{
"session_id": "20260526_070136_e248717a",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-26T11:50:33.079567",
"last_updated": "2026-05-26T11:50:41.334229",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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% — 2,175/2,200 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: Qwen TS:11435; gpt-5.5; AutoShorts GPU RTX A5000 16GB.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [98% — 1,349/1,375 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. New visual standard for proof shorts: detailed AI-generated real UI/screen images with readable concrete text (e.g. Outlook-style invite/login/calendar), no extra focus lines/arrows/boxes; active-word subtitles; export MP4 with explicit 9:16 SAR/DAR.\n\nConversation started: Tuesday, May 26, 2026 07:01 AM\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": "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": "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": "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": 125,
"messages": [
{
"role": "user",
"content": "[M G] Aspect ratio stimmt nicht, bzw oben und unten sind schwarze balken und das sichtbare bild ist in der höhe zusammengestaucht."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_SzTTY979Trq3jNtoUiptAls5",
"call_id": "call_SzTTY979Trq3jNtoUiptAls5",
"response_item_id": "fc_041f677d7aa57418016a152937c5f0819196d75d08e46b6cf0",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Considering video response**\n\nI think I need to respond regarding the previous video, but I might need more context about AutoShorts. Maybe I should ask the user for clarification? I could suggest a fix using ffmpeg, but I wonder if that's necessary given the current situation. The user mentioned aspect ratio, which is important for social media shorts. It could be useful to explore skills related to social media shorts or growth while addressing this.",
"reasoning_content": "**Considering video response**\n\nI think I need to respond regarding the previous video, but I might need more context about AutoShorts. Maybe I should ask the user for clarification? I could suggest a fix using ffmpeg, but I wonder if that's necessary given the current situation. The user mentioned aspect ratio, which is important for social media shorts. It could be useful to explore skills related to social media shorts or growth while addressing this.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFSk3kI_hclDPvBtw2nE0r_AZIS1oAL8jlGoopdcNqOT7UdhovaDHHN3V9Ghk-SDyGbcBC99UwRk8l9_TOR8qzI1yFrE6GJI2lBVvbIHuakFQ6A_X_0LR_mFczbYeQoUasrIm1Xwhb1APHx4zXMxrEq0CIenYXiLGsRnVwEqOY4QqFD5sMcUZmRCOZL_Axkj4ibbU0OF4nPR-4zmT5_yR6_6zZNzYUpHJ9bi4IQVx_PEhUXB3KDQ3cz1vI4LjO4AV9bShky0vhtxgZCG-CyhxsUhHVIdmCSGNhMMnGVth3mitvmBjpgqF2OnXJCdN3soGy7LeCUkffrgexxSouUDlwKwcRfYzTrKuv-0ihDmBsvNVd3SUFIpd8LO1svw0d1s7D9keV2-JSeXSAyFh0bps2qeM4KCqCStsnrgAwkoxBlGzh9zutXDj-_ZGasq-2wbZolHm0cM_srWqBKrcumkuQGLFTKTd-PfxgyzIOtSupFon95ixnmgwqyICXuBoxzLgzvbbVJJiyPrf-kyvP0FBWmLZAgpMuRn8dv_ot69YbXgokcep-fHS9oxLoIgaLg49uxT8xFK2ZtgQt8vaEThUZS2-vYzWyB_4RYukEmIh0eRJEhfCNFx99LO4xX7dfYuPGEht7TgJzQRBhDA_PBbLaWFom0Jk5xkEbMCzqSQ0Ke2EwC54fEGRAO4qt8CKjsCe0b31JfNDbRegp4t_Qp1ZBifW3limcDaGwJmV_dIKGfMMZtabGuqermIz-LtlYPZs-_iILRgQ8A0W4gYDJPNAVwYTjRFCerWeT4GyaZCtp_kmqX2nfq0Y-Qz6EcXHJ03mdW-W-1gZrbg5tS3aAapDP_h0torRWvpX1wwalS-zRRcw1QHCaI_I2NCxtCAH9xHBk5EKdgqvbbgW9kVlC9xR9Q4NlCeXaqApIhvvysf9zBNp1TS7zuI8hoFHrxqstWT6iUiHQa_vnYr2wPp3eUKqgYELbKgBo5ZclrBH7jcP1gyzFePyMVWlQ7cmZbPg2owRnRHtSix23DRfATyQx-EDDOeoLo4EWuMajClqVCwj7crmm4G96YiO7QyoB_efvb-5-DRwjmBJU4-Jx5tRmTQePRzoN9C67Te50traUnbAD_HB8WNuatQguNOAnfOEG-_DauBrj7eE267-tAzskD5IvZDkESsp3w7gisf40hejSrxEIKcEZW1ffQSktVIQ79PRFqt4zay9XnUFBFxWhoDJdNWza0TcQwrrGicN8i5Afi_1Izob4yUucnD0soo2xHewtQuy4GpVpSS5Jd_Hjjxt2PNP6JiqBVVgm-3oDhC3siegDpZbLMP3Bmg04gcK9e3Bsgxc4qZ1gobvSj_QJ_x3dJS_tA==",
"id": "rs_041f677d7aa57418016a15293353dc8191a723934f839d030b",
"summary": [
{
"type": "summary_text",
"text": "**Considering video response**\n\nI think I need to respond regarding the previous video, but I might need more context about AutoShorts. Maybe I should ask the user for clarification? I could suggest a fix using ffmpeg, but I wonder if that's necessary given the current situation. The user mentioned aspect ratio, which is important for social media shorts. It could be useful to explore skills related to social media shorts or growth while addressing this."
}
]
}
]
},
{
"role": "tool",
"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. 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. If a final render is rejected as preview-overlay-dominated or not visual-first, follow `references/visual-first-final-render-repair.md`: mark the render rejected/not-postable, fix Wan output parsing, enforce final overlay limits, produce only a short visual proof under `APPROVED_FOR_VISUAL_PROOF`, and do not run another full final until the visual direction is accepted. If even the visual proof is rejected as abstract/generic/dark or “blind T2V”, switch to `references/keyframe-first-final-visuals.md`: plan and approve start/end/style keyframes first, then run only a short I2V/first-last-frame proof with minimal renderer-owned overlays. If the user says the production method itself is wrong, keyframes are not I2V-ready, or an animatic was incorrectly substituted for a failed Wan proof, follow `references/visual-production-reset-i2v-first.md`: reject/audit the proof, stop content rendering, run technical I2V sanity first, then generate at most three styleframes for direction review. If deterministic layout proofs are useful as composition blueprints but rejected as too wireframe/PowerPoint for final style, switch to `references/premium-ai-styleframe-direction.md`: use the layouts only as mechanism blueprints, generate at most 3 tightly briefed premium text-free AI styleframes, deliver a contact sheet with verified media metadata, and keep Wan/I2V/endframes/full-final blocked until style direction approval. If those premium styleframes are better quality but still too abstract, switch to `references/real-life-premium-styleframes.md`: anchor the mechanism in a realistic work/meeting/desk scene, keep all readable overlays renderer-owned, generate at most 3 new real-life styleframes plus contact sheet, and still block Wan/I2V/endframes/full-final until explicit approval. If the user approves one or more real-life styleframe directions but wants to compare A/B/C before Wan, use `references/prerelease-animatic-preview.md`: build only a small AI-keyframe + deterministic-motion animatic per approved direction, reuse existing voiceover/captions, deliver MP4/contact/QA sheets with metadata, and keep Wan/I2V/final/posting blocked. If the user then rejects the renderer-added circles/arrows/labels/boxes/decision-target markers as destroying the premium look, switch to `references/clean-image-cut-no-overlay-pilot.md`: render only AI images + voiceover + clean subtitles, with a hard subtitles-only NoOverlayPolicy and no Wan/I2V/full-final/posting. If the user performs a strategic reset because the pilot is pretty but semantically weak, stop optimizing that candidate and follow `references/proof-short-real-problem-reset.md`: archive the old pilot as a learning case, enforce the Pretty Empty Visual Blocker, and build a screen/mockup-led Proof Short around an immediately visible real viewer problem before any premium/Wan production. If the next review focuses on image provider quality/provenance or subtitles running off-screen, follow `references/image-provider-audit-and-caption-safezone.md`: audit the configured image provider/model/tier from config + provider registry + cache metadata, run at most one neutral capability test, keep UI/invoice mockups deterministic, and add tested caption layout safe-zone bounds before re-rendering. If an approved Proof Short feels too visually flat and the user asks why no AI images were used, follow `references/proof-short-ai-visual-upgrade.md`: use AI images as a controlled text-free atmosphere/background/keyframe layer while keeping invoices, emails, payment routes, labels, facts, and captions deterministic renderer-owned. For the concrete rendering/QA pattern — small 2–3 keyframe pass, contact-sheet inspection, discard pseudo-text frames, blur/darken/subordinate AI backgrounds, and report used/discarded keyframes — see `references/proof-short-ai-background-rendering-qa.md`. For the follow-up A/B test where the user asks whether pure KI images can match or beat hybrid mockups, see `references/clean-ai-cut-vs-hybrid-mechanism.md`: compare clean image-only premium feel against mechanism clarity, keep word-aligned subtitles, and consider `90% clean AI images + 10% minimal deterministic mechanism` as the likely next style.\\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 - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - After a Proof Short concept is approved for usefulness/clarity, do not interpret `ProofShortMode` as a permanent ban on AI images. If the user wants the video “aufgepeppt”, add a controlled AI visual-upgrade layer: 2–3 text-free premium backgrounds/keyframes, while all invoice/email/UI/payment-route text, labels, facts, captions, claims, and red-flag overlays remain deterministic renderer-owned. See `references/proof-short-ai-visual-upgrade.md`.\\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 - For final renders, Wan must carry the visual scene, not merely provide a blurred/looped background underneath preview cards. Use full-bleed, scene-specific Wan clips, minimal renderer-owned overlays, and a final overlay policy that blocks preview frames, large UI cards/textboxes, debug bars, and overlay overload. If architecture is rejected, create a short visual proof first; see `references/visual-first-final-render-repair.md`.\\n - If a visual proof still looks like blind text-to-video or random abstract AI output, stop T2V and move to a keyframe-first workflow: generate/approve 1–2 text-free start/end/style keyframes for the scene, then attempt only a short Wan I2V/first-last-frame proof. See `references/keyframe-first-final-visuals.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 provider word-boundary events before sending another final-near clip. For EdgeTTS previews, request `boundary='WordBoundary'` and render active-word highlights from those offsets; do not merely distribute words evenly over sentence SRT durations. 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 - If the user says the ending is confusing, rushed, or does not offer a real solution, treat this as a content-blocking issue, not a minor copy tweak. Rewrite the final third so non-technical viewers are explicitly walked through the concrete action: what to close/avoid, what to open/check instead, how they know it worked, and the memorable rule/fazit. Avoid vague imperatives like “open the service yourself” unless immediately unpacked with a concrete example such as “close the invite; open the calendar/work app from the normal icon or bookmark; if the meeting is real, it will still be there.”\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style.\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style. Treat them as composition blueprints and run a small, tightly prompted premium AI styleframe pass instead: AI supplies glassmorphism/product-film quality and atmosphere; the renderer still owns labels, arrows, captions, facts, and final assembly. See `references/premium-ai-styleframe-direction.md`.\\n - If premium AI styleframes improve quality but still feel like abstract SaaS symbolism, do not continue iterating abstract funnels/rings. Move to real-life anchored work scenes: desk/laptop, empty meeting room/glass board, or project table/work evidence, with only subtle glass/holographic mechanism overlays. See `references/real-life-premium-styleframes.md`.\\n - If real-life styleframe directions are accepted but the user has not approved Wan/I2V yet, insert a pre-release animatic comparison step rather than jumping to endframes or video generation. Create small per-direction AI keyframe sets, deterministic push-ins/crossfades, existing voiceover, static active-word captions, contact sheets, and QA frame sheets; report Telegram delivery metadata. See `references/prerelease-animatic-preview.md`.\\nIf the user rejects the added visual explanation overlays as making the result look like a technical explainer board, treat this as a hard style failure. For the clean pilot, use only AI images, voiceover, and clean subtitles; block circles, arrows, labels, boxes, connector lines, decision-target markers, debug/diagram elements, and caption boxes/bars. See `references/clean-image-cut-no-overlay-pilot.md`. If the user then asks whether pure KI-Bilder can achieve the same or better effect, run an A/B proof against the hybrid version and assess premium look vs mechanism clarity; the likely final style may be `90% clean AI images + 10% minimal renderer-owned mechanism`. See `references/clean-ai-cut-vs-hybrid-mechanism.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.\\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. 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. See `references/word-boundary-caption-sync-and-solution-gate.md` for the stronger EdgeTTS `WordBoundary` timing pattern and the explicit solution/fazit gate for Proof Shorts.\\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/visual-first-final-render-repair.md` for the repaired final-render architecture after a rejected pilot: mark rejected renders as not-postable, avoid preview-board overlays, enforce `FinalOverlayPolicy`, validate ComfyUI/Wan output histories robustly, and use `APPROVED_FOR_VISUAL_PROOF` for one short visual proof before any second full final.\\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 keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not overcorrect from “validate with Proof Shorts” into “never use AI images.” Once the real problem/rule is approved, AI images can legitimately improve perceived quality as text-free atmosphere, backgrounds, keyframes, or thumbnails. 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"reasoning_content": "**Inspecting repo files**\n\nI need to inspect the files in the repository. My current working directory is hermes-agent, but I think the autos runtime might be relevant too. So, I'll search for recent mp4 files. It makes sense to use the search_files function to do that. Just need to make sure I get the right search parameters to pull up those files efficiently. Let's see what I can find!",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Run generated Wan2.2 I2V clip jobs through ComfyUI.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import json\\n 6|from datetime import datetime, timezone\\n 7|from pathlib import Path\\n 8|from typing import Any\\n 9|\\n 10|from autoshorts.i2v_clip_jobs import build_native_wan22_i2v_workflow\\n 11|\\n 12|\\n 13|def _utc_now() -> str:\\n 14| return datetime.now(timezone.utc).isoformat()\\n 15|\\n 16|\\n 17|def _empty_summary(total: int) -> dict[str, int]:\\n 18| return {\\\"total\\\": total, \\\"succeeded\\\": 0, \\\"skipped\\\": 0, \\\"failed\\\": 0}\\n 19|\\n 20|\\n 21|def _skip_result(job: dict[str, Any], reason: str) -> dict[str, Any]:\\n 22| return {\\n 23| \\\"job_id\\\": job[\\\"job_id\\\"],\\n 24| \\\"scene_id\\\": job[\\\"scene_id\\\"],\\n 25| \\\"seed\\\": job[\\\"seed\\\"],\\n 26| \\\"status\\\": \\\"skipped\\\",\\n 27| \\\"reason\\\": reason,\\n 28| \\\"keyframe_path\\\": job[\\\"keyframe_path\\\"],\\n 29| \\\"output_path\\\": job[\\\"output_path\\\"],\\n 30| }\\n 31|\\n 32|\\n 33|def _success_result(job: dict[str, Any], client_result: dict[str, Any]) -> dict[str, Any]:\\n 34| return {\\n 35| \\\"job_id\\\": job[\\\"job_id\\\"],\\n 36| \\\"scene_id\\\": job[\\\"scene_id\\\"],\\n 37| \\\"seed\\\": job[\\\"seed\\\"],\\n 38| \\\"status\\\": \\\"success\\\",\\n 39| \\\"prompt_id\\\": client_result.get(\\\"prompt_id\\\"),\\n 40| \\\"output_path\\\": client_result.get(\\\"output_path\\\", job[\\\"output_path\\\"]),\\n 41| }\\n 42|\\n 43|\\n 44|def _failed_result(job: dict[str, Any], error: Exception | dict[str, Any]) -> dict[str, Any]:\\n 45| detail = error if isinstance(error, dict) else {\\\"error\\\": type(error).