{
"session_id": "20260523_110720_f2697b",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-23T11:05:19.661848",
"last_updated": "2026-05-23T11:32:26.205061",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,191/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; use `github_pat_`/`ghp_` cell; never quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [99% — 1,371/1,375 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§\nUser wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\n§\nTelegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\n§\nFinance: no real amounts/IDs/API keys in chat; credentials via terminal/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: mappings learned; provider calls explicit/cache-first.\n\nConversation started: Saturday, May 23, 2026 11:07 AM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "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": 363,
"messages": [
{
"role": "user",
"content": "[M G] Ok, nächster schritt"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_8oTsPqSJeb2Lxwhp3grsOkmr",
"call_id": "call_8oTsPqSJeb2Lxwhp3grsOkmr",
"response_item_id": "fc_0ebac54e0d9e8557016a113ed59c24819190c070e8f2345add",
"type": "function",
"function": {
"name": "session_search",
"arguments": "{}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Clarifying next steps**\n\nI’m trying to understand if I need context for the answer, especially previous information. The phrase “nächster schritt” likely refers to past actions. I think I should use a recent search to gather fresh information. Also, is the project context Hermes relevant here? It seems like there might be no specific skill needed, but things feel a bit ambiguous. I definitely need to run a recent session search to clarify this.",
"reasoning_content": "**Clarifying next steps**\n\nI’m trying to understand if I need context for the answer, especially previous information. The phrase “nächster schritt” likely refers to past actions. I think I should use a recent search to gather fresh information. Also, is the project context Hermes relevant here? It seems like there might be no specific skill needed, but things feel a bit ambiguous. I definitely need to run a recent session search to clarify this.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqET7VOh7HrZ76_FRutmUw-s4aCXcopvalyEK2mcD7t8ZtC80_Umo_YkhoCczQheHeqyELkfxDlvOM53B3uENFGBHHSVdCLbrtsKIfKdSAQpbYFgZ2f1jk8xjhLbI-M7aa0DdgTNR8MBskIk-rJki9OCqk3ELe2ecD0n66Y4aGbtw9f4SsiasQz0TwqjUjkZG1nlZCaAW8zZjNUQj8p7QPVKE6RjyElZ3Zo9q6H9vyKGirC6qaCJgwc4sYiMFg-YQpcLy28ri9sxiUfw103ZrK-VX_DYshHz6PP-T2i_6ujEx-J_fznjm1EsMIhOkiG8QvtXuRGetPzfehal59DCN1AIEmYlPUCB8GlJybGq2a1K-w9BRNaBFZhrYml8K-lmCQvEUqHR-vzi955lsOJG6JzLBA5mxmDX04nCL_ptdsWR_5iTcL2xvOZ7QzI-uGOFA_pexUya72n0DotaBLvzLzwf3AJ1FsjDopL94OgAfhiFxUm18piIYzw1J24EtfJg9UVQ5kGt3Fm6rcbmJUOFl02-kZ_kYcNmNHga-ttchn2CH-i0vJfWl56ASE15mxQ3rlo5vQaijx3OO8nDbXagZLD2OxZ5Jb8ZSjtFD4LwPWpLMZHHVH0wSDM8ZUrY7Sj1QhNR4CKpFvemaoaU7AH4fVcJoEXlmn441LLRN6_g0xdqX0YPrhrK75k0Js6ifa3q8hzCNXdIAVxi7BWi9QHBcpxYFxsn47fsnt9Atr-HTCWLXfIlGgNpJduj3YglU5svHaMnjPeXVa-4Ve6bmk9tCwEAaoKJGtIt-YJB8MkQfLoM_842SxhAxGZjuvPozmBqtGYtry0qgsqCMdS43LHNVPSdCtE4MaeUrfiyxQ8lon-DokCuwUzPNhSop34aPVFluIyxBxKZ1QerZkymW6BhkVlGTASp9EVvGjOlV_pQ4S7fp8BncZ68iVAzbHuw6HC2urPye4SFMz4sKaTeZkDKi13rHC0kXpevabaybUjJsBMwExfdffEvGVumHSlXC49WUoKV_HCZDB-p6Xal1PVozrxpT2bf6L6FEFfADQccmp7VtX-K7AxU-DBzKxGYZr0wT8NPuMvUII-4MNi2FTK_r3EEjiVhakuLbeWMdzonjdCTeO9g-7Nd9djhNp1DMY8jhOy3uzhQRgR-XO7a907UHkqu3DXk8cBlEgTucgluC4_m5tVLMi5c476Go0BI78RW2lzztJ",
"id": "rs_0ebac54e0d9e8557016a113ed306648191af0e8fba4bba1fb8",
"summary": [
{
"type": "summary_text",
"text": "**Clarifying next steps**\n\nI’m trying to understand if I need context for the answer, especially previous information. The phrase “nächster schritt” likely refers to past actions. I think I should use a recent search to gather fresh information. Also, is the project context Hermes relevant here? It seems like there might be no specific skill needed, but things feel a bit ambiguous. I definitely need to run a recent session search to clarify this."
}
]
}
]
},
{
"role": "tool",
"content": "[session_search] (936 chars result)",
"tool_call_id": "call_8oTsPqSJeb2Lxwhp3grsOkmr"
},
{
"role": "user",
"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 125 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_MNVRy4Ng42QPsbNAvuT7xF4k",
"call_id": "call_MNVRy4Ng42QPsbNAvuT7xF4k",
"response_item_id": "fc_02aa21bd75cf0633016a11440d4040819199ca80f4c7ee6076",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Renderer-Abhängigkeiten und Repo-Status prüfen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Renderer-v1-Tests schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_MNVRy4Ng42QPsbNAvuT7xF4k"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_rL9LpNa5WeHwyw8u2V9JbbGm",
"call_id": "call_rL9LpNa5WeHwyw8u2V9JbbGm",
"response_item_id": "fc_02aa21bd75cf0633016a114414efd48191a0cf98f83b876276",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py\", \"content\": \"import json\\nfrom pathlib import Path\\n\\nfrom autoshorts.preview_renderer import (\\n build_preview_frames,\\n render_preview_video,\\n write_frame_images,\\n)\\n\\n\\ndef _manifest() -> dict:\\n path = Path...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py (50 lines)",
"tool_call_id": "call_rL9LpNa5WeHwyw8u2V9JbbGm"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_PSHzgd6x95rsfmHrVdWg7YMS",
"call_id": "call_PSHzgd6x95rsfmHrVdWg7YMS",
"response_item_id": "fc_02aa21bd75cf0633016a114434d4788191b15855d1f06d1e41",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_preview_renderer.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest tests/test_preview_renderer.py -q` -> exit 2, 1 lines output",
"tool_call_id": "call_PSHzgd6x95rsfmHrVdWg7YMS"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_6qTiBIiCS5xyN7rV3DH9HMcK",
"call_id": "call_6qTiBIiCS5xyN7rV3DH9HMcK",
"response_item_id": "fc_02aa21bd75cf0633016a114438356c8191834db3e81d706e57",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Renderer-v1-Tests schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Deterministischen Preview-Renderer implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_6qTiBIiCS5xyN7rV3DH9HMcK"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_837U7ceTUEsYWmXJETGf1zCe",
"call_id": "call_837U7ceTUEsYWmXJETGf1zCe",
"response_item_id": "fc_02aa21bd75cf0633016a11443b1e008191a77b2f57bbeeaaf4",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\": \"\\\"\\\"\\\"Deterministic preview renderer for AutoShortsBot render manifests.\\n\\nThis v1 renderer intentionally uses manifest-driven cards/placeholders instead of\\nAI-generated video. It renders a simple vertica...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (206 lines)",
"tool_call_id": "call_837U7ceTUEsYWmXJETGf1zCe"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_fMjjxOdPFh7y0poQkNs1Nv9V",
"call_id": "call_fMjjxOdPFh7y0poQkNs1Nv9V",
"response_item_id": "fc_02aa21bd75cf0633016a1144751fa481919a8c7853a03033a6",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"old_string\":\" \\\"render_manifest\\\",\\n \\\"retention\\\",\",\"new_string\":\" \\\"preview_renderer\\\",\\n \\\"render_manifest\\\",\\n \\\"retention\\\",\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py (456 chars result)",
"tool_call_id": "call_fMjjxOdPFh7y0poQkNs1Nv9V"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_wtAgBgOSsJCjWloXNpprT0Nh",
"call_id": "call_wtAgBgOSsJCjWloXNpprT0Nh",
"response_item_id": "fc_02aa21bd75cf0633016a11447a58408191ad1329e5620eeae6",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"old_string\":\" font: ImageFont.ImageFont,\",\"new_string\":\" font: Any,\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (539 chars result)",
"tool_call_id": "call_wtAgBgOSsJCjWloXNpprT0Nh"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_PV5wuilG2VzXoV1zF3XKmXJL",
"call_id": "call_PV5wuilG2VzXoV1zF3XKmXJL",
"response_item_id": "fc_02aa21bd75cf0633016a11447fadbc819183cf06a67bb09554",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_preview_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"... [100%]\\n3 passed in 1.01s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_PV5wuilG2VzXoV1zF3XKmXJL"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Tft5cmufQb2VWgzqhAylfKaE",
"call_id": "call_Tft5cmufQb2VWgzqhAylfKaE",
"response_item_id": "fc_02aa21bd75cf0633016a114484339081919f1def0d0934c6fc",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Deterministischen Preview-Renderer implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Erstes ai-004 Testvideo lokal rendern und prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_Tft5cmufQb2VWgzqhAylfKaE"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_FT9sFxM8zQgOHTJ5Cl6meszJ",
"call_id": "call_FT9sFxM8zQgOHTJ5Cl6meszJ",
"response_item_id": "fc_02aa21bd75cf0633016a114487319481919e845c3e4eeadf03",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\": \"\\\"\\\"\\\"Render preview MP4 videos from AutoShortsBot renderer manifests.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file_...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_preview.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 1507, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_FT9sFxM8zQgOHTJ5Cl6meszJ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Ivb2PWx4KgSGAxmU5hfsUMWv",
"call_id": "call_Ivb2PWx4KgSGAxmU5hfsUMWv",
"response_item_id": "fc_02aa21bd75cf0633016a1144927f788191a0f763b403acbd66",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"rm -rf data/previews/testbatch_002_top3\\npython3 scripts/render_preview.py data/render_manifests/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json --out-dir data/previews/testbatch_002_top...[truncated]\", \"timeout\": 120, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `rm -rf data/previews/testbatch_002_top3\npython3 scripts/render_preview.py dat...` -> exit 0, 1 lines output",
"tool_call_id": "call_Ivb2PWx4KgSGAxmU5hfsUMWv"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_K6UQ7OQ2XQDF2fqYGK9xGfpq",
"call_id": "call_K6UQ7OQ2XQDF2fqYGK9xGfpq",
"response_item_id": "fc_02aa21bd75cf0633016a11449da46c81918a8d5acf4dbd27b2",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Erstes ai-004 Testvideo lokal rendern und prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_K6UQ7OQ2XQDF2fqYGK9xGfpq"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_3RSsy6NW8jAaCaoIRhmLGT7p",
"call_id": "call_3RSsy6NW8jAaCaoIRhmLGT7p",
"response_item_id": "fc_02aa21bd75cf0633016a1144a0cb708191bab4fc1358e0ab1f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest -q\ngit status --short\ngit diff --stat` -> exit 0, 1 lines output",
"tool_call_id": "call_3RSsy6NW8jAaCaoIRhmLGT7p"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_YXhBFBdBO3hJlfttufvxuLrm",
"call_id": "call_YXhBFBdBO3hJlfttufvxuLrm",
"response_item_id": "fc_02aa21bd75cf0633016a1144a97878819197cfe54a5ed58fcb",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":120,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/.gitignore\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Inspecting project dependencies**\n\nI'm trying to figure out if I need to generate a preview without tracking. The status doesn't show data or previews, so I might need to check .gitignore. It seems the pyproject dependencies are empty, although my code imports PIL, so I should add Pillow as an optional dependency. Even though tests pass, the package needs to declare this dependency. I’ll check the .gitignore for data/previews; generated media might not need to be committed either.",
"reasoning_content": "**Inspecting project dependencies**\n\nI'm trying to figure out if I need to generate a preview without tracking. The status doesn't show data or previews, so I might need to check .gitignore. It seems the pyproject dependencies are empty, although my code imports PIL, so I should add Pillow as an optional dependency. Even though tests pass, the package needs to declare this dependency. I’ll check the .gitignore for data/previews; generated media might not need to be committed either.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUSpxLFnY53BpFbS_ypa5M7_tUioO9BduVCVg7eDOSOIPSO9BZJK2bMkozpQ84lfmwr-HqxEVZUEWyXfjs0T33hVf-P1V86kgECsgzld4HpqDAAxFrUfxSA7AIMukIE5ndSiUaJhUIB4b6FG9al3mOZc_GBddSP_j5fNQN5BRg-XLxzhkMD_5cJN5lxkmq65GXOV9sQeNY96yqkuQvn-LZ14QZzcG-KSC9eeMe9XxxTlbnqpqDRDfdHMR33y4akJCAyOHav2hugDdRglItMOYubVCDX2lhJ4z_sSi_3ann9iVcvIbH-bUyv-j4wMzCwPNjig27WPCjEQbMTd1u-73sXOpbcGwKSrtAf8T4-feLK-yYNHQGTw__Fo7a0cVTxtnxYQs4nCE5eCNXXtBzr3gM-MUj48FcUrN5Ek10xKduIuufzuQS-FLZlq1U-iN8FrGr7Z_3Fq0oka7GDNmw-9ghBR5WmioQ3uXQ4YwfrfW4WoGWSTySqfebxRMhwvOplfG5Q86Mfa-K9UJMtY6976z7K_fmMUQbnEQ7EM6pYgt0tlu4dVfQnh8AbWHzEtBo_23cvl4Jf-46BEOs6zO_mhxo7fy3GrKZVvxC04OOqXZGxbN7D7pQgmhSHoep-l1GB7KkmbtxP4WHlJaPW5GZu31hvDG4x2h0PiKdHE-Onzw4cT6kcAOmRav2yU250ybDjiBR58TOYvj7FEU4SiOCvy2uhoKftEK1UqrkkhriVhpTe09AAwh4JJSLlBWYJ_tq9oG1HWZtCzxJIoWEMdiBVJXHBG_V8Yzhz3vx67rdmDga7HwLrW4ymeF7b362f4k6PDS69iP96OYldOR1ADdb7j6wfT9xRT5Dw3OLTRNjJ5XdD0VDj7PV9u8DTvcmPC2HCjnmiim8yB7Jd---sDDJ42bOuYGSKXmJjeSb0p2BiSoRlZIrUAkly-M81ldahcwBt7nocgGRiskp7WUkqdejATJHQUpsfrrMbBKjCHZKc289sqDSI84n56VmpY4twVfMRmTWfJxliXAuCkt8e3VfEuOdJ9P7q3MzzLn5x8gRSNfB8xZA9eRWtgZYHL5U7ncNYroHldCIt0Y_CiYBMJu-Q5PoClrANbU27ZqlR4vf0TCHp9H3mlK0PbBll7CSGPszba-HKFMgBrWwCwXLCyKUvEuHTk6uaxDmg8VK_bZP5ixveoGpymDRMFp5kvH07OgRV7oJGBzVe_Wv9npGODHDs6wbpjUWb7AksO3ZH_kqnelzRF1Zme7nvOx3gYB99EUzglVajSpqBHTilMvS26tCxJx_pkuSQTGa8c38DTStYZcFsIEICd3uYTokLc9llQPhFknN_kveIreBC183ukC4vDX__tvsS9L0dia2EZrJYkLFEBldTq5PM6pbRYuAhH_EcLrYvhLgcednJ4waC9Cly2o-V7RjUW7ZpAF_orYvZh55yLS-Blo34dr8mmGs2BdD92QjXO-02wmb040drKgOnzVkdSxR8cPywTLeMZroxcbtmTT-w4_DQ0EoPxCdqgXcIpvUx0ggkcAHC0VvKQlIa0RVL43OjrDcVwU6uAdq8nZNO4dJU=",
"id": "rs_02aa21bd75cf0633016a1144a5bb58819187c4d3c096efc12a",
"summary": [
{
"type": "summary_text",
"text": "**Inspecting project dependencies**\n\nI'm trying to figure out if I need to generate a preview without tracking. The status doesn't show data or previews, so I might need to check .gitignore. It seems the pyproject dependencies are empty, although my code imports PIL, so I should add Pillow as an optional dependency. Even though tests pass, the package needs to declare this dependency. I’ll check the .gitignore for data/previews; generated media might not need to be committed either."
}
]
}
]
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/.gitignore from line 1 (740 chars)",
"tool_call_id": "call_YXhBFBdBO3hJlfttufvxuLrm"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_YAevRR2Dy83drYT3zEgA2IDa",
"call_id": "call_YAevRR2Dy83drYT3zEgA2IDa",
"response_item_id": "fc_02aa21bd75cf0633016a1144ac5ba08191b2fc332ae95f30df",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"old_string\":\"dependencies = []\",\"new_string\":\"dependencies = [\\\"Pillow>=10.0\\\"]\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/pyproject.toml\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/pyproject.toml (563 chars result)",
"tool_call_id": "call_YAevRR2Dy83drYT3zEgA2IDa"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_xPZ2xVfIHwJycwe2HGRy3ldx",
"call_id": "call_xPZ2xVfIHwJycwe2HGRy3ldx",
"response_item_id": "fc_02aa21bd75cf0633016a1144b0f6b481919ce168250d9e0082",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest -q\ngit status --short\ngit diff --stat` -> exit 0, 1 lines output",
"tool_call_id": "call_xPZ2xVfIHwJycwe2HGRy3ldx"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_VG8FhiFA3qSPQJkXKPN6FAHz",
"call_id": "call_VG8FhiFA3qSPQJkXKPN6FAHz",
"response_item_id": "fc_02aa21bd75cf0633016a1144b7c56881918b6674b58792650d",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/preview_renderer.py autoshorts/__init__.py scripts/render_preview.py tests/test_preview_renderer.py pyproject.toml\\ngit commit -m \\\"feat: add manifest preview renderer\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `set -euo pipefail\ngit add autoshorts/preview_renderer.py autoshorts/__init__....` -> exit 0, 1 lines output",
"tool_call_id": "call_VG8FhiFA3qSPQJkXKPN6FAHz"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_mS4G9t8Bw0ZiSbKhQetzk753",
"call_id": "call_mS4G9t8Bw0ZiSbKhQetzk753",
"response_item_id": "fc_02aa21bd75cf0633016a1144bb93648191a38b1b7ef3853cb7",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s...[truncated]\", \"timeout\": 120, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `set -euo pipefail\nexport GITHUB_TOKEN=$(tr -d '\\n\\r' < /home/agent/.hermes/se...` -> exit 0, 1 lines output",
"tool_call_id": "call_mS4G9t8Bw0ZiSbKhQetzk753"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_4Qkg35QC52EQnU0kQeJU2LSC",
"call_id": "call_4Qkg35QC52EQnU0kQeJU2LSC",
"response_item_id": "fc_02aa21bd75cf0633016a1144c6bc948191aaece30f8ee0f8fa",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=noprint_wrappers=1 data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q\nffprobe -v error -select_...` -> exit 0, 1 lines output",
"tool_call_id": "call_4Qkg35QC52EQnU0kQeJU2LSC"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_zy5D8ukgpFsDzPuJdGdEytYm",
"call_id": "call_zy5D8ukgpFsDzPuJdGdEytYm",
"response_item_id": "fc_02aa21bd75cf0633016a1144cceff081918485a6d866525e07",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_zy5D8ukgpFsDzPuJdGdEytYm"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti.",
"reasoning": "**Finalizing media path**\n\nI need to create a concise final version in German, specifically including the media path as MP4. I should ensure that the absolute path is included; it might mean that a local path is ignored but technically still exists. I’ll definitely mention any final commits along with tests. It’s important to keep everything clear and precise, so the user has all the necessary details in one final output!",
"reasoning_content": "**Finalizing media path**\n\nI need to create a concise final version in German, specifically including the media path as MP4. I should ensure that the absolute path is included; it might mean that a local path is ignored but technically still exists. I’ll definitely mention any final commits along with tests. It’s important to keep everything clear and precise, so the user has all the necessary details in one final output!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUTSWF2Skq0pWeIVc-MIIThcv0-09F09Q8RS7d78thgTwEFhNn87GpQ9uE_sRvWR_BMeu-Aoxfk2r97jkXO1cDr6ztUnvYQx5VtgC70plbVXWmVYdPRdLOOI9t4rD_6fb69rGzR3H6mpg_Axv4cT6VgoARGuhNnoY5nDEhFEERtvVa7RoK9rtFGoCCOHiEBjSG6WQWvDapZW2rpZ4tDzyYEgNvhWT--OAntKDTqZU7xJeQGMd5sTQ_F_AQTgxcAqhU7vAtLtjf0771tVfnGBg6JuJAIc1TKxaQT2jG2DJ0hjiLcNRZtxWe5VhjGbshpgg_oxg8X63AEyF1wX86YHQQAg1pcF-cN1mPL6iFJGFM2A-cvY1bq7VEHSM2L9aQPqspYmxXoh9or50lFgMZAZ-cPgCxG6uexIZiFTUaiyxBFLh_scRmcQDGeWTMPvvjBJR9CGxQGYFHR1toEpkLzeMJf5hHJcOdqNN4_6rreKZcqu5upDQTn5AXC0CLfMKKmixurUNaFZLIK28_gxeHIPkxLfnKRcafH9gZHAD_MQcNgbh1YhnmNIvx_1ZysbqUX36U3JaukA4C4e3ND_tk3vipuw5uxHfIIu1zB41S9p3fD7lecy4oRnd-_gDO_EXJSCkL6oIqka7_sBsqLwhIPSKO-nYhq46ZQaGS6XOrxEw2dBwsNU-8TkpHXu_oqLgaQUGhMcH-qHg47rZkYirsKIjnFY_AdP3Xu7wizhstsxzUcK4Jm0msFjOXbRuzxn6S6ZKDZNw1URUiqHrOSp5SMmBXpMX-6JqE4va8etVrVJKvItxCBOekkC-QXaLDOg_eSLv45h78nyMnRQyNNbpfCF9_5rj-Fbh0VMGGFMNV7Z7nHDgaEjHJtdJuQJ16DwfW_Dzw5B17BO58CWlfwiOwfTkLngXTKcZgGMAj90blkGl20K0zaqyvrxu096FmZDcEdFiTM5cQ3tnDjmhDL_Jdn17IxqUu3pFRnaXb8ZkvKUtDebvjbHjDUaQJPVsAmZzfR1yX76jnYPtu2mCasT-ChgMuhBga0syXRWL4LCBSGH-JSLCx0Pb4wKVGdWGitm1oHSHBV6b-zWWTWaDq5urHALrYPOjA==",
"id": "rs_02aa21bd75cf0633016a1144d09c3c8191b916c52143aabd81",
"summary": [
{
"type": "summary_text",
"text": "**Finalizing media path**\n\nI need to create a concise final version in German, specifically including the media path as MP4. I should ensure that the absolute path is included; it might mean that a local path is ignored but technically still exists. I’ll definitely mention any final commits along with tests. It’s important to keep everything clear and precise, so the user has all the necessary details in one final output!"
}
]
}
],
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti."
}
],
"id": "msg_02aa21bd75cf0633016a1144d2ba708191ad5619de97d234fe",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Der text ist teilweise gross und teilweise sehr klein geschrieben. Mach nun weiter."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_jrO9l42RtYVxqZxuDqgEUmHe",
"call_id": "call_jrO9l42RtYVxqZxuDqgEUmHe",
"response_item_id": "fc_02aa21bd75cf0633016a114801be60819189318313e0c6cb2c",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"test-driven-development\"}"
}
},
{
"id": "call_D2ofhFUiq1crFVBQWRzFYlzw",
"call_id": "call_D2ofhFUiq1crFVBQWRzFYlzw",
"response_item_id": "fc_02aa21bd75cf0633016a114801be7081919e855123ef4df968",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
},
{
"id": "call_bOW0RYcevGqIAyi943Vvto28",
"call_id": "call_bOW0RYcevGqIAyi943Vvto28",
"response_item_id": "fc_02aa21bd75cf0633016a114801be7c81918e8f17f5a1ab2795",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"github-pr-workflow\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Evaluating video rendering**\n\nI need to take action regarding the user’s feedback about inconsistent text size in the video. I’ll check the renderer to see what needs fixing. We should load the relevant skills since a compliance scan is mandatory, and although they’re already loaded, I want to ensure everything works smoothly. I think using test-driven development (TDD) to adjust the font scaling for consistency will be helpful. I might check the frames or render the first frame. Let's load the skills and get started!",
"reasoning_content": "**Evaluating video rendering**\n\nI need to take action regarding the user’s feedback about inconsistent text size in the video. I’ll check the renderer to see what needs fixing. We should load the relevant skills since a compliance scan is mandatory, and although they’re already loaded, I want to ensure everything works smoothly. I think using test-driven development (TDD) to adjust the font scaling for consistency will be helpful. I might check the frames or render the first frame. Let's load the skills and get started!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUgBVtu19gD4wfr6twPEyBaahmgGimhs8zWfSCyrzQzJhKrmTStiQFcGttqgWWUYaCILFcxtQbqBfn7_sVC7C7-Eql35xBJ24I4RO2gMe7aEIdedd_4tYEV-UZNBCnNI0nP8StVZRp6_0Y3LdTxsvgW52yxro6AKifWuTFheqSSCSDrAvDifR_mnfOZCcNOfVghGu-P9cjEEaQpyxcBGQBn4n-4eRBhF76k77eU-mnE7DDdovK-n1s9eLIzZWR8SHA8pT0WAwAw_xz3Hvl8P0_JizfgnuC6Be44LW2KHPMxCaywbsnHqdriuLWaS1h0p0EExsRqYeptNEYb-qpe7tBCWftbQYPpNiy_1Z8e4JDZNtLwe81vKO2hji2jWkgAsNvSk66UEEBj30lCZxKFSaVYhGcpiq_yet-DNT9h5SSNi8yCqiEN8W0Rzl0pfvXbzxgR0T881NvujPLlCDcrhS2TkG1jLTXKMW4y0gqs9F_Zueyn0YDArPC0-gFW81MSMC8lh8ZunGD4uH05fttEE5zaNC7rOJcZwae4ZPDSPwJDtx5YOZPHIZAC-JaiO4GgXvJBAqnKhjjn8wngf47lyD4OlojrEffX36jcsUctXwLzrXWfRnFKcZl_T3MpHWMPrxdn-cjPxs0H1P8_7zzqBz80AuQHuSwtlglIbATEYmz_AWAl6Kw5gs0Vx3vRlAC8H2kJycjVNeM_qHwROQ2z-uFiLIfysPIVmYPcdSp63ezQdN33C7ZYFis7-CR6AGfwmkBWfLS2QJaU8yY4m2WWuxooRJm2kkOsVo10UX2tW7nlgvUDGgcpIEBY4jZTkpnyACcC9B5fHlhddgjG_OTS84ibNUtie2Y-v59hariAVgRtqb5GmmywW6BxV6l84GORsCIOQ0FuuDRWDipq7siJCbU8EhY3jDtTEDgI1CjhggD-qrUxWNimdE-sFq5dN9tPfNy6UBC88KCHajXvfudodrCZyMKbkMf-cZQYvC_7GDH9-zS4XHSDzZW-szBZpAxq65hB071fzBZgjOzh7GHzP375D1EW-3t6sW-ldZaQrm4alHfLgtbiNMPdxpbRz0PpO4YRPqjBAjXQcQ64RyKmrOnmAn73Qb6tyMSQAKJDMav8mE7zmCF7y5Y9e2f8eDVZju7PiOMuU1AOzjGolZcJgD598TjmE3EnOrLcZBFUDbJ6rLmqN2aExhpKdZvKXJnbWjc7fEBtZFbvbhCq11aOrJCM4f9QcsLk06uYOOs11kb750GFcUbvbMG6sfNcxN_GddsnuAUEjfT9iuxufpFlMYKbMVoW4rgPfKo2-jt1hgUbGfQ43fMoKRHmnuKkaly1hpVYPoEu2eeo6f8vX5FlbpwkX2Kdj4hfYnKpQORxVXsE8gwc=",
"id": "rs_02aa21bd75cf0633016a1147fe9ac08191b5e35a04b54b5429",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating video rendering**\n\nI need to take action regarding the user’s feedback about inconsistent text size in the video. I’ll check the renderer to see what needs fixing. We should load the relevant skills since a compliance scan is mandatory, and although they’re already loaded, I want to ensure everything works smoothly. I think using test-driven development (TDD) to adjust the font scaling for consistency will be helpful. I might check the frames or render the first frame. Let's load the skills and get started!"
}
]
}
]
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_jrO9l42RtYVxqZxuDqgEUmHe"
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_D2ofhFUiq1crFVBQWRzFYlzw"
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_bOW0RYcevGqIAyi943Vvto28"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_CV1pGlPWOkk6TcIX1PX8ZVnB",
"call_id": "call_CV1pGlPWOkk6TcIX1PX8ZVnB",
"response_item_id": "fc_02aa21bd75cf0633016a1148044a2881918d18a337f1b564a0",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"file_path\":\"references/manifest-preview-renderer.md\",\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[skill_view] name=social-media-shorts-automation (3,343 chars)",
"tool_call_id": "call_CV1pGlPWOkk6TcIX1PX8ZVnB"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_6oshjN8i2YO900gb8wC0NCPT",
"call_id": "call_6oshjN8i2YO900gb8wC0NCPT",
"response_item_id": "fc_02aa21bd75cf0633016a11480875a481918c794faf8dd58ba8",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"todos\": [{\"content\": \"Renderer-Feedback reproduzieren und aktuelle Frames/Tests prüfen\", \"id\": \"1\", \"status\": \"in_progress\"}, {\"content\": \"TDD: Konsistente Textgrössen-/Lesbarkeits-Tests hinzufügen und RED verifizieren\", \"id\": \"2\", \"status\": \"pending\"}, {\"content\": \"Adaptive Font-Fit und konsistente Text-Hierarchie implementieren\", \"id\": \"3\", \"status\": \"pending\"}, {\"content\": \"Preview neu rendern, visuell/technisch prüfen\", \"id\": \"4\", \"status\": \"pending\"}, {\"content\": \"Full test suite, commit, push und remote verifizieren\", \"id\": \"5\", \"status\": \"pending\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_6oshjN8i2YO900gb8wC0NCPT"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_gKhz4JJJbgZcG55Yp2wbNrch",
"call_id": "call_gKhz4JJJbgZcG55Yp2wbNrch",
"response_item_id": "fc_02aa21bd75cf0633016a11480fdf1481919cc1049da8673675",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\",\"limit\":260}"
}
},
{
"id": "call_X4pX3d0gBX6M8Nr1aA7lLlzk",
"call_id": "call_X4pX3d0gBX6M8Nr1aA7lLlzk",
"response_item_id": "fc_02aa21bd75cf0633016a11480fdf4c8191920aef441613e600",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py\",\"limit\":220}"
}
},
{
"id": "call_6FBeJ2e3W84hGIDYoraq94dl",
"call_id": "call_6FBeJ2e3W84hGIDYoraq94dl",
"response_item_id": "fc_02aa21bd75cf0633016a11480fdf6481919e6df98ae74c119e",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py from line 1 (9,671 chars)",
"tool_call_id": "call_gKhz4JJJbgZcG55Yp2wbNrch"
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py from line 1 (2,169 chars)",
"tool_call_id": "call_X4pX3d0gBX6M8Nr1aA7lLlzk"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q` -> exit 0, 1 lines output",
"tool_call_id": "call_6FBeJ2e3W84hGIDYoraq94dl"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_caMww15Sv6BgRg3TNZWgJ1sf",
"call_id": "call_caMww15Sv6BgRg3TNZWgJ1sf",
"response_item_id": "fc_02aa21bd75cf0633016a1148178e288191aee1e9f21805328e",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"python3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nfrom autoshorts.preview_renderer import build_preview_frames, write_frame_images\\nm=json.loads(Path('data/render_manifests/testbatch_002_top3/ai-00...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 - <<'PY'\nimport json\nfrom pathlib import Path\nfrom autoshorts.preview...` -> exit 0, 1 lines output",
"tool_call_id": "call_caMww15Sv6BgRg3TNZWgJ1sf"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_lAo6CaUNA9ANeC7ZTuixKEZv",
"call_id": "call_lAo6CaUNA9ANeC7ZTuixKEZv",
"response_item_id": "fc_02aa21bd75cf0633016a11481c99c0819198cf1b3cd65a19b8",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_ai004_frames_old/frame_001_scene_01.png\",\"question\":\"Assess the typography and layout. Is the main text too small or inconsistent for a vertical Shorts preview?\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[vision_analyze] 'Assess the typography and layout. Is the main text' (238 chars)",
"tool_call_id": "call_lAo6CaUNA9ANeC7ZTuixKEZv"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_51Ewb319ubXSqJyteK6LI8ax",
"call_id": "call_51Ewb319ubXSqJyteK6LI8ax",
"response_item_id": "fc_02aa21bd75cf0633016a114828efc48191931683e395b7dd1b",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Renderer-Feedback reproduzieren und aktuelle Frames/Tests prüfen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Konsistente Textgrössen-/Lesbarkeits-Tests hinzufügen und RED verifizieren\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning for font testing**\n\nI need to address some testing requirements for fitting. It seems like I have to add metadata for font sizes in the frames, but the current frame dictionary is lacking. I'll create tests focusing on main font sizes between 31 and 45, with a maximum line count of 4. Retention might be an issue if it's too small. I'll also need to clarify whether the user meant uppercase/lowercase or if they were referring to different font sizes. Consistent sizing is essential here.\n**Enhancing text size and consistency**\n\nI need to improve the main text size to make it larger and more consistent. The body font should fit well within the card layout, and the retention goal shouldn't be too small. I’m considering using a pill design at the bottom with a size of around 0.75 for better visibility. Additionally, I should reduce the blank space in the card. I’ll implement these changes and plan to update my to-do list accordingly.",
"reasoning_content": "**Planning for font testing**\n\nI need to address some testing requirements for fitting. It seems like I have to add metadata for font sizes in the frames, but the current frame dictionary is lacking. I'll create tests focusing on main font sizes between 31 and 45, with a maximum line count of 4. Retention might be an issue if it's too small. I'll also need to clarify whether the user meant uppercase/lowercase or if they were referring to different font sizes. Consistent sizing is essential here.\n**Enhancing text size and consistency**\n\nI need to improve the main text size to make it larger and more consistent. The body font should fit well within the card layout, and the retention goal shouldn't be too small. I’m considering using a pill design at the bottom with a size of around 0.75 for better visibility. Additionally, I should reduce the blank space in the card. I’ll implement these changes and plan to update my to-do list accordingly.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUgoQJzEuO8fzg5KrxMFfnVkpcRfBPm_2pXDPooC553go-eQAuaeqshY_OrZ8KrJCaKqyKKcsUYoPih75rKNJ5oFAkSfD13pbv4uWGl5wj5mIwIWUMEMeVrChitArXWbVdqstu5osxRcwHtzI9qX5UiGYKllh9T23ZjVKxreTXIK9EpwqISXfrkZXVuu6eoMwxQQJXZZ21X2Sk2tKgHZ1m088XDPfZgoAEDFg1ej4dtvjGhLy6gF3aizM70_l5_ojj78s4PdFpFBhZW-V9k6g9YJ_S6kC3pYFffdoRfBjl4PDY1SUNdEEoYs3gIpA5XFw0psEl4T7FraPj8xSIegl-hwRYOPRD9_ZxWblcB8LHWvBgbD4iZBg6ZFi4uawROiqiJlqahjPDx7t0CI-yZTsj-rUKvtu9Llm9Bpf0MZdCQE9E0lCn7o9Mz0ZSjqoCbji701tqyRPQPwkwDvG3jZB-hAHAdwgf2rEt_Gb5JNUwh7I2gO16gAs82ArYmPzG_Yoz84MxezNLdBAdhtQrkr9RoDsoFJ4ITNS5qtHxu5Th3HmuzGBQTZ4adMMfGb4a1NezpLF5eZGHerFJm3uSY5nKNn2cGrspQOUpdvR0bOH6MUQb55hX3e3HapgRElhXHYz-_XrNHuAPy0RbAansiQX4ODuz32dGW3UW6PCrexaRr4dlp4PYdGJIlATMHEaVqkzikIPsnONG-AgvrOS81T8V2waeqgTv6b6clrRGavwNcJr9j2DPJI1OMRIXq5mFyrYG-K1jSbHmJN65_tP0xksNV6CSCpwm5YTVXFm6YPBRyWrp0vxSX_vCf-BJMwsK9CkzZKcnROWMjxSUmP-C6kIx3KzCkPSdQFFQNzgmGvxvjFSwfckxzxFMgwKLp-R1HDzT_yvnSr_BqNw8DrroCe33ta76-1PM34T8KblPsnMOnluVrzpbR2CTQKySIyq6q8xSyGAWKWUwh04Ojv1D1pCh2KoOie1DPfwH9HdP4poSCdxPVQTAiH3NalbpcrUkinEVQJ4B5SAN8Yvd02BeDsuYQJ3M0FqiaOBeSnrco4_Zt01SZpey97-JX2TtmzKGdlQMn0-nCJ3q-iAdS17r8j7_vwEGkv2IJ8826LvHr84WY2urxQJRBprVQeHy2lNj75GFCzHcZQk1u0oJP6tS_VMY3CyKBu5O1cx_WIHdXE3CrwmUI2O8jILtapDGBi1pgLxe5iDF0ls1fIAlLvjv0Rd_CgsCm4zt7OrSLVVZ3FF8QrWat_2FJlIfS2IU8LXeyq8h_9BhghENHKf7jmXANM_INvQ1EmlZWjqgoKV6SR3sdEcuADl2DVMrtcjccEr2aXcJENTuk_MefdQJNsE0iW0Yq_Bhj6USsJnhFOieQD2rF2wup7sktj4bGOYpainLREUU_H1ByM0Y3JRA7II5GReYgd0fAP6REgCNM5RS5FTfAzTh0xwRnXa1AxyOkndonb_jSVL7raXRF8y7HVt1LCcK4VIFL_kS2UuAeWiyN5qhATStCSwQ7CkYFwyvmbEDgnjQHGf9vAu8fClfQiXTAaQ-uBcg-yzvd79i913OCsCqjg1nY9Ze7ezMAhPIg7e_oJjtGKEPBA8YPo69l2Q2W3TWY7JUgieYZmP4KOQlbYXAlNjhk-JFsun0Pox7qtQzcV4kgLo5SESmfqF7UyFl6hnL2EmLwkEVUouuA235ooEVlqHSfv_CWpqUY-bCqtyS_LLucRWPETgMnNzEgeuhTU8JCxk_wInwDgNG6xVz-HbCrnwUrsNDiU9oJ61TzYtGbEuXeJQ1pgy9eVVT0h9-XgMWE_rDj74e8zCD-FBQBBA8M6qyEfQs_wykmXjoSu5VX9d07vz6KgkKGTuDHJLLLUqNiBAgYeKL2cxHdRggOHXo1XncipEZznBNVXIEojnXo0IcxHj-mD7FdpXtxlYyfeZzfdN1KxrKH3awow_cDTAj1XtvIgfYF09IjygkuYlqPah5c5qB7PSIy5rqeZN75_nKl1HRooMsJmlCeLr6kgeajPk2eGJPlTMZhEQ81JjQGivKHr9tAzxZ1KnQpnRmlR4DUGCacVec57ojFCQtMAzzhlG6YZnPzYGizkfA39T10D6LgTAa_xl7roV36zqteSiONXBq-58t8RYQfHRqCD6SC1n0aghwyxKp8BjRQXUoiZF5ToesjGKJI-aUEw3U_Jd_JJTHKGdWHrJVeLprXvKka9MP_b9W2LCZ0r1mNz_HIeHzACdxiy_WyylDRqFioRmv0yIq-LYQA2Pf-1AYii9u4wPAeQVE2627vJGLFnlTtWttfju2WQU1jDteLl7clTIbPxtxehmh87MsU7untMeEPJ-KWaOFOehbewiY38WAas9_Hz",
"id": "rs_02aa21bd75cf0633016a11482110a08191bc2ae7dc94d28077",
"summary": [
{
"type": "summary_text",
"text": "**Planning for font testing**\n\nI need to address some testing requirements for fitting. It seems like I have to add metadata for font sizes in the frames, but the current frame dictionary is lacking. I'll create tests focusing on main font sizes between 31 and 45, with a maximum line count of 4. Retention might be an issue if it's too small. I'll also need to clarify whether the user meant uppercase/lowercase or if they were referring to different font sizes. Consistent sizing is essential here."
},
{
"type": "summary_text",
"text": "**Enhancing text size and consistency**\n\nI need to improve the main text size to make it larger and more consistent. The body font should fit well within the card layout, and the retention goal shouldn't be too small. I’m considering using a pill design at the bottom with a size of around 0.75 for better visibility. Additionally, I should reduce the blank space in the card. I’ll implement these changes and plan to update my to-do list accordingly."
}
]
}
]
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_51Ewb319ubXSqJyteK6LI8ax"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_X3z8x5FzR1f9mb9qzHHGZm1s",
"call_id": "call_X3z8x5FzR1f9mb9qzHHGZm1s",
"response_item_id": "fc_02aa21bd75cf0633016a11482d5d008191aa42d3f6420b1334",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py\", \"old_string\": \"def test_write_frame_images_outputs_png_contact_sheet_inputs(tmp_path):\\n manifest = _manifest()\\n frames = build_preview_frames(manifest, width=270, height=480)\\n\\n paths = write_frame_images(fr...[truncated]\", \"new_string\": \"def test_preview_frames_keep_main_text_readable_and_consistent():\\n manifest = _manifest()\\n\\n frames = build_preview_frames(manifest, width=540, height=960)\\n\\n main_sizes = [frame[\\\"typography\\\"][...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.py (1,250 chars result)",
"tool_call_id": "call_X3z8x5FzR1f9mb9qzHHGZm1s"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_WlPA0YEkL3chSsIiClSfVkj6",
"call_id": "call_WlPA0YEkL3chSsIiClSfVkj6",
"response_item_id": "fc_02aa21bd75cf0633016a11483753488191890a3d343c9de981",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_preview_renderer.py::test_preview_frames_keep_main_text_readable_and_consistent -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest tests/test_preview_renderer.py::test_preview_frames_keep_ma...` -> exit 1, 1 lines output",
"tool_call_id": "call_WlPA0YEkL3chSsIiClSfVkj6"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_gERm4TO9HKGrAbbJnr2N9ACd",
"call_id": "call_gERm4TO9HKGrAbbJnr2N9ACd",
"response_item_id": "fc_02aa21bd75cf0633016a11483e6cf88191a956c923853d5fbc",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Konsistente Textgrössen-/Lesbarkeits-Tests hinzufügen und RED verifizieren\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Adaptive Font-Fit und konsistente Text-Hierarchie implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_gERm4TO9HKGrAbbJnr2N9ACd"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_8LBBs5eXYTvPhbaKxcWd8N87",
"call_id": "call_8LBBs5eXYTvPhbaKxcWd8N87",
"response_item_id": "fc_02aa21bd75cf0633016a1148464fbc8191979342bb216daa1b",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\": \"patch\", \"patch\": \"*** Begin Patch\\n*** Update File: /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\\n@@\\n def _wrap_text(text: str, width_chars: int) -> str:\\n return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\"...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning a patch implementation**\n\nI need to implement functions like _line_metrics and _fit_wrapped_text that return the wrapped text, font, size, and line count. For a width of 540, I should consider minimum sizes. There might be a main constant around 38 to help fit down. I need to estimate characters based on size and loop from max_size to min_size. I’ll ensure to handle line counts and height constraints while using draw textbbox. Careful execution is key here.",
"reasoning_content": "**Planning a patch implementation**\n\nI need to implement functions like _line_metrics and _fit_wrapped_text that return the wrapped text, font, size, and line count. For a width of 540, I should consider minimum sizes. There might be a main constant around 38 to help fit down. I need to estimate characters based on size and loop from max_size to min_size. I’ll ensure to handle line counts and height constraints while using draw textbbox. Careful execution is key here.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUhGMF8fUlSNvA2nGLs-s6Y_Z27BWSXesOCSogwD5hgWfcevRpC7-5CtbUXBkhN5gyYxjK86KWWmMWyjUBgwYpe8FbbW1joPvEd-qikvQ_Oy3Y7SvqyRUberL4h77sWbWUqG7MvSViWz8UY322d_DMoN6q9MSvnvYoAH0r5PpZY8qj3mUEAvhzMb_EAGUMiUr3k5h0MSoyBBI_c3tf5Lrx3Q7DGwNwgX1eiAv7BmBmBKmIAfBml2q7jN2Iuxvr-oSL-ieIEaQIbpdZBDYZFlfLewnOWZkAJqrSNMEeIp6o37vTRXbTSfbLGVD38Kp9sFoZgGj8PO8tIQSzh1NxC0g8XngrphVh9XWNpRUX32mLIwDzYhE22JJneJzHlBBDzOF-4TpWybW6zewmN9F-7XlHszyHGCeAg8Buaskj4sLatd3ewit0ToyAElgPr6dIS43ukufGnE2Mj_cGkygDXrwTA7tvu08MUFVMmuU0frO5K3pbAQV8MnwemXVpJUaw_QdXgeLJH-qEefVDiTvq2FZdC-MqJD0v2yB0vwVeuoPU5CvkS-L8oid7q_pRwMITdj8ITS1koUElws9B0pu54AfGKKzjh5qJXjYiqCiHCMJyKhgpr-Nm04Z3K1BEbRJT95Y0N-fSLGUdgFKQnG93fvBxnetCewWgPYTlaVoQkYZMoAxNGL94pTVInAcFjbC9iXuHdLtMF46R0t7qVjshYc3tdgvQ4bZO2auuddYZtYZmWubMFQYn2S1NR7UvNReZkMuSqyrYkBlWCx84WZXPCl1Oih9so4fikd07YaGFs3n2XPcnilJ9qSzJfGjoVhCfcgBFOaRf7qic8STdBhF9ED024WWCZObTy6PTR0LArg2CjrnZScvGjD9mimXQ8MaW5pL0fL0cQHWfQ1Q9SqQiKolCz7-MgcDdVJlZjqSMNaxEgVw2zPUO_VhCU6NCUz5LBpywzLd4aXpFsNXi9jp9NXkwyTn43T8EVFcHcYYG3a8T21wnIq0Qql6OdmOBh4GKHTlVxgWimtFMGSO1ITAeJqijQJpUfi-SUfSyFvdHYObT_bWIFM138P_K1QHotPHHTDeYxwua1N7CkDNf7KToYpB-lwDZfDN9wAz8H6HP2VFMaEqJjPJTD6OGHuXEmAxBZzJUILroUAiFGeZKgHE7HOsU6uLo2IbhqLtxwiux34YurOzDumtxXztiG1n0j4PzvdQgneyCLv4LDS7ryoV2oShFlB-Z5UP81yi4FUZQqg_HzEuXpLxLbbwXOQWjbOqSLE8BCLtJbVZ7AsnDwouZ7UZk19EWCoVW_-GijpWV8bhqkeHSRGiEYDNqz3i4D43w5KTKK5e1oZc6eEBlGjeqbLQDt-w2VPC7WzAXI1fFBat1svAiVKSOzlEt198CA18JwWUXjN19j3orhkwT9-zir_dZmtm7nTxOwKUgtReZbOtKSMTk0yRwuvS11_ihRVTtWsiiE0r3TSSPzTJjW36nDywVFCl_1f_NQ9zpGkwydg8mz_Cfp_c6Wr44nmTol7xZy7BqucMrQ2yfhXwbNZ1HJqldl-kA==",
"id": "rs_02aa21bd75cf0633016a1148424db08191925d7762f0dc2441",
"summary": [
{
"type": "summary_text",
"text": "**Planning a patch implementation**\n\nI need to implement functions like _line_metrics and _fit_wrapped_text that return the wrapped text, font, size, and line count. For a width of 540, I should consider minimum sizes. There might be a main constant around 38 to help fit down. I need to estimate characters based on size and loop from max_size to min_size. I’ll ensure to handle line counts and height constraints while using draw textbbox. Careful execution is key here."
}
]
}
]
},
{
"role": "tool",
"content": "[patch] patch in ? (282 chars result)",
"tool_call_id": "call_8LBBs5eXYTvPhbaKxcWd8N87"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_M8CcSwQh84ervuQJRYpu8SE2",
"call_id": "call_M8CcSwQh84ervuQJRYpu8SE2",
"response_item_id": "fc_02aa21bd75cf0633016a1148661ff481918117cc573fcd469f",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\", \"old_string\": \"def _wrap_text(text: str, width_chars: int) -> str:\\n return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\".join(text.split()), width=width_chars))\\n\\n\\ndef _gradient\", \"new_string\": \"def _wrap_text(text: str, width_chars: int) -> str:\\n return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\".join(text.split()), width=width_chars))\\n\\n\\ndef _text_block_size(draw: ImageDraw.ImageDraw, text: str, font: An...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Troubleshooting patch failure**\n\nI’m considering whether the patch failure is due to an absolute error. I think I’ll use Python to read the sections carefully. It seems like using smaller patches might be a good strategy to address the issue. I’m curious about how that might help resolve it, so I’ll explore that route. Let's try breaking down the problem into manageable parts! It feels like we might find a solution that way.",
"reasoning_content": "**Troubleshooting patch failure**\n\nI’m considering whether the patch failure is due to an absolute error. I think I’ll use Python to read the sections carefully. It seems like using smaller patches might be a good strategy to address the issue. I’m curious about how that might help resolve it, so I’ll explore that route. Let's try breaking down the problem into manageable parts! It feels like we might find a solution that way.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUhmdlk2s8xscQ-2d_FoPHHKyjeb5-7irKf0bPFn2Nhv3q32o4TdnTGBeAlpGnOIxAVCUKudzU-f9pk30XDiDU21d0Klp3WW_3Lm2a6ff2BVHb4gdbEEozfibuaMELrO_9Yg3GWgkX8iIVPsk4NK3ACpYBXVY2a0soNYrrhDs6guQtrESmK_CJiG1gczlMhLCaJ2GUU8nxhsGxeu8N0kvuODMFRmPJ-FHusd5RnbMASdmfv9LTCh_aDN2Y_Uz9Dpx_GG-tuPbxjf0D01FK-KBoVuXz-RaXov6_r_xX27jcV_JjeLeZGy938_Qiq9Itv523YSjDJ6wj46-HQoDsDnORCpCc2sWF-tNxrAWeMIalWdjjk7FbLi_NUmbDcrwvtOt7_NEgqO-DrHYHytJMP6N6J-pqFcjeM22S_4_504xsv8BpuXUcRnJmApTYur654yQRRccvhFE_HElYKEmH7s1WXzbQRvfP-oCnfQEPllZXOft6dk78evg_KT7WKYF45cmova7JmbSABsPwFH_h9dIsjXWULeMAdDwtDpdPa9bBejopMs59Alx5VNyTg0nbS6hvUfQz_Q6fCqa9f56P0yUvI1fT5ODDgCDXOIOIZu1g0QrfPAvAO1Eoy5ky7_WAIfQCcXC_rrbTDstABv0GGu8OkQmBT_uKx5KFQjKMjAcZgNS-yqyjWx3huf-6ZfLg_lvSYXVPbAzldbreiVq77ZywiZpZckv9jbGKwPx9LluryapY6I7eCbaZQABCRjha-1KzUPYdUk6pfIZ0VaE5Th-MXNBKO61zv8qGfge-qdtE_Pl3eyI0T6Z3kouC1aikIeO8iCHUEi_QrtercIo_oQK1OLW5XaADXJKH9e35m24W2mWeoCxhSmHqbsssCMGlUeEPTNQKzy7dFGsTEnZGNaSQV63hfhx_LE-4hnlKW33Qi3AtuqoNsFVxdTYdwOS9-j09F_9jm5L6oJheKYLWSqYoB8JOwiOSa9lBNt3k0puyB4zJr0x9kZKhrSihtmhHqvGHh-BX2UxSi47PsqrLlKdQ5JLw==",
"id": "rs_02aa21bd75cf0633016a114863b9c081918a9061bbf4da6fb2",
"summary": [
{
"type": "summary_text",
"text": "**Troubleshooting patch failure**\n\nI’m considering whether the patch failure is due to an absolute error. I think I’ll use Python to read the sections carefully. It seems like using smaller patches might be a good strategy to address the issue. I’m curious about how that might help resolve it, so I’ll explore that route. Let's try breaking down the problem into manageable parts! It feels like we might find a solution that way."
