{
"session_id": "20260525_170059_c017b0",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-25T14:28:57.702586",
"last_updated": "2026-05-25T18:33:06.974742",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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) [99% — 1,370/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN. Positioning: hidden mechanisms, not AI tips; AI 30–40%. Format: fail→mechanism→fix→takeaway; captions static 3–7w phrase with active word highlight. Visuals: layouts=blueprints; premium text-free AI styleframes; renderer owns labels/arrows/facts; Wan only after approval.\n\nConversation started: Monday, May 25, 2026 06:31 PM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_doc_read",
"description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
"parameters": {
"type": "object",
"properties": {
"doc_token": {
"type": "string",
"description": "The document token (from the document URL or comment context)."
}
},
"required": [
"doc_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_add_comment",
"description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"content": {
"type": "string",
"description": "The comment text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comment_replies",
"description": "List all replies in a comment thread on a Feishu document.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to list replies for."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"page_size": {
"type": "integer",
"description": "Number of replies per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token",
"comment_id"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comments",
"description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"is_whole": {
"type": "boolean",
"description": "If true, only return whole-document comments.",
"default": false
},
"page_size": {
"type": "integer",
"description": "Number of comments per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_reply_comment",
"description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to reply to."
},
"content": {
"type": "string",
"description": "The reply text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"comment_id",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown  and the gateway will deliver it.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"aspect_ratio": {
"type": "string",
"enum": [
"landscape",
"square",
"portrait"
],
"description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
"default": "landscape"
}
},
"required": [
"prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
"default": "replace"
},
"path": {
"type": "string",
"description": "REQUIRED when mode='replace'. File path to edit."
},
"old_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit",
"close"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "send_message",
"description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"send",
"list"
],
"description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
},
"target": {
"type": "string",
"description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)"
},
"message": {
"type": "string",
"description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "session_search",
"description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
},
"role_filter": {
"type": "string",
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
},
"limit": {
"type": "integer",
"description": "Max sessions to summarize (default: 3, max: 5).",
"default": 3
}
}
}
}
},
{
"type": "function",
"function": {
"name": "skill_manage",
"description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"create",
"patch",
"edit",
"delete",
"write_file",
"remove_file"
],
"description": "The action to perform."
},
"name": {
"type": "string",
"description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
},
"content": {
"type": "string",
"description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
},
"category": {
"type": "string",
"description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
},
"file_path": {
"type": "string",
"description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
},
"file_content": {
"type": "string",
"description": "Content for the file. Required for 'write_file'."
},
"absorbed_into": {
"type": "string",
"description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."
}
},
"required": [
"action",
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skill_view",
"description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')."
},
"file_path": {
"type": "string",
"description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
}
},
"required": [
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skills_list",
"description": "List available skills (name + description). Use skill_view(name) to load full content.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional category filter to narrow results"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "The command to execute on the VM"
},
"background": {
"type": "boolean",
"description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"pty": {
"type": "boolean",
"description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
"default": false
},
"notify_on_complete": {
"type": "boolean",
"description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
"default": false
},
"watch_patterns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "video_analyze",
"description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
"parameters": {
"type": "object",
"properties": {
"video_url": {
"type": "string",
"description": "Video URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
}
},
"required": [
"video_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https), local file path, or data: URL to load."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
}
},
"required": [
"image_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "write_file",
"description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
},
"content": {
"type": "string",
"description": "Complete content to write to the file"
}
},
"required": [
"path",
"content"
]
}
}
}
],
"message_count": 195,
"messages": [
{
"role": "user",
"content": "[M G] Die subtitles sind nicht genau gleichzeitig mit dem voiceover. Dies ist irritierend beim hören und mitlesen. Harmozi aufleuchten der einzelnen wörter genau im zeitpunkt des sprechens (wie es Tiktok pber seinen integrierten subtitles-generator macht) wirkt angenehmer und professioneller. Zudem frage ich mich, ob es am ende des video nicht eine zusammenfassung oder fazit braucht. Eine wirkliche lösung wird einem nicht angeboten. Nach dem schauen des videos denkt sich der nitzer jetzt nicht; ,wow, wie interessant, das löst nun mein problem…‘"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_5w0MiJx3l4GjdDPnCLLgYQ1w",
"call_id": "call_5w0MiJx3l4GjdDPnCLLgYQ1w",
"response_item_id": "fc_0ba9aa9428d34ee7016a13e87338488191ad724077c9f0b84a",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
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"role": "assistant",
"content": "Sir, **Sprint 2.3 — Real-Life Premium Styleframe + I2V Proof Prep ist erledigt.**\n\nAusgeführt:\n\n- 3 neue AI-Styleframes A/B/C\n- Real-Life-/Work-Scene-Basis\n- Contact Sheet\n- Quality Gate\n- Delivery per Telegram\n\nNicht ausgeführt:\n\n- kein Wan\n- kein I2V\n- kein T2V\n- kein Endframe\n- kein Full-Final\n- keine Render-Serie\n- kein Posting\n- keine Plattform-API\n\n## 1. Revision Audit + Prompts\n\nRevision Audit:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/premium_styleframe_revision_audit.json\n```\n\nPrompts:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_prompts.json\n```\n\nDelivery Manifest:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_delivery_manifest.json\n```\n\nTelegram Delivery:\n\n```text\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n\ntarget:\ntelegram:JARVIS Command Center / topic 4850\n\nchat_id:\n-1003907117629\n```\n\n## 2. Contact Sheet A/B/C\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_contact_sheet_ABC.png\n\n```text\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_contact_sheet_ABC.png\n\nfile_exists:\ntrue\n\nfile_size:\n1172784 bytes\n\nsha256:\nedd996f32f1f672977dee95ebde69c0df998325bc49fd7bf77744ddaf0540e8c\n\nmedia_type:\nimage/png\n\ndimensions:\n1440x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\n## 3. Variante A — Desk / Laptop Scene\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_A.png\n\n```text\nvariant_id:\nA\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n1828729 bytes\n\nsha256:\n52892b0c366981aba992cf016d55c1b091cb9c024baa34dfd9363cc50cab68aa\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — Desk/Laptop**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine beobachtet**\n- Mechanismus: Karten + unverbundener Decision Target sichtbar; Summary/Compression eher subtil\n- Caption-Safe-Area: **gut**\n- Risiko: braucht später renderer-owned Pfeil/Label, sonst liest es eher als generischer Data Flow\n\nKurz:\n\n```text\nCleanste echte Arbeitsplatzszene, aber Mechanismus schwächer als B/C.\n```\n\n## 4. Variante B — Meeting Room / Glass Board\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_B.png\n\n```text\nvariant_id:\nB\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_B.png\n\nfile_exists:\ntrue\n\nfile_size:\n1745510 bytes\n\nsha256:\n70da5c3d820c5a1b28e8a6ad6afec09d713ec811a3ea2b7af89af94d38f95d69\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — leerer Meetingraum / Glass Board**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine beobachtet**\n- Mechanismus: **klarster Flow in Summary Capsule + separater Decision Target Node**\n- Caption-Safe-Area: **exzellent — untere Tischfläche ruhig**\n- Risiko: etwas corporate, aber premium und am wenigsten cluttered\n\nKurz:\n\n```text\nSicherster Gewinner: realer Arbeitskontext + klarer Mechanismus + beste Caption-Fläche.\n```\n\n## 5. Variante C — Project Table / Work Evidence\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_C.png\n\n```text\nvariant_id:\nC\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n2308943 bytes\n\nsha256:\na295e458b6dbf3fa2fa198a325c52a3b46c7054665bb9419234399ceef0235bd\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — Project Table / Tablet / Notizen**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine lesbaren Texte/Logos**\n- Mechanismus: **visuell stärkster Compression-Effekt + separater Decision Target**\n- Caption-Safe-Area: **gut, aber Bild insgesamt voller**\n- Risiko: einige dekorative Linien auf Karten wirken nahe an Pseudo-Schrift; vor I2V müsste man vereinfachen/cleanen\n\nKurz:\n\n```text\nVisuell stärkste Szene, aber am riskantesten wegen Busy-ness und pseudo-writing-artiger Linien.\n```\n\n## 6. Empfehlung\n\nMeine Empfehlung:\n\n```text\nB\n```\n\nBegründung:\n\n- B ist am besten als **Produktionsbasis**:\n - realer Meeting-/Work-Kontext\n - keine Menschen/Hände/Gesichter\n - kein lesbarer Text\n - klarster Informationsfluss\n - Decision Target sichtbar getrennt\n - beste Caption-Safe-Area\n - geringstes I2V-Risiko\n\nC ist optisch spannender, aber riskanter. A ist sauber, aber der Mechanismus ist zu subtil.\n\n## 7. Endframe-Plan für Gewinner B\n\nDa B nach meinen Gates klar am stärksten als Produktionsbasis ist, habe ich **nur einen Endframe-Plan** erstellt — **kein Endframe-Bild generiert**.\n\nGespeichert im Manifest:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_delivery_manifest.json\n```\n\nPlan:\n\n```text\nKeep same meeting-room camera, lighting, glass-board background, and empty room.\nInformation cards have moved into the same horizontal summary capsule, which becomes smoother/brighter and more generic.\nDecision Target node stays upper-right, dim and unconnected, with no label baked into image.\nPreserve lower table area as caption-safe.\nNo readable text, no humans, no logos.\nRenderer later adds small NO DECISION TARGET label/arrow and captions.\n```\n\n## 8. Nächste erlaubte Kommandos\n\nWenn B passt:\n\n```text\nAPPROVED_REAL_LIFE_STYLEFRAME_DIRECTION ai-output-autopsy-decision-summary-38s B\n```\n\nDanach erlaubt:\n\n- passender Endframe im gleichen Stil\n- kurzer 5–6s Wan I2V Proof\n- kein Full-Final\n\nWenn Anpassung gewünscht:\n\n```text\nREVISE_REAL_LIFE_STYLEFRAME_DIRECTION ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn alle abgelehnt:\n\n```text\nREJECT_REAL_LIFE_STYLEFRAME_DIRECTIONS ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nBis zu Ihrer visuellen Freigabe bleibt alles blockiert.",
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"text": "Sir, **Sprint 2.3 — Real-Life Premium Styleframe + I2V Proof Prep ist erledigt.**\n\nAusgeführt:\n\n- 3 neue AI-Styleframes A/B/C\n- Real-Life-/Work-Scene-Basis\n- Contact Sheet\n- Quality Gate\n- Delivery per Telegram\n\nNicht ausgeführt:\n\n- kein Wan\n- kein I2V\n- kein T2V\n- kein Endframe\n- kein Full-Final\n- keine Render-Serie\n- kein Posting\n- keine Plattform-API\n\n## 1. Revision Audit + Prompts\n\nRevision Audit:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/premium_styleframe_revision_audit.json\n```\n\nPrompts:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_prompts.json\n```\n\nDelivery Manifest:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_delivery_manifest.json\n```\n\nTelegram Delivery:\n\n```text\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n\ntarget:\ntelegram:JARVIS Command Center / topic 4850\n\nchat_id:\n-1003907117629\n```\n\n## 2. Contact Sheet A/B/C\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_contact_sheet_ABC.png\n\n```text\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_contact_sheet_ABC.png\n\nfile_exists:\ntrue\n\nfile_size:\n1172784 bytes\n\nsha256:\nedd996f32f1f672977dee95ebde69c0df998325bc49fd7bf77744ddaf0540e8c\n\nmedia_type:\nimage/png\n\ndimensions:\n1440x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\n## 3. Variante A — Desk / Laptop Scene\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_A.png\n\n```text\nvariant_id:\nA\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n1828729 bytes\n\nsha256:\n52892b0c366981aba992cf016d55c1b091cb9c024baa34dfd9363cc50cab68aa\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — Desk/Laptop**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine beobachtet**\n- Mechanismus: Karten + unverbundener Decision Target sichtbar; Summary/Compression eher subtil\n- Caption-Safe-Area: **gut**\n- Risiko: braucht später renderer-owned Pfeil/Label, sonst liest es eher als generischer Data Flow\n\nKurz:\n\n```text\nCleanste echte Arbeitsplatzszene, aber Mechanismus schwächer als B/C.\n```\n\n## 4. Variante B — Meeting Room / Glass Board\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_B.png\n\n```text\nvariant_id:\nB\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_B.png\n\nfile_exists:\ntrue\n\nfile_size:\n1745510 bytes\n\nsha256:\n70da5c3d820c5a1b28e8a6ad6afec09d713ec811a3ea2b7af89af94d38f95d69\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — leerer Meetingraum / Glass Board**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine beobachtet**\n- Mechanismus: **klarster Flow in Summary Capsule + separater Decision Target Node**\n- Caption-Safe-Area: **exzellent — untere Tischfläche ruhig**\n- Risiko: etwas corporate, aber premium und am wenigsten cluttered\n\nKurz:\n\n```text\nSicherster Gewinner: realer Arbeitskontext + klarer Mechanismus + beste Caption-Fläche.\n```\n\n## 5. Variante C — Project Table / Work Evidence\n\nMEDIA:/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_C.png\n\n```text\nvariant_id:\nC\n\nfile_path:\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/s03_real_life_styleframe_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n2308943 bytes\n\nsha256:\na295e458b6dbf3fa2fa198a325c52a3b46c7054665bb9419234399ceef0235bd\n\nmedia_type:\nimage/png\n\ndimensions:\n1024x1536\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5862\n```\n\nBewertung:\n\n- Auto-reject: **nein**\n- Real-Life-Anker: **stark — Project Table / Tablet / Notizen**\n- Menschen/Hände/Gesichter: **keine beobachtet**\n- Lesbarer Text/Logo/UI: **keine lesbaren Texte/Logos**\n- Mechanismus: **visuell stärkster Compression-Effekt + separater Decision Target**\n- Caption-Safe-Area: **gut, aber Bild insgesamt voller**\n- Risiko: einige dekorative Linien auf Karten wirken nahe an Pseudo-Schrift; vor I2V müsste man vereinfachen/cleanen\n\nKurz:\n\n```text\nVisuell stärkste Szene, aber am riskantesten wegen Busy-ness und pseudo-writing-artiger Linien.\n```\n\n## 6. Empfehlung\n\nMeine Empfehlung:\n\n```text\nB\n```\n\nBegründung:\n\n- B ist am besten als **Produktionsbasis**:\n - realer Meeting-/Work-Kontext\n - keine Menschen/Hände/Gesichter\n - kein lesbarer Text\n - klarster Informationsfluss\n - Decision Target sichtbar getrennt\n - beste Caption-Safe-Area\n - geringstes I2V-Risiko\n\nC ist optisch spannender, aber riskanter. A ist sauber, aber der Mechanismus ist zu subtil.\n\n## 7. Endframe-Plan für Gewinner B\n\nDa B nach meinen Gates klar am stärksten als Produktionsbasis ist, habe ich **nur einen Endframe-Plan** erstellt — **kein Endframe-Bild generiert**.\n\nGespeichert im Manifest:\n\n```text\n/tmp/autoshorts_real_life_styleframes_sprint23/ai-output-autopsy-decision-summary-38s/real_life_styleframe_delivery_manifest.json\n```\n\nPlan:\n\n```text\nKeep same meeting-room camera, lighting, glass-board background, and empty room.\nInformation cards have moved into the same horizontal summary capsule, which becomes smoother/brighter and more generic.\nDecision Target node stays upper-right, dim and unconnected, with no label baked into image.\nPreserve lower table area as caption-safe.\nNo readable text, no humans, no logos.\nRenderer later adds small NO DECISION TARGET label/arrow and captions.\n```\n\n## 8. Nächste erlaubte Kommandos\n\nWenn B passt:\n\n```text\nAPPROVED_REAL_LIFE_STYLEFRAME_DIRECTION ai-output-autopsy-decision-summary-38s B\n```\n\nDanach erlaubt:\n\n- passender Endframe im gleichen Stil\n- kurzer 5–6s Wan I2V Proof\n- kein Full-Final\n\nWenn Anpassung gewünscht:\n\n```text\nREVISE_REAL_LIFE_STYLEFRAME_DIRECTION ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\nWenn alle abgelehnt:\n\n```text\nREJECT_REAL_LIFE_STYLEFRAME_DIRECTIONS ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nBis zu Ihrer visuellen Freigabe bleibt alles blockiert."
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"id": "rs_05cdea51f45b90d7016a146752fa8481918875ffe2d278b210",
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{
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"text": "**Considering caption overlays**\n\nI'm thinking about using captions for the visuals, but should they be minimal? Maybe a small overlay at the top could work, like “PRE-RELEASE A,” but that seems a bit debug-ish. It might be better to use subtle overlays like translucent arrows or rings. I also wonder about adding labels for clarity, but don’t want to clutter the visuals. Captions seem crucial, particularly with the need for static segments and highlights. Let’s ensure everything aligns with the user's requirements!"
},
{
"type": "summary_text",
"text": "**Planning keyframe delivery**\n\nI’m considering creating a keyframe set directory and then zipping it up for delivery. The user requested Keyframe-Set A, so I should make sure to include that along with a contact sheet. I think sending it as a zip file makes sense since it keeps everything organized and easy for the user. So, I’ll focus on preparing the keyframe set and the contact sheet for a smooth delivery!"
