{ "session_id": "20260524_171930_769d7a", "model": "gpt-5.5", "base_url": "https://chatgpt.com/backend-api/codex", "platform": "telegram", "session_start": "2026-05-24T13:55:49.357315", "last_updated": "2026-05-24T18:06:57.367661", "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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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 ![alt](url) and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [98% — 2,167/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [97% — 1,335/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts preference: free/local, approval-first, TDD slices, deterministic artifacts, human approval, no side effects until approved. Approval previews in German; final videos in English. Prefer motion-led mechanism-reveal Shorts; quality > speed; avoid static slideshows/generic avatars/paid credits/artifacts.\n\nConversation started: Sunday, May 24, 2026 06:05 PM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "feishu_doc_read", "description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.", "parameters": { "type": "object", "properties": { "doc_token": { "type": "string", "description": "The document token (from the document URL or comment context)." } }, "required": [ "doc_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_add_comment", "description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "content": { "type": "string", "description": "The comment text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "content" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comment_replies", "description": "List all replies in a comment thread on a Feishu document.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to list replies for." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "page_size": { "type": "integer", "description": "Number of replies per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token", "comment_id" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comments", "description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "is_whole": { "type": "boolean", "description": "If true, only return whole-document comments.", "default": false }, "page_size": { "type": "integer", "description": "Number of comments per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_reply_comment", "description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to reply to." }, "content": { "type": "string", "description": "The reply text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "comment_id", "content" ] } } }, { "type": "function", "function": { "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) 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": 208, "messages": [ { "role": "user", "content": "[M G] co/Civitai/Qwen-Image-2512-GGUF \"Civitai/Qwen-Image-2512-GGUF · Hugging Face\"\nSollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?\n für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.\n\nMeine Rangliste für deinen Usecase:\n\n| Zweck | Empfehlung | Warum |\n| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Beste kostenlose GGUF-Bildgenerierung in ComfyUI | Qwen-Image-2512-GGUF | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |\n| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2]) |\n| Schnell / weniger VRAM / experimentell | Z-Image Turbo GGUF | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3]) |\n| Alternative zu Qwen, stark aber schwerer | FLUX.2-dev-GGUF | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4]) |\n| Aktuell stärkstes Open-Weights-Modell allgemein | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5]) |\n\nKurz gesagt:\nFür deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.\n\nDie passende Auswahl nach VRAM:\n\n| VRAM | Modell/Quantisierung |\n| ----------: | ------------------------------------------------------ |\n| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |\n| 16 GB | Q5 oder Q6 testen |\n| 24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität |\n| <8 GB | eher Z-Image Turbo GGUF statt Qwen |\n\nWichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])\n\nMeine konkrete Empfehlung:\nInstalliere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.\n\n[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF \"unsloth/Qwen-Image-2512-GGUF · Hugging Face\"\n[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF \"unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face\"\n[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF \"jayn7/Z-Image-Turbo-GGUF · Hugging Face\"\n[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF \"orabazes/FLUX.2-dev-GGUF · Hugging Face\"\n[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image \"Text to Image Leaderboard - Top AI Image Models\"\n[6]: https://huggingface.\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen." }, { "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. 49 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": "[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. 209 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 ---", "tool_calls": [ { "id": "call_N5Ot8sgWfogJyYo7pKIrM3En", "call_id": "call_N5Ot8sgWfogJyYo7pKIrM3En", "response_item_id": "fc_060035eb87c40e14016a131098692c8191b20c538e32c59ee0", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"command -v ffmpeg || true; command -v ffprobe || true; git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!", "reasoning_content": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExCYHgcgAnwHILgSNiEUl1N9obiL1G2tl_8G4pAWHiQajbJYPFrl_hX-O5xK4i9ydX2rFzjRi8cI__dNFnlqqqpML16a9Q4oCehnueqJ4p-oiN5P2SRCcya8t5QlAYqPySF1ABEYPfCxkxdXBNxNDjfce8Xk7yLllK4FzJSG5latd6FdzmpRmVbNvljNXIP2A0T7inUqaSeW6e1Tux8QMxq5rRK9KP8SkgGV1-tmK7OAAkdl9OBQTuySNdqaTIb3gM9ya63bFlNQkwwjqVG866jl-3SAcVHVlTd1sDbMSb6DTlPBimZUFbU7fyoTF-ixAD1JXnY0cRl4fwfCh81HGqY9fRK-ba9ujHJ7ikcG3js03wTT9PYqgTlIC9WB4VsD6D_y9G5BRx9IkI9mRxqoq3YOonTDknHlpxSvzmBxxtEt3-NHQO9OiWlZMSFqJ4hbmi59f6lUof62jqXZnAez1k8t8tSUuhFkbtbgnw62yZNe3ZXEd0L9zBoy0cEN6czKEXpfxB9KNiXK1C7fTITvxWKTWVqEliXtTqhBxY-8gUe8BRfHGC-qAfhPvJRwPpL3Vh-Q246oJ7s0kGeh0d2I7N1lxMb1uIzT1LmpnPQWBz8sazT5hpvsCNe4MUXyHishB-PIWy-aVtL34rbdwno8_BAm0SQiSesIYGQ3WgmDFdn4Dkuz2aD5RWqQ5T4ZnhOtPk4jm2uT7gkYElPnIVG674yK4H8lsOEs4JWdIStVa0CWezxZDVWfYM7VN2HbkyjeVb614dbX9OQSn5tE8mt9ZIOIi11Ty80Vcgqc_NjyRSqUV4-3uoFbxSD3wr8r0XVj1XHeyDBIobvZfok-efp-ZBJUZF3KUc2uIitpCptrvOq5iGIOxbMGM_6at_D5DZ-yB1HLSh5KIC0YSVq3YAX6BOEIKdSJi6iFFKSqwe7M_DGGcZCiMs8ZNhVko7bhV2w4eZx3Bv8tuUejM2XLm11TH-2_ncijeS8-Q9ymi32-1PCl1hyjISSTLGZpdmRGZKrmJBrI", "id": "rs_060035eb87c40e14016a13109569e081918165d278415a9904", "summary": [ { "type": "summary_text", "text": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!" } ] } ] }, { "role": "tool", "content": "{\"output\": \"/usr/bin/ffmpeg\\n/usr/bin/ffprobe\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_N5Ot8sgWfogJyYo7pKIrM3En" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_B1l3vy7tRMttV4dwSqflmL00", "call_id": "call_B1l3vy7tRMttV4dwSqflmL00", "response_item_id": "fc_060035eb87c40e14016a13109fbd088191b1e510686766f93d", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"import json\\nimport shutil\\nimport subprocess\\nfrom pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_renderer.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!", "reasoning_content": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExCfJ-MXyd-FuW2Yzn9oPo8oCTm3sfhjduYUah-vISyn2wMq54luo2GuvIJ6WFcZ8Wg4pv8XHLxlcnBy8ufqN-8oryjNmR6O6zLnWYv4vf8xF2scKPGAYEhv_Hyeo0p2aLPbjpnPk1kCisUcxFnGBIJiI-axcjouonFTXOQKxzTP2QUjqbQMDmsWEJQ3_iFhuSiiS_Rc98x1rRm0DUev4WvETXZ-M3UHpkUfH9-2Aje5uYhIvSx3-kTmc0uBvPhbwXctrMEqNaE0miNraaYMIiB9PQrE2aNxor-QswevJqO42esuWgcQSrxeJ-sHem-kyPXqMwx-V4_LerAqlmztu99_DRJy3y-A7qhVyj-u19ghSOs8Y3lUNqm__dGnYO7ZoBmiDem8yz4NbrXPdyDTgewLlUjKPMfeoPvkoCxT7_3bfBiHN2MxPEYER8PZFOlA-BQlqa8yL70PYIXOfhp96SfIsIvYgVbnXJzCrz7KftbTCSH3SMbRoSjBqgsDZFGTOq7AAKTo8OTb6Z1Xq3DmHGXVAd8n8qGfmAyRME24nBWTd_2KmXGg13MNa-xheNTGke31E4YkxIP955FnSePQ14M4AAFjXHONHdnt3cy9a9u1y37JOG1DldN_Mr87XzdxqBPfA_I_yag_9vtHn0qodRtAVNYgW-kCdf51Q7uh4SxOtthNAMpZyvulp6UaFpla8k6nqhLvF1WTwM3niBfcbZO05D3HtmpdRS6IYY8vGQFYM1NEjRh-0oY_tRCE6cTESvpnfE4yQawjp6f8EtOKz0gAInzYdQqMZJju6DpKQsAZ2sfZF5-COLLi5qaSs51CE_VVeg-KuzHD_qlOgK75ppL3Q9xLHwVg8rQiLgX4LT6zk7-G8i3RO3lKv3yHx6J8F-yJJvDYQ4aLy7dIGHNAsgbVh7XEFs0IY86MSEOvQxBOODFy4jmRPcNqEc0SmsgBWijapF8jybzjgs0k8-I8ZQ6NJHseDaA7o4JGZQjLTQAU7bxTKMJfIeJqKud8T8n-gYUwi69v1cPG1gPmPTmPyrxiRw3yAjmadVRegQGZcsP-INilwIdxByei856FxjtdVndskx8Xnag8i9dQDacEVKtUTg==", "id": "rs_060035eb87c40e14016a13109d11f48191a8f5197327698a8f", "summary": [ { "type": "summary_text", "text": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!" } ] } ] }, { "role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_renderer.py (99 lines)", "tool_call_id": "call_B1l3vy7tRMttV4dwSqflmL00" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_qhXSLVz2PPk168HKHsUX2yF2", "call_id": "call_qhXSLVz2PPk168HKHsUX2yF2", "response_item_id": "fc_060035eb87c40e14016a1310b562ac81919f055049f18c428b", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ffmpeg_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_ffmpeg_renderer.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_qhXSLVz2PPk168HKHsUX2yF2" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_EjgkkXfsV2fyE3oILAvpzrDT", "call_id": "call_EjgkkXfsV2fyE3oILAvpzrDT", "response_item_id": "fc_060035eb87c40e14016a1310bec9688191a0c38c23e1288eac", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"FFmpeg rendering boundary for local preview videos.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\nimport subprocess\\n\\nfrom autoshorts.rendering.f...