__name__, \\\"message\\\": str(error)}\\n 46| return {\\n 47| \\\"job_id\\\": job[\\\"job_id\\\"],\\n 48| \\\"scene_id\\\": job[\\\"scene_id\\\"],\\n 49| \\\"seed\\\": job[\\\"seed\\\"],\\n 50| \\\"status\\\": \\\"failed\\\",\\n 51| \\\"detail\\\": detail,\\n 52| \\\"keyframe_path\\\": job[\\\"keyframe_path\\\"],\\n 53| \\\"output_path\\\": job[\\\"output_path\\\"],\\n 54| }\\n 55|\\n 56|\\n 57|def run_i2v_clip_jobs(\\n 58| package: dict[str, Any],\\n 59| *,\\n 60| client: Any,\\n 61| limit: int | None = None,\\n 62| dry_run: bool = False,\\n 63| poll_seconds: float = 5,\\n 64| timeout_seconds: int = 900,\\n 65|) -> dict[str, Any]:\\n 66| \\\"\\\"\\\"Run clip jobs from a package and return a structured run report.\\\"\\\"\\\"\\n 67| jobs = package.get(\\\"jobs\\\", [])[:limit]\\n 68| summary = _empty_summary(len(jobs))\\n 69| results: list[dict[str, Any]] = []\\n 70|\\n 71| for job in jobs:\\n 72| keyframe_path = Path(job[\\\"keyframe_path\\\"])\\n 73| if not keyframe_path.exists():\\n 74| summary[\\\"skipped\\\"] += 1\\n 75| results.append(_skip_result(job, \\\"missing_keyframe\\\"))\\n 76| continue\\n 77| if Path(job[\\\"output_path\\\"]).exists():\\n 78| summary[\\\"skipped\\\"] += 1\\n 79| results.append(_skip_result(job, \\\"output_exists\\\"))\\n 80| continue\\n 81| if dry_run:\\n 82| summary[\\\"skipped\\\"] += 1\\n 83| results.append(_skip_result(job, \\\"dry_run_ready\\\"))\\n 84| continue\\n 85|\\n 86| workflow = build_native_wan22_i2v_workflow(job, uploaded_image_name=job[\\\"remote_image_name\\\"])\\n 87| try:\\n 88| client_result = client.run_i2v_workflow(\\n 89| workflow=workflow,\\n 90| keyframe_path=keyframe_path,\\n 91| remote_image_name=job[\\\"remote_image_name\\\"],\\n 92| output_path=Path(job[\\\"output_path\\\"]),\\n 93| poll_seconds=poll_seconds,\\n 94| timeout_seconds=timeout_seconds,\\n 95| )\\n 96| if client_result.get(\\\"status\\\") == \\\"success\\\":\\n 97| summary[\\\"succeeded\\\"] += 1\\n 98| results.append(_success_result(job, client_result))\\n 99| else:\\n 100| summary[\\\"failed\\\"] += 1\\n 101| results.append(_failed_result(job, client_result))\\n 102| except Exception as exc: # pragma: no cover - tested through behavior shape, not specific network exception\\n 103| summary[\\\"failed\\\"] += 1\\n 104| results.append(_failed_result(job, exc))\\n 105|\\n 106| return {\\n 107| \\\"schema_version\\\": \\\"i2v_run_report.v1\\\",\\n 108| \\\"created_at\\\": _utc_now(),\\n 109| \\\"video\\\": package.get(\\\"video\\\", {}),\\n 110| \\\"summary\\\": summary,\\n 111| \\\"results\\\": results,\\n 112| }\\n 113|\\n 114|\\n 115|def write_run_report(report: dict[str, Any], output_file: Path) -> Path:\\n 116| \\\"\\\"\\\"Write an I2V run report as JSON.\\\"\\\"\\\"\\n 117| output_file.parent.mkdir(parents=True, exist_ok=True)\\n 118| output_file.write_text(json.dumps(report, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 119| return output_file\\n 120|\", \"total_lines\": 119, \"file_size\": 4053, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Build executable Wan2.2 I2V clip jobs from prompt plans.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import json\\n 6|from pathlib import Path\\n 7|from typing import Any\\n 8|\\n 9|\\n 10|NATIVE_WAN22_CLIENT_ID = \\\"autoshorts-i2v\\\"\\n 11|\\n 12|\\n 13|def _video_slug(prompt_plan: dict[str, Any]) -> str:\\n 14| return prompt_plan[\\\"video\\\"][\\\"id\\\"]\\n 15|\\n 16|\\n 17|def _scene_keyframe_path(keyframe_root: Path, video_slug: str, scene_id: str) -> Path:\\n 18| return keyframe_root / video_slug / f\\\"{scene_id}.png\\\"\\n 19|\\n 20|\\n 21|def _scene_output_path(output_root: Path, video_slug: str, scene_id: str, seed: int) -> Path:\\n 22| return output_root / video_slug / scene_id / f\\\"seed_{seed}.mp4\\\"\\n 23|\\n 24|\\n 25|def build_i2v_clip_jobs(\\n 26| prompt_plan: dict[str, Any],\\n 27| keyframe_root: Path,\\n 28| output_root: Path,\\n 29| seeds: list[int],\\n 30|) -> list[dict[str, Any]]:\\n 31| \\\"\\\"\\\"Create seeded clip jobs for each Wan2.2 I2V scene.\\\"\\\"\\\"\\n 32| video_slug = _video_slug(prompt_plan)\\n 33| jobs: list[dict[str, Any]] = []\\n 34| for scene in prompt_plan[\\\"scenes\\\"]:\\n 35| for seed in seeds:\\n 36| scene_id = scene[\\\"scene_id\\\"]\\n 37| keyframe_path = _scene_keyframe_path(keyframe_root, video_slug, scene_id)\\n 38| output_path = _scene_output_path(output_root, video_slug, scene_id, seed)\\n 39| jobs.append(\\n 40| {\\n 41| \\\"schema_version\\\": \\\"i2v_clip_job.v1\\\",\\n 42| \\\"job_id\\\": f\\\"{scene_id}_seed_{seed}\\\",\\n 43| \\\"video_id\\\": video_slug,\\n 44| \\\"scene_id\\\": scene_id,\\n 45| \\\"purpose\\\": scene[\\\"purpose\\\"],\\n 46| \\\"seed\\\": seed,\\n 47| \\\"status\\\": \\\"ready\\\" if keyframe_path.exists() else \\\"needs_keyframe\\\",\\n 48| \\\"keyframe_path\\\": str(keyframe_path),\\n 49| \\\"output_path\\\": str(output_path),\\n 50| \\\"remote_image_name\\\": f\\\"{video_slug}_{scene_id}.png\\\",\\n 51| \\\"prompt_spec\\\": scene,\\n 52| \\\"review\\\": {\\n 53| \\\"human_review_required\\\": True,\\n 54| \\\"criteria\\\": [\\n 55| \\\"subject clear in first 0.5s\\\",\\n 56| \\\"motion supports retention beat\\\",\\n 57| \\\"no baked text/logos/watermarks\\\",\\n 58| \\\"no distracting deformation/flicker\\\",\\n 59| \\\"clean space remains for captions\\\",\\n 60| ],\\n 61| },\\n 62| }\\n 63| )\\n 64| return jobs\\n 65|\\n 66|\\n 67|def _filename_prefix(job: dict[str, Any]) -> str:\\n 68| return f\\\"autoshorts_{job['video_id']}_{job['scene_id']}_seed_{job['seed']}\\\"\\n 69|\\n 70|\\n 71|def build_native_wan22_i2v_workflow(job: dict[str, Any], uploaded_image_name: str) -> dict[str, Any]:\\n 72| \\\"\\\"\\\"Build an API-format native ComfyUI Wan2.2 I2V workflow for a clip job.\\\"\\\"\\\"\\n 73| spec = job[\\\"prompt_spec\\\"]\\n 74| comfy = spec[\\\"comfyui\\\"]\\n 75| width = int(comfy[\\\"resolution\\\"][\\\"width\\\"])\\n 76| height = int(comfy[\\\"resolution\\\"][\\\"height\\\"])\\n 77| return {\\n 78| \\\"1\\\": {\\n 79| \\\"class_type\\\": \\\"UNETLoader\\\",\\n 80| \\\"inputs\\\": {\\\"unet_name\\\": comfy[\\\"model\\\"], \\\"weight_dtype\\\": \\\"default\\\"},\\n 81| },\\n 82| \\\"2\\\": {\\n 83| \\\"class_type\\\": \\\"CLIPLoader\\\",\\n 84| \\\"inputs\\\": {\\\"clip_name\\\": comfy[\\\"text_encoder\\\"], \\\"type\\\": \\\"wan\\\", \\\"device\\\": \\\"default\\\"},\\n 85| },\\n 86| \\\"3\\\": {\\n 87| \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n 88| \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"i2v_motion_prompt\\\"]},\\n 89| },\\n 90| \\\"4\\\": {\\n 91| \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n 92| \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"negative_prompt\\\"]},\\n 93| },\\n 94| \\\"5\\\": {\\\"class_type\\\": \\\"VAELoader\\\", \\\"inputs\\\": {\\\"vae_name\\\": comfy[\\\"vae\\\"]}},\\n 95| \\\"6\\\": {\\\"class_type\\\": \\\"LoadImage\\\", \\\"inputs\\\": {\\\"image\\\": uploaded_image_name}},\\n 96| \\\"7\\\": {\\n 97| \\\"class_type\\\": \\\"ImageScale\\\",\\n 98| \\\"inputs\\\": {\\n 99| \\\"image\\\": [\\\"6\\\", 0],\\n 100| \\\"upscale_method\\\": \\\"lanczos\\\",\\n 101| \\\"width\\\": width,\\n 102| \\\"height\\\": height,\\n 103| \\\"crop\\\": \\\"disabled\\\",\\n 104| },\\n 105| },\\n 106| \\\"8\\\": {\\n 107| \\\"class_type\\\": \\\"Wan22ImageToVideoLatent\\\",\\n 108| \\\"inputs\\\": {\\n 109| \\\"vae\\\": [\\\"5\\\", 0],\\n 110| \\\"width\\\": width,\\n 111| \\\"height\\\": height,\\n 112| \\\"length\\\": int(comfy[\\\"frames\\\"]),\\n 113| \\\"batch_size\\\": 1,\\n 114| \\\"start_image\\\": [\\\"7\\\", 0],\\n 115| },\\n 116| },\\n 117| \\\"9\\\": {\\n 118| \\\"class_type\\\": \\\"KSampler\\\",\\n 119| \\\"inputs\\\": {\\n 120| \\\"model\\\": [\\\"1\\\", 0],\\n 121| \\\"seed\\\": int(job[\\\"seed\\\"]),\\n 122| \\\"steps\\\": int(comfy[\\\"steps\\\"]),\\n 123| \\\"cfg\\\": float(comfy[\\\"cfg\\\"]),\\n 124| \\\"sampler_name\\\": \\\"uni_pc\\\",\\n 125| \\\"scheduler\\\": \\\"simple\\\",\\n 126| \\\"positive\\\": [\\\"3\\\", 0],\\n 127| \\\"negative\\\": [\\\"4\\\", 0],\\n 128| \\\"latent_image\\\": [\\\"8\\\", 0],\\n 129| \\\"denoise\\\": 1.0,\\n 130| },\\n 131| },\\n 132| \\\"10\\\": {\\\"class_type\\\": \\\"VAEDecode\\\", \\\"inputs\\\": {\\\"samples\\\": [\\\"9\\\", 0], \\\"vae\\\": [\\\"5\\\", 0]}},\\n 133| \\\"11\\\": {\\\"class_type\\\": \\\"CreateVideo\\\", \\\"inputs\\\": {\\\"images\\\": [\\\"10\\\", 0], \\\"fps\\\": float(comfy[\\\"fps\\\"])}},\\n 134| \\\"12\\\": {\\n 135| \\\"class_type\\\": \\\"SaveVideo\\\",\\n 136| \\\"inputs\\\": {\\n 137| \\\"video\\\": [\\\"11\\\", 0],\\n 138| \\\"filename_prefix\\\": _filename_prefix(job),\\n 139| \\\"format\\\": \\\"mp4\\\",\\n 140| \\\"codec\\\": \\\"h264\\\",\\n 141| },\\n 142| },\\n 143| }\\n 144|\\n 145|\\n 146|def write_i2v_clip_jobs(\\n 147| prompt_plan: dict[str, Any],\\n 148| output_file: Path,\\n 149| keyframe_root: Path,\\n 150| generated_clip_root: Path,\\n 151| seeds: list[int],\\n 152|) -> Path:\\n 153| \\\"\\\"\\\"Write a clip-job package JSON.\\\"\\\"\\\"\\n 154| output_file.parent.mkdir(parents=True, exist_ok=True)\\n 155| package = {\\n 156| \\\"schema_version\\\": \\\"i2v_clip_jobs.v1\\\",\\n 157| \\\"video\\\": prompt_plan[\\\"video\\\"],\\n 158| \\\"strategy\\\": prompt_plan[\\\"strategy\\\"],\\n 159| \\\"keyframe_root\\\": str(keyframe_root),\\n 160| \\\"generated_clip_root\\\": str(generated_clip_root),\\n 161| \\\"jobs\\\": build_i2v_clip_jobs(prompt_plan, keyframe_root, generated_clip_root, seeds),\\n 162| }\\n 163| output_file.write_text(json.dumps(package, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 164| return output_file\\n 165|\", \"total_lines\": 164, \"file_size\": 5945, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Small ComfyUI REST client used by AutoShortsBot clip jobs.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import json\\n 6|import time\\n 7|import urllib.parse\\n 8|import urllib.request\\n 9|from pathlib import Path\\n 10|from typing import Any\\n 11|\\n 12|\\n 13|class UrllibComfyUITransport:\\n 14| \\\"\\\"\\\"HTTP transport for local/Tailscale ComfyUI instances.\\\"\\\"\\\"\\n 15|\\n 16| def upload_image(self, base_url: str, path: Path, *, remote_name: str, overwrite: bool = True) -> dict[str, Any]:\\n 17| boundary = \\\"----AutoShortsBotBoundary\\\"\\n 18| body: list[bytes] = []\\n 19|\\n 20| def add(part: str | bytes) -> None:\\n 21| body.append(part.encode() if isinstance(part, str) else part)\\n 22|\\n 23| add(\\n 24| f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"image\\\"; filename=\\\"{remote_name}\\\"\\\\r\\\\n'\\n 25| \\\"Content-Type: image/png\\\\r\\\\n\\\\r\\\\n\\\"\\n 26| )\\n 27| add(path.read_bytes())\\n 28| add(\\\"\\\\r\\\\n\\\")\\n 29| add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"type\\\"\\\\r\\\\n\\\\r\\\\ninput\\\\r\\\\n')\\n 30| add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"overwrite\\\"\\\\r\\\\n\\\\r\\\\n{str(overwrite).lower()}\\\\r\\\\n')\\n 31| add(f\\\"--{boundary}--\\\\r\\\\n\\\")\\n 32| request = urllib.request.Request(\\n 33| f\\\"{base_url}/upload/image\\\",\\n 34| data=b\\\"\\\".join(body),\\n 35| headers={\\\"Content-Type\\\": f\\\"multipart/form-data; boundary={boundary}\\\"},\\n 36| )\\n 37| return json.loads(urllib.request.urlopen(request, timeout=120).read().decode())\\n 38|\\n 39| def post_json(self, base_url: str, endpoint: str, payload: dict[str, Any]) -> dict[str, Any]:\\n 40| request = urllib.request.Request(\\n 41| f\\\"{base_url}{endpoint}\\\",\\n 42| data=json.dumps(payload).encode(),\\n 43| headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n 44| )\\n 45| raw = urllib.request.urlopen(request, timeout=120).read().decode()\\n 46| return json.loads(raw) if raw else {}\\n 47|\\n 48| def wait_for_completion(\\n 49| self,\\n 50| base_url: str,\\n 51| prompt_id: str,\\n 52| *,\\n 53| poll_seconds: float,\\n 54| timeout_seconds: int,\\n 55| ) -> dict[str, Any]:\\n 56| deadline = time.monotonic() + timeout_seconds\\n 57| while time.monotonic() < deadline:\\n 58| queue = json.loads(urllib.request.urlopen(f\\\"{base_url}/queue\\\", timeout=30).read().decode())\\n 59| running = queue.get(\\\"queue_running\\\", [])\\n 60| pending = queue.get(\\\"queue_pending\\\", [])\\n 61| if not running and not pending:\\n 62| history = json.loads(urllib.request.urlopen(f\\\"{base_url}/history/{prompt_id}\\\", timeout=60).read().decode())\\n 63| return history.get(prompt_id, {})\\n 64| time.sleep(poll_seconds)\\n 65| raise TimeoutError(f\\\"ComfyUI prompt did not finish within {timeout_seconds}s: {prompt_id}\\\")\\n 66|\\n 67| def download_view(self, base_url: str, output: dict[str, Any], destination: Path) -> Path:\\n 68| params = urllib.parse.urlencode(\\n 69| {\\n 70| \\\"filename\\\": output[\\\"filename\\\"],\\n 71| \\\"subfolder\\\": output.get(\\\"subfolder\\\", \\\"\\\"),\\n 72| \\\"type\\\": output.get(\\\"type\\\", \\\"output\\\"),\\n 73| }\\n 74| )\\n 75| destination.parent.mkdir(parents=True, exist_ok=True)\\n 76| destination.write_bytes(urllib.request.urlopen(f\\\"{base_url}/view?{params}\\\", timeout=300).read())\\n 77| return destination\\n 78|\\n 79|\\n 80|class ComfyUIClient:\\n 81| \\\"\\\"\\\"Minimal client for uploading keyframes, running workflows, and downloading MP4 outputs.\\\"\\\"\\\"\\n 82|\\n 83| def __init__(self, base_url: str, *, transport: Any | None = None):\\n 84| self.base_url = base_url.rstrip(\\\"/\\\")\\n 85| self.transport = transport or UrllibComfyUITransport()\\n 86|\\n 87| def run_i2v_workflow(\\n 88| self,\\n 89| *,\\n 90| workflow: dict[str, Any],\\n 91| keyframe_path: Path,\\n 92| remote_image_name: str,\\n 93| output_path: Path,\\n 94| poll_seconds: float = 5,\\n 95| timeout_seconds: int = 900,\\n 96| ) -> dict[str, Any]:\\n 97| upload = self.transport.upload_image(self.base_url, keyframe_path, remote_name=remote_image_name, overwrite=True)\\n 98| prompt_response = self.transport.post_json(\\n 99| self.base_url,\\n 100| \\\"/prompt\\\",\\n 101| {\\\"prompt\\\": workflow, \\\"client_id\\\": \\\"autoshorts-i2v\\\"},\\n 102| )\\n 103| prompt_id = prompt_response[\\\"prompt_id\\\"]\\n 104| history = self.transport.wait_for_completion(\\n 105| self.base_url,\\n 106| prompt_id,\\n 107| poll_seconds=poll_seconds,\\n 108| timeout_seconds=timeout_seconds,\\n 109| )\\n 110| status = history.get(\\\"status\\\", {})\\n 111| if status.get(\\\"status_str\\\") != \\\"success\\\":\\n 112| return {\\\"status\\\": \\\"failed\\\", \\\"prompt_id\\\": prompt_id, \\\"history\\\": history, \\\"upload\\\": upload}\\n 113| output = _first_video_output(history)\\n 114| self.transport.download_view(self.base_url, output, output_path)\\n 115| return {\\n 116| \\\"status\\\": \\\"success\\\",\\n 117| \\\"prompt_id\\\": prompt_id,\\n 118| \\\"output_path\\\": str(output_path),\\n 119| \\\"remote_output\\\": output,\\n 120| \\\"upload\\\": upload,\\n 121| }\\n 122|\\n 123|\\n 124|def _first_video_output(history: dict[str, Any]) -> dict[str, Any]:\\n 125| for node_output in history.get(\\\"outputs\\\", {}).values():\\n 126| for item in node_output.get(\\\"images\\\", []):\\n 127| if str(item.get(\\\"filename\\\", \\\"\\\")).lower().endswith(\\\".mp4\\\"):\\n 128| return item\\n 129| raise ValueError(\\\"No MP4 output found in ComfyUI history\\\")\\n 130|\", \"total_lines\": 129, \"file_size\": 5168, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/fix_vertical_aspect.py\",\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Normalize MP4s for mobile 9:16 playback.\\n\\nThis is intentionally a post-render boundary: it does not alter source clips;\\nit writes a square-pixel 1080x1920 H.264 export with explicit SAR/DAR metadata.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport subprocess\\nfrom pathlib import Path\\n\\n\\nTARGET_WIDTH = 1080\\nTARGET_HEIGHT = 1920\\n\\n\\ndef normalize_vertical_mp4(input_path: Path, output_path: Path) -> dict[str, object]:\\n output_path.parent.mkdir(parents=True, exist_ok=True)\\n video_filter = (\\n f\\\"scale={TARGET_WIDTH}:{TARGET_HEIGHT}:force_original_aspect_ratio=increase,\\\"\\n f\\\"crop={TARGET_WIDTH}:{TARGET_HEIGHT},\\\"\\n \\\"setsar=1,setdar=9/16,format=yuv420p\\\"\\n )\\n cmd = [\\n \\\"ffmpeg\\\",\\n \\\"-y\\\",\\n \\\"-hide_banner\\\",\\n \\\"-loglevel\\\",\\n \\\"error\\\",\\n \\\"-i\\\",\\n str(input_path),\\n \\\"-vf\\\",\\n video_filter,\\n \\\"-c:v\\\",\\n \\\"libx264\\\",\\n \\\"-preset\\\",\\n \\\"veryfast\\\",\\n \\\"-crf\\\",\\n \\\"18\\\",\\n \\\"-movflags\\\",\\n \\\"+faststart\\\",\\n \\\"-an\\\",\\n str(output_path),\\n ]\\n subprocess.check_call(cmd)\\n probe = json.loads(\\n subprocess.check_output(\\n [\\n \\\"ffprobe\\\",\\n \\\"-v\\\",\\n \\\"error\\\",\\n \\\"-select_streams\\\",\\n \\\"v:0\\\",\\n \\\"-show_entries\\\",\\n \\\"stream=width,height,sample_aspect_ratio,display_aspect_ratio\\\",\\n \\\"-show_entries\\\",\\n \\\"format=duration\\\",\\n \\\"-of\\\",\\n \\\"json\\\",\\n str(output_path),\\n ],\\n text=True,\\n )\\n )\\n stream = probe[\\\"streams\\\"][0]\\n return {\\n \\\"output_path\\\": str(output_path),\\n \\\"width\\\": stream[\\\"width\\\"],\\n \\\"height\\\": stream[\\\"height\\\"],\\n \\\"sample_aspect_ratio\\\": stream.get(\\\"sample_aspect_ratio\\\"),\\n \\\"display_aspect_ratio\\\": stream.get(\\\"display_aspect_ratio\\\"),\\n \\\"duration\\\": float(probe.get(\\\"format\\\", {}).get(\\\"duration\\\", 0.0)),\\n }\\n\\n\\ndef main() -> None:\\n parser = argparse.ArgumentParser(description=\\\"Normalize MP4 to mobile-safe 9:16 1080x1920.\\\")\\n parser.add_argument(\\\"input\\\", type=Path)\\n parser.add_argument(\\\"output\\\", type=Path)\\n args = parser.parse_args()\\n print(json.dumps(normalize_vertical_mp4(args.input, args.output), indent=2))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"}"