}
]
}
]
},
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{
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"content": "[process] wait session=proc_7a68025f2bbe",
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{
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{
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"content": "[process] wait session=proc_7a68025f2bbe",
"tool_call_id": "call_jDLJHYmN7HTfNrtlAYdhRoYe"
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{
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}
}
],
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},
{
"role": "tool",
"content": "[todo] updated task list",
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{
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"content": "",
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"id": "call_3Vlg9uGnqxoqqkryUzqWTfq1",
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"finish_reason": "tool_calls",
"reasoning": "**Evaluating media delivery**\n\nI need to inspect the MP4 durations — the audio is 34.968 seconds, which is shorter than the requested candidate of 38 seconds. I might need to shorten the last scene. A video contact sheet is essential, and I'll extract visual QA frames using ffprobe. It seems like I need to deliver several media files: an MP4, a contact sheet, and zip files for each. I’ll need to verify file sizes and update the manifest before sending through Telegram.",
"reasoning_content": "**Evaluating media delivery**\n\nI need to inspect the MP4 durations — the audio is 34.968 seconds, which is shorter than the requested candidate of 38 seconds. I might need to shorten the last scene. A video contact sheet is essential, and I'll extract visual QA frames using ffprobe. It seems like I need to deliver several media files: an MP4, a contact sheet, and zip files for each. I’ll need to verify file sizes and update the manifest before sending through Telegram.",
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"id": "rs_05cdea51f45b90d7016a14685763288191bc1b14c29fa21361",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating media delivery**\n\nI need to inspect the MP4 durations — the audio is 34.968 seconds, which is shorter than the requested candidate of 38 seconds. I might need to shorten the last scene. A video contact sheet is essential, and I'll extract visual QA frames using ffprobe. It seems like I need to deliver several media files: an MP4, a contact sheet, and zip files for each. I’ll need to verify file sizes and update the manifest before sending through Telegram."
}
]
}
]
},
{
"role": "tool",
"content": "[read_file] read /tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/prerelease_animatic_manifest_AC.json from line 1 (15,848 chars)",
"tool_call_id": "call_3Vlg9uGnqxoqqkryUzqWTfq1"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Un196L6bW30EPKUtml6FAEd3",
"call_id": "call_Un196L6bW30EPKUtml6FAEd3",
"response_item_id": "fc_05cdea51f45b90d7016a146864ee2c81918b730eebc2ed6f6e",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"for v in A C; do mkdir -p /tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_${v}/qa_frames; for t in 1 6 16 23 32; do ffmpeg -y -ss $t -i /tmp/autoshorts_prer...[truncated]\", \"timeout\": 120, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Considering visual QA for video frames**\n\nI need to perform a visual QA of the screenshots, possibly using ffmpeg for extraction. I’m considering creating a preview contact sheet of video frames, even if it wasn't specifically requested. Keyframe contact is important, so I need to ensure the video isn't black and that captions are visible. I should sample frames to generate a montage and vision. Alright, I’ll go ahead and use the terminal for this operation!",
"reasoning_content": "**Considering visual QA for video frames**\n\nI need to perform a visual QA of the screenshots, possibly using ffmpeg for extraction. I’m considering creating a preview contact sheet of video frames, even if it wasn't specifically requested. Keyframe contact is important, so I need to ensure the video isn't black and that captions are visible. I should sample frames to generate a montage and vision. Alright, I’ll go ahead and use the terminal for this operation!",
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{
"type": "summary_text",
"text": "**Considering visual QA for video frames**\n\nI need to perform a visual QA of the screenshots, possibly using ffmpeg for extraction. I’m considering creating a preview contact sheet of video frames, even if it wasn't specifically requested. Keyframe contact is important, so I need to ensure the video isn't black and that captions are visible. I should sample frames to generate a montage and vision. Alright, I’ll go ahead and use the terminal for this operation!"
}
]
}
]
},
{
"role": "tool",
"content": "[terminal] ran `for v in A C; do mkdir -p /tmp/autoshorts_prerelease_animatic_sprint24/ai-out...` -> exit 0, 1 lines output",
"tool_call_id": "call_Un196L6bW30EPKUtml6FAEd3"
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{
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"content": "[vision_analyze] 'QA this A video frame sheet: are frames visible, c' (316 chars)",
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"arguments": "{\"image_url\":\"/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/qa_video_frames_C.jpg\",\"question\":\"QA this C video frame sheet: are frames visible, captions present with active-highlight style, no obvious humans/hands/logos/readable AI-generated text? Any severe issue before sending?\"}"
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"arguments": "{\"code\": \"from pathlib import Path\\nimport json, hashlib\\nROOT=Path('/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s')\\nman=json.loads((ROOT/'prerelease_animatic_manifest_AC.jso...[truncated]\"}"
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"finish_reason": "tool_calls",
"reasoning": "**Reviewing output text and visuals**\n\nIt seems the vision output isn’t showing the text even though the tool indicates it’s loaded. I can check the images, but there are some text labels, like \"BLOCKERS...\" which is interesting. The generated C 16 has these small white marks that might be an issue. The user asked for no pseudo-writing, so I need to consider if I should automatically reject it. I’ll also update the manifest regarding the final MP4 duration.\n**Organizing media files**\n\nI’m looking at the audio and captions, which need to end at 34.959, while the video is at 34.968, so that’s good to note. I might send a message with the videos, contact sheets, and zips; I wonder if large zips around 15MB are okay. To avoid flooding, I should send one message for A and another for C. My last attempt handled multiple media, but maybe it’s safer to limit attachments to two mp4s, two contact sheets, and two zips per message. I'll update the manifest and message IDs afterwards.",
"reasoning_content": "**Reviewing output text and visuals**\n\nIt seems the vision output isn’t showing the text even though the tool indicates it’s loaded. I can check the images, but there are some text labels, like \"BLOCKERS...\" which is interesting. The generated C 16 has these small white marks that might be an issue. The user asked for no pseudo-writing, so I need to consider if I should automatically reject it. I’ll also update the manifest regarding the final MP4 duration.\n**Organizing media files**\n\nI’m looking at the audio and captions, which need to end at 34.959, while the video is at 34.968, so that’s good to note. I might send a message with the videos, contact sheets, and zips; I wonder if large zips around 15MB are okay. To avoid flooding, I should send one message for A and another for C. My last attempt handled multiple media, but maybe it’s safer to limit attachments to two mp4s, two contact sheets, and two zips per message. I'll update the manifest and message IDs afterwards.",
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"id": "rs_05cdea51f45b90d7016a14687ef2908191bd4a605a02de77cc",
"summary": [
{
"type": "summary_text",
"text": "**Reviewing output text and visuals**\n\nIt seems the vision output isn’t showing the text even though the tool indicates it’s loaded. I can check the images, but there are some text labels, like \"BLOCKERS...\" which is interesting. The generated C 16 has these small white marks that might be an issue. The user asked for no pseudo-writing, so I need to consider if I should automatically reject it. I’ll also update the manifest regarding the final MP4 duration."
},
{
"type": "summary_text",
"text": "**Organizing media files**\n\nI’m looking at the audio and captions, which need to end at 34.959, while the video is at 34.968, so that’s good to note. I might send a message with the videos, contact sheets, and zips; I wonder if large zips around 15MB are okay. To avoid flooding, I should send one message for A and another for C. My last attempt handled multiple media, but maybe it’s safer to limit attachments to two mp4s, two contact sheets, and two zips per message. I'll update the manifest and message IDs afterwards."
}
]
}
]
},
{
"role": "tool",
"content": "[execute_code] `from pathlib import Path import json, hashlib ROOT=Path('/tm...` (1 lines output)",
"tool_call_id": "call_RPVOtmbfnyXNth265MVRJP9h"
},
{
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"tool_calls": [
{
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"response_item_id": "fc_05cdea51f45b90d7016a14689332b481918ccc3b33265c47b8",
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"function": {
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}
],
"finish_reason": "tool_calls"
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{
"role": "tool",
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"tool_call_id": "call_IW2Qo23AWnNPLBgrPi3IdOno"
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{
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"tool_calls": [
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"type": "function",
"function": {
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{
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"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"5886\", \"mirrored\": true}",
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"content": "Sir, **Sprint 2.4 — Pre-Release Animatic Preview A/C ist abgeschlossen.**\n\nAusgeführt:\n\n- Version A Pre-Release MP4\n- Version C Pre-Release MP4\n- Keyframe-Sets A/C\n- Contact Sheets A/C\n- QA Frame Sheets A/C\n- bestehendes Voiceover genutzt\n- bestehende static active-word Caption-Logik genutzt\n- deterministische Push-ins / Crossfades / leichte Motion\n\nNicht ausgeführt:\n\n- **kein Wan**\n- **kein I2V**\n- **kein T2V**\n- **kein Full-Final**\n- **kein Posting**\n- **keine Plattform-API**\n- keine Render-Serie über A/C hinaus\n\n## Delivery\n\nTelegram Versand verifiziert:\n\n```text\nA message_id: 5886\nC message_id: 5891\ntarget: telegram:JARVIS Command Center / topic 4850\nchat_id: -1003907117629\ntelegram_delivery_verified: true\n```\n\nManifest:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/prerelease_animatic_manifest_AC.json\n```\n\nPlan/Audit:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/sprint24_plan_audit.json\n```\n\nPrompts:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/prerelease_keyframe_prompts_AC.json\n```\n\n---\n\n# Version A — Clean Desk / Laptop\n\n## MP4 A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/prerelease_animatic_A.mp4\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/prerelease_animatic_A.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n9595863 bytes\n\nsha256:\naf841ea4b3e6531ba04a63110a1e2d7c1ec8a6f0ca4b526a064de675fde6c4b4\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## Contact Sheet A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/contact_sheet_keyframes_A.png\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/contact_sheet_keyframes_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n2368997 bytes\n\nsha256:\n8abde5cb511fde842ef4a731fb23e79e0633e8fc369b5436afcb24a60ccbca71\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## QA Frame Sheet A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/qa_video_frames_A.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/qa_video_frames_A.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n119508 bytes\n\nsha256:\n7310cc96a4c6519c730e04a1b3b99491fa23d6c79cd18afa28cb92738777a89f\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## Keyframe Set ZIP A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/keyframe_set_A.zip\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/keyframe_set_A.zip\n\nfile_exists:\ntrue\n\nfile_size:\n12159230 bytes\n\nsha256:\n9daa4ad86ae97739407d1a7ed66892688f6cb8c1c4f4f9f12330f004628c4f3b\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\nKurzbewertung A:\n\n- Bildsprache: sauber, hell, ruhig, premium.\n- Arbeitswelt-Anker: stark durch Desk/Laptop.\n- Mechanismus: verständlich, aber subtiler als C.\n- Captions: static phrase + Active-Word-Highlight vorhanden.\n- Risiko: etwas weniger Social-Punch; dafür produktionssicherer.\n\n---\n\n# Version C — Project Table / Work Evidence\n\n## MP4 C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/prerelease_animatic_C.mp4\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/prerelease_animatic_C.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n13198110 bytes\n\nsha256:\nc053cbc3927aea905c83733e54dc1d51bb776a13a120cadf469e5263e193a023\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## Contact Sheet C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/contact_sheet_keyframes_C.png\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/contact_sheet_keyframes_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n2795310 bytes\n\nsha256:\n18e5d07f30735834222b775a7cb9574b166ca3229df94c5f508d70e48ef23295\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## QA Frame Sheet C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/qa_video_frames_C.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/qa_video_frames_C.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n121050 bytes\n\nsha256:\nda1b9371639cef86e5013e08bbcabc8b62fbf32d3472d81462d1739e62862610\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## Keyframe Set ZIP C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/keyframe_set_C.zip\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/keyframe_set_C.zip\n\nfile_exists:\ntrue\n\nfile_size:\n15191806 bytes\n\nsha256:\n67dd927ec7030505efbc4236d3c4a71446bac0c3a5dd3cf2d3b0a5abcfe62cbf\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\nKurzbewertung C:\n\n- Bildsprache: stärker, kontrastreicher, social-tauglicher.\n- Arbeitswelt-Anker: stark durch Project Table / Work Evidence.\n- Mechanismus: visuell energischer und auffälliger als A.\n- Captions: static phrase + Active-Word-Highlight vorhanden.\n- Risiko: etwas busy; einzelne Karten-/Fragmentflächen kommen näher an Pseudo-Writing-Risiko, aber kein lesbarer Text beobachtet.\n\n---\n\n## Szenenstruktur\n\nBeide Versionen nutzen dieselbe Struktur:\n\n```text\ns01_fail: 0.000 – 4.418s\ns02_bad_output: 4.418 – 14.431s\ns03_mechanism: 14.431 – 19.109s\ns04_fix: 19.109 – 30.397s\ns05_takeaway: 30.397 – 34.968s mit Audio\n```\n\nHinweis:\n\n```text\nSilent base render = 38.0s\nAudio-bound preview MP4 = 34.968s\n```\n\nDas finale MP4 wurde mit `-shortest` auf die bestehende Voiceover-Länge geschnitten. Keine künstliche stille Reststrecke. Trocken, aber sinnvoll.\n\n## Caption-Verhalten\n\nVerwendet:\n\n```text\nstatic phrase segments + active-word highlight\n```\n\nAlso:\n\n- Phrase bleibt stabil auf dem Bildschirm.\n- Nur das aktuell gesprochene Wort wird hervorgehoben.\n- Kein One-Word-Teleprompter.\n- Kein hektisches Sliding-Window.\n\n## Vergleich A vs C\n\nA:\n\n- sauberer\n- ruhiger\n- glaubwürdiger als spätere I2V-Basis\n- weniger Risiko bei Pseudo-Schrift / Busy-ness\n- etwas weniger Aufmerksamkeitssog\n\nC:\n\n- stärkerer Social Impact\n- mehr visuelle Energie\n- besserer “Work Evidence”-Look\n- näher an TrueTraceShorts-Retention\n- aber riskanter für spätere Wan/I2V wegen Busy-ness und Pseudo-Writing-Nähe\n\n## Meine Empfehlung\n\nWenn das Ziel maximale Produktionssicherheit ist:\n\n```text\nA\n```\n\nWenn das Ziel stärkere Shorts-Wirkung ist:\n\n```text\nC\n```\n\nMeine vorsichtige Empfehlung:\n\n```text\nC, aber nur mit Cleanup-Anweisung vor Endframe/Wan:\nweniger Karten, keine Pseudo-Line-Marks, ruhigere untere Caption-Zone.\n```\n\nWenn Sie direkt in den nächsten kontrollierten Schritt wollen, wäre mein Favorit:\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s C\n```\n\nmit Zusatz:\n\n```text\n\"Cleanup before endframe: reduce busy card fragments, avoid pseudo-writing, preserve project-table energy.\"\n```\n\n## Nächste erlaubte Kommandos\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s A\n```\n\noder:\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s C\n```\n\noder:\n\n```text\nREVISE_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\noder:\n\n```text\nREJECT_PRERELEASE_DIRECTIONS ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nErst danach:\n\n- Endframe\n- kurzer Wan-I2V-Proof\n- später echter Finalrender",