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location.", "reasoning_content": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExC-U6GLfQrls9Y24e_1j6AovRJt8Ww7EZAyQmBcqPSWFkNceP5A9jiGS8dmzi8RxXamHRQcd2_-Ft7AnZx1_Hfbszj9v3ivJbXbveCRamtnbIyFkAXYRW0PdotH8tNNSui5lCI43aW3j8OrxsTSvc0E-r8DUu40566vy1mHX4tbqHvhrGNNouUDl2Xec_USaA7MShDe8nl0fKJ5cl2Jvn0NRynYuGzEK-1Ofy8wl0nVSw7IA6oxpQq9QQZwjMql5WpsRqFVAM-fer5oHiv9JwDFXR6MXxViR5ZxT9NrJVJvO_VcrvrKJtsDmxfIaXEBf4F3FIoWggYCyQ4RKDh_BOZcKURfgA0ZyfO4PegoLECKXC7vutDGk62nKsp0XyBL9n7Utgi7_sDLkLbXd8GdO1UOz0KEsvO6s-gWegh-GSTB1YC9yKZHgW0QVauHejeMX_odHFCCpAwRmHOsk5e29Jhb7sXr1KJxoTZuy502ly-si-t9VkFqsHB0IwVtVDQ26voOJxnNL4HMqyRyLiSKY9oAkpGntPOjjNInFCf5fB_Hig9YslDkK0vH_w9gbuJ6j58V0p3ndL4kYDAl7-gjmYdnyUNjnA9XDQZ6vvUhMRRuXNEK6DGojezz9SIrjEnRzfgVBmQRgUTBOWacIomMRIPMldCj1BViZ8szStQ1Bg0GMLYOm6jGHRkcrxXfAuZwFOZlGgHJiiXn578uoDLMU4-eOjsQwz-Dt2TohXYblihwRT8OMPSLdB5d-VKw6tSgI5jMQD6CCW8CzK9XdxiMFcFY8N0s-O0WcbAkdmLOH7HM-TyaxYenTetegCBOF-ti5zGIE4i3TG5E_BRTNcQWT2LfZXDsGpo_5c5Kb9CtY7xRzojj4hvkDeFpF1zIm3TTvJ5NmZL4-31b5UPa5EH7mR8LdyswI_Td5ayrM8z6UPPNXTO8hmh1iFTLyJ_jtvFLY9It4EaUoaHqqtZRJj_x2cKeNRePPo7K76GShLg_vxA57ZMNvU1qgOFc3rFYDSns8QA9LZlBBdDAxwgirXp2tYKpoHnPWOLKK1kzFD7xFwgkL3Cw7LDlZPEq2Gapa7s2KTxNLVYNLd2e9x_MS0lvqY3iqpWCh8FO3pc5KswjhKJVDxNJXRvVZk5i0IbBUj9TmhQFGl_S9KSS2iMEQM46h1k5hoS4Gg1Ii2eZBCwOMqz1jnRPoH40Z-LdqCOk8NPgklON6jVZ2s4qi69NZOiQs0-fDF8i6PXViEENz10Uq35Jn8gBNsEw50yFY2Hkm5WazRSQ7TlYd0IbcR0aJlN7PaMDkaXEjWdcrIrcdfrgAbqtPNlq6LdfQFCVj805-XUa9zJMklLSBVuqRE23sMQCv7KdgqdtPEe3IGqoojnl5bdVbLs-ckHRSr1Nlu6VUkbQ0lDu", "id": "rs_060035eb87c40e14016a1310bac45c8191baef96d981dfb9e2", "summary": [ { "type": "summary_text", "text": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location." } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 1952, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_EjgkkXfsV2fyE3oILAvpzrDT" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_JnXrXT6YaOvHJ1i3tgEn7Xqa", "call_id": "call_JnXrXT6YaOvHJ1i3tgEn7Xqa", "response_item_id": "fc_060035eb87c40e14016a1310cc2c7481918a532354a81a7195", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ffmpeg_renderer.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"... 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FFmpeg is used only by the explicit local preview renderer boundary and remains an external system prerequisite.\\n\\n## Planned architecture\",\"old_string\":\"Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is not used yet.\\n\\n## Planned architecture\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (741 chars result)", "tool_call_id": "call_hcRUmSKGLAp9Q9bZt4AWFjFV" }, { "role": "assistant", "content": "Need manual verify.", "tool_calls": [ { "id": "call_oiC2uTDChOCKKR5v8IgErSHU", "call_id": "call_oiC2uTDChOCKKR5v8IgErSHU", "response_item_id": "fc_060035eb87c40e14016a13110207f4819198efa0e8183c50f8", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"python -m pytest tests -q && python - <<'PY'\\nfrom tempfile import TemporaryDirectory\\nfrom pathlib import Path\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_d...[truncated]\", \"timeout\": 240, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Need manual verify." } ], "id": "msg_060035eb87c40e14016a131101bf4c819190590dee93304280", "phase": "commentary" } ] }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests -q && python - <<'PY'\nfrom tempfile import TemporaryDi...` -> exit 0, 1 lines output", "tool_call_id": "call_oiC2uTDChOCKKR5v8IgErSHU" }, { "role": "assistant", "content": "Need git commit.", "tool_calls": [ { "id": "call_zGo0h2Opda1zMB6GduEOQ4p4", "call_id": "call_zGo0h2Opda1zMB6GduEOQ4p4", "response_item_id": "fc_060035eb87c40e14016a13111da2888191ae676caba4715e99", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/rendering/ffmpeg_renderer.py tests/test_ffmpeg_renderer.py && git commit -m \\\"feat: render local ffmpeg preview videos\\\" && git log --oneline -n 10 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Need git commit." } ], "id": "msg_060035eb87c40e14016a13111d526c8191948475aae66a3bed", "phase": "commentary" } ] }, { "role": "tool", "content": "[terminal] ran `git status --short && git add README.md autoshorts/rendering/ffmpeg_renderer....` -> exit 0, 1 lines output", "tool_call_id": "call_zGo0h2Opda1zMB6GduEOQ4p4" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg MP4 Preview Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\n## Neue API\n\n```python\nrender_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\nWir haben damit den ersten echten lokalen Video-Artefakt-Schritt.\n\n## FFmpegRenderResult\n\n```python\nFFmpegRenderResult(\n script_id=...,\n output_path=...,\n concat_file_path=...,\n returncode=0,\n stdout=...,\n stderr=...,\n side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\"),\n external_calls=(\"ffmpeg\",),\n)\n```\n\nAlso diesmal klar: **ja**, hier wird geschrieben und **ja**, hier wird FFmpeg aufgerufen. Kein stilles Herumgeschmuggel in einem “Formatter”. Vorbildlich langweilig.\n\n## Was der Renderer macht\n\n`render_ffmpeg_preview_video()`:\n\n1. validiert `FFmpegConcatPlan`\n2. schreibt `.concat.txt`\n3. ruft `ffmpeg`\n4. erzeugt `.mp4`\n5. nutzt:\n - concat demuxer\n - `fps=`\n - `format=yuv420p`\n - `+faststart`\n\n## Tests\n\nNeue Tests prüfen:\n\n- concat file wird geschrieben\n- MP4 wird erzeugt\n- MP4 ist non-empty\n- Result metadata korrekt\n- Side Effects korrekt:\n ```text\n (\"write_concat\", \"call_ffmpeg\", \"write_mp4\")\n ```\n- External Calls korrekt:\n ```text\n (\"ffmpeg\",)\n ```\n- invalid plan wird rejected\n- `ffprobe` Smoke Test, falls verfügbar:\n - width `540`\n - height `960`\n - fps `24`\n - duration ungefähr `35s`\n\nTests skippen sauber, falls `ffmpeg` / `ffprobe` nicht installiert sind.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_renderer'\n```\n\nGREEN:\n\n```text\n3 passed\n```\n\nFull suite:\n\n```text\n109 passed in 5.96s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\npreview.mp4 True True\npreview.concat.txt True\n('write_concat', 'call_ffmpeg', 'write_mp4') ('ffmpeg',) 0\n```\n\nBedeutung:\n\n- Script korrekt\n- MP4 existiert\n- MP4 ist non-empty\n- concat file existiert\n- Side Effects korrekt\n- External Call korrekt\n- FFmpeg returncode `0`\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_renderer`\n- explizite FFmpeg Boundary\n- `.concat.txt` + `.mp4`\n- `yuv420p`\n- `+faststart`\n- FFmpeg als externes System-Prerequisite\n\n## Commit\n\n```text\n89a0ebe feat: render local ffmpeg preview videos\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Aktueller Stand der Pipeline\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt brauchen wir einen Demo-CLI für den kompletten lokalen Preview-Render:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo\n```\n\nZiel:\n\n- deterministic Demo Content bauen\n- PNG Frames schreiben\n- FFmpegConcatPlan bauen\n- MP4 rendern\n- Metadaten ausgeben:\n - output path\n - concat file path\n - frame count\n - side effects\n - external calls\n- keine Telegram Calls\n- keine Plattform Calls\n- keine AI Calls\n- keine Secrets\n\nDann haben wir einen echten lokalen Command, der einen reviewbaren MP4-Timing-Preview erzeugt. Noch nicht hübsch wie finaler Content — aber als Timing-/Approval-Dummy endlich konkret sichtbar.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg MP4 Preview Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\n## Neue API\n\n```python\nrender_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\nWir haben damit den ersten echten lokalen Video-Artefakt-Schritt.\n\n## FFmpegRenderResult\n\n```python\nFFmpegRenderResult(\n script_id=...,\n output_path=...,\n concat_file_path=...,\n returncode=0,\n stdout=...,\n stderr=...,\n side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\"),\n external_calls=(\"ffmpeg\",),\n)\n```\n\nAlso diesmal klar: **ja**, hier wird geschrieben und **ja**, hier wird FFmpeg aufgerufen. Kein stilles Herumgeschmuggel in einem “Formatter”. Vorbildlich langweilig.\n\n## Was der Renderer macht\n\n`render_ffmpeg_preview_video()`:\n\n1. validiert `FFmpegConcatPlan`\n2. schreibt `.concat.txt`\n3. ruft `ffmpeg`\n4. erzeugt `.mp4`\n5. nutzt:\n - concat demuxer\n - `fps=`\n - `format=yuv420p`\n - `+faststart`\n\n## Tests\n\nNeue Tests prüfen:\n\n- concat file wird geschrieben\n- MP4 wird erzeugt\n- MP4 ist non-empty\n- Result metadata korrekt\n- Side Effects korrekt:\n ```text\n (\"write_concat\", \"call_ffmpeg\", \"write_mp4\")\n ```\n- External Calls korrekt:\n ```text\n (\"ffmpeg\",)\n ```\n- invalid plan wird rejected\n- `ffprobe` Smoke Test, falls verfügbar:\n - width `540`\n - height `960`\n - fps `24`\n - duration ungefähr `35s`\n\nTests skippen sauber, falls `ffmpeg` / `ffprobe` nicht installiert sind.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_renderer'\n```\n\nGREEN:\n\n```text\n3 passed\n```\n\nFull suite:\n\n```text\n109 passed in 5.96s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\npreview.mp4 True True\npreview.concat.txt True\n('write_concat', 'call_ffmpeg', 'write_mp4') ('ffmpeg',) 0\n```\n\nBedeutung:\n\n- Script korrekt\n- MP4 existiert\n- MP4 ist non-empty\n- concat file existiert\n- Side Effects korrekt\n- External Call korrekt\n- FFmpeg returncode `0`\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_renderer`\n- explizite FFmpeg Boundary\n- `.concat.txt` + `.mp4`\n- `yuv420p`\n- `+faststart`\n- FFmpeg als externes System-Prerequisite\n\n## Commit\n\n```text\n89a0ebe feat: render local ffmpeg preview videos\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Aktueller Stand der Pipeline\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt brauchen wir einen Demo-CLI für den kompletten lokalen Preview-Render:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo\n```\n\nZiel:\n\n- deterministic Demo Content bauen\n- PNG Frames schreiben\n- FFmpegConcatPlan bauen\n- MP4 rendern\n- Metadaten ausgeben:\n - output path\n - concat file path\n - frame count\n - side effects\n - external calls\n- keine Telegram Calls\n- keine Plattform Calls\n- keine AI Calls\n- keine Secrets\n\nDann haben wir einen echten lokalen Command, der einen reviewbaren MP4-Timing-Preview erzeugt. 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After that, I should implement the command-line interface (CLI) as requested. It’s essential to outline the next steps clearly. I’ll make sure to use the appropriate tools to complete this task effectively. Alright, let’s get going and figure this out together!", "reasoning_content": "**Implementing CLI and Next Steps**\n\nI need to take action by loading the relevant skills. After that, I should implement the command-line interface (CLI) as requested. It’s essential to outline the next steps clearly. I’ll make sure to use the appropriate tools to complete this task effectively. Alright, let’s get going and figure this out together!