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"reasoning_content": "**Normalizing video outputs**\n\nI need to think about whether a patch is necessary to normalize after the download, which might require ffmpeg. Maybe it's best to keep the final assembly separate from ComfyUI. The user has expressed some complaints, so I'm considering creating a `normalize_vertical_mp4` function. However, I want to avoid overcomplicating things. It might be a good idea to add a helper script to fix the vertical aspect and ensure the user gets the corrected output they want.",
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"text": "**Preparing for video automation**\n\nI definitely need to act and consider using AutoShorts. I’m also thinking about loading relevant skills I might need beforehand. Maybe I had the social skill loaded from earlier, but each response needs to ensure that skill is mandatory before replying. I should look into social media shorts automation, perhaps GitHub for resources. I need to prepare for the next video and inspect the repository to understand the upcoming post. Let’s figure out the next steps!"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/final-motion-hybrid-rendering.md\", \"content\": \"# Final Motion Hybrid Rendering\\n\\nUse this pattern for mechanism-reveal Shorts where clarity matters more than cinematic generation.\\n\\n## Core rule\\n\\nAI image/video provides only text-free atmosphere and motion texture. The deterministic renderer owns all truth-bearing layers:\\n\\n- captions and subtitles;\\n- UI/email/card text;\\n- arrows, labels, numbers, and diagrams;\\n- factual claims and final assembly.\\n\\nDo not ask an image/video model to spell workflow labels or explain the mechanism. It will eventually produce fake UI text, malformed words, or pretty but unclear motion.\\n\\n## Recommended pipeline\\n\\n```text\\nFinal voiceover script\\n→ final video plan / scene timeline\\n→ scene-specific text-free image prompts\\n→ procedural motion renderer overlays\\n→ TTS voiceover\\n→ compact phrase-boundary subtitles\\n→ FFmpeg final assembly\\n→ visual QA frames\\n```\\n\\n## Scene-specific ChatGPT Image/Codex backgrounds\\n\\nWhen the user points out that ChatGPT Image is available through Codex, prefer it for fast text-free keyframe/background texture before reaching for local Qwen Image/ComfyUI. Local Qwen/ComfyUI remains useful for private/local batches or cost control, but for this class of mechanism-reveal prototype the lowest-friction path is often:\\n\\n```text\\nChatGPT Image/Codex background(s)\\n→ deterministic renderer overlays\\n→ FFmpeg assembly\\n```\\n\\nFor each production scene, create one background prompt that explicitly says:\\n\\n```text\\nVertical 9:16 cinematic background\\nNo readable text\\nNo logos\\nNo watermarks\\nNo UI labels\\nNo fake letters\\nLeave central negative space for renderer-owned overlays\\n```\\n\\nUse scene-specific mood, not baked-in explanations:\\n\\n- clean demo: calm laptop/workspace/dashboard glow;\\n- weird input: slightly messy email-workflow atmosphere, amber warning mood;\\n- wrong routing: abstract AI path energy, subtle red warning tension;\\n- mechanism split: minimal dark technical background with space for diagram overlay;\\n- final rule: calm premium blue/green tech background.\\n\\nStore generated media outside Git; commit only plans, prompts, tests, and docs.\\n\\n## Renderer-owned mechanism overlays\\n\\nA good beginner-friendly AI-systems Short should make the mechanism visible. Example pattern:\\n\\n```text\\nClean demo\\n→ weird email appears\\n→ AI confidently routes it wrong\\n→ three-way split:\\n Normal → Go through\\n Unclear → Review\\n Weird/Risky → Stop\\n→ final rule\\n```\\n\\nRender labels, arrows, cards, and captions locally. Check that arrows sit behind labels and that subtitles do not obscure the diagram.\\n\\n## Subtitle polish for final-motion videos\\n\\nUse compact phrase-boundary subtitles for final-near renders:\\n\\n- 1–4 word cues;\\n- split on semantic punctuation where possible;\\n- smaller than preview captions;\\n- placed high enough above bottom UI/safe zones;\\n- avoid cross-clause chunks like `ANYTHING DECIDE THREE`.\\n\\nFor a 1080x1920 ASS burn-in baseline, a useful starting point is:\\n\\n```text\\nFontsize: 72\\nMarginV: 320\\nMax lines: 2\\n```\\n\\nAlways inspect at least one critical mechanism frame after subtitle burn-in before exporting or sending.\\n\\n## Approval package after first-public export\\n\\nOnce a 1080x1920 candidate exists, do not stop at \\\"video rendered\\\". Prepare a side-effect-free final approval package before any delivery/platform step:\\n\\n```text\\nmedia path\\nSHA256 checksum\\nplatform labels\\ntitle variants\\ncaption + hashtags\\nhook\\nfinal/review language metadata\\nquality notes\\napproval commands\\n```\\n\\nGuardrails:\\n\\n- `requires_human_approval=True`\\n- `side_effects=()`\\n- no Telegram/platform calls inside the package builder\\n- no implicit action words like `publish` or `post`\\n- verify the checksum and ffprobe metadata from the actual file before presenting approval metadata\\n\\nThis turns a rendered MP4 into an auditable review artifact instead of a loose file path.\\n\\n## QA gates\\n\\nBefore treating a render as a first-public candidate, verify:\\n\\n- first 2 seconds communicate the concrete problem;\\n- top text, subtitles, and voiceover come from one source of truth;\\n- no generated image contains readable fake text/logos;\\n- mechanism labels are readable on mobile;\\n- subtitles do not compete with the core diagram;\\n- audio and video durations match closely;\\n- generated media remains ignored by Git.\\n\", \"file_type\": \".md\"}",
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Fix the outcome.\\n\\nGerman explanation:\\n\\n> Wir zeigen, warum digitale Dinge im Alltag schiefgehen — und welche kleine Systemregel sie repariert.\\n\\nKeep `Systems over prompts.` as a usable tagline, but interpret it broadly: not just better prompts, but better digital thinking, work, attention, decision, safety, and workflow systems.\\n\\nThe bot is a learning editorial system, not a content automaton:\\n\\n```text\\nResearch signal → hypothesis → ContentBrief → ScriptDraft → VideoCandidate\\n→ score/gate → review/preview → human approval → manual post pack\\n→ analytics feedback → next experiment\\n```\\n\\n## Content mix target\\n\\nFor the next strategic test batch, avoid an all-AI feed. Suggested mix for ~60 videos:\\n\\n- 35% — AI That Actually Helps / AI Output Autopsy\\n- 20% — Digital Self-Defense: scams, phishing, fake reviews, privacy/data traps, hallucination risks\\n- 20% — Attention & Decision Systems: focus, to-dos, meetings, decisions, overload\\n- 15% — Workflow Teardowns: email, calendar, notes, automations, broken work systems\\n- 10% — Algorithm & Social Search Literacy: feeds, search, recommendations, content consumption\\n\\nAI remains important, but should usually be 30–40% of the content universe, not 100%.\\n\\n## Recommended series\\n\\n- `AI Output Autopsy` — bad AI answer → why it sounds right but fails → rule.\\n- `Digital Red Flags` — normal-looking digital situation → one warning signal exposes the trap.\\n- `One Broken Workflow` / `Workflow Teardown` — mini-workflow fails → missing boundary/decision/exception path.\\n- `Decision Design` — people ask tools for answers when they need decision criteria.\\n- `Attention Traps` — normal digital behavior explained as an attention mechanism; avoid medical claims.\\n- `Search Smarter` — TikTok/YouTube/Google/AI search done badly vs. better.\\n- `Worklife Systems` — meetings, email, notes, calendar, to-dos.\\n\\n## Standard video structure\\n\\nUse `visible fail → identification → mechanism → fix → before/after → memorable takeaway`:\\n\\n1. 0–1.5s: visible conflict in the first frame, not just text.\\n2. 1.5–5s: viewer recognition / identity.\\n3. 5–14s: mechanism reveal — why it happens.\\n4. 14–28s: practical fix — rule, phrase, template, checklist, or decision frame.\\n5. 28–38s: before/after.\\n6. final 2–4s: memorable sentence with save/share/follow reason.\\n\\nEvery video must answer:\\n\\n- What concretely goes wrong?\\n- Why does it happen?\\n- What is the simple correction?\\n- What sentence should stick?\\n- Why would the viewer save or share it?\\n- Why would they follow this channel/series?\\n\\n## ContentBrief fields to add or require\\n\\nAdd these fields before scripting so JARVIS cannot generate generic AI-topic sludge:\\n\\n- `audience_need`: boredom | urgency | fear | competence | clarity | status | safety | curiosity\\n- `job_to_be_done`: “When I …, I want to …, so I can …”\\n- `viewer_identity`: beginner | creator | solo_founder | office_worker | student | parent | small_team | freelancer\\n- `viewer_emotion_before`: confused | annoyed | overwhelmed | skeptical | curious | embarrassed | anxious\\n- `viewer_emotion_after`: relieved | smarter | in_control | warned | motivated | equipped\\n- `share_reason`: “I would send this to someone because …”\\n- `save_reason`: “I would save this because …”\\n- `follow_reason`: “I would follow this channel because …”\\n- `search_intent_phrase`: platform-native problem/search phrase\\n- `series_id`: ai_output_autopsy | digital_red_flags | decision_design | workflow_teardown | attention_traps | search_smarter | worklife_systems\\n- `saturation_risk`: low | medium | high\\n- `human_texture`: real example | mini case | tested prompt | screen recording | personal observation | source-based claim\\n- `claim_risk`: low | medium | high\\n- `ai_disclosure_required`: yes | no | unclear\\n\\n## 100-point scoring model\\n\\nScore candidates on viewer behavior, not internal cleverness:\\n\\n- Problem Urgency — 15\\n- First-Frame Clarity — 12\\n- Hook Curiosity — 12\\n- Mechanism Value — 12\\n- Practical Payoff — 15\\n- Retention Path — 10\\n- Share/Save Potential — 10\\n- Trust/Human Texture — 8\\n- Series Fit — 4\\n- Saturation Defense — 2\\n\\nHard-block if:\\n\\n- no concrete problem\\n- no clear solution\\n- first frame is only text / no visible conflict\\n- hook sounds like a generic AI tip\\n- claim is not sourceable\\n- too similar to the last ~10 videos\\n- no save/share/follow reason\\n- realistic AI-generated content has unclear disclosure status\\n\\n## Research, claims, and compliance layers\\n\\nInsert a research layer before scripting:\\n\\n```text\\nSignal → hypothesis → brief → script → candidate → score → render\\n```\\n\\nUseful planned modules:\\n\\n- `autoshorts.research.trend_signals`\\n- `autoshorts.research.social_search`\\n- `autoshorts.research.topic_saturation`\\n- `autoshorts.claims.fact_gate`\\n- `autoshorts.strategy.experiment`\\n- `autoshorts.compliance.ai_disclosure`\\n\\nEvery factual claim should carry source/risk. Any `unclear` disclosure should block or require manual review before publishing.\\n\\n## Visual grammar\\n\\nMotion must explain meaning, not just interrupt attention. Add/maintain a visual grammar keyed by mechanism type:\\n\\n- missing boundary → open container / leaking information\\n- decision gap → three paths, one lights up\\n- false confidence → foggy cards becoming clear with constraints\\n- urgency trap → timer + red urgency line\\n- overload → stack of cards sorted by one rule\\n- before/after → split-screen with marked difference\\n- exception path → workflow line breaks, detour appears\\n- attention capture → cursor/thought pulled by app tiles\\n- hidden cost → free/cheap path reveals later cost\\n\\nPotential module: `autoshorts.rendering.visual_grammar`.\\n\\n## Captions, voice, and human texture\\n\\n- Forced alignment is a top priority. If captions drift, simplify visuals before tolerating bad sync.\\n- Support three caption modes:\\n - `word_by_word` — fast hooks / high-energy TikTok style\\n - `phrase_by_phrase` — clearer for complex explanations\\n - `keyword_pulse` — calmer premium look for key ideas\\n- Avoid the vibe: “an AI voice explaining an AI tip about AI.”\\n- Require human texture: real/credible mini-situation, tested prompt, screen recording, personal observation, or source-based claim.\\n\\n## Review package v2\\n\\nFuture Telegram/content review packages should include strategy and risk, not only visuals:\\n\\n- Candidate ID, version, media hash, script hash\\n- Series, audience need, viewer emotion before/after\\n- Hook, first-frame description, open loop, mechanism, takeaway\\n- Save/share/follow reason\\n- Platform fit per YouTube/TikTok/Instagram\\n- Claims: source + risk\\n- AI disclosure: yes/no/unclear per platform\\n- Policy/brand/saturation/repetition risks\\n- Experiment hypothesis and expected winning metric\\n\\nApproval should bind to exact version/hash.\\n\\n## Analytics early\\n\\nDo not wait for auto-publishing to add analytics. 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types: {visual_types}.\\n 98|**Risk Notes:** {package['risk_notes']}\\n 99|\\n 100|**Freigabe-Befehle:**\\n 101|{commands}\\n 102|\\\"\\\"\\\".strip() + \\\"\\\\n\\\"\\n 103|\\n 104|\\n 105|def write_review_packages(\\n 106| batch: dict[str, Any],\\n 107| manifest_dir: Path,\\n 108| preview_dir: Path,\\n 109| output_dir: Path,\\n 110|) -> list[Path]:\\n 111| \\\"\\\"\\\"Write one Telegram review package Markdown file per render manifest.\\\"\\\"\\\"\\n 112| output_dir.mkdir(parents=True, exist_ok=True)\\n 113| ideas = _idea_index(batch)\\n 114| paths: list[Path] = []\\n 115|\\n 116| for manifest_path in sorted(manifest_dir.glob(\\\"*.json\\\")):\\n 117| manifest = json.loads(manifest_path.read_text(encoding=\\\"utf-8\\\"))\\n 118| video_id = manifest[\\\"video\\\"][\\\"id\\\"]\\n 119| if video_id not in ideas:\\n 120| raise KeyError(f\\\"No idea found for manifest video id: {video_id}\\\")\\n 121| preview_path = preview_dir / f\\\"{manifest_path.stem}.mp4\\\"\\n 122| package = build_review_package(ideas[video_id], manifest, preview_path)\\n 123| output_path = output_dir / f\\\"{manifest_path.stem}.md\\\"\\n 124| output_path.write_text(format_review_package(package), encoding=\\\"utf-8\\\")\\n 125| paths.append(output_path)\\n 126|\\n 127| return paths\\n 128|\", \"total_lines\": 127, \"file_size\": 4363, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a growth-oriented pipeline for creating social-media shorts with a human approval loop.\\n 4|\\n 5|## Current phase\\n 6|\\n 7|Phase 1 prepares a semi-automatic testbatch workflow:\\n 8|\\n 9|1. Generate/curate scripts for three content pillars.\\n 10|2. Score each idea before rendering.\\n 11|3. Render previews locally.\\n 12|4. Send drafts/previews to Telegram for approval.\\n 13|5. Post manually or semi-automatically only after approval.\\n 14|6. Capture analytics and feed results back into the next batch.\\n 15|\\n 16|## Content pillars\\n 17|\\n 18|- `life_optimization` — practical micro-systems for overloaded adults.\\n 19|- `ai_life_systems` — AI as a servant for better life systems.\\n 20|- `faithtok` — Christian encouragement for overloaded modern life.\\n 21|\\n 22|## First testbatch\\n 23|\\n 24|The first batch contains 9 ideas:\\n 25|\\n 26|- 3 Life Optimization\\n 27|- 3 AI Life Systems\\n 28|- 3 FaithTok\\n 29|\\n 30|Most are short growth candidates; selected items are 60+ seconds for TikTok Creator Rewards testing.\\n 31|\\n 32|## Secrets\\n 33|\\n 34|Do not commit secrets, `.env` files, OAuth tokens, generated credentials, or produced media files. Runtime secrets stay outside the repository.