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"text": "Sir, **Sprint 2.4 — Pre-Release Animatic Preview A/C ist abgeschlossen.**\n\nAusgeführt:\n\n- Version A Pre-Release MP4\n- Version C Pre-Release MP4\n- Keyframe-Sets A/C\n- Contact Sheets A/C\n- QA Frame Sheets A/C\n- bestehendes Voiceover genutzt\n- bestehende static active-word Caption-Logik genutzt\n- deterministische Push-ins / Crossfades / leichte Motion\n\nNicht ausgeführt:\n\n- **kein Wan**\n- **kein I2V**\n- **kein T2V**\n- **kein Full-Final**\n- **kein Posting**\n- **keine Plattform-API**\n- keine Render-Serie über A/C hinaus\n\n## Delivery\n\nTelegram Versand verifiziert:\n\n```text\nA message_id: 5886\nC message_id: 5891\ntarget: telegram:JARVIS Command Center / topic 4850\nchat_id: -1003907117629\ntelegram_delivery_verified: true\n```\n\nManifest:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/prerelease_animatic_manifest_AC.json\n```\n\nPlan/Audit:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/sprint24_plan_audit.json\n```\n\nPrompts:\n\n```text\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/prerelease_keyframe_prompts_AC.json\n```\n\n---\n\n# Version A — Clean Desk / Laptop\n\n## MP4 A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/prerelease_animatic_A.mp4\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/prerelease_animatic_A.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n9595863 bytes\n\nsha256:\naf841ea4b3e6531ba04a63110a1e2d7c1ec8a6f0ca4b526a064de675fde6c4b4\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## Contact Sheet A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/contact_sheet_keyframes_A.png\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/contact_sheet_keyframes_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n2368997 bytes\n\nsha256:\n8abde5cb511fde842ef4a731fb23e79e0633e8fc369b5436afcb24a60ccbca71\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## QA Frame Sheet A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/qa_video_frames_A.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/qa_video_frames_A.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n119508 bytes\n\nsha256:\n7310cc96a4c6519c730e04a1b3b99491fa23d6c79cd18afa28cb92738777a89f\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\n## Keyframe Set ZIP A\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/keyframe_set_A.zip\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_A/keyframe_set_A.zip\n\nfile_exists:\ntrue\n\nfile_size:\n12159230 bytes\n\nsha256:\n9daa4ad86ae97739407d1a7ed66892688f6cb8c1c4f4f9f12330f004628c4f3b\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5886\n```\n\nKurzbewertung A:\n\n- Bildsprache: sauber, hell, ruhig, premium.\n- Arbeitswelt-Anker: stark durch Desk/Laptop.\n- Mechanismus: verständlich, aber subtiler als C.\n- Captions: static phrase + Active-Word-Highlight vorhanden.\n- Risiko: etwas weniger Social-Punch; dafür produktionssicherer.\n\n---\n\n# Version C — Project Table / Work Evidence\n\n## MP4 C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/prerelease_animatic_C.mp4\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/prerelease_animatic_C.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n13198110 bytes\n\nsha256:\nc053cbc3927aea905c83733e54dc1d51bb776a13a120cadf469e5263e193a023\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## Contact Sheet C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/contact_sheet_keyframes_C.png\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/contact_sheet_keyframes_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n2795310 bytes\n\nsha256:\n18e5d07f30735834222b775a7cb9574b166ca3229df94c5f508d70e48ef23295\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## QA Frame Sheet C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/qa_video_frames_C.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/qa_video_frames_C.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n121050 bytes\n\nsha256:\nda1b9371639cef86e5013e08bbcabc8b62fbf32d3472d81462d1739e62862610\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\n## Keyframe Set ZIP C\n\nMEDIA:/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/keyframe_set_C.zip\n\n```text\nfile_path:\n/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/keyframe_set_C.zip\n\nfile_exists:\ntrue\n\nfile_size:\n15191806 bytes\n\nsha256:\n67dd927ec7030505efbc4236d3c4a71446bac0c3a5dd3cf2d3b0a5abcfe62cbf\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5891\n```\n\nKurzbewertung C:\n\n- Bildsprache: stärker, kontrastreicher, social-tauglicher.\n- Arbeitswelt-Anker: stark durch Project Table / Work Evidence.\n- Mechanismus: visuell energischer und auffälliger als A.\n- Captions: static phrase + Active-Word-Highlight vorhanden.\n- Risiko: etwas busy; einzelne Karten-/Fragmentflächen kommen näher an Pseudo-Writing-Risiko, aber kein lesbarer Text beobachtet.\n\n---\n\n## Szenenstruktur\n\nBeide Versionen nutzen dieselbe Struktur:\n\n```text\ns01_fail: 0.000 – 4.418s\ns02_bad_output: 4.418 – 14.431s\ns03_mechanism: 14.431 – 19.109s\ns04_fix: 19.109 – 30.397s\ns05_takeaway: 30.397 – 34.968s mit Audio\n```\n\nHinweis:\n\n```text\nSilent base render = 38.0s\nAudio-bound preview MP4 = 34.968s\n```\n\nDas finale MP4 wurde mit `-shortest` auf die bestehende Voiceover-Länge geschnitten. Keine künstliche stille Reststrecke. Trocken, aber sinnvoll.\n\n## Caption-Verhalten\n\nVerwendet:\n\n```text\nstatic phrase segments + active-word highlight\n```\n\nAlso:\n\n- Phrase bleibt stabil auf dem Bildschirm.\n- Nur das aktuell gesprochene Wort wird hervorgehoben.\n- Kein One-Word-Teleprompter.\n- Kein hektisches Sliding-Window.\n\n## Vergleich A vs C\n\nA:\n\n- sauberer\n- ruhiger\n- glaubwürdiger als spätere I2V-Basis\n- weniger Risiko bei Pseudo-Schrift / Busy-ness\n- etwas weniger Aufmerksamkeitssog\n\nC:\n\n- stärkerer Social Impact\n- mehr visuelle Energie\n- besserer “Work Evidence”-Look\n- näher an TrueTraceShorts-Retention\n- aber riskanter für spätere Wan/I2V wegen Busy-ness und Pseudo-Writing-Nähe\n\n## Meine Empfehlung\n\nWenn das Ziel maximale Produktionssicherheit ist:\n\n```text\nA\n```\n\nWenn das Ziel stärkere Shorts-Wirkung ist:\n\n```text\nC\n```\n\nMeine vorsichtige Empfehlung:\n\n```text\nC, aber nur mit Cleanup-Anweisung vor Endframe/Wan:\nweniger Karten, keine Pseudo-Line-Marks, ruhigere untere Caption-Zone.\n```\n\nWenn Sie direkt in den nächsten kontrollierten Schritt wollen, wäre mein Favorit:\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s C\n```\n\nmit Zusatz:\n\n```text\n\"Cleanup before endframe: reduce busy card fragments, avoid pseudo-writing, preserve project-table energy.\"\n```\n\n## Nächste erlaubte Kommandos\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s A\n```\n\noder:\n\n```text\nAPPROVED_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s C\n```\n\noder:\n\n```text\nREVISE_PRERELEASE_DIRECTION ai-output-autopsy-decision-summary-38s \"Änderungswunsch\"\n```\n\noder:\n\n```text\nREJECT_PRERELEASE_DIRECTIONS ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nErst danach:\n\n- Endframe\n- kurzer Wan-I2V-Proof\n- später echter Finalrender"
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"content": "[M G] JARVIS, wir korrigieren jetzt die Richtung nochmals deutlich.\n\nDie KI-Bilder sind inzwischen hochwertiger, aber die darübergelegten Kreise, Pfeile, Labels, Boxen und Markierungen zerstören den Look. Das Video wirkt dadurch wieder wie ein technisches Erklärboard.\n\nAb jetzt für diesen Pilot:\n\nNur KI-Bilder + Voiceover + Subtitles.\n\nKeine zusätzlichen visuellen Erklär-Overlays.\n\nNeue Regel:\n\n* keine Kreise\n* keine Pfeile\n* keine Labels\n* keine Decision-Target-Markierung\n* keine Linien\n* keine Kästchen\n* keine Rahmen\n* keine Stempel\n* keine Debug-Elemente\n* keine Diagramm-Overlays\n* keine künstlich draufgesetzten Felder\n* keine Caption-Box mit Rahmen\n\nErlaubt:\n\n* KI-Bilder\n* Voiceover\n* Subtitles\n* dezenter Textschatten / Outline für Lesbarkeit\n* subtile Kamera-Motion\n* Crossfades\n* leichte Parallax, falls sauber möglich\n\nSubtitles:\n\n* static phrase segments mit active-word highlight bleibt korrekt\n* aber ohne Rahmenbox\n* keine grosse dunkle Subtitle-Fläche\n* kein sichtbarer Container\n* maximal dezenter Schatten oder leichte Kontur\n* active word darf gelb/highlight sein\n* restliche Wörter weiss\n* mobile-safe unten positioniert\n* ruhig, hochwertig, nicht TikTok-chaotisch\n\nNeuer Sprint:\n\nSprint 2.5 — Clean Image Cut, No Overlay Pilot\n\nZiel:\nErzeuge ein sauberes Pre-Release-Video nur aus den besten KI-Bildern, Voiceover und Subtitles. Keine Erklärgrafik-Overlays.\n\n1. Beste Richtung verwenden\n\nNutze primär Version C als Basis, weil sie visuell stärker wirkt.\n\nAber:\n\n* verwende nur saubere KI-Bilder ohne zusätzliche Kreise/Labels/Pfeile\n* falls C zu busy ist, nutze A als ruhigere Vergleichsversion\n* keine neuen AI-Bilder, falls die vorhandenen Keyframes ausreichen\n* falls ein Bild bereits eingebrannte Markierungen enthält, nicht verwenden oder durch cleanes Bild ersetzen\n\n2. Clean Cut erstellen\n\nErzeuge zuerst genau ein Clean-Cut-Video:\n\nCandidate:\nai-output-autopsy-decision-summary-38s\n\nVersion:\nclean_image_cut_C_v1\n\nInhalt:\n\n* bestehendes Voiceover\n* bestehende Subtitles, aber ohne Rahmen/Box\n* KI-Bilder als Szenenbasis\n* langsame Push-ins\n* leichte Pans\n* sanfte Crossfades\n* keine Wan-Animation\n* keine I2V\n* keine T2V\n* kein Full-Final\n\nZiel:\nWir beurteilen zuerst, ob die Bildsprache und Story als hochwertiges Image-Sequence-Video funktioniert, wenn keine störenden Overlays draufliegen.\n\n3. Optional A-Vergleich nur wenn günstig\n\nWenn es ohne grossen Aufwand möglich ist:\nErzeuge zusätzlich eine Clean-Cut-Version A.\n\nAber Priorität:\nC clean.\n\nKeine lange Render-Serie. Maximal C und optional A.\n\n4. Keine Overlays ausser Subtitles\n\nBitte baue eine harte NoOverlayPolicy für diesen Clean Cut:\n\nallowed_overlay_types:\n\n* subtitles only\n\nblocked_overlay_types:\n\n* arrows\n* labels\n* circles\n* boxes\n* stamps\n* debug text\n* diagram elements\n* fake UI\n* captions background rectangle\n* full-width subtitle bar\n* decision target markers\n* connector lines\n\nWenn eines dieser Elemente im RenderManifest auftaucht:\nblockieren.\n\n5. Subtitles neu stylen\n\nSubtitle-Stil:\n\n* kein Rahmen\n* keine Box\n* keine dunkle Fläche über die ganze Breite\n* weisser Text\n* aktives Wort gelb\n* leichter Schatten oder dezente Outline\n* phrase segment bleibt stabil\n* highlight wandert durch den Satzabschnitt\n* unten genügend Abstand zur Plattform-UI\n* nicht zu gross\n* nicht zu klein\n* hochwertiger Look\n\n6. Wan jetzt noch nicht für das ganze Video\n\nBitte jetzt noch keinen Wan-Full-Render und keinen I2V-Full-Render starten.\n\nBegründung:\nWir wissen noch nicht, ob Wan aus diesen Bildern einen ansprechenden Film macht. Erst muss der Clean Image Cut visuell funktionieren.\n\n7. Danach optional ein einziger Wan-I2V-Micro-Proof\n\nWenn der Clean Cut erzeugt ist und technisch sauber aussieht, darfst du optional zusätzlich genau einen kurzen Micro-Proof vorbereiten, aber noch nicht zwingend ausführen:\n\n* 3–5 Sekunden\n* eine einzige Szene\n* ein einziges sauberes Startbild\n* kein Overlay\n* kein Subtitle nötig\n* nur prüfen: kann Wan aus diesem Bild schöne, subtile Bewegung machen?\n\nFalls du ihn ausführst:\n\n* nur ein Micro-Proof\n* kein zweiter Versuch ohne Freigabe\n* kein Full-Final\n\n8.\nLieferung\n\nBitte sende:\n\n* Clean Cut C MP4\n* optional Clean Cut A MP4\n* Contact Sheet der verwendeten Bilder\n* Manifest mit Liste der verwendeten Bilddateien\n* Subtitle Style Preview / Beispiel-Frame\n\nFür jedes Artefakt:\n\n* file_path\n* file_exists\n* file_size\n* sha256\n* duration\n* resolution\n* telegram_delivery_verified\n* telegram_message_id\n\n9. Bewertung im Abschlussbericht\n\nBitte bewerte:\n\n* wirkt es ohne Overlays hochwertiger?\n* tragen die Bilder die Story genug?\n* sind die Subtitles angenehm?\n* ist C besser als A?\n* lohnt sich Wan-I2V überhaupt?\n* oder reicht Image Cut + subtile Motion für den ersten Test?\n\n10. Keine Freigabe für Posting\n\nNicht ausführen:\n\n* kein Posting\n* keine Plattform-API\n* kein Full-Final\n* keine Render-Serie\n* kein Wan-I2V für das ganze Video\n\nNächste Kommandos danach:\n\nWenn Clean Cut C passt:\n\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s C\n\nWenn A besser ist:\n\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s A\n\nWenn nur Subtitles geändert werden sollen:\n\nREVISE_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s subtitles \"Änderungswunsch\"\n\nWenn ein einzelner Wan-Micro-Proof erlaubt ist:\n\nAPPROVED_SINGLE_WAN_MICRO_PROOF ai-output-autopsy-decision-summary-38s \n\nWenn die Richtung nicht reicht:\n\nREJECT_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s \"Grund\"\n\nWichtigster Grundsatz:\nWir prüfen jetzt, ob ein hochwertiges, ruhiges Bildsequenz-Video mit sauberem Voiceover und guten Subtitles bereits überzeugend wirkt. Keine Erklärgrafik mehr über die Bilder legen."
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}
}
],
"tool_call_id": "call_Lnt53MuwqrOdTbP7tbiZrOrs"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Revising video crops**\n\nI need to ensure the video still has subtle lines and no overlay, but it hasn't been delivered yet. I plan to revise it before sending out. I should render the cleaned version with specific cropping instructions. For scenes, I will create cleaned sources manually and set specific crop settings to exclude certain elements. It's important to ensure the script fits without cropping out source markings, and we may need to adjust the subtitles for better visibility on mobile devices.\n**Updating script for video**\n\nI need to ensure that some parts may be less readable without a box, so I'll create a script variant, possibly \"clean_image_cut_C_v1,\" to make sure it's the actual clean version. Since it hasn't been delivered yet, should I overwrite and use the same paths? I'll apply a deterministic crop to hide some rings, ensuring images are 1080x1920 PNG files. I want to avoid any circular artifacts, especially in frame 6. I'll fix the script to create cleaned versions from the keyframes while defining the correct aspect ratio too.\n**Adjusting crop settings**\n\nThe cover fit at 1080x1920 will crop the width from 1280 to 1080 at a scale of 1.25, which only allows for removing 200px. This isn’t enough to hide the ring if the center is at x=850. I need to crop further to hide the right side, targeting a crop width of around 820 from 1024 and resizing accordingly. A scale for zooming in at 1.35 or 1.5 would help, with a focal point set around 0.38.",
"finish_reason": "tool_calls",
"reasoning_content": "**Revising video crops**\n\nI need to ensure the video still has subtle lines and no overlay, but it hasn't been delivered yet. I plan to revise it before sending out. I should render the cleaned version with specific cropping instructions. For scenes, I will create cleaned sources manually and set specific crop settings to exclude certain elements. It's important to ensure the script fits without cropping out source markings, and we may need to adjust the subtitles for better visibility on mobile devices.\n**Updating script for video**\n\nI need to ensure that some parts may be less readable without a box, so I'll create a script variant, possibly \"clean_image_cut_C_v1,\" to make sure it's the actual clean version. Since it hasn't been delivered yet, should I overwrite and use the same paths? I'll apply a deterministic crop to hide some rings, ensuring images are 1080x1920 PNG files. I want to avoid any circular artifacts, especially in frame 6. I'll fix the script to create cleaned versions from the keyframes while defining the correct aspect ratio too.\n**Adjusting crop settings**\n\nThe cover fit at 1080x1920 will crop the width from 1280 to 1080 at a scale of 1.25, which only allows for removing 200px. This isn’t enough to hide the ring if the center is at x=850. I need to crop further to hide the right side, targeting a crop width of around 820 from 1024 and resizing accordingly. A scale for zooming in at 1.35 or 1.5 would help, with a focal point set around 0.38.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": 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2rkwABUhnKrHcwR_HVsOooJ3IvlANwuOjJR4HXR7kDPFqewOm5mPEjMvXbT5-4dxOgWXxOeQzUOVD5qXNXpK94zfUXD_Vjexlz4UhoDQw2q99wLDUUM9sUPosoZNFJk3swI0wJA6kFW5trwpI1AKeCshwYaVklcmZoGAJdTsauk7LqNXVUiKj9n8Vp-v1qZGipwVuRQ1WPUDfeu7jIezzkv1lACuLbwVY7jnGUUganDV7JlfEfwdxqWYt5G90W4j5LEaPEWJ8uorFuFKbOkrjWwbjAN9cH2fgVboTj6PQxo1IKmEFs8d5LRuCqSvJKNBNs7zu5es0khuNWyn2STsjOHqTUzPO7D7KbsyIX-u2ysV37YOxr77FJhu80ZkSP-x6LCNiiCkGYeBrp6KmEea2vuVUxla02qzqBygxyGDmEyVO485w0JNqVucVlQzQxJWZgq3crTHKD_F3RxpKLS7FOct58vv_l03o18wgX7pY5XNOQQiHH6j05Cy6N6Hf3NO0n6gvyuAHvzHZGWxWygaTk9aJy3WbI5HewrWc3WI7VUjJnoo2sOi0kzKwM3kQdyeHBaMA75Xd6Gt0sCaEtWsx1q_JeBddx30jGdGxy-j-1a6YyMSprnVPPSh8FD7IVtLn0sJt1ax9n9DqgSO7yGnGnbmIYY9qnWuohtTc-ahHvwYXW0OIYi9yQ3j3vpO1RXmNSRBb8_uT_6Niz3NFonoSLkgDrn92edrUX8XE0orX6DOmSbOaldpt8ZU9esZVcrSiZFaEn4p_Rn2na-TCTBB9A7if-SEfXCPKOadxzv0MseibtCRLllGZUlofWXEL0QGyMynvfVYAqPj-p8bKGZBHFekmBAZuI850YJVd-AW8P90adkGq5nNCmNmF68RbgLhYGZKV-vgfvmmUNy5SI8fJsV-DmXFbid_nklp-Kp8a9P3TWc7OLAzgyR2uFpy1xHUFZrijjEx9LfMg94to9FcD3evdpWHfI5w_rqjldJUXidARI1NL1-xsoEoP5dYXZh1Dc78FKgAAtHcA0nleiTKVd_jAo23SfXcKcXYMS3ZZXlzBkr5aqJdMiP_QGUoI-NXQA_fFiEXRhupEPdbt0qLInk3UrSAjdEMdNvcxKLQ5UdJyvomuHYKK3wuNopEsKl_urKG6-Owtm8dJE1pzQ6sseqcwCcQ7nmO9hU1LwcQUS5vJ7GfOOUCJlWfBrSaT8BiUoPhX69ojiYvK_5aILmaU-U0XdU1buMdVTYMRZVRIqT-AT_WlwskcHDB-ykAXAmHzMPANsoIMNPnr_yEJpMpRwweO_ALy_HSoOPZUGuVDlkPObLgyZYiKZVmnXjv-Yy-q7b4ZnAo-XhIwv40wmDFcRROdmD23dssLR7imdp5D7ggDVCXKC48He3LufEylByA3yv1e1otcqhWDrFBrB7wvb-S8-SIIL6y1g1hwTf3waQQyHKq4uiPT4AlKgnrdSYXSIucgTmPu0nTeAWVXdst4gflzkQyCF5GLBEpBs90A_a1teQdGCbQYBceUM2NMbhCuQbfyo2WhKLl0_09jODJXXwI6ZhHsNwQwBJyXckKzRE8NrEVefQuHgK31OZ9hJKhyEW0JlAya2KTdkgeHFmgh6k4_6I-1Iu2riExxjDja2nf7yO2NaLGHm1WJYgC2NAGtaOCSQreLbipTZowtEBBw4MkoG1mPCcIPxL2DW__2X5LlxIhHh1oJs_3tWOp5lkNZBzfH81Yce_uTWlcdijx2uT43ve2EGAbz3FyiMvBJoqeD6X0uFkWuIMr0sVKk-TJVSljWF6DnuxS5-HEIVFA64rBknEOW7JfS8V6PZT8GK7FZDhAuBnNeta_8PGvOAMW32R0ZWEsXQySw-FpyJjgXLfJNqNq05Oea-dPcMj_B3OcXc12dURphOuDY3dAkr5yWPXxHCnOGuNUJbvykqe4yrMLuBXF9dHZRnZgR9ZGxSzRKZttcIVWYuTJi6v9QpSOxfXMHaFSTpFkyhvaMwIr43RU0NpUKLRsGP-xWLvJjAhNvgX5btcOH_qCQ5EodI_jKVO43zuEgrG-RRj3tqmfv7E2FJ8c8WmyBy3ktsmkvYXaraQ5FyHvO7H8wDrOK44sWESwulWFAtIEcvrF5_jrcfABdZLi7tVgBevqb2qtTSX3zknrKkNECXz7YHcL8efxELV-RpNKOfDVQI736nJP9aDxPEQ==",
"id": "rs_05cdea51f45b90d7016a1476539dfc8191a7533dca026f9b53",
"summary": [
{
"type": "summary_text",
"text": "**Revising video crops**\n\nI need to ensure the video still has subtle lines and no overlay, but it hasn't been delivered yet. I plan to revise it before sending out. I should render the cleaned version with specific cropping instructions. For scenes, I will create cleaned sources manually and set specific crop settings to exclude certain elements. It's important to ensure the script fits without cropping out source markings, and we may need to adjust the subtitles for better visibility on mobile devices."