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExMFHz5d7dIFrubb1M4fM8npUSLewg_qliNwpsrOENhjlkr1jjuXpbL_9YhUwHfx8QGs97Q00vl5belWWNZEnvBmAUZO5hP4yeesU-r4oMogafOr_nbfJ3VxBQyYwxHsfFxNXbj4kN3sPP_gYCpghEFG254wGSAViMaUM-bfe8anqyI-nSoFIi-pZu7pG49qUCfons6U7XY6xmO_mc2fE7Gycv0BbtijPJHJ35z8S3rRNrlcjjV1d94rC0zrYrQaMfAXRd8EuKGue-jljJ6ajipBxSULWT13z29_zwcfWjjHtpA9eN70U6ov3lWdzL1UR3qQc931Yu4aPkzWnrK49MTUgtRhFeD5BTUKhqPkWSXMkmVsRWq9UddilNpTRB1r1YJgBRs-9gHS5XYEw6Xxr_WAjH8--u8Da1iDmdxoIaOojowhgzSFbk8VjoDiazazo9dAqkZcf-UeHMjli1CdZ7yI6UyFsOj3_xIW4moDoU5KSKjRQFIctjNmqxEQ-r6PGMPXyTbfPhdLHhbpZAKjJKeWIF-rbrbG9ZsD9XypwRxnV4nQk_PyX3DPimsP1B6H4HLFhqzKyBh2WqevfAYFEXHFsbCu80iQcBZcmMTIr_wDlpr6JzYBYwlaoBDrEBskbiol60MFU9_02QJNuybj8WHjjL1Gsbq68_jLGjVFC1WI8cCX-6s9uXTIGRSf1jI3PitVlH0wnLkIv4iasFAI91vNpg_yILSIl_Yki0U85i4RYMkG5WaCTk36Q8xIo9Ufudq3NF71SgUaQVyj8EPuTOp_5Kqglg-vLdk_xyCk1IYIA7yrfbyRdydyLgbnv1V8i5QM_Py07vf1h-5ELqDKyLGcX0lILQSS9Eyz6VfdaTWUJPFKCVto-amrlD97RdhjLp0P2KXn88SaN5pnd0mNnabkBqjnF4nUAx2TTt6qKL9uUu4BRPzWfXPsGsPhsaf4tpa7-9dmrakvtuNjvZWPksAQ0f36CiFlKu5Bpm2dv8UTqKZBodWm3syXhhsguHU9HMtgLTvXR96FyZ_tnYrQOF_Q8lWBMJnk_zNsAxYEHTvT-CU=", "id": "rs_060035eb87c40e14016a131301c5a48191bf4083cbea54149f", "summary": [ { "type": "summary_text", "text": "**Implementing CLI and Next Steps**\n\nI need to take action by loading the relevant skills. 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Alright, let’s get going and figure this out together!" } ] } ] }, { "role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_mbQecwMWLQnX3dAgKD1HdhJU" }, { "role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_CZLGTjFFJoNegpwtfjBYFbzO" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_mUUvi6zNFHcWxtrNoH1itcg8", "call_id": "call_mUUvi6zNFHcWxtrNoH1itcg8", "response_item_id": "fc_060035eb87c40e14016a13131106f88191a6ce74e4b96faab7", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"import shutil\\nfrom pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.cli.demo_local_preview_video import main\\n\\npytestmark = pytest.mark.skipif(shutil.which(\\\"ffmpeg\\\") is None, reason=\\\"ffmpeg is requi...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git.", "reasoning_content": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExMRxADC70NJSOPEKelkfHmOb1h0ArNfy8CiwS2apkPbt7dA4w85MaxoY-jb45aWldON0t056zhR8-q8pmNbnthK64YST2VG6NKMNn7QrJJULimtDjURE3-ZvMIaLpomiVS2mUOlpPG5MFgft7fDdfzwAnKkt5dUxaax3Kq8KAE8__FX8SwcMKoBDX3W3WfLfaIArhYOLkYSf0KRz1GPtQM5YVBEZEkavAQz8C-ti811wnW2ZNpX5GjPrecFlyyVzNdnh2wQojFHhuXxFL6YfmePs6LCBgWyd8OVbR-JYn5eQK2-OB9DAE6hqOSXZZoySPzF7vDsL5rdZImEH4zHr1oKEZUqKbDMtKcPBHkJ4dhgCyWxEsqjQQzyOAWcCf5iJlxic54uCShwy2Lj9IjYiBjTuADY59uD_9mrFjgsSibs6SfkHra7JeQwHSvKiujjFg9UF9WUar36bGRjs_DqiXdNlNIWfHOZoNeE0YIH7s8Xsu7scR_qbB2azw7LfchA-Mf90DyjkTZqGOr7rDIu0LLI6UTiHv_apmPD6S-OEUdvz6JKm892G7tBcquE7tT7oVGgZZSleLreQHyDlHuQqjsNiOc03jgh2ZyI0kz8JOed3MBDwzC7P0DMRzeNN8tDdZW1nSFY75TZQvC246LMN4fwDQCNWjS_eWgWTFAbQSpVKLRVeuzu2L8VVq7Hy4C8_OAnIFC9vANn9fxmJtRRDvImZohddGx0ljppnBM2FocYuJjNeLgEHE_K4iSLWY7zElURSaSRPJqWzLLsAHgYVwxmmbf49NqLOOBVIn3p2c7hPf-8nguaRCdPZArLfXT0ppsrAVFSvtCpYL5biaFRIFiFmCCd3UBucIKsqWM2q-svw1buojOn2WChjAxBLLYITYuKg_6PK6JzlTPS4b1E4s_hVWevhPI9oNNGP9scAZEXckzw2PNUNuJ7RnDWf4hI0GJZbVpXpuwVrhnNRHfbTlbaxicOKiR69olwGhIYR01LeFTidM2zBYEtiamkMgd6CNpIhaq3eQ_bok-hIbMseD0JSkfRCDwevmvPbTSL_LE9pI7REwBeWhUFZ73mrJotU7Kij4XJNa1qqtBnLw40vVjqzVfrsbfVWb8WKJovLzBkpviCSc_BaG8k7hpq2Ufb9HMuUVNVsQTiAkcY9lbRwyY561LE94mexuZTYLWo1LLk0VLeaU0U886ExG4285xmc6z0r9OSpr3YIYJ8JauOMLE5xfYuYIvNO-2CAkBdfBkyc2wQdXcjGQhKogKlTmFLeeDCkhmUvbULc6tGJ7O5Q1fU2XpFQlxHx_Cp7fSpCWTaxmRBRn8wgwcsRr5qBjybxEkZacEZHPl6QXqRSW7VtE2itRAHZXqIyX-ULYNr13FAtuax4vDkY3_EcMSBJu-GpwyyCOS5HoxlITG8Vt2J1xfxtIBo-cXcaV0f47wloP3k6cEBHGM_JMZR6iU9SBc5bCJm7QtzTW8q4vPdmEHi3dzFugPM2TZQd7fnGQGPaBHhyZXB6mlnlgL0nAUJS9S9emZ4L6H_dHPbv9CuAWf4EEuJCMRfwp24lmy5eGnH1lW2VSC-oro61aYfsB3ljuwkRY53BX8fGqA0MDcBbd0Dki0Hmwms82jCqCucDbgjhxjVcHNO9fvNQ_K-5ZbkJawnJo95q7v8pHZ5ORgQTwO0ydHTKunl4alBuQeI5I9dS0jYk9rdrns9Be7WKihamRbYH-3C0a9TbZkEAQDQjUETYW7XZbj3y3JaOT43kBIdj5INVNjHf6vTVkDKFGafFyo3DtvAaDYbRmtGREycqCJ5mRw0B31MA3LpYsS3_Zktd4U8qLI5NGiD53lnl9U1lba_tuugnFDzKQcNIrgrNzvr-TXMzB86R5kElf-KCGU21pQCIDXg0it_z6VqcvX-apIn8ptdWUlg5vsK7xYvbdBh4XvPow==", "id": "rs_060035eb87c40e14016a13130a2e0c819180d588e5a7a755f7", "summary": [ { "type": "summary_text", "text": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git." } ] } ] }, { "role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py (33 lines)", "tool_call_id": "call_mUUvi6zNFHcWxtrNoH1itcg8" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_WFWl0WnDVFNZnXMh2KGrn55U", "call_id": "call_WFWl0WnDVFNZnXMh2KGrn55U", "response_item_id": "fc_060035eb87c40e14016a131319a4408191a6da05d7d0f5e2e4", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_local_preview_video_cli.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_demo_local_preview_video_cli.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_WFWl0WnDVFNZnXMh2KGrn55U" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_FyYTsG9izfK37BgpzSbG3Fsi", "call_id": "call_FyYTsG9izfK37BgpzSbG3Fsi", "response_item_id": "fc_060035eb87c40e14016a131320fa288191b17d167568cb90c1", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"Render the deterministic local MP4 timing preview demo.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport sys\\nfrom collections.abc import Sequence\\nfrom pathlib import Path\\nfrom typing ...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning CLI Implementation**\n\nI'm thinking I need to implement a Command Line Interface (CLI) for my project. I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. I’m excited to see how it all comes together!", "reasoning_content": "**Planning CLI Implementation**\n\nI'm thinking I need to implement a Command Line Interface (CLI) for my project. I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. 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I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. I’m excited to see how it all comes together!" } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 2994, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_FyYTsG9izfK37BgpzSbG3Fsi" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_IN525eA2OOt8MwxNdBEoxVCq", "call_id": "call_IN525eA2OOt8MwxNdBEoxVCq", "response_item_id": "fc_060035eb87c40e14016a131331ea84819182c3eec222bba36f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_local_preview_video_cli.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \". [100%]\\n1 passed in 2.64s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_IN525eA2OOt8MwxNdBEoxVCq" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_DJxwrpXkp5jd3aaC74V3vKEJ", "call_id": "call_DJxwrpXkp5jd3aaC74V3vKEJ", "response_item_id": "fc_060035eb87c40e14016a13133aa4308191998ad9ca4283e234", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests -q` -> exit 0, 1 lines output", "tool_call_id": "call_DJxwrpXkp5jd3aaC74V3vKEJ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_B4OE9sYcqjMLTMSYKGxREIpM", "call_id": "call_B4OE9sYcqjMLTMSYKGxREIpM", "response_item_id": "fc_060035eb87c40e14016a13134b3df48191900f94bee7dde758", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.cli.demo_local_preview_video` — local CLI entry point that renders deterministic PNG frames and an MP4 timing preview.\\n- `autoshorts.cli.demo_render_preview` — local CLI entry point that...[truncated]\", \"old_string\": \"- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning README patch**\n\nI see that I need to make a README patch and there's a manual run needed to the /tmp directory. I'll make sure to commit those changes afterward. It seems straightforward, but I want to ensure that everything is correct and clear. Taking these steps will help maintain clarity and proper documentation as I go along. Let's proceed carefully to avoid any mistakes!", "reasoning_content": "**Planning README patch**\n\nI see that I need to make a README patch and there's a manual run needed to the /tmp directory. I'll make sure to commit those changes afterward. It seems straightforward, but I want to ensure that everything is correct and clear. Taking these steps will help maintain clarity and proper documentation as I go along. 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I'll make sure to commit those changes afterward. It seems straightforward, but I want to ensure that everything is correct and clear. Taking these steps will help maintain clarity and proper documentation as I go along. Let's proceed carefully to avoid any mistakes!" } ] } ] }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (879 chars result)", "tool_call_id": "call_B4OE9sYcqjMLTMSYKGxREIpM" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_UsxrgoSZgdRKbRxXqCySReTw", "call_id": "call_UsxrgoSZgdRKbRxXqCySReTw", "response_item_id": "fc_060035eb87c40e14016a131355c354819196f50b86f6eb0796", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"The renderer exports `yuv420p` MP4 with `+faststart`. Tests skip if `ffmpeg`/`ffprobe` are unavailable and otherwise smoke-probe width, height, frame rate, and approximate duration.\\n\\nRun the determini...[truncated]\", \"old_string\": \"The renderer exports `yuv420p` MP4 with `+faststart`. Tests skip if `ffmpeg`/`ffprobe` are unavailable and otherwise smoke-probe width, height, frame rate, and approximate duration.\\n\\n## Social media d...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,122 chars result)", "tool_call_id": "call_UsxrgoSZgdRKbRxXqCySReTw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_8UREYusvfXzhLFkVWisX3nyI", "call_id": "call_8UREYusvfXzhLFkVWisX3nyI", "response_item_id": "fc_060035eb87c40e14016a131362cb408191bcec5f2cfd5c7699", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"rm -rf /tmp/autoshorts-demo-local-preview && python -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview && python - <<'PY'\\nfrom pathlib import Path\\np = Path('/tmp/a...[truncated]\", \"timeout\": 240, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `rm -rf /tmp/autoshorts-demo-local-preview && python -m autoshorts.cli.demo_lo...` -> exit 0, 1 lines output", "tool_call_id": "call_8UREYusvfXzhLFkVWisX3nyI" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_QhEziWB9BtnzruGlCcNQ6lLq", "call_id": "call_QhEziWB9BtnzruGlCcNQ6lLq", "response_item_id": "fc_060035eb87c40e14016a1313768ff88191afa70a60f4477000", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/cli/demo_local_preview_video.py tests/test_demo_local_preview_video_cli.py && git commit -m \\\"feat: add demo local preview video cli\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `git status --short && git add README.md autoshorts/cli/demo_local_preview_vid...