}
]
}
]
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (3,011 chars result)",
"tool_call_id": "call_M8CcSwQh84ervuQJRYpu8SE2"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_5vsl1r0usdMZjGffWKUxxnxr",
"call_id": "call_5vsl1r0usdMZjGffWKUxxnxr",
"response_item_id": "fc_02aa21bd75cf0633016a11487c406c81918f26ec2080ad656b",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\",\"offset\":110,\"limit\":100}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py from line 110 (5,305 chars)",
"tool_call_id": "call_5vsl1r0usdMZjGffWKUxxnxr"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_F3iyuzahOQgF3oh4eGoKLpyr",
"call_id": "call_F3iyuzahOQgF3oh4eGoKLpyr",
"response_item_id": "fc_02aa21bd75cf0633016a11488200e88191a6fd1ff0703319d1",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\": \"replace\", \"old_string\": \"def _render_scene_image(item: dict[str, Any], manifest: dict[str, Any], width: int, height: int) -> Image.Image:\\n start, end = BACKGROUND_BY_TYPE.get(item[\\\"type\\\"], ((24, 24, 27), (64, 64, 64)))\\n ...[truncated]\", \"new_string\": \"def _render_scene_image(\\n item: dict[str, Any], manifest: dict[str, Any], width: int, height: int\\n) -> tuple[Image.Image, dict[str, int]]:\\n start, end = BACKGROUND_BY_TYPE.get(item[\\\"type\\\"], ((24...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (3,961 chars result)",
"tool_call_id": "call_F3iyuzahOQgF3oh4eGoKLpyr"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_xV3DeoAqPecoWsqmsnO4CySB",
"call_id": "call_xV3DeoAqPecoWsqmsnO4CySB",
"response_item_id": "fc_02aa21bd75cf0633016a1148a8f51c8191a5cff46bdd956354",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\": \"replace\", \"new_string\": \" frames: list[dict[str, Any]] = []\\n for item in visual_items:\\n image, typography = _render_scene_image(item, manifest, width, height)\\n frames.append(\\n {\\n ...[truncated]\", \"old_string\": \" frames: list[dict[str, Any]] = []\\n for item in visual_items:\\n frames.append(\\n {\\n \\\"scene_id\\\": item[\\\"id\\\"],\\n \\\"start_ms\\\": item[\\\"start_ms\\\"],\\n ...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (1,046 chars result)",
"tool_call_id": "call_xV3DeoAqPecoWsqmsnO4CySB"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_uS64pzrUs0BApZ1Z7YlnlfkJ",
"call_id": "call_uS64pzrUs0BApZ1Z7YlnlfkJ",
"response_item_id": "fc_02aa21bd75cf0633016a1148b191408191aff9f4e26ea827b5",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" return int(width), int(height)\",\"old_string\":\" return width, height\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py (660 chars result)",
"tool_call_id": "call_uS64pzrUs0BApZ1Z7YlnlfkJ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_f6P4fuD0NBFOsksjOtMqWOTR",
"call_id": "call_f6P4fuD0NBFOsksjOtMqWOTR",
"response_item_id": "fc_02aa21bd75cf0633016a1148b687f88191aef27ab15b85e8c0",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_preview_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \".... [100%]\\n4 passed in 0.92s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_f6P4fuD0NBFOsksjOtMqWOTR"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_unMECi7UgFjEhCicPfisNdiY",
"call_id": "call_unMECi7UgFjEhCicPfisNdiY",
"response_item_id": "fc_02aa21bd75cf0633016a1148bcddc88191ba6b325edf97d90d",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Adaptive Font-Fit und konsistente Text-Hierarchie implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Preview neu rendern, visuell/technisch prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_unMECi7UgFjEhCicPfisNdiY"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_hlKqBvnx2s414ZZtsMqa7Fa2",
"call_id": "call_hlKqBvnx2s414ZZtsMqa7Fa2",
"response_item_id": "fc_02aa21bd75cf0633016a1148c05ca48191a6a19ecf80d94853",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"rm -rf data/previews/testbatch_002_top3 /tmp/autoshorts_ai004_frames_new\\npython3 scripts/render_preview.py data/render_manifests/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json --out-di...[truncated]\", \"timeout\": 180, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `rm -rf data/previews/testbatch_002_top3 /tmp/autoshorts_ai004_frames_new\npyth...` -> exit 0, 1 lines output",
"tool_call_id": "call_hlKqBvnx2s414ZZtsMqa7Fa2"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_gwLHaGil24wqjWS1vrZAZ9hV",
"call_id": "call_gwLHaGil24wqjWS1vrZAZ9hV",
"response_item_id": "fc_02aa21bd75cf0633016a1148cc00748191ace81001e7957d69",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_ai004_frames_new/frame_001_scene_01.png\",\"question\":\"Assess the typography after the fix. Is the main text more consistent/readable for a vertical Shorts preview? Mention any obvious remaining issue briefly.\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[vision_analyze] 'Assess the typography after the fix. Is the main t' (285 chars)",
"tool_call_id": "call_gwLHaGil24wqjWS1vrZAZ9hV"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_kKYUR8m33O1Eqc8qLpdfuVxC",
"call_id": "call_kKYUR8m33O1Eqc8qLpdfuVxC",
"response_item_id": "fc_02aa21bd75cf0633016a1148d45f0c81918102a8b77204e290",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"for manifest in data/render_manifests/testbatch_002_top3/*.json; do\\n python3 scripts/render_preview.py \\\"$manifest\\\" --out-dir data/previews/testbatch_002_top3 --width 540 --height 960 --fps 24 >/dev/null\\ndone\\npython3 - <<'PY'\\nfrom pathlib import Path\\nfor p in sorted(Path('data/previews/testbatch_002_top3').glob('*.mp4')):\\n print(p.name, p.stat().st_size)\\nPY\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Evaluating rendering process**\n\nI’m considering whether I need to inspect all frames or use a montage. It might be sufficient to run tests and push changes, but first, I could render the top three previews. The user mentioned generating all of them, even though we only fixed ai-004. Currently, the CLI only allows rendering one, but I could loop through to render all three locally without code changes. I also need to commit code and update the skill regarding typography consistency feedback. Let's focus on tests before the final steps.",
"reasoning_content": "**Evaluating rendering process**\n\nI’m considering whether I need to inspect all frames or use a montage. It might be sufficient to run tests and push changes, but first, I could render the top three previews. The user mentioned generating all of them, even though we only fixed ai-004. Currently, the CLI only allows rendering one, but I could loop through to render all three locally without code changes. I also need to commit code and update the skill regarding typography consistency feedback. Let's focus on tests before the final steps.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUjUIwlYNAHnhI_AAwiFtzB49MUr3eIsna1xBy_jA1smQUHlNkba-7ZO2YFkB1_LQ9p9e52oC0wKhYISY5Tlnlhl9ftpFuWNK3zMcQp6cDi8wQ_SdqsUE-Mz9Hzm0PitbpM8OjlOply0neviU3-hkWwmKHAqkmahPormYUqKMSOxZWVqfdLXfp4-udi6KRvdWDTwxWsVjd64DlhzfA-610VMvmD-vM_PC7047tp4cao8O4XpByfHKYvB496vilWgXC-kPEwoAwgtJK7tfd0WAGbkILE6EQwhBIqKgPx2cdktsYaY4xkNBI5ZJE5NJNUGWNU1Lf34x8DNVjLnsFf14NbbGhNYpYPw5lY-z5z4PlPTzTGJ9PKOAggYdp9Xu3msGbJ3B04i1xoxqKBC4V268PXDW8iOD_XOENp73dBJT_LezLUT5s8yqblqAke5OsjJXO0AJ9SpCNMw2m2pDSO0gJ5aCnHsq-E9TLxKQB7UDbl0xnySZtVgR-OpzyAlLovDSO1g1gPNjB3w2y-zEd0Xs0fsm69De5pGmWTMwMvznUi2CSvCjOLGbgKiet-rq8byiCHNXmI2rZ-07-feC1_F4zw17yA64E4Io4E2W9LZ_UjU5Fa7_S_KqRVGdGVIBlLZ-Xrvns9b4oyNzadO5DWYlKaU069pFjaLNxRVfKuj0jyCADq9HL6f4EcYyrW5w46eSyfBelw1BupGf7-gbqLDvKgtZGZ_c_2DSxC97SaojeEZ2dwH-07l3xHJnFjmGitT_sP8zCO_xRA8_VAywKjS0_kOChOUuNM5TQQD7MhsbTBXT4-yzK8RAkH3XeoyHThGw3YPCmYjqKQNhuwZwLNALKst3oBLRt4wUtPsgNburkC7SZyQyHZb0YuOeFpXdzOn04EZ-xzaYBzZ1EGeLlE563He4E9WKPhkp2ewLj7reawTfHrUyY1BceHZJ0FLyP9b-K-X9YGLgKhlHAdkY-XDPuE8FjXUuDMT2iQVsszKNQDNZ11MRSkS_4I-0qvZsF9OyiG9ZuL-Fxn252gnbI9QY7Hy18aWY4CX8NRBhXYX6qmVjgOASfO61Ogzo-X54QBJBs9Puha1N38Ea1SkEd3txf7M6AQ6G_HN6neb8e_xzx9zVkmCgaVQn9y2275lqOH2yvs_Ap34CKEdYBkMdz7ST0r3NkmgcRwD8ciCndPpBoQSr6rtAkdcv2TKr0xYUizbAS1BzVKDitMxGg7R72mPqyC8ebc3TAbnauFKXHiU8Ke2LZVckLgnNBV_syB_B4B6IFIe0HLuCCzsSkaMlbC8hWNX1mvSoWHZfokxX8nBTDwlYeKEf1ewiVPZkKKOnqhzFuYzAdpmhOFC6-uSZqegx35Owg3cYKciskuOrQsddTkuirCd_3UOsVWP6Bn_Mv1Fy0_h63kdakE7QlLUyWGHy5uJ_Il1IYYd-ce4b1VHFt8nicudpmMuTAk1bfb3ziSy7fsIYY-2rcmyJJd-L7uumCnc8Vag_QvvVSC48e9g3bk4mznYHCLCXdUN6FgJ7tG4iDRYoGQnEmKldYMcy4zMIRddc5feeOFeBXmVhaeA9IULKhh64i2UzgT4yz25xxnY7arZD9iVO6Jdx8ZJr4FfC3ooIwllh5f2Ksft1cVk86IOf33F2DuiKRJZmnV1NbTpcQnJiQ9vNe6yCxMvfhX34CQjDSdCpRhsCSRKGl-HJPC61QU79iLm5A_CNLnv9_CCUdM-BTf6Q8mSYBV4XY4uXADodRwEXvYrDcKFFu2ku3kf4b7WnvaIXl2F-YH8RnHMsBiV2WYdRy46x9Hzk9R4oKaZ7Q==",
"id": "rs_02aa21bd75cf0633016a1148cfb6a88191ad3ad9d75fa236cc",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating rendering process**\n\nI’m considering whether I need to inspect all frames or use a montage. It might be sufficient to run tests and push changes, but first, I could render the top three previews. The user mentioned generating all of them, even though we only fixed ai-004. Currently, the CLI only allows rendering one, but I could loop through to render all three locally without code changes. I also need to commit code and update the skill regarding typography consistency feedback. Let's focus on tests before the final steps."
}
]
}
]
},
{
"role": "tool",
"content": "[terminal] ran `for manifest in data/render_manifests/testbatch_002_top3/*.json; do\n python3...` -> exit 0, 1 lines output",
"tool_call_id": "call_kKYUR8m33O1Eqc8qLpdfuVxC"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_BdJSQ3pPgcTc0kcOV8Pc3K7Y",
"call_id": "call_BdJSQ3pPgcTc0kcOV8Pc3K7Y",
"response_item_id": "fc_02aa21bd75cf0633016a1148e426288191a05199643b0d24a2",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Preview neu rendern, visuell/technisch prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_BdJSQ3pPgcTc0kcOV8Pc3K7Y"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_GmK2XIEW318teExAxesA6jLw",
"call_id": "call_GmK2XIEW318teExAxesA6jLw",
"response_item_id": "fc_02aa21bd75cf0633016a1148e8205c8191a25e5d50eb67bb66",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest -q\ngit status --short\ngit diff --stat` -> exit 0, 1 lines output",
"tool_call_id": "call_GmK2XIEW318teExAxesA6jLw"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_AtkRg4CadUuQsT48JedD18TB",
"call_id": "call_AtkRg4CadUuQsT48JedD18TB",
"response_item_id": "fc_02aa21bd75cf0633016a1148edac748191a302aa2645ba50ec",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-automation\", \"file_path\": \"references/manifest-preview-renderer.md\", \"old_string\": \"## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset...[truncated]\", \"new_string\": \"## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"Patched references/manifest-preview-renderer.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_AtkRg4CadUuQsT48JedD18TB"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_vIv1vw5KlP5VMquUzhNA5unz",
"call_id": "call_vIv1vw5KlP5VMquUzhNA5unz",
"response_item_id": "fc_02aa21bd75cf0633016a1148f5fe5881919cf43f030f9de524",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/preview_renderer.py tests/test_preview_renderer.py\\ngit commit -m \\\"fix: improve preview renderer text sizing\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"[feat/initial-growth-engine 3cc201b] fix: improve preview renderer text sizing\\n 2 files changed, 101 insertions(+), 13 deletions(-)\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_vIv1vw5KlP5VMquUzhNA5unz"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_UOfdRxDrPXjEaliMSnVcdpXJ",
"call_id": "call_UOfdRxDrPXjEaliMSnVcdpXJ",
"response_item_id": "fc_02aa21bd75cf0633016a1148fc2ca0819197894d7410da1dc0",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s...[truncated]\", \"timeout\": 120, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `set -euo pipefail\nexport GITHUB_TOKEN=$(tr -d '\\n\\r' < /home/agent/.hermes/se...` -> exit 0, 1 lines output",
"tool_call_id": "call_UOfdRxDrPXjEaliMSnVcdpXJ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_qp3c9L2muSLPeECLPfLXtIS1",
"call_id": "call_qp3c9L2muSLPeECLPfLXtIS1",
"response_item_id": "fc_02aa21bd75cf0633016a114908376c819185d67c7bfb760bbd",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=noprint_wrappers=1 data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q\nffprobe -v error -select_...` -> exit 0, 1 lines output",
"tool_call_id": "call_qp3c9L2muSLPeECLPfLXtIS1"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_9eb54HmA6Vmh60lndX13rs32",
"call_id": "call_9eb54HmA6Vmh60lndX13rs32",
"response_item_id": "fc_02aa21bd75cf0633016a114913f12c81919015179fd29ed56f",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_9eb54HmA6Vmh60lndX13rs32"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\nDu hattest recht: Der Preview-Renderer hatte eine zu grobe Font-Logik. Manche Texte wirkten relativ groß, andere zu klein — klassischer „wir haben einen Renderer gebaut und jetzt tut er Renderer-Dinge“-Moment.\n\n## Gefixt\n\nIch habe den Renderer erweitert um:\n\n- **adaptive Font-Fitting**\n- konsistentere Text-Hierarchie\n- Mindestgrößen für Haupttext und Retention-Text\n- maximale Zeilenanzahl pro Textblock\n- Typography-Metadaten pro Frame\n- Tests gegen zu kleine/zu stark schwankende Textgrößen\n\n## Konkrete neue Test-Regeln\n\nFür `540x960` Preview:\n\n```text\nmain_font_size >= 34\nmax(main_font_size) - min(main_font_size) <= 8\nretention_font_size >= 22\nmain_line_count <= 4\nretention_line_count <= 3\n```\n\nDamit verhindert der Renderer genau diesen Fehler künftig automatisch.\n\n## Neu gerendert\n\nIch habe die Top-3 Previews neu erzeugt:\n\n```text\ndata/previews/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.mp4\n ai-005-the-4-box-prompt-for-mental-overload.mp4\n attention-002-the-algorithm-does-not-need-you-happy.mp4\n```\n\nAktuelle Typografie-Prüfung für `ai-004`:\n\n```text\nmain_font_size: 34–42\nretention_font_size: 25–26\n```\n\nTechnische Prüfung `ai-004`:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nAktualisiertes Preview:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Tests / Git\n\nFull suite:\n\n```text\n25 passed in 0.96s\n```\n\nNeuer Commit:\n\n```text\n3cc201b fix: improve preview renderer text sizing\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nebenbei\n\nIch habe die zugehörige Renderer-Skill-Notiz ergänzt, damit wir uns diesen Typografie-Fehler nicht beim nächsten Video wieder als „neue Erkenntnis“ verkaufen.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\nDu hattest recht: Der Preview-Renderer hatte eine zu grobe Font-Logik. Manche Texte wirkten relativ groß, andere zu klein — klassischer „wir haben einen Renderer gebaut und jetzt tut er Renderer-Dinge“-Moment.\n\n## Gefixt\n\nIch habe den Renderer erweitert um:\n\n- **adaptive Font-Fitting**\n- konsistentere Text-Hierarchie\n- Mindestgrößen für Haupttext und Retention-Text\n- maximale Zeilenanzahl pro Textblock\n- Typography-Metadaten pro Frame\n- Tests gegen zu kleine/zu stark schwankende Textgrößen\n\n## Konkrete neue Test-Regeln\n\nFür `540x960` Preview:\n\n```text\nmain_font_size >= 34\nmax(main_font_size) - min(main_font_size) <= 8\nretention_font_size >= 22\nmain_line_count <= 4\nretention_line_count <= 3\n```\n\nDamit verhindert der Renderer genau diesen Fehler künftig automatisch.\n\n## Neu gerendert\n\nIch habe die Top-3 Previews neu erzeugt:\n\n```text\ndata/previews/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.mp4\n ai-005-the-4-box-prompt-for-mental-overload.mp4\n attention-002-the-algorithm-does-not-need-you-happy.mp4\n```\n\nAktuelle Typografie-Prüfung für `ai-004`:\n\n```text\nmain_font_size: 34–42\nretention_font_size: 25–26\n```\n\nTechnische Prüfung `ai-004`:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nAktualisiertes Preview:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Tests / Git\n\nFull suite:\n\n```text\n25 passed in 0.96s\n```\n\nNeuer Commit:\n\n```text\n3cc201b fix: improve preview renderer text sizing\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nebenbei\n\nIch habe die zugehörige Renderer-Skill-Notiz ergänzt, damit wir uns diesen Typografie-Fehler nicht beim nächsten Video wieder als „neue Erkenntnis“ verkaufen."
}
],
"id": "msg_02aa21bd75cf0633016a11491c61f8819183ad2eb273bb312b",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Nachwievor ist die hälfte des texts in grosser schrift und der text in der unteren hälfte kleiner geschrieben… wie wird das endergebnis aussehen? Videosequenzen, oder wieder statische bilder, so wie wir das schon hatten? Wieso bist du der meinung dass diese videos dann besser sind als der content den wir schon hatten? Einfach nur statische bilder wäre, denke ich, etwas zu langweilig. Aktuell bewegt sich ja alles und vodeos werden photorealistisch erstellt. Kling, Hailou, … kaum von realität zu unterscheiden, wie kanal ,chloe vs history‘."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_EHxDY9HekU6hkRNzm2s8gTUC",
"call_id": "call_EHxDY9HekU6hkRNzm2s8gTUC",
"response_item_id": "fc_02aa21bd75cf0633016a114a5810948191a69e76d2ca590854",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEUpYOLukYtvd4p90qpVkNx0Y-66KxVFfpKIGyqa9pC_X5Jv7AFB4LnQsAgVUBUFbbniLo8lryhHS68NYvdAfDZDxHnZZU7nEflHs9SCSO2NNSa7-Qs6DDIeZyD5JIWZtlYGK8n1eoQKE0ntoKaqj6j6ioehqgSDiCJgcacrmQc7MsrWc7MO_Z515OfE3tc8TyBaaDEMq7rXf1YmNz-Umv1dNWzZGXaWbl-EQMcWBBLoceN9i6u7_bICWFUf7xPFEgoXL3QmRYyLl8S-LsGiyUn4cVI2QU3C6UolVlNpwpBYNeaQyxWR1RSkOV_JmlYK0FiseHeZ56h7XxkH9QwVqxw4eNSKFcktWKqEz92sPjxCzu1AZ5lewS__WYcM9QZrY7OgndSCgbP9OcxXrRlbqIeLfA6n93HDeNEJ4th6PVqzZur__fB6ni8RqSxhFEhQATXppZEFGFUotjDS1MScVZhkZYWqYRKgEb5iqi_0WqZZSzx8cbBrFRVdvXcFhoRDAc0POWO8JUJkbZ1rA2BlX-1pDNfErxGlDk0Sx1DVQLp5BShJJ_G9Uh42SeWkRjlAZm7ejp0BW6e9hsSDJ07lA1tXl0HcGHP0jahnV6_j2Lbmub2V2m0DB3mdIculQn613i2po2uOEzbOqYkS7JoR0LDOJsV2EYVwGUPtGcx0uUTlmfD6onOGjCDkFHLKAnDh65bQP-6qoeXhD0xbH_giHHSA9inAXZ-8FzacoUWu4p7brLzja2c98ClJ_Ktrb53U051ZAQKYIHe0iIOhAXdX8IaDpC2ON1n4d4VGIznzvHcKsw9BlF0xkih-JJ8ST6V73k17FpwWVXrjeM9vIKH89dpiqMFxaII3NlN49zGmmGsBJPZQ7zx66oWP33J5IZfYR7jIc4NNpx-koZ2XPfxx20FCbH5N1sqX2FLJI_5MzOT87x4N917xhJDWNW79lclQLLzlOyyg89y8bUsAjeN7L-l8Ja6ZTM05_jm9QhplAuYNz4sH0DzMu8OayJ6iqNQL8OqvANNuNX-vY-8_tVsJOuYnIH3lnKG5419xpcP48TFDPyoyCaw3iwD9H03naf3SSKX8sz-eIDhzx12LC4u2sWKwuydhnzQjLPifb4s0zLKnGabWgK5-8Z7vtBCx1vY9oELTKMQzXmGflMilIA6D933CjUJsIsw53FfLtAzIYpDe0ApV4F1jso3W8xRE_UUaL0Hjtfxdpfqzi5q8CAYLaCsaJGPWR4rsTAgp-VttMA0CWPaPF_UuVHiVeRGPKEFe_HBZmxiQTzT2PNQpQDzxh0f90wwb9AUMx6Jg3EXxNWhYGqUih4hrEsH-VVNKG_yH_X60johu5dD87vQJGtOzMloHTarOovaEYqG8NHU5xbfFKZV0xLQZLpvVRk_GBTynYAIEaY08PksFiTveP1-zYwLweHv3eXAh3ZTVBtTk0lwzVhDnhnPctBGJ1CzpmB8B7VRnOj6lRNFMstPg14mf_Bi-QN1Bj-sVGN05l6XgGDYae2hApZXJU2ovfOQv0qIBXxd5L6EWj0wXB1bwenm5bs4XsW49-V4VXdqBUV6dDmq2-p1x_MIih9WPBQS_Kr3467yrTccMyf2RmDgFp-Mrw5XNhdLj7ywwrOuTrFRy4YAp5v0Xo9ZObSJjktE8UmiW0Z2LTh3h90b6hU-HJxuqHqqVclbVNtfx7Kh_pnqPKBLG1V_jmHjE_uuCxy_h5iRZjWWT4Rh5dkHn4Jam24reuqXt_pNWOAUScbxOmEu4r8rIUmiaq0Y4u1G_YuD07-5DniS2A_SCKtXNq9bno1N6Rx0VrOjnzer_W8juLBqJ0kXPcAtWknoluKRgaN_2PMtI_26prUnCWw8U4wWEfT2h7Z3siWe0E5vJ6andNpNm8ywISFf0xe0pHzNk_fDkmR8XOkW1MIyRhi6eIl8Icdm_MiV25as6CvknNLR1hIJYp9poq7ZWXhWEMxmjHKWo7yrk9tmEcPHNKCMn5Qi4UiM_1ghcks_aYQgKECYGjdZAYbln-aUXbL3VdVdYkc7xzHb3-LYHB9XzR4tBVU9v6GEhCQbqwRHSjSctl5QxPbH_P3WJyOh3KFHOOtdi9EO6IBu31eHQrpY45ttRCtMEGC6tmnbgNW66cAz9W6B_szwmhP-jFWRUnTStwWl-bBlJE10vYzmdKzRHQ3vcYAO8ePGKcbVtupQcLbMKULqX9auC7pJN9MRRrQxm90vuZffl47i2IIjnXjGIwqYgtSBKmIp-c7EkDqLX4gWu7dPQ9FM1W1QavlHKQ6xP9B2NiMxUYArhvtsilvbTvwsPEqvkKf8zt9iai24ffu56M1fudVOxaXLv-byUQ4bxTpHVdLtvPAPslJ-5yBDliUwDo-lxl2fJA2ZEpiH4XmavebioqGMzFGFWMFTcjbjCFIe-sjiDZzITjRXFI_zgdkdfjzfhYrWRYQO546ECv5dN_OiTKOqOLeVprW4SJ_FSRW1oFBe0yogoaFKOVmK7GVLICeJP4Plz9ZZSkTVV7SBfhl2izaMUCUB55p-okGRHFqNzApkme2anxRgKsQ2SEm5Bm6V4W8xzFs37lZ4HDJ_SZ4u3xHq167Zltd2a4W2xlo4lYQItFmAibihBTpAsS8prNq13MU2wF46eG05iY_DDGFLYHuLB2Kd5dC25EA6Veq19sqoN9ZPuGtMtBc6hUFzS_IdYuMD346EiAZGVpEpt2mPY4Po8oJbBTdmyKlLVH3DDRnvXl60z_k00Y",
"id": "rs_02aa21bd75cf0633016a114a529460819198c9c29758a6055a",
"summary": []
}
]
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_EHxDY9HekU6hkRNzm2s8gTUC"
},
{
"role": "assistant",
"content": "Ja — völlig berechtigter Einwand, Sir.\n\n## Kurzfassung\n\n**Wenn das Endergebnis nur statische Cards mit Text bleibt, ist es nicht gut genug.** \nDann ist es bestenfalls ein strukturierter Prototyp, aber kein konkurrenzfähiger Short.\n\nDer aktuelle Renderer ist **nicht** das Zielprodukt. Er ist ein **Preview-/Timing-Renderer**, um Script, Hook, Szenenfolge und Textgewicht zu prüfen. Als finales Content-Format wäre das zu langweilig — da stimme ich dir zu.\n\n## Zum Textproblem\n\nDu hast auch beim Text recht: Aktuell gibt es zwei Ebenen:\n\n- großer Haupttext im weißen Card-Bereich\n- kleinerer Retention-/Erklärungstext unten\n\nDas ist technisch Absicht im Preview-Renderer, aber visuell wirkt es wie: „halber Text wichtig, halber Text Fußnote“. Für TikTok/Shorts ist das falsch.\n\nIm finalen Video sollte es eher so sein:\n\n- **weniger Text pro Frame**\n- Text nicht als Absatz\n- große Hook-/Keyword-Captions\n- schnelle Textwechsel\n- Voiceover trägt den Inhalt\n- Bild/Video erzählt mit\n\nAlso nicht:\n\n```text\ngroße Headline\nkleiner erklärender Absatz\n```\n\nSondern eher:\n\n```text\nSTOP ASKING AI TO THINK FOR YOU\n↓\nASK IT TO ARGUE WITH YOU\n↓\nWEAKEST PART?\n↓\nSMART CRITIC?\n↓\nCOMMENT: CRITIC\n```\n\nKurz, groß, rhythmisch.\n\n## Wie soll das Endergebnis aussehen?\n\nNicht statische Bilder.\n\nDas bessere Ziel wäre:\n\n## 1. Cinematic / AI-video-led Shorts\n\nFür bestimmte Formate:\n\n- kurze videorealistische Szenen\n- Kling / Hailuo / Runway / Pika / ähnliche Generatoren als Asset-Lieferanten\n- mehrere 3–5s Clips\n- schnelle Schnitte\n- große Captions\n- Voiceover\n- Musik/SFX\n- motion captions\n\nBeispiel-Look:\n\n```text\nAI-generated cinematic clip\n+ kinetic captions\n+ voiceover\n+ sound design\n+ fast edit\n```\n\nDas ist näher an dem, was du mit „Chloe vs History“ meinst: nicht Texttafeln, sondern visuelle Mini-Szenen.\n\n## 2. Screen-recording / tutorial-led Shorts\n\nFür AI-Life-System-Themen wahrscheinlich stärker als photorealistische KI-Bilder:\n\n- echte/simulierte Screen Recordings\n- Prompt wird live aufgebaut\n- Cursor/Typing\n- Zooms\n- Highlight Boxes\n- Vorher/Nachher\n- Captions darüber\n\nFür `ai-004` wäre z.B. besser:\n\n```text\nScene 1:\nMensch starrt auf ChatGPT-Fenster: “Give me ideas”\nCaption: STOP ASKING AI TO THINK FOR YOU\n\nScene 2:\nPrompt wird gelöscht und ersetzt:\n“Argue with my idea.”\n\nScene 3:\nSplit-screen:\nLazy Prompt vs Critic Prompt\n\nScene 4:\nAI gibt Gegenargumente aus\nMensch markiert eine Schwachstelle\n\nScene 5:\nCTA:\nComment CRITIC\n```\n\nDas wäre deutlich besser als statische Cards.\n\n## Warum ich trotzdem diesen Preview-Renderer gebaut habe\n\nNicht weil ich glaube, dass das Endvideo so aussehen soll.\n\nSondern weil wir zuerst eine **kontrollierbare Struktur** brauchen:\n\n```text\nScript\n→ Shot Plan\n→ Render Manifest\n→ Preview\n→ Asset Generation\n→ Final Render\n```\n\nOhne diese Struktur passiert sonst schnell das klassische Problem:\n\n> „Mach mir ein cooles KI-Video“ \n> Ergebnis: hübsch, aber unkontrolliert, inkonsistent, nicht zur Hook passend.\n\nDer Preview-Renderer prüft nur:\n\n- Ist die Szenenfolge logisch?\n- Ist der Hook stark?\n- Passt die Dauer?\n- Ist zu viel Text drin?\n- Wo brauchen wir echte Clips?\n- Wo reicht Screen Recording?\n- Wo muss ein AI-video Clip her?\n\nEr ist also eher ein **Storyboard-Video**, nicht das finale Video.\n\n## Warum wären unsere Videos besser als der alte Content?\n\nNur wenn wir drei Dinge verbessern:\n\n### 1. Besseres Scripting\n\nNicht generisch:\n\n```text\nHere are 5 productivity tips...\n```\n\nSondern:\n\n```text\nStop asking AI to think for you. Ask it to argue with you.\n```\n\nDas ist schon besser.\n\n### 2. Bessere visuelle Umsetzung\n\nNicht:\n\n```text\nstatische KI-Bilder + Text\n```\n\nSondern:\n\n```text\nCinematic clips / screen recordings / motion captions / fast cuts\n```\n\nDas fehlt aktuell noch.\n\n### 3. Bessere Retention-Struktur\n\nAlle 2–3 Sekunden ein neuer visueller Reiz:\n\n- Hook\n- Pattern interrupt\n- payoff\n- example\n- CTA\n\nDas ist im Manifest vorbereitet, aber im Preview noch nicht hochwertig umgesetzt.\n\n## Ehrliche Einschätzung\n\nAktueller Stand:\n\n```text\nScript/Plan: brauchbar\nPreview-Renderer: nützlich, aber optisch nicht final\nFinal-Video-Qualität: noch nicht erreicht\n```\n\nWenn wir jetzt einfach mit statischen Cards weitermachen, wäre das strategisch zu schwach.\n\nDein Einwand ist also nicht nur kosmetisch — er ändert die Richtung.\n\n## Besserer nächster Schritt\n\nIch würde jetzt nicht weiter am Card-Renderer polieren.\n\nStattdessen:\n\n## Asset Pipeline v2 bauen\n\nFür jede Szene im Render Manifest soll entschieden werden:\n\n```text\nasset_mode:\n cinematic_ai_video\n screen_recording\n kinetic_text\n card_animation\n broll\n```\n\nDann pro Szene:\n\n- Prompt für Kling/Hailuo/Runway erzeugen\n- Dauer: 3–5 Sekunden\n- Kamerabewegung\n- Motiv\n- Stil\n- No-text-in-video-Regel\n- Caption separat im Renderer\n- später Clips zusammenfügen\n\n## Für `ai-004` konkret\n\nIch würde die visuelle Richtung so ändern:\n\n- keine weißen Textkarten als Hauptformat\n- stattdessen:\n - 40% Screen Recording / UI Simulation\n - 30% cinematic human-at-desk / decision moment\n - 20% kinetic captions\n - 10% cards nur für Prompt-Fragmente\n\nDas wäre viel näher an modernem Short-Content.\n\n## Fazit\n\nDu hast recht:\n\n**Statische Bilder sind zu langweilig.** \n**Der aktuelle Preview ist nicht das Ziel.** \n**Das Endergebnis muss Videosequenzen, Bewegung, Captions und ggf. AI-video Assets nutzen.**\n\nIch würde als Nächstes den Plan entsprechend umbauen:\n\n```text\nPreview Renderer bleibt nur Storyboard\nFinal Renderer bekommt echte Asset Slots\nAI-video Prompt Generator für Kling/Hailuo-artige Clips\nScreen-recording Generator für AI/tutorial Szenen\ndanach erst finaler MP4-Schnitt\n```\n\nDas ist der richtige nächste Schritt.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Ja — völlig berechtigter Einwand, Sir.\n\n## Kurzfassung\n\n**Wenn das Endergebnis nur statische Cards mit Text bleibt, ist es nicht gut genug.** \nDann ist es bestenfalls ein strukturierter Prototyp, aber kein konkurrenzfähiger Short.\n\nDer aktuelle Renderer ist **nicht** das Zielprodukt. Er ist ein **Preview-/Timing-Renderer**, um Script, Hook, Szenenfolge und Textgewicht zu prüfen. Als finales Content-Format wäre das zu langweilig — da stimme ich dir zu.\n\n## Zum Textproblem\n\nDu hast auch beim Text recht: Aktuell gibt es zwei Ebenen:\n\n- großer Haupttext im weißen Card-Bereich\n- kleinerer Retention-/Erklärungstext unten\n\nDas ist technisch Absicht im Preview-Renderer, aber visuell wirkt es wie: „halber Text wichtig, halber Text Fußnote“. Für TikTok/Shorts ist das falsch.\n\nIm finalen Video sollte es eher so sein:\n\n- **weniger Text pro Frame**\n- Text nicht als Absatz\n- große Hook-/Keyword-Captions\n- schnelle Textwechsel\n- Voiceover trägt den Inhalt\n- Bild/Video erzählt mit\n\nAlso nicht:\n\n```text\ngroße Headline\nkleiner erklärender Absatz\n```\n\nSondern eher:\n\n```text\nSTOP ASKING AI TO THINK FOR YOU\n↓\nASK IT TO ARGUE WITH YOU\n↓\nWEAKEST PART?\n↓\nSMART CRITIC?\n↓\nCOMMENT: CRITIC\n```\n\nKurz, groß, rhythmisch.\n\n## Wie soll das Endergebnis aussehen?\n\nNicht statische Bilder.\n\nDas bessere Ziel wäre:\n\n## 1. Cinematic / AI-video-led Shorts\n\nFür bestimmte Formate:\n\n- kurze videorealistische Szenen\n- Kling / Hailuo / Runway / Pika / ähnliche Generatoren als Asset-Lieferanten\n- mehrere 3–5s Clips\n- schnelle Schnitte\n- große Captions\n- Voiceover\n- Musik/SFX\n- motion captions\n\nBeispiel-Look:\n\n```text\nAI-generated cinematic clip\n+ kinetic captions\n+ voiceover\n+ sound design\n+ fast edit\n```\n\nDas ist näher an dem, was du mit „Chloe vs History“ meinst: nicht Texttafeln, sondern visuelle Mini-Szenen.\n\n## 2. Screen-recording / tutorial-led Shorts\n\nFür AI-Life-System-Themen wahrscheinlich stärker als photorealistische KI-Bilder:\n\n- echte/simulierte Screen Recordings\n- Prompt wird live aufgebaut\n- Cursor/Typing\n- Zooms\n- Highlight Boxes\n- Vorher/Nachher\n- Captions darüber\n\nFür `ai-004` wäre z.B. besser:\n\n```text\nScene 1:\nMensch starrt auf ChatGPT-Fenster: “Give me ideas”\nCaption: STOP ASKING AI TO THINK FOR YOU\n\nScene 2:\nPrompt wird gelöscht und ersetzt:\n“Argue with my idea.”\n\nScene 3:\nSplit-screen:\nLazy Prompt vs Critic Prompt\n\nScene 4:\nAI gibt Gegenargumente aus\nMensch markiert eine Schwachstelle\n\nScene 5:\nCTA:\nComment CRITIC\n```\n\nDas wäre deutlich besser als statische Cards.\n\n## Warum ich trotzdem diesen Preview-Renderer gebaut habe\n\nNicht weil ich glaube, dass das Endvideo so aussehen soll.\n\nSondern weil wir zuerst eine **kontrollierbare Struktur** brauchen:\n\n```text\nScript\n→ Shot Plan\n→ Render Manifest\n→ Preview\n→ Asset Generation\n→ Final Render\n```\n\nOhne diese Struktur passiert sonst schnell das klassische Problem:\n\n> „Mach mir ein cooles KI-Video“ \n> Ergebnis: hübsch, aber unkontrolliert, inkonsistent, nicht zur Hook passend.\n\nDer Preview-Renderer prüft nur:\n\n- Ist die Szenenfolge logisch?\n- Ist der Hook stark?\n- Passt die Dauer?\n- Ist zu viel Text drin?\n- Wo brauchen wir echte Clips?\n- Wo reicht Screen Recording?\n- Wo muss ein AI-video Clip her?\n\nEr ist also eher ein **Storyboard-Video**, nicht das finale Video.\n\n## Warum wären unsere Videos besser als der alte Content?\n\nNur wenn wir drei Dinge verbessern:\n\n### 1. Besseres Scripting\n\nNicht generisch:\n\n```text\nHere are 5 productivity tips...\n```\n\nSondern:\n\n```text\nStop asking AI to think for you. Ask it to argue with you.\n```\n\nDas ist schon besser.\n\n### 2. Bessere visuelle Umsetzung\n\nNicht:\n\n```text\nstatische KI-Bilder + Text\n```\n\nSondern:\n\n```text\nCinematic clips / screen recordings / motion captions / fast cuts\n```\n\nDas fehlt aktuell noch.\n\n### 3. Bessere Retention-Struktur\n\nAlle 2–3 Sekunden ein neuer visueller Reiz:\n\n- Hook\n- Pattern interrupt\n- payoff\n- example\n- CTA\n\nDas ist im Manifest vorbereitet, aber im Preview noch nicht hochwertig umgesetzt.\n\n## Ehrliche Einschätzung\n\nAktueller Stand:\n\n```text\nScript/Plan: brauchbar\nPreview-Renderer: nützlich, aber optisch nicht final\nFinal-Video-Qualität: noch nicht erreicht\n```\n\nWenn wir jetzt einfach mit statischen Cards weitermachen, wäre das strategisch zu schwach.\n\nDein Einwand ist also nicht nur kosmetisch — er ändert die Richtung.\n\n## Besserer nächster Schritt\n\nIch würde jetzt nicht weiter am Card-Renderer polieren.\n\nStattdessen:\n\n## Asset Pipeline v2 bauen\n\nFür jede Szene im Render Manifest soll entschieden werden:\n\n```text\nasset_mode:\n cinematic_ai_video\n screen_recording\n kinetic_text\n card_animation\n broll\n```\n\nDann pro Szene:\n\n- Prompt für Kling/Hailuo/Runway erzeugen\n- Dauer: 3–5 Sekunden\n- Kamerabewegung\n- Motiv\n- Stil\n- No-text-in-video-Regel\n- Caption separat im Renderer\n- später Clips zusammenfügen\n\n## Für `ai-004` konkret\n\nIch würde die visuelle Richtung so ändern:\n\n- keine weißen Textkarten als Hauptformat\n- stattdessen:\n - 40% Screen Recording / UI Simulation\n - 30% cinematic human-at-desk / decision moment\n - 20% kinetic captions\n - 10% cards nur für Prompt-Fragmente\n\nDas wäre viel näher an modernem Short-Content.\n\n## Fazit\n\nDu hast recht:\n\n**Statische Bilder sind zu langweilig.** \n**Der aktuelle Preview ist nicht das Ziel.** \n**Das Endergebnis muss Videosequenzen, Bewegung, Captions und ggf. AI-video Assets nutzen.**\n\nIch würde als Nächstes den Plan entsprechend umbauen:\n\n```text\nPreview Renderer bleibt nur Storyboard\nFinal Renderer bekommt echte Asset Slots\nAI-video Prompt Generator für Kling/Hailuo-artige Clips\nScreen-recording Generator für AI/tutorial Szenen\ndanach erst finaler MP4-Schnitt\n```\n\nDas ist der richtige nächste Schritt."
}
],
"id": "msg_02aa21bd75cf0633016a114a5a648c819199560b7b3bec5284",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] ok, baue weiter."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_wh8tVRO5Ipzfaw9DmeU4UEVw",
"call_id": "call_wh8tVRO5Ipzfaw9DmeU4UEVw",
"response_item_id": "fc_02aa21bd75cf0633016a114f3cdbdc8191bc59831e55298929",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"test-driven-development\"}"
}
},
{
"id": "call_1kuOIvvnAoo8tlQvq4wp9DZz",
"call_id": "call_1kuOIvvnAoo8tlQvq4wp9DZz",
"response_item_id": "fc_02aa21bd75cf0633016a114f3cdbf08191847b7d35076be166",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
},
{
"id": "call_WzyZBZHjRfcRXEHaEXRj7pf9",
"call_id": "call_WzyZBZHjRfcRXEHaEXRj7pf9",
"response_item_id": "fc_02aa21bd75cf0633016a114f3cdbf8819188d1982537dad27e",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"github-pr-workflow\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning implementation steps**\n\nI need to take action now. It seems like I might need to use the skills that are already loaded, but I have to ensure the new turn incorporates the relevant updates. I likely need to implement asset pipeline version 2, and I want to use test-driven development (TDD) as well. Let’s not forget to inspect the repository to see what we have to work with.",
"reasoning_content": "**Planning implementation steps**\n\nI need to take action now. It seems like I might need to use the skills that are already loaded, but I have to ensure the new turn incorporates the relevant updates. I likely need to implement asset pipeline version 2, and I want to use test-driven development (TDD) as well. Let’s not forget to inspect the repository to see what we have to work with.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEU88ZnI7mow4bgA_L4zIALbWpbIAIPk48ToqbzVxe_SILjhdByl2FfY_V0QN7_daw3tPZMMNSV436-svz7TaxhPFrR7m5mINyWF0ol50uiaPTNQd6-qzyDSAhqA_TfY4BhmPnSYuCru0XQv1mJcNEimsT2Bu7x-ehww0dLyasWXd0u3XqMXFjn1Kv0jgjVqPhwlBJJK9eG8mfrXA8VB3_z-RNXqFkESrRD3kF_mtwqBHl1gTCSz-h0MPoiYUr-SMLCzWzufZlVW_x4Kfdf1oz0tV_osCZ36uzyyFxxxMenCfkUcSbngfBS710BNEU9fmnElUPsRX5MoNgZsPVRg2jF64X_rdj3Z9KME_Sffvg6PA1-BmV4rKae7q00-BtKopIqg7rCkvzXkSXwcxn0za4hGAxk9EjXy-eCGkBwuNHGD1axyg9oMw_9DYEDBk4Njnt-NEvaHkDnE1NBl5jeH-TbKdoIZRwfVtA0TyjhjZ0KG3jWPYSDURw5xPKTH4HpzAfFknM4QbSftAdrrR4SRFr9FrlbnWI_vWQCmiKIRaBHgSlLNNIsAjdeNAdVdUVTswxe06EB2IgjmyuiYfrZcpsLfd7VROUor7_Mw9ub4DvbrXnoo4rlgvEsUibfGO-g4lgSg_pmee5gOD0u6LVOaHJO-0oIRePDPOS0OFI4OEPOTCd5mYAoDuU_T9OBAf7LVyx7CFRVNRWcSlOLh0Z9YTQau02ybI6GVqb2lgIPwkIae7lnh1h-PVivcnMyuTpGpJlgNp-SEbcfBOBxY0pGrqP6sXLRN7DnS-3RDuxEFKbXF0F1gWgTlX0YJgt_q7Yoypo_yLkSKTkKranEfQwJ9Sif30oq3ObWADNbuZdLBE5O6Ft278AbdxqSr7IExSS2WTZa_205clFJrIdOnrxvsJxUzu-y3z0DN8_Od6R1mzAPvzgJ8P6ggF3Gnnihku3TzSmxDTpY82dzLE1_Fksne1jwdqd3Cb9LSqFkAV9vwufLQatYMdwzyPKQhbL0FzGGth3BAuHRYZsbOhDYw3yIJk4gNtBfdvjrqo6Uevx0gp4NJRX2y_h1fLIMFJLI7CRuT8mppSZpFAoVKiXQyGsskJ3ylDcjlt90G5C8SkCEg6ap6jKDM=",
"id": "rs_02aa21bd75cf0633016a114f3a3af0819195a47de95a4a3d2c",
"summary": [
{
"type": "summary_text",
"text": "**Planning implementation steps**\n\nI need to take action now. It seems like I might need to use the skills that are already loaded, but I have to ensure the new turn incorporates the relevant updates. I likely need to implement asset pipeline version 2, and I want to use test-driven development (TDD) as well. Let’s not forget to inspect the repository to see what we have to work with."
}
]
}
]
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_wh8tVRO5Ipzfaw9DmeU4UEVw"
},
{
"role": "tool",
"content": "[skill_view] name=social-media-shorts-automation (8,000 chars)",
"tool_call_id": "call_1kuOIvvnAoo8tlQvq4wp9DZz"
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_WzyZBZHjRfcRXEHaEXRj7pf9"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_X1iiNJo0uUOGoid6vZwmMknd",
"call_id": "call_X1iiNJo0uUOGoid6vZwmMknd",
"response_item_id": "fc_02aa21bd75cf0633016a114f402fc08191bb33d0ed0f6704c8",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status prüfen und bestehende Manifest-/Shot-Plan-Struktur lesen\", \"status\": \"in_progress\"}, {\"id\": \"2\", \"content\": \"TDD: Asset-Plan-Tests für cinematic/screen/kinetic Modi schreiben und RED verifizieren\", \"status\": \"pending\"}, {\"id\": \"3\", \"content\": \"Asset-Plan-Builder v2 implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Asset-Pläne für Top-3 generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Full test suite, commit, push und remote verifizieren\", \"status\": \"pending\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_X1iiNJo0uUOGoid6vZwmMknd"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_fD3BTcj2XoxkataXVHihXhiR",
"call_id": "call_fD3BTcj2XoxkataXVHihXhiR",
"response_item_id": "fc_02aa21bd75cf0633016a114f48a680819194910f262ce6a372",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
},
{
"id": "call_IjKSen4Y04YJTFE3bZEnjAVa",
"call_id": "call_IjKSen4Y04YJTFE3bZEnjAVa",
"response_item_id": "fc_02aa21bd75cf0633016a114f48a69c8191835ec96794d89eef",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":240,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
},
{
"id": "call_c03otYKBCmCnxXxwIhMjRzbG",
"call_id": "call_c03otYKBCmCnxXxwIhMjRzbG",
"response_item_id": "fc_02aa21bd75cf0633016a114f48a6a88191858e63356fd1499f",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":220,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/shot_planner.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q` -> exit 0, 1 lines output",
"tool_call_id": "call_fD3BTcj2XoxkataXVHihXhiR"
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py from line 1 (7,461 chars)",
"tool_call_id": "call_IjKSen4Y04YJTFE3bZEnjAVa"
},
{
"role": "tool",
"content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/shot_planner.py from line 1 (8,370 chars)",
"tool_call_id": "call_c03otYKBCmCnxXxwIhMjRzbG"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_tCLD3dcgZVuNYsic0LEjWZIO",
"call_id": "call_tCLD3dcgZVuNYsic0LEjWZIO",
"response_item_id": "fc_02aa21bd75cf0633016a114f5540848191997fa87b4beeeb50",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Repo-Status prüfen und bestehende Manifest-/Shot-Plan-Struktur lesen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Asset-Plan-Tests für cinematic/screen/kinetic Modi schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEU9V4x2G73q6iKi6AAi_1MnC7-__TOG-0-rY2ip8ABTAPfEGqYEdRFXM8bsF78LlehTV-HTZrgRRpUYj5xAx-91JZKPZQaT6J-MiZzSm25EYVrtmUerCVF3Zt2Cly5gbbAjCUEZa9we5nu8zpwctVBdZGt7QjfzIXgIyAWE3WElGL1on_26Mb0BqZ0LsEt35cZUDz-5W6c8Ta9mAimbPcVGWJJvy9dF1sF79iFF7ZI-0zvQR7uVSDktLUECda5rx9NndQTDqK992JRoK2j_me6ClSx3iIseXRsd1CbQ5Zlu7S2Fdl_C0emUU4eGiKqwICcWV_K-Y3LFJyiiVxvrA4O_SG2qu2cQ_fkkmrtrdHmTimTLNmMjwlGTJCxfARwtqgdyKDI-xwFVLdessSjL0J8wQITm5H0NSpQWuqYSA0tuEH-kjaym_FE05poDPDtS2UzLtIzNS5kQsppz4_jG_Z2r3lEH8GgSr7Kock4XfIaOLwVyIEv_rMGl9jLvlnK7obzcpPFTzhUjYobjs7pj1rp3tMa_yKoLuO80XtCh_NoGuoAqVHDQlcsXJbh9x5RCMI6D-1qWwAg0IwwRpZ_E-En9kpSsigvkj3s9VkXkJLWE8PsIhQA9fpQ15YDNZ161WmstsFpRImX7ljiYzge8ialCSTNcU_tKhgfLazU9aZjNDaB7lztop_ArDp1WI4H3gB6pXzUetU0vsSpnaWoZzWF1Nsi8PX2XADS1GJ1aRHJazsYAng3RPsIwkFntlZJ9JDG7_ER-fbQlI3OXMaO-zd4amHILEVekbCZ6EMGEkPAdu1m0_vTUOUa5kOB830QAwXMy1gu3_-USJdk6yZxt5oUGCUldgPpyBHP9UMoDHRcPnei3ogiS2M0VfsZkpDWph4vobhhzctbZ0H02PA6ZHxu_w3xx-ALBQAt3OR6827-Y_Hy2FOcAD9djg9EyONsB7MSjFp_2D1yGrSM5UmDEK-N-AWUD3M7DgVUnW18nQxxKi337_MIoLdqJqyH-38hhYJyUkV-jtNveH6K_Bi2XR520mqJwsOFq0_MsYMCxlViMZUh6DqtCGcigjiBegbZv65y3UWM9HgQBnPwoh3y6Itf0i_Q6snlzCItoQDuEHJSPubDlC7v2W6XpamKye4edv1lKyXa3jD_lySKzmD0-7et7bqCxYnSxfwKt5e8ciuZSyM6L-pkYbJRtFnYCCMXbeLrN6F4WWzX3Gegp4zT0SWr7vuUfZpLDAUVLGLxVnDAahH9Le0dh0U91ueEYIgpMlSAkG24Ndlk1DeZKqBfnCBGkd2vzF7TLC7Y3ZYSfIGPeKRmtD6ZXQ8DsJ_taB9EWF0bZxSHM-zhwclNwJdWDjvrJ8OoMjDp2xO0rKszi6ZGtE95aFbLPfdpqf-9UCROoa9B0wDgiQfW65KymLslYTjAlVdpJ2Ty1XtuN8-TFRJ8AvS03qxDzlMMF_vCr-CtvfqWCcoPzC0erruaBj4wzXbDS8xmTYMJK9VlWSZ7vbEsBRDDgKVfrVuabHlYpDWK9nKDqgDfLBLumirMYxTe7ra0PxLTxrobjgn8SJ3f80KerJsvu_SBdghZJMe2sBSA166ka97gWA0h6wRiIU-PH8c6HJzawZN7OmbUOzy-remG1ex0xc7IQNQQbiJO1ga_QE8ZVPsSgWm0BymzeBCU5JpSCyYO-LYgpaEslluhIt9LvPg81Qf8sSA0PU0LDUx1NvyyByt1V_RjbxpcgQp6Jc7MmlKeX2cvGl6VFlZMdGR87dgTD3Jh5nMD2e0FVEqYh88M_r6EgBKfEOokn4Kb7VW4rfMKaD6EHDiURX22xAnou67pcQy8P0pZZrvu8nYuPbro878GnluLx-gNTy8TM5KlTNPLaI-eP_bVtYjha7ltlRo2QDK6FxfkjEmT45_ULXaVifWdAl91jgWBxxWiqnNBbXZJmMem-1CUdVmxe5U7Ip_9jkXd-F9aVo_uWayOUGM_DPP3KD6mIUBPgQWqt-A9KS69naz2kqJshxLVCDXNxmn4GnFjv3uGwHtWliNeaxju-UJSVDJr43f6kT0ahLzJb-hqVA0DSJoNNlHzrR0YCrY6G6XDZ-UIOqjocVZ-I7NTyek9pcnwiTHn0Z7IpyvR3LSothcGvikXjOKRhOMlPmHTOc8gImtYNkmsNAIFSAe_MF7egGTtvxDJJR3Ukli13BTseFm0M95CAB7sOEzT3OGcsl39ftnA7jpLsWwNEYXduM_sKoJ7UV4f_35geWardrUzUeh_BB5gkrTc264KZW8qX2hLznBVgNSxJub_0s7cOKsfrDnnEPRSlM-k1WyCkg3ytf_VijYTn0VRJabAAASEkDlS_qcfEM5-4AVuzjLGhXLsFV3BZXSlhHDCNFNyDPRZUDa0b-6pHnvmZi6wFiusdUE83-v86346nQvZ_Mb76x",
"id": "rs_02aa21bd75cf0633016a114f5072348191920b4458c3d5a51c",
"summary": []
}
]
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_tCLD3dcgZVuNYsic0LEjWZIO"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_qybKZFHHh9wOPM29xLbXF86k",
"call_id": "call_qybKZFHHh9wOPM29xLbXF86k",
"response_item_id": "fc_02aa21bd75cf0633016a114f5946f0819190a990f251f4cf73",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\", \"content\": \"import json\\nfrom pathlib import Path\\n\\nfrom autoshorts.asset_plan import build_asset_plan, write_asset_plans\\n\\n\\ndef _manifest(name: str = \\\"ai-004-stop-asking-ai-to-think-for-you\\\") -> dict:\\n path = Pa...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py (70 lines)",
"tool_call_id": "call_qybKZFHHh9wOPM29xLbXF86k"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_A5tWdOCX4M0vJ8OYPxtVagAG",
"call_id": "call_A5tWdOCX4M0vJ8OYPxtVagAG",
"response_item_id": "fc_02aa21bd75cf0633016a114f6db92c8191b9f10d766e51c00e",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_asset_plan.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest tests/test_asset_plan.py -q` -> exit 2, 1 lines output",
"tool_call_id": "call_A5tWdOCX4M0vJ8OYPxtVagAG"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_wCVsTWBVTAsVPDhBH52If8YM",
"call_id": "call_wCVsTWBVTAsVPDhBH52If8YM",
"response_item_id": "fc_02aa21bd75cf0633016a114f71b118819192c449c23ba0123f",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Asset-Plan-Tests für cinematic/screen/kinetic Modi schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Asset-Plan-Builder v2 implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_wCVsTWBVTAsVPDhBH52If8YM"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Qao5BPw9Oeu6z0If0CSkzcPR",
"call_id": "call_Qao5BPw9Oeu6z0If0CSkzcPR",
"response_item_id": "fc_02aa21bd75cf0633016a114f795d0881919287824e9932033b",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\": \"\\\"\\\"\\\"Asset planning for final, motion-led AutoShortsBot renders.\\n\\nThe preview renderer is only a storyboard. This module converts render manifests\\ninto explicit asset-generation plans so the final video...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 7508, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_Qao5BPw9Oeu6z0If0CSkzcPR"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_KtkK8NrKnziES2O0Fh7FWBZx",
"call_id": "call_KtkK8NrKnziES2O0Fh7FWBZx",
"response_item_id": "fc_02aa21bd75cf0633016a114fa66f2481918d621222cd56651b",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\",\"old_string\":\"__all__ = [\\n \\\"growth_plan\\\",\",\"new_string\":\"__all__ = [\\n \\\"asset_plan\\\",\\n \\\"growth_plan\\\",\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py (483 chars result)",
"tool_call_id": "call_KtkK8NrKnziES2O0Fh7FWBZx"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_SzRRg7A2tcyI0Hm0isyvJpBj",
"call_id": "call_SzRRg7A2tcyI0Hm0isyvJpBj",
"response_item_id": "fc_02aa21bd75cf0633016a114fad67b48191af42799a3605cad6",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_asset_plan.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \".... [100%]\\n4 passed in 0.04s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_SzRRg7A2tcyI0Hm0isyvJpBj"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_b7ddNKIcVD99t16Z9g4BeWaI",
"call_id": "call_b7ddNKIcVD99t16Z9g4BeWaI",
"response_item_id": "fc_02aa21bd75cf0633016a114fb1c65481918cd54debebed7f68",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Asset-Plan-Builder v2 implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Asset-Pläne für Top-3 generieren und prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_b7ddNKIcVD99t16Z9g4BeWaI"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_az3KvczXaRT8MYw1CJ1Q3VMh",
"call_id": "call_az3KvczXaRT8MYw1CJ1Q3VMh",
"response_item_id": "fc_02aa21bd75cf0633016a114fb541248191bcf8231877e6ce2c",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\": \"\\\"\\\"\\\"Generate final-render asset plans from AutoShortsBot render manifests.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(_...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_asset_plans.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 1265, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_az3KvczXaRT8MYw1CJ1Q3VMh"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_GvZHv5RMnATTfmoic4K6Pnaf",
"call_id": "call_GvZHv5RMnATTfmoic4K6Pnaf",
"response_item_id": "fc_02aa21bd75cf0633016a114fc01518819197d22b906b26800d",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"rm -rf data/asset_plans/testbatch_002_top3\\npython3 scripts/generate_asset_plans.py --manifest-dir data/render_manifests/testbatch_002_top3 --out data/asset_plans/testbatch_002_top3\\npython3 - <<'PY'\\nim...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `rm -rf data/asset_plans/testbatch_002_top3\npython3 scripts/generate_asset_pla...` -> exit 0, 1 lines output",
"tool_call_id": "call_GvZHv5RMnATTfmoic4K6Pnaf"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Jq8O1OGxaw6TVvj44fAi6UxQ",
"call_id": "call_Jq8O1OGxaw6TVvj44fAi6UxQ",
"response_item_id": "fc_02aa21bd75cf0633016a114fc6e1348191b3510404d95a26b1",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Asset-Pläne für Top-3 generieren und prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_Jq8O1OGxaw6TVvj44fAi6UxQ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_9cHtvfrbu4YOHMWBWcpL0bWt",
"call_id": "call_9cHtvfrbu4YOHMWBWcpL0bWt",
"response_item_id": "fc_02aa21bd75cf0633016a114fd2509081919ea2882b612cd9a7",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python3 -m pytest -q\ngit status --short\ngit diff --stat` -> exit 0, 1 lines output",
"tool_call_id": "call_9cHtvfrbu4YOHMWBWcpL0bWt"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_7wdAlsWxDj075TSI6kjvPZpD",
"call_id": "call_7wdAlsWxDj075TSI6kjvPZpD",
"response_item_id": "fc_02aa21bd75cf0633016a114fdfe06c8191a532e21506ae1271",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\": \"write_file\", \"file_content\": \"# AI-video asset planning for motion-led Shorts\\n\\nUse this when a shorts pipeline has scripts, shot plans, and render manifests but the user rejects static card/slideshow output.\\n\\n## Durable pattern\\n\\nT...[truncated]\", \"file_path\": \"references/ai-video-asset-planning.md\", \"name\": \"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Deciding on untracked files**\n\nI’m facing a decision about an unknown untracked review package. There are 32 tests that passed, but the untracked ones are a bit strange. If I only commit our changes, the full tests afterward may still include those untracked tests. That's a complicated situation since \"clean\" refers to existing untracked files. I wonder if these untracked files were previous changes from the user. Ultimately, I need to ensure we only commit our own changes and then push.\n**Updating skill references**\n\nI need to make sure there are no large media files, but JSON is fine. There's also the option to update the skill after discovering that the workflow asset plan v2 should patch the skill to prevent forgetting. Maybe this is required after a complex task? I can patch the existing skill since it lacks the final AI video asset pipeline. I might also create a supporting reference file and link it to the skill update. First, though, I need to commit.",
"reasoning_content": "**Deciding on untracked files**\n\nI’m facing a decision about an unknown untracked review package. There are 32 tests that passed, but the untracked ones are a bit strange. If I only commit our changes, the full tests afterward may still include those untracked tests. That's a complicated situation since \"clean\" refers to existing untracked files. I wonder if these untracked files were previous changes from the user. Ultimately, I need to ensure we only commit our own changes and then push.\n**Updating skill references**\n\nI need to make sure there are no large media files, but JSON is fine. There's also the option to update the skill after discovering that the workflow asset plan v2 should patch the skill to prevent forgetting. Maybe this is required after a complex task? I can patch the existing skill since it lacks the final AI video asset pipeline. I might also create a supporting reference file and link it to the skill update. First, though, I need to commit.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEU_fNHmYB9_5qga-yY0Z4zIod0Ttrx7JnM4BxP122hV0u2-KTHTHHntCSCQ0dZNy3t-80ilNqiPGcFXbbbiFDqGOq3i5ntbuDZVGMyxTzlQlGl9fn4W9j29GUZMgA21R0SS-uLsBtS5bMBVn6otKANVsBEVtpOYeOsvk7k8f87f4qmFFqx5DEW4iXNmwDT2fnNckLJPsBLWOvz-DMhOj8ZpiknLUa4oKsMmvRhXzLlEqe5YuIxCw5owOe6KEgNmPJ9kMWGmyvoHtdpzqWaAAXEC1s73tqotgfjivizNgi-JzLahVBZUjt7RAt-9B4bTJFnW98Zf7G6sqOFstDZtPleXyaS8lK-QSEBowyp1OGMy_UoILQPxV2Lte0BAdu46Q5NGJfFFvr9AsA7KHjEwHq7omgpJjZtUH-pvJNKqEwPKokvmZuJNeNwlYUgmcrjRLJJRilVMS4CvJg_rKG3szaIggdqCd3H8eRc99ML7-DgcUNMuJc6GnlX1u8XxTLqL_K3SvMt6LahUC0-1LZD9HK8lirj8bjVObiIS7OZWPy-7difPy0baOiDKksI89mTsvyyNMk7T2XHcibgFYVaesnSIGcE-pPWJYhqwy28nZit2nF6KdCAwGF6OU-YDkGrUP7WPrTGsrFp4LugzJfpB7t5Hp856WPzgv_hBFvhXoa62rcxwLxLC-UM1eiN6nK8mwgWcDD3jlfNhad219bFfRE9gHV6dRy9ADyMWa-PrPt4ba9CSfFGnL0F60XzDUcaCH222BVrLx9LQQBUc1dNuI5D_R_c29Xr7ekOn4a4StWukiCd0n4ITvYKYCmOxOpVvubnufRydzVmOY21APVSyNGiVdPZ4U2SpuN2KtQpZnC6_bXDyy0RLfYp29jNiwG1Mo_zDKQk7txGjWw45GymiqURYMGt2LxLQqO6T9oK7F3pRM5DuI0u4Wa39EiNLSu4JGGnS1au1j8TnNcGe_A3xnMjpHsBR7gIg6hhxgweLoRjImefVO3nWBcGx-v-wBFv2kOo_zlcUbtwLI9QttYnWBlQmHwaoqrTi5ggqogyKTrur1mFdmUR8_LOs-VUY-IiSsMI4hbXeDNfGPikNSpWrZFUkM_M4HBrrlJJPncXr4y0QHUYmE3yLsSIqvO-a2mhCAHdp13URhhS0LZ4C3-ATAUC-rpdzoDYenYQqldckKO9J31UnAAkKs8Ai8KzQaruzwj-g4vF_MiQ9_6MwQdDel6FxyxBEiqhGPY6xIpa8E8HjSFz_rC-P_4Rh7KrR6kfqeaZV3ErgzEmdL_3Cqhg1us9fKSc8ZnIFOXNLbsmpHnLBPpEO9d-V-NXKG5FA5kIrOy3bPHBA9ZjOGvMEUwtf2STa8z_EDFLd12Xnx48jX--k89N5vk89qE4Rf3E2qNFm7-TwlVrIYnk-37aJjqSA7Hc67o8FmtkLIPiBszr-aR-5C73DmZbFFer8tygEaZmu9Pwoifq8QTp_EjFmA7s9KbVUnwbGypr_aBvDIJnPFx4NHdHpKE1DL_ZyGbkDRrDPaitg6jlFYsp1uDOjjYF38NMi57XGPQKqwWPi6dUIoGmVemZdlvPZoM_lJ-Gg8tNAOEHA3MTl_qPdhHN0AUY_L5ezt_rMgvONLWykZBzw6FRfyNKp3W3A7Ot4L2IdJ_EswQwARC9fZLjDvA-6HCUHkhoXCyu4BMpuDY2Vp7DqlAfXs2j56bXQ1QizyA6rHsGJg9sRWdEI3kciofzbT47rgIrYOKxBpjpX93JWoSZIvruNugh1Gu6yTT1Htn5vdpr5INJmPd7FrfUehFx4buyemf-THpjWq4W429_-Iwi-64u5nzKzTLZxI0SR7EInI8kt2WmtSxaivpPlTXkgVv5wIBEXZvPO4t4z-8c7HzzVOH9k940LfTAoJDIbZEiywL7F3745xU_5nLsJGnBhMvrHaq_1XWsYM4FPmU9RYog0dNA95hFn8ojEdw10P8zjTPyvQ25blZzoU4u3yvASAo9LrJXeBimVdNQd3Mp2TKp5y37RiJx7NdtyYnd_aIGWWt5Jj9yV7ZbhN4_UayK1KyKQ8sUqLxZeDMWK5k2XBY61DA87FR6Z0Y5Yoi17YcrHXyMZ_EINjHsTzBTbDeFtYVdWFuG_gqsS3o0jPDUeensNay2Z0TxFOMnOuVBeHd8dzgHuMQuCF0x2LfebKspzIqcnoyUHwLrS5b9g4xy-vqHkYZwbUp9M7a37fW4kebtqanUCpFiZM7ouSpSZAmqkXw4K_VQ7tvI6RLXhiL31yAF-QgHnJbtcq5flTbFfrFsIzQ5BiMCpnxzumK2hVQBbb4CGGSD3PVPkR734H1fSFL4oIHp4oReleXJzWXILY1KE07UpdjZmNqN4ttFncmpbNNpw2lWi98JbDqQoFWnmegcrzRtY2boqZLQMKk1siPZJpRuCukqWMptU4eKRnKCVmXCKMMNScNWFZOpjRoSByQNejlqa9cI2uZ_Jnhnn2wIpP7o4v8LIb6uoi8KB7Yo_5SA4n8JYl_r-ND929q-0I-rMIdM1EoKDwgqLoPIluN4NVUQ3P685-VSvbieiGq33n7bEED9o7NhdwaW37LK1TtN6O32Zik4ZuvfnMlp320lCWNqLzoR7VCE2N2wObWEDXyydRA090ZnbMHeMn3AfR8Osg7WkGhXOAYdzxN5vORVtxBuR7djEvxiTj92q1D9gjZKH3fWbbAMC_27-An6M4Fiv26Nc3xylQl66cBcPCXn_qcVU5pfcmVqZC5FlMYPWsfUJ00H-ytysqxNO56V9qa_1UJozB9uvf7R7L_yEcgHnDXk1c506HUPLvlme7eVWZ13b6db6L915RTtzVYJ1V9qNlRzjmHtBYNOPd9fdVto5BpzVPCNgc4spzMUmjWuqKTfrXD4INB2RBYNjXJhkV7aC144pMUjA=",
"id": "rs_02aa21bd75cf0633016a114fd7f58881919d036484f175f120",
"summary": [
{
"type": "summary_text",
"text": "**Deciding on untracked files**\n\nI’m facing a decision about an unknown untracked review package. There are 32 tests that passed, but the untracked ones are a bit strange. If I only commit our changes, the full tests afterward may still include those untracked tests. That's a complicated situation since \"clean\" refers to existing untracked files. I wonder if these untracked files were previous changes from the user. Ultimately, I need to ensure we only commit our own changes and then push."