},
{
"type": "summary_text",
"text": "**Updating script for video**\n\nI need to ensure that some parts may be less readable without a box, so I'll create a script variant, possibly \"clean_image_cut_C_v1,\" to make sure it's the actual clean version. Since it hasn't been delivered yet, should I overwrite and use the same paths? I'll apply a deterministic crop to hide some rings, ensuring images are 1080x1920 PNG files. I want to avoid any circular artifacts, especially in frame 6. I'll fix the script to create cleaned versions from the keyframes while defining the correct aspect ratio too."
},
{
"type": "summary_text",
"text": "**Adjusting crop settings**\n\nThe cover fit at 1080x1920 will crop the width from 1280 to 1080 at a scale of 1.25, which only allows for removing 200px. This isn’t enough to hide the ring if the center is at x=850. I need to crop further to hide the right side, targeting a crop width of around 820 from 1024 and resizing accordingly. A scale for zooming in at 1.35 or 1.5 would help, with a focal point set around 0.38."
}
]
}
],
"tool_calls": [
{
"id": "call_nlHyJ26KVTxhdT4FnRUWUn7Q",
"call_id": "call_nlHyJ26KVTxhdT4FnRUWUn7Q",
"response_item_id": "fc_05cdea51f45b90d7016a147670d45c81919db7d3e45baa6149",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"from pathlib import Path\\nfrom PIL import Image, ImageDraw, ImageFont\\nimport json, hashlib, subprocess, shutil, zipfile\\nROOT=Path('/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s')\\nSRC=Path('/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s')\\nPREV=Path('/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s')\\nW,H=1080,1920; FPS=24; DUR=38.0\\nAUDIO=PREV/'voiceover.mp3'; CAPS=json.loads((PREV/'caption_static_segments_v3.json').read_text())\\nSCENES=[('s01_fail',0,4.418),('s02_bad_output',4.418,14.431),('s03_mechanism',14.431,19.109),('s04_fix',19.109,30.397),('s05_takeaway',30.397,38.0)]\\nFONT=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',54)\\nFONT_SMALL=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',34)\\nfiles=['s01_fail_01.png','s02_bad_output_01.png','s03_mechanism_01_approved_styleframe.png','s03_mechanism_02_mid.png','s04_fix_01.png','s05_takeaway_01.png']\\n# Crop focus intentionally avoids top/right baked target rings from previous exploratory styleframes.\\nfocal_C={\\n 's01_fail_01.png':(.45,.52,.92),'s02_bad_output_01.png':(.36,.54,.78),'s03_mechanism_01_approved_styleframe.png':(.34,.52,.76),'s03_mechanism_02_mid.png':(.34,.52,.76),'s04_fix_01.png':(.36,.52,.78),'s05_takeaway_01.png':(.38,.52,.80)}\\nfocal_A={k:(.42,.52,.84) for k in files}\\ndef sha(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest()\\ndef crop_clean(src, fx, fy, rel_w):\\n im=Image.open(src).convert('RGB'); iw,ih=im.size\\n cw=int(iw*rel_w); ch=int(cw*16/9)\\n if ch>ih:\\n ch=int(ih*rel_w); cw=int(ch*9/16)\\n cx=int(iw*fx); cy=int(ih*fy)\\n left=max(0,min(iw-cw,cx-cw//2)); top=max(0,min(ih-ch,cy-ch//2))\\n return im.crop((left,top,left+cw,top+ch)).resize((W,H),Image.Resampling.LANCZOS)\\ndef scene_for_t(t):\\n for s,a,b in SCENES:\\n if a<=t970:\\n font=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',48)\\n widths=[d.textbbox((0,0),w,font=font,stroke_width=3)[2] for w in words]\\n total=sum(widths)+gap*(len(words)-1)\\n x=(W-total)//2; y=H-315; cx=x\\n for i,w in enumerate(words):\\n fill=(255,215,70,255) if i==active else (255,255,255,245)\\n d.text((cx+2,y+3),w,font=font,fill=(0,0,0,95))\\n d.text((cx,y),w,font=font,fill=fill,stroke_width=3,stroke_fill=(0,0,0,155))\\n cx+=widths[i]+gap\\n return img\\ndef ken(base,t,a,b):\\n p=max(0,min(1,(t-a)/(b-a))); z=1+0.025*p\\n cw,ch=int(W/z),int(H/z); left=(W-cw)//2; top=int((H-ch)//2 - 8*p)\\n return base.crop((left,top,left+cw,top+ch)).resize((W,H),Image.Resampling.LANCZOS)\\ndef probe(p):\\n return json.loads(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration,size:stream=codec_type,width,height,r_frame_rate','-of','json',str(p)]))\\ndef meta(p):\\n p=Path(p); m={'file_path':str(p),'file_exists':p.exists(),'file_size':p.stat().st_size,'sha256':sha(p),'media_type':'video/mp4' if p.suffix=='.mp4' else ('application/zip' if p.suffix=='.zip' else ('image/jpeg' if p.suffix.lower() in ['.jpg','.jpeg'] else 'image/png')),'telegram_delivery_attempted':False,'telegram_delivery_verified':False,'telegram_message_id':None}\\n if p.suffix=='.mp4':\\n pr=probe(p); m['duration']=float(pr['format']['duration'])\\n for st in pr['streams']:\\n if st.get('codec_type')=='video': m['resolution']=f\\\"{st.get('width')}x{st.get('height')}\\\"; m['fps']=st.get('r_frame_rate')\\n else:\\n m['duration']='n/a'\\n try:\\n im=Image.open(p); m['resolution']=f'{im.width}x{im.height}'\\n except Exception: m['resolution']='n/a'\\n return m\\ndef make_contact(v,imgs):\\n sheet=Image.new('RGB',(1080,2560),(18,22,32)); d=ImageDraw.Draw(sheet)\\n d.text((45,30),f'Clean Image Cut {v} — cleaned crops used',font=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',42),fill=(245,248,255))\\n for i,p in enumerate(imgs):\\n im=Image.open(p).resize((405,720)); x=70+(i%2)*525; y=120+(i//2)*790\\n sheet.paste(im,(x,y)); d.text((x,y+730),p.stem.replace('_clean',''),font=FONT_SMALL,fill=(230,238,255))\\n out=ROOT/f'version_{v}/clean_contact_sheet_{v}.png'; sheet.save(out,quality=95); return out\\ndef render(v):\\n srcd=SRC/f'version_{v}/keyframes'; outd=ROOT/f'version_{v}'; cleand=outd/'used_images_cleaned'; cleand.mkdir(parents=True,exist_ok=True)\\n foc=focal_C if v=='C' else focal_A\\n clean=[]\\n for f in files:\\n im=crop_clean(srcd/f,*foc[f]); p=cleand/f.replace('.png','_clean.png'); im.save(p); clean.append(p)\\n scene_imgs={'s01_fail':[Image.open(clean[0]).convert('RGB')],'s02_bad_output':[Image.open(clean[1]).convert('RGB')],'s03_mechanism':[Image.open(clean[2]).convert('RGB'),Image.open(clean[3]).convert('RGB')],'s04_fix':[Image.open(clean[4]).convert('RGB')],'s05_takeaway':[Image.open(clean[5]).convert('RGB')]}\\n silent=outd/f'clean_image_cut_{v}_v1_silent.mp4'; final=outd/f'clean_image_cut_{v}_v1.mp4'\\n proc=subprocess.Popen(['ffmpeg','-y','-f','rawvideo','-vcodec','rawvideo','-pix_fmt','rgb24','-s',f'{W}x{H}','-r',str(FPS),'-i','-','-an','-c:v','libx264','-preset','medium','-crf','18','-pix_fmt','yuv420p',str(silent)],stdin=subprocess.PIPE)\\n for fi in range(int(DUR*FPS)):\\n t=fi/FPS; s,a,b=scene_for_t(t); bases=scene_imgs[s]\\n if len(bases)>1:\\n alpha=max(0,min(1,((t-a)/(b-a)-.28)/.55)); frame=Image.blend(ken(bases[0],t,a,b),ken(bases[1],t,a,b),alpha)\\n else: frame=ken(bases[0],t,a,b)\\n if t>0 and t-a<.22: frame=Image.blend(Image.new('RGB',(W,H),(8,9,12)),frame,(t-a)/.22)\\n frame=draw_caption(frame.convert('RGBA'),t).convert('RGB'); proc.stdin.write(frame.tobytes())\\n proc.stdin.close(); proc.wait(); assert proc.returncode==0\\n subprocess.run(['ffmpeg','-y','-i',str(silent),'-i',str(AUDIO),'-map','0:v:0','-map','1:a:0','-c:v','copy','-c:a','aac','-b:a','192k','-shortest',str(final)],check=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n contact=make_contact(v,clean)\\n qdir=outd/'qa_frames'; qdir.mkdir(exist_ok=True)\\n times=[1,6,16,23,32]\\n for t in times: subprocess.run(['ffmpeg','-y','-ss',str(t),'-i',str(final),'-frames:v','1',str(qdir/f'frame_{t}s.jpg')],stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL,check=True)\\n sheet=Image.new('RGB',(1080,480),(18,22,32)); 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}
}
],
"tool_call_id": "call_gx8KOxhn8qLm1CO52LTBBFI4"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Assessing image choices**\n\nI’m considering using neutral images from s01, s02, or s03 for all scenes, and I think a voiceover alone could suffice for the storytelling. For a clean cut, I can pick images that portray a work scene to avoid the explicit graphics in s04/s05. The idea is to reuse approved project table images, which seem more natural. The focus will be on aesthetic appeal without any circles or rings, keeping things simple and effective.\n**Deciding on circular objects**\n\nI’m thinking we might still have a circular object, like a plant pot or a cup, that can be used as a real item instead of just an overlay. For creating version 1, I need to overwrite some elements. For C, using a source map seems helpful: maybe using s01 for s04, which has no lines, and s02 for s05, ensuring we crop out any rings. It might be nice to use s04 but crop it from the center-left to avoid those lines on the right.",
"finish_reason": "tool_calls",
"reasoning_content": "**Assessing image choices**\n\nI’m considering using neutral images from s01, s02, or s03 for all scenes, and I think a voiceover alone could suffice for the storytelling. For a clean cut, I can pick images that portray a work scene to avoid the explicit graphics in s04/s05. The idea is to reuse approved project table images, which seem more natural. The focus will be on aesthetic appeal without any circles or rings, keeping things simple and effective.\n**Deciding on circular objects**\n\nI’m thinking we might still have a circular object, like a plant pot or a cup, that can be used as a real item instead of just an overlay. For creating version 1, I need to overwrite some elements. For C, using a source map seems helpful: maybe using s01 for s04, which has no lines, and s02 for s05, ensuring we crop out any rings. It might be nice to use s04 but crop it from the center-left to avoid those lines on the right.",
"codex_reasoning_items": [
{
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"id": "rs_05cdea51f45b90d7016a147757898c8191b105196227264695",
"summary": [
{
"type": "summary_text",
"text": "**Assessing image choices**\n\nI’m considering using neutral images from s01, s02, or s03 for all scenes, and I think a voiceover alone could suffice for the storytelling. For a clean cut, I can pick images that portray a work scene to avoid the explicit graphics in s04/s05. The idea is to reuse approved project table images, which seem more natural. The focus will be on aesthetic appeal without any circles or rings, keeping things simple and effective."
},
{
"type": "summary_text",
"text": "**Deciding on circular objects**\n\nI’m thinking we might still have a circular object, like a plant pot or a cup, that can be used as a real item instead of just an overlay. For creating version 1, I need to overwrite some elements. For C, using a source map seems helpful: maybe using s01 for s04, which has no lines, and s02 for s05, ensuring we crop out any rings. It might be nice to use s04 but crop it from the center-left to avoid those lines on the right."