` -> exit 0, 1 lines output", "tool_call_id": "call_QhEziWB9BtnzruGlCcNQ6lLq" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Local Preview Video CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview\n```\n\nDer läuft jetzt komplett durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Verifiziert\n\nOutput:\n\n```text\nScript: ai_life_systems-ai-weekly-review-routine\nPreview video: /tmp/autoshorts-demo-local-preview/preview.mp4\nConcat file: /tmp/autoshorts-demo-local-preview/preview.concat.txt\nFrame count: 5\nPreview format: 540x960 @ 24fps\nFrame side effects: write_png\nSide effects: write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\nexists True\nsize 174359\nframes 5\n```\n\nTests:\n\n```text\n110 passed in 8.46s\n```\n\nCommit:\n\n```text\n496b0ac feat: add demo local preview video cli\n```\n\nArbeitsverzeichnis sauber.\n\nHier ist der erzeugte aktuelle Timing-Preview:\n\nMEDIA:/tmp/autoshorts-demo-local-preview/preview.mp4\n\n---\n\n## Was das jetzt ist\n\nDas ist ein **technischer Timing-/Structure-Preview**, noch kein finaler Content-Preview.\n\nEr zeigt:\n\n- 5 Szenen\n- Timing\n- Hook/Caption Text\n- Scene Labels\n- Asset Mode\n- Motion Instructions\n- grobe vertikale Cards\n\nEr zeigt noch **nicht**:\n\n- echte Motion Graphics\n- Voiceover\n- Musik/SFX\n- AI-/Qwen-/Wan-Visuals\n- final polish\n- echte captions synced to audio\n\nAlso: Es ist der Rohbau. Noch nicht die Villa. Aber immerhin steht kein Zirkuszelt mehr auf der Baustelle.\n\n---\n\n# Nächste Schritte bis zum effektiven Content-Preview zur Freigabe\n\n## Phase 1 — Approval Package mit MP4 + Social Draft\n\nNächster sinnvoller Schritt:\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nZielobjekt:\n\n```python\nContentPreviewApprovalPackage(\n candidate_id=...,\n preview_video_path=...,\n social_review_text=...,\n approval_commands=(...),\n requires_human_approval=True,\n side_effects=(),\n)\n```\n\nDamit haben wir ein einheitliches Ding, das sagt:\n\n> “Hier ist das Video-Preview + Caption/Titel/Hashtags + Freigabe-Kommandos.”\n\n## Phase 2 — Telegram-safe Preview Delivery\n\nDanach:\n\n```text\nContentPreviewApprovalPackage\n→ TelegramPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- Video path vorhanden\n- Approval mandatory\n- kein Upload zu Plattformen\n- kein Auto-Senden ohne explizite Adapter-Grenze\n- keine `publish/post`-Sprache\n\nDann können wir lokal erzeugen:\n\n```text\nMEDIA:/.../preview.mp4\n+\nReview Text\n+\nFreigabe-Kommandos\n```\n\n## Phase 3 — Demo CLI für echten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_content_preview.py\ntests/test_demo_content_preview_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_content_preview --out-dir data/previews/demo-content-preview\n```\n\nOutput:\n\n```text\nPreview video: data/previews/demo-content-preview/preview.mp4\nReview text length: ...\nRequires human approval: yes\nSide effects: write_png, write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\n```\n\nUnd der Text enthält:\n\n```text\n## Content Preview Review\n\nVideo:\n\n\nCandidate:\n...\n\nPlatforms:\nYouTube Shorts, TikTok, Instagram Reels\n\nHook:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nAblehnen\nÄndern: <...>\n```\n\n**Das ist der erste echte Freigabe-Preview.**\n\n## Phase 4 — Renderer v2: weniger statisch\n\nAktuell sind es Scene Cards. Für “Content Preview, das nicht beleidigend statisch wirkt” brauchen wir danach:\n\n```text\nautoshorts/rendering/motion_preview.py\n```\n\nZiel:\n\n- simple Ken Burns / pan / zoom\n- kinetic text intro\n- scene transitions\n- progress bar\n- captions renderer-owned\n- sichtbare Bewegung alle 1–2 Sekunden\n\nNoch lokal, noch ohne AI-Video.\n\n## Phase 5 — TTS / Voiceover\n\nDann:\n\n```text\nScriptDraft\n→ VoiceoverScript\n→ TTS audio\n→ caption timing\n```\n\nFür dich relevant:\n\n- Default Voice laut Profil: `de-DE-KillianNeural`\n- Wenn Content englisch bleibt, müssten wir eine englische Voice wählen.\n- Output:\n - `.mp3` oder `.wav`\n - später `.srt` / caption timings\n\nDanach:\n\n```text\nMP4 visual preview\n+ TTS audio\n→ audio-synced preview\n```\n\nDas wäre schon deutlich näher an einem echten Content-Preview.\n\n## Phase 6 — Qwen Image / Wan / ComfyUI Assets\n\nErst danach würde ich Qwen/Wan anbinden:\n\n```text\nRenderManifest Scene\n→ Keyframe Prompt\n→ Qwen Image Keyframe\n→ Wan I2V Clip\n→ imported/local asset\n→ deterministic final assembly\n```\n\nWichtig bleibt:\n\n- keine baked-in text in AI assets\n- captions/labels/arrows renderer-owned\n- AI-Video nur für Textur/Bewegung\n- Review jedes Clips vor finaler Assembly\n\n## Meine empfohlene Reihenfolge ab jetzt\n\nKurz und praktisch:\n\n```text\n1. ContentPreviewApprovalPackage\n2. TelegramPreviewDelivery / local delivery object\n3. demo_content_preview CLI\n4. Motion Preview Renderer v2\n5. TTS + synced captions\n6. Qwen/Wan asset layer\n7. Final review package\n8. Erst nach expliziter Freigabe: Plattformadapter\n```\n\n## Direkt nächster Task\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nDamit verbinden wir den vorhandenen MP4-Preview mit dem Social-Media-Freigabetext zu einem echten Content-Preview-Paket. Danach kann ich daraus den Telegram-fähigen Freigabeentwurf machen.", "reasoning": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval.", "reasoning_content": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExOCkjz5xWQK9etJL4VTp7rHPMlVJVtgGoTKWUXufjYstY68jbpg_xCMgSz0zehPScjnIe9mQsmsBohttYOBaYLfb-hrSGglLOszh2XndUm5h3MU3VK5QRbeG9UzoSFlgnx4-DgHE5lVqEgFsZb1y0CeAVpbcRo0PfECCeSyj08hRyz2rurZplWTzLEFtl_wwAuuKzoZB_aXzcfp8D2RSAAmkl1Cn9RyP3uClY-BEygHIbezBYQxsHITpg5N_T9EV6iS6jIq0qQpYbJHuiavxLgeJSo42tab2wAU_9RKRNUf9BdNcQn1mEMV_NLsLY1yLEf9Aq3AP5_cvddsN1kYLjQNH9C01Qjj5VlGxLIBU77c5OBGuc_qhk5_Ipqmh1et571LlD4Ze2rd8rFayVMrfnr4CmA_1S7KAVnbbTjslnzZlNqXRUK-azbvDuVYSZK12qgeAUpP6Hw-1gkuyvAiHPDwrjtb8u4lN0W8JLko2fsu3CoCPTIh8zgbqE3TRCHbqaSS7AVMdA3Y_AMRxa9gNTwwQvzrBONR9zsM5nB3rm3mrdtAg0zQG6RZlzF3KrIvouSxL49pi1a1ewHCICag0TO_W0xvflkfYCaOSeHtThWBnpBFhiTCEYSFtlF0KXRXq2fhwGVFaO5eoramEfWuDjIRTyqhjHgVReUSY8EVQ016Cj96D-tOoEZfRV3Xgm5nSITf1rq4uqW9TTnqAxnNvAvm-IpqF3bUXXAi_30kaBgNiDlAIRrh1TTRXmk69IsyqrSNLw2r0TfqB8fnzJk3OU4u7wfX5ESdWGLxNkIkfb8yBfYdYo_SHA4nBdFjmTu3LNbLmgyZJVuNLFEKAvW-gKXrRvX-RlxoE3zhPgB1ut9k1u6zRC2C8BRb_Jp9NHlZyy3q6zJjsJYMfOdIwa1h4-r9F-RMyPsDjC_Ujiygukqz2mhxbtXrBf-UzbECjyzLI6jKnZfdhgYJoDGVrz_ZHMmsBOAZYn8Met0MSHpPP_dhrHTDL4UcrIhuGGy05IggrKp_rbGYnIxuY3ql9q6IuOPhIMolxyf5thMQAjo87iJjOtfG96K7qyG-4boxdX_ai1wexepTv8bwxWqxEI7WVbyhi1I_KX18Kn1Hgj1q-twn6YdSanSTV2VqCgXGUdkIKl49y1Re-SEpuv2nbfKYIVcp519Te9tI1geXhcp1K07UQ-oSgrCHjcEHInM3jsxF2U2hXkZHu9jB40jX9HKWDYgtfvftf2Wgp4meGj6bPfKD575yhq15ZMoqb9YGZKr2y9J8CV8nkfiL7VxyUMTGuuepImpd5Fn2T5f0YCsdOpiAgnQD1UiCjRT9AhlMitf5I_nIXzBCRQKWpUEjNJWJuMvjFg6aCo1vW6qvCRFEQzXlmSYI8CjBCuSZFot46H-kjom5W9Bux9ZxJOzNKR00dnGT2KVbSw-CoBkrI6eirqIR509RWFiPQ3HZJtd3qvTNk9xQ7sQrqpw5TIjDG9fW06pz5l2oWLmxly7Vzlxfe6cXNZKdgS8hO6vIlzgooCwRn8r6EC2yw7Obt309vI4rhI_onY7G_MVA1IuKI5gxHXP3DfKHKTwgZdRqYZ9Rg-6BPhN6ofkroYiWznfcVYGfm9xdMv3A-N-R7KL9zDl_A7cHrGSGcZyn2qPURVpzDKqahj2nXh8VuqhuA9c3bpKkenkP-2TvzrngwcSi7obQjAiJBi0ZbUKJjoA3qauICv-PnBgeLRi1QdBik7A4vIIi3jD3Pq51M0ca3-Z0M8k-FSB7rvD1D0GXyXCpiRBcqX_Ug_QeMpZ8ADvcoHaF0I5N3L912bLoiTkWVtCq8mePsB9gibqRlWycCR6f4nDz8AnrTrJVPv2sixP-RL0cwEyZoYhUzoqk3GDUzdF3LpboDReGGvBigY3sCTeXAlL46AVV4VNA39OYZ02qsJZjgML9GZIyqjv01b3Ir1bxvcrA-wI8zx5YI4iPHoB9Qw4mjxDfMxIw8PzL8_eg2o25Fpi2ODWUAHjKfOCpYtPvplaQn5rBst8G7HHHpJu8vY823R-BWEI6IhoojtfKhNZrigGqYgcLpcW6ZVJtb9rA4Tue4-jQNaDAuaZxP7W7e0R0nKJFJCL9", "id": "rs_060035eb87c40e14016a13137bb2e0819181a353f3ac1f0169", "summary": [ { "type": "summary_text", "text": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval." } ] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Local Preview Video CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview\n```\n\nDer läuft jetzt komplett durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Verifiziert\n\nOutput:\n\n```text\nScript: ai_life_systems-ai-weekly-review-routine\nPreview video: /tmp/autoshorts-demo-local-preview/preview.mp4\nConcat file: /tmp/autoshorts-demo-local-preview/preview.concat.txt\nFrame count: 5\nPreview format: 540x960 @ 24fps\nFrame side effects: write_png\nSide effects: write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\nexists True\nsize 174359\nframes 5\n```\n\nTests:\n\n```text\n110 passed in 8.46s\n```\n\nCommit:\n\n```text\n496b0ac feat: add demo local preview video cli\n```\n\nArbeitsverzeichnis sauber.\n\nHier ist der erzeugte aktuelle Timing-Preview:\n\nMEDIA:/tmp/autoshorts-demo-local-preview/preview.mp4\n\n---\n\n## Was das jetzt ist\n\nDas ist ein **technischer Timing-/Structure-Preview**, noch kein finaler Content-Preview.\n\nEr zeigt:\n\n- 5 Szenen\n- Timing\n- Hook/Caption Text\n- Scene Labels\n- Asset Mode\n- Motion Instructions\n- grobe vertikale Cards\n\nEr zeigt noch **nicht**:\n\n- echte Motion Graphics\n- Voiceover\n- Musik/SFX\n- AI-/Qwen-/Wan-Visuals\n- final polish\n- echte captions synced to audio\n\nAlso: Es ist der Rohbau. Noch nicht die Villa. Aber immerhin steht kein Zirkuszelt mehr auf der Baustelle.\n\n---\n\n# Nächste Schritte bis zum effektiven Content-Preview zur Freigabe\n\n## Phase 1 — Approval Package mit MP4 + Social Draft\n\nNächster sinnvoller Schritt:\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nZielobjekt:\n\n```python\nContentPreviewApprovalPackage(\n candidate_id=...,\n preview_video_path=...,\n social_review_text=...,\n approval_commands=(...),\n requires_human_approval=True,\n side_effects=(),\n)\n```\n\nDamit haben wir ein einheitliches Ding, das sagt:\n\n> “Hier ist das Video-Preview + Caption/Titel/Hashtags + Freigabe-Kommandos.”\n\n## Phase 2 — Telegram-safe Preview Delivery\n\nDanach:\n\n```text\nContentPreviewApprovalPackage\n→ TelegramPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- Video path vorhanden\n- Approval mandatory\n- kein Upload zu Plattformen\n- kein Auto-Senden ohne explizite Adapter-Grenze\n- keine `publish/post`-Sprache\n\nDann können wir lokal erzeugen:\n\n```text\nMEDIA:/.../preview.mp4\n+\nReview Text\n+\nFreigabe-Kommandos\n```\n\n## Phase 3 — Demo CLI für echten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_content_preview.py\ntests/test_demo_content_preview_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_content_preview --out-dir data/previews/demo-content-preview\n```\n\nOutput:\n\n```text\nPreview video: data/previews/demo-content-preview/preview.mp4\nReview text length: ...