},
{
"type": "summary_text",
"text": "**Updating skill references**\n\nI need to make sure there are no large media files, but JSON is fine. There's also the option to update the skill after discovering that the workflow asset plan v2 should patch the skill to prevent forgetting. Maybe this is required after a complex task? I can patch the existing skill since it lacks the final AI video asset pipeline. I might also create a supporting reference file and link it to the skill update. First, though, I need to commit."
}
]
}
]
},
{
"role": "tool",
"content": "[skill_manage] name=social-media-shorts-automation (247 chars)",
"tool_call_id": "call_7wdAlsWxDj075TSI6kjvPZpD"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_MlVW18YTxxQQdA9mSuBBXDol",
"call_id": "call_MlVW18YTxxQQdA9mSuBBXDol",
"response_item_id": "fc_02aa21bd75cf0633016a114fea59148191822cc991e42fadfb",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-automation\", \"old_string\": \" - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: render vertical scene/card frames from manifest fields, assemble them with FFm...[truncated]\", \"new_string\": \" - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: render vertical scene/card frames from manifest fields, assemble them with FFm...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_MlVW18YTxxQQdA9mSuBBXDol"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ZRRW24RQwBXe96V6QA6Sayo7",
"call_id": "call_ZRRW24RQwBXe96V6QA6Sayo7",
"response_item_id": "fc_02aa21bd75cf0633016a114ff136b88191b10f178fb3c52132",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-automation\", \"new_string\": \"See `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-short...[truncated]\", \"old_string\": \"See `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-short...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_ZRRW24RQwBXe96V6QA6Sayo7"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_sA3cV3wyY8Njrsy12fGZeObo",
"call_id": "call_sA3cV3wyY8Njrsy12fGZeObo",
"response_item_id": "fc_02aa21bd75cf0633016a114ffa72588191bc7d9d0becea599f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/asset_plan.py autoshorts/__init__.py scripts/generate_asset_plans.py tests/test_asset_plan.py data/asset_plans/testbatch_002_top3\\ngit commit -m \\\"feat: add motion-led asset planning\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `set -euo pipefail\ngit add autoshorts/asset_plan.py autoshorts/__init__.py scr...` -> exit 0, 1 lines output",
"tool_call_id": "call_sA3cV3wyY8Njrsy12fGZeObo"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_CB6ZKQZ8Ui43gxrnp3wcovnx",
"call_id": "call_CB6ZKQZ8Ui43gxrnp3wcovnx",
"response_item_id": "fc_02aa21bd75cf0633016a1150009424819185817318e3400f10",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s...[truncated]\", \"timeout\": 120, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `set -euo pipefail\nexport GITHUB_TOKEN=$(tr -d '\\n\\r' < /home/agent/.hermes/se...` -> exit 0, 1 lines output",
"tool_call_id": "call_CB6ZKQZ8Ui43gxrnp3wcovnx"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_DfM3y6rQzNSW5pKabbcCoOQv",
"call_id": "call_DfM3y6rQzNSW5pKabbcCoOQv",
"response_item_id": "fc_02aa21bd75cf0633016a11500badf08191973b2606b371da31",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\np=Path('data/asset_plans/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json')\\ndata=json.loads(p.read_text())\\nprint('asset_plan', p)\\nfor s in data['scenes']:\\n print(s['scene_id'], s['asset_mode'], s['duration_seconds'], s['provider_family'])\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q\npython3 - <<'PY'\nimport j...` -> exit 0, 1 lines output",
"tool_call_id": "call_DfM3y6rQzNSW5pKabbcCoOQv"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_0wsns5mWhoa6b5onRLHx2Txl",
"call_id": "call_0wsns5mWhoa6b5onRLHx2Txl",
"response_item_id": "fc_02aa21bd75cf0633016a115012265881918e7164aec71c1b4f",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Full test suite, commit, push und remote verifizieren\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_0wsns5mWhoa6b5onRLHx2Txl"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\nIch habe die Richtung jetzt bewusst geändert: **weg vom Card-/Storyboard-Renderer als vermeintliches Endprodukt**, hin zu einer **motion-led Asset Pipeline v2**.\n\n## Gebaut: Asset Plan v2\n\nNeue Komponente:\n\n```text\nautoshorts/asset_plan.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_asset_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_asset_plan.py\n```\n\nGenerierte Asset-Pläne:\n\n```text\ndata/asset_plans/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.json\n ai-005-the-4-box-prompt-for-mental-overload.json\n attention-002-the-algorithm-does-not-need-you-happy.json\n```\n\n## Was sich dadurch ändert\n\nDer Preview-Renderer bleibt jetzt explizit nur:\n\n```text\nStoryboard / Timing Preview\n```\n\nNicht finaler Content.\n\nDer neue Asset Plan entscheidet pro Szene:\n\n```text\ncinematic_ai_video\nscreen_recording_simulation\nanimated_card\nkinetic_text_overlay\n```\n\nUnd setzt:\n\n```text\nstatic_card_limit = 0\n```\n\nAlso: **keine finalen statischen Textkarten**.\n\n## Beispiel `ai-004`\n\nDer finale Render-Plan für `ai-004` ist jetzt:\n\n```text\nscene_01 → cinematic_ai_video 3s Kling/Hailuo/Runway-style\nscene_02 → screen_recording_simulation 5s local renderer\nscene_03 → animated_card 6s local renderer\nscene_04 → screen_recording_simulation 6s local renderer\nscene_05 → kinetic_text_overlay 6s local renderer\n```\n\nDamit wäre das Endprodukt nicht mehr:\n\n```text\nstatische Bilder + Text\n```\n\nsondern:\n\n```text\nphotorealistische Clip-Sequenz\n+ UI-/Screen-Recording-Simulation\n+ animierte Cards\n+ große kinetic captions\n+ finaler Schnitt\n```\n\n## Provider-ready Prompting\n\nFür cinematic Szenen erzeugt der Plan jetzt direkt Prompt-Felder wie:\n\n```text\nPhotorealistic vertical 9:16 short video clip...\ncinematic lighting...\nrealistic motion...\nLeave clean negative space for captions...\ndo not include readable text in the generated video.\n```\n\nPlus Negative Prompt:\n\n```text\nno readable text\nno subtitles\nno logos\nno watermarks\nno fake UI text\nno slideshow\nno static image\nno uncanny faces\n```\n\nDas ist genau wichtig, weil Kling/Hailuo/Runway sonst gerne Text, Logos oder kaputte UI in den Clip backen — sehr nett von ihnen, aber leider meistens Müll.\n\n## Für `attention-002`\n\nDer Plan nutzt mehr visuelle Bewegung:\n\n```text\ncinematic_ai_video\ncinematic_ai_video\nanimated_card\nscreen_recording_simulation\nkinetic_text_overlay\n```\n\nAlso eher:\n\n- Person mit Phone / Feed / Licht auf Gesicht\n- Scroll-/Pause-Moment\n- Incentive-Vergleich als animierte Card\n- Home-screen cleanup\n- CTA als kinetic caption\n\n## Tests / Push\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.asset_plan`\n- GREEN: Asset Plan Builder\n- Full suite:\n\n```text\n32 passed in 0.96s\n```\n\nNeuer Commit:\n\n```text\nb60981b feat: add motion-led asset planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Hinweis\n\nIm Working Tree liegen noch untracked Dateien zu einem separaten Telegram-Review-Package:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nDie habe ich nicht angefasst und nicht in diesen Commit aufgenommen, damit wir keine fremden/parallel entstandenen Änderungen vermischen. Saubere Buchführung, wie langweilig — und deshalb nützlich.\n\n## Nächster sinnvoller Schritt\n\nJetzt als nächstes:\n\n```text\nAsset Plan v2\n→ AI-video prompt export package\n→ Clip placeholders / provider job specs\n→ final renderer liest echte Clip-Dateien statt Cards\n```\n\nPraktisch: Ich baue als Nächstes einen **provider job exporter**, der aus dem Asset Plan für jede `cinematic_ai_video`-Szene saubere Kling/Hailuo/Runway-Job-Specs erzeugt. Danach können wir entweder manuell Clips erzeugen oder später API-Integration anschließen.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\nIch habe die Richtung jetzt bewusst geändert: **weg vom Card-/Storyboard-Renderer als vermeintliches Endprodukt**, hin zu einer **motion-led Asset Pipeline v2**.\n\n## Gebaut: Asset Plan v2\n\nNeue Komponente:\n\n```text\nautoshorts/asset_plan.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_asset_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_asset_plan.py\n```\n\nGenerierte Asset-Pläne:\n\n```text\ndata/asset_plans/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.json\n ai-005-the-4-box-prompt-for-mental-overload.json\n attention-002-the-algorithm-does-not-need-you-happy.json\n```\n\n## Was sich dadurch ändert\n\nDer Preview-Renderer bleibt jetzt explizit nur:\n\n```text\nStoryboard / Timing Preview\n```\n\nNicht finaler Content.\n\nDer neue Asset Plan entscheidet pro Szene:\n\n```text\ncinematic_ai_video\nscreen_recording_simulation\nanimated_card\nkinetic_text_overlay\n```\n\nUnd setzt:\n\n```text\nstatic_card_limit = 0\n```\n\nAlso: **keine finalen statischen Textkarten**.\n\n## Beispiel `ai-004`\n\nDer finale Render-Plan für `ai-004` ist jetzt:\n\n```text\nscene_01 → cinematic_ai_video 3s Kling/Hailuo/Runway-style\nscene_02 → screen_recording_simulation 5s local renderer\nscene_03 → animated_card 6s local renderer\nscene_04 → screen_recording_simulation 6s local renderer\nscene_05 → kinetic_text_overlay 6s local renderer\n```\n\nDamit wäre das Endprodukt nicht mehr:\n\n```text\nstatische Bilder + Text\n```\n\nsondern:\n\n```text\nphotorealistische Clip-Sequenz\n+ UI-/Screen-Recording-Simulation\n+ animierte Cards\n+ große kinetic captions\n+ finaler Schnitt\n```\n\n## Provider-ready Prompting\n\nFür cinematic Szenen erzeugt der Plan jetzt direkt Prompt-Felder wie:\n\n```text\nPhotorealistic vertical 9:16 short video clip...\ncinematic lighting...\nrealistic motion...\nLeave clean negative space for captions...\ndo not include readable text in the generated video.\n```\n\nPlus Negative Prompt:\n\n```text\nno readable text\nno subtitles\nno logos\nno watermarks\nno fake UI text\nno slideshow\nno static image\nno uncanny faces\n```\n\nDas ist genau wichtig, weil Kling/Hailuo/Runway sonst gerne Text, Logos oder kaputte UI in den Clip backen — sehr nett von ihnen, aber leider meistens Müll.\n\n## Für `attention-002`\n\nDer Plan nutzt mehr visuelle Bewegung:\n\n```text\ncinematic_ai_video\ncinematic_ai_video\nanimated_card\nscreen_recording_simulation\nkinetic_text_overlay\n```\n\nAlso eher:\n\n- Person mit Phone / Feed / Licht auf Gesicht\n- Scroll-/Pause-Moment\n- Incentive-Vergleich als animierte Card\n- Home-screen cleanup\n- CTA als kinetic caption\n\n## Tests / Push\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.asset_plan`\n- GREEN: Asset Plan Builder\n- Full suite:\n\n```text\n32 passed in 0.96s\n```\n\nNeuer Commit:\n\n```text\nb60981b feat: add motion-led asset planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Hinweis\n\nIm Working Tree liegen noch untracked Dateien zu einem separaten Telegram-Review-Package:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nDie habe ich nicht angefasst und nicht in diesen Commit aufgenommen, damit wir keine fremden/parallel entstandenen Änderungen vermischen. Saubere Buchführung, wie langweilig — und deshalb nützlich.\n\n## Nächster sinnvoller Schritt\n\nJetzt als nächstes:\n\n```text\nAsset Plan v2\n→ AI-video prompt export package\n→ Clip placeholders / provider job specs\n→ final renderer liest echte Clip-Dateien statt Cards\n```\n\nPraktisch: Ich baue als Nächstes einen **provider job exporter**, der aus dem Asset Plan für jede `cinematic_ai_video`-Szene saubere Kling/Hailuo/Runway-Job-Specs erzeugt. Danach können wir entweder manuell Clips erzeugen oder später API-Integration anschließen."
}
],
"id": "msg_02aa21bd75cf0633016a1150195034819196cf0b66e0e1caf6",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] ok bau weiter. den separaten Working tree kannst du ignorieren, ich habe ausversehen in den Falschen telegram-kanal geschrieben. Hier noch einige ideen, wie unser content aussehen könnte:. Wir sollten nicht “KI-Video-Content” bauen, sondern ein halbautomatisches “Retention-Studio” für Shorts. Der Unterschied ist wichtig: Nicht der KI-Look zählt, sondern ob die ersten 1–2 Sekunden einen offenen Loop erzeugen, ob alle 1–2 Sekunden visuell etwas passiert und ob der Zuschauer am Ende denkt: „Davon will ich mehr.“\nDeine Beobachtung ist absolut richtig: statische KI-Bilder + KI-Voiceover + “Was wäre wenn…” ist bereits KI-Müll-Massenware. Damit gewinnt man heute nur noch zufällig oder kurzfristig.\nMeine klare Empfehlung\nDer beste Ansatz für Hermes wäre:\nKI-generierte, animierte Erklärvideos im Stil von “mechanism reveal / visual explanation / mini-investigation” – aber mit eigener Nische, eigener Dramaturgie und eigener Datenbasis.\nAlso nicht:\n“Was wäre, wenn ein Römer heute ein iPhone sieht?”\nSondern eher:\n“Warum du beim Autokauf fast immer gegen einen versteckten Algorithmus verlierst.”\n“Warum ein kleines Bauprojekt plötzlich 1 Million teurer wird.”\n“Warum Shorts dich nicht wegen dem Inhalt halten, sondern wegen offenen Loops.”\n“Wie ein Betrug funktioniert – Frame für Frame erklärt.”\n“Warum manche KI-Agenten Geld verbrennen, bevor sie produktiv werden.”\nDas wäre näher an @zackdfilms, aber nicht als Kopie. Zack D. Films hat laut YouTube aktuell rund 27 Mio. Abonnenten und macht kurze animierte Erklärvideos mit sehr klarem visuellen Payoff. Genau daran ist spannend: Es ist nicht einfach Voiceover. Es ist visuelles Verständnis.\nWas wir von schnell wachsenden Kanälen adaptieren sollten\nNicht die Themen kopieren. Sondern die Mechaniken.\n1. Zack-D-Films-Mechanik: “Zeig den unsichtbaren Mechanismus”\nDas Erfolgsprinzip ist:\nHook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung\nBeispiel für deinen Kanal:\nHook:\n“Dieser Occasion-Käufer denkt, er spart 4’000 Franken. In Wirklichkeit verliert er gerade Geld.”\nVisual:\n3D-Auto, Preisschild, Käufer klickt, dann “Röntgenblick” ins Auto: Wertverlust, Reparaturrisiko, Standtage, Händler-Marge, Finanzierung.\nPayoff:\n“Der teuerste Fehler war nicht der Kaufpreis. Es war der Zeitpunkt.”\nDas ist viel stärker als ein Avatar, der in die Kamera redet.\n2. MrBeast-/Jenny-Hoyos-Mechanik: “Sofortiges Versprechen”\nJedes Video braucht in den ersten 1–2 Sekunden ein klares Versprechen:\n“In 20 Sekunden verstehst du, warum…”\n“Das sieht harmlos aus, aber…”\n“Die meisten sehen nur X. Die Wahrheit ist Y.”\n“Dieser eine Fehler kostet…”\nNicht: “Heute erklären wir…”\n3. TikTok-Creative-Code-Mechanik: Struktur, Stimulation, Sound\nTikTok selbst empfiehlt für performante Videos eine klare Struktur aus Hook, Body, Close, dynamisches Editing, Bewegung, Text-Overlays und Sound/Voice/SFX als Aufmerksamkeitstreiber. Das ist genau das Gegenteil von statischen KI-Bildern.\nAvatare: Ja, aber nicht als Hauptformat\nRealistische KI-Avatare, die sprechen, sind verführerisch. Aber ich würde sie nicht als Kernformat nehmen.\nWarum?\nErstens wirken viele Avatare nach 3 Sekunden generisch. Zweitens erzeugt ein sprechender Avatar selten hohe Retention, wenn er nur erklärt. Drittens gibt es Policy- und Vertrauensrisiken, wenn realistische KI-Menschen wie echte Experten wirken. YouTube verlangt Disclosure, wenn realistische Inhalte mit synthetischen Medien erstellt wurden; bei sensiblen Themen wie Finanzen, Gesundheit, News oder Wahlen kann ein Label auch direkt im Video erscheinen. \nBesser: Avatar nur als wiederkehrende Nebenfigur. Zum Beispiel:\n“JARVIS scannt den Fall.”\nDann sieht man kurz einen KI-Analysten, aber der Hauptteil ist animierte Visualisierung: Diagramme, Objekte, Simulationen, Screenshots, Daten, 3D-Szenen.\nRe-Kuratieren erfolgreicher Videos: Ja, aber vorsichtig\nDas ist strategisch stark, aber es darf nicht “kopieren und neu vertonen” werden.\nYouTube sagt klar: Reused Content ist problematisch, wenn man vorhandenes Material ohne deutliche eigene Kommentare, substanzielle Änderung oder eigenen Bildungs-/Unterhaltungswert wiederverwendet. Erlaubt ist Wiederverwendung eher dann, wenn ein klarer eigener Spin, Storyline, Kommentar oder substanzielle Bearbeitung dazukommt. \nFür Hermes heisst das:\nErlaubt / sinnvoll:\nerfolgreiche Videos analysieren\nHook-Struktur extrahieren\nThema neu recherchieren\neigene Daten/Quellen verwenden\neigene Animation generieren\neigene These / eigene Auflösung bauen\nNicht sinnvoll:\nVideo herunterladen\nmit KI nachbauen\nleicht umschreiben\ngleiche Szenenfolge\ngleiche Pointe\nDas wäre riskant, austauschbar und langfristig nicht monetarisierungssicher. YouTube betont ausserdem, dass Content original/authentisch sein soll und nicht massenproduziert oder repetitiv wirken darf. \nDas Format, das ich für dich bauen würde\nIch würde einen Kanal nicht als “KI erklärt…” positionieren, sondern als:\n“Hidden Systems” / “Unsichtbare Mechanismen”\nThema: Dinge, die Menschen täglich betreffen, aber nicht verstehen.\nVisuell: 3D-Animation, Daten, Simulationen, Röntgenblick, UI-Animationen, Mini-Szenen.\nStimme: hochwertige menschlich klingende Voice, aber nicht lahmer AI-Narrator.\nLänge: 22–38 Sekunden.\nFrequenz: 2–4 Shorts pro Tag testen, aber nur mit klaren Qualitätsregeln.\nDie besten Content-Säulen für dich\n1. \nGeldfallen & Alltagsökonomie\nSehr gut monetarisierbar, stark für Follow.\nBeispiele:\n“Warum du bei Leasing fast immer die falsche Zahl vergleichst”\n“Warum 0%-Finanzierung nicht gratis ist”\n“Warum der Autohändler deinen alten Wagen anders bewertet als du”\n“Wie 100 Franken pro Monat heimlich zu 30’000 Franken werden”\n“Warum Krankenkassenprämien psychologisch perfekt weh tun”\n2. \nAuto- & Handels-Mechaniken\nPasst zu deinem Autotrading-Script und kann später ein Produkt/Tool stützen.\nBeispiele:\n“Der Moment, in dem ein Auto unverkäuflich wird”\n“Warum manche Autos 3 Tage online sind und andere 90”\n“So erkennt ein Algorithmus einen schlechten Occasion-Deal”\n“Warum ein tiefer Preis manchmal ein Warnsignal ist”\n“Der unsichtbare Kampf zwischen Käufer, Händler und Plattform”\n3. \nKI-Agenten realistisch erklärt\nHier kannst du dich positiv vom Hype unterscheiden.\nBeispiele:\n“Warum KI-Agenten oft scheitern, obwohl sie schlau wirken”\n“Was passiert, wenn ein Agent ohne Kontrolle tradet”\n“Der Unterschied zwischen Chatbot und echtem Agenten”\n“Warum dein KI-Agent zuerst langweilige Arbeit lernen muss”\n“Wie ein Agent dein Leben organisieren könnte – aber nur mit Regeln”\n4. \nSchweiz erklärt als System\nDas wäre deine lokale Differenzierung. Weniger Konkurrenz, höhere Glaubwürdigkeit.\nBeispiele:\n“Warum ein Gemeindekredit plötzlich Millionen kostet”\n“Wie ein Einwohnerrat wirklich entscheidet”\n“Warum Bauen in der Schweiz so teuer ist”\n“Warum ein Bahnhofprojekt 10 Jahre dauern kann”\n“Wie Steuergeld durch politische Prozesse fliesst”\n5. \nGefährliche Denkfehler\nSehr stark für Retention, weil es persönlich relevant ist.\nBeispiele:\n“Warum dein Gehirn schlechte Deals schönrechnet”\n“Warum du Verlusten hinterherrennst”\n“Warum Menschen schlechten News mehr glauben”\n“Warum ‘ich weiss es besser’ teuer wird”\n“Warum Charts dich zum falschen Zeitpunkt überzeugen”\nMein Favorit: “Mechanism Explainer Shorts”\nDie Formel:\n0.0–1.5 Sek.: Pattern Interrupt\nEin Objekt explodiert, fällt, wird durchleuchtet, friert ein oder verwandelt sich.\n1.5–5 Sek.: Problem\n“Das sieht nach einem guten Deal aus.”\n5–18 Sek.: Mechanismus\nRöntgenblick / Animation / Datenlayer / Mini-Simulation.\n18–28 Sek.: Twist\n“Aber der Preis ist nicht das Problem. Die Liquidität ist das Problem.”\n28–35 Sek.: Follow-Trigger\n“Folge, wenn du versteckte Systeme sehen willst, bevor sie dich Geld kosten.”\nDas gibt Hermes eine klare Produktionslogik.\nTechnischer Hermes-Workflow\nHermes sollte nicht einfach “poste jeden Tag 10 Videos” machen. Das endet in KI-Müll. Besser:\nPhase 1: Trend- und Format-Mining\nHermes sammelt täglich:\n50 erfolgreiche Shorts aus 5–10 Zielnischen\nHook-Typ\nVideolänge\nSchnittrhythmus\nvisueller Stil\nKommentar-Trigger\nLike/View-Verhältnis, soweit sichtbar\nWiederholbare Formate\nDann klassifiziert er:\nMechanism Reveal\nMistake Breakdown\nHidden Cost\nBefore/After\nMyth vs Reality\nSimulation\n“Nobody notices this…”\n“This tiny detail changes everything…”\nPhase 2: Ideen-Scoring\nJede Idee bekommt einen Score:\npersönlicher Nutzen\nvisueller Mechanismus\nÜberraschung\nDatenbezug\nKommentarpotenzial\nSerienfähigkeit\nMonetarisierungspotenzial\nProduktionsaufwand\nNur Ideen mit hohem Score werden produziert.\nPhase 3: Skript mit Retention-Logik\nHermes erstellt nicht einfach Text, sondern ein Shot-by-Shot-Skript:\nZeit\nBild\nText\nBewegung\nSound\n0–1.5s\nAuto mit rotem Warnsignal\n“Dieser Deal sieht gut aus…”\nKamera-Zoom\nBass-Hit\n1.5–4s\nPreis fällt\n“…bis du den versteckten Fehler siehst.”\nFreeze + Pfeil\nWhoosh\n4–12s\nRöntgenblick Auto\n“Der Preis ist nur eine Schicht.”\nLayers erscheinen\nClicks\n12–24s\nMarkt-/Standtage-Simulation\n“Das Risiko steckt hier.”\nGraph bewegt sich\nTension\n24–32s\nAuflösung\n“Nicht kaufen, wenn…”\nSnap to conclusion\nDrop\nPhase 4: Video-Generierung\nNicht mit einem einzigen Tool, sondern modular:\nBlender / Runway / Kling / Veo / Hailuo für Szenen\nAfter Effects / CapCut Templates / Remotion für dynamische Overlays\nElevenLabs / hochwertiges TTS für Voice\nSFX-Library für Pattern Interrupts\nHermes für Schnittlogik, Titel, Beschreibung, Posting\nDer eigentliche Qualitätshebel ist nicht das KI-Video-Tool. Es ist die visuelle Regie.\nEin konkretes Pilotformat\nIch würde mit dieser Serie starten:\n“Unsichtbare Systeme in 30 Sekunden”\nClaim:\n“Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.”\nErste 10 Videos:\n“Warum ein billiges Auto teuer sein kann”\n“Warum 0%-Finanzierung nicht kostenlos ist”\n“Warum KI-Agenten ohne Regeln gefährlich werden”\n“Warum ein Bauprojekt fast nie beim ersten Preis bleibt”\n“Warum Shorts dich nach 1 Sekunde verlieren”\n“Warum Menschen schlechten Deals zustimmen”\n“Warum Banken Gebühren verstecken können, ohne sie zu verstecken”\n“Warum ein Occasion-Preis manchmal zu tief ist”\n“Warum politische Projekte so langsam wirken”\n“Warum dein Gehirn Verluste schlimmer bewertet als Gewinne”\nDas ist hochwertiger als “Was wäre wenn…”, weil es echte Probleme erklärt.\nFollow-Trigger, der funktionieren kann\nNicht am Ende sagen:\n“Like und Follow für mehr.”\nSondern:\n“Folge, wenn du versteckte Kosten sehen willst, bevor sie dich treffen.”\n“Ich zeige jeden Tag ein System, das Menschen falsch verstehen.”\n“Morgen: warum Leasing psychologisch perfekt gebaut ist.”\n“Teil 2 zeigt den Trick, den fast niemand berechnet.”\nDer Follow entsteht durch Serienversprechen, nicht durch Bitte.\nWas ich nicht machen würde\nIch würde keinen reinen KI-Avatar-Kanal bauen.\nIch würde keinen Zack-D-Films-Klon bauen.\nIch würde keine fremden Videos recyceln.\nIch würde keinen generischen Wissenskanal machen.\nIch würde auch nicht “maximal automatisieren” als Hauptziel setzen. Besser:\n80 % automatisiert, 20 % menschliche Qualitätskontrolle.\nDiese 20 % entscheiden, ob es nach wertvoller Marke aussieht oder nach KI-Fabrik.\nBeste strategische Positionierung\nFür dich sehe ich drei starke Richtungen:\nRichtung\nReichweitenpotenzial\nMonetarisierung\nDifferenzierung\nHidden Money Systems\nhoch\nsehr hoch\ngut\nAI Agents Explained\nmittel–hoch\nsehr hoch\nsehr gut\nSwiss Systems Explained\nmittel\nmittel–hoch\nsehr stark lokal\nGeneral Mystery/History\nhoch\nmittel\nschwach, viel Konkurrenz\nKI-Avatar-News\nmittel\nmittel\nriskant/generisch\nMeine Wahl wäre:\nHidden Money & AI Systems\nWarum?\nEs verbindet deine bestehenden Projekte: Hermes, Autotrading, Finanzen, KI-Agenten, Politik/Systemverständnis. Es ist nicht nur Reichweiten-Content, sondern kann später in Newsletter, Tools, Beratung, Affiliate, Software oder Kurse führen.\nMinimaler Startplan für Hermes\nWoche 1:\n30 erfolgreiche Shorts analysieren, davon 10 von Zack-D-Films-artigen Explainern, 10 Money-/Business-Shorts, 10 AI-/Tech-Shorts.\nWoche 2:\n20 eigene Skripte erstellen, aber nur 5 produzieren.\nWoche 3:\nTäglich 2 Shorts posten, jeweils mit unterschiedlichen Hook-Typen.\nWoche 4:\nNur nach Daten entscheiden:\nRetention nach 1 Sekunde\nRetention nach 3 Sekunden\ndurchschnittliche Wiedergabedauer\nWiederholungen\nKommentare pro 1’000 Views\nFollows pro 1’000 Views\nDann skaliert Hermes nur die Gewinnerformate.\nMein Urteil\nDer stärkste Weg ist:\nNicht KI-Avatar. Nicht statische Bilder. Nicht “Was wäre wenn”. Sondern animierte, datenbasierte Mechanismus-Erklärvideos mit starkem Hook, klarer visueller Bewegung und echtem Nutzen.\nAlso: Zack-D-Films-Prinzip + Money/System/AI-Nische + Hermes-Retention-Analyse.\nDas kann sich deutlich von KI-Müll unterscheiden, weil jedes Video eine echte Frage beantwortet und visuell etwas zeigt, das man vorher nicht verstanden hat. / evtl. kann man das konzept auch zusätzlich auf andere bereiche anwenden…"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_1Nio1373TZYkpT6jlcomJe9i",
"call_id": "call_1Nio1373TZYkpT6jlcomJe9i",
"response_item_id": "fc_02aa21bd75cf0633016a1155f6200c8191872f94a938b9a458",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"todos\": [{\"id\": \"1\", \"content\": \"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\", \"status\": \"in_progress\"}, {\"id\": \"2\", \"content\": \"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\", \"status\": \"pending\"}, {\"id\": \"3\", \"content\": \"Retention-Studio-Strategiemodul implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Pilot-Ideen/Format-Config generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}]}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Building a strategy for retention studio**\n\nI’m thinking we need to act here and possibly build the concept into the repository. First, I should check what's untracked since the user mentioned ignoring a separate working tree, which I can leave. I also need to implement the retention studio concept, including strategy configuration, format taxonomy, scoring, and an explainer spec. I definitely want to use Test-Driven Development (TDD), creating tests for the mechanism's format and content pillars within the repo.",
"reasoning_content": "**Building a strategy for retention studio**\n\nI’m thinking we need to act here and possibly build the concept into the repository. First, I should check what's untracked since the user mentioned ignoring a separate working tree, which I can leave. I also need to implement the retention studio concept, including strategy configuration, format taxonomy, scoring, and an explainer spec. I definitely want to use Test-Driven Development (TDD), creating tests for the mechanism's format and content pillars within the repo.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEVX2JpZxCWjUMvhcjjAATHWM4Jnew8dzWVCJIHSZsVZU-l2P2wtpIfeJUluo1J-gbVdxigKKzuiFr1nWdPfGS3Xyj0xWjWe06frrkadLaIedFSPqxmENmMIx7DewgcxfBQwzoAdBhIHEuQC7TwnFve6qpAU_6NfYOVr617w0odpRs33JAbEm9qFL2ObOq2exBJLBDXG_onF3bUCtWFEscLCjmaK-ZBImWdEhq9T0ON2gDLM9pJm-MoOJ2YLXcFZIIwWg2hnT0OTQS6hD-LNdRjW1SEZZPXy_kPSt0iYau8QcTTnK8gPiZTeUNIjlYlJVIEcJC_XwI1wUAvBGmg01Rc0lOXhdc6z3EcmKzWZaR-kKB8Hw5emV11W1Kod5Uwii_rZz4T7wAzyPIWC8HsHDlrKpnSTz8DnW7K1Iz10cEoy4tFg17Mvc-f7WcazpCFTObwAjxsyF8YwWYatpQZAr72j9d55K3U18ov2QUP35BhkNxxhlrAjB8SVPxbK7qfD4wTDt3LxJbMs7zWSyfufT21kiuh3Klmis9CnIBVtcZ5FoSf9XvOqG7eOHiJ0WS8XUfp6VrDK9mGix6jaB2rKMGFR0l14z9B-dVXWeRPZhcdQYtkT29FDgU-iiTc3n_kKECT357vku7PfRGDWDyxDkaoOcKUuBI4fiKLw_s8TFjKM4Y9Vclj_fP9Qu-A40PAg9zQMesRP4DnmxQYcokO8Y1t-7DfTDNlBH2YwWdpsYtjRGu0yekehhg6rSBReHB7Qkg4Q_XbS83egHJgcUJ3RJZzbkIX3MxH8UgNTjMNiamDmFxBvIoEvorOih_axcDYzsIgw9YZrOVNc75hNMBKgNSXsVsLOMcBR8oW8_DKMXFwPjS_DkkPE7Ku-Kdq-gwp475k4e5nUmA2C97IPUCq_XqtQYsjSGX2YhvqNzLS72A9q1fbgQ50EU3qmZyXe5QmDp7CPZ5oRjrakdy-FzSQjyvDl-iifIzuDPjfxRy4iXxlRJQndrrso1ExT7t8HsFM0Kb1m2eZT582BtH0hnsPgV-qB-iIuAA2pKK9tQUfD7RroThmXCtYS7c08_YiP9587mYuc2IGfn7PfnfQZSVcMNGj4RdFYd28uo7aE_O_s7Xtsxb_4nVMuHGI0_wT_u4Ny0ItPRXBaq4t_dCIOX_brw3eBx4ZT3jnV_PKR0IBUHjf2iO8pefuNyvnUqbapv7GkhRByWGEZGzJs9NtRk6HtcVXbhrVHic5LdwuNNUFLacN_f_2EQ00zPD4uHyTripvRa416h4zO1_tKLrC0uZRENtLRl2olOoa67o_IXvh-TpjOOKQiQavS7yvQSQ_UTXgtNRVHmziy-ifjJfeORDH6VioaAO4uxH4OXqLJSu-Apmu0j3I5DjHQwguEczZu5XjAY0yFL",
"id": "rs_02aa21bd75cf0633016a1155f208a48191b89779304cbdebac",
"summary": [
{
"type": "summary_text",
"text": "**Building a strategy for retention studio**\n\nI’m thinking we need to act here and possibly build the concept into the repository. First, I should check what's untracked since the user mentioned ignoring a separate working tree, which I can leave. I also need to implement the retention studio concept, including strategy configuration, format taxonomy, scoring, and an explainer spec. I definitely want to use Test-Driven Development (TDD), creating tests for the mechanism's format and content pillars within the repo."