\\n 35|\", \"total_lines\": 34, \"file_size\": 1087, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Local/free motion renderer for AutoShortsBot production jobs.\\n 2|\\n 3|The renderer intentionally uses only local Pillow/FFmpeg drawing and compositing.\\n 4|It does not call paid video-generation providers. The output is a deterministic\\n 5|motion prototype: procedural backgrounds, car/mechanism layers, kinetic captions,\\n 6|and beat-synced movement based on the free production job specs.\\n 7|\\\"\\\"\\\"\\n 8|\\n 9|from __future__ import annotations\\n 10|\\n 11|import json\\n 12|import math\\n 13|import shutil\\n 14|import subprocess\\n 15|import tempfile\\n 16|import textwrap\\n 17|from pathlib import Path\\n 18|from typing import Any\\n 19|\\n 20|from PIL import Image, ImageDraw, ImageFont\\n 21|\\n 22|PAID_VIDEO_PROVIDERS = {\\\"kling\\\", \\\"hailuo\\\", \\\"runway\\\", \\\"veo\\\", \\\"pika\\\", \\\"luma\\\"}\\n 23|LOCAL_RENDER_PROVIDERS = {\\\"local_renderer\\\", \\\"local_ffmpeg\\\"}\\n 24|\\n 25|\\n 26|PALETTE_BY_MODE = {\\n 27| \\\"cinematic_ai_video\\\": ((7, 12, 25), (30, 64, 175), (248, 113, 113)),\\n 28| \\\"mechanism_animation\\\": ((2, 6, 23), (15, 118, 110), (251, 191, 36)),\\n 29| \\\"kinetic_text_overlay\\\": ((15, 23, 42), (88, 28, 135), (34, 211, 238)),\\n 30| \\\"screen_recording_simulation\\\": ((8, 13, 28), (14, 116, 144), (96, 165, 250)),\\n 31| \\\"animated_card\\\": ((17, 24, 39), (120, 53, 15), (251, 146, 60)),\\n 32|}\\n 33|\\n 34|\\n 35|def _font(size: int, bold: bool = False) -> ImageFont.FreeTypeFont | ImageFont.ImageFont:\\n 36| candidates = [\\n 37| \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n 38| \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf\\\",\\n 39| ]\\n 40| for path in candidates:\\n 41| if Path(path).exists():\\n 42| return ImageFont.truetype(path, size=size)\\n 43| return ImageFont.load_default()\\n 44|\\n 45|\\n 46|def _wrap(text: str, width_chars: int) -> str:\\n 47| return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\".join(text.split()), width=max(8, width_chars)))\\n 48|\\n 49|\\n 50|def _gradient(width: int, height: int, start: tuple[int, int, int], end: tuple[int, int, int]) -> Image.Image:\\n 51| image = Image.new(\\\"RGB\\\", (width, height), start)\\n 52| draw = ImageDraw.Draw(image)\\n 53| for y in range(height):\\n 54| ratio = y / max(1, height - 1)\\n 55| color = tuple(round(start[i] * (1 - ratio) + end[i] * ratio) for i in range(3))\\n 56| draw.line([(0, y), (width, y)], fill=color)\\n 57| return image\\n 58|\\n 59|\\n 60|def _ease(progress: float) -> float:\\n 61| progress = min(1.0, max(0.0, progress))\\n 62| return 0.5 - math.cos(progress * math.pi) / 2\\n 63|\\n 64|\\n 65|def _caption_layout(text: str, width: int, height: int, pulse: float) -> dict[str, Any]:\\n 66| \\\"\\\"\\\"Calculate a caption layout that stays inside the vertical safe area.\\\"\\\"\\\"\\n 67| text_max_width = round(width * 0.78)\\n 68| max_box_width = round(width * 0.88)\\n 69| max_block_height = round(height * 0.21)\\n 70| pad = max(8, round(width * 0.035))\\n 71| preferred_size = max(18, round(width * (0.06 + 0.012 * pulse)))\\n 72| min_size = max(10, round(width * 0.038))\\n 73|\\n 74| scratch = Image.new(\\\"RGB\\\", (width, height))\\n 75| draw = ImageDraw.Draw(scratch)\\n 76| for size in range(preferred_size, min_size - 1, -1):\\n 77| font = _font(size, bold=True)\\n 78| # Width-based wrapping alone is too optimistic for German compound words;\\n 79| # verify actual pixel width and shrink/re-wrap until it fits.\\n 80| wrap_chars = max(8, round(text_max_width / (size * 0.5)))\\n 81| wrapped = _wrap(text, wrap_chars)\\n 82| raw_lines = wrapped.splitlines()\\n 83| if len(raw_lines) > 4:\\n 84| raw_lines = raw_lines[:3] + [\\\" \\\".join(raw_lines[3:])]\\n 85| spacing = max(4, round(size * 0.20))\\n 86| lines = []\\n 87| too_wide = False\\n 88| for line in raw_lines:\\n 89| box = draw.textbbox((0, 0), line, font=font)\\n 90| line_width = box[2] - box[0]\\n 91| line_height = box[3] - box[1]\\n 92| if line_width > text_max_width:\\n 93| too_wide = True\\n 94| break\\n 95| lines.append({\\\"text\\\": line, \\\"width\\\": line_width, \\\"height\\\": line_height})\\n 96| block_height = sum(line[\\\"height\\\"] for line in lines) + spacing * max(0, len(lines) - 1)\\n 97| if lines and not too_wide and block_height <= max_block_height:\\n 98| y = height - round(height * 0.205) - block_height // 2\\n 99| box = [\\n 100| round((width - max_box_width) / 2),\\n 101| y - pad,\\n 102| round((width + max_box_width) / 2),\\n 103| y + block_height + pad,\\n 104| ]\\n 105| return {\\n 106| \\\"font\\\": font,\\n 107| \\\"font_size\\\": size,\\n 108| \\\"lines\\\": lines,\\n 109| \\\"spacing\\\": spacing,\\n 110| \\\"box\\\": box,\\n 111| \\\"text_max_width\\\": text_max_width,\\n 112| \\\"start_y\\\": y,\\n 113| }\\n 114|\\n 115| font = _font(min_size, bold=True)\\n 116| line = text\\n 117| suffix = \\\"…\\\" if len(text) > 1 else \\\"\\\"\\n 118| while line:\\n 119| candidate = line.rstrip() + suffix\\n 120| box = draw.textbbox((0, 0), candidate, font=font)\\n 121| if box[2] - box[0] <= text_max_width:\\n 122| break\\n 123| line = line[:-1]\\n 124| candidate = (line.rstrip() + suffix) if line else \\\"…\\\"\\n 125| box = draw.textbbox((0, 0), candidate, font=font)\\n 126| return {\\n 127| \\\"font\\\": font,\\n 128| \\\"font_size\\\": min_size,\\n 129| \\\"lines\\\": [{\\\"text\\\": candidate, \\\"width\\\": box[2] - box[0], \\\"height\\\": box[3] - box[1]}],\\n 130| \\\"spacing\\\": max(4, round(min_size * 0.20)),\\n 131| \\\"box\\\": [round(width * 0.06), round(height * 0.72), round(width * 0.94), round(height * 0.84)],\\n 132| \\\"text_max_width\\\": text_max_width,\\n 133| \\\"start_y\\\": round(height * 0.76),\\n 134| }\\n 135|\\n 136|\\n 137|def _draw_caption(draw: ImageDraw.ImageDraw, text: str, width: int, height: int, accent: tuple[int, int, int], pulse: float) -> None:\\n 138| layout = _caption_layout(text, width, height, pulse)\\n 139| draw.rounded_rectangle(\\n 140| layout[\\\"box\\\"],\\n 141| radius=round(width * 0.035),\\n 142| fill=(255, 255, 255),\\n 143| outline=accent,\\n 144| width=max(2, width // 180),\\n 145| )\\n 146| y = layout[\\\"start_y\\\"]\\n 147| for line in layout[\\\"lines\\\"]:\\n 148| x = (width - line[\\\"width\\\"]) // 2\\n 149| draw.text((x, y), line[\\\"text\\\"], font=layout[\\\"font\\\"], fill=(15, 23, 42))\\n 150| y += line[\\\"height\\\"] + layout[\\\"spacing\\\"]\\n 151|\\n 152|\\n 153|def _draw_car(draw: ImageDraw.ImageDraw, width: int, height: int, progress: float, accent: tuple[int, int, int]) -> None:\\n 154| cx = width // 2\\n 155| cy = round(height * 0.43)\\n 156| scale = width / 540\\n 157| bob = round(math.sin(progress * math.tau * 2) * 6 * scale)\\n 158| car_w = round(width * 0.58)\\n 159| car_h = round(height * 0.12)\\n 160| left = cx - car_w // 2\\n 161| right = cx + car_w // 2\\n 162| top = cy - car_h // 2 + bob\\n 163| bottom = cy + car_h // 2 + bob\\n 164| draw.rounded_rectangle([left, top, right, bottom], radius=round(24 * scale), fill=(226, 232, 240), outline=accent, width=max(2, round(4 * scale)))\\n 165| cabin = [left + round(car_w * 0.23), top - round(car_h * 0.55), right - round(car_w * 0.25), top + round(car_h * 0.15)]\\n 166| draw.rounded_rectangle(cabin, radius=round(14 * scale), fill=(148, 163, 184), outline=(226, 232, 240), width=max(1, round(2 * scale)))\\n 167| wheel_r = round(width * 0.045)\\n 168| for wx in (left + round(car_w * 0.22), right - round(car_w * 0.22)):\\n 169| draw.ellipse([wx - wheel_r, bottom - wheel_r, wx + wheel_r, bottom + wheel_r], fill=(2, 6, 23), outline=(248, 250, 252), width=max(2, round(3 * scale)))\\n 170| angle = progress * math.tau * 3\\n 171| draw.line([wx, bottom, wx + math.cos(angle) * wheel_r, bottom + math.sin(angle) * wheel_r], fill=accent, width=max(1, round(3 * scale)))\\n 172| # x-ray scan line and warning pulse\\n 173| scan_x = left + round(car_w * _ease(progress))\\n 174| draw.line([(scan_x, top - round(60 * scale)), (scan_x, bottom + round(70 * scale))], fill=accent, width=max(2, round(5 * scale)))\\n 175| pulse_r = round((30 + 38 * _ease((progress * 2) % 1)) * scale)\\n 176| draw.ellipse([cx - pulse_r, cy - pulse_r + bob, cx + pulse_r, cy + pulse_r + bob], outline=accent, width=max(1, round(3 * scale)))\\n 177|\\n 178|\\n 179|def _draw_mechanism_layers(draw: ImageDraw.ImageDraw, width: int, height: int, progress: float, accent: tuple[int, int, int], twist: bool) -> None:\\n 180| labels = [\\\"Preis\\\", \\\"Reparatur\\\", \\\"Standtage\\\", \\\"Marge\\\", \\\"Liquidität\\\"]\\n 181| visible = max(1, min(len(labels), math.ceil(progress * len(labels))))\\n 182| top = round(height * 0.24)\\n 183| layer_h = round(height * 0.072)\\n 184| gap = round(height * 0.022)\\n 185| for index, label in enumerate(labels[:visible]):\\n 186| p = _ease(min(1.0, max(0.0, progress * len(labels) - index)))\\n 187| x_offset = round((1 - p) * width * 0.45)\\n 188| y = top + index * (layer_h + gap)\\n 189| color = accent if (twist and index == 0) else (226, 232, 240)\\n 190| fill = (30, 41, 59) if not (twist and index == 0) else (127, 29, 29)\\n 191| draw.rounded_rectangle(\\n 192| [round(width * 0.12) + x_offset, y, round(width * 0.88) + x_offset, y + layer_h],\\n 193| radius=round(width * 0.026),\\n 194| fill=fill,\\n 195| outline=color,\\n 196| width=max(2, width // 180),\\n 197| )\\n 198| f = _font(max(14, round(width * 0.046)), bold=True)\\n 199| draw.text((round(width * 0.17) + x_offset, y + round(layer_h * 0.22)), label, font=f, fill=(248, 250, 252))\\n 200| meter_w = round(width * 0.24 * p)\\n 201| draw.rounded_rectangle(\\n 202| [round(width * 0.58) + x_offset, y + round(layer_h * 0.35), round(width * 0.58) + x_offset + meter_w, y + round(layer_h * 0.63)],\\n 203| radius=round(width * 0.015),\\n 204| fill=color,\\n 205| )\\n 206| car_y = round(height * 0.72)\\n 207| draw.line([(round(width * 0.5), top + visible * (layer_h + gap)), (round(width * 0.5), car_y - round(height * 0.05))], fill=accent, width=max(2, width // 160))\\n 208| _draw_car(draw, width, height, progress, accent)\\n 209|\\n 210|\\n 211|def _draw_kinetic_words(draw: ImageDraw.ImageDraw, width: int, height: int, text: str, progress: float, accent: tuple[int, int, int]) -> None:\\n 212| words = text.split()\\n 213| current = words[: max(1, min(len(words), math.ceil(progress * max(1, len(words)))))]\\n 214| main = \\\" \\\".join(current[-5:])\\n 215| size = max(28, round(width * (0.105 + 0.025 * math.sin(progress * math.tau * 3))))\\n 216| font = _font(size, bold=True)\\n 217| wrapped = _wrap(main, max(8, round(width / (size * 0.48))))\\n 218| lines = wrapped.splitlines()[:3]\\n 219| total_h = 0\\n 220| boxes = []\\n 221| for line in lines:\\n 222| box = draw.textbbox((0, 0), line, font=font)\\n 223| boxes.append(box)\\n 224| total_h += box[3] - box[1]\\n 225| y = height // 2 - total_h // 2\\n 226| for line, box in zip(lines, boxes):\\n 227| line_w = box[2] - box[0]\\n 228| x = (width - line_w) // 2 + round(math.sin(progress * math.tau * 2) * width * 0.025)\\n 229| draw.text((x + 3, y + 3), line, font=font, fill=(15, 23, 42))\\n 230| draw.text((x, y), line, font=font, fill=(248, 250, 252))\\n 231| y += box[3] - box[1] + round(size * 0.18)\\n 232| glow = round(width * (0.12 + 0.08 * _ease((progress * 3) % 1)))\\n 233| draw.ellipse([width // 2 - glow, round(height * 0.18) - glow, width // 2 + glow, round(height * 0.18) + glow], outline=accent, width=max(2, width // 140))\\n 234|\\n 235|\\n 236|def _render_frame(job: dict[str, Any], width: int, height: int, frame_index: int, total_frames: int) -> Image.Image:\\n 237| mode = job[\\\"asset_mode\\\"]\\n 238| start, end, accent = PALETTE_BY_MODE.get(mode, PALETTE_BY_MODE[\\\"cinematic_ai_video\\\"])\\n 239| progress = frame_index / max(1, total_frames - 1)\\n 240| # simulated handheld push-in: brighten and offset gradient slightly over time\\n 241| image = _gradient(width, height, start, end)\\n 242| draw = ImageDraw.Draw(image)\\n 243|\\n 244| for i in range(9):\\n 245| y = round((i / 8) * height + math.sin(progress * math.tau + i) * height * 0.014)\\n 246| draw.line([(0, y), (width, y + round(height * 0.08))], fill=tuple(min(255, c + 16) for c in start), width=max(1, width // 90))\\n 247|\\n 248| if mode == \\\"mechanism_animation\\\":\\n 249| _draw_mechanism_layers(draw, width, height, progress, accent, twist=\\\"teuerste Fehler\\\" in job.get(\\\"overlay_text\\\", \\\"\\\"))\\n 250| elif mode == \\\"kinetic_text_overlay\\\":\\n 251| _draw_kinetic_words(draw, width, height, job[\\\"overlay_text\\\"], progress, accent)\\n 252| else:\\n 253| _draw_car(draw, width, height, progress, accent)\\n 254| # local parallax objects / risk tags\\n 255| tag_font = _font(max(12, round(width * 0.038)), bold=True)\\n 256| for idx, label in enumerate([\\\"-4'000?\\\", \\\"Risiko\\\", \\\"Timing\\\"]):\\n 257| p = (progress + idx * 0.18) % 1\\n 258| x = round(width * (0.1 + 0.72 * p))\\n 259| y = round(height * (0.17 + 0.08 * idx + 0.015 * math.sin(progress * math.tau * 2 + idx)))\\n 260| draw.rounded_rectangle([x, y, x + round(width * 0.22), y + round(height * 0.045)], radius=round(width * 0.02), fill=(248, 250, 252), outline=accent, width=max(1, width // 220))\\n 261|\", \"total_lines\": 368, \"file_size\": 16961, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 368 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 261| draw.text((x + round(width * 0.018), y + round(height * 0.01)), label, font=tag_font, fill=(15, 23, 42))\\n 262|\\n 263| title_font = _font(max(11, round(width * 0.031)), bold=False)\\n 264| draw.text((round(width * 0.06), round(height * 0.045)), f\\\"{job['scene_id']} · {mode.replace('_', ' ')}\\\", font=title_font, fill=(226, 232, 240))\\n 265| _draw_caption(draw, job[\\\"overlay_text\\\"], width, height, accent, _ease((progress * 4) % 1))\\n 266| return image\\n 267|\\n 268|\\n 269|def local_render_jobs(production_jobs: dict[str, Any]) -> list[dict[str, Any]]:\\n 270| \\\"\\\"\\\"Return only executable local render jobs, excluding text-only LLM refinement.\\\"\\\"\\\"\\n 271| providers = {job[\\\"provider\\\"] for job in production_jobs[\\\"jobs\\\"]}\\n 272| forbidden = providers & PAID_VIDEO_PROVIDERS\\n 273| if forbidden:\\n 274| raise ValueError(f\\\"Paid video providers are forbidden in local render: {sorted(forbidden)}\\\")\\n 275| return [job for job in production_jobs[\\\"jobs\\\"] if job[\\\"provider\\\"] in LOCAL_RENDER_PROVIDERS]\\n 276|\\n 277|\\n 278|def _write_frames(render_jobs: list[dict[str, Any]], frame_dir: Path, width: int, height: int, fps: int) -> tuple[int, int]:\\n 279| frame_dir.mkdir(parents=True, exist_ok=True)\\n 280| frame_number = 0\\n 281| motion_events = 0\\n 282| for job in render_jobs:\\n 283| scene_frames = max(1, round(float(job[\\\"duration_seconds\\\"]) * fps))\\n 284| motion_events += max(2, math.ceil(float(job[\\\"duration_seconds\\\"]) / 2))\\n 285| for local_index in range(scene_frames):\\n 286| image = _render_frame(job, width, height, local_index, scene_frames)\\n 287| image.save(frame_dir / f\\\"frame_{frame_number:06d}.png\\\")\\n 288| frame_number += 1\\n 289| return frame_number, motion_events\\n 290|\\n 291|\\n 292|def render_local_motion_video(\\n 293| production_jobs: dict[str, Any],\\n 294| output_path: Path,\\n 295| width: int = 540,\\n 296| height: int = 960,\\n 297| fps: int = 24,\\n 298|) -> dict[str, Any]:\\n 299| \\\"\\\"\\\"Render local/free production jobs into a vertical MP4 and metadata JSON.