}
]
}
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"arguments": "{\"content\":\"# Quick strict-clean C rerender: uses only existing AI images, deterministic crops, no renderer overlays except subtitles.\\nfrom pathlib import Path\\nfrom PIL import Image, ImageDraw, ImageFont\\nimport json, hashlib, subprocess, zipfile\\nROOT=Path('/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s'); SRC=Path('/tmp/autoshorts_prerelease_animatic_sprint24/ai-output-autopsy-decision-summary-38s/version_C/keyframes'); PREV=Path('/tmp/autoshorts_preview_v2/ai-output-autopsy-decision-summary-38s')\\nW,H=1080,1920; FPS=24; DUR=38.0; AUDIO=PREV/'voiceover.mp3'; CAPS=json.loads((PREV/'caption_static_segments_v3.json').read_text())\\nSCENES=[('s01_fail',0,4.418),('s02_bad_output',4.418,14.431),('s03_mechanism',14.431,19.109),('s04_fix',19.109,30.397),('s05_takeaway',30.397,38.0)]\\nFONT=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',54); SMALL=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',34)\\n# selected sources avoid explicit fix diagrams/connector-line images; repeated images are acceptable for clean image cut review.\\nMAP={'s01_fail':'s01_fail_01.png','s02_bad_output':'s02_bad_output_01.png','s03_mechanism':'s03_mechanism_02_mid.png','s04_fix':'s01_fail_01.png','s05_takeaway':'s02_bad_output_01.png'}\\nFOCUS={'s01_fail':(.42,.52,.78),'s02_bad_output':(.18,.55,.58),'s03_mechanism':(.20,.52,.58),'s04_fix':(.48,.52,.70),'s05_takeaway':(.18,.55,.58)}\\ndef sha(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest()\\ndef crop(src,fx,fy,rel):\\n im=Image.open(src).convert('RGB'); iw,ih=im.size; cw=int(iw*rel); ch=int(cw*16/9)\\n if ch>ih: ch=int(ih*rel); cw=int(ch*9/16)\\n cx=int(iw*fx); cy=int(ih*fy); l=max(0,min(iw-cw,cx-cw//2)); t=max(0,min(ih-ch,cy-ch//2))\\n return im.crop((l,t,l+cw,t+ch)).resize((W,H),Image.Resampling.LANCZOS)\\ndef scene_for_t(t):\\n for s,a,b in SCENES:\\n if a<=t970:\\n font=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',48); widths=[d.textbbox((0,0),w,font=font,stroke_width=3)[2] for w in words]; total=sum(widths)+gap*(len(words)-1)\\n x=(W-total)//2; y=H-315; cx=x\\n for i,w in enumerate(words):\\n fill=(255,215,70,255) if i==active else (255,255,255,245)\\n d.text((cx+2,y+3),w,font=font,fill=(0,0,0,95)); d.text((cx,y),w,font=font,fill=fill,stroke_width=3,stroke_fill=(0,0,0,155)); cx+=widths[i]+gap\\n return img\\ndef kb(base,t,a,b):\\n p=max(0,min(1,(t-a)/(b-a))); z=1+0.022*p; cw,ch=int(W/z),int(H/z); return base.crop(((W-cw)//2,(H-ch)//2,(W+cw)//2,(H+ch)//2)).resize((W,H),Image.Resampling.LANCZOS)\\nout=ROOT/'version_C'; used=out/'used_images_cleaned'; used.mkdir(parents=True,exist_ok=True)\\nimgs={}; used_files=[]\\nfor s,f in MAP.items():\\n im=crop(SRC/f,*FOCUS[s]); p=used/f'{s}_strict_clean.png'; im.save(p); used_files.append(p); imgs[s]=im\\nsilent=out/'clean_image_cut_C_v1_silent.mp4'; final=out/'clean_image_cut_C_v1.mp4'\\nproc=subprocess.Popen(['ffmpeg','-y','-f','rawvideo','-vcodec','rawvideo','-pix_fmt','rgb24','-s',f'{W}x{H}','-r',str(FPS),'-i','-','-an','-c:v','libx264','-preset','medium','-crf','18','-pix_fmt','yuv420p',str(silent)],stdin=subprocess.PIPE)\\nfor fi in range(int(DUR*FPS)):\\n t=fi/FPS; s,a,b=scene_for_t(t); frame=kb(imgs[s],t,a,b)\\n if t>0 and t-a<.22: frame=Image.blend(Image.new('RGB',(W,H),(8,9,12)),frame,(t-a)/.22)\\n frame=drawcap(frame.convert('RGBA'),t).convert('RGB'); proc.stdin.write(frame.tobytes())\\nproc.stdin.close(); proc.wait(); assert proc.returncode==0\\nsubprocess.run(['ffmpeg','-y','-i',str(silent),'-i',str(AUDIO),'-map','0:v:0','-map','1:a:0','-c:v','copy','-c:a','aac','-b:a','192k','-shortest',str(final)],check=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n# contact\\nsheet=Image.new('RGB',(1080,2560),(18,22,32)); d=ImageDraw.Draw(sheet); d.text((45,30),'Clean Image Cut C — strict selected clean crops',font=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',40),fill=(245,248,255))\\nfor i,p in enumerate(used_files):\\n im=Image.open(p).resize((405,720)); x=70+(i%2)*525; y=120+(i//2)*790; sheet.paste(im,(x,y)); d.text((x,y+730),p.stem,font=SMALL,fill=(230,238,255))\\ncontact=out/'clean_contact_sheet_C.png'; sheet.save(contact,quality=95)\\n# preview frames\\nqdir=out/'qa_frames'; qdir.mkdir(exist_ok=True)\\ntimes=[1,6,16,23,32]\\nfor tt in times: subprocess.run(['ffmpeg','-y','-ss',str(tt),'-i',str(final),'-frames:v','1',str(qdir/f'frame_{tt}s.jpg')],stdout=subprocess.DEVNULL,stderr=subprocess.DEVNULL,check=True)\\nprev=out/'subtitle_style_preview_C.jpg'; prevsheet=Image.new('RGB',(1080,480),(18,22,32)); d=ImageDraw.Draw(prevsheet)\\nfor i,tt in enumerate(times):\\n im=Image.open(qdir/f'frame_{tt}s.jpg').resize((216,384)); x=i*216; prevsheet.paste(im,(x,60)); d.text((x+10,14),f'C {tt}s',font=SMALL,fill=(245,248,255))\\nprevsheet.save(prev,quality=92)\\nz=out/'clean_used_images_C.zip'\\nwith zipfile.ZipFile(z,'w',compression=zipfile.ZIP_DEFLATED) as zipf:\\n for p in used_files: zipf.write(p,f'used_images_cleaned/{p.name}')\\n zipf.write(contact,contact.name); zipf.write(prev,prev.name)\\ndef probe(p): return json.loads(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration,size:stream=codec_type,width,height,r_frame_rate','-of','json',str(p)]))\\ndef meta(p):\\n p=Path(p); m={'file_path':str(p),'file_exists':p.exists(),'file_size':p.stat().st_size,'sha256':sha(p),'media_type':'video/mp4' if p.suffix=='.mp4' else ('application/zip' if p.suffix=='.zip' else ('image/jpeg' if p.suffix.lower() in ['.jpg','.jpeg'] else 'image/png')),'telegram_delivery_attempted':False,'telegram_delivery_verified':False,'telegram_message_id':None}\\n if p.suffix=='.mp4':\\n pr=probe(p); m['duration']=float(pr['format']['duration'])\\n for st in pr['streams']:\\n if st.get('codec_type')=='video': m['resolution']=f\\\"{st.get('width')}x{st.get('height')}\\\"; m['fps']=st.get('r_frame_rate')\\n else:\\n m['duration']='n/a'\\n try: im=Image.open(p); m['resolution']=f'{im.width}x{im.height}'\\n except Exception: m['resolution']='n/a'\\n return m\\nmanifest=json.loads((ROOT/'clean_image_cut_manifest_AC.json').read_text())\\nmanifest['versions']['C']={'version_id':'clean_image_cut_C_v1','used_images':[meta(p) for p in used_files],'artifacts':{'mp4':meta(final),'silent_mp4':meta(silent),'contact_sheet':meta(contact),'subtitle_style_preview':meta(prev),'used_images_zip':meta(z)},'overlay_types':['subtitles_only'],'blocked_overlay_types_present':[],'subtitle_style':'no box, no bar, white text, yellow active word, subtle shadow/outline','cleaning_note':'strict deterministic crops/reused existing clean frames; 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}
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"tool_call_id": "call_XblnjSi0zHWEk7sijbRzggIh"
},
{
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"content": "",
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{
"id": "call_D9xlMQ3cbjbU2DNuzqf5KGj1",
"call_id": "call_D9xlMQ3cbjbU2DNuzqf5KGj1",
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},
{
"role": "tool",
"name": "process",
"content": "{\"status\": \"exited\", \"exit_code\": 0, \"output\": \"l,\\n \\\"duration\\\": 38.0,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"contact_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1745778,\\n \\\"sha256\\\": \\\"8a0dfebb4deadbe935dabb41670d44ed14bbc1b7f2f594aecaa33a4e38e969e8\\\",\\n \\\"media_type\\\": \\\"image/png\\\",\\n \\\"telegram_delivery_attempted\\\": false,\\n \\\"telegram_delivery_verified\\\": false,\\n \\\"telegram_message_id\\\": null,\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x2560\\\"\\n },\\n \\\"subtitle_style_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 91870,\\n \\\"sha256\\\": \\\"890bf7c4c5c916a51be39a181bbd3d9f6e1a6d8b862f9499c0fcdbb290a35104\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"telegram_delivery_attempted\\\": false,\\n \\\"telegram_delivery_verified\\\": false,\\n \\\"telegram_message_id\\\": null,\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x480\\\"\\n },\\n \\\"used_images_zip\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 10834800,\\n \\\"sha256\\\": \\\"54536f53e9600e8e685cce66209ac234c240eda8f6fe9a6c93ce821897629799\\\",\\n \\\"media_type\\\": \\\"application/zip\\\",\\n \\\"telegram_delivery_attempted\\\": false,\\n \\\"telegram_delivery_verified\\\": false,\\n \\\"telegram_message_id\\\": null,\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n }\\n },\\n \\\"overlay_types\\\": [\\n \\\"subtitles_only\\\"\\n ],\\n \\\"blocked_overlay_types_present\\\": [],\\n \\\"subtitle_style\\\": \\\"no box, no bar, white text, yellow active word, subtle shadow/outline\\\",\\n \\\"cleaning_note\\\": \\\"strict deterministic crops/reused existing clean frames; avoided obvious baked target-ring/connector/key visual markings; no new AI images\\\"\\n}\\n\"}",
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},
{
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"tool_calls": [
{
"id": "call_tLXm1Q8Z7qogw5BekgorcEPi",
"call_id": "call_tLXm1Q8Z7qogw5BekgorcEPi",
"response_item_id": "fc_05cdea51f45b90d7016a1477f00c548191b5a7135507812f80",
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"arguments": "{\"image_url\":\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\",\"question\":\"Final QA for strict clean C: actual thumbnails only (ignore top timing labels in the QA sheet). Are there any added overlay circles/arrows/labels/boxes/diagram elements/caption boxes? Are subtitles only white/yellow with subtle shadow/outline? Are obvious target rings gone?\"}"
}
}
]
},
{
"role": "tool",
"name": "vision_analyze",
"content": [
{
"type": "text",
"text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Final QA for strict clean C: actual thumbnails only (ignore top timing labels in the QA sheet). Are there any added overlay circles/arrows/labels/boxes/diagram elements/caption boxes? Are subtitles only white/yellow with subtle shadow/outline? Are obvious target rings gone?"
},
{
"type": "image_url",
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}
}
],
"tool_call_id": "call_tLXm1Q8Z7qogw5BekgorcEPi"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
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{
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"encrypted_content": "gAAAAABqFHf3jhOMLS4Y2OcKDIMYiJqOq23n6ns-k8e8zbaxLpV5TbP65Cv7jRYMj2V2oWiYO7d4UkqsOJhjKK691WYuFApOROjkVV44oBqPOUbJ95N_GqCIJGaKP6OvAPNhO7L-G5tSSk4KYiXDPw-Y8A0-5q5Db_ebGGIP8DAe_iPYiRkVsrkvqFf7J8-gFyYjs3cfztIf9t-SdJTN9_LWKNw7a0CsryFHSKwZKjeEYtST9qBpZs2I4fMA47PUCxlODxaYRnZjeZIYQY3G3V0m2Bs-04XjKFa6Gx6cJJn0owZW7jTYXxqjOx2v8paA4MpcmSt97mrl6q09FssodnUjOP0Gxf60fsQ-5pgrfqxNjtOSyCLKncx-ciZn4m1l13ptOy85oLI17k53Ar33grTS7NpKwTK2HedwKxVeStTAu2OHqX39Iz3XDfKrmWoIDQDM5qMk6MFO1WvFcHlM5bSrNw5f0XArjHOJ53p_4Qq6zJIQyFBd6nwAe5cJbQ1v3XgS0nQ2AqmmJdwwo4ot3FxLn3T4f-f4g8rLihg0MR5On2Me3Z6v2Sa4Fzv-406JchgGhn3fI6cKT4SmMo2-1QwDSeclJkcNd36gfZ_CawWJf_Nd68XsEPrMLnd5PlKgyO-cf3xr2bN6ZxbDXUmx_yVdINmuan21I9ePJFzvx-ynBwVlbML-B1Sq6zMub2jxOEV5q7MARQ8j0yD_SiduJWvX0fqJTz-x5WKupHHuHVT25xvM1W_n_FKvd_ZgaVL641Ib0n0dcJ8LbHHJFf13jRtF_wF73GcOfGesS_yPwzf7aU8la249gL34kF7A86ReTo2yg767-ZKRRDdZ4gG4pmnAYPoijz2rS_8_0G7QeHu3gYk2ZgccItU5hMeoXcyQGpuWrgyNDzmq_deZIxhqBRVOF5VE8Yy0gbwkD_M2luC4w5_aQeUZYh_lN3DUFhQkq9h7mO2qTAIyLHiRmDRnvQDzWXvhSZa3kMCpNZtR5mw7wEwKBQTNn7mSq4aroVTdueTwzDj4IJOwZcp7bXS2IVhHZVzRBgiTGgvFalg5v7QaBCx_8nZYGoahjumw5k_RtyLuGurwSV_BhNEQx3TJLgaxiSk05B5ucKBmXVRMbvGZKt6-YHloRHwPQfjuLSf5bjVYpknAURtF2n53eIKeuE4DKOIdypMt15sKYJ7Cm5smxWPkFVxNFU30x3ub0kZgydix_JrJabXh6vaASIgU3WU1pY-i29YGcewZn0YXjwVBIpAOlUm4RP6dGMSH901RFspvU8LRz9FbpnZP6yubLQJ3DzKlWTpOr7T5LWrVJPWLr1JTH5EyMhamKcLPbeOT_Pq_M4x_8EvjATc_8YKI-Mt5HDDdKEfZqDeKAusOKePyHT7-kNuSj8Wi3xQW_Jdu_AW7j1EA7EwKetUks1vA586hcyk9he7VNg==",
"id": "rs_05cdea51f45b90d7016a1477f56310819196c0c7caa4bb6eb7",
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}
],
"tool_calls": [
{
"id": "call_Fybr8qyydDFYz65Kag7YGyhA",
"call_id": "call_Fybr8qyydDFYz65Kag7YGyhA",
"response_item_id": "fc_05cdea51f45b90d7016a1477f78fa48191a5e781f4e7086929",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\",\"question\":\"QA optional clean A preview frames: actual thumbnails only. Are there any added overlay circles/arrows/labels/boxes/diagram elements/caption boxes? Are subtitles only white/yellow with subtle shadow/outline? Any obvious baked target-ring/circle issue that should prevent delivery?\"}"
}
}
]
},
{
"role": "tool",
"name": "vision_analyze",
"content": [
{
"type": "text",
"text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA optional clean A preview frames: actual thumbnails only. Are there any added overlay circles/arrows/labels/boxes/diagram elements/caption boxes? Are subtitles only white/yellow with subtle shadow/outline? Any obvious baked target-ring/circle issue that should prevent delivery?"
},
{
"type": "image_url",
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}
}
],
"tool_call_id": "call_Fybr8qyydDFYz65Kag7YGyhA"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Revising rendering choices**\n\nI’m considering whether A's baked rings or connectors should be delivered. It seems I could rerender strict A by selecting neutral frames and cropping appropriately. For example, I can use s01/s02 without the rings, and perhaps s03 with the left/center crop. Since the user’s priority is C, I might only offer A if it’s budget-friendly. Let’s also consider rendering A strictly for comparison, avoiding any rings in the process. I think I'm ready to proceed!",
"finish_reason": "tool_calls",
"reasoning_content": "**Revising rendering choices**\n\nI’m considering whether A's baked rings or connectors should be delivered. It seems I could rerender strict A by selecting neutral frames and cropping appropriately. For example, I can use s01/s02 without the rings, and perhaps s03 with the left/center crop. Since the user’s priority is C, I might only offer A if it’s budget-friendly. Let’s also consider rendering A strictly for comparison, avoiding any rings in the process. I think I'm ready to proceed!",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_05cdea51f45b90d7016a147802e9a08191b446f74d28389d3d",
"summary": [
{
"type": "summary_text",
"text": "**Revising rendering choices**\n\nI’m considering whether A's baked rings or connectors should be delivered. It seems I could rerender strict A by selecting neutral frames and cropping appropriately. For example, I can use s01/s02 without the rings, and perhaps s03 with the left/center crop. Since the user’s priority is C, I might only offer A if it’s budget-friendly. Let’s also consider rendering A strictly for comparison, avoiding any rings in the process. I think I'm ready to proceed!"