}
]
}
]
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_1Nio1373TZYkpT6jlcomJe9i"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_YXoE7sRCbLLgdc8mp0CU7fHX",
"call_id": "call_YXoE7sRCbLLgdc8mp0CU7fHX",
"response_item_id": "fc_02aa21bd75cf0633016a1155ff4ef08191ac0ce6990979cd57",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
},
{
"id": "call_W7XxkzAAAbtB6rdSXg6aoEZO",
"call_id": "call_W7XxkzAAAbtB6rdSXg6aoEZO",
"response_item_id": "fc_02aa21bd75cf0633016a1155ff4f088191bd92b62f1e28e239",
"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"limit\":80,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot\",\"pattern\":\"*.py\",\"target\":\"files\"}"
}
},
{
"id": "call_uLSYeOG5uZ1gEbzDEpqijNpI",
"call_id": "call_uLSYeOG5uZ1gEbzDEpqijNpI",
"response_item_id": "fc_02aa21bd75cf0633016a1155ff4f1081918ecc9267efc2004b",
"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"limit\":80,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot\",\"pattern\":\"*.json\",\"target\":\"files\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `git status --short --branch && python3 -m pytest -q` -> exit 0, 1 lines output",
"tool_call_id": "call_YXoE7sRCbLLgdc8mp0CU7fHX"
},
{
"role": "tool",
"content": "[search_files] files search for '*.py' in /home/agent/jarvis_runtime/AutoShortsBot -> 27 matches",
"tool_call_id": "call_W7XxkzAAAbtB6rdSXg6aoEZO"
},
{
"role": "tool",
"content": "[search_files] files search for '*.json' in /home/agent/jarvis_runtime/AutoShortsBot -> 15 matches",
"tool_call_id": "call_uLSYeOG5uZ1gEbzDEpqijNpI"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_AMZnVe0LxYNrbnLmkmpOBmGS",
"call_id": "call_AMZnVe0LxYNrbnLmkmpOBmGS",
"response_item_id": "fc_02aa21bd75cf0633016a115606e97081919fa6b52b132f5c45",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[todo] updated task list",
"tool_call_id": "call_AMZnVe0LxYNrbnLmkmpOBmGS"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_2dmGFhrOE03ZKWkFNFAIfjyZ",
"call_id": "call_2dmGFhrOE03ZKWkFNFAIfjyZ",
"response_item_id": "fc_02aa21bd75cf0633016a11560b05c0819182242e12fd528cf4",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_retention_studio.py\", \"content\": \"from autoshorts.retention_studio import (\\n PILOT_IDEAS,\\n build_mechanism_explainer,\\n build_retention_studio_strategy,\\n score_idea,\\n)\\n\\n\\ndef test_strategy_positions_channel_as_retention_stud...[truncated]\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/tests/test_retention_studio.py (71 lines)",
"tool_call_id": "call_2dmGFhrOE03ZKWkFNFAIfjyZ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_0NE14QBFLU898CREQvERneA3",
"call_id": "call_0NE14QBFLU898CREQvERneA3",
"response_item_id": "fc_02aa21bd75cf0633016a11561cc0388191b318a09e0288336d",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_retention_studio.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"==================================== ERRORS ====================================\\n_______________ ERROR collecting tests/test_retention_studio.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_retention_studio.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_retention_studio.py:1: in \\n from autoshorts.retention_studio import (\\nE ModuleNotFoundError: No module named 'autoshorts.retention_studio'\\n=========================== short test summary info ============================\\nERROR tests/test_retention_studio.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.12s\", \"exit_code\": 2, \"error\": null}",
"tool_call_id": "call_0NE14QBFLU898CREQvERneA3"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_HHiOpyq2OrMcyXRDYReurpHb",
"call_id": "call_HHiOpyq2OrMcyXRDYReurpHb",
"response_item_id": "fc_02aa21bd75cf0633016a115622415c81918c7f55c427c6b301",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Retention-Studio-Strategiemodul implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Retention-Studio-Strategiemodul implementieren\", \"status\": \"in_progress\"}, {\"id\": \"4\", \"content\": \"Pilot-Ideen/Format-Config generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 2, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}",
"tool_call_id": "call_HHiOpyq2OrMcyXRDYReurpHb"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_fyD67iJC7MFmddGLFakgY2Gb",
"call_id": "call_fyD67iJC7MFmddGLFakgY2Gb",
"response_item_id": "fc_02aa21bd75cf0633016a115657067481919f5a556cf39da4de",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"\\\"\\\"\\\"Retention Studio strategy for mechanism-explainer Shorts.\\n\\nThis module captures the strategic shift from generic AI-video output to a\\nsemi-automatic retention studio: format mining, idea scoring, mechanism-reveal\\nscripts, motion-led visuals, and human quality gates.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom typing import Any\\n\\nPILOT_IDEAS: list[dict[str, Any]] = [\\n {\\n \\\"title\\\": \\\"Warum ein billiges Auto teuer sein kann\\\",\\n \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n \\\"hook\\\": \\\"Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\\",\\n \\\"mechanism\\\": \\\"Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\\\",\\n \\\"twist\\\": \\\"Der teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.\\\",\\n \\\"visual_mechanism\\\": \\\"3D car x-ray with price tag layers and risk meters\\\",\\n \\\"surprise\\\": 9,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 8,\\n \\\"series_potential\\\": 9,\\n \\\"monetization_potential\\\": 10,\\n \\\"production_effort\\\": 6,\\n },\\n {\\n \\\"title\\\": \\\"Warum 0%-Finanzierung nicht kostenlos ist\\\",\\n \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n \\\"hook\\\": \\\"0 Prozent klingt gratis. Genau deshalb funktioniert es.\\\",\\n \\\"mechanism\\\": \\\"Cash price, margin, fees, lock-in and opportunity cost split into layers.\\\",\\n \\\"twist\\\": \\\"Die Zinsen verschwinden nicht. Sie verstecken sich nur im System.\\\",\\n \\\"visual_mechanism\\\": \\\"contract transforms into hidden cost layers\\\",\\n \\\"surprise\\\": 8,\\n \\\"data_basis\\\": 7,\\n \\\"comment_potential\\\": 8,\\n \\\"series_potential\\\": 9,\\n \\\"monetization_potential\\\": 10,\\n \\\"production_effort\\\": 5,\\n },\\n {\\n \\\"title\\\": \\\"Warum KI-Agenten ohne Regeln gefährlich werden\\\",\\n \\\"pillar\\\": \\\"ai_agents_explained\\\",\\n \\\"hook\\\": \\\"Dieser Agent wirkt produktiv. Bis er ohne Kontrolle Geld verbrennt.\\\",\\n \\\"mechanism\\\": \\\"Goal, tool access, missing approval gates, runaway loops and audit trail visualized as a circuit.\\\",\\n \\\"twist\\\": \\\"Der Fehler ist nicht Intelligenz. Der Fehler ist fehlende Begrenzung.\\\",\\n \\\"visual_mechanism\\\": \\\"agent control loop with approval gate and red runaway path\\\",\\n \\\"surprise\\\": 8,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 9,\\n \\\"series_potential\\\": 10,\\n \\\"monetization_potential\\\": 10,\\n \\\"production_effort\\\": 5,\\n },\\n {\\n \\\"title\\\": \\\"Der Unterschied zwischen Chatbot und echtem Agenten\\\",\\n \\\"pillar\\\": \\\"ai_agents_explained\\\",\\n \\\"hook\\\": \\\"Ein Chatbot antwortet. Ein Agent verändert Dinge.\\\",\\n \\\"mechanism\\\": \\\"Intent, tools, memory, approvals and execution are revealed as separate layers.\\\",\\n \\\"twist\\\": \\\"Autonomie beginnt nicht beim Modell, sondern beim Zugriff.\\\",\\n \\\"visual_mechanism\\\": \\\"split-screen chatbot bubble vs agent toolchain\\\",\\n \\\"surprise\\\": 7,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 8,\\n \\\"series_potential\\\": 10,\\n \\\"monetization_potential\\\": 9,\\n \\\"production_effort\\\": 4,\\n },\\n {\\n \\\"title\\\": \\\"Warum ein Bauprojekt fast nie beim ersten Preis bleibt\\\",\\n \\\"pillar\\\": \\\"swiss_systems_explained\\\",\\n \\\"hook\\\": \\\"Dieser Baukredit sieht fix aus. Dann beginnt die Schweiz.\\\",\\n \\\"mechanism\\\": \\\"Permits, objections, procurement, inflation, scope creep and political process stack over time.\\\",\\n \\\"twist\\\": \\\"Die Überraschung ist nicht der Mehrpreis. Es ist die Anzahl unsichtbarer Schleifen.\\\",\\n \\\"visual_mechanism\\\": \\\"construction budget timeline with process gates and cost bubbles\\\",\\n \\\"surprise\\\": 8,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 7,\\n \\\"series_potential\\\": 9,\\n \\\"monetization_potential\\\": 7,\\n \\\"production_effort\\\": 7,\\n },\\n {\\n \\\"title\\\": \\\"Wie ein Einwohnerrat wirklich entscheidet\\\",\\n \\\"pillar\\\": \\\"swiss_systems_explained\\\",\\n \\\"hook\\\": \\\"Das sieht wie eine Abstimmung aus. Eigentlich ist es ein Filtersystem.\\\",\\n \\\"mechanism\\\": \\\"Motion, committee, budget, party positions, amendments and final vote visualized as a flow.\\\",\\n \\\"twist\\\": \\\"Die Entscheidung fällt oft, bevor die Schlussabstimmung beginnt.\\\",\\n \\\"visual_mechanism\\\": \\\"council chamber turns into decision flowchart\\\",\\n \\\"surprise\\\": 7,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 7,\\n \\\"series_potential\\\": 8,\\n \\\"monetization_potential\\\": 6,\\n \\\"production_effort\\\": 6,\\n },\\n {\\n \\\"title\\\": \\\"Warum dein Gehirn schlechte Deals schönrechnet\\\",\\n \\\"pillar\\\": \\\"dangerous_thinking_errors\\\",\\n \\\"hook\\\": \\\"Dein Gehirn merkt diesen schlechten Deal. Dann verteidigt es ihn.\\\",\\n \\\"mechanism\\\": \\\"Sunk cost, confirmation bias and loss aversion appear as competing brain layers.\\\",\\n \\\"twist\\\": \\\"Du kaufst nicht den Deal. Du kaufst die Rechtfertigung.\\\",\\n \\\"visual_mechanism\\\": \\\"brain courtroom where biases argue over a bad deal\\\",\\n \\\"surprise\\\": 9,\\n \\\"data_basis\\\": 7,\\n \\\"comment_potential\\\": 9,\\n \\\"series_potential\\\": 10,\\n \\\"monetization_potential\\\": 8,\\n \\\"production_effort\\\": 5,\\n },\\n {\\n \\\"title\\\": \\\"Warum du Verlusten hinterherrennst\\\",\\n \\\"pillar\\\": \\\"dangerous_thinking_errors\\\",\\n \\\"hook\\\": \\\"Der Verlust ist vorbei. Trotzdem zahlst du weiter.\\\",\\n \\\"mechanism\\\": \\\"Loss aversion, sunk cost and identity protection visualized as a trap door.\\\",\\n \\\"twist\\\": \\\"Du rettest nicht dein Geld. Du rettest dein altes Ich.\\\",\\n \\\"visual_mechanism\\\": \\\"money trail into trap with brain pulling it back\\\",\\n \\\"surprise\\\": 8,\\n \\\"data_basis\\\": 7,\\n \\\"comment_potential\\\": 8,\\n \\\"series_potential\\\": 9,\\n \\\"monetization_potential\\\": 8,\\n \\\"production_effort\\\": 5,\\n },\\n {\\n \\\"title\\\": \\\"Warum Shorts dich nicht wegen dem Inhalt halten\\\",\\n \\\"pillar\\\": \\\"hidden_attention_systems\\\",\\n \\\"hook\\\": \\\"Du bleibst nicht wegen dem Thema. Du bleibst wegen der offenen Schleife.\\\",\\n \\\"mechanism\\\": \\\"Hook, open loop, pattern interrupt, payoff delay and reward pulse shown as timeline layers.\\\",\\n \\\"twist\\\": \\\"Retention ist oft Architektur, nicht Interesse.\\\",\\n \\\"visual_mechanism\\\": \\\"shorts timeline with dopamine gates and loop markers\\\",\\n \\\"surprise\\\": 9,\\n \\\"data_basis\\\": 8,\\n \\\"comment_potential\\\": 9,\\n \\\"series_potential\\\": 10,\\n \\\"monetization_potential\\\": 8,\\n \\\"production_effort\\\": 5,\\n },\\n {\\n \\\"title\\\": \\\"Warum Banken Gebühren verstecken können, ohne sie zu verstecken\\\",\\n \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n \\\"hook\\\": \\\"Diese Gebühr ist sichtbar. Genau deshalb übersiehst du sie.\\\",\\n \\\"mechanism\\\": \\\"Statement line items, percentage framing, monthly smoothing and comparison blindness.\\\",\\n \\\"twist\\\": \\\"Das Versteck ist nicht Geheimhaltung. Es ist Normalisierung.\\\",\\n \\\"visual_mechanism\\\": \\\"bank statement zooms into visible-but-ignored fee layers\\\",\\n \\\"surprise\\\": 8,\\n \\\"data_basis\\\": 7,\\n \\\"comment_potential\\\": 8,\\n \\\"series_potential\\\": 9,\\n \\\"monetization_potential\\\": 9,\\n \\\"production_effort\\\": 5,\\n },\\n]\\n\\n\\ndef build_retention_studio_strategy() -> dict[str, Any]:\\n \\\"\\\"\\\"Return the strategic operating model for the channel.\\\"\\\"\\\"\\n return {\\n \\\"positioning\\\": \\\"Hidden Money & AI Systems\\\",\\n \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n \\\"claim\\\": \\\"Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\\\",\\n \\\"operating_model\\\": \\\"semi_automatic_retention_studio\\\",\\n \\\"not_a\\\": [\\\"ki_avatar_news\\\", \\\"static_ai_slideshow\\\", \\\"reused_content_clone\\\"],\\n \\\"core_formula\\\": [\\n \\\"0.0-1.5s pattern interrupt\\\",\\n \\\"1.5-5s problem\\\",\\n \\\"5-18s mechanism reveal\\\",\\n \\\"18-28s twist\\\",\\n \\\"28-35s follow trigger\\\",\\n ],\\n \\\"format_taxonomy\\\": [\\n \\\"mechanism_reveal\\\",\\n \\\"mistake_breakdown\\\",\\n \\\"hidden_cost\\\",\\n \\\"before_after\\\",\\n \\\"myth_vs_reality\\\",\\n \\\"mini_simulation\\\",\\n \\\"nobody_notices_this\\\",\\n ],\\n \\\"content_pillars\\\": [\\n \\\"hidden_money_systems\\\",\\n \\\"auto_trade_mechanics\\\",\\n \\\"ai_agents_explained\\\",\\n \\\"swiss_systems_explained\\\",\\n \\\"dangerous_thinking_errors\\\",\\n ],\\n \\\"quality_gates\\\": {\\n \\\"static_card_limit\\\": 0,\\n \\\"first_two_seconds_must_open_loop\\\": True,\\n \\\"visual_change_every_seconds\\\": 2,\\n \\\"human_review_percent\\\": 20,\\n \\\"reused_content_allowed\\\": False,\\n \\\"synthetic_media_disclosure_required_when_realistic\\\": True,\\n },\\n \\\"metrics\\\": [\\n \\\"retention_1s\\\",\\n \\\"retention_3s\\\",\\n \\\"average_view_duration\\\",\\n \\\"rewatches\\\",\\n \\\"comments_per_1000_views\\\",\\n \\\"follows_per_1000_views\\\",\\n ],\\n }\\n\\n\\ndef score_idea(idea: dict[str, Any]) -> dict[str, Any]:\\n \\\"\\\"\\\"Score an idea for production priority.\\\"\\\"\\\"\\n weights = {\\n \\\"surprise\\\": 1.6,\\n \\\"data_basis\\\": 1.2,\\n \\\"comment_potential\\\": 1.2,\\n \\\"series_potential\\\": 1.4,\\n \\\"monetization_potential\\\": 1.4,\\n }\\n weighted = sum(float(idea.get(key, 0)) * weight for key, weight in weights.items())\\n visual_bonus = 12 if _has_specific_visual_mechanism(str(idea.get(\\\"visual_mechanism\\\", \\\"\\\"))) else 0\\n effort_penalty = max(0, float(idea.get(\\\"production_effort\\\", 5)) - 5) * 2\\n total = round(min(100, weighted / sum(weights.values()) * 8.8 + visual_bonus - effort_penalty))\\n if total >= 80:\\n decision = \\\"produce\\\"\\n elif total >= 65:\\n decision = \\\"develop\\\"\\n else:\\n decision = \\\"reject\\\"\\n return {\\\"total\\\": total, \\\"decision\\\": decision}\\n\\n\\ndef _has_specific_visual_mechanism(visual_mechanism: str) -> bool:\\n generic_terms = {\\\"generic montage\\\", \\\"talking avatar\\\", \\\"static image\\\", \\\"slideshow\\\"}\\n if visual_mechanism.lower().strip() in generic_terms:\\n return False\\n return any(\\n token in visual_mechanism.lower()\\n for token in [\\\"x-ray\\\", \\\"röntgen\\\", \\\"layer\\\", \\\"simulation\\\", \\\"timeline\\\", \\\"flow\\\", \\\"split\\\", \\\"3d\\\", \\\"circuit\\\"]\\n )\\n\\n\\ndef build_mechanism_explainer(idea: dict[str, Any]) -> dict[str, Any]:\\n \\\"\\\"\\\"Build a shot-by-shot mechanism explainer skeleton from a scored idea.\\\"\\\"\\\"\\n follow_trigger = \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\"\\n return {\\n \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n \\\"title\\\": idea[\\\"title\\\"],\\n \\\"pillar\\\": idea[\\\"pillar\\\"],\\n \\\"target_seconds\\\": 34,\\n \\\"follow_trigger\\\": follow_trigger,\\n \\\"beats\\\": [\\n {\\n \\\"beat\\\": \\\"pattern_interrupt\\\",\\n \\\"start\\\": 0.0,\\n \\\"end\\\": 1.4,\\n \\\"voiceover\\\": idea[\\\"hook\\\"],\\n \\\"visual\\\": f\\\"{idea['visual_mechanism']} freezes mid-action with warning signal.\\\",\\n \\\"visual_motion\\\": \\\"hard zoom, freeze-frame, red signal pulse\\\",\\n \\\"sound_cue\\\": \\\"bass hit + camera snap\\\",\\n },\\n {\\n \\\"beat\\\": \\\"problem\\\",\\n \\\"start\\\": 1.4,\\n \\\"end\\\": 5.0,\\n \\\"voiceover\\\": \\\"Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\\",\\n \\\"visual\\\": \\\"surface layer peels away to reveal the hidden system underneath.\\\",\\n \\\"visual_motion\\\": \\\"layer peel + parallax push\\\",\\n \\\"sound_cue\\\": \\\"whoosh + low tension tick\\\",\\n },\\n {\\n \\\"beat\\\": \\\"mechanism\\\",\\n \\\"start\\\": 5.0,\\n \\\"end\\\": 18.0,\\n \\\"voiceover\\\": idea[\\\"mechanism\\\"],\\n \\\"visual\\\": idea[\\\"visual_mechanism\\\"],\\n \\\"visual_motion\\\": \\\"sequential layer reveal every 1-2 seconds\\\",\\n \\\"sound_cue\\\": \\\"clicks per revealed layer\\\",\\n },\\n {\\n \\\"beat\\\": \\\"twist\\\",\\n \\\"start\\\": 18.0,\\n \\\"end\\\": 28.0,\\n \\\"voiceover\\\": idea[\\\"twist\\\"],\\n \\\"visual\\\": \\\"camera snaps from obvious cause to real cause; wrong layer fades out.\\\",\\n \\\"visual_motion\\\": \\\"snap zoom + wrong answer collapses\\\",\\n \\\"sound_cue\\\": \\\"drop + silence beat\\\",\\n },\\n {\\n \\\"beat\\\": \\\"follow_trigger\\\",\\n \\\"start\\\": 28.0,\\n \\\"end\\\": 34.0,\\n \\\"voiceover\\\": follow_trigger,\\n \\\"visual\\\": \\\"series tag appears over a clean animated system map.\\\",\\n \\\"visual_motion\\\": \\\"map draws itself, final node glows\\\",\\n \\\"sound_cue\\\": \\\"resolve hit + soft riser\\\",\\n },\\n ],\\n }\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/retention_studio.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 12472, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_fyD67iJC7MFmddGLFakgY2Gb"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_cOEeaETMj4Km3kSqSX1a42La",
"call_id": "call_cOEeaETMj4Km3kSqSX1a42La",
"response_item_id": "fc_02aa21bd75cf0633016a11569a2bbc8191b361bff7910db2b7",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"render_manifest\\\",\\n \\\"retention\\\",\\n \\\"retention_studio\\\",\",\"old_string\":\" \\\"render_manifest\\\",\\n \\\"retention\\\",\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\n@@ -6,6 +6,7 @@\\n \\\"preview_renderer\\\",\\n \\\"render_manifest\\\",\\n \\\"retention\\\",\\n+ \\\"retention_studio\\\",\\n \\\"script_drafts\\\",\\n \\\"shot_planner\\\",\\n \\\"voiceover_finalizer\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_cOEeaETMj4Km3kSqSX1a42La"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_sWAZjCsSy27jFukNwPlF4Mkn",
"call_id": "call_sWAZjCsSy27jFukNwPlF4Mkn",
"response_item_id": "fc_02aa21bd75cf0633016a1156a03d7081919e1c598d83aaeb08",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_retention_studio.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \".... [100%]\\n4 passed in 0.04s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_sWAZjCsSy27jFukNwPlF4Mkn"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_cK6JLWBRQSV9dX1tfJLjuuvn",
"call_id": "call_cK6JLWBRQSV9dX1tfJLjuuvn",
"response_item_id": "fc_02aa21bd75cf0633016a1156b3a9588191896fd80b7564b1ed",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Retention-Studio-Strategiemodul implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Pilot-Ideen/Format-Config generieren und prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Retention-Studio-Strategiemodul implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Ideen/Format-Config generieren und prüfen\", \"status\": \"in_progress\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 1, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}",
"tool_call_id": "call_cK6JLWBRQSV9dX1tfJLjuuvn"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_7woNNgdgL11siGQODYTtvQxx",
"call_id": "call_7woNNgdgL11siGQODYTtvQxx",
"response_item_id": "fc_02aa21bd75cf0633016a1156baf6008191862d5563452207d6",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"\\\"\\\"\\\"Generate Retention Studio strategy artifacts and pilot mechanism explainers.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.retention_studio import (\\n PILOT_IDEAS,\\n build_mechanism_explainer,\\n build_retention_studio_strategy,\\n score_idea,\\n)\\n\\n\\ndef main() -> None:\\n output_dir = Path(\\\"data/retention_studio\\\")\\n output_dir.mkdir(parents=True, exist_ok=True)\\n\\n strategy = build_retention_studio_strategy()\\n (output_dir / \\\"strategy.json\\\").write_text(\\n json.dumps(strategy, indent=2, ensure_ascii=False) + \\\"\\\\n\\\",\\n encoding=\\\"utf-8\\\",\\n )\\n\\n scored_ideas = []\\n explainers = []\\n for idea in PILOT_IDEAS:\\n scored = {**idea, \\\"score\\\": score_idea(idea)}\\n scored_ideas.append(scored)\\n explainers.append(build_mechanism_explainer(idea))\\n\\n (output_dir / \\\"pilot_ideas.json\\\").write_text(\\n json.dumps(scored_ideas, indent=2, ensure_ascii=False) + \\\"\\\\n\\\",\\n encoding=\\\"utf-8\\\",\\n )\\n (output_dir / \\\"mechanism_explainers.json\\\").write_text(\\n json.dumps(explainers, indent=2, ensure_ascii=False) + \\\"\\\\n\\\",\\n encoding=\\\"utf-8\\\",\\n )\\n\\n print(f\\\"Wrote strategy and {len(scored_ideas)} pilot ideas to {output_dir}\\\")\\n for idea in scored_ideas:\\n print(f\\\"{idea['score']['total']:03d} {idea['score']['decision']:7s} {idea['pillar']}: {idea['title']}\\\")\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_retention_studio.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 1559, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_7woNNgdgL11siGQODYTtvQxx"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_HXwajqQaHfFJ2mQTnSFbaTfo",
"call_id": "call_HXwajqQaHfFJ2mQTnSFbaTfo",
"response_item_id": "fc_02aa21bd75cf0633016a1156c98e1c8191a688e21f0a0410a5",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"rm -rf data/retention_studio\\npython3 scripts/generate_retention_studio.py\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nroot=Path('data/retention_studio')\\nfor p in sorted(root.glob('*.json')):\\n data=json.loads(p.read_text())\\n print(p.name, type(data).__name__, len(data) if isinstance(data, list) else sorted(data.keys())[:5])\\nideas=json.loads((root/'pilot_ideas.json').read_text())\\nprint('produce_count', sum(1 for i in ideas if i['score']['decision']=='produce'))\\nprint('top3', [(i['score']['total'], i['title']) for i in sorted(ideas, key=lambda x: x['score']['total'], reverse=True)[:3]])\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"Wrote strategy and 10 pilot ideas to data/retention_studio\\n088 produce hidden_money_systems: Warum ein billiges Auto teuer sein kann\\n086 produce hidden_money_systems: Warum 0%-Finanzierung nicht kostenlos ist\\n079 develop ai_agents_explained: Warum KI-Agenten ohne Regeln gefährlich werden\\n086 produce ai_agents_explained: Der Unterschied zwischen Chatbot und echtem Agenten\\n077 develop swiss_systems_explained: Warum ein Bauprojekt fast nie beim ersten Preis bleibt\\n073 develop swiss_systems_explained: Wie ein Einwohnerrat wirklich entscheidet\\n076 develop dangerous_thinking_errors: Warum dein Gehirn schlechte Deals schönrechnet\\n071 develop dangerous_thinking_errors: Warum du Verlusten hinterherrennst\\n090 produce hidden_attention_systems: Warum Shorts dich nicht wegen dem Inhalt halten\\n084 produce hidden_money_systems: Warum Banken Gebühren verstecken können, ohne sie zu verstecken\\nmechanism_explainers.json list 10\\npilot_ideas.json list 10\\nstrategy.json dict ['claim', 'content_pillars', 'core_formula', 'format_taxonomy', 'metrics']\\nproduce_count 5\\ntop3 [(90, 'Warum Shorts dich nicht wegen dem Inhalt halten'), (88, 'Warum ein billiges Auto teuer sein kann'), (86, 'Warum 0%-Finanzierung nicht kostenlos ist')]\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_HXwajqQaHfFJ2mQTnSFbaTfo"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_soGSo40wDQNZ5bRSR6BbYJss",
"call_id": "call_soGSo40wDQNZ5bRSR6BbYJss",
"response_item_id": "fc_02aa21bd75cf0633016a1156d05dac81918fa53531bfe59ee2",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Pilot-Ideen/Format-Config generieren und prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Retention-Studio-Strategiemodul implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Ideen/Format-Config generieren und prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}",
"tool_call_id": "call_soGSo40wDQNZ5bRSR6BbYJss"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_DXSQknTX5OVVJlb6uAk1ealZ",
"call_id": "call_DXSQknTX5OVVJlb6uAk1ealZ",
"response_item_id": "fc_02aa21bd75cf0633016a1156d4b4e08191aa53c86ecbbcecf8",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \".................................... [100%]\\n36 passed in 1.08s\\n M autoshorts/__init__.py\\n?? autoshorts/retention_studio.py\\n?? autoshorts/review_package.py\\n?? data/retention_studio/\\n?? data/telegram_reviews/\\n?? scripts/generate_retention_studio.py\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_retention_studio.py\\n?? tests/test_review_package.py\\n autoshorts/__init__.py | 1 +\\n 1 file changed, 1 insertion(+)\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_DXSQknTX5OVVJlb6uAk1ealZ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_nA7moBKSFM5czSikxBHbdUm9",
"call_id": "call_nA7moBKSFM5czSikxBHbdUm9",
"response_item_id": "fc_02aa21bd75cf0633016a1156da7f6881918a85e24f6a195a05",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"write_file\",\"file_content\":\"# Retention Studio strategy for mechanism-explainer Shorts\\n\\nUse this when a shorts project should move beyond generic AI-video output into a semi-automatic retention studio.\\n\\n## Core distinction\\n\\nDo not optimize for \\\"KI-Look\\\". Optimize for retention mechanics:\\n\\n- first 1-2 seconds open a loop\\n- visual change every 1-2 seconds\\n- every scene reveals an invisible mechanism\\n- the viewer ends with: \\\"I want to see more of these systems\\\"\\n\\n## Recommended positioning\\n\\nBest fit for this user's projects:\\n\\n```text\\nHidden Money & AI Systems\\nSeries: Unsichtbare Systeme in 30 Sekunden\\nClaim: Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\\n```\\n\\nAvoid pure KI-avatar news, static AI slideshows, reused-content clones, or generic \\\"what if\\\" videos.\\n\\n## Mechanism Explainer formula\\n\\n```text\\n0.0-1.5s Pattern interrupt\\n1.5-5s Problem\\n5-18s Mechanism reveal\\n18-28s Twist\\n28-35s Follow trigger / series promise\\n```\\n\\nThe key visual mechanic is: **show the invisible mechanism**.\\n\\nExamples:\\n\\n- car price -> x-ray layers: repair risk, stand days, margin, financing\\n- AI agent -> control loop: goal, tools, approval gate, runaway path\\n- building project -> timeline: permits, objections, procurement, inflation, scope creep\\n- bad deal -> brain/courtroom: sunk cost, confirmation bias, loss aversion\\n\\n## Idea scoring dimensions\\n\\nScore ideas for:\\n\\n- surprise\\n- data basis\\n- comment potential\\n- series potential\\n- monetization potential\\n- production effort penalty\\n- specific visual mechanism bonus\\n\\nProduce only high-scoring ideas. Develop medium ideas. Reject generic montage ideas.\\n\\n## Production principle\\n\\nBuild artifacts in this order:\\n\\n```text\\nstrategy -> scored ideas -> mechanism explainer -> shot plan -> render manifest -> asset plan -> final renderer\\n```\\n\\nFor final render, use motion-led asset modes:\\n\\n- cinematic AI video\\n- screen-recording simulation\\n- animated card\\n- kinetic text overlay\\n\\nKeep static cards at `0` for final output.\\n\\n## Testing checks\\n\\nTests should assert:\\n\\n- strategy is not positioned as AI-video factory\\n- `static_card_limit == 0`\\n- mechanism explainer has all five beats\\n- pattern interrupt ends within 1.5 seconds\\n- every beat has visual motion and sound cue\\n- weak generic ideas are rejected\\n- pilot ideas cover money, AI, Swiss systems, and thinking-error pillars\",\"file_path\":\"references/retention-studio-strategy.md\",\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"File 'references/retention-studio-strategy.md' written to skill 'social-media-shorts-automation'.\", \"path\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation/references/retention-studio-strategy.md\"}",
"tool_call_id": "call_nA7moBKSFM5czSikxBHbdUm9"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_nJDQ6P4QYmXWh774Qucmdl7o",
"call_id": "call_nJDQ6P4QYmXWh774Qucmdl7o",
"response_item_id": "fc_02aa21bd75cf0633016a1156e9b5a08191aeb78c1bda35ee35",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"Treat the system as a **Shorts Growth Engine**, 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, platform fit, and analytics feedback.\",\"new_string\":\"Treat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_nJDQ6P4QYmXWh774Qucmdl7o"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_g8OD2CSWeenYltByoaLB19WV",
"call_id": "call_g8OD2CSWeenYltByoaLB19WV",
"response_item_id": "fc_02aa21bd75cf0633016a1156f1c6648191a9317214b38aba37",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/retention_studio.py autoshorts/__init__.py scripts/generate_retention_studio.py tests/test_retention_studio.py data/retention_studio\\ngit commit -m \\\"feat: add retention studio strategy\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"[feat/initial-growth-engine a9fe897] feat: add retention studio strategy\\n 7 files changed, 1192 insertions(+)\\n create mode 100644 autoshorts/retention_studio.py\\n create mode 100644 data/retention_studio/mechanism_explainers.json\\n create mode 100644 data/retention_studio/pilot_ideas.json\\n create mode 100644 data/retention_studio/strategy.json\\n create mode 100644 scripts/generate_retention_studio.py\\n create mode 100644 tests/test_retention_studio.py\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_g8OD2CSWeenYltByoaLB19WV"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_fGLsWjHp7SdP3bexEodBboGc",
"call_id": "call_fGLsWjHp7SdP3bexEodBboGc",
"response_item_id": "fc_02aa21bd75cf0633016a11570171348191b043442ab7d7f707",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push >/tmp/autoshorts_push10.log 2>&1 || { sed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push10.log; exit 1; }\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\nprintf 'branch=%s\\\\nlocal=%s\\\\nremote=%s\\\\n' \\\"$BRANCH\\\" \\\"$LOCAL\\\" \\\"$REMOTE\\\"\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\nsed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push10.log\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"branch=feat/initial-growth-engine\\nlocal=a9fe897616a9c8e8a65160460d70dc8be039c769\\nremote=a9fe897616a9c8e8a65160460d70dc8be039c769\\nTo https://github.com/Gamexgit/AutoShortsBot.git\\n b60981b..a9fe897 feat/initial-growth-engine -> feat/initial-growth-engine\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_fGLsWjHp7SdP3bexEodBboGc"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_oMHDpKmmbBUX3rIhER25vxUh",
"call_id": "call_oMHDpKmmbBUX3rIhER25vxUh",
"response_item_id": "fc_02aa21bd75cf0633016a115710afb081918609b67c691397f8",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nideas=json.loads(Path('data/retention_studio/pilot_ideas.json').read_text())\\nprint('pilot_ideas', len(ideas))\\nprint('produce_count', sum(1 for i in ideas if i['score']['decision']=='produce'))\\nprint('top3')\\nfor i in sorted(ideas, key=lambda x: x['score']['total'], reverse=True)[:3]:\\n print(i['score']['total'], i['pillar'], i['title'])\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\n?? autoshorts/review_package.py\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n.................................... [100%]\\n36 passed in 0.99s\\npilot_ideas 10\\nproduce_count 5\\ntop3\\n90 hidden_attention_systems Warum Shorts dich nicht wegen dem Inhalt halten\\n88 hidden_money_systems Warum ein billiges Auto teuer sein kann\\n86 hidden_money_systems Warum 0%-Finanzierung nicht kostenlos ist\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_oMHDpKmmbBUX3rIhER25vxUh"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_S0nyjf1qJ1HdasZryYMaCtvB",
"call_id": "call_S0nyjf1qJ1HdasZryYMaCtvB",
"response_item_id": "fc_02aa21bd75cf0633016a115717c0fc8191a09881bcd93563b5",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Neue Retention-Studio-Anforderungen in Projektstruktur einordnen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Tests für Mechanism-Explainer-Format, Pillars und Scoring schreiben\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Retention-Studio-Strategiemodul implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Ideen/Format-Config generieren und prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_S0nyjf1qJ1HdasZryYMaCtvB"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\nIch habe deine Richtung jetzt nicht nur „notiert“, sondern als **produktive Systemlogik** in AutoShortsBot eingebaut.\n\n## Gebaut: Retention Studio Strategie\n\nNeue Komponente:\n\n```text\nautoshorts/retention_studio.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_retention_studio.py\n```\n\nNeue Tests:\n\n```text\ntests/test_retention_studio.py\n```\n\nGenerierte Strategie-Artefakte:\n\n```text\ndata/retention_studio/\n strategy.json\n pilot_ideas.json\n mechanism_explainers.json\n```\n\n## Was jetzt im System verankert ist\n\nDer Bot ist nicht mehr konzeptionell:\n\n```text\nKI-Video-Fabrik\n```\n\nsondern:\n\n```text\nsemi_automatic_retention_studio\n```\n\nPositionierung:\n\n```text\nHidden Money & AI Systems\n```\n\nSerie:\n\n```text\nUnsichtbare Systeme in 30 Sekunden\n```\n\nClaim:\n\n```text\nWir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\n```\n\n## Explizit ausgeschlossen\n\nIm Strategie-Modul steht jetzt klar:\n\n```text\nnot_a:\n - ki_avatar_news\n - static_ai_slideshow\n - reused_content_clone\n```\n\nUnd als Quality Gate:\n\n```text\nstatic_card_limit = 0\n```\n\nDamit ist die Richtung sauber: **keine finalen statischen KI-Bilder-/Textkarten-Videos**.\n\n## Mechanism-Explainer-Formel eingebaut\n\nJedes Format folgt jetzt dieser Dramaturgie:\n\n```text\n0.0–1.5s Pattern Interrupt\n1.5–5s Problem\n5–18s Mechanism Reveal\n18–28s Twist\n28–35s Follow Trigger\n```\n\nTests prüfen u.a.:\n\n- Pattern Interrupt endet ≤ 1.5 Sekunden\n- jeder Beat hat visuelle Bewegung\n- jeder Beat hat Sound Cue\n- kein generisches „Follow for more“\n- Follow Trigger ist Serienversprechen statt Betteln\n\n## Format-Taxonomie\n\nEingebaut:\n\n```text\nmechanism_reveal\nmistake_breakdown\nhidden_cost\nbefore_after\nmyth_vs_reality\nmini_simulation\nnobody_notices_this\n```\n\n## Content-Pillars\n\nEingebaut:\n\n```text\nhidden_money_systems\nauto_trade_mechanics\nai_agents_explained\nswiss_systems_explained\ndangerous_thinking_errors\n```\n\nDazu zusätzlich aus deinen Ideen:\n\n```text\nhidden_attention_systems\n```\n\n## Pilot-Ideen eingebaut\n\n10 Pilot-Ideen wurden als strukturierte Daten erzeugt und gescored.\n\nTop 3 nach Score:\n\n```text\n90 — hidden_attention_systems\nWarum Shorts dich nicht wegen dem Inhalt halten\n\n88 — hidden_money_systems\nWarum ein billiges Auto teuer sein kann\n\n86 — hidden_money_systems\nWarum 0%-Finanzierung nicht kostenlos ist\n```\n\nProduktionsempfehlung:\n\n```text\nproduce_count = 5 von 10\n```\n\n## Beispiel: Mechanism Explainer\n\nFür „Warum ein billiges Auto teuer sein kann“ erzeugt das System jetzt sinngemäß:\n\n```text\nPattern interrupt:\nDieser Occasion-Käufer denkt, er spart 4'000 Franken.\nVisual: 3D car x-ray, warning pulse\nSound: bass hit\n\nProblem:\nDas sieht harmlos aus, bis du den Mechanismus dahinter siehst.\nVisual: surface layer peels away\n\nMechanism:\nRöntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\nVisual: sequential layer reveal every 1–2 seconds\n\nTwist:\nDer teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.\nVisual: wrong layer collapses\n\nFollow trigger:\nMorgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\nVisual: system map draws itself\n```\n\nDas ist genau die Richtung: **visuelles Verständnis**, nicht Avatar-Gelaber. Endlich weniger „KI erklärt Dinge vor Hintergrundbild“, mehr „hier ist der Mechanismus, Sir“.\n\n## Tests / Git\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.retention_studio`\n- GREEN: Strategiemodul implementiert\n- Full suite:\n\n```text\n36 passed in 0.99s\n```\n\nNeuer Commit:\n\n```text\na9fe897 feat: add retention studio strategy\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nIch habe die Social-Shorts-Skill-Notiz erweitert um:\n\n```text\nRetention Studio strategy\nMechanism Explainer Shorts\nHidden Money & AI Systems\nstatic_card_limit = 0\n```\n\nDamit bleibt diese neue Richtung dauerhaft abrufbar.\n\n## Zu den untracked Telegram-Review-Dateien\n\nIch habe sie, wie gewünscht, ignoriert und **nicht** committet:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir aus dem besten Pilot eine echte Produktionskette bauen:\n\n```text\nRetention Studio idea\n→ Mechanism Explainer\n→ Shot Plan\n→ Render Manifest\n→ Asset Plan\n→ Provider Job Specs\n→ Final Motion Render\n```\n\nIch würde als ersten echten Pilot nehmen:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nWarum? Es passt stark zu Money/Auto, hat klares visuelles Objekt, hohe Monetarisierung und kann mit 3D-/X-Ray-/Layer-Animation sehr gut funktionieren.\n\nNächster Build-Schritt wäre also:\n\n```text\nMechanism Explainer → Shot Plan Adapter\n```\n\nDamit nicht nur alte AI-Life-Ideen durch die Pipeline laufen, sondern dieses neue Retention-Studio-Format.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\nIch habe deine Richtung jetzt nicht nur „notiert“, sondern als **produktive Systemlogik** in AutoShortsBot eingebaut.\n\n## Gebaut: Retention Studio Strategie\n\nNeue Komponente:\n\n```text\nautoshorts/retention_studio.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_retention_studio.py\n```\n\nNeue Tests:\n\n```text\ntests/test_retention_studio.py\n```\n\nGenerierte Strategie-Artefakte:\n\n```text\ndata/retention_studio/\n strategy.json\n pilot_ideas.json\n mechanism_explainers.json\n```\n\n## Was jetzt im System verankert ist\n\nDer Bot ist nicht mehr konzeptionell:\n\n```text\nKI-Video-Fabrik\n```\n\nsondern:\n\n```text\nsemi_automatic_retention_studio\n```\n\nPositionierung:\n\n```text\nHidden Money & AI Systems\n```\n\nSerie:\n\n```text\nUnsichtbare Systeme in 30 Sekunden\n```\n\nClaim:\n\n```text\nWir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\n```\n\n## Explizit ausgeschlossen\n\nIm Strategie-Modul steht jetzt klar:\n\n```text\nnot_a:\n - ki_avatar_news\n - static_ai_slideshow\n - reused_content_clone\n```\n\nUnd als Quality Gate:\n\n```text\nstatic_card_limit = 0\n```\n\nDamit ist die Richtung sauber: **keine finalen statischen KI-Bilder-/Textkarten-Videos**.\n\n## Mechanism-Explainer-Formel eingebaut\n\nJedes Format folgt jetzt dieser Dramaturgie:\n\n```text\n0.0–1.5s Pattern Interrupt\n1.5–5s Problem\n5–18s Mechanism Reveal\n18–28s Twist\n28–35s Follow Trigger\n```\n\nTests prüfen u.a.:\n\n- Pattern Interrupt endet ≤ 1.5 Sekunden\n- jeder Beat hat visuelle Bewegung\n- jeder Beat hat Sound Cue\n- kein generisches „Follow for more“\n- Follow Trigger ist Serienversprechen statt Betteln\n\n## Format-Taxonomie\n\nEingebaut:\n\n```text\nmechanism_reveal\nmistake_breakdown\nhidden_cost\nbefore_after\nmyth_vs_reality\nmini_simulation\nnobody_notices_this\n```\n\n## Content-Pillars\n\nEingebaut:\n\n```text\nhidden_money_systems\nauto_trade_mechanics\nai_agents_explained\nswiss_systems_explained\ndangerous_thinking_errors\n```\n\nDazu zusätzlich aus deinen Ideen:\n\n```text\nhidden_attention_systems\n```\n\n## Pilot-Ideen eingebaut\n\n10 Pilot-Ideen wurden als strukturierte Daten erzeugt und gescored.\n\nTop 3 nach Score:\n\n```text\n90 — hidden_attention_systems\nWarum Shorts dich nicht wegen dem Inhalt halten\n\n88 — hidden_money_systems\nWarum ein billiges Auto teuer sein kann\n\n86 — hidden_money_systems\nWarum 0%-Finanzierung nicht kostenlos ist\n```\n\nProduktionsempfehlung:\n\n```text\nproduce_count = 5 von 10\n```\n\n## Beispiel: Mechanism Explainer\n\nFür „Warum ein billiges Auto teuer sein kann“ erzeugt das System jetzt sinngemäß:\n\n```text\nPattern interrupt:\nDieser Occasion-Käufer denkt, er spart 4'000 Franken.\nVisual: 3D car x-ray, warning pulse\nSound: bass hit\n\nProblem:\nDas sieht harmlos aus, bis du den Mechanismus dahinter siehst.\nVisual: surface layer peels away\n\nMechanism:\nRöntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\nVisual: sequential layer reveal every 1–2 seconds\n\nTwist:\nDer teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.\nVisual: wrong layer collapses\n\nFollow trigger:\nMorgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\nVisual: system map draws itself\n```\n\nDas ist genau die Richtung: **visuelles Verständnis**, nicht Avatar-Gelaber. Endlich weniger „KI erklärt Dinge vor Hintergrundbild“, mehr „hier ist der Mechanismus, Sir“.\n\n## Tests / Git\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.retention_studio`\n- GREEN: Strategiemodul implementiert\n- Full suite:\n\n```text\n36 passed in 0.99s\n```\n\nNeuer Commit:\n\n```text\na9fe897 feat: add retention studio strategy\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nIch habe die Social-Shorts-Skill-Notiz erweitert um:\n\n```text\nRetention Studio strategy\nMechanism Explainer Shorts\nHidden Money & AI Systems\nstatic_card_limit = 0\n```\n\nDamit bleibt diese neue Richtung dauerhaft abrufbar.\n\n## Zu den untracked Telegram-Review-Dateien\n\nIch habe sie, wie gewünscht, ignoriert und **nicht** committet:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir aus dem besten Pilot eine echte Produktionskette bauen:\n\n```text\nRetention Studio idea\n→ Mechanism Explainer\n→ Shot Plan\n→ Render Manifest\n→ Asset Plan\n→ Provider Job Specs\n→ Final Motion Render\n```\n\nIch würde als ersten echten Pilot nehmen:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nWarum? Es passt stark zu Money/Auto, hat klares visuelles Objekt, hohe Monetarisierung und kann mit 3D-/X-Ray-/Layer-Animation sehr gut funktionieren.\n\nNächster Build-Schritt wäre also:\n\n```text\nMechanism Explainer → Shot Plan Adapter\n```\n\nDamit nicht nur alte AI-Life-Ideen durch die Pipeline laufen, sondern dieses neue Retention-Studio-Format."
}
],
"id": "msg_02aa21bd75cf0633016a11571ca93c8191b654d4091bc3ba10",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] ok, baue weiter."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_PZJ41ErKMavGlksUN4VDWDpp",
"call_id": "call_PZJ41ErKMavGlksUN4VDWDpp",
"response_item_id": "fc_02aa21bd75cf0633016a115832b02c8191af0bb6ff3cb5a90e",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"test-driven-development\"}"
}
},
{
"id": "call_JldWFrEHINSamnEzoIVRJQ7q",
"call_id": "call_JldWFrEHINSamnEzoIVRJQ7q",
"response_item_id": "fc_02aa21bd75cf0633016a115832b1e481919c63f56becd97a35",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
},
{
"id": "call_qGtIgM15OCkVcWfQk371SKmX",
"call_id": "call_qGtIgM15OCkVcWfQk371SKmX",
"response_item_id": "fc_02aa21bd75cf0633016a115832b1f4819186bfc2d79d9a3f6d",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"github-pr-workflow\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning skill adaptation**\n\nI need to take action and consider loading relevant skills. I already have some, but we might need to scan and load new ones. Using Test-Driven Development (TDD) could be helpful here, but I need to be careful about anything untracked. I should probably build an adapter mechanism and create an explainer for it. I’m wondering how to structure this plan effectively!",
"reasoning_content": "**Planning skill adaptation**\n\nI need to take action and consider loading relevant skills. I already have some, but we might need to scan and load new ones. Using Test-Driven Development (TDD) could be helpful here, but I need to be careful about anything untracked. I should probably build an adapter mechanism and create an explainer for it. I’m wondering how to structure this plan effectively!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEVgyI5icjOnwGklEmP6Q5dQkWlUOwFoYmJL1hBKQXSZ3U_8fqNKPUB1EelWqoCU04Ahira-cto71tAK7xcMEA_CFaX12f9V53RgME8WtKx4VisSILaPpmgguGWISfg60mm6t4xca6QZOC02K31BLwkX0kbZ7Qx_9xjLhXA8RJAxYiyO72S6oHJpvP3WOrH6kSWnH3QC_WF14tqoH9NlPgnewO88jtw9YDKo6HtDmJDvqBwf4VLVGRmYji2NqTvyihqSsBPOEZM0sc9bl7VkkoZDdDcYolP4SBZt9mEZ47w9YO3cR7KB81CAycrMyXS4IsjsjZX9N5WlmJburPTw77N5MxAFRoyhv_VL2l6B73w8H6iypywDGBDYtrIPm5P3xLQuaqRGR7xupaRmE7_Bbnzk98BsI5wWMBbVYjOsWAAnxzMwHNvnCrGmksolxbQ-gqIHB_N9c1OQBdL2nlFw1I94eIvbYzoNz1z4Qa29RiCq7gaanYb1Ff8oe4423vjO9g7FbHMVeHK0Z4-4_J8TGNeGNNIkQZkrOHJ3gs0asMVYg2afuiexr4UWW3pLcz-joJ6XaiCIcDaeJF0EISZayzTisXDl6gqSBzlzmYTdGa4w0UC_dV-JwSTwLzCfxV7xClD5GaWRRpcFqrfj1xQlwYpAjHdt0kehWU8ClVJmZKXz8rztLZL2G0MS4qQbpEn_rCipEkBFYf5yq_PrYQztfarjDrKOlvvBCnFpVxoRt4b57TfAEixlWkfFNewXXE-6_NXdyLJdXHMu9hpkiIA-H8_ti0H9RtC-h6VAAwL267jxFobTfqSPB60BsPwy5I0hJuIg3c_gqkdwzlqy_ctNdQp4rElSszNRhe7maWBFWPXiXXKXPMXGbFiA_lVpvKIAYNZCkmgX19wWhRiTR3ctDBgAmmLMvtrvLFbidI0Z4vR39nbNSR14KCqIB7QcFlITQHA2lIN6GdoPbn6YYwux_a2d7O3IS-xuMyp87LZrzbs7uSBMg-Sq5Zs5EPZEJLYsWnumHW0gnVco207AVuxTVYkRfKR23gZL6qItbcpx_Q2EY6Tx2NS4_ptvC7A04C__4Gp2OwR4-lUxn0-ZjOpf4HamMHTGb0C0IiOH4qnvNmDUboDru53a2q5pZOqn8sfz-8U_s",
"id": "rs_02aa21bd75cf0633016a11582ddd888191afb296f86aa131ca",
"summary": [
{
"type": "summary_text",
"text": "**Planning skill adaptation**\n\nI need to take action and consider loading relevant skills. I already have some, but we might need to scan and load new ones. Using Test-Driven Development (TDD) could be helpful here, but I need to be careful about anything untracked. I should probably build an adapter mechanism and create an explainer for it. I’m wondering how to structure this plan effectively!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
"tool_call_id": "call_PZJ41ErKMavGlksUN4VDWDpp"
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\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\\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\\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 - 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 Render only drafts above the chosen threshold, e.g. 8/10.\\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: render vertical scene/card frames from manifest fields, assemble them with FFmpeg, then use the MP4 for timing/approval review. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output or expects Kling/Hailuo-quality motion, add an `asset_plan.v2` layer that maps each scene to `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, or `kinetic_text_overlay` with provider-ready prompts and no baked text. See `references/ai-video-asset-planning.md`.\\n\\n6. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\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\\n7. **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/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/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. 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\\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 over-optimize renderer quality before testing whether the content format earns retention.\\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 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/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/manifest-preview-renderer.md\", \"references/ai-video-asset-planning.md\", \"references/monetization-and-approval-notes.md\", \"references/retention-led-shorts.md\", \"references/retention-render-planning.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\"}",
"tool_call_id": "call_JldWFrEHINSamnEzoIVRJQ7q"
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}",
"tool_call_id": "call_qGtIgM15OCkVcWfQk371SKmX"
},
{
"role": "assistant",
"content": "Need implement adapter. Check files maybe use existing shot planner data shape. Need not touch untracked.",
"tool_calls": [
{
"id": "call_ShtQ6eHZpjdsiuGMyEgDWSxX",
"call_id": "call_ShtQ6eHZpjdsiuGMyEgDWSxX",
"response_item_id": "fc_02aa21bd75cf0633016a115838e0448191829fc79e5507961a",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"todos\":[{\"content\":\"Repo-Status und Retention-Studio-Artefakte prüfen\",\"id\":\"1\",\"status\":\"in_progress\"},{\"content\":\"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\",\"id\":\"2\",\"status\":\"pending\"},{\"content\":\"Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"pending\"},{\"content\":\"Pilot-Shot-Plan für billiges Auto generieren und prüfen\",\"id\":\"4\",\"status\":\"pending\"},{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"pending\"}]}"
}
}
],
"finish_reason": "tool_calls",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Need implement adapter. Check files maybe use existing shot planner data shape. Need not touch untracked."
}
],
"id": "msg_02aa21bd75cf0633016a11583870ac81919e50e07d11ed3268",
"phase": "commentary"
}
]
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"in_progress\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"pending\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 4, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}",
"tool_call_id": "call_ShtQ6eHZpjdsiuGMyEgDWSxX"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ioI3LYVMPxCOu6jSjEVSrNKH",
"call_id": "call_ioI3LYVMPxCOu6jSjEVSrNKH",
"response_item_id": "fc_02aa21bd75cf0633016a115843c0748191afcfd31d460fb7e4",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
},
{
"id": "call_xKRCtTMTN8eE0WfozoTVPCI1",
"call_id": "call_xKRCtTMTN8eE0WfozoTVPCI1",
"response_item_id": "fc_02aa21bd75cf0633016a115843c08c8191a0de3fa433b7d22e",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":260,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/retention_studio.py\"}"
}
},
{
"id": "call_bJeHkPnCwQhQP1Kyy1mvz7OP",
"call_id": "call_bJeHkPnCwQhQP1Kyy1mvz7OP",
"response_item_id": "fc_02aa21bd75cf0633016a115843c09881919161c445957d2dff",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":120,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/retention_studio/mechanism_explainers.json\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\n?? autoshorts/review_package.py\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n.................................... [100%]\\n36 passed in 1.05s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_ioI3LYVMPxCOu6jSjEVSrNKH"
},
{
"role": "tool",
"content": "{\"content\": \" 1|\\\"\\\"\\\"Retention Studio strategy for mechanism-explainer Shorts.\\n 2|\\n 3|This module captures the strategic shift from generic AI-video output to a\\n 4|semi-automatic retention studio: format mining, idea scoring, mechanism-reveal\\n 5|scripts, motion-led visuals, and human quality gates.\\n 6|\\\"\\\"\\\"\\n 7|\\n 8|from __future__ import annotations\\n 9|\\n 10|from typing import Any\\n 11|\\n 12|PILOT_IDEAS: list[dict[str, Any]] = [\\n 13| {\\n 14| \\\"title\\\": \\\"Warum ein billiges Auto teuer sein kann\\\",\\n 15| \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n 16| \\\"hook\\\": \\\"Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\\",\\n 17| \\\"mechanism\\\": \\\"Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\\\",\\n 18| \\\"twist\\\": \\\"Der teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.\\\",\\n 19| \\\"visual_mechanism\\\": \\\"3D car x-ray with price tag layers and risk meters\\\",\\n 20| \\\"surprise\\\": 9,\\n 21| \\\"data_basis\\\": 8,\\n 22| \\\"comment_potential\\\": 8,\\n 23| \\\"series_potential\\\": 9,\\n 24| \\\"monetization_potential\\\": 10,\\n 25| \\\"production_effort\\\": 6,\\n 26| },\\n 27| {\\n 28| \\\"title\\\": \\\"Warum 0%-Finanzierung nicht kostenlos ist\\\",\\n 29| \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n 30| \\\"hook\\\": \\\"0 Prozent klingt gratis. Genau deshalb funktioniert es.\\\",\\n 31| \\\"mechanism\\\": \\\"Cash price, margin, fees, lock-in and opportunity cost split into layers.\\\",\\n 32| \\\"twist\\\": \\\"Die Zinsen verschwinden nicht. Sie verstecken sich nur im System.\\\",\\n 33| \\\"visual_mechanism\\\": \\\"contract transforms into hidden cost layers\\\",\\n 34| \\\"surprise\\\": 8,\\n 35| \\\"data_basis\\\": 7,\\n 36| \\\"comment_potential\\\": 8,\\n 37| \\\"series_potential\\\": 9,\\n 38| \\\"monetization_potential\\\": 10,\\n 39| \\\"production_effort\\\": 5,\\n 40| },\\n 41| {\\n 42| \\\"title\\\": \\\"Warum KI-Agenten ohne Regeln gefährlich werden\\\",\\n 43| \\\"pillar\\\": \\\"ai_agents_explained\\\",\\n 44| \\\"hook\\\": \\\"Dieser Agent wirkt produktiv. Bis er ohne Kontrolle Geld verbrennt.\\\",\\n 45| \\\"mechanism\\\": \\\"Goal, tool access, missing approval gates, runaway loops and audit trail visualized as a circuit.\\\",\\n 46| \\\"twist\\\": \\\"Der Fehler ist nicht Intelligenz. Der Fehler ist fehlende Begrenzung.\\\",\\n 47| \\\"visual_mechanism\\\": \\\"agent control loop with approval gate and red runaway path\\\",\\n 48| \\\"surprise\\\": 8,\\n 49| \\\"data_basis\\\": 8,\\n 50| \\\"comment_potential\\\": 9,\\n 51| \\\"series_potential\\\": 10,\\n 52| \\\"monetization_potential\\\": 10,\\n 53| \\\"production_effort\\\": 5,\\n 54| },\\n 55| {\\n 56| \\\"title\\\": \\\"Der Unterschied zwischen Chatbot und echtem Agenten\\\",\\n 57| \\\"pillar\\\": \\\"ai_agents_explained\\\",\\n 58| \\\"hook\\\": \\\"Ein Chatbot antwortet. Ein Agent verändert Dinge.\\\",\\n 59| \\\"mechanism\\\": \\\"Intent, tools, memory, approvals and execution are revealed as separate layers.\\\",\\n 60| \\\"twist\\\": \\\"Autonomie beginnt nicht beim Modell, sondern beim Zugriff.\\\",\\n 61| \\\"visual_mechanism\\\": \\\"split-screen chatbot bubble vs agent toolchain\\\",\\n 62| \\\"surprise\\\": 7,\\n 63| \\\"data_basis\\\": 8,\\n 64| \\\"comment_potential\\\": 8,\\n 65| \\\"series_potential\\\": 10,\\n 66| \\\"monetization_potential\\\": 9,\\n 67| \\\"production_effort\\\": 4,\\n 68| },\\n 69| {\\n 70| \\\"title\\\": \\\"Warum ein Bauprojekt fast nie beim ersten Preis bleibt\\\",\\n 71| \\\"pillar\\\": \\\"swiss_systems_explained\\\",\\n 72| \\\"hook\\\": \\\"Dieser Baukredit sieht fix aus. Dann beginnt die Schweiz.\\\",\\n 73| \\\"mechanism\\\": \\\"Permits, objections, procurement, inflation, scope creep and political process stack over time.\\\",\\n 74| \\\"twist\\\": \\\"Die Überraschung ist nicht der Mehrpreis. Es ist die Anzahl unsichtbarer Schleifen.\\\",\\n 75| \\\"visual_mechanism\\\": \\\"construction budget timeline with process gates and cost bubbles\\\",\\n 76| \\\"surprise\\\": 8,\\n 77| \\\"data_basis\\\": 8,\\n 78| \\\"comment_potential\\\": 7,\\n 79| \\\"series_potential\\\": 9,\\n 80| \\\"monetization_potential\\\": 7,\\n 81| \\\"production_effort\\\": 7,\\n 82| },\\n 83| {\\n 84| \\\"title\\\": \\\"Wie ein Einwohnerrat wirklich entscheidet\\\",\\n 85| \\\"pillar\\\": \\\"swiss_systems_explained\\\",\\n 86| \\\"hook\\\": \\\"Das sieht wie eine Abstimmung aus. Eigentlich ist es ein Filtersystem.\\\",\\n 87| \\\"mechanism\\\": \\\"Motion, committee, budget, party positions, amendments and final vote visualized as a flow.\\\",\\n 88| \\\"twist\\\": \\\"Die Entscheidung fällt oft, bevor die Schlussabstimmung beginnt.\\\",\\n 89| \\\"visual_mechanism\\\": \\\"council chamber turns into decision flowchart\\\",\\n 90| \\\"surprise\\\": 7,\\n 91| \\\"data_basis\\\": 8,\\n 92| \\\"comment_potential\\\": 7,\\n 93| \\\"series_potential\\\": 8,\\n 94| \\\"monetization_potential\\\": 6,\\n 95| \\\"production_effort\\\": 6,\\n 96| },\\n 97| {\\n 98| \\\"title\\\": \\\"Warum dein Gehirn schlechte Deals schönrechnet\\\",\\n 99| \\\"pillar\\\": \\\"dangerous_thinking_errors\\\",\\n 100| \\\"hook\\\": \\\"Dein Gehirn merkt diesen schlechten Deal. Dann verteidigt es ihn.\\\",\\n 101| \\\"mechanism\\\": \\\"Sunk cost, confirmation bias and loss aversion appear as competing brain layers.\\\",\\n 102| \\\"twist\\\": \\\"Du kaufst nicht den Deal. Du kaufst die Rechtfertigung.\\\",\\n 103| \\\"visual_mechanism\\\": \\\"brain courtroom where biases argue over a bad deal\\\",\\n 104| \\\"surprise\\\": 9,\\n 105| \\\"data_basis\\\": 7,\\n 106| \\\"comment_potential\\\": 9,\\n 107| \\\"series_potential\\\": 10,\\n 108| \\\"monetization_potential\\\": 8,\\n 109| \\\"production_effort\\\": 5,\\n 110| },\\n 111| {\\n 112| \\\"title\\\": \\\"Warum du Verlusten hinterherrennst\\\",\\n 113| \\\"pillar\\\": \\\"dangerous_thinking_errors\\\",\\n 114| \\\"hook\\\": \\\"Der Verlust ist vorbei. Trotzdem zahlst du weiter.\\\",\\n 115| \\\"mechanism\\\": \\\"Loss aversion, sunk cost and identity protection visualized as a trap door.\\\",\\n 116| \\\"twist\\\": \\\"Du rettest nicht dein Geld. Du rettest dein altes Ich.\\\",\\n 117| \\\"visual_mechanism\\\": \\\"money trail into trap with brain pulling it back\\\",\\n 118| \\\"surprise\\\": 8,\\n 119| \\\"data_basis\\\": 7,\\n 120| \\\"comment_potential\\\": 8,\\n 121| \\\"series_potential\\\": 9,\\n 122| \\\"monetization_potential\\\": 8,\\n 123| \\\"production_effort\\\": 5,\\n 124| },\\n 125| {\\n 126| \\\"title\\\": \\\"Warum Shorts dich nicht wegen dem Inhalt halten\\\",\\n 127| \\\"pillar\\\": \\\"hidden_attention_systems\\\",\\n 128| \\\"hook\\\": \\\"Du bleibst nicht wegen dem Thema. Du bleibst wegen der offenen Schleife.\\\",\\n 129| \\\"mechanism\\\": \\\"Hook, open loop, pattern interrupt, payoff delay and reward pulse shown as timeline layers.\\\",\\n 130| \\\"twist\\\": \\\"Retention ist oft Architektur, nicht Interesse.\\\",\\n 131| \\\"visual_mechanism\\\": \\\"shorts timeline with dopamine gates and loop markers\\\",\\n 132| \\\"surprise\\\": 9,\\n 133| \\\"data_basis\\\": 8,\\n 134| \\\"comment_potential\\\": 9,\\n 135| \\\"series_potential\\\": 10,\\n 136| \\\"monetization_potential\\\": 8,\\n 137| \\\"production_effort\\\": 5,\\n 138| },\\n 139| {\\n 140| \\\"title\\\": \\\"Warum Banken Gebühren verstecken können, ohne sie zu verstecken\\\",\\n 141| \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n 142| \\\"hook\\\": \\\"Diese Gebühr ist sichtbar. Genau deshalb übersiehst du sie.\\\",\\n 143| \\\"mechanism\\\": \\\"Statement line items, percentage framing, monthly smoothing and comparison blindness.\\\",\\n 144| \\\"twist\\\": \\\"Das Versteck ist nicht Geheimhaltung. Es ist Normalisierung.\\\",\\n 145| \\\"visual_mechanism\\\": \\\"bank statement zooms into visible-but-ignored fee layers\\\",\\n 146| \\\"surprise\\\": 8,\\n 147| \\\"data_basis\\\": 7,\\n 148| \\\"comment_potential\\\": 8,\\n 149| \\\"series_potential\\\": 9,\\n 150| \\\"monetization_potential\\\": 9,\\n 151| \\\"production_effort\\\": 5,\\n 152| },\\n 153|]\\n 154|\\n 155|\\n 156|def build_retention_studio_strategy() -> dict[str, Any]:\\n 157| \\\"\\\"\\\"Return the strategic operating model for the channel.\\\"\\\"\\\"\\n 158| return {\\n 159| \\\"positioning\\\": \\\"Hidden Money & AI Systems\\\",\\n 160| \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n 161| \\\"claim\\\": \\\"Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\\\",\\n 162| \\\"operating_model\\\": \\\"semi_automatic_retention_studio\\\",\\n 163| \\\"not_a\\\": [\\\"ki_avatar_news\\\", \\\"static_ai_slideshow\\\", \\\"reused_content_clone\\\"],\\n 164| \\\"core_formula\\\": [\\n 165| \\\"0.0-1.5s pattern interrupt\\\",\\n 166| \\\"1.5-5s problem\\\",\\n 167| \\\"5-18s mechanism reveal\\\",\\n 168| \\\"18-28s twist\\\",\\n 169| \\\"28-35s follow trigger\\\",\\n 170| ],\\n 171| \\\"format_taxonomy\\\": [\\n 172| \\\"mechanism_reveal\\\",\\n 173| \\\"mistake_breakdown\\\",\\n 174| \\\"hidden_cost\\\",\\n 175| \\\"before_after\\\",\\n 176| \\\"myth_vs_reality\\\",\\n 177| \\\"mini_simulation\\\",\\n 178| \\\"nobody_notices_this\\\",\\n 179| ],\\n 180| \\\"content_pillars\\\": [\\n 181| \\\"hidden_money_systems\\\",\\n 182| \\\"auto_trade_mechanics\\\",\\n 183| \\\"ai_agents_explained\\\",\\n 184| \\\"swiss_systems_explained\\\",\\n 185| \\\"dangerous_thinking_errors\\\",\\n 186| ],\\n 187| \\\"quality_gates\\\": {\\n 188| \\\"static_card_limit\\\": 0,\\n 189| \\\"first_two_seconds_must_open_loop\\\": True,\\n 190| \\\"visual_change_every_seconds\\\": 2,\\n 191| \\\"human_review_percent\\\": 20,\\n 192| \\\"reused_content_allowed\\\": False,\\n 193| \\\"synthetic_media_disclosure_required_when_realistic\\\": True,\\n 194| },\\n 195| \\\"metrics\\\": [\\n 196| \\\"retention_1s\\\",\\n 197| \\\"retention_3s\\\",\\n 198| \\\"average_view_duration\\\",\\n 199| \\\"rewatches\\\",\\n 200| \\\"comments_per_1000_views\\\",\\n 201| \\\"follows_per_1000_views\\\",\\n 202| ],\\n 203| }\\n 204|\\n 205|\\n 206|def score_idea(idea: dict[str, Any]) -> dict[str, Any]:\\n 207| \\\"\\\"\\\"Score an idea for production priority.\\\"\\\"\\\"\\n 208| weights = {\\n 209| \\\"surprise\\\": 1.6,\\n 210| \\\"data_basis\\\": 1.2,\\n 211| \\\"comment_potential\\\": 1.2,\\n 212| \\\"series_potential\\\": 1.4,\\n 213| \\\"monetization_potential\\\": 1.4,\\n 214| }\\n 215| weighted = sum(float(idea.get(key, 0)) * weight for key, weight in weights.items())\\n 216| visual_bonus = 12 if _has_specific_visual_mechanism(str(idea.get(\\\"visual_mechanism\\\", \\\"\\\"))) else 0\\n 217| effort_penalty = max(0, float(idea.get(\\\"production_effort\\\", 5)) - 5) * 2\\n 218| total = round(min(100, weighted / sum(weights.values()) * 8.8 + visual_bonus - effort_penalty))\\n 219| if total >= 80:\\n 220| decision = \\\"produce\\\"\\n 221| elif total >= 65:\\n 222| decision = \\\"develop\\\"\\n 223| else:\\n 224| decision = \\\"reject\\\"\\n 225| return {\\\"total\\\": total, \\\"decision\\\": decision}\\n 226|\\n 227|\\n 228|def _has_specific_visual_mechanism(visual_mechanism: str) -> bool:\\n 229| generic_terms = {\\\"generic montage\\\", \\\"talking avatar\\\", \\\"static image\\\", \\\"slideshow\\\"}\\n 230| if visual_mechanism.lower().strip() in generic_terms:\\n 231| return False\\n 232| return any(\\n 233| token in visual_mechanism.lower()\\n 234| for token in [\\\"x-ray\\\", \\\"röntgen\\\", \\\"layer\\\", \\\"simulation\\\", \\\"timeline\\\", \\\"flow\\\", \\\"split\\\", \\\"3d\\\", \\\"circuit\\\"]\\n 235| )\\n 236|\\n 237|\\n 238|def build_mechanism_explainer(idea: dict[str, Any]) -> dict[str, Any]:\\n 239| \\\"\\\"\\\"Build a shot-by-shot mechanism explainer skeleton from a scored idea.\\\"\\\"\\\"\\n 240| follow_trigger = \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\"\\n 241| return {\\n 242| \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n 243| \\\"title\\\": idea[\\\"title\\\"],\\n 244| \\\"pillar\\\": idea[\\\"pillar\\\"],\\n 245| \\\"target_seconds\\\": 34,\\n 246| \\\"follow_trigger\\\": follow_trigger,\\n 247| \\\"beats\\\": [\\n 248| {\\n 249| \\\"beat\\\": \\\"pattern_interrupt\\\",\\n 250| \\\"start\\\": 0.0,\\n 251| \\\"end\\\": 1.4,\\n 252| \\\"voiceover\\\": idea[\\\"hook\\\"],\\n 253| \\\"visual\\\": f\\\"{idea['visual_mechanism']} freezes mid-action with warning signal.\\\",\\n 254| \\\"visual_motion\\\": \\\"hard zoom, freeze-frame, red signal pulse\\\",\\n 255| \\\"sound_cue\\\": \\\"bass hit + camera snap\\\",\\n 256| },\\n 257| {\\n 258| \\\"beat\\\": \\\"problem\\\",\\n 259| \\\"start\\\": 1.4,\\n 260| \\\"end\\\": 5.0,\\n 261|\", \"total_lines\": 294, \"file_size\": 12472, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 294 lines)\", \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_xKRCtTMTN8eE0WfozoTVPCI1"
},
{
"role": "tool",
"content": "{\"content\": \" 1|[\\n 2| {\\n 3| \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n 4| \\\"title\\\": \\\"Warum ein billiges Auto teuer sein kann\\\",\\n 5| \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n 6| \\\"target_seconds\\\": 34,\\n 7| \\\"follow_trigger\\\": \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\",\\n 8| \\\"beats\\\": [\\n 9| {\\n 10| \\\"beat\\\": \\\"pattern_interrupt\\\",\\n 11| \\\"start\\\": 0.0,\\n 12| \\\"end\\\": 1.4,\\n 13| \\\"voiceover\\\": \\\"Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\\",\\n 14| \\\"visual\\\": \\\"3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\\\",\\n 15| \\\"visual_motion\\\": \\\"hard zoom, freeze-frame, red signal pulse\\\",\\n 16| \\\"sound_cue\\\": \\\"bass hit + camera snap\\\"\\n 17| },\\n 18| {\\n 19| \\\"beat\\\": \\\"problem\\\",\\n 20| \\\"start\\\": 1.4,\\n 21| \\\"end\\\": 5.0,\\n 22| \\\"voiceover\\\": \\\"Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\\",\\n 23| \\\"visual\\\": \\\"surface layer peels away to reveal the hidden system underneath.\\\",\\n 24| \\\"visual_motion\\\": \\\"layer peel + parallax push\\\",\\n 25| \\\"sound_cue\\\": \\\"whoosh + low tension tick\\\"\\n 26| },\\n 27| {\\n 28| \\\"beat\\\": \\\"mechanism\\\",\\n 29| \\\"start\\\": 5.0,\\n 30| \\\"end\\\": 18.0,\\n 31| \\\"voiceover\\\": \\\"Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\\\",\\n 32| \\\"visual\\\": \\\"3D car x-ray with price tag layers and risk meters\\\",\\n 33| \\\"visual_motion\\\": \\\"sequential layer reveal every 1-2 seconds\\\",\\n 34| \\\"sound_cue\\\": \\\"clicks per revealed layer\\\"\\n 35| },\\n 36| {\\n 37| \\\"beat\\\": \\\"twist\\\",\\n 38| \\\"start\\\": 18.0,\\n 39| \\\"end\\\": 28.0,\\n 40| \\\"voiceover\\\": \\\"Der teuerste Fehler ist nicht der Kaufpreis, sondern der Zeitpunkt.\\\",\\n 41| \\\"visual\\\": \\\"camera snaps from obvious cause to real cause; wrong layer fades out.\\\",\\n 42| \\\"visual_motion\\\": \\\"snap zoom + wrong answer collapses\\\",\\n 43| \\\"sound_cue\\\": \\\"drop + silence beat\\\"\\n 44| },\\n 45| {\\n 46| \\\"beat\\\": \\\"follow_trigger\\\",\\n 47| \\\"start\\\": 28.0,\\n 48| \\\"end\\\": 34.0,\\n 49| \\\"voiceover\\\": \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\",\\n 50| \\\"visual\\\": \\\"series tag appears over a clean animated system map.\\\",\\n 51| \\\"visual_motion\\\": \\\"map draws itself, final node glows\\\",\\n 52| \\\"sound_cue\\\": \\\"resolve hit + soft riser\\\"\\n 53| }\\n 54| ]\\n 55| },\\n 56| {\\n 57| \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n 58| \\\"title\\\": \\\"Warum 0%-Finanzierung nicht kostenlos ist\\\",\\n 59| \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n 60| \\\"target_seconds\\\": 34,\\n 61| \\\"follow_trigger\\\": \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\",\\n 62| \\\"beats\\\": [\\n 63| {\\n 64| \\\"beat\\\": \\\"pattern_interrupt\\\",\\n 65| \\\"start\\\": 0.0,\\n 66| \\\"end\\\": 1.4,\\n 67| \\\"voiceover\\\": \\\"0 Prozent klingt gratis. Genau deshalb funktioniert es.\\\",\\n 68| \\\"visual\\\": \\\"contract transforms into hidden cost layers freezes mid-action with warning signal.\\\",\\n 69| \\\"visual_motion\\\": \\\"hard zoom, freeze-frame, red signal pulse\\\",\\n 70| \\\"sound_cue\\\": \\\"bass hit + camera snap\\\"\\n 71| },\\n 72| {\\n 73| \\\"beat\\\": \\\"problem\\\",\\n 74| \\\"start\\\": 1.4,\\n 75| \\\"end\\\": 5.0,\\n 76| \\\"voiceover\\\": \\\"Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\\",\\n 77| \\\"visual\\\": \\\"surface layer peels away to reveal the hidden system underneath.\\\",\\n 78| \\\"visual_motion\\\": \\\"layer peel + parallax push\\\",\\n 79| \\\"sound_cue\\\": \\\"whoosh + low tension tick\\\"\\n 80| },\\n 81| {\\n 82| \\\"beat\\\": \\\"mechanism\\\",\\n 83| \\\"start\\\": 5.0,\\n 84| \\\"end\\\": 18.0,\\n 85| \\\"voiceover\\\": \\\"Cash price, margin, fees, lock-in and opportunity cost split into layers.\\\",\\n 86| \\\"visual\\\": \\\"contract transforms into hidden cost layers\\\",\\n 87| \\\"visual_motion\\\": \\\"sequential layer reveal every 1-2 seconds\\\",\\n 88| \\\"sound_cue\\\": \\\"clicks per revealed layer\\\"\\n 89| },\\n 90| {\\n 91| \\\"beat\\\": \\\"twist\\\",\\n 92| \\\"start\\\": 18.0,\\n 93| \\\"end\\\": 28.0,\\n 94| \\\"voiceover\\\": \\\"Die Zinsen verschwinden nicht. Sie verstecken sich nur im System.\\\",\\n 95| \\\"visual\\\": \\\"camera snaps from obvious cause to real cause; wrong layer fades out.\\\",\\n 96| \\\"visual_motion\\\": \\\"snap zoom + wrong answer collapses\\\",\\n 97| \\\"sound_cue\\\": \\\"drop + silence beat\\\"\\n 98| },\\n 99| {\\n 100| \\\"beat\\\": \\\"follow_trigger\\\",\\n 101| \\\"start\\\": 28.0,\\n 102| \\\"end\\\": 34.0,\\n 103| \\\"voiceover\\\": \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\",\\n 104| \\\"visual\\\": \\\"series tag appears over a clean animated system map.\\\",\\n 105| \\\"visual_motion\\\": \\\"map draws itself, final node glows\\\",\\n 106| \\\"sound_cue\\\": \\\"resolve hit + soft riser\\\"\\n 107| }\\n 108| ]\\n 109| },\\n 110| {\\n 111| \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n 112| \\\"title\\\": \\\"Warum KI-Agenten ohne Regeln gefährlich werden\\\",\\n 113| \\\"pillar\\\": \\\"ai_agents_explained\\\",\\n 114| \\\"target_seconds\\\": 34,\\n 115| \\\"follow_trigger\\\": \\\"Morgen zeige ich das nächste versteckte System, bevor es dich Geld kostet.\\\",\\n 116| \\\"beats\\\": [\\n 117| {\\n 118| \\\"beat\\\": \\\"pattern_interrupt\\\",\\n 119| \\\"start\\\": 0.0,\\n 120| \\\"end\\\": 1.4,\\n 121|\", \"total_lines\": 542, \"file_size\": 22268, \"truncated\": true, \"hint\": \"Use offset=121 to continue reading (showing 1-120 of 542 lines)\", \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_bJeHkPnCwQhQP1Kyy1mvz7OP"
},
{
"role": "assistant",
"content": "Need TDD tests.",
"tool_calls": [
{
"id": "call_cRs8kOyvmMTlYdPhRsMzjZ1t",
"call_id": "call_cRs8kOyvmMTlYdPhRsMzjZ1t",
"response_item_id": "fc_02aa21bd75cf0633016a11584caccc81918d5a6204c0b68742",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Repo-Status und Retention-Studio-Artefakte prüfen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Need TDD tests."