\nRequires human approval: yes\nSide effects: write_png, write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\n```\n\nUnd der Text enthält:\n\n```text\n## Content Preview Review\n\nVideo:\n\n\nCandidate:\n...\n\nPlatforms:\nYouTube Shorts, TikTok, Instagram Reels\n\nHook:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nAblehnen\nÄndern: <...>\n```\n\n**Das ist der erste echte Freigabe-Preview.**\n\n## Phase 4 — Renderer v2: weniger statisch\n\nAktuell sind es Scene Cards. Für “Content Preview, das nicht beleidigend statisch wirkt” brauchen wir danach:\n\n```text\nautoshorts/rendering/motion_preview.py\n```\n\nZiel:\n\n- simple Ken Burns / pan / zoom\n- kinetic text intro\n- scene transitions\n- progress bar\n- captions renderer-owned\n- sichtbare Bewegung alle 1–2 Sekunden\n\nNoch lokal, noch ohne AI-Video.\n\n## Phase 5 — TTS / Voiceover\n\nDann:\n\n```text\nScriptDraft\n→ VoiceoverScript\n→ TTS audio\n→ caption timing\n```\n\nFür dich relevant:\n\n- Default Voice laut Profil: `de-DE-KillianNeural`\n- Wenn Content englisch bleibt, müssten wir eine englische Voice wählen.\n- Output:\n - `.mp3` oder `.wav`\n - später `.srt` / caption timings\n\nDanach:\n\n```text\nMP4 visual preview\n+ TTS audio\n→ audio-synced preview\n```\n\nDas wäre schon deutlich näher an einem echten Content-Preview.\n\n## Phase 6 — Qwen Image / Wan / ComfyUI Assets\n\nErst danach würde ich Qwen/Wan anbinden:\n\n```text\nRenderManifest Scene\n→ Keyframe Prompt\n→ Qwen Image Keyframe\n→ Wan I2V Clip\n→ imported/local asset\n→ deterministic final assembly\n```\n\nWichtig bleibt:\n\n- keine baked-in text in AI assets\n- captions/labels/arrows renderer-owned\n- AI-Video nur für Textur/Bewegung\n- Review jedes Clips vor finaler Assembly\n\n## Meine empfohlene Reihenfolge ab jetzt\n\nKurz und praktisch:\n\n```text\n1. ContentPreviewApprovalPackage\n2. TelegramPreviewDelivery / local delivery object\n3. demo_content_preview CLI\n4. Motion Preview Renderer v2\n5. TTS + synced captions\n6. Qwen/Wan asset layer\n7. Final review package\n8. Erst nach expliziter Freigabe: Plattformadapter\n```\n\n## Direkt nächster Task\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nDamit verbinden wir den vorhandenen MP4-Preview mit dem Social-Media-Freigabetext zu einem echten Content-Preview-Paket. Danach kann ich daraus den Telegram-fähigen Freigabeentwurf machen." } ], "id": "msg_060035eb87c40e14016a131382decc8191b71fae6b08824198", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] Den zeilenumbruch musst du noch optimieren. Der text ist teilweise nicht lesbar. Mach diese freigabevorschau videos grundsätzlich in deutsch (sodass ich das konzept vesser verstehe), die finalen videos dann in englisch." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_29kWbiEFBFMkHo21tp3NWFzY", "call_id": "call_29kWbiEFBFMkHo21tp3NWFzY", "response_item_id": "fc_060035eb87c40e14016a1317079a4c8191b867c1f41b6ee803", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } }, { "id": "call_N0CtnOJN2PmDMygeGmQDf7a3", "call_id": "call_N0CtnOJN2PmDMygeGmQDf7a3", "response_item_id": "fc_060035eb87c40e14016a1317079a6881918a834fa6c28a41e7", "type": "function", "function": { "name": "skill_view", "arguments": "{\"file_path\":\"references/manifest-preview-renderer.md\",\"name\":\"social-media-shorts-automation\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExcHrnfFUvebwZgitOGEaXUmVyx0TxrVQhzbBiZyNM12UfFhM1Nuh_q9z17azpLBzWzMSYcY8Hncc5mPCoOBq44UK1Va2BFRvc0AX4E1tfcr9qhEWICXyCOFcckqvYO4xrQ0c2uaQzFXu94of4dJOV5OfkiJAWu_zrEOfhFMWlMFG7izTzc4T0hKNUmp7o-kDHKP4nV9h48YVKpnUb1e0om2JQi4hi-FX_p3N6eKwyfSFV7GS_zWb8mRMdPOPSPrVTl56OvGsNAb8iKYpLbZb8jG7UVAsEnS3NV8eH0VKADG3LqzCU7ulxAqRIpkQgJ8D4hKB3z_jXJZ8TgPdnSqaPcLedH8bC6lTCFri0NI_QH9yUKJRQesiU1W-bNpx1QHQ05bFo316ovEdRCXWoZ-fofw5ZN45Cme1SjIvPecF8eNfS65V8wpaT0Ioc7VjwHKeBvdGsnZ1g_vxY5O4N-RLZTplq56U2PScpXj_UxLW9MtBb8Sp4Faf8DoXkkO1b5Tm8yCbKpaHvYGes26eC8kWAMzE-ax6uGydDPhEoT4VeyagyLb1kGTSMXSE0TVueF7tj8edYwv9Efd1SswfA15yTjGhYzn5jyPVnZnsMXmfwHO95-ZFTwaRcX-6f-lHRVXA6KjzxYYO-xylM7j9tWv-g7QMkdYNl3b4IahJiiR3p3FQQLFgSC7uZ6bHjsNnnYnKJVEesZShuEucAxd-qZYBfC8mr-w79GDvbdfyq5ggTx0LuB_Edz0hGLxHoel_vVfDsQUw8tsnf8ikFcHIctfaif4YnqFwhlDON-F4U61Iw35DOEyLzfPX1o3TV6LYGqAgbhe5FGxno8RIgNPs18QL5VhEGVz-gRxbEm0iunG5zvd3RiomWJfqXS-yDNJVVjFae7R9NUAfIld2ZfVPWuuJZ-rS7p25kdhDbkBg-C8qwcW8Y4LD29-poktc31Gk8n1UDkCguBF9DTkhY9bDEn9C3mhxH8DJWKKX-iLDz3HhPYAOero2n2_dbbh5cTzbuBGDbc2uli9XrK7QTVriKK5_CwFX-_2hoUQDEux1HkqxGeORW51J8zdONGbkum6uVwOVUGT9p1sGp4IbN3UAkDmto0iwPZgc_Pi2XXkij11e-VrYaSumLy7Kv9hweR1joE5eDkjUMvn42OBTjmK-y3yxqeycLUyBAk5f6MPOiyO09tfo0jo8Dxz0hYYQ7HA1Nb_1MdENQ8sLT7I_NrYnoIUveCdMaTD6TX66_nQxPtz3xJbdf48xD1UXszygsPTD6kOvTyk6QIhwRUSOzThBJtKvh_OOvNwEqUs51zigGdUqLYdQTT1uSt0__k5e5swkSFrojhIM7nurntNN_2Bk4ZR4QU6xRT-TX4gdRWJyecWPZmdDWU0fXyvRhghn7YDk2LfRZ_3XR9WtVnxoont2OTP79MjMj626lyLDbdYewU264l-vxK8JCmeYShPIsMvh8H7A_vv", "id": "rs_060035eb87c40e14016a131704accc8191a1394023553ea4e0", "summary": [] } ] }, { "role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_29kWbiEFBFMkHo21tp3NWFzY" }, { "role": "tool", "content": "[skill_view] name=social-media-shorts-automation (5,015 chars)", "tool_call_id": "call_N0CtnOJN2PmDMygeGmQDf7a3" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_76kWYFKlvRzggFnok0a7kg5M", "call_id": "call_76kWYFKlvRzggFnok0a7kg5M", "response_item_id": "fc_060035eb87c40e14016a13170d54b081918b2b0d0e0e3afe09", "type": "function", "function": { "name": "memory", "arguments": "{\"action\":\"add\",\"target\":\"user\",\"content\":\"AutoShorts preference: Freigabevorschau-/Approval-Preview-Videos grundsätzlich auf Deutsch erstellen, damit das Konzept verständlicher ist; finale Social-Media-Videos danach auf Englisch.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[memory] add on user", "tool_call_id": "call_76kWYFKlvRzggFnok0a7kg5M" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_yCiPXsK7STot2vIuCyLmS9DD", "call_id": "call_yCiPXsK7STot2vIuCyLmS9DD", "response_item_id": "fc_060035eb87c40e14016a131712aed0819184c2f6b35015593c", "type": "function", "function": { "name": "memory", "arguments": "{\"action\":\"replace\",\"old_text\":\"AutoShorts preference: free/local approval-first Retention Studio\",\"target\":\"user\",\"content\":\"AutoShorts preference: free/local, approval-first, TDD slices, deterministic artifacts, human approval, no side effects until approved. 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Prefer motion-led mechanism-reveal Shorts; quality > speed; avoid static slideshows/generic avatars/paid credits/artifacts.\",\"old_text\":\"AutoShorts preference: free/local approval-first Retention Studio\",\"target\":\"user\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[memory] replace on user", "tool_call_id": "call_t3BdMEhRhmNNABxLJx7eQEk0" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_RZ5a34l1TbGvgQLeL1drdsZN", "call_id": "call_RZ5a34l1TbGvgQLeL1drdsZN", "response_item_id": "fc_060035eb87c40e14016a131734be2c81919387302cfefc1488", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\",\"limit\":240}" } }, { "id": "call_tpFMaQc6afm4Si23EQmBkmS4", "call_id": "call_tpFMaQc6afm4Si23EQmBkmS4", "response_item_id": "fc_060035eb87c40e14016a131734be448191afc775ef4da6203d", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\",\"limit\":200}" } }, { "id": "call_FmwxbRejcDaMEZOslULvboQB", "call_id": "call_FmwxbRejcDaMEZOslULvboQB", "response_item_id": "fc_060035eb87c40e14016a131734be50819199eb88420cae816d", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py\",\"limit\":260}" } }, { "id": "call_cPV2DePdLkjnl5OaDKLg6pcY", "call_id": "call_cPV2DePdLkjnl5OaDKLg6pcY", "response_item_id": "fc_060035eb87c40e14016a131734be5881918205def3584231fe", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short\",\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"timeout\":120}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExc0CxNh_K3FsRTwTM0MAg0M_-l39sSyGMtddWB82WvO5XZO9EUgeGO2Lsw5F8xwT5iOEHadabGw_ivdjdKKjpxMMgdbtnDNut9AEOKM020yAmHixfW8DEpr4XgTyLKGUxFOW--_dKv4suHI_QUsag89MHyCgfG5IqUOzdOtS1jCVUqkELyXbs-7JSoAQze5-JNW0P7wUZM6gmWDx0l7fnde9mRc2pxQYM_RGDHe1U_u0hxSa9Vfbit49KPtnzt7rQPTVu6KylVCB81yECdLA6FlXcUCU2UKCvW3ZluVz7CUF4JSZQYKtA3ypl3hnHPAz1cw53SlG1r9PeHH6fVe48otz1cIxDL4gbDlagzZQYe87Zp93L07_8O7zhwPHijaVKSak3vqC_OS51LaLOCsm88omqiByTOnhs_SVV2LpQxOODP1TxZdf_KQuCenRFOAy831vw_0SpDvhIPHkVl7Q-0WEiUSve3eiRXQxl2L-xdpLdz8HHjmZlP0vO1KRd7IMrvRjbA0dm60OZ8we1g7G81_GrRgo7EmUj8Tp35j0BJlmRepUbm1fVmQAu7ohoy9A08p374aE-ishaO9Q0bzGulymGL67hpCzE_T2xpvuKbHLU-A91PvE91xGzLNdlQKz1_EBNWLRCAAyhUcpgvfK4YnlWY3Hocgp2rlRJbFdU5e8Hirl2lwnInjKwYdF9JKHYOZWb9d7Yggylvxz0io6ipIga0TPx-Kj1USEf6sWI-u-Ot9rNYMAPy8bVVzNPWgIAsTeLWfOAvn-PsGIxXWPCXzNHXj9YgUEoH63hFfSvE1RQHm362Dq2d2TwEvG9SS62FnIcdCL1W6Pudn-Hn0ukNV_sNgBzk2NlrvgSl58OmDQgJS0xJOseD_eR_668v8SiuOzleFX0pqhFJk5QYtsl1VczHYxfecR988CsHt85ipwaF5hqHX7CZg9vBx89nevVk3", "id": "rs_060035eb87c40e14016a13173128008191bb99daeba44f7bbc", "summary": [] } ] }, { "role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py from line 1 (4,889 chars)", "tool_call_id": "call_RZ5a34l1TbGvgQLeL1drdsZN" }, { "role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py from line 1 (3,814 chars)", "tool_call_id": "call_tpFMaQc6afm4Si23EQmBkmS4" }, { "role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py from line 1 (6,060 chars)", "tool_call_id": "call_FmwxbRejcDaMEZOslULvboQB" }, { "role": "tool", "content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_cPV2DePdLkjnl5OaDKLg6pcY" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_A33yvxEGBLenzAHWJbrjC172", "call_id": "call_A33yvxEGBLenzAHWJbrjC172", "response_item_id": "fc_060035eb87c40e14016a131746b9908191a50e51dfe1be3321", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"old_string\":\"from autoshorts.rendering.local_frame_writer import LocalFrameWriteResult, write_local_preview_frames\\n\",\"new_string\":\"from autoshorts.rendering.local_frame_writer import (\\n LocalFrameWriteResult,\\n localize_frame_copy,\\n wrap_text_to_pixel_width,\\n write_local_preview_frames,\\n)\\n\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Updating local frame tests**\n\nI’m realizing the CLI output should be text rather than video. It looks like I need a local frame file instead of OCR. Pure tests might be sufficient, so I should consider updating `local_frame_writer.py` with new functions. It might make sense to write tests first using ImageDraw for text bounding boxes. I wonder if I need to import `localize_frame_copy` and `wrap_text_to_pixel_width` since they seem to be missing. It’s essential to ensure the writer can accept `approval_language` too.", "reasoning_content": "**Updating local frame tests**\n\nI’m realizing the CLI output should be text rather than video. It looks like I need a local frame file instead of OCR. Pure tests might be sufficient, so I should consider updating `local_frame_writer.py` with new functions. It might make sense to write tests first using ImageDraw for text bounding boxes. I wonder if I need to import `localize_frame_copy` and `wrap_text_to_pixel_width` since they seem to be missing. 