTS audio → final MP4\\n```\\n\\nIf the single Wan clip is shorter than the final video, looping/upscaling under deterministic overlays is acceptable for a controlled assessment, but disclose it clearly as a limitation in the review package.\\n\\n## Review package\\n\\nInclude:\\n\\n- Wan background path and prompt plan/history\\n- final MP4 path, SHA256, duration, resolution, fps\\n- audio path and duration\\n- caption/overlay manifest\\n- whether forced alignment was used\\n- exact note that this was one Wan call/render, not a variant series\\n- no posting/platform API performed\\n\\n## Pitfalls\\n\\n- Do not let Wan generate text, labels, UI, captions, or fake numbers; those often become unreadable and unreviewable.\\n- Do not hide that a single short Wan clip was looped if the final video is longer.\\n- Do not spin up extra seeds/variants after an “exactly one final render” approval; ask for a second-render approval first.\\n\",\"file_path\":\"references/wan22-one-render-pilot-background.md\",\"name\":\"comfyui\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"comfyui\",\"new_string\":\"- `qwen-image-gguf-local.md` — Qwen-Image-2512-GGUF model choice, VRAM/quantization ladder, and orchestration guidance for using Qwen keyframe generation alongside Wan/Wan2.2 video without assuming concurrent GPU residency.\\n- `wan22-one-render-pilot-background.md` — controlled “exactly one final pilot render” pattern: one text-free Wan2.2 background/motion layer, deterministic overlays/captions/audio, resource-gate checks, and transparent limitation reporting when the clip is looped/upscaled.\",\"old_string\":\"- `qwen-image-gguf-local.md` — Qwen-Image-2512-GGUF model choice, VRAM/quantization ladder, and orchestration guidance for using Qwen keyframe generation alongside Wan/Wan2.2 video without assuming concurrent GPU residency.\"}"
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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. If the user explicitly approves exactly one final pilot render, do not run seed batches; use the one-render background pattern in `references/wan22-one-render-pilot-background.md` and report any loop/upscale limitations transparently. 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. 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`.\"}"
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"content": "Skill library updated.\n\n## Updated skills\n\n- `social-media-shorts-automation`\n - Added reference:\n - `references/static-active-word-captions-and-final-pilot-render.md`\n - Patched `SKILL.md` to make the corrected caption style explicit:\n - static 3–7 word phrase segments\n - only active spoken word highlights\n - highlight moves left-to-right\n - no default one-word-only captions\n - no hectic all-words-changing window\n - Added final-pilot render gate / review-package pattern:\n - `APPROVED_FOR_PREVIEW_RENDER`\n - `APPROVED_FOR_FINAL_RENDER`\n - no posting/platform API without separate approval\n - Wan text-free background only\n - deterministic overlays/captions\n\n- `comfyui`\n - Added reference:\n - `references/wan22-one-render-pilot-background.md`\n - Patched `SKILL.md` with the “exactly one final pilot render” Wan2.2 pattern:\n - one text-free Wan background/motion layer\n - no seed batch unless explicitly approved\n - deterministic renderer owns all captions/labels/facts\n - disclose loop/upscale limitations if one short Wan clip is reused\n\n## Captured learning\n\nThe main durable correction was the user’s subtitle preference:\n\n> Subtitle phrase stays static; only the currently spoken word highlights and moves left-to-right through the segment.\n\nThis is now embedded in the relevant class-level Shorts automation skill, not only memory.",
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"text": "Skill library updated.\n\n## Updated skills\n\n- `social-media-shorts-automation`\n - Added reference:\n - `references/static-active-word-captions-and-final-pilot-render.md`\n - Patched `SKILL.md` to make the corrected caption style explicit:\n - static 3–7 word phrase segments\n - only active spoken word highlights\n - highlight moves left-to-right\n - no default one-word-only captions\n - no hectic all-words-changing window\n - Added final-pilot render gate / review-package pattern:\n - `APPROVED_FOR_PREVIEW_RENDER`\n - `APPROVED_FOR_FINAL_RENDER`\n - no posting/platform API without separate approval\n - Wan text-free background only\n - deterministic overlays/captions\n\n- `comfyui`\n - Added reference:\n - `references/wan22-one-render-pilot-background.md`\n - Patched `SKILL.md` with the “exactly one final pilot render” Wan2.2 pattern:\n - one text-free Wan background/motion layer\n - no seed batch unless explicitly approved\n - deterministic renderer owns all captions/labels/facts\n - disclose loop/upscale limitations if one short Wan clip is reused\n\n## Captured learning\n\nThe main durable correction was the user’s subtitle preference:\n\n> Subtitle phrase stays static; only the currently spoken word highlights and moves left-to-right through the segment.\n\nThis is now embedded in the relevant class-level Shorts automation skill, not only memory."
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