}
],
"id": "msg_02aa21bd75cf0633016a11584c79c4819198eb244f57f51aec",
"phase": "commentary"
}
]
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"in_progress\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 3, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}",
"tool_call_id": "call_cRs8kOyvmMTlYdPhRsMzjZ1t"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_pTQnj4zQDQ02vfLr90VUXd1F",
"call_id": "call_pTQnj4zQDQ02vfLr90VUXd1F",
"response_item_id": "fc_02aa21bd75cf0633016a115850dd8c8191baf1461e626c8fd4",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py\",\"content\":\"from autoshorts.mechanism_pipeline import (\\n build_mechanism_shot_plan,\\n write_mechanism_shot_plans,\\n)\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\\n\\ndef _auto_explainer():\\n return build_mechanism_explainer(PILOT_IDEAS[0])\\n\\n\\ndef test_build_mechanism_shot_plan_preserves_retention_beats_and_timing():\\n explainer = _auto_explainer()\\n\\n plan = build_mechanism_shot_plan(explainer)\\n\\n assert plan[\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n assert plan[\\\"series\\\"] == \\\"Unsichtbare Systeme in 30 Sekunden\\\"\\n assert plan[\\\"estimated_seconds\\\"] == 34\\n assert [scene[\\\"purpose\\\"] for scene in plan[\\\"scenes\\\"]] == [\\n \\\"pattern_interrupt\\\",\\n \\\"problem\\\",\\n \\\"mechanism_reveal\\\",\\n \\\"twist\\\",\\n \\\"follow_trigger\\\",\\n ]\\n assert plan[\\\"scenes\\\"][0][\\\"start\\\"] == 0.0\\n assert plan[\\\"scenes\\\"][0][\\\"end\\\"] <= 1.5\\n assert plan[\\\"scenes\\\"][2][\\\"asset_type\\\"] == \\\"mechanism_animation\\\"\\n assert all(scene[\\\"visual_motion\\\"] for scene in plan[\\\"scenes\\\"])\\n assert all(scene[\\\"sound_cue\\\"] for scene in plan[\\\"scenes\\\"])\\n\\n\\ndef test_mechanism_shot_plan_uses_motion_led_asset_types_not_static_cards():\\n plan = build_mechanism_shot_plan(_auto_explainer())\\n\\n asset_types = [scene[\\\"asset_type\\\"] for scene in plan[\\\"scenes\\\"]]\\n\\n assert \\\"static_card\\\" not in asset_types\\n assert \\\"cinematic_ai_video\\\" in asset_types\\n assert \\\"mechanism_animation\\\" in asset_types\\n assert \\\"kinetic_text_overlay\\\" in asset_types\\n assert plan[\\\"final_render_intent\\\"][\\\"static_card_limit\\\"] == 0\\n assert plan[\\\"final_render_intent\\\"][\\\"visual_change_every_seconds\\\"] <= 2\\n\\n\\ndef test_mechanism_shot_plan_generates_caption_beats_from_voiceover():\\n plan = build_mechanism_shot_plan(_auto_explainer())\\n\\n assert len(plan[\\\"caption_beats\\\"]) == 5\\n assert plan[\\\"caption_beats\\\"][0][\\\"start\\\"] == 0\\n assert \\\"Occasion\\\" in plan[\\\"caption_beats\\\"][0][\\\"text\\\"]\\n assert plan[\\\"caption_beats\\\"][-1][\\\"start\\\"] == 28\\n assert \\\"versteckte\\\" in plan[\\\"caption_beats\\\"][-1][\\\"text\\\"].lower()\\n\\n\\ndef test_write_mechanism_shot_plans_outputs_markdown(tmp_path):\\n explainer = _auto_explainer()\\n\\n paths = write_mechanism_shot_plans([explainer], tmp_path)\\n\\n assert len(paths) == 1\\n assert paths[0].suffix == \\\".md\\\"\\n text = paths[0].read_text(encoding=\\\"utf-8\\\")\\n assert \\\"## Mechanism shot plan\\\" in text\\n assert \\\"pattern_interrupt\\\" in text\\n assert \\\"mechanism_animation\\\" in text\\n assert \\\"Sound cue\\\" in text\\n\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 2463, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.mechanism_pipeline\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}",
"tool_call_id": "call_pTQnj4zQDQ02vfLr90VUXd1F"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ie1FvlIV50L7XSLE2MpAIGyn",
"call_id": "call_ie1FvlIV50L7XSLE2MpAIGyn",
"response_item_id": "fc_02aa21bd75cf0633016a115867a0348191b520a35c06b6b678",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_mechanism_pipeline.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"==================================== ERRORS ====================================\\n______________ ERROR collecting tests/test_mechanism_pipeline.py _______________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_mechanism_pipeline.py:1: in \\n from autoshorts.mechanism_pipeline import (\\nE ModuleNotFoundError: No module named 'autoshorts.mechanism_pipeline'\\n=========================== short test summary info ============================\\nERROR tests/test_mechanism_pipeline.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.10s\", \"exit_code\": 2, \"error\": null}",
"tool_call_id": "call_ie1FvlIV50L7XSLE2MpAIGyn"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_T6mrJi2hfPFQ58YeH5h0GPPS",
"call_id": "call_T6mrJi2hfPFQ58YeH5h0GPPS",
"response_item_id": "fc_02aa21bd75cf0633016a11586dc4e0819186b1e057ec34d550",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"in_progress\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 2, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}",
"tool_call_id": "call_T6mrJi2hfPFQ58YeH5h0GPPS"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_kQQmUHvnmjpTYwz4AQEu6pqy",
"call_id": "call_kQQmUHvnmjpTYwz4AQEu6pqy",
"response_item_id": "fc_02aa21bd75cf0633016a11587219008191b9170214572a13ad",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"\\\"\\\"\\\"Adapters from Retention Studio mechanism explainers into production shot plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nfrom autoshorts.script_drafts import _slug\\n\\nBEAT_TO_PURPOSE = {\\n \\\"pattern_interrupt\\\": \\\"pattern_interrupt\\\",\\n \\\"problem\\\": \\\"problem\\\",\\n \\\"mechanism\\\": \\\"mechanism_reveal\\\",\\n \\\"twist\\\": \\\"twist\\\",\\n \\\"follow_trigger\\\": \\\"follow_trigger\\\",\\n}\\n\\nBEAT_TO_ASSET_TYPE = {\\n \\\"pattern_interrupt\\\": \\\"cinematic_ai_video\\\",\\n \\\"problem\\\": \\\"cinematic_ai_video\\\",\\n \\\"mechanism\\\": \\\"mechanism_animation\\\",\\n \\\"twist\\\": \\\"mechanism_animation\\\",\\n \\\"follow_trigger\\\": \\\"kinetic_text_overlay\\\",\\n}\\n\\n\\ndef _plan_id(explainer: dict[str, Any]) -> str:\\n return f\\\"{_slug(explainer['pillar'])}-{_slug(explainer['title'])}\\\"\\n\\n\\ndef _overlay_text(voiceover: str, limit: int = 72) -> str:\\n cleaned = \\\" \\\".join(voiceover.split())\\n if len(cleaned) <= limit:\\n return cleaned\\n return cleaned[:limit].rsplit(\\\" \\\", 1)[0].rstrip(\\\".,;:\\\")\\n\\n\\ndef _scene_from_beat(index: int, beat: dict[str, Any]) -> dict[str, Any]:\\n beat_name = beat[\\\"beat\\\"]\\n return {\\n \\\"scene\\\": index,\\n \\\"purpose\\\": BEAT_TO_PURPOSE[beat_name],\\n \\\"start\\\": beat[\\\"start\\\"],\\n \\\"end\\\": beat[\\\"end\\\"],\\n \\\"asset_type\\\": BEAT_TO_ASSET_TYPE[beat_name],\\n \\\"on_screen_text\\\": _overlay_text(beat[\\\"voiceover\\\"]),\\n \\\"voiceover\\\": beat[\\\"voiceover\\\"],\\n \\\"visual_direction\\\": beat[\\\"visual\\\"],\\n \\\"visual_motion\\\": beat[\\\"visual_motion\\\"],\\n \\\"sound_cue\\\": beat[\\\"sound_cue\\\"],\\n \\\"retention_goal\\\": _retention_goal(beat_name),\\n }\\n\\n\\ndef _retention_goal(beat_name: str) -> str:\\n goals = {\\n \\\"pattern_interrupt\\\": \\\"Open a visual loop immediately; make the viewer need the hidden explanation.\\\",\\n \\\"problem\\\": \\\"Make the surface-level interpretation feel incomplete.\\\",\\n \\\"mechanism\\\": \\\"Reveal the invisible system through moving layers, not narration alone.\\\",\\n \\\"twist\\\": \\\"Reframe the obvious cause into the real cause.\\\",\\n \\\"follow_trigger\\\": \\\"Promise the next hidden system without a generic like/follow plea.\\\",\\n }\\n return goals[beat_name]\\n\\n\\ndef build_mechanism_shot_plan(explainer: dict[str, Any]) -> dict[str, Any]:\\n \\\"\\\"\\\"Convert a Retention Studio mechanism explainer into a motion-led shot plan.\\\"\\\"\\\"\\n scenes = [_scene_from_beat(index, beat) for index, beat in enumerate(explainer[\\\"beats\\\"], start=1)]\\n caption_beats = [\\n {\\\"start\\\": int(beat[\\\"start\\\"]), \\\"text\\\": beat[\\\"voiceover\\\"]}\\n for beat in explainer[\\\"beats\\\"]\\n ]\\n return {\\n \\\"id\\\": _plan_id(explainer),\\n \\\"series\\\": explainer[\\\"series\\\"],\\n \\\"title\\\": explainer[\\\"title\\\"],\\n \\\"pillar\\\": explainer[\\\"pillar\\\"],\\n \\\"estimated_seconds\\\": int(explainer[\\\"target_seconds\\\"]),\\n \\\"caption_beats\\\": caption_beats,\\n \\\"scenes\\\": scenes,\\n \\\"final_render_intent\\\": {\\n \\\"static_card_limit\\\": 0,\\n \\\"visual_change_every_seconds\\\": 2,\\n \\\"primary_style\\\": \\\"mechanism_reveal_motion_explainer\\\",\\n \\\"captions\\\": \\\"large kinetic overlays; voiceover carries detail; no paragraph cards\\\",\\n },\\n }\\n\\n\\ndef format_mechanism_shot_plan(plan: dict[str, Any]) -> str:\\n scene_blocks = []\\n for scene in plan[\\\"scenes\\\"]:\\n scene_blocks.append(\\n f\\\"### Scene {scene['scene']} — {scene['purpose']}\\\\n\\\\n\\\"\\n f\\\"- Timing: {scene['start']}s–{scene['end']}s\\\\n\\\"\\n f\\\"- Asset type: {scene['asset_type']}\\\\n\\\"\\n f\\\"- On-screen text: {scene['on_screen_text']}\\\\n\\\"\\n f\\\"- Voiceover: {scene['voiceover']}\\\\n\\\"\\n f\\\"- Visual direction: {scene['visual_direction']}\\\\n\\\"\\n f\\\"- Visual motion: {scene['visual_motion']}\\\\n\\\"\\n f\\\"- Sound cue: {scene['sound_cue']}\\\\n\\\"\\n f\\\"- Retention goal: {scene['retention_goal']}\\\"\\n )\\n scenes = \\\"\\\\n\\\\n\\\".join(scene_blocks)\\n beats = \\\"\\\\n\\\".join(f\\\"- {beat['start']:02d}s: {beat['text']}\\\" for beat in plan[\\\"caption_beats\\\"])\\n intent = plan[\\\"final_render_intent\\\"]\\n return f\\\"\\\"\\\"# {plan['title']}\\n\\n- ID: `{plan['id']}`\\n- Series: {plan['series']}\\n- Pillar: `{plan['pillar']}`\\n- Estimated duration: {plan['estimated_seconds']}s\\n\\n## Mechanism shot plan\\n\\n{scenes}\\n\\n## Caption beats\\n\\n{beats}\\n\\n## Final render intent\\n\\n- Static card limit: {intent['static_card_limit']}\\n- Visual change every seconds: {intent['visual_change_every_seconds']}\\n- Primary style: {intent['primary_style']}\\n- Captions: {intent['captions']}\\n\\\"\\\"\\\"\\n\\n\\ndef write_mechanism_shot_plans(explainers: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write mechanism shot plans as Markdown files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for explainer in explainers:\\n plan = build_mechanism_shot_plan(explainer)\\n path = output_dir / f\\\"{plan['id']}.md\\\"\\n path.write_text(format_mechanism_shot_plan(plan), encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 4898, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_kQQmUHvnmjpTYwz4AQEu6pqy"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_HWMAKcMjpe36arx7x6tSG2Gf",
"call_id": "call_HWMAKcMjpe36arx7x6tSG2Gf",
"response_item_id": "fc_02aa21bd75cf0633016a115892da80819195c222676432e2f5",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"growth_plan\\\",\\n \\\"mechanism_pipeline\\\",\\n \\\"preview_renderer\\\",\",\"old_string\":\" \\\"growth_plan\\\",\\n \\\"preview_renderer\\\",\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\n@@ -3,6 +3,7 @@\\n __all__ = [\\n \\\"asset_plan\\\",\\n \\\"growth_plan\\\",\\n+ \\\"mechanism_pipeline\\\",\\n \\\"preview_renderer\\\",\\n \\\"render_manifest\\\",\\n \\\"retention\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_HWMAKcMjpe36arx7x6tSG2Gf"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_tC9osqYwqtNLETslhYWpoYrZ",
"call_id": "call_tC9osqYwqtNLETslhYWpoYrZ",
"response_item_id": "fc_02aa21bd75cf0633016a1158a0751881919df1a1515cb3d749",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_mechanism_pipeline.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \".... [100%]\\n4 passed in 0.03s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_tC9osqYwqtNLETslhYWpoYrZ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_8SLcx4jMvY9hsIIN65K8cGL3",
"call_id": "call_8SLcx4jMvY9hsIIN65K8cGL3",
"response_item_id": "fc_02aa21bd75cf0633016a1158a672c08191b0f3811e257a0520",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"\\\"\\\"\\\"Generate mechanism-explainer shot plans from Retention Studio artifacts.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.mechanism_pipeline import write_mechanism_shot_plans\\n\\n\\ndef main() -> None:\\n parser = argparse.ArgumentParser(description=\\\"Generate mechanism shot plans.\\\")\\n parser.add_argument(\\\"--explainers\\\", default=\\\"data/retention_studio/mechanism_explainers.json\\\")\\n parser.add_argument(\\\"--out\\\", default=\\\"data/mechanism_shot_plans\\\")\\n parser.add_argument(\\\"--title-contains\\\", default=None)\\n args = parser.parse_args()\\n\\n explainers = json.loads(Path(args.explainers).read_text(encoding=\\\"utf-8\\\"))\\n if args.title_contains:\\n needle = args.title_contains.lower()\\n explainers = [item for item in explainers if needle in item[\\\"title\\\"].lower()]\\n\\n paths = write_mechanism_shot_plans(explainers, Path(args.out))\\n print(f\\\"Generated {len(paths)} mechanism shot plans in {args.out}\\\")\\n for path in paths:\\n print(path)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_shot_plans.py\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"bytes_written\": 1207, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_8SLcx4jMvY9hsIIN65K8cGL3"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_PzBDEV8yaHeR6rrutpu6vZtq",
"call_id": "call_PzBDEV8yaHeR6rrutpu6vZtq",
"response_item_id": "fc_02aa21bd75cf0633016a1158b4c6e48191bb6e8247d115ee83",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Pilot-Shot-Plan für billiges Auto generieren und prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"in_progress\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 1, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}",
"tool_call_id": "call_PzBDEV8yaHeR6rrutpu6vZtq"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_zbySla8BWGxA622QURnBJ2Uj",
"call_id": "call_zbySla8BWGxA622QURnBJ2Uj",
"response_item_id": "fc_02aa21bd75cf0633016a1158b918cc8191882ac9ca3be202cb",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"rm -rf data/mechanism_shot_plans\\npython3 scripts/generate_mechanism_shot_plans.py --title-contains 'billiges Auto' --out data/mechanism_shot_plans\\npython3 - <<'PY'\\nfrom pathlib import Path\\nfiles=sorted(Path('data/mechanism_shot_plans').glob('*.md'))\\nprint('files', len(files))\\nfor p in files:\\n print(p)\\n print('\\\\n'.join(p.read_text(encoding='utf-8').splitlines()[:40]))\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"Generated 1 mechanism shot plans in data/mechanism_shot_plans\\ndata/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md\\nfiles 1\\ndata/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md\\n# Warum ein billiges Auto teuer sein kann\\n\\n- ID: `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`\\n- Series: Unsichtbare Systeme in 30 Sekunden\\n- Pillar: `hidden_money_systems`\\n- Estimated duration: 34s\\n\\n## Mechanism shot plan\\n\\n### Scene 1 — pattern_interrupt\\n\\n- Timing: 0.0s–1.4s\\n- Asset type: cinematic_ai_video\\n- On-screen text: Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\n- Voiceover: Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\n- Visual direction: 3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\\n- Visual motion: hard zoom, freeze-frame, red signal pulse\\n- Sound cue: bass hit + camera snap\\n- Retention goal: Open a visual loop immediately; make the viewer need the hidden explanation.\\n\\n### Scene 2 — problem\\n\\n- Timing: 1.4s–5.0s\\n- Asset type: cinematic_ai_video\\n- On-screen text: Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\n- Voiceover: Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\n- Visual direction: surface layer peels away to reveal the hidden system underneath.\\n- Visual motion: layer peel + parallax push\\n- Sound cue: whoosh + low tension tick\\n- Retention goal: Make the surface-level interpretation feel incomplete.\\n\\n### Scene 3 — mechanism_reveal\\n\\n- Timing: 5.0s–18.0s\\n- Asset type: mechanism_animation\\n- On-screen text: Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und\\n- Voiceover: Röntgenblick durch Preis, Reparaturrisiko, Standtage, Händler-Marge und Liquidität.\\n- Visual direction: 3D car x-ray with price tag layers and risk meters\\n- Visual motion: sequential layer reveal every 1-2 seconds\\n- Sound cue: clicks per revealed layer\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_zbySla8BWGxA622QURnBJ2Uj"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_2OsupX7Syp9cWSaWjHSLT1c0",
"call_id": "call_2OsupX7Syp9cWSaWjHSLT1c0",
"response_item_id": "fc_02aa21bd75cf0633016a1158c2b25481918cf77a24ba09cee6",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Pilot-Shot-Plan für billiges Auto generieren und prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}",
"tool_call_id": "call_2OsupX7Syp9cWSaWjHSLT1c0"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_6G1bvyKo9Uo7OVg2pOwGqqgi",
"call_id": "call_6G1bvyKo9Uo7OVg2pOwGqqgi",
"response_item_id": "fc_02aa21bd75cf0633016a1158ca599c819189bf269574d85756",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"........................................ [100%]\\n40 passed in 0.96s\\n M autoshorts/__init__.py\\n?? autoshorts/mechanism_pipeline.py\\n?? autoshorts/review_package.py\\n?? data/mechanism_shot_plans/\\n?? data/telegram_reviews/\\n?? scripts/generate_mechanism_shot_plans.py\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_mechanism_pipeline.py\\n?? tests/test_review_package.py\\n autoshorts/__init__.py | 1 +\\n 1 file changed, 1 insertion(+)\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_6G1bvyKo9Uo7OVg2pOwGqqgi"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_JC3xz3YC2YQirlvAzKKMqgCz",
"call_id": "call_JC3xz3YC2YQirlvAzKKMqgCz",
"response_item_id": "fc_02aa21bd75cf0633016a1158d1d42c81918ca5d9a057c1856c",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/retention-studio-strategy.md\",\"old_string\":\"Build artifacts in this order:\\n\\n```text\\nstrategy -> scored ideas -> mechanism explainer -> shot plan -> render manifest -> asset plan -> final renderer\\n```\",\"new_string\":\"Build artifacts in this order:\\n\\n```text\\nstrategy -> scored ideas -> mechanism explainer -> mechanism shot plan -> render manifest -> asset plan -> final renderer\\n```\\n\\nMechanism explainers should be adapted into production shot plans without losing beat timing, visual motion, sound cues, or the no-static-cards rule. The shot plan is where `pattern_interrupt`, `problem`, `mechanism_reveal`, `twist`, and `follow_trigger` become renderer-facing scenes.\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"message\": \"Patched references/retention-studio-strategy.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_JC3xz3YC2YQirlvAzKKMqgCz"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_AYfWJHimIwyf46r4liRq5Kgq",
"call_id": "call_AYfWJHimIwyf46r4liRq5Kgq",
"response_item_id": "fc_02aa21bd75cf0633016a1158d8a93881919f6dc96ebf1f1bfc",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/mechanism_pipeline.py autoshorts/__init__.py scripts/generate_mechanism_shot_plans.py tests/test_mechanism_pipeline.py data/mechanism_shot_plans\\ngit commit -m \\\"feat: adapt mechanism explainers to shot plans\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"[feat/initial-growth-engine 5644c8b] feat: adapt mechanism explainers to shot plans\\n 5 files changed, 323 insertions(+)\\n create mode 100644 autoshorts/mechanism_pipeline.py\\n create mode 100644 data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md\\n create mode 100644 scripts/generate_mechanism_shot_plans.py\\n create mode 100644 tests/test_mechanism_pipeline.py\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_AYfWJHimIwyf46r4liRq5Kgq"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_cvB2qTRSXaJKMEI3fbjcP6xs",
"call_id": "call_cvB2qTRSXaJKMEI3fbjcP6xs",
"response_item_id": "fc_02aa21bd75cf0633016a1158e0566c81919e3087ecccb07e51",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push >/tmp/autoshorts_push11.log 2>&1 || { sed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push11.log; exit 1; }\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\nprintf 'branch=%s\\\\nlocal=%s\\\\nremote=%s\\\\n' \\\"$BRANCH\\\" \\\"$LOCAL\\\" \\\"$REMOTE\\\"\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\nsed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push11.log\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"branch=feat/initial-growth-engine\\nlocal=5644c8beacb0d638929e65af4f110b7d70cff0cf\\nremote=5644c8beacb0d638929e65af4f110b7d70cff0cf\\nTo https://github.com/Gamexgit/AutoShortsBot.git\\n a9fe897..5644c8b feat/initial-growth-engine -> feat/initial-growth-engine\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_cvB2qTRSXaJKMEI3fbjcP6xs"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_B3XTucuFvbcXFZQVBOtVDd90",
"call_id": "call_B3XTucuFvbcXFZQVBOtVDd90",
"response_item_id": "fc_02aa21bd75cf0633016a1158ef24648191abf3b615d298eec6",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\npython3 - <<'PY'\\nfrom pathlib import Path\\np=Path('data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md')\\nprint('shot_plan_exists', p.exists())\\nprint('lines', len(p.read_text(encoding='utf-8').splitlines()))\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\n?? autoshorts/review_package.py\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n........................................ [100%]\\n40 passed in 0.97s\\nshot_plan_exists True\\nlines 78\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_B3XTucuFvbcXFZQVBOtVDd90"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ZTaF3knneVkEgjCBwnluACyc",
"call_id": "call_ZTaF3knneVkEgjCBwnluACyc",
"response_item_id": "fc_02aa21bd75cf0633016a1158fa298c81919f4783ea7a35d4fa",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und Retention-Studio-Artefakte prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Explainer→Shot-Plan-Adapter testen und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Shot-Plan für billiges Auto generieren und prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_ZTaF3knneVkEgjCBwnluACyc"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\nIch habe jetzt den nächsten Pipeline-Schritt gebaut:\n\n```text\nMechanism Explainer\n→ Mechanism Shot Plan\n```\n\nDamit läuft das neue Retention-Studio-Format nicht mehr nur als Strategiepapier herum — sehr britisch formuliert: es hat nun tatsächlich Beine.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/mechanism_pipeline.py\n```\n\nCLI:\n\n```text\nscripts/generate_mechanism_shot_plans.py\n```\n\nTests:\n\n```text\ntests/test_mechanism_pipeline.py\n```\n\nGenerierter Pilot-Shot-Plan:\n\n```text\ndata/mechanism_shot_plans/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md\n```\n\n## Was der Adapter macht\n\nEr nimmt einen Retention-Studio Mechanism Explainer mit Beats:\n\n```text\npattern_interrupt\nproblem\nmechanism\ntwist\nfollow_trigger\n```\n\nund erzeugt daraus einen renderer-nahen Shot Plan mit:\n\n- Timing\n- Asset Type\n- On-screen Text\n- Voiceover\n- Visual Direction\n- Visual Motion\n- Sound Cue\n- Retention Goal\n- Caption Beats\n- Final Render Intent\n\n## Pilot: „Warum ein billiges Auto teuer sein kann“\n\nJetzt als Shot Plan:\n\n```text\nScene 1 — pattern_interrupt\n0.0s–1.4s\nAsset type: cinematic_ai_video\nVisual: 3D car x-ray with price tag layers and risk meters freezes mid-action\nMotion: hard zoom, freeze-frame, red signal pulse\nSound: bass hit + camera snap\n\nScene 2 — problem\n1.4s–5.0s\nAsset type: cinematic_ai_video\nVisual: surface layer peels away to reveal hidden system\nMotion: layer peel + parallax push\n\nScene 3 — mechanism_reveal\n5.0s–18.0s\nAsset type: mechanism_animation\nVisual: 3D car x-ray with price tag layers and risk meters\nMotion: sequential layer reveal every 1–2 seconds\n\nScene 4 — twist\n18.0s–28.0s\nAsset type: mechanism_animation\nVisual: wrong cause fades out, real cause appears\nMotion: snap zoom + wrong answer collapses\n\nScene 5 — follow_trigger\n28.0s–34.0s\nAsset type: kinetic_text_overlay\nVisual: animated system map\nMotion: map draws itself, final node glows\n```\n\n## Wichtig\n\nDer Shot Plan trägt jetzt explizit:\n\n```text\nstatic_card_limit = 0\nvisual_change_every_seconds = 2\nprimary_style = mechanism_reveal_motion_explainer\n```\n\nAlso keine finalen statischen Cards. Der Preview-Renderer bleibt nur Storyboard-Werkzeug.\n\n## Tests / Git\n\nTDD sauber:\n\n- RED: fehlendes `autoshorts.mechanism_pipeline`\n- GREEN: Adapter + CLI\n- Full suite:\n\n```text\n40 passed in 0.97s\n```\n\nNeuer Commit:\n\n```text\n5644c8b feat: adapt mechanism explainers to shot plans\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nIch habe die Retention-Studio-Skill-Notiz ebenfalls angepasst:\n\n```text\nstrategy\n→ scored ideas\n→ mechanism explainer\n→ mechanism shot plan\n→ render manifest\n→ asset plan\n→ final renderer\n```\n\n## Ignoriert wie gewünscht\n\nDie versehentlich im falschen Telegram-Kanal entstandenen untracked Review-Dateien bleiben unberührt:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt:\n\n```text\nMechanism Shot Plan\n→ Render Manifest\n→ Asset Plan\n```\n\nFür den neuen Pilot, nicht mehr für die alten AI-Life-Top-3.\n\nDanach haben wir für „Warum ein billiges Auto teuer sein kann“ eine komplette Produktionskette bis zu provider-ready AI-video Jobs und finaler Schnittlogik.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\nIch habe jetzt den nächsten Pipeline-Schritt gebaut:\n\n```text\nMechanism Explainer\n→ Mechanism Shot Plan\n```\n\nDamit läuft das neue Retention-Studio-Format nicht mehr nur als Strategiepapier herum — sehr britisch formuliert: es hat nun tatsächlich Beine.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/mechanism_pipeline.py\n```\n\nCLI:\n\n```text\nscripts/generate_mechanism_shot_plans.py\n```\n\nTests:\n\n```text\ntests/test_mechanism_pipeline.py\n```\n\nGenerierter Pilot-Shot-Plan:\n\n```text\ndata/mechanism_shot_plans/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md\n```\n\n## Was der Adapter macht\n\nEr nimmt einen Retention-Studio Mechanism Explainer mit Beats:\n\n```text\npattern_interrupt\nproblem\nmechanism\ntwist\nfollow_trigger\n```\n\nund erzeugt daraus einen renderer-nahen Shot Plan mit:\n\n- Timing\n- Asset Type\n- On-screen Text\n- Voiceover\n- Visual Direction\n- Visual Motion\n- Sound Cue\n- Retention Goal\n- Caption Beats\n- Final Render Intent\n\n## Pilot: „Warum ein billiges Auto teuer sein kann“\n\nJetzt als Shot Plan:\n\n```text\nScene 1 — pattern_interrupt\n0.0s–1.4s\nAsset type: cinematic_ai_video\nVisual: 3D car x-ray with price tag layers and risk meters freezes mid-action\nMotion: hard zoom, freeze-frame, red signal pulse\nSound: bass hit + camera snap\n\nScene 2 — problem\n1.4s–5.0s\nAsset type: cinematic_ai_video\nVisual: surface layer peels away to reveal hidden system\nMotion: layer peel + parallax push\n\nScene 3 — mechanism_reveal\n5.0s–18.0s\nAsset type: mechanism_animation\nVisual: 3D car x-ray with price tag layers and risk meters\nMotion: sequential layer reveal every 1–2 seconds\n\nScene 4 — twist\n18.0s–28.0s\nAsset type: mechanism_animation\nVisual: wrong cause fades out, real cause appears\nMotion: snap zoom + wrong answer collapses\n\nScene 5 — follow_trigger\n28.0s–34.0s\nAsset type: kinetic_text_overlay\nVisual: animated system map\nMotion: map draws itself, final node glows\n```\n\n## Wichtig\n\nDer Shot Plan trägt jetzt explizit:\n\n```text\nstatic_card_limit = 0\nvisual_change_every_seconds = 2\nprimary_style = mechanism_reveal_motion_explainer\n```\n\nAlso keine finalen statischen Cards. Der Preview-Renderer bleibt nur Storyboard-Werkzeug.\n\n## Tests / Git\n\nTDD sauber:\n\n- RED: fehlendes `autoshorts.mechanism_pipeline`\n- GREEN: Adapter + CLI\n- Full suite:\n\n```text\n40 passed in 0.97s\n```\n\nNeuer Commit:\n\n```text\n5644c8b feat: adapt mechanism explainers to shot plans\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nIch habe die Retention-Studio-Skill-Notiz ebenfalls angepasst:\n\n```text\nstrategy\n→ scored ideas\n→ mechanism explainer\n→ mechanism shot plan\n→ render manifest\n→ asset plan\n→ final renderer\n```\n\n## Ignoriert wie gewünscht\n\nDie versehentlich im falschen Telegram-Kanal entstandenen untracked Review-Dateien bleiben unberührt:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt:\n\n```text\nMechanism Shot Plan\n→ Render Manifest\n→ Asset Plan\n```\n\nFür den neuen Pilot, nicht mehr für die alten AI-Life-Top-3.\n\nDanach haben wir für „Warum ein billiges Auto teuer sein kann“ eine komplette Produktionskette bis zu provider-ready AI-video Jobs und finaler Schnittlogik."
}
],
"id": "msg_02aa21bd75cf0633016a11590059b88191a30aef47c19855c3",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] ok nächster schritt"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Considering build requirements**\n\nI’m thinking about how we need to continue building, and I'll need to load the relevant skills. I should inspect the current manifest to see if it expects markdown shot plans rather than JSON. There's a thought about using test-driven development (TDD) and whether I should implement a parse mechanism for the shot plan. It might be better to generate the render manifest directly from the mechanism shot plan dictionary. Also, I need to check the existing render_manifest functions from the shot plan.",
"finish_reason": "tool_calls",
"reasoning_content": "**Considering build requirements**\n\nI’m thinking about how we need to continue building, and I'll need to load the relevant skills. I should inspect the current manifest to see if it expects markdown shot plans rather than JSON. There's a thought about using test-driven development (TDD) and whether I should implement a parse mechanism for the shot plan. It might be better to generate the render manifest directly from the mechanism shot plan dictionary. Also, I need to check the existing render_manifest functions from the shot plan.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW5QgsimNbKWe9lmper6Q8XJaGIwY0BCPachmHmHJao0LKitxazRDs0UKXRwevQhFVtJwXHUGjPa3aM7FNDgDQiuCo2qunhD_U1rZQ-QClHVhpZjwgAfvjUAt2Bb0bl1mxKJpF3K-r83422r2LiaY5zGnpgZAWRdtxBBYqW14Lno0mhs4XqDdsRZUlnFbsMCIn_ZzzwL5rGSBRnoeuetOIhrLb1fLNbhMV6fvYtvWd0JHMIR1_VX7k0Y3Ic1TY0UvF3g7i0MtPNdw-F5O-gWYADQZIr-v0jB4kHEXYvHFoJdlF8g1q5oJoEDcyBkMDwvctuxjm2Bv3ajBAwiARWyW-Ow5pdvKHCw3WZ3pGl2OdIEfmGrWCvaofSNY7JubtB9shbz7yKTOk_9cHirnWlLdRYz5vn2yrK4TfoYkjwZ7_k4jFOaNvdbEirQUEt07onciQXHr31gp5pG_5EpyGX6kRNtdpanWI70M8SoNgXPVnwTNAI8bDMh70iW_Of6A0V5ATtdbXtyu4E2T1rI-6hTtTBB-geUQ1P12Gwrj1u2Gt4rlicCzY-mfX6ZDFahwLpzuDxtNxQSJnr3LXBeElnLH-R8FcGsoJ-5b4-m1e3-_YdhFRvzTsoS1HHCT-td5xC6WfQr_w2ZEJWWTHpqQ6s_d04UgdjgqFcoiokx3IKKMAtvltKhHeFNoxAP-pBg6TyCZy-psCpA7Rv1UCKgWD49dTYKH2VXgQDN7kIGvhvNz-gGJTRwILLNVs15xeVz-6H3gp7uSd6SDyHHgd3pgYvCkUr36ziBCGmG-ST490Cfn4ACgg-DjtJHF0CbfHXvqhqcvDAmmPlt1pfeJNUOPEMEIm4C0UDopQfnhhZN6rNkxa6slWUh6sC2tA03frJhes7DqbgeHVEfsH8vaTtF9mj414VFcnzZ5a4dObQRRgkfENm2lIL0N9__9CMNaxFYJ_JOJGpclPc6o1UsNsJm-3lhmJv9mQVv28dp3z6wCZDKF1YOfzvhOFN8cqRvP2bhFuM3epCoVZO4Octp1BRcAm6XgvFhnMYzrYagnNghqyvM3rv73_SeapS-yrd4dUTIJ3FsoiscxrhsuZIiYjhPMleMNpjFpp4CoIvRmEy9JlMZlMz1XPt5sWSG10QqyPn76EG_aiXQOwRW9rB6V5kWlTfe_HMy3cmSFEQTqeHWaT3wPy35K-mZcFSPOL_uUVmjoPL5Vhs5zKHVHMXsnJeJXAh2goJyuY0weXwr3BAXWfe6Ttlmul90JXkfv2gKeDDZpTH7H9vlTuuNlzTtvC3Ew6BOQeCDTpwcH1-4mJM4WlKkgKxodfFhsJ_3flHclO2VnnaJ90-l",
"id": "rs_02aa21bd75cf0633016a116e4b9f1081918984c329f01b6ccd",
"summary": [
{
"type": "summary_text",
"text": "**Considering build requirements**\n\nI’m thinking about how we need to continue building, and I'll need to load the relevant skills. I should inspect the current manifest to see if it expects markdown shot plans rather than JSON. There's a thought about using test-driven development (TDD) and whether I should implement a parse mechanism for the shot plan. It might be better to generate the render manifest directly from the mechanism shot plan dictionary. Also, I need to check the existing render_manifest functions from the shot plan."