}
]
}
],
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{
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"call_id": "call_BV6XMl0v8NJGuBkZg08oBiAU",
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"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/tmp/rerender_strict_clean_c.py')\\ns=p.read_text()\\ns=s.replace(\\\"version_C/keyframes\\\", \\\"version_A/keyframes\\\")\\ns=s.replace(\\\"MAP={'s01_fail':'s01_fail_01.png','s02_bad_output':'s02_bad_output_01.png','s03_mechanism':'s03_mechanism_02_mid.png','s04_fix':'s01_fail_01.png','s05_takeaway':'s02_bad_output_01.png'}\\\", \\\"MAP={'s01_fail':'s01_fail_01.png','s02_bad_output':'s02_bad_output_01.png','s03_mechanism':'s03_mechanism_02_mid.png','s04_fix':'s01_fail_01.png','s05_takeaway':'s02_bad_output_01.png'}\\\")\\ns=s.replace(\\\"FOCUS={'s01_fail':(.42,.52,.78),'s02_bad_output':(.18,.55,.58),'s03_mechanism':(.20,.52,.58),'s04_fix':(.48,.52,.70),'s05_takeaway':(.18,.55,.58)}\\\", \\\"FOCUS={'s01_fail':(.42,.52,.78),'s02_bad_output':(.18,.55,.58),'s03_mechanism':(.22,.52,.60),'s04_fix':(.42,.52,.78),'s05_takeaway':(.18,.55,.58)}\\\")\\ns=s.replace(\\\"out=ROOT/'version_C'\\\", \\\"out=ROOT/'version_A'\\\")\\ns=s.replace(\\\"f'C {tt}s'\\\", \\\"f'A {tt}s'\\\")\\ns=s.replace(\\\"Clean Image Cut C — strict selected clean crops\\\", \\\"Clean Image Cut A — strict selected clean crops\\\")\\ns=s.replace(\\\"manifest['versions']['C']\\\", \\\"manifest['versions']['A']\\\")\\ns=s.replace(\\\"'version_id':'clean_image_cut_C_v1'\\\", \\\"'version_id':'clean_image_cut_A_v1'\\\")\\ns=s.replace(\\\"clean_image_cut_C_v1\\\", \\\"clean_image_cut_A_v1\\\")\\ns=s.replace(\\\"subtitle_style_preview_C\\\", \\\"subtitle_style_preview_A\\\")\\ns=s.replace(\\\"clean_contact_sheet_C\\\", \\\"clean_contact_sheet_A\\\")\\ns=s.replace(\\\"clean_used_images_C\\\", \\\"clean_used_images_A\\\")\\nPath('/tmp/rerender_strict_clean_a.py').write_text(s)\\nprint('written')\\nPY\\npython /tmp/rerender_strict_clean_a.py\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"written\\nffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, rawvideo, from 'fd:':\\n Duration: N/A, start: 0.000000, bitrate: 1194393 kb/s\\n Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 1080x1920, 1194393 kb/s, 24 tbr, 24 tbn\\nStream mapping:\\n Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))\\n[libx264 @ 0x566483d14440] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\\n[libx264 @ 0x566483d14440] profile High, level 4.0, 4:2:0, 8-bit\\n[libx264 @ 0x566483d14440] 264 - core 164 r3108 31e19f9 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=9 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=24 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=18.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\\nOutput #0, mp4, to '/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1_silent.mp4':\\n Metadata:\\n encoder : Lavf60.16.100\\n Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, progressive), 1080x1920, q=2-31, 24 fps, 12288 tbn\\n Metadata:\\n encoder : Lavc60.31.102 libx264\\n Side data:\\n cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\\nframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 3 fps=0.8 q=23.0 size= 0kB time=00:00:00.04 bitrate= 9.2kbits/s speed=0.0112x \\rframe= 11 fps=2.6 q=23.0 size= 256kB time=00:00:00.37 bitrate=5593.4kbits/s speed=0.0879x \\rframe= 19 fps=3.9 q=23.0 size= 256kB time=00:00:00.70 bitrate=2961.2kbits/s speed=0.147x \\rframe= 27 fps=5.0 q=23.0 size= 256kB time=00:00:01.04 bitrate=2013.6kbits/s speed=0.194x \\rframe= 34 fps=5.8 q=23.0 size= 256kB time=00:00:01.33 bitrate=1573.2kbits/s speed=0.227x \\rframe= 42 fps=6.6 q=23.0 size= 512kB time=00:00:01.66 bitrate=2516.8kbits/s speed=0.261x \\rframe= 49 fps=7.1 q=23.0 size= 512kB time=00:00:01.95 bitrate=2142.0kbits/s speed=0.283x \\rframe= 56 fps=7.5 q=23.0 size= 512kB time=00:00:02.25 bitrate=1864.3kbits/s speed=0.302x \\rframe= 63 fps=7.9 q=23.0 size= 512kB time=00:00:02.54 bitrate=1650.4kbits/s speed=0.318x \\rframe= 70 fps=8.2 q=23.0 size= 512kB time=00:00:02.83 bitrate=1480.5kbits/s speed=0.332x \\rframe= 77 fps=8.5 q=23.0 size= 768kB time=00:00:03.12 bitrate=2013.4kbits/s speed=0.345x \\rframe= 84 fps=8.8 q=23.0 size= 768kB time=00:00:03.41 bitrate=1841.5kbits/s speed=0.356x \\rframe= 91 fps=9.0 q=23.0 size= 768kB time=00:00:03.70 bitrate=1696.7kbits/s speed=0.366x \\rframe= 98 fps=9.2 q=23.0 size= 768kB time=00:00:04.00 bitrate=1573.0kbits/s speed=0.375x \\rframe= 105 fps=9.3 q=23.0 size= 768kB time=00:00:04.29 bitrate=1466.1kbits/s speed=0.382x \\rframe= 111 fps=9.4 q=23.0 size= 1024kB time=00:00:04.54 bitrate=1847.1kbits/s speed=0.386x \\rframe= 118 fps=9.6 q=23.0 size= 1024kB time=00:00:04.83 bitrate=1735.7kbits/s speed=0.392x \\rframe= 125 fps=9.7 q=23.0 size= 1280kB time=00:00:05.12 bitrate=2046.1kbits/s speed=0.398x \\rframe= 132 fps=9.8 q=23.0 size= 1280kB time=00:00:05.41 bitrate=1935.9kbits/s speed=0.403x \\rframe= 139 fps=9.9 q=23.0 size= 1280kB time=00:00:05.70 bitrate=1837.0kbits/s speed=0.408x \\rframe= 146 fps= 10 q=23.0 size= 1280kB time=00:00:06.00 bitrate=1747.7kbits/s speed=0.413x \\rframe= 153 fps= 10 q=23.0 size= 1280kB time=00:00:06.29 bitrate=1666.7kbits/s speed=0.418x \\rframe= 161 fps= 10 q=23.0 size= 1280kB time=00:00:06.62 bitrate=1582.8kbits/s speed=0.425x \\rframe= 168 fps= 10 q=23.0 size= 1280kB time=00:00:06.91 bitrate=1516.1kbits/s speed=0.43x \\rframe= 176 fps= 11 q=23.0 size= 1536kB time=00:00:07.20 bitrate=1745.7kbits/s speed=0.433x \\rframe= 183 fps= 11 q=23.0 size= 1536kB time=00:00:07.54 bitrate=1668.5kbits/s speed=0.439x \\rframe= 190 fps= 11 q=23.0 size= 1536kB time=00:00:07.83 bitrate=1606.4kbits/s speed=0.443x \\rframe= 197 fps= 11 q=23.0 size= 1536kB time=00:00:08.12 bitrate=1548.7kbits/s speed=0.446x \\rframe= 204 fps= 11 q=23.0 size= 1536kB time=00:00:08.41 bitrate=1495.0kbits/s speed=0.449x \\rframe= 211 fps= 11 q=23.0 size= 1536kB time=00:00:08.70 bitrate=1445.0kbits/s speed=0.452x \\rframe= 219 fps= 11 q=23.0 size= 1536kB time=00:00:09.04 bitrate=1391.7kbits/s speed=0.456x \\rframe= 226 fps= 11 q=23.0 size= 1792kB time=00:00:09.33 bitrate=1572.9kbits/s speed=0.458x \\rframe= 233 fps= 11 q=23.0 size= 1792kB time=00:00:09.62 bitrate=1525.2kbits/s speed=0.461x \\rframe= 240 fps= 11 q=23.0 size= 1792kB time=00:00:09.91 bitrate=1480.4kbits/s speed=0.463x \\rframe= 247 fps= 11 q=23.0 size= 1792kB time=00:00:10.20 bitrate=1438.1kbits/s speed=0.465x \\rframe= 255 fps= 11 q=23.0 size= 1792kB time=00:00:10.54 bitrate=1392.6kbits/s speed=0.469x \\rframe= 262 fps= 11 q=23.0 size= 1792kB time=00:00:10.83 bitrate=1355.1kbits/s speed=0.471x \\rframe= 271 fps= 12 q=23.0 size= 2048kB time=00:00:11.16 bitrate=1502.5kbits/s speed=0.474x \\rframe= 278 fps= 12 q=23.0 size= 2048kB time=00:00:11.50 bitrate=1458.9kbits/s speed=0.478x \\rframe= 286 fps= 12 q=23.0 size= 2048kB time=00:00:11.83 bitrate=1417.8kbits/s speed=0.481x \\rframe= 294 fps= 12 q=23.0 size= 2048kB time=00:00:12.16 bitrate=1379.0kbits/s speed=0.484x \\rframe= 300 fps= 12 q=23.0 size= 2048kB time=00:00:12.41 bitrate=1351.2kbits/s speed=0.484x \\rframe= 307 fps= 12 q=23.0 size= 2048kB time=00:00:12.70 bitrate=1320.2kbits/s speed=0.485x \\rframe= 313 fps= 12 q=23.0 size= 2048kB time=00:00:12.95 bitrate=1294.7kbits/s speed=0.485x \\rframe= 321 fps= 12 q=23.0 size= 2048kB time=00:00:13.25 bitrate=1266.2kbits/s speed=0.486x \\rframe= 326 fps= 12 q=23.0 size= 2304kB time=00:00:13.50 bitrate=1398.1kbits/s speed=0.486x \\rframe= 332 fps= 12 q=23.0 size= 2304kB time=00:00:13.75 bitrate=1372.7kbits/s speed=0.486x \\rframe= 340 fps= 12 q=20.0 size= 2304kB time=00:00:14.04 bitrate=1344.2kbits/s speed=0.487x \\rframe= 346 fps= 12 q=23.0 size= 2304kB time=00:00:14.33 bitrate=1316.8kbits/s speed=0.488x \\rframe= 353 fps= 12 q=23.0 size= 2560kB time=00:00:14.62 bitrate=1434.0kbits/s speed=0.489x \\rframe= 360 fps= 12 q=23.0 size= 2560kB time=00:00:14.91 bitrate=1405.9kbits/s speed=0.489x \\rframe= 367 fps= 12 q=23.0 size= 2560kB time=00:00:15.20 bitrate=1379.0kbits/s speed=0.49x \\rframe= 375 fps= 12 q=23.0 size= 2816kB time=00:00:15.54 bitrate=1484.3kbits/s speed=0.492x \\rframe= 383 fps= 12 q=23.0 size= 2816kB time=00:00:15.87 bitrate=1453.2kbits/s speed=0.494x \\rframe= 391 fps= 12 q=23.0 size= 2816kB time=00:00:16.20 bitrate=1423.3kbits/s speed=0.496x \\rframe= 399 fps= 12 q=23.0 size= 2816kB time=00:00:16.54 bitrate=1394.6kbits/s speed=0.498x \\rframe= 407 fps= 12 q=23.0 size= 3072kB time=00:00:16.87 bitrate=1491.3kbits/s speed=0.499x \\rframe= 414 fps= 12 q=23.0 size= 3072kB time=00:00:17.16 bitrate=1466.0kbits/s speed=0.499x \\rframe= 421 fps= 12 q=23.0 size= 3072kB time=00:00:17.45 bitrate=1441.5kbits/s speed= 0.5x \\rframe= 428 fps= 12 q=23.0 size= 3072kB time=00:00:17.75 bitrate=1417.8kbits/s speed=0.501x \\rframe= 436 fps= 12 q=23.0 size= 3328kB time=00:00:18.04 bitrate=1511.1kbits/s speed=0.501x \\rframe= 442 fps= 12 q=23.0 size= 3328kB time=00:00:18.33 bitrate=1487.1kbits/s speed=0.502x \\rframe= 450 fps= 12 q=23.0 size= 3328kB time=00:00:18.62 bitrate=1463.8kbits/s speed=0.502x \\rframe= 456 fps= 12 q=23.0 size= 3328kB time=00:00:18.91 bitrate=1441.2kbits/s speed=0.503x \\rframe= 464 fps= 12 q=23.0 size= 3584kB time=00:00:19.20 bitrate=1528.5kbits/s speed=0.504x \\rframe= 470 fps= 12 q=23.0 size= 3584kB time=00:00:19.50 bitrate=1505.7kbits/s speed=0.504x \\rframe= 477 fps= 12 q=23.0 size= 3584kB time=00:00:19.79 bitrate=1483.5kbits/s speed=0.505x \\rframe= 485 fps= 12 q=23.0 size= 3584kB time=00:00:20.12 bitrate=1458.9kbits/s speed=0.506x \\rframe= 492 fps= 12 q=23.0 size= 3840kB time=00:00:20.41 bitrate=1540.8kbits/s speed=0.507x \\rframe= 499 fps= 12 q=23.0 size= 3840kB time=00:00:20.70 bitrate=1519.1kbits/s speed=0.508x \\rframe= 507 fps= 12 q=23.0 size= 3840kB time=00:00:21.00 bitrate=1498.0kbits/s speed=0.508x \\rframe= 514 fps= 12 q=23.0 size= 3840kB time=00:00:21.29 bitrate=1477.5kbits/s speed=0.509x \\rframe= 520 fps= 12 q=23.0 size= 3840kB time=00:00:21.58 bitrate=1457.5kbits/s speed=0.509x \\rframe= 527 fps= 12 q=23.0 size= 3840kB time=00:00:21.87 bitrate=1438.1kbits/s speed=0.51x \\rframe= 535 fps= 12 q=23.0 size= 4096kB time=00:00:22.20 bitrate=1510.9kbits/s speed=0.511x \\rframe= 544 fps= 12 q=23.0 size= 4096kB time=00:00:22.54 bitrate=1488.6kbits/s speed=0.512x \\rframe= 550 fps= 12 q=23.0 size= 4096kB time=00:00:22.83 bitrate=1469.6kbits/s speed=0.513x \\rframe= 557 fps= 12 q=23.0 size= 4096kB time=00:00:23.12 bitrate=1451.0kbits/s speed=0.513x \\rframe= 564 fps= 12 q=23.0 size= 4096kB time=00:00:23.41 bitrate=1432.9kbits/s speed=0.514x \\rframe= 571 fps= 12 q=23.0 size= 4096kB time=00:00:23.70 bitrate=1415.3kbits/s speed=0.514x \\rframe= 579 fps= 12 q=23.0 size= 4352kB time=00:00:24.00 bitrate=1485.5kbits/s speed=0.514x \\rframe= 586 fps= 12 q=23.0 size= 4352kB time=00:00:24.33 bitrate=1465.1kbits/s speed=0.515x \\rframe= 594 fps= 12 q=23.0 size= 4352kB time=00:00:24.66 bitrate=1445.4kbits/s speed=0.516x \\rframe= 601 fps= 12 q=23.0 size= 4352kB time=00:00:24.95 bitrate=1428.5kbits/s speed=0.517x \\rframe= 609 fps= 12 q=23.0 size= 4352kB time=00:00:25.29 bitrate=1409.6kbits/s speed=0.518x \\rframe= 617 fps= 12 q=23.0 size= 4352kB time=00:00:25.62 bitrate=1391.3kbits/s speed=0.519x \\rframe= 625 fps= 13 q=23.0 size= 4608kB time=00:00:25.95 bitrate=1454.2kbits/s speed=0.52x \\rframe= 634 fps= 13 q=23.0 size= 4608kB time=00:00:26.29 bitrate=1435.8kbits/s speed=0.521x \\rframe= 641 fps= 13 q=23.0 size= 4608kB time=00:00:26.62 bitrate=1417.8kbits/s speed=0.522x \\rframe= 649 fps= 13 q=23.0 size= 4608kB time=00:00:26.95 bitrate=1400.3kbits/s speed=0.522x \\rframe= 657 fps= 13 q=23.0 size= 4608kB time=00:00:27.25 bitrate=1385.3kbits/s speed=0.523x \\rframe= 663 fps= 13 q=23.0 size= 4608kB time=00:00:27.54 bitrate=1370.6kbits/s speed=0.523x \\rframe= 670 fps= 13 q=23.0 size= 4864kB time=00:00:27.83 bitrate=1431.6kbits/s speed=0.524x \\rframe= 678 fps= 13 q=23.0 size= 4864kB time=00:00:28.16 bitrate=1414.7kbits/s speed=0.524x \\rframe= 685 fps= 13 q=23.0 size= 4864kB time=00:00:28.45 bitrate=1400.2kbits/s speed=0.524x \\rframe= 693 fps= 13 q=23.0 size= 4864kB time=00:00:28.79 bitrate=1384.0kbits/s speed=0.525x \\rframe= 701 fps= 13 q=20.0 size= 4864kB time=00:00:29.12 bitrate=1368.1kbits/s speed=0.526x \\rframe= 709 fps= 13 q=23.0 size= 4864kB time=00:00:29.45 bitrate=1352.6kbits/s speed=0.527x \\rframe= 717 fps= 13 q=23.0 size= 5120kB time=00:00:29.79 bitrate=1407.9kbits/s speed=0.528x \\rframe= 726 fps= 13 q=23.0 size= 5120kB time=00:00:30.12 bitrate=1392.3kbits/s speed=0.53x \\rframe= 733 fps= 13 q=23.0 size= 5376kB time=00:00:30.45 bitrate=1445.9kbits/s speed=0.53x \\rframe= 742 fps= 13 q=23.0 size= 5376kB time=00:00:30.79 bitrate=1430.3kbits/s speed=0.531x \\rframe= 749 fps= 13 q=23.0 size= 5632kB time=00:00:31.12 bitrate=1482.3kbits/s speed=0.532x \\rframe= 757 fps= 13 q=23.0 size= 5632kB time=00:00:31.45 bitrate=1466.6kbits/s speed=0.533x \\rframe= 766 fps= 13 q=23.0 size= 5632kB time=00:00:31.79 bitrate=1451.3kbits/s speed=0.534x \\rframe= 773 fps= 13 q=23.0 size= 5632kB time=00:00:32.12 bitrate=1436.2kbits/s speed=0.535x \\rframe= 781 fps= 13 q=23.0 size= 5632kB time=00:00:32.45 bitrate=1421.4kbits/s speed=0.535x \\rframe= 789 fps= 13 q=23.0 size= 5888kB time=00:00:32.79 bitrate=1470.9kbits/s speed=0.536x \\rframe= 797 fps= 13 q=23.0 size= 5888kB time=00:00:33.12 bitrate=1456.1kbits/s speed=0.537x \\rframe= 805 fps= 13 q=23.0 size= 5888kB time=00:00:33.45 bitrate=1441.6kbits/s speed=0.538x \\rframe= 813 fps= 13 q=23.0 size= 5888kB time=00:00:33.79 bitrate=1427.4kbits/s speed=0.539x \\rframe= 823 fps= 13 q=23.0 size= 5888kB time=00:00:34.16 bitrate=1411.8kbits/s speed=0.54x \\rframe= 831 fps= 13 q=23.0 size= 5888kB time=00:00:34.54 bitrate=1396.4kbits/s speed=0.541x \\rframe= 840 fps= 13 q=23.0 size= 5888kB time=00:00:34.91 bitrate=1381.4kbits/s speed=0.542x \\rframe= 848 fps= 13 q=23.0 size= 6144kB time=00:00:35.25 bitrate=1427.9kbits/s speed=0.543x \\rframe= 856 fps= 13 q=23.0 size= 6144kB time=00:00:35.58 bitrate=1414.5kbits/s speed=0.544x \\r[out#0/mp4 @ 0x566483d12d40] video:6496kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.178424%\\nframe= 912 fps= 14 q=-1.0 Lsize= 6508kB 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{
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"call_id": "call_2qgpwPbGjfzOMUOKYr7uIxFN",
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}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"policy-select\", \"content\": \"Sprint-2.5 NoOverlayPolicy/Manifest vorbereiten und vorhandene C/A-Keyframes auswählen\", \"status\": \"completed\"}, {\"id\": \"render-c\", \"content\": \"Clean-Cut C rendern: KI-Bilder + Voiceover + rahmenlose Active-Word-Subtitles\", \"status\": \"completed\"}, {\"id\": \"render-a\", \"content\": \"Optional Clean-Cut A rendern, falls ohne Zusatzaufwand möglich\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Contact Sheets, Subtitle-Preview-Frames, Metadaten/Hashes/ffprobe verifizieren\", \"status\": \"completed\"}, {\"id\": \"deliver-report\", \"content\": \"Medien per Telegram liefern und Abschlussbewertung berichten\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}",
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},
{
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"arguments": "{\"action\":\"send\",\"target\":\"telegram:JARVIS Command Center / topic 4850\",\"message\":\"Sprint 2.5 — Clean Image Cut, No Overlay Pilot — Version C\\n\\nClean Cut C MP4:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\\n\\nContact Sheet C:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\\n\\nSubtitle Style Preview C:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\\n\\nUsed Images ZIP C:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\"}"
}
}
]
},
{
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"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"5914\", \"warnings\": [\"Failed to send media /tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4: Timed out\"], \"mirrored\": true}",
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"arguments": "{\"action\":\"send\",\"message\":\"Sprint 2.5 — Optional Clean Image Cut A\\n\\nClean Cut A MP4:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\\n\\nContact Sheet A:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\\n\\nSubtitle Style Preview A:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\\n\\nUsed Images ZIP A:\\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\",\"target\":\"telegram:JARVIS Command Center / topic 4850\"}"
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},
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"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"5921\", \"mirrored\": true}",
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{
"id": "call_ojBro56cAxMuqEb8UzIE2Qov",
"call_id": "call_ojBro56cAxMuqEb8UzIE2Qov",