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It looks like I need a local frame file instead of OCR. Pure tests might be sufficient, so I should consider updating `local_frame_writer.py` with new functions. It might make sense to write tests first using ImageDraw for text bounding boxes. I wonder if I need to import `localize_frame_copy` and `wrap_text_to_pixel_width` since they seem to be missing. It’s essential to ensure the writer can accept `approval_language` too." } ] } ] }, { "role": "tool", "content": "{\"error\": \"path required\"}", "tool_call_id": "call_A33yvxEGBLenzAHWJbrjC172" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_hhsOOoKmo4EHYwcBgkhtVoqx", "call_id": "call_hhsOOoKmo4EHYwcBgkhtVoqx", "response_item_id": "fc_060035eb87c40e14016a131749b2388191b73209a54ad618ca", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\",\"old_string\":\"from autoshorts.rendering.local_frame_writer import LocalFrameWriteResult, write_local_preview_frames\\n\",\"new_string\":\"from autoshorts.rendering.local_frame_writer import (\\n LocalFrameWriteResult,\\n localize_frame_copy,\\n wrap_text_to_pixel_width,\\n write_local_preview_frames,\\n)\\n\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py (1,423 chars result)", "tool_call_id": "call_hhsOOoKmo4EHYwcBgkhtVoqx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_eCSdXPTrtrT1zOaYVFSCVofJ", "call_id": "call_eCSdXPTrtrT1zOaYVFSCVofJ", "response_item_id": "fc_060035eb87c40e14016a13175278388191a7f30a3437d3a491", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def test_write_local_preview_frames_writes_valid_png_scene_cards(tmp_path):\\n plan = _plan()\\n\\n result = write_local_preview_frames(plan, output_dir=tmp_path, approval_language=\\\"de\\\")\\n\\n assert i...[truncated]\", \"old_string\": \"def test_write_local_preview_frames_writes_valid_png_scene_cards(tmp_path):\\n plan = _plan()\\n\\n result = write_local_preview_frames(plan, output_dir=tmp_path)\\n\\n assert isinstance(result, LocalF...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py (1,066 chars result)", "tool_call_id": "call_eCSdXPTrtrT1zOaYVFSCVofJ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_oaItu5KWusa0ad12gVTs1qFP", "call_id": "call_oaItu5KWusa0ad12gVTs1qFP", "response_item_id": "fc_060035eb87c40e14016a13175cf4208191bbaf97e93e0ad508", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def test_write_local_preview_frames_preserves_plan_order_and_paths(tmp_path):\\n plan = _plan()\\n\\n result = write_local_preview_frames(plan, output_dir=tmp_path, approval_language=\\\"de\\\")\\n\", \"old_string\": \"def test_write_local_preview_frames_preserves_plan_order_and_paths(tmp_path):\\n plan = _plan()\\n\\n result = write_local_preview_frames(plan, output_dir=tmp_path)\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py (1,047 chars result)", "tool_call_id": "call_oaItu5KWusa0ad12gVTs1qFP" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_GJSfTsJ3J1BAR8o34JK0UXn8", "call_id": "call_GJSfTsJ3J1BAR8o34JK0UXn8", "response_item_id": "fc_060035eb87c40e14016a13176375b48191a850a0bd0a0ca93a", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \" with pytest.raises(ValueError, match=\\\"output_dir must be explicit and normalized\\\"):\\n write_local_preview_frames(plan, output_dir=unsafe_dir)\\n\\n\\ndef test_localize_frame_copy_defaults_to_germa...[truncated]\", \"old_string\": \" with pytest.raises(ValueError, match=\\\"output_dir must be explicit and normalized\\\"):\\n write_local_preview_frames(plan, output_dir=unsafe_dir)\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py (1,457 chars result)", "tool_call_id": "call_GJSfTsJ3J1BAR8o34JK0UXn8" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_eCloY72oQab3ZcI4JHdycOik", "call_id": "call_eCloY72oQab3ZcI4JHdycOik", "response_item_id": "fc_060035eb87c40e14016a13176e99188191a25608241c2163e7", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_local_frame_writer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_local_frame_writer.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_eCloY72oQab3ZcI4JHdycOik" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dKQqpf44N3TuCj6kQslLbhmw", "call_id": "call_dKQqpf44N3TuCj6kQslLbhmw", "response_item_id": "fc_060035eb87c40e14016a131775b1cc8191821a005c8f5b6bc7", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"Write deterministic local preview PNG scene cards.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\nfrom textwrap import wrap\\n\\nfrom PIL import Imag...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"id": "rs_060035eb87c40e14016a131772fdbc81919b2a89d869130ee4", "summary": [] } ] }, { "role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py (219 lines)", "tool_call_id": "call_dKQqpf44N3TuCj6kQslLbhmw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dWyggnRbEPQm6v6mcrcmxTjG", "call_id": "call_dWyggnRbEPQm6v6mcrcmxTjG", "response_item_id": "fc_060035eb87c40e14016a1317a7d1348191975d6ccbb3d750f4", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_local_frame_writer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Testing font properties**\n\nI’m thinking about addressing a potential issue with the line height, especially if font size is a float. 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I just want to make sure everything works as intended, so I’m hopeful that the tests will provide clarity!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExenbL8owYRAbw01L1ffZgww80IgoKXkLwI76Nv2n1eSGQLacjNzWe1-3nJixgRpWtWyVtC9BWteN58NRLpoWkbmNv4pKAnik98NbNKS-I48McADBObjsXUK5nkv9orkuZAgbN0RA-j6YgTCiqbKnxozLoaRs0oiAoB4l-s95Ypl3y9nN_V0TeoxbKQGF97MKcUIvM00E9I-dSlZmgKjAEfCyHkuJPY-PHluyi_jzi9O8QzMrU2rPFbsPrdac-jq0QRbmv2cG7fMxJblNZR6x55NfdMUAqv1GMtpTD3sthJ-Mr_sUIggJ8J6_MSqcFij38BAdELmrb0jhVgp5LyjgGPK4VaJhoiUZW_ejWCVLdhNhaReMc2-v13AdkktrzCTtmc4_lETHdCgafsH-Qw6_N8KE8YK27n7G9UJcTfnEvMQ34X2P_QAg0QS5rtLuZ--Fhw2O1AprLTjFA90ryvZMKIj3lEYCEfoU_qZZQPA2d167daDRf7qr3KGdn0eKbuCChx0-XRJdipJpE3Qw3TxMbB7KcEplvb4wZOJHTcpMdNVJKz_NPYNC_rDjzen-kTUQK9bUsRjM9WHhGVc2vHBDVh7bCxzUZbG3gNqE57kcYWyCQDaL4kyUAhtOYJlW4DJJtmLMi3Ybu3EOMUvr1d7AOahMfW9PFbvWRX00zoYHvIqNJHty03PE3FpI-onWajPot7MzQQ-N1KztBhS_tDb4SFMd4ZCqNNZ4aHHeq-nlHbYWESqXxtN8fqC-ey3v19x7GwTuo82OaPtQvEMYAWjYUkFc3mXy9ZWAi54VZ5YFP9lOC2nYVktADvEgLFO-duyA-KxWaOlx_eF4-cA7YQ8-_VcwWlj_YFs03P_fen14xOosQeed26qVaOOL0mRpuGmRhr-MEqWXzzAxt4L4zRXgigLXnOjCz82rdbVk0T_qEtzbX7rta8EK5CNwNNSYo-ZW74GmRMoFFkaHdlANCffyoHhnpbg6x_gEq7q7CTzv-dE6qszFoGglpLlNC6W_iu9Fcq1", "id": "rs_060035eb87c40e14016a1317a553fc81919f15208e36742be9", "summary": [ { "type": "summary_text", "text": "**Testing font properties**\n\nI’m thinking about addressing a potential issue with the line height, especially if font size is a float. I want to ensure it runs smoothly, so I’ll run some tests to check for any runtime issues. This could help pinpoint whether there's a problem that needs fixing. I just want to make sure everything works as intended, so I’m hopeful that the tests will provide clarity!" } ] } ] }, { "role": "tool", "content": "{\"output\": \"...... [100%]\\n6 passed in 0.34s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_dWyggnRbEPQm6v6mcrcmxTjG" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_N2YmAOenerXqdQkikSLqguMn", "call_id": "call_N2YmAOenerXqdQkikSLqguMn", "response_item_id": "fc_060035eb87c40e14016a1317b13b848191b80b25a2e789cd9a", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Cleaning and Testing Code**\n\nI’m thinking that maybe using pyright isn’t a must, but I should clean things up a bit. I want to write files for diagnostics, and possibly patch them. It might be useful to add tests for a local preview in German. I’ll need to ensure that the default writer for German is functional through a specific function. Plus, I want to render a new video and visually inspect it, but I might need comprehensive tests in case something fails unexpectedly.", "reasoning_content": "**Cleaning and Testing Code**\n\nI’m thinking that maybe using pyright isn’t a must, but I should clean things up a bit. I want to write files for diagnostics, and possibly patch them. It might be useful to add tests for a local preview in German. I’ll need to ensure that the default writer for German is functional through a specific function. 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I want to write files for diagnostics, and possibly patch them. It might be useful to add tests for a local preview in German. I’ll need to ensure that the default writer for German is functional through a specific function. Plus, I want to render a new video and visually inspect it, but I might need comprehensive tests in case something fails unexpectedly." } ] } ] }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests -q` -> exit 0, 1 lines output", "tool_call_id": "call_N2YmAOenerXqdQkikSLqguMn" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_9GNii9kxqL4mteHua44BxO5x", "call_id": "call_9GNii9kxqL4mteHua44BxO5x", "response_item_id": "fc_060035eb87c40e14016a1317c329b48191b0e9a19f2a422928", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert \\\"Preview format: 540x960 @ 24fps\\\" in captured.out\\n assert \\\"Approval language: de\\\" in captured.out\\n assert \\\"Frame side effects: write_png\\\" in captured.out\\n\",\"old_string\":\" assert \\\"Preview format: 540x960 @ 24fps\\\" in captured.out\\n assert \\\"Frame side effects: write_png\\\" in captured.out\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering verification steps**\n\nI need to generate a video and view the screenshot to verify things. It might be necessary to update the CLI output to include a mention of German. I wonder if I should run tests for the demo output as well. Perhaps I could create a patch test to assert \"Approval language: de\" and the corresponding code. Should I use Test-Driven Development (TDD)? Yes, adding a test that fails could be a good idea!", "reasoning_content": "**Considering verification steps**\n\nI need to generate a video and view the screenshot to verify things. It might be necessary to update the CLI output to include a mention of German. I wonder if I should run tests for the demo output as well. Perhaps I could create a patch test to assert \"Approval language: de\" and the corresponding code. Should I use Test-Driven Development (TDD)? Yes, adding a test that fails could be a good idea!