\\\"\\\"\\\"\\n 300| ffmpeg = shutil.which(\\\"ffmpeg\\\")\\n 301| if not ffmpeg:\\n 302| raise RuntimeError(\\\"ffmpeg is required to render local motion MP4 files\\\")\\n 303|\\n 304| render_jobs = local_render_jobs(production_jobs)\\n 305| output_path.parent.mkdir(parents=True, exist_ok=True)\\n 306| total_duration: float = sum((float(job[\\\"duration_seconds\\\"]) for job in render_jobs), 0.0)\\n 307|\\n 308| with tempfile.TemporaryDirectory(prefix=\\\"autoshorts_local_motion_\\\") as tmp:\\n 309| frame_dir = Path(tmp) / \\\"frames\\\"\\n 310| frame_count, motion_event_count = _write_frames(render_jobs, frame_dir, width, height, fps)\\n 311| command = [\\n 312| ffmpeg,\\n 313| \\\"-y\\\",\\n 314| \\\"-framerate\\\",\\n 315| str(fps),\\n 316| \\\"-i\\\",\\n 317| str(frame_dir / \\\"frame_%06d.png\\\"),\\n 318| \\\"-vf\\\",\\n 319| \\\"format=yuv420p\\\",\\n 320| \\\"-movflags\\\",\\n 321| \\\"+faststart\\\",\\n 322| str(output_path),\\n 323| ]\\n 324| completed = subprocess.run(command, check=True, capture_output=True, text=True)\\n 325|\\n 326| metadata_path = output_path.with_suffix(\\\".json\\\")\\n 327| metadata = {\\n 328| \\\"schema_version\\\": \\\"local_motion_render.v1\\\",\\n 329| \\\"video_id\\\": production_jobs[\\\"video\\\"][\\\"id\\\"],\\n 330| \\\"source_schema_version\\\": production_jobs[\\\"schema_version\\\"],\\n 331| \\\"output_path\\\": str(output_path),\\n 332| \\\"width\\\": width,\\n 333| \\\"height\\\": height,\\n 334| \\\"fps\\\": fps,\\n 335| \\\"frame_count\\\": frame_count,\\n 336| \\\"scene_count\\\": len(render_jobs),\\n 337| \\\"rendered_duration_seconds\\\": int(total_duration) if total_duration.is_integer() else total_duration,\\n 338| \\\"motion_event_count\\\": motion_event_count,\\n 339| \\\"paid_credit_providers_used\\\": [],\\n 340| \\\"budget_policy\\\": production_jobs[\\\"budget_policy\\\"],\\n 341| \\\"scenes\\\": [\\n 342| {\\n 343| \\\"scene_id\\\": job[\\\"scene_id\\\"],\\n 344| \\\"asset_mode\\\": job[\\\"asset_mode\\\"],\\n 345| \\\"provider\\\": job[\\\"provider\\\"],\\n 346| \\\"execution_mode\\\": job[\\\"execution_mode\\\"],\\n 347| \\\"duration_seconds\\\": job[\\\"duration_seconds\\\"],\\n 348| \\\"motion_directive\\\": job[\\\"motion_directive\\\"],\\n 349| }\\n 350| for job in render_jobs\\n 351| ],\\n 352| \\\"ffmpeg_stderr_tail\\\": completed.stderr[-2000:],\\n 353| }\\n 354| metadata_path.write_text(json.dumps(metadata, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 355|\\n 356| return {\\n 357| \\\"output_path\\\": output_path,\\n 358| \\\"metadata_path\\\": metadata_path,\\n 359| \\\"video_id\\\": production_jobs[\\\"video\\\"][\\\"id\\\"],\\n 360| \\\"width\\\": width,\\n 361| \\\"height\\\": height,\\n 362| \\\"fps\\\": fps,\\n 363| \\\"frame_count\\\": frame_count,\\n 364| \\\"scene_count\\\": len(render_jobs),\\n 365| \\\"rendered_duration_seconds\\\": metadata[\\\"rendered_duration_seconds\\\"],\\n 366| \\\"motion_event_count\\\": motion_event_count,\\n 367| \\\"paid_credit_providers_used\\\": [],\\n 368| }\\n 369|\", \"total_lines\": 368, \"file_size\": 16961, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Deterministic preview renderer for AutoShortsBot render manifests.\\n 2|\\n 3|This v1 renderer intentionally uses manifest-driven cards/placeholders instead of\\n 4|AI-generated video. It renders a simple vertical MP4 suitable for timing and\\n 5|approval review before investing in polished assets.\\n 6|\\\"\\\"\\\"\\n 7|\\n 8|from __future__ import annotations\\n 9|\\n 10|import shutil\\n 11|import subprocess\\n 12|import tempfile\\n 13|import textwrap\\n 14|from pathlib import Path\\n 15|from typing import Any\\n 16|\\n 17|from PIL import Image, ImageDraw, ImageFont\\n 18|\\n 19|BACKGROUND_BY_TYPE = {\\n 20| \\\"kinetic_text\\\": ((19, 23, 38), (79, 70, 229)),\\n 21| \\\"screen_recording\\\": ((15, 23, 42), (14, 165, 233)),\\n 22| \\\"card_animation\\\": ((17, 24, 39), (245, 158, 11)),\\n 23| \\\"stock_broll\\\": ((28, 25, 23), (34, 197, 94)),\\n 24| \\\"generated_background\\\": ((24, 24, 27), (168, 85, 247)),\\n 25|}\\n 26|\\n 27|\\n 28|def _font(size: int, bold: bool = False) -> ImageFont.FreeTypeFont | ImageFont.ImageFont:\\n 29| candidates = [\\n 30| \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n 31| \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf\\\",\\n 32| ]\\n 33| for path in candidates:\\n 34| if Path(path).exists():\\n 35| return ImageFont.truetype(path, size=size)\\n 36| return ImageFont.load_default()\\n 37|\\n 38|\\n 39|def _wrap_text(text: str, width_chars: int) -> str:\\n 40| return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\".join(text.split()), width=width_chars))\\n 41|\\n 42|\\n 43|def _text_block_size(draw: ImageDraw.ImageDraw, text: str, font: Any, spacing: int) -> tuple[int, int]:\\n 44| lines = text.splitlines() or [\\\"\\\"]\\n 45| boxes = [draw.textbbox((0, 0), line, font=font) for line in lines]\\n 46| width = max((box[2] - box[0] for box in boxes), default=0)\\n 47| height = sum(box[3] - box[1] for box in boxes) + spacing * max(0, len(lines) - 1)\\n 48| return int(width), int(height)\\n 49|\\n 50|\\n 51|def _fit_text_block(\\n 52| draw: ImageDraw.ImageDraw,\\n 53| text: str,\\n 54| max_width: int,\\n 55| max_height: int,\\n 56| max_lines: int,\\n 57| preferred_size: int,\\n 58| min_size: int,\\n 59| bold: bool,\\n 60|) -> dict[str, Any]:\\n 61| \\\"\\\"\\\"Choose a readable font size and wrapping that fits a text box.\\\"\\\"\\\"\\n 62| cleaned = \\\" \\\".join(text.split())\\n 63| for size in range(preferred_size, min_size - 1, -1):\\n 64| wrap_chars = max(10, round(max_width / (size * 0.54)))\\n 65| wrapped = _wrap_text(cleaned, wrap_chars)\\n 66| lines = wrapped.splitlines()\\n 67| font = _font(size, bold=bold)\\n 68| spacing = max(4, round(size * 0.25))\\n 69| block_width, block_height = _text_block_size(draw, wrapped, font, spacing)\\n 70| if len(lines) <= max_lines and block_width <= max_width and block_height <= max_height:\\n 71| return {\\n 72| \\\"text\\\": wrapped,\\n 73| \\\"font\\\": font,\\n 74| \\\"font_size\\\": size,\\n 75| \\\"line_count\\\": len(lines),\\n 76| \\\"spacing\\\": spacing,\\n 77| }\\n 78|\\n 79| fallback_font = _font(min_size, bold=bold)\\n 80| fallback_chars = max(10, round(max_width / (min_size * 0.54)))\\n 81| fallback = _wrap_text(cleaned, fallback_chars)\\n 82| return {\\n 83| \\\"text\\\": fallback,\\n 84| \\\"font\\\": fallback_font,\\n 85| \\\"font_size\\\": min_size,\\n 86| \\\"line_count\\\": len(fallback.splitlines()),\\n 87| \\\"spacing\\\": max(4, round(min_size * 0.25)),\\n 88| }\\n 89|\\n 90|\\n 91|def _gradient(width: int, height: int, start: tuple[int, int, int], end: tuple[int, int, int]) -> Image.Image:\\n 92| image = Image.new(\\\"RGB\\\", (width, height), start)\\n 93| draw = ImageDraw.Draw(image)\\n 94| for y in range(height):\\n 95| ratio = y / max(1, height - 1)\\n 96| color = tuple(round(start[i] * (1 - ratio) + end[i] * ratio) for i in range(3))\\n 97| draw.line([(0, y), (width, y)], fill=color)\\n 98| return image\\n 99|\\n 100|\\n 101|def _draw_multiline_center(\\n 102| draw: ImageDraw.ImageDraw,\\n 103| xy: tuple[int, int],\\n 104| text: str,\\n 105| font: Any,\\n 106| fill: tuple[int, int, int],\\n 107| spacing: int = 8,\\n 108|) -> None:\\n 109| lines = text.splitlines()\\n 110| line_boxes = [draw.textbbox((0, 0), line, font=font) for line in lines]\\n 111| line_heights = [box[3] - box[1] for box in line_boxes]\\n 112| total_height = sum(line_heights) + spacing * max(0, len(lines) - 1)\\n 113| y = xy[1] - total_height // 2\\n 114| for line, box, line_height in zip(lines, line_boxes, line_heights):\\n 115| line_width = box[2] - box[0]\\n 116| draw.text((xy[0] - line_width // 2, y), line, font=font, fill=fill)\\n 117| y += line_height + spacing\\n 118|\\n 119|\\n 120|def _render_scene_image(\\n 121| item: dict[str, Any], manifest: dict[str, Any], width: int, height: int\\n 122|) -> tuple[Image.Image, dict[str, int]]:\\n 123| start, end = BACKGROUND_BY_TYPE.get(item[\\\"type\\\"], ((24, 24, 27), (64, 64, 64)))\\n 124| image = _gradient(width, height, start, end)\\n 125| draw = ImageDraw.Draw(image)\\n 126|\\n 127| margin = round(width * 0.075)\\n 128| title_font = _font(max(18, round(width * 0.06)), bold=True)\\n 129| label_font = _font(max(12, round(width * 0.032)), bold=False)\\n 130| small_font = _font(max(12, round(width * 0.034)), bold=False)\\n 131|\\n 132| # Safe area and content card.\\n 133| card_top = round(height * 0.18)\\n 134| card_bottom = round(height * 0.78)\\n 135| draw.rounded_rectangle(\\n 136| [margin, card_top, width - margin, card_bottom],\\n 137| radius=round(width * 0.04),\\n 138| fill=(255, 255, 255),\\n 139| outline=(226, 232, 240),\\n 140| width=max(2, width // 180),\\n 141| )\\n 142|\\n 143| draw.text((margin, round(height * 0.055)), manifest[\\\"video\\\"][\\\"id\\\"], font=label_font, fill=(226, 232, 240))\\n 144| draw.text((margin, round(height * 0.09)), item[\\\"type\\\"].replace(\\\"_\\\", \\\" \\\").upper(), font=title_font, fill=(255, 255, 255))\\n 145|\\n 146| scene_label = item[\\\"id\\\"].replace(\\\"_\\\", \\\" \\\").upper()\\n 147| draw.text((margin * 1.4, card_top + margin), scene_label, font=small_font, fill=(71, 85, 105))\\n 148|\\n 149| main_fit = _fit_text_block(\\n 150| draw,\\n 151| item[\\\"text\\\"],\\n 152| max_width=width - margin * 4,\\n 153| max_height=round(height * 0.24),\\n 154| max_lines=4,\\n 155| preferred_size=max(22, round(width * 0.078)),\\n 156| min_size=max(18, round(width * 0.063)),\\n 157| bold=True,\\n 158| )\\n 159| _draw_multiline_center(\\n 160| draw,\\n 161| (width // 2, round(height * 0.45)),\\n 162| main_fit[\\\"text\\\"],\\n 163| main_fit[\\\"font\\\"],\\n 164| (15, 23, 42),\\n 165| spacing=main_fit[\\\"spacing\\\"],\\n 166| )\\n 167|\\n 168| retention_fit = _fit_text_block(\\n 169| draw,\\n 170| item.get(\\\"retention_goal\\\", \\\"\\\"),\\n 171| max_width=width - margin * 4,\\n 172| max_height=round(height * 0.14),\\n 173| max_lines=3,\\n 174| preferred_size=max(14, round(width * 0.048)),\\n 175| min_size=max(12, round(width * 0.041)),\\n 176| bold=False,\\n 177| )\\n 178| _draw_multiline_center(\\n 179| draw,\\n 180| (width // 2, round(height * 0.69)),\\n 181| retention_fit[\\\"text\\\"],\\n 182| retention_fit[\\\"font\\\"],\\n 183| (71, 85, 105),\\n 184| spacing=retention_fit[\\\"spacing\\\"],\\n 185| )\\n 186|\\n 187| timing = f\\\"{item['start_ms'] / 1000:.1f}s – {(item['start_ms'] + item['duration_ms']) / 1000:.1f}s\\\"\\n 188| draw.text((margin, height - round(height * 0.075)), timing, font=label_font, fill=(226, 232, 240))\\n 189| draw.text((width - margin - draw.textlength(\\\"PREVIEW\\\", font=label_font), height - round(height * 0.075)), \\\"PREVIEW\\\", font=label_font, fill=(226, 232, 240))\\n 190| return image, {\\n 191| \\\"main_font_size\\\": main_fit[\\\"font_size\\\"],\\n 192| \\\"main_line_count\\\": main_fit[\\\"line_count\\\"],\\n 193| \\\"retention_font_size\\\": retention_fit[\\\"font_size\\\"],\\n 194| \\\"retention_line_count\\\": retention_fit[\\\"line_count\\\"],\\n 195| }\\n 196|\\n 197|\\n 198|def build_preview_frames(manifest: dict[str, Any], width: int = 540, height: int = 960) -> list[dict[str, Any]]:\\n 199| \\\"\\\"\\\"Build one representative image per visual timeline item.\\\"\\\"\\\"\\n 200| visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n 201| frames: list[dict[str, Any]] = []\\n 202| for item in visual_items:\\n 203| image, typography = _render_scene_image(item, manifest, width, height)\\n 204| frames.append(\\n 205| {\\n 206| \\\"scene_id\\\": item[\\\"id\\\"],\\n 207| \\\"start_ms\\\": item[\\\"start_ms\\\"],\\n 208| \\\"duration_ms\\\": item[\\\"duration_ms\\\"],\\n 209| \\\"text\\\": item[\\\"text\\\"],\\n 210| \\\"typography\\\": typography,\\n 211| \\\"image\\\": image,\\n 212| }\\n 213| )\\n 214| return frames\\n 215|\\n 216|\\n 217|def write_frame_images(frames: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n 218| \\\"\\\"\\\"Write preview frames as numbered PNG files.\\\"\\\"\\\"\\n 219| output_dir.mkdir(parents=True, exist_ok=True)\\n 220| paths: list[Path] = []\\n 221| for index, frame in enumerate(frames, start=1):\\n 222| path = output_dir / f\\\"frame_{index:03d}_{frame['scene_id']}.png\\\"\\n 223| frame[\\\"image\\\"].save(path)\\n 224| paths.append(path)\\n 225| return paths\\n 226|\\n 227|\\n 228|def _write_concat_file(frames: list[dict[str, Any]], image_paths: list[Path], concat_path: Path) -> None:\\n 229| lines: list[str] = []\\n 230| for frame, image_path in zip(frames, image_paths):\\n 231| seconds = max(0.5, frame[\\\"duration_ms\\\"] / 1000)\\n 232| lines.append(f\\\"file '{image_path.as_posix()}'\\\")\\n 233| lines.append(f\\\"duration {seconds:.3f}\\\")\\n 234| if image_paths:\\n 235| lines.append(f\\\"file '{image_paths[-1].as_posix()}'\\\")\\n 236| concat_path.write_text(\\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 237|\\n 238|\\n 239|def render_preview_video(\\n 240| manifest: dict[str, Any],\\n 241| output_path: Path,\\n 242| width: int = 540,\\n 243| height: int = 960,\\n 244| fps: int = 24,\\n 245|) -> dict[str, Any]:\\n 246| \\\"\\\"\\\"Render a manifest-driven preview MP4 using Pillow frames and ffmpeg.\\\"\\\"\\\"\\n 247| ffmpeg = shutil.which(\\\"ffmpeg\\\")\\n 248| if not ffmpeg:\\n 249| raise RuntimeError(\\\"ffmpeg is required to render preview MP4 files\\\")\\n 250|\\n 251| output_path.parent.mkdir(parents=True, exist_ok=True)\\n 252| frames = build_preview_frames(manifest, width=width, height=height)\\n 253| with tempfile.TemporaryDirectory(prefix=\\\"autoshorts_preview_\\\") as tmp:\\n 254| tmp_dir = Path(tmp)\\n 255| image_paths = write_frame_images(frames, tmp_dir / \\\"frames\\\")\\n 256| concat_path = tmp_dir / \\\"concat.txt\\\"\\n 257| _write_concat_file(frames, image_paths, concat_path)\\n 258| command = [\\n 259| ffmpeg,\\n 260| \\\"-y\\\",\\n 261|\", \"total_lines\": 279, \"file_size\": 10275, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 279 lines)\", \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"limit\":80,\"offset\":239,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
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"content": "{\"content\": \" 239|def render_preview_video(\\n 240| manifest: dict[str, Any],\\n 241| output_path: Path,\\n 242| width: int = 540,\\n 243| height: int = 960,\\n 244| fps: int = 24,\\n 245|) -> dict[str, Any]:\\n 246| \\\"\\\"\\\"Render a manifest-driven preview MP4 using Pillow frames and ffmpeg.\\\"\\\"\\\"\\n 247| ffmpeg = shutil.which(\\\"ffmpeg\\\")\\n 248| if not ffmpeg:\\n 249| raise RuntimeError(\\\"ffmpeg is required to render preview MP4 files\\\")\\n 250|\\n 251| output_path.parent.mkdir(parents=True, exist_ok=True)\\n 252| frames = build_preview_frames(manifest, width=width, height=height)\\n 253| with tempfile.TemporaryDirectory(prefix=\\\"autoshorts_preview_\\\") as tmp:\\n 254| tmp_dir = Path(tmp)\\n 255| image_paths = write_frame_images(frames, tmp_dir / \\\"frames\\\")\\n 256| concat_path = tmp_dir / \\\"concat.txt\\\"\\n 257| _write_concat_file(frames, image_paths, concat_path)\\n 258| command = [\\n 259| ffmpeg,\\n 260| \\\"-y\\\",\\n 261| \\\"-f\\\",\\n 262| \\\"concat\\\",\\n 263| \\\"-safe\\\",\\n 264| \\\"0\\\",\\n 265| \\\"-i\\\",\\n 266| str(concat_path),\\n 267| \\\"-vf\\\",\\n 268| f\\\"fps={fps},format=yuv420p\\\",\\n 269| \\\"-movflags\\\",\\n 270| \\\"+faststart\\\",\\n 271| str(output_path),\\n 272| ]\\n 273| completed = subprocess.run(command, check=True, capture_output=True, text=True)\\n 274| return {\\n 275| \\\"output_path\\\": output_path,\\n 276| \\\"frame_count\\\": len(frames),\\n 277| \\\"fps\\\": fps,\\n 278| \\\"stderr\\\": completed.stderr,\\n 279| }\\n 280|\", \"total_lines\": 279, \"file_size\": 10275, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"limit\":100,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot\",\"pattern\":\"tts|edge|voiceover|subtitle|caption|ass|srt\",\"target\":\"content\",\"file_glob\":\"*.py\"}"
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"reasoning": "**Preparing content for next post**\n\nI think I should consider using the existing render manifest to help streamline things. The user asked for the next post, so I might need to package everything for \"attention-002.\" I'll have to check if there's a TTS (text-to-speech) pipeline we can use for the final video. It could be useful to enhance the content! I'll look into the options for that and see how to proceed.",
"reasoning_content": "**Preparing content for next post**\n\nI think I should consider using the existing render manifest to help streamline things. The user asked for the next post, so I might need to package everything for \"attention-002.\" I'll have to check if there's a TTS (text-to-speech) pipeline we can use for the final video. It could be useful to enhance the content! I'll look into the options for that and see how to proceed.",
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"text": "**Preparing content for next post**\n\nI think I should consider using the existing render manifest to help streamline things. The user asked for the next post, so I might need to package everything for \"attention-002.\" I'll have to check if there's a TTS (text-to-speech) pipeline we can use for the final video. It could be useful to enhance the content! I'll look into the options for that and see how to proceed."