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"arguments": "{\"code\":\"from pathlib import Path\\nimport json\\nROOT=Path('/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s')\\nman=json.loads((ROOT/'clean_image_cut_manifest_AC.json').read_text())\\n# C: first batch had MP4 timeout warning; MP4 retried successfully as 5916. Other C artifacts in 5914.\\nfor k,item in man['versions']['C']['artifacts'].items():\\n item['telegram_delivery_attempted']=True\\n item['telegram_delivery_verified']=True\\n item['telegram_message_id']='5916' if k=='mp4' else '5914'\\n item['telegram_target']='telegram:JARVIS Command Center / topic 4850'\\n# A all delivered in 5921\\nfor item in man['versions']['A']['artifacts'].values():\\n item['telegram_delivery_attempted']=True\\n item['telegram_delivery_verified']=True\\n item['telegram_message_id']='5921'\\n item['telegram_target']='telegram:JARVIS Command Center / topic 4850'\\nman['thread_delivery']={'C_batch_message_id':'5914','C_mp4_retry_message_id':'5916','A_batch_message_id':'5921','manifest_message_id':'5924','chat_id':'-1003907117629','target':'telegram:JARVIS Command Center / topic 4850'}\\nman['qa_notes']={\\n 'C':'Strict cleaned C uses deterministic crops/reused existing clean frames to avoid obvious baked rings/connectors. Final QA preview shows no added circles, arrows, labels, boxes, diagram overlays, or caption boxes. Subtitles are white/yellow only with subtle outline/shadow.',\\n 'A':'Optional strict cleaned A also removes obvious target-ring markings and uses subtitles only. It is cleaner/brighter but less visually forceful than C.'\\n}\\nman['assessment']={\\n 'looks_better_without_overlays':True,\\n 'story_carried_by_images':'Mostly yes for premium mood and work context; mechanism specificity now depends on voiceover/subtitles rather than graphics.',\\n 'subtitles':'Much calmer; no box/bar; static phrases with yellow active-word highlight are more premium.',\\n 'C_vs_A':'C feels more cinematic/social, A is cleaner and safer but softer.',\\n 'wan_i2v_worth_it':'Not yet for whole video; first evaluate clean cut. If approved, run only one scene-level micro-proof.',\\n 'image_cut_sufficiency':'Likely enough for a first test if user accepts story relying on VO/subtitles.'\\n}\\n(ROOT/'clean_image_cut_manifest_AC.json').write_text(json.dumps(man,indent=2))\\nsummary={v:{'version_id':man['versions'][v]['version_id'],'mp4':man['versions'][v]['artifacts']['mp4'],'contact':man['versions'][v]['artifacts']['contact_sheet'],'subtitle_preview':man['versions'][v]['artifacts']['subtitle_style_preview'],'zip':man['versions'][v]['artifacts']['used_images_zip']} for v in ['C','A']}\\nprint(json.dumps(summary,indent=2))\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"C\\\": {\\n \\\"version_id\\\": \\\"clean_image_cut_C_v1\\\",\\n \\\"mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 8366916,\\n \\\"sha256\\\": \\\"3871314b9abc3fca6a5366b9ca3dff2ea5d5151fea69c82abf8e599fbaf5f6fd\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5916\\\",\\n \\\"duration\\\": 34.968,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"contact\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1745778,\\n \\\"sha256\\\": \\\"8a0dfebb4deadbe935dabb41670d44ed14bbc1b7f2f594aecaa33a4e38e969e8\\\",\\n \\\"media_type\\\": \\\"image/png\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5914\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x2560\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"subtitle_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 91870,\\n \\\"sha256\\\": \\\"890bf7c4c5c916a51be39a181bbd3d9f6e1a6d8b862f9499c0fcdbb290a35104\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5914\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x480\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"zip\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 10834800,\\n \\\"sha256\\\": \\\"54536f53e9600e8e685cce66209ac234c240eda8f6fe9a6c93ce821897629799\\\",\\n \\\"media_type\\\": \\\"application/zip\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5914\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n }\\n },\\n \\\"A\\\": {\\n \\\"version_id\\\": \\\"clean_image_cut_A_v1\\\",\\n \\\"mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 6706631,\\n \\\"sha256\\\": \\\"23d15ee103ca10038e1ca2bc9337a850ca87d381c50e77af49242e2d6631badd\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5921\\\",\\n \\\"duration\\\": 34.968,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"contact\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1586799,\\n \\\"sha256\\\": \\\"77257763d0e52138f4c3bd8a3abf3ebc92d928109d02e02414212dcba69d690c\\\",\\n \\\"media_type\\\": \\\"image/png\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5921\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x2560\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"subtitle_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 99894,\\n \\\"sha256\\\": \\\"97b1c6c60d90daa522c667171ff4423b147fa55d783f5497f1c31a89a1409616\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5921\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x480\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"zip\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 9118648,\\n \\\"sha256\\\": \\\"f86999fe5717c57be188b4add2a84e5395bf8fd0994423dbc5b483bf0ea9d7ca\\\",\\n \\\"media_type\\\": \\\"application/zip\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5921\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n }\\n }\\n}\\n\", \"tool_calls_made\": 0, \"duration_seconds\": 0.26}",
"tool_call_id": "call_ojBro56cAxMuqEb8UzIE2Qov"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_5gM1Cz5thVPVPQ6gXUge4il2",
"call_id": "call_5gM1Cz5thVPVPQ6gXUge4il2",
"response_item_id": "fc_05cdea51f45b90d7016a1478c15530819183e27a974071caf0",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Medien per Telegram liefern und Abschlussbewertung berichten\",\"id\":\"deliver-report\",\"status\":\"completed\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"policy-select\", \"content\": \"Sprint-2.5 NoOverlayPolicy/Manifest vorbereiten und vorhandene C/A-Keyframes auswählen\", \"status\": \"completed\"}, {\"id\": \"render-c\", \"content\": \"Clean-Cut C rendern: KI-Bilder + Voiceover + rahmenlose Active-Word-Subtitles\", \"status\": \"completed\"}, {\"id\": \"render-a\", \"content\": \"Optional Clean-Cut A rendern, falls ohne Zusatzaufwand möglich\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Contact Sheets, Subtitle-Preview-Frames, Metadaten/Hashes/ffprobe verifizieren\", \"status\": \"completed\"}, {\"id\": \"deliver-report\", \"content\": \"Medien per Telegram liefern und Abschlussbewertung berichten\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_5gM1Cz5thVPVPQ6gXUge4il2"
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"content": "Sir, **Sprint 2.5 — Clean Image Cut, No Overlay Pilot ist abgeschlossen.**\n\nKernkorrektur umgesetzt:\n\n- **keine Kreise**\n- **keine Pfeile**\n- **keine Labels**\n- **keine Decision-Target-Markierungen**\n- **keine Linien**\n- **keine Kästchen**\n- **keine Rahmen**\n- **keine Stempel**\n- **keine Debug-Elemente**\n- **keine Diagramm-Overlays**\n- **keine künstlich draufgesetzten Felder**\n- **keine Caption-Box / kein Subtitle-Bar**\n\nErlaubt und verwendet:\n\n- KI-Bilder\n- bestehendes Voiceover\n- Subtitles\n- dezente Textkontur/Schatten\n- langsame Push-ins\n- leichte Pans\n- sanfte Crossfades\n\nNicht ausgeführt:\n\n- kein Wan\n- kein I2V\n- kein T2V\n- kein Full-Final\n- kein Posting\n- keine Plattform-API\n- keine neuen AI-Bilder\n\n## Wichtige Korrektur während QA\n\nBeim ersten Clean-Cut-C-Versuch waren in einigen ursprünglichen KI-Bildern noch **eingebrannte Target-Rings / Connector-Anmutungen** sichtbar. \nIch habe deshalb **keine neuen AI-Bilder erzeugt**, sondern die vorhandenen Bilder deterministisch gecroppt bzw. nur sauberere vorhandene Frames wiederverwendet.\n\nErgebnis:\n\n```text\nstrict deterministic crops/reused existing clean frames\nno new AI images\nobvious baked target-ring/connector/key visual markings avoided\n```\n\nDas ist nicht so glamourös wie ein Cannes Case Study Deck, aber deutlich weniger beleidigend fürs Auge.\n\n---\n\n# 1. NoOverlayPolicy\n\nPolicy Manifest:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_cut_policy_manifest.json\n```\n\nDelivery / Used Image Manifest:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_image_cut_manifest_AC.json\n```\n\nPolicy:\n\n```text\nallowed_overlay_types:\n- subtitles_only\n```\n\nBlocked:\n\n```text\narrows\nlabels\ncircles\nboxes\nstamps\ndebug_text\ndiagram_elements\nfake_ui\ncaptions_background_rectangle\nfull_width_subtitle_bar\ndecision_target_markers\nconnector_lines\nframes\ncaption_box\n```\n\nResult:\n\n```text\npolicy_result: pass\nblocked_overlay_types_present: []\n```\n\n---\n\n# 2. Primary Deliverable — Clean Image Cut C\n\n## MP4 C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\n\n```text\nversion:\nclean_image_cut_C_v1\n\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n8366916 bytes\n\nsha256:\n3871314b9abc3fca6a5366b9ca3dff2ea5d5151fea69c82abf8e599fbaf5f6fd\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5916\n```\n\nHinweis:\n\n- Im ersten Telegram-Batch hatte der MP4-Upload einen Timeout.\n- Der MP4 wurde danach separat erfolgreich erneut gesendet.\n\n## Contact Sheet C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n1745778 bytes\n\nsha256:\n8a0dfebb4deadbe935dabb41670d44ed14bbc1b7f2f594aecaa33a4e38e969e8\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\nresolution:\n1080x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n## Subtitle Style Preview C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n91870 bytes\n\nsha256:\n890bf7c4c5c916a51be39a181bbd3d9f6e1a6d8b862f9499c0fcdbb290a35104\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\nresolution:\n1080x480\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n## Used Images ZIP C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\n\nfile_exists:\ntrue\n\nfile_size:\n10834800 bytes\n\nsha256:\n54536f53e9600e8e685cce66209ac234c240eda8f6fe9a6c93ce821897629799\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n---\n\n# 3. Optional Comparison — Clean Image Cut A\n\nDa es ohne großen Zusatzaufwand möglich war, habe ich auch A als Vergleich erzeugt — ebenfalls strict clean.\n\n## MP4 A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\n\n```text\nversion:\nclean_image_cut_A_v1\n\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n6706631 bytes\n\nsha256:\n23d15ee103ca10038e1ca2bc9337a850ca87d381c50e77af49242e2d6631badd\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Contact Sheet A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n1586799 bytes\n\nsha256:\n77257763d0e52138f4c3bd8a3abf3ebc92d928109d02e02414212dcba69d690c\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\nresolution:\n1080x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Subtitle Style Preview A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n99894 bytes\n\nsha256:\n97b1c6c60d90daa522c667171ff4423b147fa55d783f5497f1c31a89a1409616\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\nresolution:\n1080x480\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Used Images ZIP A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\n\nfile_exists:\ntrue\n\nfile_size:\n9118648 bytes\n\nsha256:\nf86999fe5717c57be188b4add2a84e5395bf8fd0994423dbc5b483bf0ea9d7ca\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n---\n\n# 4. Manifest Delivery\n\n```text\npolicy manifest message_id:\n5924\n\ndelivery/used-image manifest message_id:\n5924\n\ntelegram_delivery_verified:\ntrue\n```\n\nManifest paths:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_cut_policy_manifest.json\n\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_image_cut_manifest_AC.json\n```\n\n---\n\n# 5. Subtitle Style\n\nImplemented:\n\n```text\nstatic phrase segments\nactive word highlight = yellow\ninactive words = white\nbackground_box = false\ncaption_bar = false\nvisible_container = false\nshadow_or_outline = subtle\n```\n\nAlso:\n\n- keine Box\n- kein Rahmen\n- keine dunkle Fläche\n- keine Vollbreiten-Bar\n- phrase segment bleibt stabil\n- Highlight wandert durch den Abschnitt\n\n## QA\n\nFinal QA Preview C:\n\n```text\nno added circles/arrows/labels/boxes/diagram overlays/caption boxes visible\nsubtitles white/yellow with subtle outline only\nobvious target rings removed via strict crops / source selection\n```\n\nFinal QA Preview A:\n\n```text\nsame policy pass\ncleaner/brighter\nless visual force than C\n```\n\n---\n\n# 6. Bewertung\n\n## Wirkt es ohne Overlays hochwertiger?\n\nJa. Deutlich.\n\nDie Bilder atmen wieder. Keine technischen Erklärboard-Narben mehr. \nDas Video wirkt ruhiger, wertiger und weniger wie ein Debug-Screen, der sich für Content hält.\n\n## Tragen die Bilder die Story genug?\n\nTeilweise ja.\n\n- Die Bilder tragen **Mood, Arbeitswelt, Premium-Look**.\n- Die präzise Mechanik wird jetzt stärker durch **Voiceover + Subtitles** getragen.\n- Das ist für diesen Clean-Cut-Test korrekt, weil wir bewusst keine Erklärgrafik mehr wollen.\n\n## Sind die Subtitles angenehmer?\n\nJa.\n\n- Keine Caption-Box.\n- Kein dunkler Balken.\n- Active-word Highlight bleibt klar.\n- Viel weniger TikTok-chaotisch.\n- Besserer Premium-Look.\n\n## Ist C besser als A?\n\nFür Social Impact:\n\n```text\nC > A\n```\n\nFür Ruhe / Safety:\n\n```text\nA > C\n```\n\nMeine Einschätzung:\n\n- **C** wirkt eher wie ein hochwertiger Social Short.\n- **A** wirkt sauberer, aber etwas zu brav / SaaS-clean.\n\n## Lohnt sich Wan-I2V überhaupt?\n\nNoch nicht als Full-Video.\n\nWenn Sie C mögen, würde ich höchstens einen **einzigen Wan-Micro-Proof** testen:\n\n```text\n3–5s\neine Szene\nein sauberes Startbild\nkein Overlay\nkein Subtitle\nnur subtile Bewegung prüfen\n```\n\nKein Full-Wan, kein Gesamtvideo-I2V, kein zweiter Versuch ohne Freigabe.\n\n## Reicht Image Cut + subtile Motion für ersten Test?\n\nMöglicherweise ja.\n\nFür einen ersten Test ist der Clean Image Cut wahrscheinlich näher am richtigen Stil als alle vorherigen Versionen mit Erklärgrafik. \nWenn die Retention später nicht reicht, kann Wan/I2V helfen — aber nur als dezente Motion-Schicht, nicht als semantische Rettungsaktion.\n\n---\n\n# 7. Nicht ausgeführt\n\n```text\nnew_ai_images_generated: false\nwan_used: false\ni2v_used: false\nt2v_used: false\nfull_final_render: false\nposting: false\nplatform_api: false\n```\n\n---\n\n# 8. Nächste erlaubte Kommandos\n\nWenn C passt:\n\n```text\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s C\n```\n\nWenn A besser ist:\n\n```text\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s A\n```\n\nWenn nur Subtitles geändert werden sollen:\n\n```text\nREVISE_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s subtitles \"Änderungswunsch\"\n```\n\nWenn ein einzelner Wan-Micro-Proof erlaubt ist:\n\n```text\nAPPROVED_SINGLE_WAN_MICRO_PROOF ai-output-autopsy-decision-summary-38s \n```\n\nWenn die Richtung nicht reicht:\n\n```text\nREJECT_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nPipeline bleibt blockiert für Posting, Full-Final und Full-Wan.",
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"text": "Sir, **Sprint 2.5 — Clean Image Cut, No Overlay Pilot ist abgeschlossen.**\n\nKernkorrektur umgesetzt:\n\n- **keine Kreise**\n- **keine Pfeile**\n- **keine Labels**\n- **keine Decision-Target-Markierungen**\n- **keine Linien**\n- **keine Kästchen**\n- **keine Rahmen**\n- **keine Stempel**\n- **keine Debug-Elemente**\n- **keine Diagramm-Overlays**\n- **keine künstlich draufgesetzten Felder**\n- **keine Caption-Box / kein Subtitle-Bar**\n\nErlaubt und verwendet:\n\n- KI-Bilder\n- bestehendes Voiceover\n- Subtitles\n- dezente Textkontur/Schatten\n- langsame Push-ins\n- leichte Pans\n- sanfte Crossfades\n\nNicht ausgeführt:\n\n- kein Wan\n- kein I2V\n- kein T2V\n- kein Full-Final\n- kein Posting\n- keine Plattform-API\n- keine neuen AI-Bilder\n\n## Wichtige Korrektur während QA\n\nBeim ersten Clean-Cut-C-Versuch waren in einigen ursprünglichen KI-Bildern noch **eingebrannte Target-Rings / Connector-Anmutungen** sichtbar. \nIch habe deshalb **keine neuen AI-Bilder erzeugt**, sondern die vorhandenen Bilder deterministisch gecroppt bzw. nur sauberere vorhandene Frames wiederverwendet.\n\nErgebnis:\n\n```text\nstrict deterministic crops/reused existing clean frames\nno new AI images\nobvious baked target-ring/connector/key visual markings avoided\n```\n\nDas ist nicht so glamourös wie ein Cannes Case Study Deck, aber deutlich weniger beleidigend fürs Auge.\n\n---\n\n# 1. NoOverlayPolicy\n\nPolicy Manifest:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_cut_policy_manifest.json\n```\n\nDelivery / Used Image Manifest:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_image_cut_manifest_AC.json\n```\n\nPolicy:\n\n```text\nallowed_overlay_types:\n- subtitles_only\n```\n\nBlocked:\n\n```text\narrows\nlabels\ncircles\nboxes\nstamps\ndebug_text\ndiagram_elements\nfake_ui\ncaptions_background_rectangle\nfull_width_subtitle_bar\ndecision_target_markers\nconnector_lines\nframes\ncaption_box\n```\n\nResult:\n\n```text\npolicy_result: pass\nblocked_overlay_types_present: []\n```\n\n---\n\n# 2. Primary Deliverable — Clean Image Cut C\n\n## MP4 C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\n\n```text\nversion:\nclean_image_cut_C_v1\n\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_image_cut_C_v1.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n8366916 bytes\n\nsha256:\n3871314b9abc3fca6a5366b9ca3dff2ea5d5151fea69c82abf8e599fbaf5f6fd\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5916\n```\n\nHinweis:\n\n- Im ersten Telegram-Batch hatte der MP4-Upload einen Timeout.\n- Der MP4 wurde danach separat erfolgreich erneut gesendet.\n\n## Contact Sheet C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_contact_sheet_C.png\n\nfile_exists:\ntrue\n\nfile_size:\n1745778 bytes\n\nsha256:\n8a0dfebb4deadbe935dabb41670d44ed14bbc1b7f2f594aecaa33a4e38e969e8\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\nresolution:\n1080x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n## Subtitle Style Preview C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/subtitle_style_preview_C.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n91870 bytes\n\nsha256:\n890bf7c4c5c916a51be39a181bbd3d9f6e1a6d8b862f9499c0fcdbb290a35104\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\nresolution:\n1080x480\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n## Used Images ZIP C\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_C/clean_used_images_C.zip\n\nfile_exists:\ntrue\n\nfile_size:\n10834800 bytes\n\nsha256:\n54536f53e9600e8e685cce66209ac234c240eda8f6fe9a6c93ce821897629799\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5914\n```\n\n---\n\n# 3. Optional Comparison — Clean Image Cut A\n\nDa es ohne großen Zusatzaufwand möglich war, habe ich auch A als Vergleich erzeugt — ebenfalls strict clean.\n\n## MP4 A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\n\n```text\nversion:\nclean_image_cut_A_v1\n\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_image_cut_A_v1.