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExfDObqfP4VJ-TEs9EZHisNwuEqsr4hCRgCzcGNdMk42jDmYdJ2ax0Pso0dq4Thw13lvQLu5biBCuxa60aDyluAwz0nYkHmMe0Fnr_k0iTaFYsZT-A_WQoxYA4S-EEYc9Ih9kiYHNNSyUQEgyrbf8NJjGQq9IMh6RZigtl5HUH3luomw8oBzaMC7uFfD93cAiNgWGKZ2Jp03MXejk0dLH0pxnK85MmfU-iQKWKGJhsuM_OQNv7_xe7kZKriNqJ4D5N8dT4tZiH2R5K8ULhEQt56yiwJmBL58r0gyU7MHFs_kb4OxW54UAhVEfu3QsO9qTHOeKDxX-p-_XwnJ87pkpLiLnnSxDuvSETIo1nZYMZo92GlKF2Pts4Y21hMlNnlooTrI8KlfX3-faGlt11UlYskj9X7uS2WciTCp-0-aZL67U5KqOaRuI14LObJhhDFL3mq2d7pYcVpVkkJGdsFuNnknesne4vpDq2zmOUSvwsCvYPhsPKUV4kqVK1nZxsJvNsC19PxF3TQ8VttFBjglHovbZ7xGFQa-0t0SkbtyEs-MJfvN2M3F13HPMLyYYQC5ygdY9R2T3T1DDROI4s-u9JxMwzOeKJVBe51a9atiTiYb_T7oGlpsQfwAJoeCY8XnFyCRloIaQSbTBUTrePaCzcyRy0a2tU39VJdKLlrF80NKXAzkTebSG8Hy7l3u4wmmUB3wWzf5HQGjNyZF746gmEMS22miuNljQFSiMMQX7r15ZEEgvSnXANxshEZWoUfriUoI5cQEQzGX1Sx0rdVSfkb8OlDu1gCm31cefsPIrWGp1TrXgm8RtbnfCzlFHPvC__d7kjXEjaBYM7LNi6wpm99EK7LwUa1RULo0b1Vg3--3kIIG48E59VjQL39H_caZXHioN23AOgYL2TAuyFzJPn5rSsXhxGEIo3BxkFxyjiRTZCuIrgSkpRV5_i5yYn8KMOlZ1LwuZbTBw15ibA3UDCukkpBqjhX5kZr03Jx8UxkEn778HnD4P7aXJWmNKpyUrsWL-POZCLzjYeDbIcQ3N8i0fTCyub7Rjx6vKD4evXJj99qNfOvP4glUIsyuOm7pp8ZNmVEFzDodMZoUXDJfYueDkYvienCWgtBemDYrH0uP9VeN6oito9syWldowz5VCCDuTGF5agDIiR-lPuvReSY541f08N2j-110yueBdFfxTlL1wtX2VndapDBWKssHm6Lw", "id": "rs_060035eb87c40e14016a1317bd8bac8191a2d4a6b07a4f9ac4", "summary": [ { "type": "summary_text", "text": "**Considering verification steps**\n\nI need to generate a video and view the screenshot to verify things. It might be necessary to update the CLI output to include a mention of German. I wonder if I should run tests for the demo output as well. Perhaps I could create a patch test to assert \"Approval language: de\" and the corresponding code. Should I use Test-Driven Development (TDD)? Yes, adding a test that fails could be a good idea!" } ] } ] }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py (1,168 chars result)", "tool_call_id": "call_9GNii9kxqL4mteHua44BxO5x" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_NuplBcgYS8Z7mKrOnVPyrHnp", "call_id": "call_NuplBcgYS8Z7mKrOnVPyrHnp", "response_item_id": "fc_060035eb87c40e14016a1317ed07d4819198a93394d4bd5a27", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\",\"limit\":80}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py from line 1 (1,574 chars)", "tool_call_id": "call_NuplBcgYS8Z7mKrOnVPyrHnp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_XT6Rwuuz1qkg0h3mzbzLBxqs", "call_id": "call_XT6Rwuuz1qkg0h3mzbzLBxqs", "response_item_id": "fc_060035eb87c40e14016a1317f4230c8191bc8a49f0a514d4d4", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert \\\"Frame count: 5\\\" in captured.out\\n assert \\\"Approval language: de\\\" in captured.out\\n assert \\\"Side effects: write_concat, call_ffmpeg, write_mp4\\\" in captured.out\\n\",\"old_string\":\" assert \\\"Frame count: 5\\\" in captured.out\\n assert \\\"Side effects: write_concat, call_ffmpeg, write_mp4\\\" in captured.out\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py (837 chars result)", "tool_call_id": "call_XT6Rwuuz1qkg0h3mzbzLBxqs" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_L18oCwPmkVkqO8z1eipULcoN", "call_id": "call_L18oCwPmkVkqO8z1eipULcoN", "response_item_id": "fc_060035eb87c40e14016a1317fae2548191bb108f626d28b3b4", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_local_preview_video_cli.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[terminal] ran `python -m pytest tests/test_demo_local_preview_video_cli.py -q` -> exit 1, 1 lines output", "tool_call_id": "call_L18oCwPmkVkqO8z1eipULcoN" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_drEoSpHUD8Vqa5tdWv8Gn2UP", "call_id": "call_drEoSpHUD8Vqa5tdWv8Gn2UP", "response_item_id": "fc_060035eb87c40e14016a131804d15081918da52b6f705e79e3", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \" parser.add_argument(\\n \\\"--out-dir\\\",\\n default=\\\"data/previews/demo-local-preview\\\",\\n help=\\\"Directory for PNG frames, concat file, and preview MP4.\\\",\\n )\\n parser.add_argument(...[truncated]\", \"old_string\": \" parser.add_argument(\\n \\\"--out-dir\\\",\\n default=\\\"data/previews/demo-local-preview\\\",\\n help=\\\"Directory for PNG frames, concat file, and preview MP4.\\\",\\n )\\n return parser\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering CLI arguments**\n\nI'm thinking about implementing a CLI argument for approval-language. It seems I need to write some code to do that. I'll also need to make sure to pass in the writer as well. It's all about making sure everything works smoothly together. I’ve got to ensure that my parameters are well defined. I'll need to look into the specifics of how to structure this properly for it to function as intended.", "reasoning_content": "**Considering CLI arguments**\n\nI'm thinking about implementing a CLI argument for approval-language. It seems I need to write some code to do that. I'll also need to make sure to pass in the writer as well. It's all about making sure everything works smoothly together. I’ve got to ensure that my parameters are well defined. I'll need to look into the specifics of how to structure this properly for it to function as intended.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExgEz9sqJyTKpSlUcNVtCxDSdLALkLAwFcyJuK0f7MDvGV_oFSAyHDg6ukUzJtwUi_iIqxwRxwduRS9ffoEaakJ1RwDvT34wcWj9hg3XMtd-HK0yqMGeniiQPGgjtFDGb6X5x0rq1aYGq7dOVCZ0yZLdO7U35LSu3uRDvpAOIbI0vJngH1-DQmyYiYVJi7C_q2EQRHM0pwJzGBnkVEIEgxzytkSmK4Mgg3ZYUcz0sdEAzA4KlgWckwUMswXBTrVKnVmaPLFgyqfpZbItwGQLmhx1AnsukqDmnFGSkZ0k1xG6wFpZ8-Ohmm-8KTxjDzL5Vgp30oDFTool2GWy6bEEA5j_bX063innjmGnR3ZC5pVeWF4_fJnJCkM7p0TOVtR2TRbpl0WspEj_PIPYvrRZUo8NHJpluyV6CwBab8goVPIebCRedwh3mp1CfRi-DlG52j7kPhLGuxAj6y2I8sjSHicai6zOAtCFf2UsXZJ4SB-Ts51mb8RgxyFQUKPhWpEGHVR1RdO6ld9mg4ngJLCs9kG765dfHiFH6orNA9uid-a5ck1VuA6RmrGFKFrEPWJNMpGnL6q3Ckmx8paPnDw20T0VSeO12TeWdTPlTHso563oawp2x3v_UtSi5VfBAprVoVLLBZBOCKdXNBvsw-QzxYHZGuBRrpZUaqE8Md04q4Nqdu76zREInlE8yy1U59_bsne7qKUUCM4ycJxd2zCSG2AoiRfRkMDoqLSWmQGFKktFLr0j1ndP4gZofxTMzkINkX34Qqwn-o7DGe6x8ah4M_pUpCLFtZSOzbujVlG6aB8r2qbc23s5QFMljVuytC02qB60LrFwYRDpCgAfxTowURVpaIK61XGZ0-_w29XjIMVRpy-Fys_gVNzIoJNzPPqXLiiqlFDZ1Ja-_hxtKxnfRuQHSX3bzRkgjQIRTgizQJAsQSHJHej1c3bgmCN1yNh8nJart8F9d1zvBXf1ugeHabN2xL0v1_b-uFh04JTNivQMXXQT7LP_-7QHY0nkOcnAxeqO", "id": "rs_060035eb87c40e14016a131802cf108191932c52927222870d", "summary": [ { "type": "summary_text", "text": "**Considering CLI arguments**\n\nI'm thinking about implementing a CLI argument for approval-language. It seems I need to write some code to do that. I'll also need to make sure to pass in the writer as well. It's all about making sure everything works smoothly together. I’ve got to ensure that my parameters are well defined. I'll need to look into the specifics of how to structure this properly for it to function as intended." } ] } ] }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py (838 chars result)", "tool_call_id": "call_drEoSpHUD8Vqa5tdWv8Gn2UP" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_t3KnjRabNL7h2HHHKmEbLLZn", "call_id": "call_t3KnjRabNL7h2HHHKmEbLLZn", "response_item_id": "fc_060035eb87c40e14016a13180bc7d08191a67f8aa452983558", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _build_preview_video(output_dir: Path, *, approval_language: str):\\n\",\"old_string\":\"def _build_preview_video(output_dir: Path):\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py (984 chars result)", "tool_call_id": "call_t3KnjRabNL7h2HHHKmEbLLZn" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_4An4SS316sAhU0LzamDDQiCd", "call_id": "call_4An4SS316sAhU0LzamDDQiCd", "response_item_id": "fc_060035eb87c40e14016a131811f66481919702207b6851cc69", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" frame_result = write_local_preview_frames(\\n preview_plan,\\n output_dir=output_dir / \\\"frames\\\",\\n approval_language=approval_language,\\n )\\n\",\"old_string\":\" frame_result = write_local_preview_frames(preview_plan, output_dir=output_dir / \\\"frames\\\")\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py (1,074 chars result)", "tool_call_id": "call_4An4SS316sAhU0LzamDDQiCd" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6tA0aE1rNfGkal6Evg3uTXHy", "call_id": "call_6tA0aE1rNfGkal6Evg3uTXHy", "response_item_id": "fc_060035eb87c40e14016a13181b20cc8191ba3bc04f52ca08d1", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \" preview_plan, frame_result, render_result = _build_preview_video(\\n output_dir,\\n approval_language=args.approval_language,\\n )\\n\\n print(f\\\"Script: {render_result.script_id}\\\", file=...[truncated]\", \"old_string\": \" preview_plan, frame_result, render_result = _build_preview_video(output_dir)\\n\\n print(f\\\"Script: {render_result.script_id}\\\", file=output)\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py (913 chars result)", "tool_call_id": "call_6tA0aE1rNfGkal6Evg3uTXHy" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_2NJOjqAHSGn1ej8JeSZIoqZS", "call_id": "call_2NJOjqAHSGn1ej8JeSZIoqZS", "response_item_id": "fc_060035eb87c40e14016a13182278648191a4bdf55205185271", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \" print(f\\\"Frame count: {len(frame_result.frame_paths)}\\\", file=output)\\n print(f\\\"Approval language: {args.approval_language}\\\", file=output)\\n print(f\\\"Preview format: {preview_plan.width}x{preview...[truncated]\", \"old_string\": \" print(f\\\"Frame count: {len(frame_result.frame_paths)}\\\", file=output)\\n print(f\\\"Preview format: {preview_plan.width}x{preview_plan.height} @ {preview_plan.fps}fps\\\", file=output)\\n\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py (1,010 chars result)", "tool_call_id": "call_2NJOjqAHSGn1ej8JeSZIoqZS" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dGySK2ju60HAF6UaCl8ZT83M", "call_id": "call_dGySK2ju60HAF6UaCl8ZT83M", "response_item_id": "fc_060035eb87c40e14016a13182be2d481919a876a9c2f34c982", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_local_preview_video_cli.py tests/test_local_frame_writer.