}
]
}
],
"tool_calls": [
{
"id": "call_pWBhOyvPg32XlWTF1MK6MCQH",
"call_id": "call_pWBhOyvPg32XlWTF1MK6MCQH",
"response_item_id": "fc_02aa21bd75cf0633016a116e503040819186457bce7cb10ad4",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"test-driven-development\"}"
}
},
{
"id": "call_yQ10HshKLHjNWjMJf8Bp4fDn",
"call_id": "call_yQ10HshKLHjNWjMJf8Bp4fDn",
"response_item_id": "fc_02aa21bd75cf0633016a116e5031d08191b808e2cb8826afab",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
},
{
"id": "call_7P0n05MwvyagVVU4OnoCqYzP",
"call_id": "call_7P0n05MwvyagVVU4OnoCqYzP",
"response_item_id": "fc_02aa21bd75cf0633016a116e50320081919679fac3db327c7b",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"github-pr-workflow\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_view",
"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
"tool_call_id": "call_pWBhOyvPg32XlWTF1MK6MCQH"
},
{
"role": "tool",
"name": "skill_view",
"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\\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\\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\\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 - 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 Render only drafts above the chosen threshold, e.g. 8/10.\\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: render vertical scene/card frames from manifest fields, assemble them with FFmpeg, then use the MP4 for timing/approval review. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output or expects Kling/Hailuo-quality motion, add an `asset_plan.v2` layer that maps each scene to `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, or `kinetic_text_overlay` with provider-ready prompts and no baked text. See `references/ai-video-asset-planning.md`.\\n\\n6. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\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\\n7. **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/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/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. 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\\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 over-optimize renderer quality before testing whether the content format earns retention.\\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 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/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/manifest-preview-renderer.md\", \"references/ai-video-asset-planning.md\", \"references/monetization-and-approval-notes.md\", \"references/retention-led-shorts.md\", \"references/retention-render-planning.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\"}",
"tool_call_id": "call_yQ10HshKLHjNWjMJf8Bp4fDn"
},
{
"role": "tool",
"name": "skill_view",
"content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}",
"tool_call_id": "call_7P0n05MwvyagVVU4OnoCqYzP"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_Qe0h1YOntUwIUylkMWgI9z9F",
"call_id": "call_Qe0h1YOntUwIUylkMWgI9z9F",
"response_item_id": "fc_02aa21bd75cf0633016a116e52bce8819183ca4f6739386338",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"todos\":[{\"content\":\"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\",\"id\":\"1\",\"status\":\"in_progress\"},{\"content\":\"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"pending\"},{\"content\":\"Mechanism-Render-Manifest-Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"pending\"},{\"content\":\"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\",\"id\":\"4\",\"status\":\"pending\"},{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"pending\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"in_progress\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"pending\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 4, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}",
"tool_call_id": "call_Qe0h1YOntUwIUylkMWgI9z9F"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_SzifCAI4D2XZ0yNROpo3wRik",
"call_id": "call_SzifCAI4D2XZ0yNROpo3wRik",
"response_item_id": "fc_02aa21bd75cf0633016a116e5a70b881918b0a57f3b4538a24",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
},
{
"id": "call_jJtfBf3dk0gouOZFlo9CvbWW",
"call_id": "call_jJtfBf3dk0gouOZFlo9CvbWW",
"response_item_id": "fc_02aa21bd75cf0633016a116e5a71208191b6bdb1868fa0c442",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":260,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
},
{
"id": "call_WMg2Da5tjnoBN6xbRBzAtApx",
"call_id": "call_WMg2Da5tjnoBN6xbRBzAtApx",
"response_item_id": "fc_02aa21bd75cf0633016a116e5a7130819190c75b04c99f7ddc",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":240,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\n?? autoshorts/review_package.py\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n........................................ [100%]\\n40 passed in 1.01s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_SzifCAI4D2XZ0yNROpo3wRik"
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 1|\\\"\\\"\\\"Renderer manifest generation for retention-led AutoShortsBot videos.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import json\\n 6|from pathlib import Path\\n 7|from typing import Any\\n 8|\\n 9|from autoshorts.script_drafts import _slug\\n 10|\\n 11|RESOLUTION = {\\\"width\\\": 1080, \\\"height\\\": 1920}\\n 12|ASPECT_RATIO = \\\"9:16\\\"\\n 13|FPS = 30\\n 14|\\n 15|\\n 16|ASSET_REQUIREMENT_HINTS = {\\n 17| \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n 18| \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n 19| \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n 20| \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n 21| \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n 22|}\\n 23|\\n 24|\\n 25|def _duration_ms(start: int, end: int) -> int:\\n 26| return max(500, (int(end) - int(start)) * 1000)\\n 27|\\n 28|\\n 29|def _placeholder(asset_type: str, video_id: str, scene: int) -> str:\\n 30| return f\\\"placeholder://{video_id}/scene-{scene:02d}/{asset_type}\\\"\\n 31|\\n 32|\\n 33|def _visual_style(scene: dict[str, Any]) -> dict[str, Any]:\\n 34| base = {\\n 35| \\\"safe_area_pct\\\": 8,\\n 36| \\\"font\\\": \\\"bold_sans\\\",\\n 37| \\\"caption_case\\\": \\\"sentence\\\",\\n 38| \\\"motion\\\": \\\"push_zoom\\\" if scene[\\\"scene\\\"] == 1 else \\\"cut_or_slide\\\",\\n 39| \\\"contrast\\\": \\\"high\\\",\\n 40| }\\n 41| if scene[\\\"asset_type\\\"] == \\\"card_animation\\\":\\n 42| base.update({\\\"layout\\\": \\\"minimal_cards\\\", \\\"max_words_on_screen\\\": 18})\\n 43| elif scene[\\\"asset_type\\\"] == \\\"screen_recording\\\":\\n 44| base.update({\\\"layout\\\": \\\"screen_plus_caption\\\", \\\"blur_sensitive_ui\\\": True})\\n 45| elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n 46| base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n 47| return base\\n 48|\\n 49|\\n 50|def _visual_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n 51| video_id = plan[\\\"id\\\"]\\n 52| items: list[dict[str, Any]] = []\\n 53| for scene in plan[\\\"scenes\\\"]:\\n 54| items.append(\\n 55| {\\n 56| \\\"id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n 57| \\\"track\\\": \\\"visual\\\",\\n 58| \\\"type\\\": scene[\\\"asset_type\\\"],\\n 59| \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n 60| \\\"start_ms\\\": int(scene[\\\"start\\\"]) * 1000,\\n 61| \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n 62| \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n 63| \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n 64| \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n 65| \\\"style\\\": _visual_style(scene),\\n 66| }\\n 67| )\\n 68| return items\\n 69|\\n 70|\\n 71|def _caption_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n 72| beats = plan.get(\\\"caption_beats\\\", [])\\n 73| total_ms = int(plan[\\\"estimated_seconds\\\"]) * 1000\\n 74| items: list[dict[str, Any]] = []\\n 75| for index, beat in enumerate(beats):\\n 76| start_ms = int(beat[\\\"start\\\"]) * 1000\\n 77| next_start_ms = int(beats[index + 1][\\\"start\\\"]) * 1000 if index + 1 < len(beats) else total_ms\\n 78| items.append(\\n 79| {\\n 80| \\\"id\\\": f\\\"caption_{index + 1:02d}\\\",\\n 81| \\\"track\\\": \\\"captions\\\",\\n 82| \\\"type\\\": \\\"kinetic_caption\\\",\\n 83| \\\"start_ms\\\": start_ms,\\n 84| \\\"duration_ms\\\": max(750, next_start_ms - start_ms),\\n 85| \\\"text\\\": beat[\\\"text\\\"],\\n 86| \\\"style\\\": {\\n 87| \\\"safe_area_pct\\\": 8,\\n 88| \\\"position\\\": \\\"lower_third\\\",\\n 89| \\\"max_chars_per_line\\\": 28,\\n 90| \\\"highlight_keywords\\\": True,\\n 91| },\\n 92| }\\n 93| )\\n 94| return items\\n 95|\\n 96|\\n 97|def _asset_requirements(plan: dict[str, Any]) -> dict[str, list[dict[str, Any]]]:\\n 98| requirements: dict[str, list[dict[str, Any]]] = {key: [] for key in ASSET_REQUIREMENT_HINTS}\\n 99| for scene in plan[\\\"scenes\\\"]:\\n 100| asset_type = scene[\\\"asset_type\\\"]\\n 101| requirements.setdefault(asset_type, []).append(\\n 102| {\\n 103| \\\"scene\\\": scene[\\\"scene\\\"],\\n 104| \\\"asset_ref\\\": _placeholder(asset_type, plan[\\\"id\\\"], scene[\\\"scene\\\"]),\\n 105| \\\"brief\\\": scene[\\\"visual_direction\\\"],\\n 106| \\\"hint\\\": ASSET_REQUIREMENT_HINTS.get(asset_type, \\\"Provide asset matching the visual direction.\\\"),\\n 107| }\\n 108| )\\n 109| return requirements\\n 110|\\n 111|\\n 112|def build_render_manifest(plan: dict[str, Any]) -> dict[str, Any]:\\n 113| \\\"\\\"\\\"Convert a human shot plan into a structured renderer contract.\\\"\\\"\\\"\\n 114| timeline = _visual_timeline_items(plan) + _caption_timeline_items(plan)\\n 115| track_order = {\\\"visual\\\": 0, \\\"captions\\\": 1}\\n 116| timeline.sort(key=lambda item: (item[\\\"start_ms\\\"], track_order.get(item[\\\"track\\\"], 99), item[\\\"id\\\"]))\\n 117| return {\\n 118| \\\"schema_version\\\": \\\"render_manifest.v1\\\",\\n 119| \\\"video\\\": {\\n 120| \\\"id\\\": plan[\\\"id\\\"],\\n 121| \\\"title\\\": plan[\\\"title\\\"],\\n 122| \\\"pillar\\\": plan[\\\"pillar\\\"],\\n 123| \\\"duration_ms\\\": int(plan[\\\"estimated_seconds\\\"]) * 1000,\\n 124| \\\"aspect_ratio\\\": ASPECT_RATIO,\\n 125| \\\"resolution\\\": RESOLUTION,\\n 126| \\\"fps\\\": FPS,\\n 127| },\\n 128| \\\"timeline\\\": timeline,\\n 129| \\\"asset_requirements\\\": _asset_requirements(plan),\\n 130| \\\"render_constraints\\\": {\\n 131| \\\"avoid_static_slideshow\\\": True,\\n 132| \\\"pattern_interrupt_every_seconds\\\": 3,\\n 133| \\\"first_frame_hook_required\\\": True,\\n 134| \\\"max_words_per_visual_card\\\": 18,\\n 135| \\\"human_approval_required_before_publish\\\": True,\\n 136| },\\n 137| \\\"production_notes\\\": plan.get(\\\"production_notes\\\", []),\\n 138| }\\n 139|\\n 140|\\n 141|def write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n 142| \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n 143| output_dir.mkdir(parents=True, exist_ok=True)\\n 144| paths: list[Path] = []\\n 145| for plan in plans:\\n 146| manifest = build_render_manifest(plan)\\n 147| path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.json\\\"\\n 148| path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 149| paths.append(path)\\n 150| return paths\\n 151|\", \"total_lines\": 150, \"file_size\": 5868, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_jJtfBf3dk0gouOZFlo9CvbWW"
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 1|\\\"\\\"\\\"Asset planning for final, motion-led AutoShortsBot renders.\\n 2|\\n 3|The preview renderer is only a storyboard. This module converts render manifests\\n 4|into explicit asset-generation plans so the final videos can use cinematic AI\\n 5|clips, screen-recording simulations, kinetic overlays, and animated cards instead\\n 6|of static text slides.\\n 7|\\\"\\\"\\\"\\n 8|\\n 9|from __future__ import annotations\\n 10|\\n 11|import json\\n 12|from pathlib import Path\\n 13|from typing import Any\\n 14|\\n 15|from autoshorts.script_drafts import _slug\\n 16|\\n 17|SCENE_MODE_BY_TYPE = {\\n 18| \\\"kinetic_text\\\": \\\"kinetic_text_overlay\\\",\\n 19| \\\"screen_recording\\\": \\\"screen_recording_simulation\\\",\\n 20| \\\"card_animation\\\": \\\"animated_card\\\",\\n 21| \\\"stock_broll\\\": \\\"cinematic_ai_video\\\",\\n 22| \\\"generated_background\\\": \\\"cinematic_ai_video\\\",\\n 23|}\\n 24|\\n 25|\\n 26|# Scene-level overrides keep AI/productivity videos from becoming five text cards.\\n 27|SCENE_MODE_OVERRIDES = {\\n 28| \\\"ai_life_systems\\\": {\\n 29| \\\"scene_01\\\": \\\"cinematic_ai_video\\\",\\n 30| \\\"scene_02\\\": \\\"screen_recording_simulation\\\",\\n 31| \\\"scene_03\\\": \\\"animated_card\\\",\\n 32| \\\"scene_04\\\": \\\"screen_recording_simulation\\\",\\n 33| \\\"scene_05\\\": \\\"kinetic_text_overlay\\\",\\n 34| },\\n 35| \\\"attention_design\\\": {\\n 36| \\\"scene_01\\\": \\\"cinematic_ai_video\\\",\\n 37| \\\"scene_02\\\": \\\"cinematic_ai_video\\\",\\n 38| \\\"scene_03\\\": \\\"animated_card\\\",\\n 39| \\\"scene_04\\\": \\\"screen_recording_simulation\\\",\\n 40| \\\"scene_05\\\": \\\"kinetic_text_overlay\\\",\\n 41| },\\n 42|}\\n 43|\\n 44|\\n 45|def _visual_items(manifest: dict[str, Any]) -> list[dict[str, Any]]:\\n 46| return [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n 47|\\n 48|\\n 49|def _duration_seconds(item: dict[str, Any]) -> int:\\n 50| return max(3, min(6, round(int(item[\\\"duration_ms\\\"]) / 1000)))\\n 51|\\n 52|\\n 53|def _asset_mode(item: dict[str, Any], pillar: str) -> str:\\n 54| override = SCENE_MODE_OVERRIDES.get(pillar, {}).get(item[\\\"id\\\"])\\n 55| if override:\\n 56| return override\\n 57| return SCENE_MODE_BY_TYPE.get(item[\\\"type\\\"], \\\"cinematic_ai_video\\\")\\n 58|\\n 59|\\n 60|def _camera_motion(scene_id: str, mode: str) -> str:\\n 61| if mode == \\\"cinematic_ai_video\\\":\\n 62| return \\\"slow handheld push-in, subtle parallax, natural micro-movements\\\"\\n 63| if mode == \\\"screen_recording_simulation\\\":\\n 64| return \\\"cursor movement, typing reveal, zoom to changed prompt, highlighted selection box\\\"\\n 65| if mode == \\\"animated_card\\\":\\n 66| return \\\"cards slide in one at a time, one concept per beat, slight scale pops\\\"\\n 67| return \\\"large kinetic caption words pop and slide with beat-synced emphasis\\\"\\n 68|\\n 69|\\n 70|def _cinematic_subject(pillar: str, text: str) -> str:\\n 71| if pillar == \\\"attention_design\\\":\\n 72| return (\\n 73| \\\"a realistic person holding a phone in a dim modern room, thumb hovering over an endless feed, \\\"\\n 74| \\\"then a decisive pause as the screen glow reflects on their face\\\"\\n 75| )\\n 76| return (\\n 77| \\\"a realistic creator at a desk late evening, laptop open to an AI chat, thoughtful but in control, \\\"\\n 78| \\\"hands moving between keyboard and notebook\\\"\\n 79| )\\n 80|\\n 81|\\n 82|def _generation_prompt(item: dict[str, Any], manifest: dict[str, Any], mode: str) -> str:\\n 83| video = manifest[\\\"video\\\"]\\n 84| text = item[\\\"text\\\"]\\n 85| if mode == \\\"cinematic_ai_video\\\":\\n 86| subject = _cinematic_subject(video[\\\"pillar\\\"], text)\\n 87| return (\\n 88| f\\\"Photorealistic vertical 9:16 short video clip, {subject}. \\\"\\n 89| f\\\"Mood: focused, modern, high-retention social video, cinematic lighting, realistic motion. \\\"\\n 90| f\\\"Scene intent: {item['visual_direction']} Camera: {_camera_motion(item['id'], mode)}. \\\"\\n 91| \\\"Leave clean negative space for captions; do not include readable text in the generated video.\\\"\\n 92| )\\n 93| if mode == \\\"screen_recording_simulation\\\":\\n 94| return (\\n 95| \\\"Vertical 9:16 UI/screen-recording simulation. Show an AI chat or phone interface being edited live: \\\"\\n 96| f\\\"{item['visual_direction']} Use typing, cursor movement, zoom highlights, and before/after prompt changes. \\\"\\n 97| \\\"All readable captions must be added by our renderer overlay, not baked into the clip.\\\"\\n 98| )\\n 99| if mode == \\\"animated_card\\\":\\n 100| return (\\n 101| \\\"Vertical animated explainer card sequence. Minimal dark UI, one idea per beat, animated boxes and arrows. \\\"\\n 102| f\\\"Visual task: {item['visual_direction']} Keep it moving; no static slide holds longer than one second.\\\"\\n 103| )\\n 104| return (\\n 105| \\\"Kinetic text overlay scene. Use large caption words, fast emphasis, scale pops, and clean background motion. \\\"\\n 106| f\\\"Overlay text: {text}\\\"\\n 107| )\\n 108|\\n 109|\\n 110|def _negative_prompt(mode: str) -> str:\\n 111| if mode == \\\"cinematic_ai_video\\\":\\n 112| return (\\n 113| \\\"no readable text, no subtitles, no logos, no watermarks, no distorted hands, no fake UI text, \\\"\\n 114| \\\"no slideshow, no static image, no uncanny faces\\\"\\n 115| )\\n 116| if mode == \\\"screen_recording_simulation\\\":\\n 117| return \\\"no private data, no real credentials, no illegible tiny UI, no brand logos unless approved\\\"\\n 118| if mode == \\\"animated_card\\\":\\n 119| return \\\"no dense paragraphs, no static PowerPoint slide, no tiny text, no chart junk\\\"\\n 120| return \\\"no long paragraphs, no tiny footnotes, no static card\\\"\\n 121|\\n 122|\\n 123|def _scene_asset_plan(item: dict[str, Any], manifest: dict[str, Any]) -> dict[str, Any]:\\n 124| video = manifest[\\\"video\\\"]\\n 125| mode = _asset_mode(item, video[\\\"pillar\\\"])\\n 126| return {\\n 127| \\\"scene_id\\\": item[\\\"id\\\"],\\n 128| \\\"asset_ref\\\": item[\\\"asset_ref\\\"],\\n 129| \\\"asset_mode\\\": mode,\\n 130| \\\"provider_family\\\": \\\"kling_hailuo_runway\\\" if mode == \\\"cinematic_ai_video\\\" else \\\"local_renderer\\\",\\n 131| \\\"duration_seconds\\\": _duration_seconds(item),\\n 132| \\\"overlay_text\\\": item[\\\"text\\\"],\\n 133| \\\"caption_strategy\\\": \\\"renderer_overlay_only\\\",\\n 134| \\\"no_text_in_video\\\": mode == \\\"cinematic_ai_video\\\",\\n 135| \\\"motion_directive\\\": _camera_motion(item[\\\"id\\\"], mode),\\n 136| \\\"generation_prompt\\\": _generation_prompt(item, manifest, mode),\\n 137| \\\"negative_prompt\\\": _negative_prompt(mode),\\n 138| \\\"retention_goal\\\": item[\\\"retention_goal\\\"],\\n 139| \\\"source_visual_direction\\\": item[\\\"visual_direction\\\"],\\n 140| }\\n 141|\\n 142|\\n 143|def build_asset_plan(manifest: dict[str, Any]) -> dict[str, Any]:\\n 144| \\\"\\\"\\\"Build a final-render asset plan from a render manifest.\\\"\\\"\\\"\\n 145| scenes = [_scene_asset_plan(item, manifest) for item in _visual_items(manifest)]\\n 146| modes = [scene[\\\"asset_mode\\\"] for scene in scenes]\\n 147| return {\\n 148| \\\"schema_version\\\": \\\"asset_plan.v2\\\",\\n 149| \\\"video\\\": manifest[\\\"video\\\"],\\n 150| \\\"scenes\\\": scenes,\\n 151| \\\"final_render_strategy\\\": {\\n 152| \\\"preview_renderer_role\\\": \\\"storyboard_only\\\",\\n 153| \\\"static_card_limit\\\": 0,\\n 154| \\\"must_use_motion_assets\\\": True,\\n 155| \\\"preferred_mix\\\": {\\n 156| \\\"cinematic_ai_video\\\": modes.count(\\\"cinematic_ai_video\\\"),\\n 157| \\\"screen_recording_simulation\\\": modes.count(\\\"screen_recording_simulation\\\"),\\n 158| \\\"animated_card\\\": modes.count(\\\"animated_card\\\"),\\n 159| \\\"kinetic_text_overlay\\\": modes.count(\\\"kinetic_text_overlay\\\"),\\n 160| },\\n 161| \\\"captions\\\": \\\"large kinetic overlays generated by renderer; no baked text in AI clips\\\",\\n 162| \\\"approval_required_before_publish\\\": True,\\n 163| },\\n 164| }\\n 165|\\n 166|\\n 167|def write_asset_plans(manifests: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n 168| \\\"\\\"\\\"Write asset plans as JSON files.\\\"\\\"\\\"\\n 169| output_dir.mkdir(parents=True, exist_ok=True)\\n 170| paths: list[Path] = []\\n 171| for manifest in manifests:\\n 172| plan = build_asset_plan(manifest)\\n 173| path = output_dir / f\\\"{plan['video']['id']}-{_slug(plan['video']['title'])}.json\\\"\\n 174| path.write_text(json.dumps(plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n 175| paths.append(path)\\n 176| return paths\\n 177|\", \"total_lines\": 176, \"file_size\": 7508, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_WMg2Da5tjnoBN6xbRBzAtApx"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW5n1vFmKjgIb05qtQ_OKAWl0i6Hpcz2ecw5E6W6t1CYT97eQY0zyFD-myg1bFPjxm2_RN1Xbq4gFm0oPQKisu3cg-P3Ncbr2AItodnj1eD2mtIVb6FX0X_AmYUSqqhD_nAig9pi8802yVZprNP6WfBiDd-qNivIWIaLm3MwZswK90MXyGWxSEwsmsrrb5YNVomazSfJDYWclPbvIqteNvYUxz90eG9py1xnXgK6igeTL9gAgycryTOKnGoH6Gz8hO3x8ebxn7lwAcRsk97tG2UrjZSENy4ZfHLoEZfhr5f-wmYy_vdkKoNAruKuHlx940osphPAonIJA3q4hx-KFCbXa1tx8H9LvLKv9wKf17FZtiNDk-PpWU322rPP1Tfel_0a-sQLSlwb8555B6PJ07tujLek1ItjH9FEK1lX1lnnhubIGaqr1Pqb6IhWvji_yyIXvQD0WygpW0JWS3NTWl7Yqq2awipWrAO47RTLC7ylCV6gCsKJ74cziGNBlyqRHjspx4QSyhMR5TQRaIF66Svg3vKRmc-rt49zsSi01PcU5aRN4qy7wIQppxFM8df6lyEqlIi8iezba7kYjKEf1spS_O-qyopt4ZCkY0YCOuj8amBfptvNLaFqC50Y6mh2rnwZPW8IZOUHs1WUqnTSX_fUTGefeC_8sZRNgg1IOp8kur1WwM7Z2tD1ogsz_KisI9VLC8Oi-SJvBfYLvapBkywV6UNrp1YmLUW8d9vsF_BbVk1yGL2rwk0Eq-JFfwPfX6xP5mMFHN-Etr4DWFIRzu3gSDqbIiZ6qMehKXVl-Ou4e7pJE6SPzS5EdKgI0AEDUo0URpk9glBiTUzYTUXshBQZAiC-u28tMqKZmhvI3TR_ZQOV4LFDHYhegnGtRWBLl4rdagxuPH_w8Hz2BqJkNh9GvZY0lmUhgG9dFWKu_hJ3Y3Ig_zqdJdJa1ED-h0y5sFtwD3drRLfhrRKUtkA9z3Ns4CsPysRYZbpDjpLsDgEbU7S1Tb1rccBtVmI_qxOBKg3eIeiEwjZ2a75fqpWV2b_sqy28McDBRdTXXpmyN7Zjj2ND843KAG1sPefDTwdFz3qHRL8aFgCJZS4hXwzmSyF5WGn1wqyItRUMHWoLeUfp9AfGxJQYI62dRBKrqEuMD2riMUslXHPveVaNg3R8LJL6VkJ786uSfYkWU34cC2WK5NfQNO5LAgF9V55cVP9dIak23yzX70EAcRL9EV2yYa3KfkS8m6tRb7T4-Wg-WVlEcHpAlhNwmGEijn9E_N4S3sTOjoIO6-OuRQIAkntNKUR8Te24huT8NK3zxMTEo_fndnNKLaHW-Brj8jqvvUsGdVZJIBV8mjtqBXTfVqNjt_WVxcBB2-ESewloTFo1WqOkSFStOvTtiuf7ZC-eYobtMuEC_p0Nk5ohnkwNkncSWe-n-1BqnRzNq4JWtwDd7MYEMffO7ItC5cN7LY0ACus3nBroI1UfZui_0r0xEyWFsQVrAftdmfTwwmbYhe6FVAeWGPBn3tbrQai5Zm_NuOVvPkMHYAhxn5dYNXq9LGgf3_5-wVxihd8N8NsRtrEJtqVv3WZm6cJyJpYbl63F2OavTAEhXqXFcKYqHr8wGQ_B7fSNUL7sYEBIhSyZfo5GzZ2ivV_K2T0NngnBeUueSXkZpq44BHrPVXKrQp98eJymU4g311pNj9w30YvBETGURuYctwaRbw2A0uhANrzy154AANcOTbj-ZQ6odh7326yH7H42GLbuVW_uapwNOTnCiSWO6JpI1gRKBm410st78xy8jqYJ3_s7XuBGgt5YNJcJ7sdo0-blUwlVUn0N3T_c66cxWcaOYEQ_RNC2lrxSN6bZGdbZsDzTgVjDa_c1li4oHI6S3K99L3Zi4CNoUpXOsYzjXgyHGiEUGHrSlSSjQde9odUPBZB6iT_tP0MFZRx5jtbIMsXMDblNYUPJ_226xxnKXUrJopq_g44kVMQyRTKRf5BySwa1tRLbDkt_QC7135jscod3B3Bq2MRSf464qF4XzujCWoB8dxcUKdQiDCJ6UcWSZahg7htD5U9dBFarXq0GrRj7yJX8dG2XfOi8yZWjqS6pDFIVa-2K_4ymOoQd-dejP-qaGupyMbvGfIFQyn0AK-0U66WO8CBNlUBc-mGtvyTbF7QUkj-NgSz-mHywsRqJdNzhMoL6_qr80EGQ5rQhIgE65jjPe8mbcbM84nhnV9NT3OiSwDRLbNgkan23kcuuZKJ-ys7K6Tze_JLh7cn8eNkzI24FD2gzURnAqVcGIkQ_w-stMx1tY2W5DHsNzuLk",
"id": "rs_02aa21bd75cf0633016a116e63e3608191901b40bf94651313",
"summary": []
}
],
"tool_calls": [
{
"id": "call_aMXjWg2DOSqR4Z4GU0nwJ1Tj",
"call_id": "call_aMXjWg2DOSqR4Z4GU0nwJ1Tj",
"response_item_id": "fc_02aa21bd75cf0633016a116e67c8448191965a6618169609d9",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\",\"limit\":240}"
}
}
]
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 1|import json\\n 2|from pathlib import Path\\n 3|\\n 4|from autoshorts.growth_plan import load_testbatch\\n 5|from autoshorts.render_manifest import build_render_manifest, write_render_manifests\\n 6|from autoshorts.shot_planner import build_shot_plan\\n 7|from autoshorts.voiceover_finalizer import finalize_voiceover\\n 8|\\n 9|\\n 10|def _shot_plan(script_id: str):\\n 11| batch = load_testbatch(Path(\\\"data/drafts/testbatch_002.json\\\"))\\n 12| idea = next(item for item in batch[\\\"ideas\\\"] if item[\\\"id\\\"] == script_id)\\n 13| return build_shot_plan(finalize_voiceover(idea))\\n 14|\\n 15|\\n 16|def test_build_render_manifest_maps_shot_plan_to_renderer_contract():\\n 17| plan = _shot_plan(\\\"ai-004\\\")\\n 18|\\n 19| manifest = build_render_manifest(plan)\\n 20|\\n 21| assert manifest[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n 22| assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"ai-004\\\"\\n 23| assert manifest[\\\"video\\\"][\\\"aspect_ratio\\\"] == \\\"9:16\\\"\\n 24| assert manifest[\\\"video\\\"][\\\"resolution\\\"] == {\\\"width\\\": 1080, \\\"height\\\": 1920}\\n 25| assert manifest[\\\"timeline\\\"][0][\\\"track\\\"] == \\\"visual\\\"\\n 26| assert manifest[\\\"timeline\\\"][0][\\\"start_ms\\\"] == 0\\n 27| assert manifest[\\\"timeline\\\"][0][\\\"duration_ms\\\"] > 0\\n 28| assert manifest[\\\"timeline\\\"][0][\\\"type\\\"] == \\\"kinetic_text\\\"\\n 29| assert manifest[\\\"timeline\\\"][0][\\\"text\\\"] == plan[\\\"scenes\\\"][0][\\\"on_screen_text\\\"]\\n 30| assert manifest[\\\"timeline\\\"][0][\\\"style\\\"][\\\"safe_area_pct\\\"] == 8\\n 31| assert any(item[\\\"track\\\"] == \\\"captions\\\" for item in manifest[\\\"timeline\\\"])\\n 32| assert manifest[\\\"render_constraints\\\"][\\\"avoid_static_slideshow\\\"] is True\\n 33|\\n 34|\\n 35|def test_build_render_manifest_uses_asset_placeholders_by_type():\\n 36| plan = _shot_plan(\\\"attention-002\\\")\\n 37|\\n 38| manifest = build_render_manifest(plan)\\n 39|\\n 40| visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n 41| assert any(item[\\\"type\\\"] == \\\"stock_broll\\\" for item in visual_items)\\n 42| assert any(item[\\\"type\\\"] == \\\"card_animation\\\" for item in visual_items)\\n 43| assert all(item[\\\"asset_ref\\\"].startswith(\\\"placeholder://\\\") for item in visual_items)\\n 44| assert manifest[\\\"asset_requirements\\\"][\\\"stock_broll\\\"]\\n 45| assert manifest[\\\"asset_requirements\\\"][\\\"screen_recording\\\"]\\n 46|\\n 47|\\n 48|def test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n 49| plans = [_shot_plan(\\\"ai-004\\\"), _shot_plan(\\\"ai-005\\\"), _shot_plan(\\\"attention-002\\\")]\\n 50|\\n 51| paths = write_render_manifests(plans, tmp_path)\\n 52|\\n 53| assert len(paths) == 3\\n 54| assert all(path.suffix == \\\".json\\\" for path in paths)\\n 55| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 56| assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n 57| assert \\\"timeline\\\" in payload\\n 58| assert len(payload[\\\"timeline\\\"]) >= 10\\n 59|\", \"total_lines\": 58, \"file_size\": 2517, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_aMXjWg2DOSqR4Z4GU0nwJ1Tj"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Creating a test plan**\n\nI need to directly use the existing function with the generated plan. It looks like the tests can pass partially, but I'm missing some keys. I should create a test to address that.\n\nAdditionally, I'll check if the script that generates render manifests accepts these changes. Inspecting the scripts will help me ensure everything is in place before moving forward. It’s important to get this all squared away!",
"finish_reason": "tool_calls",
"reasoning_content": "**Creating a test plan**\n\nI need to directly use the existing function with the generated plan. It looks like the tests can pass partially, but I'm missing some keys. I should create a test to address that.\n\nAdditionally, I'll check if the script that generates render manifests accepts these changes. Inspecting the scripts will help me ensure everything is in place before moving forward. It’s important to get this all squared away!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW5u-ZD9hwsvyefw0ce41Lo7DclozB2L4YB0liCuDxptcSebttA8x28d5_ID1XFaasTfr4lKUAzJg1ybUX6iVFSZGHcakFzPSb_zn0yemx5TJO5x9p_w-6kUmMlbfIUZH6woApTL-uQ-aQxFxU-Y73Erq31d67S4h3if2x0bCz1WF0nueQqAhbq1K9iGpvxvkoKcPUcFdfc-mhu6gHY2hA5jN_kZvDGmiY5uUGK2qPQfyoZ0q5BaqsCDpybcQBqDsTVEj_5Lqsu4atKBJFIqUV7v9XmaCZE2CxmxPnELf-JZDyO_hqMpr5CB3RzX3JV5Ucc79_IL8ph9fKMI84dG-NYcB3kD1-YU3XTWVh774_t91kBmo7WEqK-stg1uQyVOcToD1foiKSAss4tkycDhdxGdzl3fBAiBqkNkiPwnWh9Dphrr3U5prOT4aphvBjsNk4jTlMlj7JoJ1zM5jaLH0unX-dd2N6Lyj6J0bBtwWKs2SN_n7dKWIW5Wyy9ri9riQdf9FXAHLIX8mLQZmgFyHWJKFczVeVSzvLUm69giHKbs-9hcx71Jkf6r8u7Z2g89P3LV6XQy0m4ut2EXilYMYecijdvzigmHY85Mmj0yugorMkVsJ8yO_H0Al9FpDokBmc3FonLw5vXBV2QyM9GLmypF7grJiDbL3sQrv6e8MyaDHGfMudncePIykf12YIDdYlKLLLwOvKqsLjjkPLQFPxC9-MZg3cl9F9zkSZDa2vEXTXmPlu1OkOBHSRooB_VeB9O8Ss2GFMQiR6iIm0lxvR3KMZp4W3h4FAnkcCNBl7KJ7o4RijnfcNgieO81eXDu_ljLhDlC3X0WSgUwsRVMtohp2_xwIhQSpQhQsFK9zy1b4BsB5mwCU3A0L96gV0ludx3bx6jcf5JVcIsrSBRSPlkZwzQTQp1MG1uCYazhT9PYZB6sDMdf9CchkKB-3i_FrG8WTaE0AN7-BAxjB3OJU0Fg-jpTBQJGAl-pnF3uiGwJZcAsEsfNC0FdR3knI4vAz3KuWdohjImDBXZX4-X_1Tss3X51upDci063VtoEWaUh8qOCkXAYOqTJsBzGmoEnF4bhovn245bztp7J8Mu5SzqTJxrLcttw0MGmzgfzFW8fWa5tNR9R0EI1Lm13zi5QrmsqfImqvC7Kr8AgKOrTbppzdA==",
"id": "rs_02aa21bd75cf0633016a116e6b70a88191820b901ef6aebcfa",
"summary": [
{
"type": "summary_text",
"text": "**Creating a test plan**\n\nI need to directly use the existing function with the generated plan. It looks like the tests can pass partially, but I'm missing some keys. I should create a test to address that.\n\nAdditionally, I'll check if the script that generates render manifests accepts these changes. Inspecting the scripts will help me ensure everything is in place before moving forward. It’s important to get this all squared away!"
}
]
}
],
"tool_calls": [
{
"id": "call_gm4u7v0lNilq3HyMn4RP2WUJ",
"call_id": "call_gm4u7v0lNilq3HyMn4RP2WUJ",
"response_item_id": "fc_02aa21bd75cf0633016a116e6e97a881919c96a0278104ed53",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":180,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_render_manifests.py\"}"
}
}
]
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 1|\\\"\\\"\\\"Generate renderer-ready JSON manifests for selected AutoShortsBot scripts.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import argparse\\n 6|import sys\\n 7|from pathlib import Path\\n 8|\\n 9|PROJECT_ROOT = Path(__file__).resolve().parents[1]\\n 10|if str(PROJECT_ROOT) not in sys.path:\\n 11| sys.path.insert(0, str(PROJECT_ROOT))\\n 12|\\n 13|from autoshorts.growth_plan import load_testbatch\\n 14|from autoshorts.render_manifest import write_render_manifests\\n 15|from autoshorts.shot_planner import build_shot_plan\\n 16|from autoshorts.voiceover_finalizer import finalize_voiceover\\n 17|\\n 18|DEFAULT_SELECTED_IDS = [\\\"ai-004\\\", \\\"ai-005\\\", \\\"attention-002\\\"]\\n 19|\\n 20|\\n 21|def main() -> None:\\n 22| parser = argparse.ArgumentParser(description=\\\"Generate renderer manifest JSON files.\\\")\\n 23| parser.add_argument(\\\"--batch\\\", default=\\\"data/drafts/testbatch_002.json\\\")\\n 24| parser.add_argument(\\\"--out\\\", default=\\\"data/render_manifests/testbatch_002_top3\\\")\\n 25| parser.add_argument(\\\"--ids\\\", nargs=\\\"*\\\", default=DEFAULT_SELECTED_IDS)\\n 26| args = parser.parse_args()\\n 27|\\n 28| batch = load_testbatch(Path(args.batch))\\n 29| selected = set(args.ids)\\n 30| plans = [\\n 31| build_shot_plan(finalize_voiceover(idea))\\n 32| for idea in batch.get(\\\"ideas\\\", [])\\n 33| if idea[\\\"id\\\"] in selected\\n 34| ]\\n 35| paths = write_render_manifests(plans, Path(args.out))\\n 36|\\n 37| print(f\\\"Generated {len(paths)} render manifests in {args.out}\\\")\\n 38| for path in paths:\\n 39| print(path)\\n 40|\\n 41|\\n 42|if __name__ == \\\"__main__\\\":\\n 43| main()\\n 44|\", \"total_lines\": 43, \"file_size\": 1403, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_gm4u7v0lNilq3HyMn4RP2WUJ"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Planning script generation**\n\nI’m focusing on adding the script `generate_mechanism_render_manifests` from explainers through `build_mechanism_shot_plan`. It seems essential to consider test-driven development (TDD) here. I'll need to write tests to ensure that `render_manifest` handles the mechanism plan correctly. It looks like I’ll also have to create some failing tests to check that everything holds up under scrutiny. Let’s move forward with this thoughtful approach!",
"finish_reason": "tool_calls",
"reasoning_content": "**Planning script generation**\n\nI’m focusing on adding the script `generate_mechanism_render_manifests` from explainers through `build_mechanism_shot_plan`. It seems essential to consider test-driven development (TDD) here. I'll need to write tests to ensure that `render_manifest` handles the mechanism plan correctly. It looks like I’ll also have to create some failing tests to check that everything holds up under scrutiny. Let’s move forward with this thoughtful approach!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW51B9qo4KN9ngZU1Ckjf3BlH4_bEFnnIlZi7msJMxeKGJ7zYCT0Gb7wxy60iexXxZAaOsiql-jHSaPZvmp0V74WBjcuGslQz_wzDmCJi9KtO6GqAoS2iYH1CysIfob7RhkeixP7nuEeqcFkG9y-OX3ul-fBQDcKMJNRxpmgox-gRthJdDmiYFyMXZvPOZ1IkleUeuKoZvB0PHmEKKfWALeejgBgHJwFqWu_O8QAU-A__46iVXulNhPckWmyjf5YCbByQP7rmRmiORz_VAlB0GlvPNdHTtC4GeDp6jgfuImL9p3cVWCbtR6y98bman6cBMAd40r1z5qph8svM7z9imq1VHm05D0JVTk7BAepbIkJg30QSzHv6zFUOOXIvir33cLVJyYYFWn8q-mWLTdlNstJI-lq5-G5TnjXXW81gT6mzTlNU7ylFlq-LwbEJARaIvOEOM3v2joeCSe6KnDzMbeOhpuAZEAmQcDAFvDosEIGcZsUcgXwu_pEvLa7fJuGCMpJwiFQnSNIEyyjsJsaFsw8x1iOlVINNw55NjglIhYS-5wxFUGsmvmS37TnwfIxULlqzleg4a6nNoq2E1uAruTf-syvX6mVe69WXD3fwQyaiN-cxDdTGaSSa2fBomWCdCFwN6pjMYc6FVdpp8vMU2h6--efV81GZXpibLivH9jZoiRFaV2sQrXK9EJXfPjd4Lh_v9KQfs0Jpkt3xDJsJSFkYDuVId9UNigVFDhs_1G3M7q3CUjV_GDDD6xn_GR14XczxagR_OngXIb-eO2jgVEezrmzCmYX1BEGoZ6YpE2DG7jDOepr5wjnwUI9tzDLIWpvOCcvO18EEJfKX5maxXjgIrMka5RQwsveVCLuCIG7_rgt5z32HTnmety2zFweN488KKXojY6fj2s3pjKxB7jA3-4tn8Vq69hJBl7Cjp2HSKBpAM_2ApJQ_5rH-Ytpw-bhEqRjIraveHLnCxbKJyT_6KacUjALG6URkoBQ1sW9wBi4qHeRHgkBn0Cz8pGap6Def4h_52YYQIh6BXVs6jWUdHsU08KmBXb-NfB2OiTIMi9np_iSQnv1nL5MEe03gK4-vup7nfZmXcZk-cFRd_XXqz8VQWjTsfJB6dU-15DohGjUFV1nRVUWZ258EFCrOHju5cJqUY0JsPb7vzlNaBSdi4YOU0_mCJJYUBYKrKgqceE=",
"id": "rs_02aa21bd75cf0633016a116e724a508191b4cd0ab3de822dc8",
"summary": [
{
"type": "summary_text",
"text": "**Planning script generation**\n\nI’m focusing on adding the script `generate_mechanism_render_manifests` from explainers through `build_mechanism_shot_plan`. It seems essential to consider test-driven development (TDD) here. I'll need to write tests to ensure that `render_manifest` handles the mechanism plan correctly. It looks like I’ll also have to create some failing tests to check that everything holds up under scrutiny. Let’s move forward with this thoughtful approach!"
}
]
}
],
"tool_calls": [
{
"id": "call_b3fndmEZKyenQpaEeWwalkIo",
"call_id": "call_b3fndmEZKyenQpaEeWwalkIo",
"response_item_id": "fc_02aa21bd75cf0633016a116e75c694819189a35c3195d21596",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\",\"id\":\"1\",\"status\":\"completed\"},{\"content\":\"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"in_progress\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"in_progress\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"pending\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 3, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}",
"tool_call_id": "call_b3fndmEZKyenQpaEeWwalkIo"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_T0g9mZQt47Tm6cqekcNOOGfH",
"call_id": "call_T0g9mZQt47Tm6cqekcNOOGfH",
"response_item_id": "fc_02aa21bd75cf0633016a116e797e948191a28871554feaa320",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"patch\",\"patch\":\"*** Begin Patch\\n*** Update File: /home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\\n@@\\n from autoshorts.growth_plan import load_testbatch\\n+from autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\n from autoshorts.render_manifest import build_render_manifest, write_render_manifests\\n+from autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n from autoshorts.shot_planner import build_shot_plan\\n from autoshorts.voiceover_finalizer import finalize_voiceover\\n@@\\n def test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n@@\\n assert \\\"timeline\\\" in payload\\n assert len(payload[\\\"timeline\\\"]) >= 10\\n+\\n+\\n+def test_build_render_manifest_preserves_mechanism_motion_and_sound_cues():\\n+ explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n+ plan = build_mechanism_shot_plan(explainer)\\n+\\n+ manifest = build_render_manifest(plan)\\n+\\n+ visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n+ assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n+ assert [item[\\\"type\\\"] for item in visual_items] == [\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"kinetic_text_overlay\\\",\\n+ ]\\n+ assert visual_items[0][\\\"visual_motion\\\"] == \\\"hard zoom, freeze-frame, red signal pulse\\\"\\n+ assert visual_items[0][\\\"sound_cue\\\"] == \\\"bass hit + camera snap\\\"\\n+ assert visual_items[2][\\\"style\\\"][\\\"layout\\\"] == \\\"xray_layer_reveal\\\"\\n+ assert manifest[\\\"asset_requirements\\\"][\\\"mechanism_animation\\\"]\\n+ assert manifest[\\\"render_constraints\\\"][\\\"max_static_cards\\\"] == 0\\n+ assert manifest[\\\"render_constraints\\\"][\\\"visual_change_every_seconds\\\"] == 2\\n*** End Patch\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": false, \"error\": \"Patch validation failed (no files were modified):\\n • /home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py: hunk (no hint) not found — old_string and new_string are identical\"}",
"tool_call_id": "call_T0g9mZQt47Tm6cqekcNOOGfH"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW6HWLgWxGsyy8izsGZUn7zeOrc3za2n9i2VMGHPQcjSzZBLPxRsYhiVevKUHTz2V1wCc_EZlpXu8N0aL2Td4xc6IcSaUfZjMYQu2yYrxC09fUiKsNGfwcAlEgxf2oCcub2HkCwl64JcNBG3zmGbNOiDGhWhXYvRtGvhQQHNlccNcr8PqqPBOWO-ZBbyO7awtcxU5s--OdF5ByMF1xWvnAXk6SIFelIFVBIp-XRXPbqfjNOThHTaltvj6l_rLboW-8hg2uJPPq6A00yPimaTneVyH85pR0BnfaKxnZvWGFmrZB1Wrp59oZA6OMbxlwZYjNIRVa06zCiCt-yOT38M5TZQuf5r21BflGhFYPRm467GjBgmmEH-5NXk57JCKWCS2CeTcHP6X1P_-tIRWJovXLuUiacIX5mR03cyfpXEQzPLjYPML5cAhiMsNn_CwSj17b0MTBF8ar5tebCYadIx6RwouMI_ct4dIZV1kIFrGRqOOw9e3sjz8dE2TGxzSi_NRf3yhjt69VG87XXSfKsmk1IGG5c3d-I7Ajh0pD2R63567HoEa_0OaMZq57cin2YV0F5uvk8gh23v0FGLGeMDu02sDgBh5DaB_a_EzmrtAIWyw6ThPqzv2aHlUUCyN-rvZReOgO6MhGDyAVacoV4_XYy7IMtcbgtR4zpeLJKmPEqCI9OD6Vtwsxovw7x-ptHbfi31D7mP8aNRyDk46OXh2YpZ-tARyXYl0qnMO5jvkq26psUlVZUVC79Kki3j7cohDP_YCXipXhYWSjooYMun54WzRUIW-ZUqL-tLUmqLYJ5ARW869qMbtwFu4CSq0g53ntgylciV-GNv-ClwCFpuNWZ5uCAnhN6z0GsquEyv0pv4jQbDGtl7RkxZYvjMw_HlUJFMAw7LTrG8jhY_efB15nXMFwhTJ7nX1SKDY486MiVdR0kf7l6hUyH5oFSR3mAXrInD",
"id": "rs_02aa21bd75cf0633016a116e86b168819197289100a0379cec",
"summary": []
}
],
"tool_calls": [
{
"id": "call_UmggUbQ5TT6EO7iqYLVKwGoD",
"call_id": "call_UmggUbQ5TT6EO7iqYLVKwGoD",
"response_item_id": "fc_02aa21bd75cf0633016a116e872cd8819197e2885ab3d70c74",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\",\"old_string\":\"from autoshorts.growth_plan import load_testbatch\\nfrom autoshorts.render_manifest import build_render_manifest, write_render_manifests\\nfrom autoshorts.shot_planner import build_shot_plan\\nfrom autoshorts.voiceover_finalizer import finalize_voiceover\\n\",\"new_string\":\"from autoshorts.growth_plan import load_testbatch\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.render_manifest import build_render_manifest, write_render_manifests\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\nfrom autoshorts.shot_planner import build_shot_plan\\nfrom autoshorts.voiceover_finalizer import finalize_voiceover\\n\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\\n@@ -2,7 +2,9 @@\\n from pathlib import Path\\n \\n from autoshorts.growth_plan import load_testbatch\\n+from autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\n from autoshorts.render_manifest import build_render_manifest, write_render_manifests\\n+from autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n from autoshorts.shot_planner import build_shot_plan\\n from autoshorts.voiceover_finalizer import finalize_voiceover\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_UmggUbQ5TT6EO7iqYLVKwGoD"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_TV177kBqmCZxB8QXPujaTsTW",
"call_id": "call_TV177kBqmCZxB8QXPujaTsTW",
"response_item_id": "fc_02aa21bd75cf0633016a116e8ec1b48191b73eec65711c9aed",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" assert len(manifest[\\\"timeline\\\"]) >= 10\\n\\n\\ndef test_build_render_manifest_preserves_mechanism_motion_and_sound_cues():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n plan = build_mechanism_shot_plan(explainer)\\n\\n manifest = build_render_manifest(plan)\\n\\n visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n assert [item[\\\"type\\\"] for item in visual_items] == [\\n \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\n assert visual_items[0][\\\"visual_motion\\\"] == \\\"hard zoom, freeze-frame, red signal pulse\\\"\\n assert visual_items[0][\\\"sound_cue\\\"] == \\\"bass hit + camera snap\\\"\\n assert visual_items[2][\\\"style\\\"][\\\"layout\\\"] == \\\"xray_layer_reveal\\\"\\n assert manifest[\\\"asset_requirements\\\"][\\\"mechanism_animation\\\"]\\n assert manifest[\\\"render_constraints\\\"][\\\"max_static_cards\\\"] == 0\\n assert manifest[\\\"render_constraints\\\"][\\\"visual_change_every_seconds\\\"] == 2\\n\",\"old_string\":\" assert len(manifest[\\\"timeline\\\"]) >= 10\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": false, \"error\": \"Found 16 matches for old_string. Provide more context to make it unique, or use replace_all=True.\"}",
"tool_call_id": "call_TV177kBqmCZxB8QXPujaTsTW"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW6Zbl8RXzfj8YLmBsriFTHzqw5aZVQYH4knXM74txv7wsbdxMiHmuNt7pUEpjuqg1yHNXEmGHsgkVMrDUoApMB8gYE-Si3_GgTid4_khutrkPEsqF85FuDUKApbUcjiybm1a5XuRMFR0TSOx5InU_DiSLgEfhcVZX8iN5lXsorX5ReK6ohxhOZi6oXqbP5qPT8uuAItvOEyrWEgDx-MIlRjEnmmIq47UcoXRFsqxb8ivjWZBah2df3wienvl9lvzYOekQ2L8jzH4RNCwdlL6cZSZGojuqd5GsbEKEEhUcTjRagNkdZk5z-KbDQUX1_Ze8SPdNI-txDor9KtzontwRDGhJXrV0o38O0xh6HyclR2ghgB1ntQtS5yDv8n6R0nDbeECl4rb2VJoRASTPexyG9_G6OTRDcRii4DpL7MMLHOqnb0AXW1CiwgkvCdWVM5ozlku-dqSl0Vp_fWp-TwWYssYeI0oWtGeB-fG-yt0MP1IXsMRu4FcLplEO2iEh3QIjH92hf4sWd2x4YfZND8Cew77czZgoMk85kDLFD9LNTcA7NRw1_1Jq3A9W-TpmWgMeILCOznlZr64dH5ayF3CZXFK7t8bV11AuAy136Ocilsr23ckJ9kdBWJ2xZrsFXCIe8yljqysUWwPFuAXCKfYVSzCqK79PJgMrhKuZ5h0t_GUegQUT27sBr-hfhlXEmpAIwOL6Mmvl1fSK4mcgePVSYCXoytAd5FJQKVi8couHjE9xBxcGE13yaDz9hJTIjsM9XBQ2EXkn8oOmIVefkhr47iOhDc_yOnSbm98kcdatRAc867G7eAhYOMIgGBfSWGIAiCOzT9sXdYmvlN5JtGD8jiRCRR7mF86Q_Tq5EzdmRyk3QkRsuPgkm6T_aR2M4YohJp2sjbDbXjWp9CdLrXDw-DntZbD6GhtS-ZB5qLj41qR_G1a84Y9t2x1e8JN3pzFrDdsiZTI58yRSkAf18x6d4DEmY8_JJGjoeJZAgtZgHZKWg=",
"id": "rs_02aa21bd75cf0633016a116e9891788191a385a94fc483eb5b",
"summary": []
}
],
"tool_calls": [
{
"id": "call_sdSVbS6LgNJT5u30hwOy4Rk2",
"call_id": "call_sdSVbS6LgNJT5u30hwOy4Rk2",
"response_item_id": "fc_02aa21bd75cf0633016a116e995708819184438555d11e421f",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\",\"offset\":45,\"limit\":40}"
}
}
]
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 45| assert all(item[\\\"asset_ref\\\"].startswith(\\\"placeholder://\\\") for item in visual_items)\\n 46| assert manifest[\\\"asset_requirements\\\"][\\\"stock_broll\\\"]\\n 47| assert manifest[\\\"asset_requirements\\\"][\\\"screen_recording\\\"]\\n 48|\\n 49|\\n 50|def test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n 51| plans = [_shot_plan(\\\"ai-004\\\"), _shot_plan(\\\"ai-005\\\"), _shot_plan(\\\"attention-002\\\")]\\n 52|\\n 53| paths = write_render_manifests(plans, tmp_path)\\n 54|\\n 55| assert len(paths) == 3\\n 56| assert all(path.suffix == \\\".json\\\" for path in paths)\\n 57| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 58| assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n 59| assert \\\"timeline\\\" in payload\\n 60| assert len(payload[\\\"timeline\\\"]) >= 10\\n 61|\", \"total_lines\": 60, \"file_size\": 2664, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_sdSVbS6LgNJT5u30hwOy4Rk2"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_rLH39wnbEVNYA6P8obSge5eQ",
"call_id": "call_rLH39wnbEVNYA6P8obSge5eQ",
"response_item_id": "fc_02aa21bd75cf0633016a116e9e2fe8819190f66accdf6c70e8",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\"def test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n plans = [_shot_plan(\\\"ai-004\\\"), _shot_plan(\\\"ai-005\\\"), _shot_plan(\\\"attention-002\\\")]\\n\\n paths = write_render_manifests(plans, tmp_path)\\n\\n assert len(paths) == 3\\n assert all(path.suffix == \\\".json\\\" for path in paths)\\n payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n assert \\\"timeline\\\" in payload\\n assert len(payload[\\\"timeline\\\"]) >= 10\\n\\n\\ndef test_build_render_manifest_preserves_mechanism_motion_and_sound_cues():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n plan = build_mechanism_shot_plan(explainer)\\n\\n manifest = build_render_manifest(plan)\\n\\n visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n assert [item[\\\"type\\\"] for item in visual_items] == [\\n \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\n assert visual_items[0][\\\"visual_motion\\\"] == \\\"hard zoom, freeze-frame, red signal pulse\\\"\\n assert visual_items[0][\\\"sound_cue\\\"] == \\\"bass hit + camera snap\\\"\\n assert visual_items[2][\\\"style\\\"][\\\"layout\\\"] == \\\"xray_layer_reveal\\\"\\n assert manifest[\\\"asset_requirements\\\"][\\\"mechanism_animation\\\"]\\n assert manifest[\\\"render_constraints\\\"][\\\"max_static_cards\\\"] == 0\\n assert manifest[\\\"render_constraints\\\"][\\\"visual_change_every_seconds\\\"] == 2\\n\",\"old_string\":\"def test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n plans = [_shot_plan(\\\"ai-004\\\"), _shot_plan(\\\"ai-005\\\"), _shot_plan(\\\"attention-002\\\")]\\n\\n paths = write_render_manifests(plans, tmp_path)\\n\\n assert len(paths) == 3\\n assert all(path.suffix == \\\".json\\\" for path in paths)\\n payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n assert \\\"timeline\\\" in payload\\n assert len(payload[\\\"timeline\\\"]) >= 10\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\\n@@ -58,3 +58,26 @@\\n assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n assert \\\"timeline\\\" in payload\\n assert len(payload[\\\"timeline\\\"]) >= 10\\n+\\n+\\n+def test_build_render_manifest_preserves_mechanism_motion_and_sound_cues():\\n+ explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n+ plan = build_mechanism_shot_plan(explainer)\\n+\\n+ manifest = build_render_manifest(plan)\\n+\\n+ visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n+ assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n+ assert [item[\\\"type\\\"] for item in visual_items] == [\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"kinetic_text_overlay\\\",\\n+ ]\\n+ assert visual_items[0][\\\"visual_motion\\\"] == \\\"hard zoom, freeze-frame, red signal pulse\\\"\\n+ assert visual_items[0][\\\"sound_cue\\\"] == \\\"bass hit + camera snap\\\"\\n+ assert visual_items[2][\\\"style\\\"][\\\"layout\\\"] == \\\"xray_layer_reveal\\\"\\n+ assert manifest[\\\"asset_requirements\\\"][\\\"mechanism_animation\\\"]\\n+ assert manifest[\\\"render_constraints\\\"][\\\"max_static_cards\\\"] == 0\\n+ assert manifest[\\\"render_constraints\\\"][\\\"visual_change_every_seconds\\\"] == 2\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"_warning\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py was last read with offset/limit pagination (partial view). Re-read the whole file before overwriting it.\"}",
"tool_call_id": "call_rLH39wnbEVNYA6P8obSge5eQ"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_FItjhbp65yiz4iRNaB63qNim",
"call_id": "call_FItjhbp65yiz4iRNaB63qNim",
"response_item_id": "fc_02aa21bd75cf0633016a116eadefb48191ad55f6b0482e5b7f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_render_manifest.py::test_build_render_manifest_preserves_mechanism_motion_and_sound_cues -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"F [100%]\\n=================================== FAILURES ===================================\\n_____ test_build_render_manifest_preserves_mechanism_motion_and_sound_cues _____\\n\\n def test_build_render_manifest_preserves_mechanism_motion_and_sound_cues():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n plan = build_mechanism_shot_plan(explainer)\\n \\n manifest = build_render_manifest(plan)\\n \\n visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n assert [item[\\\"type\\\"] for item in visual_items] == [\\n \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\n> assert visual_items[0][\\\"visual_motion\\\"] == \\\"hard zoom, freeze-frame, red signal pulse\\\"\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE KeyError: 'visual_motion'\\n\\ntests/test_render_manifest.py:78: KeyError\\n=========================== short test summary info ============================\\nFAILED tests/test_render_manifest.py::test_build_render_manifest_preserves_mechanism_motion_and_sound_cues\\n1 failed in 0.06s\", \"exit_code\": 1, \"error\": null}",
"tool_call_id": "call_FItjhbp65yiz4iRNaB63qNim"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_AfSJIHqz75KO6yuaXddCG0Gu",
"call_id": "call_AfSJIHqz75KO6yuaXddCG0Gu",
"response_item_id": "fc_02aa21bd75cf0633016a116eb37b8c8191a1c1361ac794936a",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\",\"id\":\"2\",\"status\":\"completed\"},{\"content\":\"Mechanism-Render-Manifest-Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"in_progress\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"in_progress\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"pending\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 2, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}",
"tool_call_id": "call_AfSJIHqz75KO6yuaXddCG0Gu"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Addressing implementation issues**\n\nI need to address the issue with duration floats. The current conversion of `int(scene start)` is causing a problem where `start 1.4` becomes 1000 instead of correctly converting to seconds. I might need to apply a round function to fix this. Additionally, I should ensure the asset requirement hints include the necessary mechanism types and styles. I'll also modify the asset plan to handle these types. There might be existing tests, but I'll need to add one in `test_asset_plan`.",
"finish_reason": "tool_calls",
"reasoning_content": "**Addressing implementation issues**\n\nI need to address the issue with duration floats. The current conversion of `int(scene start)` is causing a problem where `start 1.4` becomes 1000 instead of correctly converting to seconds. I might need to apply a round function to fix this. Additionally, I should ensure the asset requirement hints include the necessary mechanism types and styles. I'll also modify the asset plan to handle these types. There might be existing tests, but I'll need to add one in `test_asset_plan`.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW678Ub-ZzjxXAYvUEJzZE6GGTRYlccicq5aVMqEvc-U56e_xqdD5cjaC2QQgmKWgD5UwY6LgPneDyERf7MghE_sed2VIVhal0JbQkskdzWnI4p4Oeenu3l9Ic64ziJV-scTxFGlfxfjfD922_l_95hJKGLxszE-TDdwlVWcT72hO0UrDpPv6-MgnZAuUqJpRncnTIL-Z-5ZJQRh2CvvAXPUSD8uORnrw9mglUTUABtgIruM7Er6aTedVuE_ofaoYVwEp5oeBJOCG50LRfbkxnjbYcOU0Fw7kkktzyYGh66EUQo0lgP4AAYQXCNuawO_bIFhxgCTyy6SdXJ3Ot-1d-d5mJKHVCV2v1WfHDwOl57EV2N4JXvE3IWBQ_QtYKcR22htUzwUzT-wV-dutYbHPvgkm9hdhySj7Gxx9m1-5UccySCQBgTaqhbpdXAdQxiImvc0h6bB8PqKq_ntRynO2W4M-hHegRboRTjruNkIDR8xK4_dgqxoNCvJYDj_nIkJnSdB0yF-tvYecI1j38UDhiZXHfDGz-QwcC0LVjZuX9cyiQSDZrOz1aN1TSDd2RKe4p8x0IaAWS5btNq_mRuEygr0PO-DLYyyy_qQx_ixoNdvoOhL7o0BA4tpqvplbkSQcLqU9OlhVmp33IYYIJLHc_qfv5REpJTFqrvrpLrudnXp5_Qe7-6mxj_0Abqa_3v-EnfzS-4sd_78FaXL-yEerdzdgwnIOtCAJCayUarAtQx-kjG9MRQYNPpUfuk_Z8MitFQa9ah7EsDszO3UOrJM7QdxqU8lhFKj0TxFnkYcJ3mYjQzDzzz8NHUdtMvcVCzg2LflyZ7ExIKBPKVa7fCWCqGweLPs92tExPlBGh9Jq59AiJZPvR-NAIO5Gy3ZoSGRO5VyrUVRXjFp2HuWW6EhAd5BzrLldNOQF0O9lFQbgUAz2LDPqUDS8cdJbPxkkp9EinsK2P-E8C1IV9VA9EtBCg0zYf_UieFcInuPBfIrOR1EHWtZsosd4auGFcjOeAuXhlfuHxrikGMDREOVWN2cCTqsJpwzLjmbxyRwiVwmmZ88eLwAF_ykXUc4fTeB3MHY-w02zv8rGDq52ndx72nqrJ_9SXpQ7fK7FFMCJEyI436YTQO6GTpiz4iSdFfKXLBWGLGqLfzFcNlACBzTyD4-WiwFLW1AnLVMfju_s7aQETa54zPGjegPnZ86V3eaJnX11kVO3is5mquW9ZXfU6b97KZ96AnEqJUedZnXZp0iQl7HpnFdlMMx1ynH4nnLr9YjAsx6WN4jHCMe7X4HYOSIhiTXK3ElOBGJ6PhpU2Anq1IMXReJERAitYH1VhN_embNHGj7O0-3ARtfxfjyYtWeKZMYDWHZDMoSHud6LwPkQJ95qMTETqSAIj0ZH3laCqQ2K1eztVNAXrNouajoGByx2P51KatNWqda3N6uuqzOIbH4Zs4Vv1d0Ef9DhDr6MEu0eUj_oEO8BYhfO9kYD4acgtWCYUhs0vuKnEEw1kQ20OhU0rDoFECTFn97v23QLM1bhUbAWqh2-_WL5OkPnDVprACelSWConZVJs5__nq35whyysXVKxxhpto3fxlgUt2sGwO_yQ8EdoVkNmFouGtvH5WiqQ==",
"id": "rs_02aa21bd75cf0633016a116eb74de081919cdd5dd0970746bc",
"summary": [
{
"type": "summary_text",
"text": "**Addressing implementation issues**\n\nI need to address the issue with duration floats. The current conversion of `int(scene start)` is causing a problem where `start 1.4` becomes 1000 instead of correctly converting to seconds. I might need to apply a round function to fix this. Additionally, I should ensure the asset requirement hints include the necessary mechanism types and styles. I'll also modify the asset plan to handle these types. There might be existing tests, but I'll need to add one in `test_asset_plan`."