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"reasoning": "**Planning video content**\n\nI'm considering using the TTS tool to generate an MP3 voiceover, but I'm not sure about including active-word subtitles since timing could be tricky. Instead, I might create phrase captions based on the script. For the user's video, it should feature relevant content. I think the next post could focus on \"Stop asking AI to think for you,\" as it’s short and looks good. I wonder if I need TTS for the video audio or if I can stick with a default voice.\n**Configuring TTS settings**\n\nI'm thinking about the TTS tool for the final English video, but I noticed it might default to de-DE, which isn’t ideal. I'm wondering if the user configured it this way or if there's only a voice option available. I could use the local edge-tts command with an English voice instead. I should check if I need additional skills for edge-tts. It's important to ensure the TTS configuration aligns with the video's language needs!",
"reasoning_content": "**Planning video content**\n\nI'm considering using the TTS tool to generate an MP3 voiceover, but I'm not sure about including active-word subtitles since timing could be tricky. Instead, I might create phrase captions based on the script. For the user's video, it should feature relevant content. I think the next post could focus on \"Stop asking AI to think for you,\" as it’s short and looks good. I wonder if I need TTS for the video audio or if I can stick with a default voice.\n**Configuring TTS settings**\n\nI'm thinking about the TTS tool for the final English video, but I noticed it might default to de-DE, which isn’t ideal. I'm wondering if the user configured it this way or if there's only a voice option available. I could use the local edge-tts command with an English voice instead. I should check if I need additional skills for edge-tts. It's important to ensure the TTS configuration aligns with the video's language needs!",
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"text": "**Planning video content**\n\nI'm considering using the TTS tool to generate an MP3 voiceover, but I'm not sure about including active-word subtitles since timing could be tricky. Instead, I might create phrase captions based on the script. For the user's video, it should feature relevant content. I think the next post could focus on \"Stop asking AI to think for you,\" as it’s short and looks good. I wonder if I need TTS for the video audio or if I can stick with a default voice."
},
{
"type": "summary_text",
"text": "**Configuring TTS settings**\n\nI'm thinking about the TTS tool for the final English video, but I noticed it might default to de-DE, which isn’t ideal. I'm wondering if the user configured it this way or if there's only a voice option available. I could use the local edge-tts command with an English voice instead. I should check if I need additional skills for edge-tts. It's important to ensure the TTS configuration aligns with the video's language needs!"
}
]
}
]
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"content": "{\"success\": true, \"name\": \"edge-tts\", \"description\": \"Text-to-speech using Microsoft Edge TTS. Supports multiple voices including German male/female options. Used for voice messages and audio output.\", \"tags\": [\"TTS\", \"Voice\", \"Audio\", \"Edge\"], \"related_skills\": [\"sherpa-onnx-tts\"], \"content\": \"---\\nname: edge-tts\\ndescription: \\\"Text-to-speech using Microsoft Edge TTS. Supports multiple voices including German male/female options. Used for voice messages and audio output.\\\"\\nversion: 1.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\nmetadata:\\n hermes:\\n tags: [TTS, Voice, Audio, Edge]\\n related_skills: [sherpa-onnx-tts]\\n---\\n\\n# Edge TTS\\n\\nText-to-speech using Microsoft Edge TTS (`edge-tts` Python package). No API key required — uses Microsoft's free Edge browser TTS engine.\\n\\n## When to Use\\n\\nUse this skill when:\\n- You need to convert text to speech for voice messages\\n- You want to test or compare different TTS voices\\n- The user asks for a voice message or speech output\\n- TTS configuration needs to be changed\\n\\n## Prerequisites\\n\\n```bash\\npip install edge-tts\\n```\\n\\n## User Voice Preference\\n\\n**Default voice: `de-DE-KillianNeural`** (German male, natural, clear)\\nSet in `~/.hermes/config.yaml` → `tts.edge.voice`.\\n\\n## German Voices Available\\n\\n| Voice | Gender | Style | Description |\\n|-------|--------|-------|-------------|\\n| `de-DE-KillianNeural` | Male | Natural | Clear, natural, slightly youthful (DEFAULT) |\\n| `de-DE-ConradNeural` | Male | Friendly | Warm, positive, slightly melodic |\\n| `de-DE-FlorianMultilingualNeural` | Male | Professional | Clear, neutral, professional |\\n| `de-DE-AmalaNeural` | Female | Friendly | Positive, warm |\\n| `de-DE-KatjaNeural` | Female | Friendly | Positive, clear |\\n| `de-DE-SeraphinaMultilingualNeural` | Female | General | Natural, clear |\\n\\n## Usage\\n\\n### Basic Synthesis\\n\\n```bash\\nedge-tts --voice de-DE-ConradNeural --text \\\"Guten Tag, Sir.\\\" --write-media output.mp3\\n```\\n\\n### Testing a Voice\\n\\n```bash\\nedge-tts --voice de-DE-FlorianMultilingualNeural --text \\\"Testnachricht\\\" --write-media /tmp/test.mp3\\n```\\n\\n### Listing Available Voices\\n\\n```bash\\npython3 -m edge_tts --list-voices | grep \\\"de-DE\\\"\\n```\\n\\n### Configuring in Hermes\\n\\nUpdate `~/.hermes/config.yaml`:\\n\\n```yaml\\ntts:\\n provider: edge\\n edge:\\n voice: de-DE-ConradNeural\\n```\\n\\n## Pitfalls\\n\\n- **Voice compatibility**: Not all voices work with all text. German text requires a German voice (`de-DE-*`).\\n- **File format**: Edge TTS outputs MP3 by default. Convert to OGG for Telegram voice messages if needed: `ffmpeg -i input.mp3 -codec:a libopus -b:a 128k output.ogg`\\n- **Gateway restart**: After changing the TTS voice in config, restart the gateway for changes to take effect.\\n- **Character limits**: Very long texts may produce unusually long audio. Split long messages before TTS.\\n- **Config verification**: After updating `tts.edge.voice` in config, always verify the change took effect by reading the config back. YAML writes can silently fail or be overwritten by subsequent edits. Check the TTS response field `voice_compatible: true` — if `false`, the voice may not have been applied.\\n- **Voice switching workflow**: Always test a voice with `edge-tts --voice --text \\\"Test\\\" --write-media /tmp/test.mp3` before committing to it. Multiple voices can sound similar; listening is the only reliable way to choose.\\n- **Config persistence**: Python one-liner YAML edits may not persist — always verify with a separate read-back step.\\n\\n## Shorts / AutoShorts Baseline Voice Use\\n\\nFor short-form content pipelines where the GPU is needed for ComfyUI/Wan/Qwen image or video generation, use EdgeTTS as the first baseline voice because it does not consume local GPU resources and is quick to iterate.\\n\\nFor caption-synchronised Shorts, request word-boundary metadata rather than approximating word timings across sentence SRT spans:\\n\\n```python\\ncommunicate = edge_tts.Communicate(text, voice, rate='+0%', boundary='WordBoundary')\\nasync for chunk in communicate.stream():\\n if chunk['type'] == 'audio':\\n ...\\n elif chunk['type'] == 'WordBoundary':\\n start_seconds = chunk['offset'] / 10_000_000\\n duration_seconds = chunk['duration'] / 10_000_000\\n word = chunk['text']\\n```\\n\\nUse these offsets to drive static 3–7 word caption phrases with only the active spoken word highlighted. This avoids the irritating lag/lead caused by sentence-duration approximation and supports the user's preferred Hormozi/TikTok-style subtitle feel.\\n\\nRecommended first English A/B voices for a precise, non-guru AI/workflow channel tone:\\n\\n```bash\\nedge-tts --voice en-GB-RyanNeural --text \\\"Most AI automations do not fail dramatically. They quietly rot.\\\" --write-media /tmp/ryan.mp3\\nedge-tts --voice en-US-ChristopherNeural --text \\\"Most AI automations do not fail dramatically. They quietly rot.\\\" --write-media /tmp/christopher.mp3\\n```\\n\\nFor AutoShorts-style production code, keep EdgeTTS behind an explicit adapter boundary:\\n\\n```text\\nTTSProviderPlan(edge_tts, voice)\\n→ EdgeTTSRequest(text, output_path.mp3)\\n→ edge-tts CLI call\\n→ EdgeTTSResult(side_effects=(\\\"call_edge_tts\\\", \\\"write_audio\\\"), external_calls=(\\\"edge-tts\\\",))\\n```\\n\\nThen mux/burn captions in a separate FFmpeg boundary:\\n\\n```text\\nMP4 timing preview + EdgeTTS MP3 + renderer-owned ASS subtitles\\n→ FFmpeg audio/caption review MP4\\n```\\n\\nImportant FFmpeg pitfall: do not blindly use `-shortest` when adding audio to a longer timing preview. It can silently truncate the video to the shorter voiceover duration. Preserve the planned video duration unless the feature explicitly asks to trim to audio length.\\n\\nCompare against any external/cloud TTS such as NVIDIA Magpie with the same 15–20 second script sample. Score clarity, pronunciation of AI/workflow terms, retention energy, artifacts, latency, cost, and operational reliability. Whisper is not a TTS model; use it later for transcript/alignment QA only.\\n\\n## Linked Resources\\n\\n- `references/german-voice-comparison.md` — German male voice comparison data from session 2026-05-07, with recommendations for voice selection.\\n- `references/autoshorts-edge-tts-audio-caption.md` — EdgeTTS boundary pattern for AutoShorts audio/caption review MP4s, including ffprobe duration alignment and the `-shortest` pitfall.\\n\\n## Voice Switching Workflow\\n\\n1. List available voices: `python3 -m edge_tts --list-voices | grep \\\"de-DE\\\"`\\n2. Test candidate voice: `edge-tts --voice --text \\\"Testnachricht\\\" --write-media /tmp/test.mp3`\\n3. Listen and decide\\n4. Update config: `tts.edge.voice: `\\n5. Restart gateway\\n\\n## Related Skills\\n\\n- `sherpa-onnx-tts` — Offline TTS with local German female voice (Eva). No internet required.\", \"path\": \"edge-tts/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/edge-tts\", \"linked_files\": {\"references\": [\"references/german-voice-comparison.md\", \"references/autoshorts-edge-tts-audio-caption.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"TTS\", \"Voice\", \"Audio\", \"Edge\"], \"related_skills\": [\"sherpa-onnx-tts\"]}}}",
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"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Repo-Zustand und nächste verfügbare Video-Kandidaten prüfen\",\"id\":\"inspect\",\"status\":\"completed\"},{\"content\":\"Nächsten Post-Kandidaten auswählen und Produktionsartefakte vorbereiten\",\"id\":\"prepare\",\"status\":\"in_progress\"},{\"content\":\"9:16/Audio/Subtitles/QA verifizieren und Review-Package liefern\",\"id\":\"qa\",\"status\":\"pending\"}]}"
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"reasoning": "**Checking installation and script creation**\n\nI need to check if edge-tts is installed to build a video using Python with edge_tts's word boundaries. I should check for the installation and write a script to generate a local package in data/post_candidates/ai-004, including an mp4 file. I wonder if any code modifications are necessary. I'll probably use a Python script with execute_code to generate the artifact, linking terminal commands and ensuring everything runs smoothly, avoiding heredoc for file creation.\n**Creating voice script and commands**\n\nI see that it suggests not to use echo/cat heredoc to create files, but running Python code in heredoc as a terminal command might still be acceptable. I think a better approach is to write the script render_next_post_ai004.py, incorporating a candidate for ai-004. The final voice script should convey a message about how to effectively use AI as a critic rather than just a thinker. \n\nAdditionally, I'll need to include edge_tts's async function for audio and word boundaries, and identify any required command packages.\n**Exploring edge_tts and rendering requirements**\n\nI could use the edge_tts library, but if it's missing, I might need to install it via pip using the terminal. For the voice, I'd like to use en-GB-RyanNeural. The project will involve rendering at 1080x1920 resolution at 30 fps with a dark gradient background, consisting of 8 scenes featuring UI cards. \n\nI'll implement word boundaries to phrase segments while highlighting active words. If this proves too complex, I might resort to phrasing captions by time instead. I need to generate frames for each segment as well!",
"reasoning_content": "**Checking installation and script creation**\n\nI need to check if edge-tts is installed to build a video using Python with edge_tts's word boundaries. I should check for the installation and write a script to generate a local package in data/post_candidates/ai-004, including an mp4 file. I wonder if any code modifications are necessary. I'll probably use a Python script with execute_code to generate the artifact, linking terminal commands and ensuring everything runs smoothly, avoiding heredoc for file creation.\n**Creating voice script and commands**\n\nI see that it suggests not to use echo/cat heredoc to create files, but running Python code in heredoc as a terminal command might still be acceptable. I think a better approach is to write the script render_next_post_ai004.py, incorporating a candidate for ai-004. The final voice script should convey a message about how to effectively use AI as a critic rather than just a thinker. \n\nAdditionally, I'll need to include edge_tts's async function for audio and word boundaries, and identify any required command packages.\n**Exploring edge_tts and rendering requirements**\n\nI could use the edge_tts library, but if it's missing, I might need to install it via pip using the terminal. For the voice, I'd like to use en-GB-RyanNeural. The project will involve rendering at 1080x1920 resolution at 30 fps with a dark gradient background, consisting of 8 scenes featuring UI cards. \n\nI'll implement word boundaries to phrase segments while highlighting active words. If this proves too complex, I might resort to phrasing captions by time instead. I need to generate frames for each segment as well!",
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"id": "rs_0653a96d40a8cbd5016a153aa01cd48191ad8b43f57ca6489f",
"summary": [
{
"type": "summary_text",
"text": "**Checking installation and script creation**\n\nI need to check if edge-tts is installed to build a video using Python with edge_tts's word boundaries. I should check for the installation and write a script to generate a local package in data/post_candidates/ai-004, including an mp4 file. I wonder if any code modifications are necessary. I'll probably use a Python script with execute_code to generate the artifact, linking terminal commands and ensuring everything runs smoothly, avoiding heredoc for file creation."
},
{
"type": "summary_text",
"text": "**Creating voice script and commands**\n\nI see that it suggests not to use echo/cat heredoc to create files, but running Python code in heredoc as a terminal command might still be acceptable. I think a better approach is to write the script render_next_post_ai004.py, incorporating a candidate for ai-004. The final voice script should convey a message about how to effectively use AI as a critic rather than just a thinker. \n\nAdditionally, I'll need to include edge_tts's async function for audio and word boundaries, and identify any required command packages."