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n6706631 bytes\n\nsha256:\n23d15ee103ca10038e1ca2bc9337a850ca87d381c50e77af49242e2d6631badd\n\nmedia_type:\nvideo/mp4\n\nduration:\n34.968s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Contact Sheet A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_contact_sheet_A.png\n\nfile_exists:\ntrue\n\nfile_size:\n1586799 bytes\n\nsha256:\n77257763d0e52138f4c3bd8a3abf3ebc92d928109d02e02414212dcba69d690c\n\nmedia_type:\nimage/png\n\nduration:\nn/a\n\nresolution:\n1080x2560\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Subtitle Style Preview A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/subtitle_style_preview_A.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n99894 bytes\n\nsha256:\n97b1c6c60d90daa522c667171ff4423b147fa55d783f5497f1c31a89a1409616\n\nmedia_type:\nimage/jpeg\n\nduration:\nn/a\n\nresolution:\n1080x480\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n## Used Images ZIP A\n\nMEDIA:/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\n\n```text\nfile_path:\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/version_A/clean_used_images_A.zip\n\nfile_exists:\ntrue\n\nfile_size:\n9118648 bytes\n\nsha256:\nf86999fe5717c57be188b4add2a84e5395bf8fd0994423dbc5b483bf0ea9d7ca\n\nmedia_type:\napplication/zip\n\nduration:\nn/a\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5921\n```\n\n---\n\n# 4. Manifest Delivery\n\n```text\npolicy manifest message_id:\n5924\n\ndelivery/used-image manifest message_id:\n5924\n\ntelegram_delivery_verified:\ntrue\n```\n\nManifest paths:\n\n```text\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_cut_policy_manifest.json\n\n/tmp/autoshorts_clean_image_cut_sprint25/ai-output-autopsy-decision-summary-38s/clean_image_cut_manifest_AC.json\n```\n\n---\n\n# 5. Subtitle Style\n\nImplemented:\n\n```text\nstatic phrase segments\nactive word highlight = yellow\ninactive words = white\nbackground_box = false\ncaption_bar = false\nvisible_container = false\nshadow_or_outline = subtle\n```\n\nAlso:\n\n- keine Box\n- kein Rahmen\n- keine dunkle Fläche\n- keine Vollbreiten-Bar\n- phrase segment bleibt stabil\n- Highlight wandert durch den Abschnitt\n\n## QA\n\nFinal QA Preview C:\n\n```text\nno added circles/arrows/labels/boxes/diagram overlays/caption boxes visible\nsubtitles white/yellow with subtle outline only\nobvious target rings removed via strict crops / source selection\n```\n\nFinal QA Preview A:\n\n```text\nsame policy pass\ncleaner/brighter\nless visual force than C\n```\n\n---\n\n# 6. Bewertung\n\n## Wirkt es ohne Overlays hochwertiger?\n\nJa. Deutlich.\n\nDie Bilder atmen wieder. Keine technischen Erklärboard-Narben mehr. \nDas Video wirkt ruhiger, wertiger und weniger wie ein Debug-Screen, der sich für Content hält.\n\n## Tragen die Bilder die Story genug?\n\nTeilweise ja.\n\n- Die Bilder tragen **Mood, Arbeitswelt, Premium-Look**.\n- Die präzise Mechanik wird jetzt stärker durch **Voiceover + Subtitles** getragen.\n- Das ist für diesen Clean-Cut-Test korrekt, weil wir bewusst keine Erklärgrafik mehr wollen.\n\n## Sind die Subtitles angenehmer?\n\nJa.\n\n- Keine Caption-Box.\n- Kein dunkler Balken.\n- Active-word Highlight bleibt klar.\n- Viel weniger TikTok-chaotisch.\n- Besserer Premium-Look.\n\n## Ist C besser als A?\n\nFür Social Impact:\n\n```text\nC > A\n```\n\nFür Ruhe / Safety:\n\n```text\nA > C\n```\n\nMeine Einschätzung:\n\n- **C** wirkt eher wie ein hochwertiger Social Short.\n- **A** wirkt sauberer, aber etwas zu brav / SaaS-clean.\n\n## Lohnt sich Wan-I2V überhaupt?\n\nNoch nicht als Full-Video.\n\nWenn Sie C mögen, würde ich höchstens einen **einzigen Wan-Micro-Proof** testen:\n\n```text\n3–5s\neine Szene\nein sauberes Startbild\nkein Overlay\nkein Subtitle\nnur subtile Bewegung prüfen\n```\n\nKein Full-Wan, kein Gesamtvideo-I2V, kein zweiter Versuch ohne Freigabe.\n\n## Reicht Image Cut + subtile Motion für ersten Test?\n\nMöglicherweise ja.\n\nFür einen ersten Test ist der Clean Image Cut wahrscheinlich näher am richtigen Stil als alle vorherigen Versionen mit Erklärgrafik. \nWenn die Retention später nicht reicht, kann Wan/I2V helfen — aber nur als dezente Motion-Schicht, nicht als semantische Rettungsaktion.\n\n---\n\n# 7. Nicht ausgeführt\n\n```text\nnew_ai_images_generated: false\nwan_used: false\ni2v_used: false\nt2v_used: false\nfull_final_render: false\nposting: false\nplatform_api: false\n```\n\n---\n\n# 8. Nächste erlaubte Kommandos\n\nWenn C passt:\n\n```text\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s C\n```\n\nWenn A besser ist:\n\n```text\nAPPROVED_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s A\n```\n\nWenn nur Subtitles geändert werden sollen:\n\n```text\nREVISE_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s subtitles \"Änderungswunsch\"\n```\n\nWenn ein einzelner Wan-Micro-Proof erlaubt ist:\n\n```text\nAPPROVED_SINGLE_WAN_MICRO_PROOF ai-output-autopsy-decision-summary-38s \n```\n\nWenn die Richtung nicht reicht:\n\n```text\nREJECT_CLEAN_IMAGE_CUT ai-output-autopsy-decision-summary-38s \"Grund\"\n```\n\nPipeline bleibt blockiert für Posting, Full-Final und Full-Wan."
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"content": "{\"success\": true, \"target\": \"user\", \"entries\": [\"**Notes:** Mein Herr. Loyaler Partner.\", \"**Name:** Sir\", \"**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\", \"**Language:** Deutsch mit englischen Fachbegriffen\", \"Kommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\", \"Kommunikationspräferenzen: **Textnachricht → Antwort per Text**\", \"Health 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.\", \"Telegram 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.\", \"Finance/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.\", \"AutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN. Positioning: hidden mechanisms, not AI tips; AI 30–40%. Format fail→mechanism→fix→takeaway. Captions static 3–7w phrase with active word highlight. Visuals: premium clean KI-image cuts; avoid labels/arrows/boxes/target markers unless approved; Wan only after approval.\"], \"usage\": \"99% — 1,367/1,375 chars\", \"entry_count\": 10, \"message\": \"Entry replaced.\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nReference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. 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.\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\nFor the user's TrueTraceShorts / AutoShortsBot strategy, do **not** default to a narrow AI/prompt-tips channel. The current strategic direction is broader: explain hidden digital everyday mechanisms — AI, work, attention, decisions, scams, productivity, workflows, social search — and end each video with one small system rule that fixes the outcome. Use the frame `Trace the hidden system. Fix the outcome.` AI should remain an important pillar, but normally only about 30–40% of the topic mix, not the whole product. See `references/truetrace-strategic-neuausrichtung.md`.\\n\\nWhen the user asks to start implementation work on this strategy, especially “Sprint 1: Strategic Spine Refactor”, follow `references/truetrace-strategic-spine-refactor-v2.md`: implement a side-effect-free V2 spine in parallel to existing V1 models, with mandatory strategic fields, hard quality gates, anti-AI-slop blockers, hash/version-bound ReviewPackageV2, and tests before integration with renderers or platform workflows.\\n\\nWhen the user asks for “Sprint 1.5” or to make the V2 spine operational without production, follow `references/truetrace-v2-operational-bridge.md`: first secure/commit the Sprint-1 baseline, then add only minimal side-effect-free bridges (`to_candidate_v2`, `workflow_v2`, `demo_review_batch_v2`), deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated non-generous scoring, anti-example/human-texture gates, minimal manual claim/disclosure stubs, LinkedIn manual metadata, and a Remotion-as-later-spike note. Keep it capped at 3 demo candidates and avoid rendering, platform APIs, dashboards, auto-posting, or 30-day batches.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - For TrueTraceShorts, prefer the structure `visible fail → identification → mechanism → fix → before/after → memorable takeaway` over generic AI-tip narration. Every video must answer: what goes wrong, why it happens, what rule fixes it, what sentence sticks, why viewers save/share it, and why they follow the series.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n - first-frame clarity\\n - mechanism value\\n - practical payoff\\n - save/share/follow reason\\n - trust/human texture\\n - series fit and saturation defense\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n For TrueTraceShorts, use the 100-point scoring and hard blockers in `references/truetrace-strategic-neuausrichtung.md`: block generic AI hooks, pure text first frames, unsourced claims, missing before/after, missing human texture, unclear AI disclosure, and candidates too similar to recent videos.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - 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 simplify captions before sending another final-near clip. For this user's AutoShorts/TrueTraceShorts default, do **not** show one word alone and do **not** slide a changing word-window every word. Use static phrase segments: keep 3–7 words fixed on screen while only the currently spoken word highlights/pulses left-to-right; replace the full segment only at a sentence/phrase boundary. See `references/static-active-word-captions-and-final-pilot-render.md`.\\n - 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`.\\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.\\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 let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not assume local Qwen/ComfyUI is the best first image step for every final-near short. If the user permits ChatGPT Image/Codex, use it for text-free scene backgrounds/keyframes when the deterministic renderer owns labels/captions and local GPU should be saved for video/model work.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not treat a keyframe animatic as a successful Wan/I2V proof. If ComfyUI/Wan I2V fails or history contains no valid video output, label any fallback MP4 as `not_wan` / composition-review-only and block full-final progression.\\n- Do not treat deterministic layout proofs as the final premium visual style when the user asked for a high-end social-video look. Layouts are for mechanism composition; after approval, use a tightly scoped premium AI styleframe pass with strict negative prompts and verified delivery before endframes/Wan.\\n- Do not mistake premium abstract glassmorphism for an approved final direction if the user wants a credible everyday work scene. When the user says the motif is still too abstract, switch to real-life work-scene styleframes with laptop/tablet/meeting-room/project-table anchors; keep motion graphics subtle and renderer-owned.\\n- If the user rejects the production method itself, do not keep iterating content visuals. Reject/audit the proof, run a technical I2V sanity test with a trivial non-content image, and only after it passes generate a small set of styleframes for direction review; see `references/visual-production-reset-i2v-first.md`.\\n- Do not keep trying blind text-to-video after the user rejects a proof as abstract, dark, generic, or semantically ungrounded. Move to keyframe-first planning, review the keyframes, then run at most one short I2V/first-last-frame proof.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/premium-ai-styleframe-direction.md\", \"references/social-draft-approval-package.md\", \"references/static-active-word-captions-and-final-pilot-render.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/visual-first-final-render-repair.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/truetrace-strategic-neuausrichtung.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/controlled-pilot-production-v2.md\", \"references/keyframe-first-final-visuals.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/pilot-preview-render-v2.md\", \"references/truetrace-strategic-spine-refactor-v2.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/real-life-premium-styleframes.md\", \"references/monetization-and-approval-notes.md\", \"references/visual-production-reset-i2v-first.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/final-motion-hybrid-rendering.md\", \"references/prerelease-animatic-preview.md\", \"references/imported-ai-clip-assembly.md\", \"references/hybrid-scene-backgrounds-codex.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\", \"references/truetrace-v2-operational-bridge.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\"}",
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"arguments": "{\"action\":\"write_file\",\"file_content\":\"# Clean Image Cut / No-Overlay Pilot\\n\\nUse this reference when the user rejects explainer-board aesthetics in an AutoShorts/TrueTraceShorts pilot and asks for a premium image-sequence video.\\n\\n## Trigger signals\\n\\n- User says circles, arrows, labels, boxes, decision-target markers, connector lines, debug marks, diagram overlays, or caption containers destroy the look.\\n- User asks for “KI-Bilder + Voiceover + Subtitles” only.\\n- User wants to test whether image sequence + subtle motion works before Wan/I2V.\\n\\n## Hard NoOverlayPolicy\\n\\nAllowed overlay types:\\n\\n```text\\nsubtitles_only\\n```\\n\\nBlocked overlay types:\\n\\n```text\\narrows\\nlabels\\ncircles\\nboxes\\nframes\\nstamps\\ndebug_text\\ndiagram_elements\\nfake_ui\\ncaption_background_rectangle\\nfull_width_subtitle_bar\\ncaption_box\\ndecision_target_markers\\nconnector_lines\\n```\\n\\nIf any blocked overlay type appears in the render manifest, block delivery and rerender. If such marks are baked into an AI image, do not use that image unless a deterministic crop removes the issue cleanly. Do not “explain” with renderer-owned graphics in this mode.\\n\\n## Subtitle style\\n\\n- Static phrase segments remain the default.\\n- Keep 3–7 words stable on screen.\\n- Active spoken word: yellow highlight.\\n- Other words: white.\\n- No visible container, no box, no dark full-width bar.\\n- Use only subtle shadow/outline for readability.\\n- Bottom mobile-safe placement; calm premium pacing.\\n\\n## Clean-cut workflow\\n\\n1. Start from existing approved AI keyframes/styleframes where possible; do not generate new AI images unless existing frames cannot be made clean.\\n2. Select the visually strongest direction first, usually the user's preferred one; optionally render one quiet comparison direction if cheap.\\n3. Inspect keyframes for baked circles/rings/connectors/pseudo-labels/fake UI/text. Reject or crop them out.\\n4. Build a deterministic image-sequence MP4:\\n - existing voiceover\\n - static active-word subtitles in clean style\\n - slow push-ins/pans\\n - gentle crossfades\\n - no Wan/I2V/T2V/full-final/posting\\n5. Generate contact sheet of used images, subtitle preview frame/sheet, and a manifest listing every used image.\\n6. QA with frame thumbnails before delivery: verify no visible blocked overlays and no caption box/bar.\\n7. Report file_path, exists, size, sha256, media_type, duration, resolution, Telegram delivery status/message ID.\\n\\n## Assessment guidance\\n\\nIn the final report, explicitly answer:\\n\\n- Does it look more premium without overlays?\\n- Do the images carry the story enough, or does the story now rely on VO/subtitles?\\n- Are subtitles comfortable and synchronized enough?\\n- Is the stronger visual direction worth its busyness versus the calmer alternative?\\n- Should Wan/I2V be tested at all?\\n\\nDefault recommendation after a clean-cut pass: do not run full Wan/I2V yet. If the clean cut works, request/await approval for exactly one 3–5s scene-level Wan/I2V micro-proof with no overlay and no subtitle, only to test subtle motion quality.\\n\\n## Common pitfall\\n\\nPreviously, renderer-owned circles/labels/arrows made premium AI images feel like a technical explainer board. For this user's pilot, that is a style failure even when the mechanism is clearer. Prefer premium mood + voiceover/subtitles over visual over-explanation.\\n\",\"file_path\":\"references/clean-image-cut-no-overlay-pilot.md\",\"name\":\"social-media-shorts-automation\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"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.\",\"new_string\":\"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.\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\" - 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`.\\n - If the user rejects 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`.\",\"old_string\":\" - 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`.\"}"
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"content": "Saved and updated.\n\n- **Memory:** Updated the AutoShorts/TrueTraceShorts user preference to reflect the new durable direction: premium clean KI-image cuts, static active-word captions, and avoiding labels/arrows/boxes/target markers unless explicitly approved.\n- **Skill:** Updated the loaded `social-media-shorts-automation` skill:\n - Added `references/clean-image-cut-no-overlay-pilot.md`\n - Patched `SKILL.md` to route future similar corrections into this clean no-overlay workflow.",
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"text": "Saved and updated.\n\n- **Memory:** Updated the AutoShorts/TrueTraceShorts user preference to reflect the new durable direction: premium clean KI-image cuts, static active-word captions, and avoiding labels/arrows/boxes/target markers unless explicitly approved.\n- **Skill:** Updated the loaded `social-media-shorts-automation` skill:\n - Added `references/clean-image-cut-no-overlay-pilot.md`\n - Patched `SKILL.md` to route future similar corrections into this clean no-overlay workflow."
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