}
]
}
],
"tool_calls": [
{
"id": "call_fODPZRJxKrWCP5yoAUKKlJiM",
"call_id": "call_fODPZRJxKrWCP5yoAUKKlJiM",
"response_item_id": "fc_02aa21bd75cf0633016a116ebbdbdc8191bc694369debcc85c",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"patch\",\"patch\":\"*** Begin Patch\\n*** Update File: /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@\\n ASSET_REQUIREMENT_HINTS = {\\n \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n+ \\\"cinematic_ai_video\\\": \\\"Needs photorealistic vertical AI-video clip; no baked text, logos, or watermarks.\\\",\\n+ \\\"mechanism_animation\\\": \\\"Needs renderer-generated mechanism/layer animation with visible movement every 1-2 seconds.\\\",\\n+ \\\"kinetic_text_overlay\\\": \\\"Generated by renderer as large beat-synced caption overlays, not paragraph cards.\\\",\\n }\\n@@\\n-def _duration_ms(start: int, end: int) -> int:\\n- return max(500, (int(end) - int(start)) * 1000)\\n+def _seconds_to_ms(value: int | float) -> int:\\n+ return round(float(value) * 1000)\\n+\\n+\\n+def _duration_ms(start: int | float, end: int | float) -> int:\\n+ return max(500, _seconds_to_ms(end) - _seconds_to_ms(start))\\n@@\\n elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n+ elif scene[\\\"asset_type\\\"] == \\\"cinematic_ai_video\\\":\\n+ base.update({\\\"layout\\\": \\\"cinematic_under_caption\\\", \\\"no_baked_text\\\": True, \\\"pace\\\": \\\"fast_cut\\\"})\\n+ elif scene[\\\"asset_type\\\"] == \\\"mechanism_animation\\\":\\n+ base.update({\\\"layout\\\": \\\"xray_layer_reveal\\\", \\\"max_words_on_screen\\\": 10, \\\"layered_motion\\\": True})\\n+ elif scene[\\\"asset_type\\\"] == \\\"kinetic_text_overlay\\\":\\n+ base.update({\\\"layout\\\": \\\"kinetic_full_frame\\\", \\\"max_words_on_screen\\\": 12, \\\"beat_synced\\\": True})\\n return base\\n@@\\n \\\"type\\\": scene[\\\"asset_type\\\"],\\n \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n- \\\"start_ms\\\": int(scene[\\\"start\\\"]) * 1000,\\n+ \\\"start_ms\\\": _seconds_to_ms(scene[\\\"start\\\"]),\\n \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n+ \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"cut_or_slide\\\"),\\n+ \\\"sound_cue\\\": scene.get(\\\"sound_cue\\\", \\\"none\\\"),\\n \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n \\\"style\\\": _visual_style(scene),\\n }\\n@@\\n- start_ms = int(beat[\\\"start\\\"]) * 1000\\n- next_start_ms = int(beats[index + 1][\\\"start\\\"]) * 1000 if index + 1 < len(beats) else total_ms\\n+ start_ms = _seconds_to_ms(beat[\\\"start\\\"])\\n+ next_start_ms = _seconds_to_ms(beats[index + 1][\\\"start\\\"]) if index + 1 < len(beats) else total_ms\\n@@\\n return {\\n@@\\n \\\"render_constraints\\\": {\\n \\\"avoid_static_slideshow\\\": True,\\n \\\"pattern_interrupt_every_seconds\\\": 3,\\n+ \\\"visual_change_every_seconds\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"visual_change_every_seconds\\\", 3),\\n \\\"first_frame_hook_required\\\": True,\\n \\\"max_words_per_visual_card\\\": 18,\\n+ \\\"max_static_cards\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 1),\\n \\\"human_approval_required_before_publish\\\": True,\\n },\\n*** End Patch\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": false, \"error\": \"Patch validation failed (no files were modified):\\n • /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py: hunk (no hint) not found — old_string and new_string are identical\"}",
"tool_call_id": "call_fODPZRJxKrWCP5yoAUKKlJiM"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEW7RLRx4cQCeF3dIj9v_VIb3ZW1VxKuvw8jeqwjmnO0micoVqP-Qrosp8IOa3YPce-TVZOHj9C6IW8WgNlHteR7W1rRPSKZZCYBkVblc-JU7ieyRP4TRTk4Q6hZOH7sngJ_GnbmhRUEsXZscMrmP5qLtZp1UGOuBeTa3UT9AnkRpYv8YkH4QMnIO4RRLr7CrifmkkPj8siqkTPrHzgnu5hpW0vZUdqG6Qklg7yC6chFh6uD16Vs71pJNDLHPFAFlNT1iOlDF2jRmSMF_R9JxHTHjmCfLCRkko2nHB0JYjMKGD_kywB91lq-ppibG_2mewhNA9M1R9E0vQE6w8I6P7gFCkW-SC6upSOx64972p0hQNz4Z0Z7ihGb-GDxpmExMeMnNNZWtpYREgbzp8aY0JUE06ADFq-AtXlvljFWYD_LygCceoucUH-g7iQkbYFJjJKoshSfQkChqdur2WQTqOltkdEOweqYeT-xBk4w4LCaHr5_z5TIATEWoqH82BOj_ZjXH1-ZbaWw-Tq0yfBL_UMLqBub_sgLP4_jv_A-lXZZtpbSQnuBzXo7ncxBUXWI7NqZUAB4iYWZjkuPRIfTN9Jk9hRmNlRftS3ioBeMz3o2MEouRNrFep2OEFED95iqF9r18yB7zCUo-89e1NyVBanPy_XtQoF_Ucw4Yyf3f6C7C3UNSpQLvceDPPiIuvej1UhHEFAyIKEIVC_KLIwjTYqeivqznlyKX25su737fENR2EQGyvyN6zMb8OTxXZ9QadnI1BBEZmlpJLtCEn0w8I8Ysgv5Ns4xkWE3_xN8psbQPr16iP1l4mPRfy1HXEdUkg57NCzNTJcaf6dXsHI9NehNmF91wczYIUEFQM9q0ERwKFp3gyPWOBQN7cAIdjBBG4f-chENN9VFggrX4RHKxpStTLBpx3z_sRdbOrvVHdpVhcBsdPtdbowOnYo5MKp5PqXU5",
"id": "rs_02aa21bd75cf0633016a116ed0ae288191b0ed33f28ddb8cf8",
"summary": []
}
],
"tool_calls": [
{
"id": "call_ZhRJpF6V0HAt5JCgt4QNzo49",
"call_id": "call_ZhRJpF6V0HAt5JCgt4QNzo49",
"response_item_id": "fc_02aa21bd75cf0633016a116ed15dbc8191be7307cba4999284",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\",\"old_string\":\"ASSET_REQUIREMENT_HINTS = {\\n \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n}\\n\\n\\ndef _duration_ms(start: int, end: int) -> int:\\n return max(500, (int(end) - int(start)) * 1000)\\n\",\"new_string\":\"ASSET_REQUIREMENT_HINTS = {\\n \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n \\\"cinematic_ai_video\\\": \\\"Needs photorealistic vertical AI-video clip; no baked text, logos, or watermarks.\\\",\\n \\\"mechanism_animation\\\": \\\"Needs renderer-generated mechanism/layer animation with visible movement every 1-2 seconds.\\\",\\n \\\"kinetic_text_overlay\\\": \\\"Generated by renderer as large beat-synced caption overlays, not paragraph cards.\\\",\\n}\\n\\n\\ndef _seconds_to_ms(value: int | float) -> int:\\n return round(float(value) * 1000)\\n\\n\\ndef _duration_ms(start: int | float, end: int | float) -> int:\\n return max(500, _seconds_to_ms(end) - _seconds_to_ms(start))\\n\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -19,11 +19,18 @@\\n \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n+ \\\"cinematic_ai_video\\\": \\\"Needs photorealistic vertical AI-video clip; no baked text, logos, or watermarks.\\\",\\n+ \\\"mechanism_animation\\\": \\\"Needs renderer-generated mechanism/layer animation with visible movement every 1-2 seconds.\\\",\\n+ \\\"kinetic_text_overlay\\\": \\\"Generated by renderer as large beat-synced caption overlays, not paragraph cards.\\\",\\n }\\n \\n \\n-def _duration_ms(start: int, end: int) -> int:\\n- return max(500, (int(end) - int(start)) * 1000)\\n+def _seconds_to_ms(value: int | float) -> int:\\n+ return round(float(value) * 1000)\\n+\\n+\\n+def _duration_ms(start: int | float, end: int | float) -> int:\\n+ return max(500, _seconds_to_ms(end) - _seconds_to_ms(start))\\n \\n \\n def _placeholder(asset_type: str, video_id: str, scene: int) -> str:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_ZhRJpF6V0HAt5JCgt4QNzo49"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_V0pXo27W7PUxGtLzB8La7FdV",
"call_id": "call_V0pXo27W7PUxGtLzB8La7FdV",
"response_item_id": "fc_02aa21bd75cf0633016a116eddcccc8191b44e98a21d39c1f0",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n elif scene[\\\"asset_type\\\"] == \\\"cinematic_ai_video\\\":\\n base.update({\\\"layout\\\": \\\"cinematic_under_caption\\\", \\\"no_baked_text\\\": True, \\\"pace\\\": \\\"fast_cut\\\"})\\n elif scene[\\\"asset_type\\\"] == \\\"mechanism_animation\\\":\\n base.update({\\\"layout\\\": \\\"xray_layer_reveal\\\", \\\"max_words_on_screen\\\": 10, \\\"layered_motion\\\": True})\\n elif scene[\\\"asset_type\\\"] == \\\"kinetic_text_overlay\\\":\\n base.update({\\\"layout\\\": \\\"kinetic_full_frame\\\", \\\"max_words_on_screen\\\": 12, \\\"beat_synced\\\": True})\\n return base\\n\",\"old_string\":\" elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n return base\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -51,6 +51,12 @@\\n base.update({\\\"layout\\\": \\\"screen_plus_caption\\\", \\\"blur_sensitive_ui\\\": True})\\n elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n+ elif scene[\\\"asset_type\\\"] == \\\"cinematic_ai_video\\\":\\n+ base.update({\\\"layout\\\": \\\"cinematic_under_caption\\\", \\\"no_baked_text\\\": True, \\\"pace\\\": \\\"fast_cut\\\"})\\n+ elif scene[\\\"asset_type\\\"] == \\\"mechanism_animation\\\":\\n+ base.update({\\\"layout\\\": \\\"xray_layer_reveal\\\", \\\"max_words_on_screen\\\": 10, \\\"layered_motion\\\": True})\\n+ elif scene[\\\"asset_type\\\"] == \\\"kinetic_text_overlay\\\":\\n+ base.update({\\\"layout\\\": \\\"kinetic_full_frame\\\", \\\"max_words_on_screen\\\": 12, \\\"beat_synced\\\": True})\\n return base\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_V0pXo27W7PUxGtLzB8La7FdV"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_8OWNmVluYVbO2EOOyq6FKxqs",
"call_id": "call_8OWNmVluYVbO2EOOyq6FKxqs",
"response_item_id": "fc_02aa21bd75cf0633016a116ee871448191a74c1e3b04786769",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n \\\"start_ms\\\": _seconds_to_ms(scene[\\\"start\\\"]),\\n \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"cut_or_slide\\\"),\\n \\\"sound_cue\\\": scene.get(\\\"sound_cue\\\", \\\"none\\\"),\\n \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n\",\"old_string\":\" \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n \\\"start_ms\\\": int(scene[\\\"start\\\"]) * 1000,\\n \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -70,10 +70,12 @@\\n \\\"track\\\": \\\"visual\\\",\\n \\\"type\\\": scene[\\\"asset_type\\\"],\\n \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n- \\\"start_ms\\\": int(scene[\\\"start\\\"]) * 1000,\\n+ \\\"start_ms\\\": _seconds_to_ms(scene[\\\"start\\\"]),\\n \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n+ \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"cut_or_slide\\\"),\\n+ \\\"sound_cue\\\": scene.get(\\\"sound_cue\\\", \\\"none\\\"),\\n \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n \\\"style\\\": _visual_style(scene),\\n }\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_8OWNmVluYVbO2EOOyq6FKxqs"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_xar7CqXWgfmbm27S7IulIpSd",
"call_id": "call_xar7CqXWgfmbm27S7IulIpSd",
"response_item_id": "fc_02aa21bd75cf0633016a11727febc081919776ec75afecb155",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" start_ms = _seconds_to_ms(beat[\\\"start\\\"])\\n next_start_ms = _seconds_to_ms(beats[index + 1][\\\"start\\\"]) if index + 1 < len(beats) else total_ms\\n\",\"old_string\":\" start_ms = int(beat[\\\"start\\\"]) * 1000\\n next_start_ms = int(beats[index + 1][\\\"start\\\"]) * 1000 if index + 1 < len(beats) else total_ms\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -88,8 +88,8 @@\\n total_ms = int(plan[\\\"estimated_seconds\\\"]) * 1000\\n items: list[dict[str, Any]] = []\\n for index, beat in enumerate(beats):\\n- start_ms = int(beat[\\\"start\\\"]) * 1000\\n- next_start_ms = int(beats[index + 1][\\\"start\\\"]) * 1000 if index + 1 < len(beats) else total_ms\\n+ start_ms = _seconds_to_ms(beat[\\\"start\\\"])\\n+ next_start_ms = _seconds_to_ms(beats[index + 1][\\\"start\\\"]) if index + 1 < len(beats) else total_ms\\n items.append(\\n {\\n \\\"id\\\": f\\\"caption_{index + 1:02d}\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_xar7CqXWgfmbm27S7IulIpSd"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_ZwIkEcOzCtm0kpAYj8YJFl9K",
"call_id": "call_ZwIkEcOzCtm0kpAYj8YJFl9K",
"response_item_id": "fc_02aa21bd75cf0633016a117286c5d48191994c5d8cf7edcd50",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"render_constraints\\\": {\\n \\\"avoid_static_slideshow\\\": True,\\n \\\"pattern_interrupt_every_seconds\\\": 3,\\n \\\"visual_change_every_seconds\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"visual_change_every_seconds\\\", 3),\\n \\\"first_frame_hook_required\\\": True,\\n \\\"max_words_per_visual_card\\\": 18,\\n \\\"max_static_cards\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 1),\\n \\\"human_approval_required_before_publish\\\": True,\\n },\\n\",\"old_string\":\" \\\"render_constraints\\\": {\\n \\\"avoid_static_slideshow\\\": True,\\n \\\"pattern_interrupt_every_seconds\\\": 3,\\n \\\"first_frame_hook_required\\\": True,\\n \\\"max_words_per_visual_card\\\": 18,\\n \\\"human_approval_required_before_publish\\\": True,\\n },\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -145,8 +145,10 @@\\n \\\"render_constraints\\\": {\\n \\\"avoid_static_slideshow\\\": True,\\n \\\"pattern_interrupt_every_seconds\\\": 3,\\n+ \\\"visual_change_every_seconds\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"visual_change_every_seconds\\\", 3),\\n \\\"first_frame_hook_required\\\": True,\\n \\\"max_words_per_visual_card\\\": 18,\\n+ \\\"max_static_cards\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 1),\\n \\\"human_approval_required_before_publish\\\": True,\\n },\\n \\\"production_notes\\\": plan.get(\\\"production_notes\\\", []),\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_ZwIkEcOzCtm0kpAYj8YJFl9K"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_zlrqT9NZmk6WIGFgNUe39QAd",
"call_id": "call_zlrqT9NZmk6WIGFgNUe39QAd",
"response_item_id": "fc_02aa21bd75cf0633016a1172903a6c819184bda42be65caf6f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_render_manifest.py::test_build_render_manifest_preserves_mechanism_motion_and_sound_cues -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \". [100%]\\n1 passed in 0.03s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_zlrqT9NZmk6WIGFgNUe39QAd"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Planning mechanism tests**\n\nI need to focus on how the CLI and asset_plan will handle different mechanism types. Writing tests within the asset_plan for the mechanism manifest is crucial, as that will help ensure everything functions properly. I’m thinking about the specific cases I need to cover and how to structure those tests to be thorough. Paying attention to these details will hopefully lead to a smoother implementation. Let’s get started on that!",
"finish_reason": "tool_calls",
"reasoning_content": "**Planning mechanism tests**\n\nI need to focus on how the CLI and asset_plan will handle different mechanism types. Writing tests within the asset_plan for the mechanism manifest is crucial, as that will help ensure everything functions properly. I’m thinking about the specific cases I need to cover and how to structure those tests to be thorough. Paying attention to these details will hopefully lead to a smoother implementation. Let’s get started on that!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXKXF3nBNZBeDDWQpXGD3aV7LDayCWV3dvI5LOD9VLcJkCR5k3uh0hn09bfhCN-ZpSnij0XdmzlY_0ywMgpmR4yZivSBSWkjwIX-jM-KX100CpbgCfonazAs5RhZih9RfK97CYkScpi3O8BLGtNOctjaumRysBIRa0JPMh9ktLIB_P4wCYmxMGPmTQibAhpb3O7ui1pEkMzuatxvERoPJPbZwNvt1o34QPcnbvIzSmv-iod0GUPscyTqvxo_QERwgm7nUBrVPVxyi9-_1ZjD49J0BCNcBhWt8ZTUZoGcRN-68QCoQrvIEVBUDFQhKAnZ599PFE7lOSe5fj-XK_CLUqtO6ZffgziP14-BGJONnzQnRC2y7NS9HcL19j-P_V-ze0d9fQhU5bzNfOD5t9FklriiyxyyJ8O0bYRJAUNK0FakiPhX9w-hxKQ4fDxjt9MU28t9wXMzzjurq6KKnBKGrcbJtP--7F2-mSX74oB9QLk0upJCKyVifqH1QXdcZTWKL3R3SdDu53KUqsxh2eL7sGyNPhuNu7hWve-7nLsi3NdS_Csdhsu_G6D456PZj-rAYqtbLhPsxrmg9KtOacdNtI7_c_IWdlvR4mzfz9ZzBsaziJHd5p8SbuZInIjgsbl6jKsSvMQWlICUhmcChC3jVNSG92bc8Isgj7lBIkUzuae-yzWPpdL9QUZdBS1bMGn_204N1QeE5e3quRAP-uvVIVmbLSAlOllWdiTHs4b9_5xDFPe8elZYDosMvMcWylJVxhgUlZQ54pKGt6klknDnFGizbKSJL5BGQlhaUHUBdzfyOQSs9H2Hx8n9q6xy36JBfloist8OHbPZCAedX-7MK1i4Im1QICKi693Wez07Mq_bGffBWTIIoDB9wNdoJv5x0nPMiheyP23fnv5xpA5Me8yR9h_BQ6rIzjqP4JzDqwFAG4BAYr1ktEtgl-GZqqB_hawuHEioISJd73wdGjetXY3PHcD9fiBbEWXY2if_pyGQmg3pQrc55TVGLKpzltlwD1C2ui6Y0Zy4NLhDsek0SEvkcR-fchK8MdNane5bsi1mGlM=",
"id": "rs_02aa21bd75cf0633016a117295ad10819187cd577578654fb1",
"summary": [
{
"type": "summary_text",
"text": "**Planning mechanism tests**\n\nI need to focus on how the CLI and asset_plan will handle different mechanism types. Writing tests within the asset_plan for the mechanism manifest is crucial, as that will help ensure everything functions properly. I’m thinking about the specific cases I need to cover and how to structure those tests to be thorough. Paying attention to these details will hopefully lead to a smoother implementation. Let’s get started on that!"
}
]
}
],
"tool_calls": [
{
"id": "call_2cdUeljjCcvmEhKdev2AYoQu",
"call_id": "call_2cdUeljjCcvmEhKdev2AYoQu",
"response_item_id": "fc_02aa21bd75cf0633016a1172970e308191b94e677df3462c3e",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"patch\",\"patch\":\"*** Begin Patch\\n*** Update File: /home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\\n@@\\n from autoshorts.asset_plan import build_asset_plan, write_asset_plans\\n+from autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\n+from autoshorts.render_manifest import build_render_manifest\\n+from autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n@@\\n def test_write_asset_plans_outputs_json(tmp_path):\\n@@\\n assert paths[0].suffix == \\\".json\\\"\\n assert \\\"scenes\\\" in payload\\n+\\n+\\n+def test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes():\\n+ explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n+ manifest = build_render_manifest(build_mechanism_shot_plan(explainer))\\n+\\n+ plan = build_asset_plan(manifest)\\n+\\n+ modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n+ assert modes == [\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"kinetic_text_overlay\\\",\\n+ ]\\n+ assert plan[\\\"final_render_strategy\\\"][\\\"preferred_mix\\\"][\\\"mechanism_animation\\\"] == 2\\n+ mechanism_scene = plan[\\\"scenes\\\"][2]\\n+ assert mechanism_scene[\\\"provider_family\\\"] == \\\"local_renderer\\\"\\n+ assert \\\"Röntgen\\\" in mechanism_scene[\\\"generation_prompt\\\"] or \\\"x-ray\\\" in mechanism_scene[\\\"generation_prompt\\\"]\\n*** End Patch\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": false, \"error\": \"Patch validation failed (no files were modified):\\n • /home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py: hunk (no hint) not found — old_string and new_string are identical\\n • /home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py: hunk (no hint) not found — Could not find a match for old_string in the file\\n\\nDid you mean one of these sections?\\n 67| \\n 68| assert len(paths) == 2\\n 69| assert all(path.suffix == \\\".json\\\" for path in paths)\\n 70| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 71| assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n 72| assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\\n---\\n 66| paths = write_asset_plans(manifests, tmp_path)\\n 67| \\n 68| assert len(paths) == 2\\n 69| assert all(path.suffix == \\\".json\\\" for path in paths)\\n 70| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 71| assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n---\\n 70| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 71| assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n 72| assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\"}",
"tool_call_id": "call_2cdUeljjCcvmEhKdev2AYoQu"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_lRcFVrwX5rvYYZ8ac8YzIawE",
"call_id": "call_lRcFVrwX5rvYYZ8ac8YzIawE",
"response_item_id": "fc_02aa21bd75cf0633016a1172a15414819198180fe51be638f5",
"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"limit\":110,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "read_file",
"content": "{\"content\": \" 1|import json\\n 2|from pathlib import Path\\n 3|\\n 4|from autoshorts.asset_plan import build_asset_plan, write_asset_plans\\n 5|\\n 6|\\n 7|def _manifest(name: str = \\\"ai-004-stop-asking-ai-to-think-for-you\\\") -> dict:\\n 8| path = Path(\\\"data/render_manifests/testbatch_002_top3\\\") / f\\\"{name}.json\\\"\\n 9| return json.loads(path.read_text(encoding=\\\"utf-8\\\"))\\n 10|\\n 11|\\n 12|def test_build_asset_plan_replaces_static_cards_with_moving_asset_modes():\\n 13| manifest = _manifest()\\n 14|\\n 15| plan = build_asset_plan(manifest)\\n 16|\\n 17| assert plan[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n 18| assert plan[\\\"video\\\"][\\\"id\\\"] == \\\"ai-004\\\"\\n 19| assert len(plan[\\\"scenes\\\"]) == 5\\n 20| modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n 21| assert \\\"cinematic_ai_video\\\" in modes\\n 22| assert \\\"screen_recording_simulation\\\" in modes\\n 23| assert \\\"kinetic_text_overlay\\\" in modes\\n 24| assert \\\"static_card\\\" not in modes\\n 25| assert all(scene[\\\"motion_directive\\\"] for scene in plan[\\\"scenes\\\"])\\n 26| assert all(scene[\\\"overlay_text\\\"] for scene in plan[\\\"scenes\\\"])\\n 27|\\n 28|\\n 29|def test_cinematic_ai_video_scenes_have_provider_ready_prompts_without_baked_text():\\n 30| manifest = _manifest()\\n 31|\\n 32| plan = build_asset_plan(manifest)\\n 33|\\n 34| cinematic = [scene for scene in plan[\\\"scenes\\\"] if scene[\\\"asset_mode\\\"] == \\\"cinematic_ai_video\\\"]\\n 35| assert cinematic\\n 36| for scene in cinematic:\\n 37| prompt = scene[\\\"generation_prompt\\\"]\\n 38| assert scene[\\\"provider_family\\\"] == \\\"kling_hailuo_runway\\\"\\n 39| assert scene[\\\"no_text_in_video\\\"] is True\\n 40| assert \\\"photorealistic\\\" in prompt.lower()\\n 41| assert \\\"vertical 9:16\\\" in prompt.lower()\\n 42| assert \\\"no readable text\\\" in scene[\\\"negative_prompt\\\"].lower()\\n 43| assert scene[\\\"duration_seconds\\\"] >= 3\\n 44| assert scene[\\\"duration_seconds\\\"] <= 6\\n 45|\\n 46|\\n 47|def test_attention_design_asset_plan_uses_broll_and_screen_recording_not_text_slideshow():\\n 48| manifest = _manifest(\\\"attention-002-the-algorithm-does-not-need-you-happy\\\")\\n 49|\\n 50| plan = build_asset_plan(manifest)\\n 51|\\n 52| modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n 53| assert \\\"cinematic_ai_video\\\" in modes\\n 54| assert \\\"screen_recording_simulation\\\" in modes\\n 55| assert \\\"animated_card\\\" in modes\\n 56| assert modes.count(\\\"kinetic_text_overlay\\\") <= 2\\n 57| assert plan[\\\"final_render_strategy\\\"][\\\"static_card_limit\\\"] == 0\\n 58|\\n 59|\\n 60|def test_write_asset_plans_outputs_json_files(tmp_path):\\n 61| manifests = [_manifest(), _manifest(\\\"attention-002-the-algorithm-does-not-need-you-happy\\\")]\\n 62|\\n 63| paths = write_asset_plans(manifests, tmp_path)\\n 64|\\n 65| assert len(paths) == 2\\n 66| assert all(path.suffix == \\\".json\\\" for path in paths)\\n 67| payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n 68| assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n 69| assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\\n 70|\", \"total_lines\": 69, \"file_size\": 2677, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_lRcFVrwX5rvYYZ8ac8YzIawE"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_YQNOdauQK25461Nf2Cac3Gu4",
"call_id": "call_YQNOdauQK25461Nf2Cac3Gu4",
"response_item_id": "fc_02aa21bd75cf0633016a1172a6ecf0819185c04cd4e744219d",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\"from autoshorts.asset_plan import build_asset_plan, write_asset_plans\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.render_manifest import build_render_manifest\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\",\"old_string\":\"from autoshorts.asset_plan import build_asset_plan, write_asset_plans\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\\n@@ -2,6 +2,9 @@\\n from pathlib import Path\\n \\n from autoshorts.asset_plan import build_asset_plan, write_asset_plans\\n+from autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\n+from autoshorts.render_manifest import build_render_manifest\\n+from autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n \\n \\n def _manifest(name: str = \\\"ai-004-stop-asking-ai-to-think-for-you\\\") -> dict:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_YQNOdauQK25461Nf2Cac3Gu4"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_sCoKGux2VlMyFpEwh4GG0FbT",
"call_id": "call_sCoKGux2VlMyFpEwh4GG0FbT",
"response_item_id": "fc_02aa21bd75cf0633016a1172b148b481918ac390cc4ebb94f7",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\\n\\n\\ndef test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n manifest = build_render_manifest(build_mechanism_shot_plan(explainer))\\n\\n plan = build_asset_plan(manifest)\\n\\n modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n assert modes == [\\n \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\n assert plan[\\\"final_render_strategy\\\"][\\\"preferred_mix\\\"][\\\"mechanism_animation\\\"] == 2\\n mechanism_scene = plan[\\\"scenes\\\"][2]\\n assert mechanism_scene[\\\"provider_family\\\"] == \\\"local_renderer\\\"\\n assert \\\"Röntgen\\\" in mechanism_scene[\\\"generation_prompt\\\"] or \\\"x-ray\\\" in mechanism_scene[\\\"generation_prompt\\\"]\\n\",\"old_string\":\" assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\\n@@ -70,3 +70,23 @@\\n payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n assert payload[\\\"schema_version\\\"] == \\\"asset_plan.v2\\\"\\n assert payload[\\\"scenes\\\"][0][\\\"scene_id\\\"] == \\\"scene_01\\\"\\n+\\n+\\n+def test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes():\\n+ explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n+ manifest = build_render_manifest(build_mechanism_shot_plan(explainer))\\n+\\n+ plan = build_asset_plan(manifest)\\n+\\n+ modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n+ assert modes == [\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"cinematic_ai_video\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"mechanism_animation\\\",\\n+ \\\"kinetic_text_overlay\\\",\\n+ ]\\n+ assert plan[\\\"final_render_strategy\\\"][\\\"preferred_mix\\\"][\\\"mechanism_animation\\\"] == 2\\n+ mechanism_scene = plan[\\\"scenes\\\"][2]\\n+ assert mechanism_scene[\\\"provider_family\\\"] == \\\"local_renderer\\\"\\n+ assert \\\"Röntgen\\\" in mechanism_scene[\\\"generation_prompt\\\"] or \\\"x-ray\\\" in mechanism_scene[\\\"generation_prompt\\\"]\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_sCoKGux2VlMyFpEwh4GG0FbT"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_LfPn4O2I6vT2CiJu8SDOAp8d",
"call_id": "call_LfPn4O2I6vT2CiJu8SDOAp8d",
"response_item_id": "fc_02aa21bd75cf0633016a1172bc7b7c8191bdf9b5f018b156f3",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_asset_plan.py::test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"F [100%]\\n=================================== FAILURES ===================================\\n______ test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes _______\\n\\n def test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes():\\n explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n manifest = build_render_manifest(build_mechanism_shot_plan(explainer))\\n \\n plan = build_asset_plan(manifest)\\n \\n modes = [scene[\\\"asset_mode\\\"] for scene in plan[\\\"scenes\\\"]]\\n> assert modes == [\\n \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\nE AssertionError: assert ['cinematic_a...tic_ai_video'] == ['cinematic_a...text_overlay']\\nE \\nE At index 2 diff: 'cinematic_ai_video' != 'mechanism_animation'\\nE Use -v to get more diff\\n\\ntests/test_asset_plan.py:82: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_asset_plan.py::test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes\\n1 failed in 0.07s\", \"exit_code\": 1, \"error\": null}",
"tool_call_id": "call_LfPn4O2I6vT2CiJu8SDOAp8d"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Implementing asset plan mapping**\n\nI need to focus on the asset plan mapping, which is crucial. I should also ensure that the preferred mix includes fixed keys. This requires adding a mechanism to facilitate this process. I wonder what sort of generation prompt I should use to make sure everything flows smoothly. It's an interesting challenge, and I'm eager to get it right! Let’s make sure I consider all necessary components accurately.",
"finish_reason": "tool_calls",
"reasoning_content": "**Implementing asset plan mapping**\n\nI need to focus on the asset plan mapping, which is crucial. I should also ensure that the preferred mix includes fixed keys. This requires adding a mechanism to facilitate this process. I wonder what sort of generation prompt I should use to make sure everything flows smoothly. It's an interesting challenge, and I'm eager to get it right! Let’s make sure I consider all necessary components accurately.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXLD465KgaOv6CDy-tVa21D4_xYRJ1-XbuL-npM-Wgclm2ayyme5x-lPuvr9lZgNnvz49XySoZUsA4Po4BYK8LBvG3xhAMd5A9JADoyvFkiI_ye_soCSuq5xSEpJuGp5dVPpr5u2jV2-X1Mz0lIrReD1SBXVTxGZjrY5kcWH0KtGLZgkE896DBrb-U_ePuMOWDYUyh01aSlzBHdemS_6I4fB1HMYiKtV1mRM7cjzFCAhMRS5oEw2fqJJxxHsXogr0-3ifc5uP9poAW4ioIv0hWGxHHN8AR5gFmH_Ssyz2reBJl9dZS29SOzI4_VJbsMxiotXY440VNSAsOp9mZE22rpw61DS2fh61WdvttYnrD5qWHaiv51ZbzJF1yImI6od4N64R2VslzspuzxXhFUC9B7P1kBcJTGFAQiE2FtjDNwK6WS-c15TDLjL2D7aCt1Cloik5STL8rywSVaE8MvHqlpuWHzlZ_kqdfR3jhcicz8SMg7JBTV2mtkI87HwloV4n1292oKI9CD2ORKRTRJO4lbO_O8CJm-LyG200740TCFUjsTgALvpvTfqi7rJAJveLOAXoZALMLnQVZiFRjGJUlcoCZS5uKgcBoTQo7HORlbvl6q9hBCevFmRjOr2zZGlR24SEfgMUsn2xKbHVPbwMFezB2gbFNOxUfwZd3FzEDciHQ5yrsXt_A24K1Ik0nTPKEoT4BEFzRRh5xh9k22ZyBgIP2aHFfkMXPQ1_gIxTxeeR6pMlWg0gxAVZtnLEOdROGWpj4076MpS-VuC4zIcPmeU7HwW-FxoBhgaI5fUVBByy27pbc8RI-MQkk3YYgldAwMA41I44f8CHVQYjMEa8_tnxPGch2BKQB1nEdYlFp0z3geiwGPpdlu9Tr57kX5xN5UIciKbxW5nKMizljeJ8xX6h9LonM6IGrJV7JY9Gd6LROQAxdbP0T4Gz9w8brr93ue_j7Ts2PgsIwZK6wiuVUCMFakFm37YObekrAToUD1SmIV5nFM_dnr9wOzZTeTck-cQ8PGc2QgMV7ZJzr2vvgOZaC30ZJ3V1j064CBMAYRGYDY=",
"id": "rs_02aa21bd75cf0633016a1172c0b510819191242aa228d54068",
"summary": [
{
"type": "summary_text",
"text": "**Implementing asset plan mapping**\n\nI need to focus on the asset plan mapping, which is crucial. I should also ensure that the preferred mix includes fixed keys. This requires adding a mechanism to facilitate this process. I wonder what sort of generation prompt I should use to make sure everything flows smoothly. It's an interesting challenge, and I'm eager to get it right! Let’s make sure I consider all necessary components accurately."
}
]
}
],
"tool_calls": [
{
"id": "call_Ceo4Ud6xxCKyIPvtnGh5VL0w",
"call_id": "call_Ceo4Ud6xxCKyIPvtnGh5VL0w",
"response_item_id": "fc_02aa21bd75cf0633016a1172c3779081919f42057ebd9b990c",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\"SCENE_MODE_BY_TYPE = {\\n \\\"kinetic_text\\\": \\\"kinetic_text_overlay\\\",\\n \\\"screen_recording\\\": \\\"screen_recording_simulation\\\",\\n \\\"card_animation\\\": \\\"animated_card\\\",\\n \\\"stock_broll\\\": \\\"cinematic_ai_video\\\",\\n \\\"generated_background\\\": \\\"cinematic_ai_video\\\",\\n \\\"cinematic_ai_video\\\": \\\"cinematic_ai_video\\\",\\n \\\"mechanism_animation\\\": \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\": \\\"kinetic_text_overlay\\\",\\n}\\n\",\"old_string\":\"SCENE_MODE_BY_TYPE = {\\n \\\"kinetic_text\\\": \\\"kinetic_text_overlay\\\",\\n \\\"screen_recording\\\": \\\"screen_recording_simulation\\\",\\n \\\"card_animation\\\": \\\"animated_card\\\",\\n \\\"stock_broll\\\": \\\"cinematic_ai_video\\\",\\n \\\"generated_background\\\": \\\"cinematic_ai_video\\\",\\n}\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n@@ -20,6 +20,9 @@\\n \\\"card_animation\\\": \\\"animated_card\\\",\\n \\\"stock_broll\\\": \\\"cinematic_ai_video\\\",\\n \\\"generated_background\\\": \\\"cinematic_ai_video\\\",\\n+ \\\"cinematic_ai_video\\\": \\\"cinematic_ai_video\\\",\\n+ \\\"mechanism_animation\\\": \\\"mechanism_animation\\\",\\n+ \\\"kinetic_text_overlay\\\": \\\"kinetic_text_overlay\\\",\\n }\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_Ceo4Ud6xxCKyIPvtnGh5VL0w"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_GZx6wKrTnoaCKn6li7l1Yq7G",
"call_id": "call_GZx6wKrTnoaCKn6li7l1Yq7G",
"response_item_id": "fc_02aa21bd75cf0633016a1172c9cd5c8191a7a7cb02656f955b",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" if mode == \\\"animated_card\\\":\\n return (\\n \\\"Vertical animated explainer card sequence. Minimal dark UI, one idea per beat, animated boxes and arrows. \\\"\\n f\\\"Visual task: {item['visual_direction']} Keep it moving; no static slide holds longer than one second.\\\"\\n )\\n if mode == \\\"mechanism_animation\\\":\\n return (\\n \\\"Vertical 9:16 mechanism-reveal animation. Build a clear visual explanation with moving layers, x-ray/röntgen reveal, \\\"\\n \\\"arrows, meters, object cutaways, and sequential labels added by our renderer. \\\"\\n f\\\"Mechanism task: {item['visual_direction']} Motion: {item.get('visual_motion', _camera_motion(item['id'], mode))}. \\\"\\n \\\"No static holds longer than one second; keep all readable text as renderer overlays.\\\"\\n )\\n return (\\n\",\"old_string\":\" if mode == \\\"animated_card\\\":\\n return (\\n \\\"Vertical animated explainer card sequence. Minimal dark UI, one idea per beat, animated boxes and arrows. \\\"\\n f\\\"Visual task: {item['visual_direction']} Keep it moving; no static slide holds longer than one second.\\\"\\n )\\n return (\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n@@ -104,6 +104,13 @@\\n \\\"Vertical animated explainer card sequence. Minimal dark UI, one idea per beat, animated boxes and arrows. \\\"\\n f\\\"Visual task: {item['visual_direction']} Keep it moving; no static slide holds longer than one second.\\\"\\n )\\n+ if mode == \\\"mechanism_animation\\\":\\n+ return (\\n+ \\\"Vertical 9:16 mechanism-reveal animation. Build a clear visual explanation with moving layers, x-ray/röntgen reveal, \\\"\\n+ \\\"arrows, meters, object cutaways, and sequential labels added by our renderer. \\\"\\n+ f\\\"Mechanism task: {item['visual_direction']} Motion: {item.get('visual_motion', _camera_motion(item['id'], mode))}. \\\"\\n+ \\\"No static holds longer than one second; keep all readable text as renderer overlays.\\\"\\n+ )\\n return (\\n \\\"Kinetic text overlay scene. Use large caption words, fast emphasis, scale pops, and clean background motion. \\\"\\n f\\\"Overlay text: {text}\\\"\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_GZx6wKrTnoaCKn6li7l1Yq7G"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_Y1MF720Z6Vta4U68iCESBitP",
"call_id": "call_Y1MF720Z6Vta4U68iCESBitP",
"response_item_id": "fc_02aa21bd75cf0633016a1172d3dd9481918a5a74280eaabbdc",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" if mode == \\\"animated_card\\\":\\n return \\\"no dense paragraphs, no static PowerPoint slide, no tiny text, no chart junk\\\"\\n if mode == \\\"mechanism_animation\\\":\\n return \\\"no static diagram, no dense labels, no tiny text, no unreadable chart junk, no generic infographic\\\"\\n return \\\"no long paragraphs, no tiny footnotes, no static card\\\"\\n\",\"old_string\":\" if mode == \\\"animated_card\\\":\\n return \\\"no dense paragraphs, no static PowerPoint slide, no tiny text, no chart junk\\\"\\n return \\\"no long paragraphs, no tiny footnotes, no static card\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n@@ -127,6 +127,8 @@\\n return \\\"no private data, no real credentials, no illegible tiny UI, no brand logos unless approved\\\"\\n if mode == \\\"animated_card\\\":\\n return \\\"no dense paragraphs, no static PowerPoint slide, no tiny text, no chart junk\\\"\\n+ if mode == \\\"mechanism_animation\\\":\\n+ return \\\"no static diagram, no dense labels, no tiny text, no unreadable chart junk, no generic infographic\\\"\\n return \\\"no long paragraphs, no tiny footnotes, no static card\\\"\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_Y1MF720Z6Vta4U68iCESBitP"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_Rea1PHX20Dz7UsmwwxeF0K9w",
"call_id": "call_Rea1PHX20Dz7UsmwwxeF0K9w",
"response_item_id": "fc_02aa21bd75cf0633016a1172dc256c819192b425df62223b89",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"provider_family\\\": \\\"kling_hailuo_runway\\\" if mode == \\\"cinematic_ai_video\\\" else \\\"local_renderer\\\",\",\"old_string\":\" \\\"provider_family\\\": \\\"kling_hailuo_runway\\\" if mode == \\\"cinematic_ai_video\\\" else \\\"local_renderer\\\",\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": false, \"error\": \"old_string and new_string are identical\"}",
"tool_call_id": "call_Rea1PHX20Dz7UsmwwxeF0K9w"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXL7y9I120Zv-ckGvV9XxQD6Is3ndOFRp4zE5m0QIdatPth-6XQB0-BLRFP3NyddVpIOWew91-yOmFwjX0u99n_NPLED2sBTeAV60UKyoZ8KbHts-kZdUsm2UeGVki1SJoA7CZ-oPWGzkfkhL1JfvWTF8oNTG775rgBGxPtgW7hbywlrfFQLs7DV_V9GG6ZhchdacaHeZQBGEj71wz-wyigpaSQmxZrlveQu776O2Yv6PlD5QX_ow8Q12FPC-MSp9bAoCAnqFBHqIfqvJkrZ-xOdTyKAkmhF0ZVYUk-sG8FD6_e_9Xu_K7HYrBN9eebHxK7anKXY-oatxj6RtnMV5H9wNU2Oc0sovW7Xjiwu0aDYkIyemX8R5-5_SMJfG-xEWfJdbNZ_2yqzvUPr5WuqVcb1DuM_b3i0lZKcirIRxgYFCegLwdB64HP6hswHlYlHXlpYUTMDsQ9kZ_s0oww_Ijp-SOngHnc-XAUVnXKe5H7-pjXAO5AA6AcVPA0j2oORlZIPB90OL0-XmtHSwdU6zmZbAfXadoGRcCm9ko4bktOpztrJDzvr7G6UPRQk22RSyUCBxXGncsojQwFpGzxKWFUs14t_0e9KBkmWsPW8GXQj3zOdlmr8Z-8Y9b3PGzZhu0GTxkpXkuPhhm7EFWJiH3xbGNd4IW2T8U7jbZ4faEBvAAHtqLF4PRtWCJAVPVbBOYe8I-JguxbRy4yGCZvdzG5ffvzVmiu4hk7vjtJmOHkU7eRarqQ5XdalMF8BGgGVve2MymyTbwpo1IIOi8h4_JW7gxZ0PQ-buVZLjjbBqyUArLWE9yFNifJuf7pQZZ4qSwBnV6JH3vj_BFObBf7WQOluPirmVhj6Gw-2sTEGb8jDxJS1ztgwtH_Wmb4VhIUGS5bFbsC_U0hShyw_RDQNv6vwmzwzs_6DwJZXxdjYZE-_PNL89RsTAIeA_D6BJDOT02r4",
"id": "rs_02aa21bd75cf0633016a1172fb61b08191882d2a78c131ef30",
"summary": []
}
],
"tool_calls": [
{
"id": "call_je3e2k3F3IQxarGVLL6vhYWp",
"call_id": "call_je3e2k3F3IQxarGVLL6vhYWp",
"response_item_id": "fc_02aa21bd75cf0633016a1172fbe7448191b5fcc78e374af03c",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"preferred_mix\\\": {\\n \\\"cinematic_ai_video\\\": modes.count(\\\"cinematic_ai_video\\\"),\\n \\\"screen_recording_simulation\\\": modes.count(\\\"screen_recording_simulation\\\"),\\n \\\"animated_card\\\": modes.count(\\\"animated_card\\\"),\\n \\\"mechanism_animation\\\": modes.count(\\\"mechanism_animation\\\"),\\n \\\"kinetic_text_overlay\\\": modes.count(\\\"kinetic_text_overlay\\\"),\\n },\\n\",\"old_string\":\" \\\"preferred_mix\\\": {\\n \\\"cinematic_ai_video\\\": modes.count(\\\"cinematic_ai_video\\\"),\\n \\\"screen_recording_simulation\\\": modes.count(\\\"screen_recording_simulation\\\"),\\n \\\"animated_card\\\": modes.count(\\\"animated_card\\\"),\\n \\\"kinetic_text_overlay\\\": modes.count(\\\"kinetic_text_overlay\\\"),\\n },\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n@@ -168,6 +168,7 @@\\n \\\"cinematic_ai_video\\\": modes.count(\\\"cinematic_ai_video\\\"),\\n \\\"screen_recording_simulation\\\": modes.count(\\\"screen_recording_simulation\\\"),\\n \\\"animated_card\\\": modes.count(\\\"animated_card\\\"),\\n+ \\\"mechanism_animation\\\": modes.count(\\\"mechanism_animation\\\"),\\n \\\"kinetic_text_overlay\\\": modes.count(\\\"kinetic_text_overlay\\\"),\\n },\\n \\\"captions\\\": \\\"large kinetic overlays generated by renderer; no baked text in AI clips\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_je3e2k3F3IQxarGVLL6vhYWp"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_XYq0sftWLAE2hVtOIAYwFyNM",
"call_id": "call_XYq0sftWLAE2hVtOIAYwFyNM",
"response_item_id": "fc_02aa21bd75cf0633016a11734f58e08191a2863b1c5cacee2f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest tests/test_asset_plan.py::test_asset_plan_maps_mechanism_manifest_to_motion_explainer_modes -q tests/test_render_manifest.py::test_build_render_manifest_preserves_mechanism_motion_and_sound_cues -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \".. [100%]\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_XYq0sftWLAE2hVtOIAYwFyNM"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Considering asset generation**\n\nI need to create a command-line interface (CLI) that generates mechanisms to render manifests. It seems the scripts for asset plans could accept a directory, and the existing generate_asset_plans could work for any manifest directory. I'll have to write a script for this. Should it be production code without tests? Maybe that’s okay, but thinking about test-driven development (TDD) for the CLI isn’t strictly necessary—yet I’m still considering it as I implement this.",
"finish_reason": "tool_calls",
"reasoning_content": "**Considering asset generation**\n\nI need to create a command-line interface (CLI) that generates mechanisms to render manifests. It seems the scripts for asset plans could accept a directory, and the existing generate_asset_plans could work for any manifest directory. I'll have to write a script for this. Should it be production code without tests? Maybe that’s okay, but thinking about test-driven development (TDD) for the CLI isn’t strictly necessary—yet I’m still considering it as I implement this.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXNafiU-phEYKwS1sgYdspOVINg40Qu4QTlrMtJUgtk6o4bQ8sKahhbuvvVV_aWUd0BgNafiXaieoJlNMDkNcz1kF2rXj1Mx7ukE1r7tTGO9VMIla4wjMC5Q8mmx4u5IqYQW1UH7bfChsSw9zsUWRv2f_P0d2RWHt_d8ciZvbrx8wwu3xNNHt4sP1chO0lQ15knulAqcM4fLrikHD4xgqPaxUD0wr41OHdu7RoCgK_B731I3Gm57Jkz3DMg4lYSvhU78bEdCPeyP7RaqVQ6JGqhoduDKoHUpTH7GX4rYgdqnkMx_EWocPH0lR2G3WHEMGgUdGRY_CxaIDef3mxI_gT4yOgqTWSk9Dp1lgqLmmUA39BxkZ6sF7U-_xyJaJ7112BC9yjyaWBl5XDZyFa734hwzwpbtxNsYJnLpOR8k4UHjaOxD6B7pgepyILzOBwImlWwIHMTRqd-5_i8ldtIIAbDMtekEKbeRnNdMA16p5ZfY-GrI5aJibtdmLGfe7D0nZMJHE2Qmuv8dsBk3xHXW5gcOkWYJAlNHIEys4--pdAeFZxFcBd_oZ363-mg_0FOdEtMLEH5Rc3RnG2a6R5rPLcA3XuPJnjrvjtPReenfdOuY4NwloWeisD9PiDtGyCJR8Ts5mahSoztwk0Yhfz4466eJ5VHGbkxumku7WhkOsVqWKiEcxL-RjlxWkFws45Az-y37w67afznVCDUkeGXcQ-zwRNe6D7Zwz3uBMoLhClMzgzetgA_zmNmbRrL2O0_WL_1qO9H0EaxdVVzNnYOiXEE93MZSVpSDgqLhVCRFVCN1DX7ZmSI21l7HLxxgxrGOmCW1aL9lKif3PdpZKlQva8Q1VyErSSoOyolK7Ns-23Vix-0sMJGesM-ApTE3B3rQm5Ps1sz-n03a7L8ja6Qtvp5xKokTMXyteUr6fq1eAWFgpTgI9H2z9SRLog7VZgAC13v1aGI-9XY3JWMA2Zy62DuJkB2ucAs7kCa0YFBG0oAZT8UJZ4BNyxn2eK5uMEfnJYfNKellGDRzZ5bhseH0Z1foFZcFt3ZEWBmj2edRJy2GYxqSII_i58hZgaI9l4O3j4uDN2JoktYu2jorTnEE8IirX5i6GOHvscSzJ_ImRIytengMPxGMnapKZ-9WthvKljnLxDa-k394o1yy1loY-_oSv88R5-8OkvwTv_TUErmM0UQqPcrae29LFUXi21RYypnhDYO2EEM-rzAvr4d9O-v5Q7L8W1d1Axj2Q0YFOp0_0iVlbMj91ZZjWzAkcOG5ntWI",
"id": "rs_02aa21bd75cf0633016a117357aa188191960b4b7954cfdaf0",
"summary": [
{
"type": "summary_text",
"text": "**Considering asset generation**\n\nI need to create a command-line interface (CLI) that generates mechanisms to render manifests. It seems the scripts for asset plans could accept a directory, and the existing generate_asset_plans could work for any manifest directory. I'll have to write a script for this. Should it be production code without tests? Maybe that’s okay, but thinking about test-driven development (TDD) for the CLI isn’t strictly necessary—yet I’m still considering it as I implement this."
}
]
}
],
"tool_calls": [
{
"id": "call_lBZBJ0l9ImaOsvHmX6KWegb4",
"call_id": "call_lBZBJ0l9ImaOsvHmX6KWegb4",
"response_item_id": "fc_02aa21bd75cf0633016a11735a33f48191a79e39c0b56ad959",
"type": "function",
"function": {
"name": "write_file",
"arguments": "{\"content\":\"\\\"\\\"\\\"Generate renderer manifests from Retention Studio mechanism explainers.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.render_manifest import write_render_manifests\\n\\n\\ndef main() -> None:\\n parser = argparse.ArgumentParser(description=\\\"Generate mechanism render manifests.\\\")\\n parser.add_argument(\\\"--explainers\\\", default=\\\"data/retention_studio/mechanism_explainers.json\\\")\\n parser.add_argument(\\\"--out\\\", default=\\\"data/render_manifests/mechanism_pilots\\\")\\n parser.add_argument(\\\"--title-contains\\\", default=None)\\n args = parser.parse_args()\\n\\n explainers = json.loads(Path(args.explainers).read_text(encoding=\\\"utf-8\\\"))\\n if args.title_contains:\\n needle = args.title_contains.lower()\\n explainers = [item for item in explainers if needle in item[\\\"title\\\"].lower()]\\n\\n plans = [build_mechanism_shot_plan(explainer) for explainer in explainers]\\n paths = write_render_manifests(plans, Path(args.out))\\n print(f\\\"Generated {len(paths)} mechanism render manifests in {args.out}\\\")\\n for path in paths:\\n print(path)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_render_manifests.py\"}"
}
}
]
},
{
"role": "tool",
"name": "write_file",
"content": "{\"bytes_written\": 1362, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_lBZBJ0l9ImaOsvHmX6KWegb4"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_DI354Q3x8qr5SIqGIWYyCbyW",
"call_id": "call_DI354Q3x8qr5SIqGIWYyCbyW",
"response_item_id": "fc_02aa21bd75cf0633016a1173670bb48191abf603449f0d69f4",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Mechanism-Render-Manifest-Adapter + CLI implementieren\",\"id\":\"3\",\"status\":\"completed\"},{\"content\":\"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\",\"id\":\"4\",\"status\":\"in_progress\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"in_progress\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 1, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}",
"tool_call_id": "call_DI354Q3x8qr5SIqGIWYyCbyW"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_giDPYoaae9e02A5HbYeIVd21",
"call_id": "call_giDPYoaae9e02A5HbYeIVd21",
"response_item_id": "fc_02aa21bd75cf0633016a11736bd8b481918a70eb58efe43588",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"rm -rf data/render_manifests/mechanism_pilots data/asset_plans/mechanism_pilots\\npython3 scripts/generate_mechanism_render_manifests.py --title-contains 'billiges Auto' --out data/render_manifests/mechanism_pilots\\npython3 scripts/generate_asset_plans.py --manifest-dir data/render_manifests/mechanism_pilots --out data/asset_plans/mechanism_pilots\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nmanifest_path=next(Path('data/render_manifests/mechanism_pilots').glob('*.json'))\\nasset_path=next(Path('data/asset_plans/mechanism_pilots').glob('*.json'))\\nmanifest=json.loads(manifest_path.read_text())\\nasset=json.loads(asset_path.read_text())\\nprint('manifest', manifest_path)\\nprint('video_id', manifest['video']['id'])\\nprint('duration_ms', manifest['video']['duration_ms'])\\nprint('visual_types', [i['type'] for i in manifest['timeline'] if i['track']=='visual'])\\nprint('constraints', manifest['render_constraints'])\\nprint('asset_plan', asset_path)\\nprint('asset_modes', [s['asset_mode'] for s in asset['scenes']])\\nprint('preferred_mix', asset['final_render_strategy']['preferred_mix'])\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"Generated 1 mechanism render manifests in data/render_manifests/mechanism_pilots\\ndata/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json\\nGenerated 1 asset plans in data/asset_plans/mechanism_pilots\\ndata/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json\\nmanifest data/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json\\nvideo_id hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\nduration_ms 34000\\nvisual_types ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\\nconstraints {'avoid_static_slideshow': True, 'pattern_interrupt_every_seconds': 3, 'visual_change_every_seconds': 2, 'first_frame_hook_required': True, 'max_words_per_visual_card': 18, 'max_static_cards': 0, 'human_approval_required_before_publish': True}\\nasset_plan data/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json\\nasset_modes ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\\npreferred_mix {'cinematic_ai_video': 2, 'screen_recording_simulation': 0, 'animated_card': 0, 'mechanism_animation': 2, 'kinetic_text_overlay': 1}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_giDPYoaae9e02A5HbYeIVd21"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Evaluating filename duplication**\n\nI noticed a filename duplication issue due to `write_render_manifests`. The existing ID contains the title, which seems unnecessary. I could improve this by adjusting the `write_render_manifests` function to avoid duplicating the title slug in the ID. While checking the existing tests, it seems they don't verify names, which is fine, but the generated filenames are unwieldy. I might consider adding an assertion to a new test to ensure clarity, even if it feels like overkill for this minor improvement.",
"finish_reason": "tool_calls",
"reasoning_content": "**Evaluating filename duplication**\n\nI noticed a filename duplication issue due to `write_render_manifests`. The existing ID contains the title, which seems unnecessary. I could improve this by adjusting the `write_render_manifests` function to avoid duplicating the title slug in the ID. While checking the existing tests, it seems they don't verify names, which is fine, but the generated filenames are unwieldy. I might consider adding an assertion to a new test to ensure clarity, even if it feels like overkill for this minor improvement.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXN7WRlfu8kgaDSCi0nftfqPYGd6k77riuQfzW3eHQ4DCYfrEh_aDEN6oJA7sDzY4MmgtDLAZXUr_EA7Z6m2pf-fvth7U3IiKjetmpI5r4Ql8Y31W2U-nZ2QM__mAdD890jfZPkJQKXpBFfm_7h-UjCsSRiDAUzZvQSI-31rIjQ2qLpsjDZuV0ZIIXnTmtZ8FUTtgyf4t-aRs7mZE0i0NZcifoLhnB7FpEg29Pi8yzpuQx8Vp2qIEL2Fy_jLcTg9vPw2wDnV2jziugrA57PLbdYvDsc79TcqeMa4ycMpR0nCFwDPi-hHSk4qYZuycJlKg4v1f8NkSz5R6o75vL2QXrOX62kzuU8qm-QTrhqHR6c-GA7o6ublc2Bxzr8KT7vH5JB5EsJzpVP6HQq4d9rTZdVii9V5WWF_GKIyYbyxSjuMHlGEAOvq4rVtLKPMBvvYPCF8UZXwxneS-1JOdQ9uvAje5SIDYtnnoIw85_dCz-GIdZBA5zeyeeoF18QPKzaYO_0kqCbRW1hj9AaSiF0PuHxuLzMPjw-bRf0lQUW3ixBmUCZ6cDr6WvwpXNw9ciGn27umCHE6eQLdHLPWVOAx0cMXm4udjuD0QeVYQHpbpeeKOUrL6NgavPcGPW0PjZ-8UspcGIOMhz-0GRrQzRRx5Q9Pp-KRXRjJl1edgaRct36xULAhKSseJNsrtIE-7HUsQgGN5K3lJI29wDDzolsbu5zSOv3aCt7hBeRjWBilS-knM8biXXuVTrux1CjQLL4zfKKUcen4sZQNskioRpGI-r1Oxor3AlgC_2rF1kUQiQMR6xBPzJLFvoFKnaKZLQGH3EAFDqw1yn2u-EYwP_SorJ4IuDk8gSQbfnfM-Gmuv7ABQwCbUYcMFwdfDCIPcVC7nxiUCkm_cCXZKjMY3JMcc3cQyiUzmuzKuMzmz__OCOqzzPoWcknBZm76nDvP4_Q7a_XiwkZdpY-DoPPzgG74Eh0GlEyDBiR0XRrCvHGgsowpszm8NWvOCmKDVaER9tpHvScOgQtzF3qUVH4NziUPZbbohTDQk45Egbcx-924aJvBD_AYT_TVXMFi9-z5uDtH_SlsYN3itXrTgjPUTSzK_oY91_DwXc_t-LqfkivmTdsvgpCWj4s9MisRsjGZGZxGdb5BgD3H4bG0gt2p4AOHVVl33hyGEV2zjSaZgzx9ryR4PYPyp2-Y5lg5wrYg2j9WomZ-rwBH6CIE7w0mcvUMsbHARRNVU58g3LBscIRAKA4EGBmgeoPSsylqhO7W6asnXJ4aPYzdpVpyM6HXLxQ_bcbXkUSsxCp3F2l-v3RvZHzhbtLSWaf6_hyoyjzpmo3mpxVAyZKO9kmV1LaNHYuC3z6ZCk45b0WgYSDi7wQ9KE4N0M4aAL0NLiIdagfDVRQuy4b2miK9TrvL7T9OERWnA0ZfzSbByj08KVwG7-RlNk04up2lSxx6ntr1A2ObgsC0vZiKIbuDuZj3VNTKPxtVyCcW-pcRFmzNETvaLTudqHnBI0MzhUS85Nh74CfAzwj4GRYFcnP3SSLu_VJvd7qI-nEEBvDVJPszBgTxSoqwNDpVSn96jNm4WtnnIbRnF_EKFkOh1K_Rw_7kHQmDYhcBgsGrsqMTPkcW-LFJQItFXti4G7iGOXsn6UBjYxN6xneka6kTOoUe4u_R8ypjt8fGVTQnXT-tiTkuYDtqvgYqNyReM0SUFQC5wz6RHBtYlZZdZmKUPXgGp-T7IYtcSyuXHjYsZxuAeD_AHsW7hwdSq8sSGI3EpFEpcgyYv63ckL62w6pmya4bqG5WSMlAe5ao1xMHRw==",
"id": "rs_02aa21bd75cf0633016a11737738188191a877f43c4d552df8",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating filename duplication**\n\nI noticed a filename duplication issue due to `write_render_manifests`. The existing ID contains the title, which seems unnecessary. I could improve this by adjusting the `write_render_manifests` function to avoid duplicating the title slug in the ID. While checking the existing tests, it seems they don't verify names, which is fine, but the generated filenames are unwieldy. I might consider adding an assertion to a new test to ensure clarity, even if it feels like overkill for this minor improvement."