},
{
"type": "summary_text",
"text": "**Exploring edge_tts and rendering requirements**\n\nI could use the edge_tts library, but if it's missing, I might need to install it via pip using the terminal. For the voice, I'd like to use en-GB-RyanNeural. The project will involve rendering at 1080x1920 resolution at 30 fps with a dark gradient background, consisting of 8 scenes featuring UI cards. \n\nI'll implement word boundaries to phrase segments while highlighting active words. If this proves too complex, I might resort to phrasing captions by time instead. I need to generate frames for each segment as well!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Repo-Zustand und nächste verfügbare Video-Kandidaten prüfen\", \"status\": \"completed\"}, {\"id\": \"prepare\", \"content\": \"Nächsten Post-Kandidaten auswählen und Produktionsartefakte vorbereiten\", \"status\": \"in_progress\"}, {\"id\": \"qa\", \"content\": \"9:16/Audio/Subtitles/QA verifizieren und Review-Package liefern\", \"status\": \"pending\"}], \"summary\": {\"total\": 3, \"pending\": 1, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}",
"tool_call_id": "call_HjCAWX47MgzCpyVzytVDDRZo"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_QN15ZCXYwMi0sTLqRlcI7gEE",
"call_id": "call_QN15ZCXYwMi0sTLqRlcI7gEE",
"response_item_id": "fc_0653a96d40a8cbd5016a153ab2fb888191b6ed1e4553282c4e",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\ntry:\\n import edge_tts\\n print('edge_tts OK', edge_tts.__version__ if hasattr(edge_tts,'__version__') else '')\\nexcept Exception as e:\\n print('edge_tts missing', type(e).__name__, str(e))\\nPY\\nwhich ffmpeg && which ffprobe\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"edge_tts OK 7.2.7\\n/usr/bin/ffmpeg\\n/usr/bin/ffprobe\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_QN15ZCXYwMi0sTLqRlcI7gEE"
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{
"role": "assistant",
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"tool_calls": [
{
"id": "call_b75VLZYdI8enISMerkHnlb0p",
"call_id": "call_b75VLZYdI8enISMerkHnlb0p",
"response_item_id": "fc_0653a96d40a8cbd5016a153ac0c7f48191af8f0d5d120da1e5",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\",\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Render next post candidate ai-004 as a 9:16 final-near approval video.\\n\\nDeterministic renderer owns all readable UI/cards/subtitles. EdgeTTS provides\\nEnglish voiceover and word-boundary timing; no platform publishing side effects.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport asyncio\\nimport hashlib\\nimport json\\nimport math\\nimport shutil\\nimport subprocess\\nimport tempfile\\nfrom pathlib import Path\\n\\nimport edge_tts\\nfrom PIL import Image, ImageDraw, ImageFont\\n\\nROOT = Path(__file__).resolve().parents[1]\\nOUT_DIR = ROOT / \\\"data\\\" / \\\"post_candidates\\\" / \\\"ai-004-stop-asking-ai-to-think-for-you\\\"\\nAUDIO = OUT_DIR / \\\"voiceover_en_gb_ryan.mp3\\\"\\nVIDEO_NO_AUDIO = OUT_DIR / \\\"video_no_audio.mp4\\\"\\nFINAL = OUT_DIR / \\\"ai-004_stop_asking_ai_to_think_for_you_1080x1920_review.mp4\\\"\\nPACKAGE = OUT_DIR / \\\"review_package.json\\\"\\nPACKAGE_MD = OUT_DIR / \\\"review_package.md\\\"\\nQA_FRAME = OUT_DIR / \\\"qa_frame_00_12.png\\\"\\n\\nWIDTH = 1080\\nHEIGHT = 1920\\nFPS = 30\\nVOICE = \\\"en-GB-RyanNeural\\\"\\n\\nSCRIPT = (\\n \\\"Stop asking AI to think for you. Ask it to argue with you. \\\"\\n \\\"When you hand AI a messy idea and say, what should I do, \\\"\\n \\\"it can sound confident before you are ready. Use it as a critic instead. \\\"\\n \\\"First: list my assumptions. Second: find the weak spots. Third: give me two alternatives. \\\"\\n \\\"Fourth: tell me what I still need to decide myself. Now AI is not replacing your judgment. \\\"\\n \\\"It is pressure-testing it. The rule is simple: never ask AI for the final answer \\\"\\n \\\"before you ask it for the argument.\\\"\\n)\\n\\nSCENES = [\\n (0.00, 4.50, \\\"HOOK\\\", \\\"Stop asking AI to think for you.\\\", \\\"Ask it to argue with you.\\\"),\\n (4.50, 9.50, \\\"VISIBLE FAIL\\\", \\\"Messy idea → instant answer\\\", \\\"Confidence arrives too early.\\\"),\\n (9.50, 14.50, \\\"MECHANISM\\\", \\\"Bad prompt: What should I do?\\\", \\\"The model fills missing judgment.\\\"),\\n (14.50, 20.00, \\\"FIX 1\\\", \\\"List my assumptions\\\", \\\"Make hidden guesses visible.\\\"),\\n (20.00, 25.50, \\\"FIX 2\\\", \\\"Find the weak spots\\\", \\\"Attack the plan before reality does.\\\"),\\n (25.50, 31.50, \\\"FIX 3\\\", \\\"Give two alternatives\\\", \\\"Options beat obedient answers.\\\"),\\n (31.50, 37.50, \\\"FIX 4\\\", \\\"What must I decide myself?\\\", \\\"Keep authority with the human.\\\"),\\n (37.50, 44.50, \\\"RULE\\\", \\\"Answer last. Argument first.\\\", \\\"Save this prompt structure.\\\"),\\n]\\n\\n\\ndef font(size: int, bold: bool = False):\\n candidates = [\\n \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf\\\",\\n ]\\n for c in candidates:\\n if Path(c).exists():\\n return ImageFont.truetype(c, size=size)\\n return ImageFont.load_default()\\n\\n\\ndef text_size(draw: ImageDraw.ImageDraw, text: str, fnt) -> tuple[int, int]:\\n box = draw.textbbox((0, 0), text, font=fnt)\\n return box[2] - box[0], box[3] - box[1]\\n\\n\\ndef wrap_by_pixels(draw: ImageDraw.ImageDraw, text: str, fnt, max_width: int) -> list[str]:\\n words = text.split()\\n lines: list[str] = []\\n line = \\\"\\\"\\n for word in words:\\n cand = (line + \\\" \\\" + word).strip()\\n if text_size(draw, cand, fnt)[0] <= max_width or not line:\\n line = cand\\n else:\\n lines.append(line)\\n line = word\\n if line:\\n lines.append(line)\\n return lines\\n\\n\\nasync def synthesize() -> list[dict[str, float | str]]:\\n OUT_DIR.mkdir(parents=True, exist_ok=True)\\n words: list[dict[str, float | str]] = []\\n communicate = edge_tts.Communicate(SCRIPT, VOICE, rate=\\\"+8%\\\", boundary=\\\"WordBoundary\\\")\\n with AUDIO.open(\\\"wb\\\") as fh:\\n async for chunk in communicate.stream():\\n if chunk[\\\"type\\\"] == \\\"audio\\\":\\n fh.write(chunk[\\\"data\\\"])\\n elif chunk[\\\"type\\\"] == \\\"WordBoundary\\\":\\n words.append(\\n {\\n \\\"word\\\": str(chunk[\\\"text\\\"]),\\n \\\"start\\\": chunk[\\\"offset\\\"] / 10_000_000,\\n \\\"duration\\\": max(0.05, chunk[\\\"duration\\\"] / 10_000_000),\\n }\\n )\\n return words\\n\\n\\ndef ffprobe_duration(path: Path) -> float:\\n raw = subprocess.check_output(\\n [\\\"ffprobe\\\", \\\"-v\\\", \\\"error\\\", \\\"-show_entries\\\", \\\"format=duration\\\", \\\"-of\\\", \\\"default=nw=1:nk=1\\\", str(path)],\\n text=True,\\n ).strip()\\n return float(raw)\\n\\n\\ndef build_segments(words: list[dict[str, float | str]]) -> list[list[dict[str, float | str]]]:\\n segments: list[list[dict[str, float | str]]] = []\\n current: list[dict[str, float | str]] = []\\n for w in words:\\n current.append(w)\\n token = str(w[\\\"word\\\"])\\n if len(current) >= 5 or token.endswith(('.', '?', '!')):\\n segments.append(current)\\n current = []\\n if current:\\n segments.append(current)\\n return segments\\n\\n\\ndef scene_for_time(t: float, duration: float):\\n # Stretch final scene if TTS runs longer than the planned visual beat.\\n for scene in SCENES:\\n if scene[0] <= t < scene[1]:\\n return scene\\n return SCENES[-1] if t <= duration + 1 else SCENES[0]\\n\\n\\ndef segment_for_time(t: float, segments):\\n for seg in segments:\\n start = float(seg[0][\\\"start\\\"])\\n end = float(seg[-1][\\\"start\\\"]) + float(seg[-1][\\\"duration\\\"]) + 0.2\\n if start <= t <= end:\\n return seg\\n return []\\n\\n\\ndef active_word_index(t: float, seg) -> int:\\n for i, w in enumerate(seg):\\n start = float(w[\\\"start\\\"])\\n end = start + float(w[\\\"duration\\\"]) + 0.08\\n if start <= t <= end:\\n return i\\n return max(0, min(len(seg) - 1, sum(1 for w in seg if float(w[\\\"start\\\"]) <= t) - 1))\\n\\n\\ndef rounded(draw, box, radius, fill, outline=None, width=1):\\n draw.rounded_rectangle(box, radius=radius, fill=fill, outline=outline, width=width)\\n\\n\\ndef draw_gradient(draw: ImageDraw.ImageDraw, frame_idx: int, total_frames: int):\\n p = frame_idx / max(1, total_frames - 1)\\n for y in range(0, HEIGHT, 4):\\n r = y / HEIGHT\\n base = (\\n int(4 + 11 * r + 8 * math.sin(p * math.tau)),\\n int(8 + 20 * r),\\n int(28 + 55 * r),\\n )\\n draw.rectangle([0, y, WIDTH, y + 4], fill=base)\\n # soft moving blobs\\n for i, col in enumerate([(37, 99, 235), (14, 165, 233), (168, 85, 247)]):\\n cx = int(WIDTH * (0.22 + 0.28 * i + 0.05 * math.sin(p * math.tau + i)))\\n cy = int(HEIGHT * (0.15 + 0.18 * i + 0.03 * math.cos(p * math.tau * 0.7 + i)))\\n rr = int(260 + 70 * math.sin(p * math.tau + i))\\n draw.ellipse([cx-rr, cy-rr, cx+rr, cy+rr], outline=tuple(min(255, c+30) for c in col), width=5)\\n\\n\\ndef draw_ui_scene(draw: ImageDraw.ImageDraw, scene, p: float):\\n _, _, label, main, sub = scene\\n small = font(34, False)\\n med = font(48, True)\\n large = font(68, True)\\n card_x1, card_y1, card_x2, card_y2 = 90, 235, 990, 1265\\n rounded(draw, [card_x1, card_y1, card_x2, card_y2], 44, (248, 250, 252), (59, 130, 246), 4)\\n draw.text((115, 95), \\\"TrueTraceShorts\\\", font=font(36, True), fill=(226, 232, 240))\\n draw.text((115, 145), \\\"Trace the hidden system. Fix the outcome.\\\", font=font(26, False), fill=(148, 163, 184))\\n rounded(draw, [115, card_y1+45, 360, card_y1+105], 24, (15, 23, 42), None)\\n draw.text((140, card_y1+58), label, font=font(28, True), fill=(125, 211, 252))\\n # simulated prompt window\\n top = card_y1 + 150\\n rounded(draw, [135, top, 945, top+280], 32, (15, 23, 42), (203, 213, 225), 2)\\n for i, c in enumerate([(248, 113, 113), (251, 191, 36), (34, 197, 94)]):\\n draw.ellipse([165+i*42, top+28, 190+i*42, top+53], fill=c)\\n draw.text((165, top+88), \\\"USER PROMPT\\\", font=font(27, True), fill=(148, 163, 184))\\n for j, line in enumerate(wrap_by_pixels(draw, main, large, 690)[:3]):\\n draw.text((165, top+135+j*82), line, font=large, fill=(248, 250, 252))\\n # AI response/checklist cards\\n base_y = top + 360\\n items = [\\\"ASSUMPTIONS\\\", \\\"WEAK SPOTS\\\", \\\"ALTERNATIVES\\\", \\\"HUMAN DECISION\\\"]\\n active = min(len(items)-1, int(p * len(items)))\\n for i, item in enumerate(items):\\n y = base_y + i * 105\\n fill = (219, 234, 254) if i <= active else (226, 232, 240)\\n outline = (59, 130, 246) if i == active else (203, 213, 225)\\n rounded(draw, [165, y, 915, y+78], 24, fill, outline, 3)\\n draw.text((200, y+19), item, font=font(30, True), fill=(15, 23, 42))\\n if i <= active:\\n draw.text((845, y+16), \\\"✓\\\", font=font(38, True), fill=(22, 163, 74))\\n # subline / rule\\n for j, line in enumerate(wrap_by_pixels(draw, sub, med, 760)[:2]):\\n w, _ = text_size(draw, line, med)\\n draw.text(((WIDTH-w)//2, 1190 + j*58), line, font=med, fill=(15, 23, 42))\\n\\n\\ndef draw_subtitles(draw: ImageDraw.ImageDraw, seg, active: int):\\n if not seg:\\n return\\n words = [str(w[\\\"word\\\"]).strip() for w in seg]\\n fnt = font(62, True)\\n pad_x, pad_y = 42, 28\\n y = 1485\\n # compute word positions centered\\n widths = [text_size(draw, w, fnt)[0] for w in words]\\n gap = 18\\n total = sum(widths) + gap * (len(words)-1)\\n x = (WIDTH - total) // 2\\n box = [max(35, x-pad_x), y-pad_y, min(WIDTH-35, x+total+pad_x), y+90+pad_y]\\n rounded(draw, box, 32, (3, 7, 18), (59, 130, 246), 3)\\n for i, (word, ww) in enumerate(zip(words, widths)):\\n fill = (250, 204, 21) if i == active else (248, 250, 252)\\n draw.text((x, y), word.upper(), font=fnt, fill=fill)\\n x += ww + gap\\n\\n\\ndef render_video(words: list[dict[str, float | str]], duration: float):\\n segments = build_segments(words)\\n total_duration = max(duration + 0.8, SCENES[-1][1])\\n total_frames = math.ceil(total_duration * FPS)\\n with tempfile.TemporaryDirectory(prefix=\\\"autoshorts_ai004_frames_\\\") as tmp:\\n frame_dir = Path(tmp) / \\\"frames\\\"\\n frame_dir.mkdir()\\n for idx in range(total_frames):\\n t = idx / FPS\\n img = Image.new(\\\"RGB\\\", (WIDTH, HEIGHT), (3, 7, 18))\\n draw = ImageDraw.Draw(img)\\n draw_gradient(draw, idx, total_frames)\\n scene = scene_for_time(t, total_duration)\\n sp = (t - scene[0]) / max(0.001, scene[1] - scene[0])\\n draw_ui_scene(draw, scene, max(0, min(1, sp)))\\n seg = segment_for_time(t, segments)\\n draw_subtitles(draw, seg, active_word_index(t, seg) if seg else 0)\\n # progress rail\\n draw.rounded_rectangle([95, 1810, 985, 1824], radius=7, fill=(30, 41, 59))\\n draw.rounded_rectangle([95, 1810, 95 + int(890 * min(1, t/total_duration)), 1824], radius=7, fill=(59, 130, 246))\\n img.save(frame_dir / f\\\"frame_{idx:06d}.jpg\\\", quality=92)\\n subprocess.check_call([\\n \\\"ffmpeg\\\", \\\"-y\\\", \\\"-hide_banner\\\", \\\"-loglevel\\\", \\\"error\\\",\\n \\\"-framerate\\\", str(FPS), \\\"-i\\\", str(frame_dir / \\\"frame_%06d.jpg\\\"),\\n \\\"-vf\\\", \\\"setsar=1,setdar=9/16,format=yuv420p\\\",\\n \\\"-c:v\\\", \\\"libx264\\\", \\\"-preset\\\", \\\"veryfast\\\", \\\"-crf\\\", \\\"18\\\",\\n \\\"-movflags\\\", \\\"+faststart\\\", str(VIDEO_NO_AUDIO)\\n ])\\n subprocess.check_call([\\n \\\"ffmpeg\\\", \\\"-y\\\", \\\"-hide_banner\\\", \\\"-loglevel\\\", \\\"error\\\",\\n \\\"-i\\\", str(VIDEO_NO_AUDIO), \\\"-i\\\", str(AUDIO),\\n \\\"-map\\\", \\\"0:v:0\\\", \\\"-map\\\", \\\"1:a:0\\\",\\n \\\"-c:v\\\", \\\"copy\\\", \\\"-c:a\\\", \\\"aac\\\", \\\"-b:a\\\", \\\"160k\\\",\\n \\\"-shortest\\\", \\\"-movflags\\\", \\\"+faststart\\\", str(FINAL)\\n ])\\n subprocess.check_call([\\n \\\"ffmpeg\\\", \\\"-y\\\", \\\"-hide_banner\\\", \\\"-loglevel\\\", \\\"error\\\",\\n \\\"-ss\\\", \\\"12\\\", \\\"-i\\\", str(FINAL), \\\"-frames:v\\\", \\\"1\\\", str(QA_FRAME)\\n ])\\n\\n\\ndef probe_video(path: Path) -> dict[str, object]:\\n meta = json.loads(subprocess.check_output([\\n \\\"ffprobe\\\", \\\"-v\\\", \\\"error\\\", \\\"-select_streams\\\", \\\"v:0\\\",\\n \\\"-show_entries\\\", \\\"stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate\\\",\\n \\\"-show_entries\\\", \\\"format=duration\\\", \\\"-of\\\", \\\"json\\\", str(path)\\n ], text=True))\\n s = meta[\\\"streams\\\"][0]\\n return {\\n \\\"width\\\": s[\\\"width\\\"], \\\"height\\\": s[\\\"height\\\"],\\n \\\"sample_aspect_ratio\\\": s.get(\\\"sample_aspect_ratio\\\"),\\n \\\"display_aspect_ratio\\\": s.get(\\\"display_aspect_ratio\\\"),\\n \\\"frame_rate\\\": s.get(\\\"r_frame_rate\\\"),\\n \\\"duration\\\": round(float(meta[\\\"format\\\"][\\\"duration\\\"]), 3),\\n }\\n\\n\\ndef sha256(path: Path) -> str:\\n h = hashlib.sha256()\\n with path.open(\\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(1024 * 1024), b\\\"\\\"):\\n h.update(chunk)\\n return h.hexdigest()\\n\\n\\ndef main() -> None:\\n if not shutil.which(\\\"ffmpeg\\\") or not shutil.which(\\\"ffprobe\\\"):\\n raise RuntimeError(\\\"ffmpeg/ffprobe required\\\")\\n OUT_DIR.mkdir(parents=True, exist_ok=True)\\n words = asyncio.run(synthesize())\\n audio_duration = ffprobe_duration(AUDIO)\\n render_video(words, audio_duration)\\n vmeta = probe_video(FINAL)\\n digest = sha256(FINAL)\\n package = {\\n \\\"schema_version\\\": \\\"autoshorts_final_review.v1\\\",\\n \\\"requires_human_approval\\\": True,\\n \\\"side_effects\\\": [],\\n \\\"candidate_id\\\": \\\"ai-004\\\",\\n \\\"title\\\": \\\"Stop asking AI to think for you\\\",\\n \\\"series\\\": \\\"AI Output Autopsy\\\",\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"final_video_language\\\": \\\"en\\\",\\n \\\"approval_language\\\": \\\"de\\\",\\n \\\"media_path\\\": str(FINAL.resolve()),\\n \\\"qa_frame_path\\\": str(QA_FRAME.resolve()),\\n \\\"sha256\\\": digest,\\n \\\"video_meta\\\": vmeta,\\n \\\"voice\\\": VOICE,\\n \\\"hook\\\": \\\"Stop asking AI to think for you. Ask it to argue with you.\\\",\\n \\\"caption\\\": \\\"AI is more useful as a critic than as a guru.\\\",\\n \\\"hashtags\\\": [\\\"#AI\\\", \\\"#Productivity\\\", \\\"#Workflow\\\", \\\"#DecisionMaking\\\", \\\"#TrueTraceShorts\\\"],\\n \\\"platform_targets\\\": [\\\"YouTube Shorts\\\", \\\"TikTok\\\", \\\"Instagram Reels\\\"],\\n \\\"quality_notes\\\": [\\n \\\"1080x1920 export with SAR 1:1 and DAR 9:16\\\",\\n \\\"EdgeTTS English voiceover with word-boundary driven active-word captions\\\",\\n \\\"Readable UI/cards are deterministic renderer-owned, not AI-generated\\\",\\n \\\"No platform posting side effects performed\\\",\\n ],\\n \\\"approval_commands\\\": [\\\"Freigabe YouTube\\\", \\\"Freigabe TikTok\\\", \\\"Freigabe Instagram\\\", \\\"Freigabe alle\\\", \\\"Ablehnen\\\", \\\"Ändern: \\\"],\\n \\\"script\\\": SCRIPT,\\n }\\n PACKAGE.write_text(json.dumps(package, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n md = f\\\"\\\"\\\"## AutoShortsBot Review — ai-004\\\\n\\\\nMEDIA:{FINAL.resolve()}\\\\n\\\\n**Titel:** Stop asking AI to think for you\\\\n**Serie:** AI Output Autopsy\\\\n**Hook:** Stop asking AI to think for you. Ask it to argue with you.\\\\n**Sprache final:** Englisch\\\\n**Zielplattformen:** YouTube Shorts, TikTok, Instagram Reels\\\\n**Format:** {vmeta['width']}x{vmeta['height']}, SAR {vmeta['sample_aspect_ratio']}, DAR {vmeta['display_aspect_ratio']}, {vmeta['duration']}s\\\\n**SHA256:** `{digest}`\\\\n\\\\n**Caption:** AI is more useful as a critic than as a guru.\\\\n**Hashtags:** {' '.join(package['hashtags'])}\\\\n\\\\n**QA:** 9:16 mobile-safe, aktive Wort-Untertitel, deterministic UI/cards, keine Posting-Side-Effects.\\\\n**QA frame:** `{QA_FRAME.resolve()}`\\\\n\\\\n**Freigabe-Befehle:**\\\\n- `Freigabe YouTube`\\\\n- `Freigabe TikTok`\\\\n- `Freigabe Instagram`\\\\n- `Freigabe alle`\\\\n- `Ablehnen`\\\\n- `Ändern: `\\\\n\\\"\\\"\\\"\\n PACKAGE_MD.write_text(md, encoding=\\\"utf-8\\\")\\n print(json.dumps({\\\"final\\\": str(FINAL), \\\"package\\\": str(PACKAGE), \\\"meta\\\": vmeta, \\\"sha256\\\": digest}, indent=2))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"}"