}
]
}
],
"tool_calls": [
{
"id": "call_4wg4BeFOgFcXCoANUjMYLikF",
"call_id": "call_4wg4BeFOgFcXCoANUjMYLikF",
"response_item_id": "fc_02aa21bd75cf0633016a11737b6f548191999acd9e247f9720",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\"def _manifest_filename(plan: dict[str, Any]) -> str:\\n title_slug = _slug(plan[\\\"title\\\"])\\n video_id = plan[\\\"id\\\"]\\n if video_id.endswith(title_slug):\\n return f\\\"{video_id}.json\\\"\\n return f\\\"{video_id}-{title_slug}.json\\\"\\n\\n\\ndef write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for plan in plans:\\n manifest = build_render_manifest(plan)\\n path = output_dir / _manifest_filename(plan)\\n path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"old_string\":\"def write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for plan in plans:\\n manifest = build_render_manifest(plan)\\n path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.json\\\"\\n path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\\n@@ -155,13 +155,21 @@\\n }\\n \\n \\n+def _manifest_filename(plan: dict[str, Any]) -> str:\\n+ title_slug = _slug(plan[\\\"title\\\"])\\n+ video_id = plan[\\\"id\\\"]\\n+ if video_id.endswith(title_slug):\\n+ return f\\\"{video_id}.json\\\"\\n+ return f\\\"{video_id}-{title_slug}.json\\\"\\n+\\n+\\n def write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for plan in plans:\\n manifest = build_render_manifest(plan)\\n- path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.json\\\"\\n+ path = output_dir / _manifest_filename(plan)\\n path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_4wg4BeFOgFcXCoANUjMYLikF"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_dBWinlTVETGz78O8uxgQLSPt",
"call_id": "call_dBWinlTVETGz78O8uxgQLSPt",
"response_item_id": "fc_02aa21bd75cf0633016a11738839048191b7c4e3bde6f89f5f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"rm -rf data/render_manifests/mechanism_pilots data/asset_plans/mechanism_pilots\\npython3 scripts/generate_mechanism_render_manifests.py --title-contains 'billiges Auto' --out data/render_manifests/mechanism_pilots\\npython3 scripts/generate_asset_plans.py --manifest-dir data/render_manifests/mechanism_pilots --out data/asset_plans/mechanism_pilots\\npython3 - <<'PY'\\nfrom pathlib import Path\\nprint([p.name for p in Path('data/render_manifests/mechanism_pilots').glob('*.json')])\\nprint([p.name for p in Path('data/asset_plans/mechanism_pilots').glob('*.json')])\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"Generated 1 mechanism render manifests in data/render_manifests/mechanism_pilots\\ndata/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\nGenerated 1 asset plans in data/asset_plans/mechanism_pilots\\ndata/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json\\n['hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json']\\n['hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann-warum-ein-billiges-auto-teuer-sein-kann.json']\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_dBWinlTVETGz78O8uxgQLSPt"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXOPNfkpZlBXVvqHCqF1C0O5k4s1xJ9KOy9j-8XBniGkImiP5UApOX8uj9mpa_GnhE2y5qhex3388qNQbFA_3wMzKNASQg0k9xCJ2VFqY3AcJHQ_dJ6e10NEvskilqkRfsad9wFhya-WIW7eMxN2FQkW13FUOJin6Jo0_QxeABvAnWK_aXPkWvw_qxvEWmLY9jWQxXC8aShpJAW9wRIrCI0Nt5uPfGvAJwS_5NbLIxI_jWJRLhWRNuJYii1iNU9re9TDfYganptfsx4TIDUfO35gvugcyp4DkQTZmvAgYK-dsxMZuUGKuvEfQb3cGkRDZrmTEqMi7QF7jNGcoOd9fMz3uuz-8vCWJOmMMDjpGYvRAwZIAzWHQ8sfgUk2Lqf2Js2hQkZBYdZDUNXEUlztapPKCXgc_J2aA6u5Gsv8KKjNhJ319arnWXLakLUhcsysIueZcby20VNlLkYYAL7vIxIQBZcSYq3e7H9qna85fdpp-Z9PJdtKIgIu_qQoIo8doCFNe5c0ZINcKOi3kwn5QAswR0jtD_0STGMnKGCAe7yUqpCex-eQcUJQhwlwfEa0lgJ_zqz3CFH7vav7J2xuQCk0wnpsl_P1h1hX-N6FnVLr_hsnDpjR0w6aO2wnum0IqwW1U6O9TTdkNKvOnvdA0x_jCzYg3hfwzbX_JW5n1mKg0KqEo8OA_0PTjM42xXbmw0VhE_9C6oOjnPQkap-_yoSwhpkDqVabbNfMoqeTQl5yqi0r0VmZwB7rD6oMgjSSFORgO8ofGbIzk8g26eFoBKn82OBoOR4FFJK5oggSuaia8d_kdWIl1JNSCDvtO1AYQ8NBIguqUFSDGVfB-LBdDd9SpbqDAyL4ZKeWSzSJc75qe12h9e1Wi7c8DIgU3QmlXZ0NPRpJTEhUJxcdYaPuB-TmzBRWD8LVNO7W1dxHvfIZiQAy2Eb4jXvtwoMrkUqZOAMg1Fi1nWh-BbhlSwmHYzSkBQ==",
"id": "rs_02aa21bd75cf0633016a11738e5e6c81918888c94cc7b645e8",
"summary": []
}
],
"tool_calls": [
{
"id": "call_Dc3dNI1jaafYbWg4CVV6Wyr0",
"call_id": "call_Dc3dNI1jaafYbWg4CVV6Wyr0",
"response_item_id": "fc_02aa21bd75cf0633016a11738fbf3c81919861976ff640f67d",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"new_string\":\"def _asset_plan_filename(video: dict[str, Any]) -> str:\\n title_slug = _slug(video[\\\"title\\\"])\\n video_id = video[\\\"id\\\"]\\n if video_id.endswith(title_slug):\\n return f\\\"{video_id}.json\\\"\\n return f\\\"{video_id}-{title_slug}.json\\\"\\n\\n\\ndef write_asset_plans(manifests: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write asset plans as JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for manifest in manifests:\\n plan = build_asset_plan(manifest)\\n path = output_dir / _asset_plan_filename(plan[\\\"video\\\"])\\n path.write_text(json.dumps(plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"old_string\":\"def write_asset_plans(manifests: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write asset plans as JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for manifest in manifests:\\n plan = build_asset_plan(manifest)\\n path = output_dir / f\\\"{plan['video']['id']}-{_slug(plan['video']['title'])}.json\\\"\\n path.write_text(json.dumps(plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"}"
}
}
]
},
{
"role": "tool",
"name": "patch",
"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\\n@@ -177,13 +177,21 @@\\n }\\n \\n \\n+def _asset_plan_filename(video: dict[str, Any]) -> str:\\n+ title_slug = _slug(video[\\\"title\\\"])\\n+ video_id = video[\\\"id\\\"]\\n+ if video_id.endswith(title_slug):\\n+ return f\\\"{video_id}.json\\\"\\n+ return f\\\"{video_id}-{title_slug}.json\\\"\\n+\\n+\\n def write_asset_plans(manifests: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write asset plans as JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for manifest in manifests:\\n plan = build_asset_plan(manifest)\\n- path = output_dir / f\\\"{plan['video']['id']}-{_slug(plan['video']['title'])}.json\\\"\\n+ path = output_dir / _asset_plan_filename(plan[\\\"video\\\"])\\n path.write_text(json.dumps(plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/asset_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_Dc3dNI1jaafYbWg4CVV6Wyr0"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_egdcVl0aBFXf1m78CFjntJyS",
"call_id": "call_egdcVl0aBFXf1m78CFjntJyS",
"response_item_id": "fc_02aa21bd75cf0633016a11739aa99081919992a99e41c99167",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"rm -rf data/render_manifests/mechanism_pilots data/asset_plans/mechanism_pilots\\npython3 scripts/generate_mechanism_render_manifests.py --title-contains 'billiges Auto' --out data/render_manifests/mechanism_pilots\\npython3 scripts/generate_asset_plans.py --manifest-dir data/render_manifests/mechanism_pilots --out data/asset_plans/mechanism_pilots\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nmanifest_path=next(Path('data/render_manifests/mechanism_pilots').glob('*.json'))\\nasset_path=next(Path('data/asset_plans/mechanism_pilots').glob('*.json'))\\nmanifest=json.loads(manifest_path.read_text())\\nasset=json.loads(asset_path.read_text())\\nprint('manifest', manifest_path.name)\\nprint('asset_plan', asset_path.name)\\nprint('visual_types', [i['type'] for i in manifest['timeline'] if i['track']=='visual'])\\nprint('start_ms', [i['start_ms'] for i in manifest['timeline'] if i['track']=='visual'])\\nprint('asset_modes', [s['asset_mode'] for s in asset['scenes']])\\nprint('preferred_mix', asset['final_render_strategy']['preferred_mix'])\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"Generated 1 mechanism render manifests in data/render_manifests/mechanism_pilots\\ndata/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\nGenerated 1 asset plans in data/asset_plans/mechanism_pilots\\ndata/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\nmanifest hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\nasset_plan hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\nvisual_types ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\\nstart_ms [0, 1400, 5000, 18000, 28000]\\nasset_modes ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\\npreferred_mix {'cinematic_ai_video': 2, 'screen_recording_simulation': 0, 'animated_card': 0, 'mechanism_animation': 2, 'kinetic_text_overlay': 1}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_egdcVl0aBFXf1m78CFjntJyS"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_8UJ4fAWcMhGD39ffqZARokRa",
"call_id": "call_8UJ4fAWcMhGD39ffqZARokRa",
"response_item_id": "fc_02aa21bd75cf0633016a1173a36eb48191becdee5026d11249",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\",\"id\":\"4\",\"status\":\"completed\"},{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"in_progress\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}",
"tool_call_id": "call_8UJ4fAWcMhGD39ffqZARokRa"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_drSFEHSRprJhUOamPWEtTyLY",
"call_id": "call_drSFEHSRprJhUOamPWEtTyLY",
"response_item_id": "fc_02aa21bd75cf0633016a1173a8fd8881919aeb71fb16bf8292",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python3 -m pytest -q\\ngit status --short\\ngit diff --stat\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \".......................................... [100%]\\n42 passed in 0.98s\\n M autoshorts/asset_plan.py\\n M autoshorts/render_manifest.py\\n M tests/test_asset_plan.py\\n M tests/test_render_manifest.py\\n?? autoshorts/review_package.py\\n?? data/asset_plans/mechanism_pilots/\\n?? data/render_manifests/mechanism_pilots/\\n?? data/telegram_reviews/\\n?? scripts/generate_mechanism_render_manifests.py\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n autoshorts/asset_plan.py | 23 ++++++++++++++++++++++-\\n autoshorts/render_manifest.py | 37 +++++++++++++++++++++++++++++++------\\n tests/test_asset_plan.py | 23 +++++++++++++++++++++++\\n tests/test_render_manifest.py | 25 +++++++++++++++++++++++++\\n 4 files changed, 101 insertions(+), 7 deletions(-)\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_drSFEHSRprJhUOamPWEtTyLY"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Considering skill updates**\n\nI think I need to consider updating my skills. If this lesson is about preserving motion or sound, using the skill management patch could be the way to go. I’ll focus on committing my changes while ignoring any untracked reviews. It feels like a straightforward approach, but I want to make sure I’m not missing anything important in the process. Maybe I should double-check the specifics before going ahead!",
"finish_reason": "tool_calls",
"reasoning_content": "**Considering skill updates**\n\nI think I need to consider updating my skills. If this lesson is about preserving motion or sound, using the skill management patch could be the way to go. I’ll focus on committing my changes while ignoring any untracked reviews. It feels like a straightforward approach, but I want to make sure I’m not missing anything important in the process. Maybe I should double-check the specifics before going ahead!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXOvMXaqk8LlNT6GmbtqF1bsNMIZwKLDIg6H7HwUcODtBBAsX3YlPbDKVXK2FygufSRYNAQWyYYvJhgIOqWO0sOCTckSaRmc2bbwGw2XfuQZmILbgpj4QryO-DCb9jg3m-4R9C_0-egPXNVDkzbyQcfZCVk_ig2sVx7pMeBObdZ80y8ztBmhC1exoThhKHdvV5-KeGuDqv0XSKSmCqWOuPEDfw5bsUQQ7gmRiSdGID6pdYWD_9Y-EFbPOHew_Qq0A79HGsp2nZtu8knswmsiY2q_8AHyxMllo4Ec_vz3bEg1Y8I01cdBWFs38YEFlQP3TOlyU5tsLzsxcR4FaTCndGiO2gBP1kjODsiMgTwS9k1Zehm3rP6Gqx7N-ZOBuWVTAlLVWEzEXFsN3gW6JafxgpRIXN370MXAeDLBzyMjH7jt-dlvEKPamVYu_Mb1IzanLigHW51333EURkqcu8FqTja7oYtxX964e_PSbzqpmYbMUiDY0-3lFouu7dTP9GNVaqO95kqj3Xqp_nrjbQxFLccUx4IZS_jAAMraeO77aT5MHf9WqRCNama6RF0tEK7McahOmMW5_nJY532VXRFtiGdhcuYsBotmh54KbXwA1NchpYITsViprXycFCJh1GFy3Rc6i_7z2Jp58HjgL1EdPxiaN8xC9NRxxaxwiu1heQihX7ghC-7D5XlGBCGSaw0BAf8sYWFnktOJHekfZuf7qn9cp9mM3SdkwKeeTWLPR6L2_N19Qrs98iU0Q4i-smhBHzda5s5HhkhwU2DFllm9jW05HfaLPQaP4fOG436D1JeAHrq4QhjysFKcrfKpiVS8YgBMoL_SDg4GoBqlT03JjurDyLmlwVm_vi_mleNQkSZxJ7NssgaBtWwZiFrQySE-rqtWd8ihGdoS2ioKPXhePsNTOLsd3-DZuqZxNMmM9PymqUf0FXqcOb_95gVISv8uGWdNGBUHKj_EjtMujhPppS4IAlpfR9OAwf7cxkUJZBlOrHE2Smib41cRE0yvggFftBKhhe_rNfxMTz8-BgPd0zPMYC10-2R95erx8BbZpd5zp5u_BAxnwTyu9GrityUe05j-weY-bfk-7hUrRynClFGiozzND0OzFCnRVgXPzlBYik8=",
"id": "rs_02aa21bd75cf0633016a1173ae39088191b5b4e3c81a1bfd23",
"summary": [
{
"type": "summary_text",
"text": "**Considering skill updates**\n\nI think I need to consider updating my skills. If this lesson is about preserving motion or sound, using the skill management patch could be the way to go. I’ll focus on committing my changes while ignoring any untracked reviews. It feels like a straightforward approach, but I want to make sure I’m not missing anything important in the process. Maybe I should double-check the specifics before going ahead!"
}
]
}
],
"tool_calls": [
{
"id": "call_fypQ5DP0odSDzeoRaYggB1Bh",
"call_id": "call_fypQ5DP0odSDzeoRaYggB1Bh",
"response_item_id": "fc_02aa21bd75cf0633016a1173aff1a08191bbe34daf2f20b29a",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"file_path\":\"references/retention-studio-strategy.md\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"Mechanism explainers should be adapted into production shot plans without losing beat timing, visual motion, sound cues, or the no-static-cards rule. The shot plan is where `pattern_interrupt`, `problem`, `mechanism_reveal`, `twist`, and `follow_trigger` become renderer-facing scenes.\",\"new_string\":\"Mechanism explainers should be adapted into production shot plans without losing beat timing, visual motion, sound cues, or the no-static-cards rule. The shot plan is where `pattern_interrupt`, `problem`, `mechanism_reveal`, `twist`, and `follow_trigger` become renderer-facing scenes. When converting those shot plans to render manifests, preserve `visual_motion` and `sound_cue` on timeline items; these are not decorative fields, they are the retention/editing contract for the final renderer.\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"Patched references/retention-studio-strategy.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_fypQ5DP0odSDzeoRaYggB1Bh"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_LAao6i9nHqHGOeDJIZAnFEco",
"call_id": "call_LAao6i9nHqHGOeDJIZAnFEco",
"response_item_id": "fc_02aa21bd75cf0633016a1173b686b88191b0f1772445d44829",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\ngit add autoshorts/asset_plan.py autoshorts/render_manifest.py scripts/generate_mechanism_render_manifests.py tests/test_asset_plan.py tests/test_render_manifest.py data/render_manifests/mechanism_pilots data/asset_plans/mechanism_pilots\\ngit commit -m \\\"feat: generate mechanism render manifests\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"[feat/initial-growth-engine 4d1e8f8] feat: generate mechanism render manifests\\n 7 files changed, 500 insertions(+), 7 deletions(-)\\n create mode 100644 data/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\n create mode 100644 data/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\n create mode 100644 scripts/generate_mechanism_render_manifests.py\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_LAao6i9nHqHGOeDJIZAnFEco"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_Ywq7KzMWFXXYzNV0rUcgvFRA",
"call_id": "call_Ywq7KzMWFXXYzNV0rUcgvFRA",
"response_item_id": "fc_02aa21bd75cf0633016a1173bdb35881918bb6462c83ff73f4",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -euo pipefail\\nexport GITHUB_TOKEN=$(tr -d '\\\\n\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push >/tmp/autoshorts_push12.log 2>&1 || { sed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push12.log; exit 1; }\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\nprintf 'branch=%s\\\\nlocal=%s\\\\nremote=%s\\\\n' \\\"$BRANCH\\\" \\\"$LOCAL\\\" \\\"$REMOTE\\\"\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\nsed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push12.log\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"branch=feat/initial-growth-engine\\nlocal=4d1e8f8f24683aaf3943103bee24ee316d9bc281\\nremote=4d1e8f8f24683aaf3943103bee24ee316d9bc281\\nTo https://github.com/Gamexgit/AutoShortsBot.git\\n 5644c8b..4d1e8f8 feat/initial-growth-engine -> feat/initial-growth-engine\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_Ywq7KzMWFXXYzNV0rUcgvFRA"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_6uOYzykbc5fg8uVHiYnIpw9F",
"call_id": "call_6uOYzykbc5fg8uVHiYnIpw9F",
"response_item_id": "fc_02aa21bd75cf0633016a1173c920588191951467e3c6ea99e8",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"git status --short --branch && python3 -m pytest -q\\npython3 - <<'PY'\\nimport json\\nfrom pathlib import Path\\nm=Path('data/render_manifests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json')\\na=Path('data/asset_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json')\\nmanifest=json.loads(m.read_text())\\nasset=json.loads(a.read_text())\\nprint('manifest_exists', m.exists())\\nprint('asset_plan_exists', a.exists())\\nprint('visual_types', [i['type'] for i in manifest['timeline'] if i['track']=='visual'])\\nprint('asset_modes', [s['asset_mode'] for s in asset['scenes']])\\nPY\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\n?? autoshorts/review_package.py\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\n.......................................... [100%]\\n42 passed in 0.99s\\nmanifest_exists True\\nasset_plan_exists True\\nvisual_types ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\\nasset_modes ['cinematic_ai_video', 'cinematic_ai_video', 'mechanism_animation', 'mechanism_animation', 'kinetic_text_overlay']\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_6uOYzykbc5fg8uVHiYnIpw9F"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_ZPT1RjLnnHboYtn3r1wXJd84",
"call_id": "call_ZPT1RjLnnHboYtn3r1wXJd84",
"response_item_id": "fc_02aa21bd75cf0633016a1173d107e481918380f24b0c989b37",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests, Commit, Push und Remote-Verifikation\",\"id\":\"5\",\"status\":\"completed\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"1\", \"content\": \"Repo-Status und bestehende Render-/Asset-Manifest-Struktur prüfen\", \"status\": \"completed\"}, {\"id\": \"2\", \"content\": \"TDD: Mechanism-Shot-Plan→Render-Manifest-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"3\", \"content\": \"Mechanism-Render-Manifest-Adapter + CLI implementieren\", \"status\": \"completed\"}, {\"id\": \"4\", \"content\": \"Pilot-Manifest und Asset-Plan für billiges Auto generieren/prüfen\", \"status\": \"completed\"}, {\"id\": \"5\", \"content\": \"Tests, Commit, Push und Remote-Verifikation\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_ZPT1RjLnnHboYtn3r1wXJd84"
},
{
"role": "assistant",
"content": "Erledigt, Sir.\n\nDer nächste Schritt ist gebaut:\n\n```text\nMechanism Shot Plan\n→ Render Manifest\n→ Asset Plan\n```\n\nFür den neuen Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Neu gebaut\n\nNeues CLI:\n\n```text\nscripts/generate_mechanism_render_manifests.py\n```\n\nErzeugte Artefakte:\n\n```text\ndata/render_manifests/mechanism_pilots/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n\ndata/asset_plans/mechanism_pilots/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Manifest ist jetzt motion-aware\n\nIch habe `render_manifest.py` erweitert, damit Mechanism-Explainer nicht wieder zu statischen Cards degenerieren.\n\nDas Manifest speichert jetzt pro visueller Szene zusätzlich:\n\n```text\nvisual_motion\nsound_cue\n```\n\nBeispiel:\n\n```text\nScene 1:\ntype: cinematic_ai_video\nvisual_motion: hard zoom, freeze-frame, red signal pulse\nsound_cue: bass hit + camera snap\n\nScene 3:\ntype: mechanism_animation\nvisual_motion: sequential layer reveal every 1-2 seconds\nsound_cue: clicks per revealed layer\n```\n\nDas ist wichtig, weil diese Felder später die eigentliche Schnitt- und Motion-Logik steuern. Ohne sie hätten wir wieder hübsch strukturierte Langeweile. Die Fachwelt nennt das vermutlich „PowerPoint mit Ambitionen“.\n\n## Timing sauberer gemacht\n\nIch habe außerdem ein Timing-Problem behoben:\n\nVorher wurden Sekunden intern mit `int()` konvertiert. \nDas hätte aus:\n\n```text\n1.4s\n```\n\neffektiv:\n\n```text\n1.0s\n```\n\ngemacht.\n\nJetzt wird korrekt in Millisekunden umgerechnet:\n\n```text\n0.0s → 0ms\n1.4s → 1400ms\n5.0s → 5000ms\n18.0s → 18000ms\n28.0s → 28000ms\n```\n\n## Pilot-Manifest\n\nFür „Warum ein billiges Auto teuer sein kann“:\n\n```text\nduration_ms: 34000\nvisual_types:\n - cinematic_ai_video\n - cinematic_ai_video\n - mechanism_animation\n - mechanism_animation\n - kinetic_text_overlay\n```\n\nRender Constraints:\n\n```text\navoid_static_slideshow: true\nvisual_change_every_seconds: 2\nmax_static_cards: 0\nfirst_frame_hook_required: true\nhuman_approval_required_before_publish: true\n```\n\n## Asset Plan\n\nDer passende Asset Plan erzeugt jetzt:\n\n```text\nasset_modes:\n - cinematic_ai_video\n - cinematic_ai_video\n - mechanism_animation\n - mechanism_animation\n - kinetic_text_overlay\n```\n\nPreferred Mix:\n\n```text\ncinematic_ai_video: 2\nmechanism_animation: 2\nkinetic_text_overlay: 1\nstatic cards: 0\n```\n\nAlso genau die neue Richtung:\n\n```text\nphotorealistische Szenen\n+ Mechanismus-/Röntgen-Animation\n+ kinetic captions\n```\n\nNicht:\n\n```text\nstatische KI-Bilder mit Text\n```\n\n## Tests / Git\n\nTDD durchgeführt:\n\n- RED: Manifest verlor `visual_motion` / `sound_cue`\n- GREEN: Manifest + Asset Plan erweitert\n\nFull suite:\n\n```text\n42 passed in 0.99s\n```\n\nNeuer Commit:\n\n```text\n4d1e8f8 feat: generate mechanism render manifests\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nDie Retention-Studio-Skill-Notiz wurde ergänzt:\n\n```text\nvisual_motion und sound_cue sind nicht dekorativ,\nsondern der Retention-/Editing-Contract für den finalen Renderer.\n```\n\n## Weiterhin ignoriert\n\nDie versehentlichen Review-Dateien bleiben uncommitted und unberührt:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt fehlt der nächste harte Baustein:\n\n```text\nAsset Plan\n→ Provider Job Specs\n```\n\nAlso für jede Szene automatisch erzeugen:\n\n- Kling/Hailuo/Runway-style Job Spec für `cinematic_ai_video`\n- lokale Renderer-Spec für `mechanism_animation`\n- kinetic caption spec für `kinetic_text_overlay`\n- klare Prompt/Negative-Prompt/Duration/Aspect/No-text-Regeln\n\nDann haben wir keine abstrakten Pläne mehr, sondern echte Produktionsaufträge für Clips und Animationen.",
"reasoning": null,
"finish_reason": "stop",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir.\n\nDer nächste Schritt ist gebaut:\n\n```text\nMechanism Shot Plan\n→ Render Manifest\n→ Asset Plan\n```\n\nFür den neuen Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Neu gebaut\n\nNeues CLI:\n\n```text\nscripts/generate_mechanism_render_manifests.py\n```\n\nErzeugte Artefakte:\n\n```text\ndata/render_manifests/mechanism_pilots/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n\ndata/asset_plans/mechanism_pilots/\n hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Manifest ist jetzt motion-aware\n\nIch habe `render_manifest.py` erweitert, damit Mechanism-Explainer nicht wieder zu statischen Cards degenerieren.\n\nDas Manifest speichert jetzt pro visueller Szene zusätzlich:\n\n```text\nvisual_motion\nsound_cue\n```\n\nBeispiel:\n\n```text\nScene 1:\ntype: cinematic_ai_video\nvisual_motion: hard zoom, freeze-frame, red signal pulse\nsound_cue: bass hit + camera snap\n\nScene 3:\ntype: mechanism_animation\nvisual_motion: sequential layer reveal every 1-2 seconds\nsound_cue: clicks per revealed layer\n```\n\nDas ist wichtig, weil diese Felder später die eigentliche Schnitt- und Motion-Logik steuern. Ohne sie hätten wir wieder hübsch strukturierte Langeweile. Die Fachwelt nennt das vermutlich „PowerPoint mit Ambitionen“.\n\n## Timing sauberer gemacht\n\nIch habe außerdem ein Timing-Problem behoben:\n\nVorher wurden Sekunden intern mit `int()` konvertiert. \nDas hätte aus:\n\n```text\n1.4s\n```\n\neffektiv:\n\n```text\n1.0s\n```\n\ngemacht.\n\nJetzt wird korrekt in Millisekunden umgerechnet:\n\n```text\n0.0s → 0ms\n1.4s → 1400ms\n5.0s → 5000ms\n18.0s → 18000ms\n28.0s → 28000ms\n```\n\n## Pilot-Manifest\n\nFür „Warum ein billiges Auto teuer sein kann“:\n\n```text\nduration_ms: 34000\nvisual_types:\n - cinematic_ai_video\n - cinematic_ai_video\n - mechanism_animation\n - mechanism_animation\n - kinetic_text_overlay\n```\n\nRender Constraints:\n\n```text\navoid_static_slideshow: true\nvisual_change_every_seconds: 2\nmax_static_cards: 0\nfirst_frame_hook_required: true\nhuman_approval_required_before_publish: true\n```\n\n## Asset Plan\n\nDer passende Asset Plan erzeugt jetzt:\n\n```text\nasset_modes:\n - cinematic_ai_video\n - cinematic_ai_video\n - mechanism_animation\n - mechanism_animation\n - kinetic_text_overlay\n```\n\nPreferred Mix:\n\n```text\ncinematic_ai_video: 2\nmechanism_animation: 2\nkinetic_text_overlay: 1\nstatic cards: 0\n```\n\nAlso genau die neue Richtung:\n\n```text\nphotorealistische Szenen\n+ Mechanismus-/Röntgen-Animation\n+ kinetic captions\n```\n\nNicht:\n\n```text\nstatische KI-Bilder mit Text\n```\n\n## Tests / Git\n\nTDD durchgeführt:\n\n- RED: Manifest verlor `visual_motion` / `sound_cue`\n- GREEN: Manifest + Asset Plan erweitert\n\nFull suite:\n\n```text\n42 passed in 0.99s\n```\n\nNeuer Commit:\n\n```text\n4d1e8f8 feat: generate mechanism render manifests\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Skill aktualisiert\n\nDie Retention-Studio-Skill-Notiz wurde ergänzt:\n\n```text\nvisual_motion und sound_cue sind nicht dekorativ,\nsondern der Retention-/Editing-Contract für den finalen Renderer.\n```\n\n## Weiterhin ignoriert\n\nDie versehentlichen Review-Dateien bleiben uncommitted und unberührt:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt fehlt der nächste harte Baustein:\n\n```text\nAsset Plan\n→ Provider Job Specs\n```\n\nAlso für jede Szene automatisch erzeugen:\n\n- Kling/Hailuo/Runway-style Job Spec für `cinematic_ai_video`\n- lokale Renderer-Spec für `mechanism_animation`\n- kinetic caption spec für `kinetic_text_overlay`\n- klare Prompt/Negative-Prompt/Duration/Aspect/No-text-Regeln\n\nDann haben wir keine abstrakten Pläne mehr, sondern echte Produktionsaufträge für Clips und Animationen."
}
],
"id": "msg_02aa21bd75cf0633016a1173d42e5c8191b1a0030f4e243d92",
"phase": "final_answer"
}
]
},
{
"role": "user",
"content": "Review the conversation above and consider saving to memory if appropriate.\n\nFocus on:\n1. Has the user revealed things about themselves — their persona, desires, preferences, or personal details worth remembering?\n2. Has the user expressed expectations about how you should behave, their work style, or ways they want you to operate?\n\nIf something stands out, save it using the memory tool. If nothing is worth saving, just say 'Nothing to save.' and stop.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXPxnriXUqOTMLUop0bZfjyWeCyElaAPQrwzone_jx872dJnCn3rtSgW73UIvRU7l_Uk4SO3QngXHYFOfWvJhD3ZW7vgviqOuCp3Vf4Nvk_YCHxNeKFoZ-JqaYU8Mx6hI58fstD39i1XKP9gdMPC3BsJmdTw2g5I45Y75cStxpWIRQupt3vmNtvEOtBR8zc3eK9Qf4g26g-7MbQvE7bXnRYX8v8pB7QXyEG6iFoseQBNYuhJdHiAiJYUK7TdkVRkkEgv0xQDGrC3xWPzK4XVhNU92rZP24qYIccQkme8ebVVkzIvG-CNcUjXEB6NdN1kZutHrGjBW1IkeIcAExycFI90cjO2IOmPbHd1rZ3783O80ECbYG9T60y4shOEp9PTrrMnSzpdyEHdh22POUL9S6pfdLD2Njk64uahqUSk2s3opaMd6OKADWRDXX-GdpkEZq6BrTvmbqEQ4hZBqHCOtJu34oD8sEtuSqthIP7rzw48wt1NQ4aEC9AuVeE8i9G1I-RkUDlcmhk9ZqwSnIp7gWMH7Iro5SGNSrktb2-PYS1tl9D3HoBekDGXzpUTj2j7kVilirMderKgH84ubFRyzZqxDvTzfzFQGL1X2ZTvL1U8_h0i5Drv6E-XGxsSoL0W4XP609UW4Vu-iC1nxemzoRD1ZmAnEHFUNLejilNSZtn5wrM5uKbRI_On3xJVmy_V54fy5b8NPsH2EPp44NXQ_dVboQb3nPWZAY1oLVTQ6vfKHgohugg0AxPcYwxZPe_okhGckP1veEI9y0lNqTA4BMBQTsrgCiADBmfUd37RVHr3GawoXGghD1YM5BhJJFG_XVFXB70EvDhutp7Iee8Qe4yC149TTfhKm-o8PCkjJaLzJxy-t4BxLItXAerHqVeHnbuTiJDY862cZeXpZOl4vD8RJO8QQUACO9O8zGuCLUPRNep0LdS-J1J7rdacyYib9fsrK9P98pa1dQxr3X7lkFsJkcQnQlbpFZ8liF-72Flc2tgABAMKd8noQ6cVL6dmL3zGd3tT4DgMRUKLc9ZLj0fhmOvXj_OqU_fQU8C44NxkfgqS4yt8yXXATokwwSMnWC-ztXml89-QM9-0rf_j-F2CQ5n_WpYUuhPyCFupDtBAipQI-AfcAvf7uYAYJtlv42DctsfyyLn9bF2u4ZzDu2spSZi-L4O_EL8dDMmBARx_YB1XD410XFh-TIebsVPsk2PGZCcGPy3fmT9AjptexEOsNV3qDDIHK3wlGviAZAvRZ_0ThG_aP4NQFghSC-sO_za5RUEdJGT9PWo-rhRZFVZ1-5xYJjtP6mhCi9gMbfrcTsr7Ktm_FafKsQ5QEkrUj5Ri6SJkM2ESzoSlqgX1KFeB4Suu9oVk2e-137bYj_fArkNxDFHcMVsmAQ2W7kxNbnDGn5_qZZ0Duw6qHcF__UftAA==",
"id": "rs_02aa21bd75cf0633016a1173effb0c8191aa1ac819abf792ac",
"summary": []
}
],
"tool_calls": [
{
"id": "call_0IxCvbGyvp6Y6fOeZVvMv6Xz",
"call_id": "call_0IxCvbGyvp6Y6fOeZVvMv6Xz",
"response_item_id": "fc_02aa21bd75cf0633016a1173f1c0a881918eab4e29b396ae76",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"add\",\"target\":\"user\",\"content\":\"Short-form content preference: User wants Hermes/AutoShorts to operate as a semi-automatic Retention Studio, not a generic KI-video factory. Preferred content style is motion-led “mechanism reveal / visual explanation / mini-investigation” Shorts with strong 1–2s open loops, visual changes every 1–2s, cinematic/animated mechanism visuals, kinetic captions, and human quality control; user dislikes static AI-image slideshows, generic AI voiceover/avatars as the main format, reused-content clones, and bland “Was wäre wenn…” KI-Müll.\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"content": "{\"success\": false, \"error\": \"Memory at 1,371/1,375 chars. Adding this entry (535 chars) would exceed the limit. Replace or remove existing entries first.\", \"current_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**\", \"User wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\", \"Telegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\", \"Finance: no real amounts/IDs/API keys in chat; credentials via terminal/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: mappings learned; provider calls explicit/cache-first.\"], \"usage\": \"1,371/1,375\"}",
"tool_call_id": "call_0IxCvbGyvp6Y6fOeZVvMv6Xz"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Evaluating memory consolidation**\n\nI'm trying to find ways to save characters in my memory. I need to consolidate entries related to voice, language, and communication. Removing \"Notes: Mein Herr. Loyaler Partner.\" could save some space, but it isn't enough. I wonder if I can replace a longer health topic entry or the finance entry instead. The finance entry is important, though, so I’m thinking about how to compress that without losing essential information and maintaining clarity. It’s a bit tricky!\n**Considering finance entry compression**\n\nI'm thinking about compressing the finance entry significantly to free up some space for a new one. I want to keep the essential information while using a shorter format. It's important to replace the current finance content with something more concise, but I need to ensure it maintains the critical details. I can use available tools to help me achieve this goal. It’s a bit tricky, but I’m determined to make it work!",
"finish_reason": "tool_calls",
"reasoning_content": "**Evaluating memory consolidation**\n\nI'm trying to find ways to save characters in my memory. I need to consolidate entries related to voice, language, and communication. Removing \"Notes: Mein Herr. Loyaler Partner.\" could save some space, but it isn't enough. I wonder if I can replace a longer health topic entry or the finance entry instead. The finance entry is important, though, so I’m thinking about how to compress that without losing essential information and maintaining clarity. It’s a bit tricky!\n**Considering finance entry compression**\n\nI'm thinking about compressing the finance entry significantly to free up some space for a new one. I want to keep the essential information while using a shorter format. It's important to replace the current finance content with something more concise, but I need to ensure it maintains the critical details. I can use available tools to help me achieve this goal. It’s a bit tricky, but I’m determined to make it work!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXQELHPyIJKkbqBnGsx8722xvNYZCzq3clkYJ0EM6LqmCXxJJQe8B2rP_4OkQI13XB-SwoQjLV3oirYLuI9JBsEh1UvZDR5sB3t2uCcTHPr52FLbtYcG-TrA5uBIL_3TQbNKUJkGzAY883knmuoUbz4a9m8haN5zOgREOkGTiSrDvVNDGqrTo247rD3TnM-cvbRDEMqoHRogqKycNICb4hAzmO9u4pAMJLYsdtspYvqr_OsWCUgcOcdvTuRZt-ZzAuNElLzKz8g7odTBvO1F_1lJx4XzYOkhTALA6gbtEqrNs-ftVAbdttFYwXgo5fDnhCmQKQ0HnVQK0udlB43nEngv0Jr2klle8glztZFRdkG2lYb8JWHAgBVyuCDrXooIpN9ocP6klUSHSzaC8mmE3AEPtRhK38bitZZmH3JjIaRa_GUBhEZWksVEqav1OTTBzkQrbcBK6VHfll8KFC4PlViV2ueFnelTjCLGmGlhmSOPeuYlkRac4djQr0fwOcHK3Z-Ku91Dm9TYuTeIEdGkr9kGpQQJfugISh3oKXU898dgnqGGUvnfXk0iC69nTpvHvcYx_LxbGXFHeFgYj5r9pwLssDITm2NzzsJbuAkL205YSHAiJnBDCmkl_yW-RTvpdYR1TxFQbwFCZuX1rzEjMSon2llCPc8GnUrzFrs-gsatSu-RpT83R16IfEalmd4vdMidiXt04CpJm43x-xCGubHunOf6T3OjbwiiP-XWF_FXPm6A0kUwdj1pwdlD6cKdgF0u3gU9MzKNzHHh0VHboJol55yFaWLvft01wi8PwNWWYKkaD0LujBMDEgRQyCikGgcL-mD7Hv8uuibHJ8GC6WhvGsP3w62Biv60IlfZnLg8GgpNeBn3pmaq4Jh0E5hrdIRr2Ish784aQ_BtOdDtSetjMplKuZybOB_6UpcuFsCEF5-6XCJRiti096ZjpzrbxB5FzvGNddcMtRmgum6fIkMkp6AbcVGr1Z5rNmyV3yYutRe1GPPidJQiOPIaS1-pN_NERVJuml-0GjbKh_SuGmu26ZN7xKlFmm_RuFg-EcQshDpsRGbm8iNL_5zVSVysaR4gTINYUfU28rSqZ-LEkxbDSZsIpVHHr2DVnaXe4ItUpFOOozVdPLYV1GHyKGdI7jh9AGQF_oQHE2PrUSa0kTwyScSrL1mTy-vb1KMJip2vBpI2o7J0EDwPfPIK3SZSScKWQDPOSLLi8nDcVsVsFIR6Uef_7fj9UzayBQixnbtzqRrhfFBqPLhISRFVMkj7VPjqKHwZUfm46MgEP3GRcjfbWmrYyBF1mLznzL2RskmP93UJphqXilIFrwRdmFudGTdKwfucL2GHuRab8EdiiBw4trJQSlp276ORGbQ4a5ev6cweTbGGnBlDQHZBNpcgYXzJpB-gbShTeZzk2Db_mN8epzkxtauRFVhLWKMJqkMF5PiZi-tK1B_31PRozaRu_g6IqFS7p2GiN1ImaOBvJspI2OcKyI16dv8Wy2uXp9Zue87RdalVESmsG0jBuMSBSSY5_ebmuGX2UDe1Vgl5ND66XWAfPiJTk8IoTcXuNxLEp8fWifuYABXo0RlFcMDNmobZeM-KwyYwbphZFmOqunTeyz8c4-xKoj7u-ubzcYoiEEqsoxcKN8LURl_9IZ5Neq8X2Aa-ddn_1ma8uZ0qSIcCzF8CTDIM63OZuICeqRA3Fe92vbiafzelWXHWmtHgSFGlGsJGUGsYLt29qDOzsg0_IbapQPHb3vM8CgNysH2W8z2Zkyk-xP83JywGkC-7yVObBZ2d0RbiDnNrRLnCAUU6ZPQd8Kxxtt88VE3qpz12plr2QR_1H_ySbSu6CG1TN9vjpp_sdivNmnMfKkLW3FL_d_4N9_CxSh3hW0EkHktYYd1-xHULun7VWF-zAZ5ILScaL12w7QsQQMxWkVL9gCH_kNTtmiYZAsBnkGZ1kyreILrADdIKJYs7dP_40K8OXqcohAjUu6IWa28f_CCBDpt6OfzM-OB0pwhXwuAOfM-KOJ6Vs3X8ffbC5joNr5IFdMufvbVdwR6rqWWpeXx81mgbs8wIxVrinWoJsCubIj_q7_cb1DOH7_4vvKr4-asohw-YsC2EoguXxhwYlh3PXrrIp0W5j8ZiZ6AiR0ziQ034a4aqo7yibcr2SjHugARvrC4omjyQbceU9FRMweBCrfgFaBUeoVBgOr85dW0o8r8mx-3XtcpvwoWQeneB73_o5eKue9ilgs0Rj78-W7VQkiVOu-fFFZzAApUDjHHJLe93qS00cTAmXktQBW3K541gbtWYrgCNehSJ4e_92Q8FLY9KGHDTU4ZqYH4y7RyVgBuWSLq4Ae_Ps13Ddj9ZBFAnM2mdLLNNoHtPq6PV5EEdw9I-BIkFSg0y_K5zc577rT-x_iFo86eZft2dGeJNLMr9qPZQg6mRA7zoNq8js4dhd9rZgo_wr_qfvM51enuzwRQIFVT7Gx5OGK9HARfVD9T6nitUwpI6yujAu2XKYJEg9qHZAq8cIzg06_-eHpcbL6uBr5DTkmRqjrkqRUYx-ECwr0zT0JWlX2T5rIaZXXwmxrgaAjOjbUw6WQ9KlIWAaRXKt43BJYM2PnkR9NWvog4baoP0QHrXjknxEeCmIi3HZcwRR4zsrZxswdYreiB9817VwIDxEnJF1U206fZq9shrlaXrkSs3iL6Fxetua6TVf3N8D7cObQoHNEEUtjHRF3D1r8ai4cOd_lgS71baLWq4ioOJzehwqUJXbcwCCN2wKGENrVOI2qvSR612WnHzteBE85uh9xe_s96KORYoKPrU_Nwzs_flKufEv-o09sd53EZXJ4hzPGGz0SJtTa-kpfT5ikk0d8hsuAlfATAC_WjtGPUOPNVgM6xsKfZcxCg0rJ4QDU0ZjIwzC8jWJ2_rEfjqR6WW8JLmusEMtbltS9pcXdXTz6mYRUz9sP8JWx_WWdOsRTcNDfZ3G-3II0db2_-vbToryG6NOHHLjku-eOFYPwbGyvRwp8v4l8yMi8FkUO7u1pyEHBHQV5rDqSoBXz87oT5NLXy0oi9QP0CiVVnP2Ho4qXc6IC2BxcW4HGyISiLcE7zh1SJRlkOTzk_-0wTY7WU91gqJTVjvB8F-FjUl02V37weKK3iH84MOZcXJTg-pT0IuasAIsRk9HOdY3leCAHQ08NQFkeRsJ_KTnOWXlZUIiLStMDNFcgpZeerziKiJ2kkSVQEGhEz7EZ14hjr6jMJ8wqhld0Ts_wyj2HfRWy_2VyucCgUmR_i0gHaHvMDMzheeOS_KKtg56abn64QnSIVu2HnizBofZaszVS_ticPXeq0FTNLy442gR6qSnD-NbKsAWHrJs3vwt2LYZRfob1B1FWgSQBGK0Vgo7rzkmrBffanbk2uUOyafV4dblYHgZCP5O8YbdJHM_JwI5BhwhppV9bnYTTD7RmMl7y9h9N76kPdmbE2ryakggiU_MCqrGoJDhoQVkqUbkswpFCnTlWJivniXE_-84jfEaEZOhXJfNIf-FxpwDTpNaor3X7wwcar83tmZhx2HD6l8ZCOcAFH_-RBE37qeHpRMhQ6AqJ8XqdgqXp-9WV14dtGltE-s83OK6lOrtlH3lc6UgSSqCOyAIUJZHhyUAYDZ1mIxVSfDJcCZmG_Hr0l_rEN0fMspbA==",
"id": "rs_02aa21bd75cf0633016a1173f93ba48191a9ac9ade7ac7314d",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating memory consolidation**\n\nI'm trying to find ways to save characters in my memory. I need to consolidate entries related to voice, language, and communication. Removing \"Notes: Mein Herr. Loyaler Partner.\" could save some space, but it isn't enough. I wonder if I can replace a longer health topic entry or the finance entry instead. The finance entry is important, though, so I’m thinking about how to compress that without losing essential information and maintaining clarity. It’s a bit tricky!"
},
{
"type": "summary_text",
"text": "**Considering finance entry compression**\n\nI'm thinking about compressing the finance entry significantly to free up some space for a new one. I want to keep the essential information while using a shorter format. It's important to replace the current finance content with something more concise, but I need to ensure it maintains the critical details. I can use available tools to help me achieve this goal. It’s a bit tricky, but I’m determined to make it work!"
}
]
}
],
"tool_calls": [
{
"id": "call_rst4tdYQyJnhRTnXIYaCzWQg",
"call_id": "call_rst4tdYQyJnhRTnXIYaCzWQg",
"response_item_id": "fc_02aa21bd75cf0633016a1174044c708191a1483bd5a5c44967",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"replace\",\"old_text\":\"Finance: no real amounts/IDs/API keys in chat\",\"content\":\"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.\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"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**\", \"User wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\", \"Telegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\", \"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.\"], \"usage\": \"98% — 1,351/1,375 chars\", \"entry_count\": 9, \"message\": \"Entry replaced.\"}",
"tool_call_id": "call_rst4tdYQyJnhRTnXIYaCzWQg"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_YA1SxOLr2F0bqIotUknYhqCV",
"call_id": "call_YA1SxOLr2F0bqIotUknYhqCV",
"response_item_id": "fc_02aa21bd75cf0633016a11740788e88191b35c6dc93630ecf2",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"add\",\"content\":\"Shorts preference: AutoShorts should be a semi-automatic Retention Studio, not a KI-video factory; user prefers motion-led mechanism-reveal/visual-explainer Shorts with strong open loops and dislikes static AI slideshows, generic avatar explainers, reused-content clones, and bland “Was wäre wenn” KI-Müll.\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"content": "{\"success\": false, \"error\": \"Memory at 1,351/1,375 chars. Adding this entry (306 chars) would exceed the limit. Replace or remove existing entries first.\", \"current_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**\", \"User wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\", \"Telegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\", \"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.\"], \"usage\": \"1,351/1,375\"}",
"tool_call_id": "call_YA1SxOLr2F0bqIotUknYhqCV"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Optimizing text entries**\n\nI’m looking to make the health management and health topic entries shorter. The user may prefer more concise information. Right now, I see that the health management entry is 306 characters, and I need to compress it down to around 130. I also want to significantly reduce the health topic by about another 150 characters. I’ll replace the current entries with these compressed versions to make everything more efficient!",
"finish_reason": "tool_calls",
"reasoning_content": "**Optimizing text entries**\n\nI’m looking to make the health management and health topic entries shorter. The user may prefer more concise information. Right now, I see that the health management entry is 306 characters, and I need to compress it down to around 130. I also want to significantly reduce the health topic by about another 150 characters. I’ll replace the current entries with these compressed versions to make everything more efficient!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXQRUEPBshgC3qAhPItHpX0-ZLEtRzly6PIRBi0h2M6N6otjbGrlYYjpO2fKNveZADr67KxSaXbPWI_8kQg4ogCyYEo-LYS2SYamHIcfwOsJ479eYpwLM0p6yv3PcNGNH-xaMp4lu7aGB7Wr2n4cYGtsLmOCXxQwBUpUMX0I1mVyVPoSA3H8otUwJiO6wr0FpMkd_dHJzkhUi3LC061TPRB1MFor7_gAREUDLtzVifbRHzcTLRwYHThS6R7f9LPggNxh9B0duhAQTH7qNcd6y2itNwTg_rCYClp8EZiF_eGArcS_-_GNvW_9howUsH1wYZO6p2O16n6zMqqObWbr5Q50pRE4xl0Ek1fDCJdG8UF5DMaBiCV2_zzUvipRyhz-pa3YpnPDAoBtqDH1BCBp5p_XpvWcr5Dua-GbWCjD-TA754Q-BRl4BdeHkLuH-T2lpz2bhtJCtFvKSfCM0Z0eyFhQm1DI3hfRNdCnC689ZfRnK3W0dAyQ0mKyIv7l3bFZFiHpTjT8Oy-V79qhEZFQPLCpQAFEtxeXA-QTnRV7u6RLjBiWayefHPCFUblisoASKZIMbT_VmiT1wdsrRNkH5L8ETI-pFE8zZ-O-p1gOHBb7bVfeJr7Qu_0M4O2plzPMdxbH1258AtZ5eQpk7kYwbq-Arqbtol8CdP3kNzQ5sYVG9mJcl9W6NKqjJFt4Aw8hGJ1runT56Yq99WcY-_EoJYwuknPD87EDSem_KWFplMwJZEMm58mGYNSbKWJ1WYp2tCG9aOiUXBfmksbQyerV1JnloIntowS6OSfPtXhNuoD7r7f6YsJPMVvJ62ctdO__fZrqdjCuob6D_SwkWXbVS-8v976m1WjGNf_JjsoPLORuqj711hS-vcvmZs0_mvMp2iveY9lQbyEkGrsmdhch1OTTb7Vdr86SOBHklY4O-U_oaIkGrflkHRJAbxHHowjm0OIDQuZ-Sd1UFAtH3AoOcp-M31_XcVWuSujB41Guv3zauEMNHqSH5iH151Ttf_hlqA8sIhHKcwMf4TgkCjdWI-K9ugjeHWldJzq2cq52FVkgVyXcPulUY6q0Klh-ekBlj4qsQoau0_jzTbVqMrtb8y6pw0R8992nX0qRUT3NunTOHAk-3HJFc98yXsmP0ZD_l1XA5tn9NSZhKHgWRCsFM8wlEmcJ84gacnO069MlYS9nhiE_8yQdeg0tb4zva6jDRLaJYg886Eixx_wjYy8980ZQ8G3K0fUr2l8Wfy0P3_rzmlmtt69Ncj1OPClAS6EgR7NUg26oTcBfPC7hpbcTiDBVhGuILj_TE3z2I8w8DGwskpucyGavbKq1xTVkp_8sR-F8n1xsbNxp--hdI2KhVRulpSy_VG2Xcah1FQ2uREz-Ulja9wsk31BMhcAmyWyPYsdOny116QnHnDgeZ2bQ9LK55oYNKiBrbSgCeEezu3jB1GY=",
"id": "rs_02aa21bd75cf0633016a11740c52788191ad8beec9ac52edc4",
"summary": [
{
"type": "summary_text",
"text": "**Optimizing text entries**\n\nI’m looking to make the health management and health topic entries shorter. The user may prefer more concise information. Right now, I see that the health management entry is 306 characters, and I need to compress it down to around 130. I also want to significantly reduce the health topic by about another 150 characters. I’ll replace the current entries with these compressed versions to make everything more efficient!"
}
]
}
],
"tool_calls": [
{
"id": "call_yfbofihOkHG1DDEtWgJi2fZ0",
"call_id": "call_yfbofihOkHG1DDEtWgJi2fZ0",
"response_item_id": "fc_02aa21bd75cf0633016a11741177808191887402263919e773",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"replace\",\"content\":\"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.\",\"old_text\":\"User wants JARVIS to act as a comprehensive health-management advisor\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"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 „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\", \"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.\"], \"usage\": \"89% — 1,227/1,375 chars\", \"entry_count\": 9, \"message\": \"Entry replaced.\"}",
"tool_call_id": "call_yfbofihOkHG1DDEtWgJi2fZ0"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_6ankEqgBtohVF2sd6KHeUw3E",
"call_id": "call_6ankEqgBtohVF2sd6KHeUw3E",
"response_item_id": "fc_02aa21bd75cf0633016a117414be388191bbe59829db1ce292",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"add\",\"content\":\"AutoShorts preference: semi-automatic Retention Studio; motion-led mechanism-reveal/visual-explainer Shorts with strong open loops. Avoid static AI slideshows, generic avatar explainers, reused-content clones, and bland “Was wäre wenn” KI-Müll.\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"content": "{\"success\": false, \"error\": \"Memory at 1,227/1,375 chars. Adding this entry (244 chars) would exceed the limit. Replace or remove existing entries first.\", \"current_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 „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\", \"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.\"], \"usage\": \"1,227/1,375\"}",
"tool_call_id": "call_6ankEqgBtohVF2sd6KHeUw3E"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqEXQctctrH6dUC8GgKdRMMnLxKHxIMylkhD-87ZrUkeEuQeLK0sfMHbxGiKky8FwBgrqW8M-N5pd-UxhuJ6UyzCSumPUJLP8zPVOr-S_QAwIbgMXF_qb8Ai8HAEfn6-I75qusdimMtxROn8ldaOej3BqmEjXaPCcLU4GVpTgWhu-mxvJSXqTn6a_hEO8zQZ3ZDviH4odAfR7Bns03dEe1SYV2Csrsk3GR89YD9A7zYfVf-dN1XVgvf7_ZxdwPQ6rUgdOQ1109a0utRSP0qHeXTFH1JGdPuS1686oLpKrIIIrx35C15nAjL6HVK3xl_9LCU5KFdMQXOQNMsdaCWMVEqAOJ35c2miMeEzoaG7M7UHiim5jw0l7x7HoWnOCw9Rp5dkDjJRI6CGU5Sxx7Qs2BpcTnmB83px-KdUIrycs9HsVIUwaPXu41iWSIq95POiKXEs5cdoghheAs1N-mLTNBoG1nuhDfgGfodEdc8hNZObS8-oScrbhjSab0B2_TNI27SVE1Wv_gkGNcr-fdiX-n8-0wsZbalr1iHUFOSll-txorMycyQLoA-8aLtZlpe-cuBKLwWIs8CXk-pMXWBED9plIIIGTjIG0MCBKh25aTJ1aqH3f365Ss2NOVlC2YXby5cCmBH-Udn_mVfIRI6J4XB0-WELwPOYBHrR6QiKldZeIIBm1DelFc0s0fIobid0urHHAVkX4DtG4XHW-DwXLlzNFnx2oujj-_2n_Tb8wf0rpOhNVFzROqLUYCBuwNLkobLliiscoAGdajCgqIE1ZCU2zhEXwQNq4XQt-IUAEsgI8VOZfSawO8R_qbosIRTjsiEK3Q8XfJqDWaZt7C2Y0n0ngurXgSbj8pJ1lO4I_WZudqERGy1KT9oOWvwTFNp98RiFa7dU7BtxM7tZUgRTykTPS_wAb6v0uLB8jsaF5xZat-XhKgM5y6IhWzU5jhDrXB5lPGF3-lawjjPXLI0D_kqQEFhQ==",
"id": "rs_02aa21bd75cf0633016a11741b450c819193fc5681a9501c27",
"summary": []
}
],
"tool_calls": [
{
"id": "call_l2KuEmr6Y18jLbuyxRMUpbJ5",
"call_id": "call_l2KuEmr6Y18jLbuyxRMUpbJ5",
"response_item_id": "fc_02aa21bd75cf0633016a11741c91ec81919b4797fc9f96ad5d",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"replace\",\"content\":\"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.\",\"old_text\":\"Telegram Topic „Gesundheit – Medizin & Strategie“\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"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.\"], \"usage\": \"74% — 1,019/1,375 chars\", \"entry_count\": 9, \"message\": \"Entry replaced.\"}",
"tool_call_id": "call_l2KuEmr6Y18jLbuyxRMUpbJ5"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_qRleben99Cl6NwFbcmeyoeaE",
"call_id": "call_qRleben99Cl6NwFbcmeyoeaE",
"response_item_id": "fc_02aa21bd75cf0633016a117422c0688191ac18e056be389379",
"type": "function",
"function": {
"name": "memory",
"arguments": "{\"action\":\"add\",\"content\":\"AutoShorts preference: semi-automatic Retention Studio; motion-led mechanism-reveal/visual-explainer Shorts with strong open loops. Avoid static AI slideshows, generic avatar explainers, reused-content clones, and bland “Was wäre wenn” KI-Müll.\",\"target\":\"user\"}"
}
}
]
},
{
"role": "tool",
"name": "memory",
"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 preference: semi-automatic Retention Studio; motion-led mechanism-reveal/visual-explainer Shorts with strong open loops. Avoid static AI slideshows, generic avatar explainers, reused-content clones, and bland “Was wäre wenn” KI-Müll.\"], \"usage\": \"92% — 1,266/1,375 chars\", \"entry_count\": 10, \"message\": \"Entry added.\"}",
"tool_call_id": "call_qRleben99Cl6NwFbcmeyoeaE"
},
{
"role": "assistant",
"content": "Saved relevant AutoShorts/Retention-Studio preference to memory.",
"reasoning": null,
"finish_reason": "stop",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Saved relevant AutoShorts/Retention-Studio preference to memory."
}
],
"id": "msg_02aa21bd75cf0633016a1174276ea8819189a66209a43af7a1",
"phase": "final_answer"
}
]
}
]
}