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"reasoning": "**Creating a generation script**\n\nI need to write a generation script and make sure the performance is okay. I might use ffmpeg to handle an image sequence and audio together. I'll set the render at 1080x1920 and 30fps, which could take some time, but that's fine. I’ll need to store frames temporarily and ensure there are no extra black bars. Visual quality assurance will be important, so I'll make sure to create the script effectively.",
"reasoning_content": "**Creating a generation script**\n\nI need to write a generation script and make sure the performance is okay. I might use ffmpeg to handle an image sequence and audio together. I'll set the render at 1080x1920 and 30fps, which could take some time, but that's fine. I’ll need to store frames temporarily and ensure there are no extra black bars. Visual quality assurance will be important, so I'll make sure to create the script effectively.",
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"summary": [
{
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"text": "**Creating a generation script**\n\nI need to write a generation script and make sure the performance is okay. I might use ffmpeg to handle an image sequence and audio together. I'll set the render at 1080x1920 and 30fps, which could take some time, but that's fine. I’ll need to store frames temporarily and ensure there are no extra black bars. Visual quality assurance will be important, so I'll make sure to create the script effectively."
}
]
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]
},
{
"role": "tool",
"content": "{\"bytes_written\": 15312, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [71:12] Type \\\"tuple[float, float]\\\" is not assignable to return type \\\"tuple[int, int]\\\"\\n \\\"float\\\" is not assignable to \\\"int\\\"\\n \\\"float\\\" is not assignable to \\\"int\\\" [reportReturnType] (Pyright)\\nERROR [97:26] Could not access item in TypedDict\\n \\\"data\\\" is not a required key in \\\"TTSChunk\\\", so access may result in runtime exception [reportTypedDictNotRequiredAccess] (Pyright)\\nERROR [101:37] Could not access item in TypedDict\\n \\\"text\\\" is not a required key in \\\"TTSChunk\\\", so access may result in runtime exception [reportTypedDictNotRequiredAccess] (Pyright)\\nERROR [102:34] Could not access item in TypedDict\\n \\\"offset\\\" is not a required key in \\\"TTSChunk\\\", so access may result in runtime exception [reportTypedDictNotRequiredAccess] (Pyright)\\nERROR [103:47] Could not access item in TypedDict\\n \\\"duration\\\" is not a required key in \\\"TTSChunk\\\", so access may result in runtime exception [reportTypedDictNotRequiredAccess] (Pyright)\\n\"}",
"tool_call_id": "call_b75VLZYdI8enISMerkHnlb0p"
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"tool_calls": [
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"id": "call_Sy6bf5KX6VPdQyLBKumQaTGZ",
"call_id": "call_Sy6bf5KX6VPdQyLBKumQaTGZ",
"response_item_id": "fc_0653a96d40a8cbd5016a153b1c3b508191a811d56c6430acf3",
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"arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\",\"old_string\":\"def text_size(draw: ImageDraw.ImageDraw, text: str, fnt) -> tuple[int, int]:\\n box = draw.textbbox((0, 0), text, font=fnt)\\n return box[2] - box[0], box[3] - box[1]\\n\",\"new_string\":\"def text_size(draw: ImageDraw.ImageDraw, text: str, fnt) -> tuple[int, int]:\\n box = draw.textbbox((0, 0), text, font=fnt)\\n return int(box[2] - box[0]), int(box[3] - box[1])\\n\"}"
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"finish_reason": "tool_calls",
"reasoning": "**Fixing type errors**\n\nI need to address some type errors I'm encountering. I think I should use a patch for this. The function `text_size` currently returns floats because of `textbbox`, so I should cast that to an integer. Also, when accessing chunks, it seems I need to use `get`. I'll make sure to implement these changes properly to ensure everything runs smoothly. This feels like a little puzzle to solve, so I'm on it!",
"reasoning_content": "**Fixing type errors**\n\nI need to address some type errors I'm encountering. I think I should use a patch for this. The function `text_size` currently returns floats because of `textbbox`, so I should cast that to an integer. Also, when accessing chunks, it seems I need to use `get`. I'll make sure to implement these changes properly to ensure everything runs smoothly. This feels like a little puzzle to solve, so I'm on it!",
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"text": "**Adjusting subtitle display**\n\nI'm working on increasing or decreasing the window height to fit subtitles correctly. The current subtitle layout at y=1248 is clipped—so I might need to limit it to one line. I’ll reduce the y value and make it dynamic, breaking subtitles into smaller segments of three words max. If they exceed the width, I’ll shrink the font size from 54 down to 38 and clean up empty words and punctuation for better spacing. I’ll patch that up!"
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"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\\n@@ -120,7 +120,9 @@\\n for w in words:\\n current.append(w)\\n token = str(w[\\\"word\\\"])\\n- if len(current) >= 5 or token.endswith(('.', '?', '!')):\\n+ # Keep active-word caption groups compact on mobile. Four/five word groups\\n+ # are readable in theory but collide in 1080-wide Shorts once uppercased.\\n+ if len(current) >= 3 or token.endswith(('.', '?', '!')):\\n segments.append(current)\\n current = []\\n if current:\\n@@ -180,7 +182,7 @@\\n _, _, label, main, sub = scene\\n small = font(34, False)\\n med = font(48, True)\\n- large = font(68, True)\\n+ large = font(58, True)\\n card_x1, card_y1, card_x2, card_y2 = 90, 235, 990, 1265\\n rounded(draw, [card_x1, card_y1, card_x2, card_y2], 44, (248, 250, 252), (59, 130, 246), 4)\\n draw.text((115, 95), \\\"TrueTraceShorts\\\", font=font(36, True), fill=(226, 232, 240))\\n@@ -189,14 +191,14 @@\\n draw.text((140, card_y1+58), label, font=font(28, True), fill=(125, 211, 252))\\n # simulated prompt window\\n top = card_y1 + 150\\n- rounded(draw, [135, top, 945, top+280], 32, (15, 23, 42), (203, 213, 225), 2)\\n+ rounded(draw, [135, top, 945, top+310], 32, (15, 23, 42), (203, 213, 225), 2)\\n for i, c in enumerate([(248, 113, 113), (251, 191, 36), (34, 197, 94)]):\\n draw.ellipse([165+i*42, top+28, 190+i*42, top+53], fill=c)\\n draw.text((165, top+88), \\\"USER PROMPT\\\", font=font(27, True), fill=(148, 163, 184))\\n for j, line in enumerate(wrap_by_pixels(draw, main, large, 690)[:3]):\\n- draw.text((165, top+135+j*82), line, font=large, fill=(248, 250, 252))\\n+ draw.text((165, top+132+j*70), line, font=large, fill=(248, 250, 252))\\n # AI response/checklist cards\\n- base_y = top + 360\\n+ base_y = top + 385\\n items = [\\\"ASSUMPTIONS\\\", \\\"WEAK SPOTS\\\", \\\"ALTERNATIVES\\\", \\\"HUMAN DECISION\\\"]\\n active = min(len(items)-1, int(p * len(items)))\\n for i, item in enumerate(items):\\n@@ -210,20 +212,27 @@\\n # subline / rule\\n for j, line in enumerate(wrap_by_pixels(draw, sub, med, 760)[:2]):\\n w, _ = text_size(draw, line, med)\\n- draw.text(((WIDTH-w)//2, 1190 + j*58), line, font=med, fill=(15, 23, 42))\\n+ draw.text(((WIDTH-w)//2, 1168 + j*54), line, font=med, fill=(15, 23, 42))\\n \\n \\n def draw_subtitles(draw: ImageDraw.ImageDraw, seg, active: int):\\n if not seg:\\n return\\n- words = [str(w[\\\"word\\\"]).strip() for w in seg]\\n+ words = [str(w[\\\"word\\\"]).strip() for w in seg if str(w[\\\"word\\\"]).strip()]\\n+ if not words:\\n+ return\\n fnt = font(62, True)\\n pad_x, pad_y = 42, 28\\n y = 1485\\n # compute word positions centered\\n- widths = [text_size(draw, w, fnt)[0] for w in words]\\n- gap = 18\\n- total = sum(widths) + gap * (len(words)-1)\\n+ gap = 22\\n+ max_total = WIDTH - 220\\n+ for size in range(62, 39, -2):\\n+ fnt = font(size, True)\\n+ widths = [text_size(draw, w, fnt)[0] for w in words]\\n+ total = sum(widths) + gap * (len(words)-1)\\n+ if total <= max_total:\\n+ break\\n x = (WIDTH - total) // 2\\n box = [max(35, x-pad_x), y-pad_y, min(WIDTH-35, x+total+pad_x), y+90+pad_y]\\n rounded(draw, box, 32, (3, 7, 18), (59, 130, 246), 3)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\"], \"lint\": {\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\": {\"status\": \"ok\", \"output\": \"\"}}}",
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"arguments": "{\"mode\":\"patch\",\"patch\":\"*** Begin Patch\\n*** Update File: /home/agent/jarvis_runtime/AutoShortsBot/scripts/render_next_post_ai004.py\\n@@\\n- # Keep active-word caption groups compact on mobile. Four/five word groups\\n- # are readable in theory but collide in 1080-wide Shorts once uppercased.\\n- if len(current) >= 3 or token.endswith(('.', '?', '!')):\\n+ # Keep active-word caption groups compact on mobile and avoid visually\\n+ # merging adjacent uppercased words.\\n+ if len(current) >= 2 or token.endswith(('.', '?', '!')):\\n@@\\n- small = font(34, False)\\n- med = font(48, True)\\n+ small = font(34, False)\\n+ med = font(48, True)\\n+ sub_font = font(42, True)\\n@@\\n- for j, line in enumerate(wrap_by_pixels(draw, sub, med, 760)[:2]):\\n- w, _ = text_size(draw, line, med)\\n- draw.text(((WIDTH-w)//2, 1168 + j*54), line, font=med, fill=(15, 23, 42))\\n+ for j, line in enumerate(wrap_by_pixels(draw, sub, sub_font, 760)[:2]):\\n+ w, _ = text_size(draw, line, sub_font)\\n+ draw.text(((WIDTH-w)//2, 1140 + j*48), line, font=sub_font, fill=(15, 23, 42))\\n@@\\n- gap = 22\\n+ gap = 40\\n*** End Patch\"}"
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"content": "[M G] Ok, erarbeite das video nun wieder mit hochwertigen, detailierten KI bildern."
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/hybrid-scene-backgrounds-codex.md\", \"content\": \"# Hybrid scene backgrounds with ChatGPT Image/Codex\\n\\nUse this when producing final-near educational/social shorts where the core value is a clear mechanism reveal, not cinematic realism alone.\\n\\n## Core pattern\\n\\n```text\\nFinalVoiceoverScript\\n→ FinalVideoPlan\\n→ SceneBackgroundPlan\\n→ ChatGPT Image/Codex text-free backgrounds\\n→ deterministic motion renderer overlays\\n→ TTS + renderer-owned subtitles\\n→ FFmpeg assembly\\n```\\n\\nAI image/video tools provide mood, texture, and scene-specific atmosphere. The deterministic renderer owns all truth-bearing content: labels, arrows, UI text, email content, numbers, subtitles, captions, and calls to action.\\n\\n## When to prefer ChatGPT Image/Codex over local Qwen/ComfyUI\\n\\nPrefer ChatGPT Image/Codex for keyframes/backgrounds when:\\n\\n- local GPU memory is also needed for a video model or other workload;\\n- the shot only needs text-free background texture;\\n- speed and iteration matter more than local model purity;\\n- generated text would be risky and should be avoided entirely.\\n\\nLocal Qwen/ComfyUI remains useful for fully local/offline workflows or when the user explicitly wants local generation. Do not default to local Qwen if Codex image generation is available and cheaper/faster for simple backgrounds.\\n\\n## Prompt guardrails\\n\\nEach scene background prompt should explicitly include:\\n\\n- `Vertical 9:16`\\n- scene-specific atmosphere, e.g. clean demo / weird email / wrong routing / three-way split / final rule\\n- `no readable text`\\n- `no logos`\\n- `no watermarks`\\n- `no UI labels`\\n- `no fake letters`\\n- `leave central negative space for renderer-owned overlays`\\n\\n## Planning artifact\\n\\nUse a pure, side-effect-free `SceneBackgroundPlan` before calling an image generator. Recommended fields per asset:\\n\\n```text\\nscene_id\\nprovider = chatgpt_image_codex\\nprompt\\noutput_path\\nrenderer_owned_overlay_required = True\\n```\\n\\nValidation should reject side effects, external calls, non-PNG outputs, duplicate scene IDs, prompts that do not explicitly forbid readable text/logos, and assets that do not require renderer-owned overlays.\\n\\n## Renderer integration\\n\\nThe motion renderer should accept a mapping rather than one global background:\\n\\n```python\\nbackground_paths = {\\n \\\"clean-demo\\\": \\\".../clean-demo.png\\\",\\n \\\"weird-email\\\": \\\".../weird-email.png\\\",\\n \\\"wrong-routing\\\": \\\".../wrong-routing.png\\\",\\n \\\"three-way-split\\\": \\\".../three-way-split.png\\\",\\n \\\"final-rule\\\": \\\".../final-rule.png\\\",\\n}\\n```\\n\\nThis keeps visual variety high while preserving deterministic overlays.\\n\\n## QA checklist\\n\\nBefore sending the review clip:\\n\\n- verify audio duration and video duration are aligned;\\n- extract representative frames, especially the densest mechanism scene;\\n- visually check mobile readability, subtitle overlap, clipping, and fake text/logos;\\n- ensure the key mechanism is clearer than a static card;\\n- keep generated media ignored by Git; commit only code, tests, and docs.\\n\\n## Beginner-friendly correction\\n\\nIf the user says the clip feels too abstract or not suitable for AI beginners, pivot the script and visuals to a concrete everyday failure first. Example: `one weird email arrives`, missing name, wrong format, unclear request. Then reveal the system principle (`Go through / Review / Stop`). Do not polish abstract architecture language before this rewrite.\", \"file_type\": \".md\"}",
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