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"....... [100%]\\n7 passed in 2.98s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_dGySK2ju60HAF6UaCl8ZT83M" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_DGOVqKjygCQ6p55j0auczEu2", "call_id": "call_DGOVqKjygCQ6p55j0auczEu2", "response_item_id": "fc_060035eb87c40e14016a13183b79208191aee668ebee0b39ee", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\": \"rm -rf /tmp/autoshorts-demo-local-preview-de && python -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview-de && python - <<'PY'\\nfrom pathlib import Path\\np=Path('/t...[truncated]\", \"timeout\": 240, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExg7JZeVR0HskdJ5UJEJtDkdxIvbJoQFfMkOeiqBv6GsobYzUZifQjQGBnf4pLkTdwnQgVFHk-sGYz0IFPUjAS9HS2Epw6rJITq9vtMVAd_quiTh3x1iXLJl8J6MWPniJJ3PdBk3o_oFfzx00BUbcjsmhsZCw1tJYDmZbm5J7H3G4P5yVZ77ZHx0piH0bKShaTQfiYsO4CMVQJYG7ZPx5oJRvxeRHjrkiMwzcsyXQFq5V8xCf2NCusB_lPKvRjLSvk3I7aaFaKUP6kvD7pYbjCY_fVhELKti4fktieHKeEqbHr_IOCnunaXDOZHBb9VASFJ7Awx2lE6IOHRpwrvcudShPEADB5HjeZ4WVr17E7_n6l3JzrAg5-IZvJBCzd-SHV2kvBD0o9TjWHJbo0U_02Ax62GEswwi6_u53UDS2lFPwAnl_PhPlwapRNQV2yaHZtZ1Z5MWX0zuGPqk26cym4h0n_3-Y3W-0wgcVOWPWTsUvf6hkDBVijZs3LZgBd3j02kF4NTecXKOyH8th5FQ0J0jwZ6kS-cylJVFNFQGhLtpHoJmoyUpzij4nSLabXNk7XcbDPVZ1CezOFkBrjemT72KzhU4bHYaJ0MjjkvV-oBFcntoUjK69jkHxL7zmovc_CXA7r55ez5GsTdznAnct_kDNshFzGryaoAhKyXRHPwHzxwK7-zmCBUgiet0rBADj-T794PtIlVICbzWYTxHWvd9uCcgYjNzMIRX2TmLjxllyzd2ESjP4afMqFKcnM9-XYLZGxA24WK7V6BL4AZqD2ORkPfOuINzPeBaEdci42jW8BW__nd6nULb88YsRcwlun7WQKaee1Y9du-T8q8FcULhe-XmTCkdeJfrfcBIB8NbEZ_dH-k6RpZSAK_3a7zKFaFayNqVPO9NBaFkil-dES9sbnITr3FYIQKbXrJSv3FHoSoBi-WvvRthXNykQz9OIIwPnvQ-RIUrB1tY512tGPiMsj3Gycczv8-welcG9BLGieTD-k60rGeQnfFaAzoUOrNMzIccPgbgR9gX_TvBqZx9-8gFCVHgAAg20zuiScqNl0fDNiwSeD14qsZkcKgRuh2mMHAr5q-H32dmHVrf84io9_sz6Wn-ph1uu9wa4hLNnPu56k7bhZ_kYnJ3ZYdq-kWXpfeOKU_QrAfMLKn0I4VC7g==", "id": "rs_060035eb87c40e14016a13183915d88191bd056d8429e53871", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `rm -rf /tmp/autoshorts-demo-local-preview-de && python -m autoshorts.cli.demo...` -> exit 0, 1 lines output", "tool_call_id": "call_DGOVqKjygCQ6p55j0auczEu2" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_mNDXnI8fW7lSoxhOWomysKLp", "call_id": "call_mNDXnI8fW7lSoxhOWomysKLp", "response_item_id": "fc_060035eb87c40e14016a13184b92208191be42f79f7919bb0e", "type": "function", "function": { "name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts-demo-local-preview-de/frames/frame-01-first_frame_hook.png\",\"question\":\"Bitte prüfe visuell, ob der Text in dieser vertikalen Preview-Card gut lesbar ist, ob die Zeilenumbrüche sinnvoll sind, und ob der sichtbare Text Deutsch ist. Gib eine kurze Einschätzung.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[vision_analyze] 'Bitte prüfe visuell, ob der Text in dieser vertika' (318 chars)", "tool_call_id": "call_mNDXnI8fW7lSoxhOWomysKLp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_L05D6x2jrViDhYqg2C9bTA4i", "call_id": "call_L05D6x2jrViDhYqg2C9bTA4i", "response_item_id": "fc_060035eb87c40e14016a131858f86481918c567246143b2d96", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"from dataclasses import dataclass\\nfrom pathlib import Path\\nfrom textwrap import wrap\\nfrom typing import Any\\n\",\"old_string\":\"from dataclasses import dataclass\\nfrom pathlib import Path\\nfrom textwrap import wrap\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Updating documentation and skills**\n\nI think I need to update the README and maybe work on a skill patch too. The user taught me a durable workflow, like handling approval previews in German and English and dealing with line wrapping for pixels. It would be good to patch the skill with a social media reference since we discovered a pitfall there. I also need to manage some diagnostics, particularly for read and write file operations, to ensure everything is clean and functioning well.", "reasoning_content": "**Updating documentation and skills**\n\nI think I need to update the README and maybe work on a skill patch too. The user taught me a durable workflow, like handling approval previews in German and English and dealing with line wrapping for pixels. It would be good to patch the skill with a social media reference since we discovered a pitfall there. 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Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal...[truncated]\", \"new_string\": \"- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal...[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_s7CWWxHkPDaivmdJX0qxkVxq" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_j7UEjwqirSJ6LXGYQqsOIN9c", "call_id": "call_j7UEjwqirSJ6LXGYQqsOIN9c", "response_item_id": "fc_060035eb87c40e14016a131c8f8218819180afcf4d894899e3", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"rm -rf /tmp/autoshorts-demo-local-preview-de && python -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview-de && python -m pytest tests -q && git status --short\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExyP4kgaDhObCldQoFVcKirXJ3hLH7K_ilOk3Sk5oDOREXpEi_pZo1lfbwx1VCcEX72wXGGNJM_gtt0slN-1jBnx1fwq_9kLf-slgE83lZhPg10YhkQqXOXuKEIts0jeWmHb9fmX6Ukpttr5IjaWQvKhAF-2JgVGLFGErDDJf-POyovoFOGw2Kgt_B8HJ2w7DAjkO5VW3OkKJPrdPKKxtFUjK1TPtfiRlMCn9TJbUDjEMrTBhzVsNXGcdGvEEcH_qatDjoxJ0R-nshu-nVTRGtQDjyjM2pA72BtDxxQcTxYNGSH-yQ0RaSMorzRs4ypUtsUFXJ15QsSNNfzt8BX-v-iux_1ToHJebLT9ax-3gezDZMHN8PBt2oom52acBkWcGZlUh1UkNxjoXqmT4BMWnZgrFtEEJE1VKER2gooje7BbCgxEajdQuh0S_qgOCvoCj4f11BlnXuCdD8cULcVaI4PBeyk5IM-6aytE3VFV_A_vGdS8ov4UzfGP1GFnpNl5Eefj0MwgNY1BGCVOiVChmWYN5nEg7rS9RCogM22UKSMK3FidyPMnPkEi-tzrLWNzNZGsR-F-NORcggIoXyIVIFgJ3XqxwT2egiuSQOTTdx3pCmG2w0UPQ-BQtp9zWVFIgHQWUXYLpYQ2fnmln_r_7ecq9PnXj4wjw4x7vKEM6m0f8wqu0AShHxDaUqWED7NkexqgFG6KMwCnylWw9YdBPoc8ogVLDYMD8onEgU520TJ5t38X-JeDF9eFA4iF9zpVEzHglK0T9naFZlmrQgFdgT-JUg80S73DGsThMVOMema99t-6sfQBdQT3JJUw4RTh6Dtt_s4Gz_2UzqNhQxtc1QLnq-yN_325CcfoQiJwvkiJSlK4_JCHhSjrpA3kBrGYVdbLefPQQzQnnBHn7ezECxiQQ_8sOG8WdQC-ZhDWcsIB6cHqoA_a3K6yqkxSSvlxLP7BXK7awPLPT72x7c6InFR0c5K7y0DsJCJMQsYOtry62RnnMcPqQ8sm-QL4bS0MtBIjMMtWpnYxNUenr-pcykJ4QA==", "id": "rs_060035eb87c40e14016a131c8c9c5c81918a0120b92312dc84", "summary": [] } ] }, { "role": "tool", "content": "[terminal] ran `rm -rf /tmp/autoshorts-demo-local-preview-de && python -m autoshorts.cli.demo...` -> exit 0, 1 lines output", "tool_call_id": "call_j7UEjwqirSJ6LXGYQqsOIN9c" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_q3f5hh8vC2Vmnl4HdBz6TzHS", "call_id": "call_q3f5hh8vC2Vmnl4HdBz6TzHS", "response_item_id": "fc_060035eb87c40e14016a131ca1fb088191a7636243c8aa2cd1", "type": "function", "function": { "name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts-demo-local-preview-de/frames/frame-01-first_frame_hook.png\",\"question\":\"Final check: Ist der Text in dieser Freigabe-Preview-Card Deutsch und gut lesbar, mit sinnvollen Zeilenumbrüchen? Kurz antworten.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "[vision_analyze] 'Final check: Ist der Text in dieser Freigabe-Previ' (260 chars)", "tool_call_id": "call_q3f5hh8vC2Vmnl4HdBz6TzHS" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_8RvV9lZjdFR6y4iwtC2IosIX", "call_id": "call_8RvV9lZjdFR6y4iwtC2IosIX", "response_item_id": "fc_060035eb87c40e14016a131cab50ec8191a8035532c6e09054", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/cli/demo_local_preview_video.py autoshorts/rendering/local_frame_writer.py tests/test_demo_local_preview_video_cli.py tests/test_local_frame_writer.py && git commit -m \\\"fix: improve German approval preview readability\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Assessing repository status**\n\nI'm confirming that I need to focus on the vision, and it’s essential for me to commit. I also realized that a skill has changed outside the repository in the /.hermes directory, which isn't tracked by git. So, my next step is to check the git status, but only for the repository itself, to make sure everything is in order. I need to keep my attention on what really matters!", "reasoning_content": "**Assessing repository status**\n\nI'm confirming that I need to focus on the vision, and it’s essential for me to commit. I also realized that a skill has changed outside the repository in the /.hermes directory, which isn't tracked by git. So, my next step is to check the git status, but only for the repository itself, to make sure everything is in order. I need to keep my attention on what really matters!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExyruuUyEsnvonIm0XzvXDJMBH7R35AEVr0-TJfVUkNWGzbAw1V060RRKAxY5ricTcES9ajBAqe-mwFAlX3M9PioMPSjFNx_1oVx3eXL44QZtM9Jbjyy5IiY-h6M3A3Y064YonzbsTPtGeh2JXa2ErpQeaZhB5Ov8PZt3oZxGP8wnyuL_TGYnYWlLHJFOwsan6Wgy8UzKrHkzaAr2idoV-0-CZ25YPbMchCMsE67kEVfy-8FGf9TpD4rLt91T9Wvo3bBv89FZMIhQmxj6Z_I42urtlTgepqzWP_MgzeIGDg4muoNS5jqqv50pi1-so9yKWWT-1xhoUeVSpMLnYu6afENstSKXyYg90fECSz5zRBKvsGrzUUIISGHyHYcCcT4jWHYh5Ib4MfeCUD7Vxb2AvW6d3yJpmQlNkIaXfuxglwY2cXkPrAFX0wd-RsT-XYJ7rBwRn4sEEouulMvLZXnsqYUVYhoUR3KHQVqFmTRmgGZxuXNzBxlkTje5wL8Khg-xIIhbbX04Tv7AbZgdW0jiPE7XP8SKOMXxNqjuLFuxDvXQHxCZtt4iBc2wdCeOui7tzlXdMvFSiDkIZrN5UswpwMOcX6ODBOIPMVfiJsDEtwsZVeB4Q4gtWWqaYtpo4Lz7Z6RhYajU94gS6CVAIEupAwSE664LiNs6tFmBSwX_9ZOi7lQ7Ea2aMQqmbOIY7v7pi7Q5cqQNE1OtiHAUakYiOicCSHpqbRLYOTuQisjTEZSC8L2yEVz0dWpG75IGunEBdccM7t8Owi4TdVTOrwdWrchHpo_wjaLhV6c-73NJ4oYcBpthg78kY4xyyiVkntO1DARg5_NaXpLNm49IGDxzMRIhXCWP1CC-iDEU9bpoC-PcfTc6l1ChWcxXxREDhg8Cbm-MwCe2Luu8JNPKlXstIFNWTLGOLrLZQhgYgpMDlqlrS_TOGVo2R4mCgfs2IDbT5r0-4ftIvqufoPoMPQ1dg6WKLSRFca3QOi8yRsPh9fvUlLJ1PL9IKqkhI7pXOtFuoF1kJoATPqCvjINv14AlRqNRhrmNWvc6FM7KguL8VdRUfJ8yAjdGStC5FOd7whwf9PbUBKgwqH1HBIfIN_-6WjVvA==", "id": "rs_060035eb87c40e14016a131ca8d6408191a444e850f8cb949c", "summary": [ { "type": "summary_text", "text": "**Assessing repository status**\n\nI'm confirming that I need to focus on the vision, and it’s essential for me to commit. I also realized that a skill has changed outside the repository in the /.hermes directory, which isn't tracked by git. So, my next step is to check the git status, but only for the repository itself, to make sure everything is in order. I need to keep my attention on what really matters!" } ] } ] }, { "role": "tool", "content": "[terminal] ran `git add README.md autoshorts/cli/demo_local_preview_video.py autoshorts/rende...` -> exit 0, 1 lines output", "tool_call_id": "call_8RvV9lZjdFR6y4iwtC2IosIX" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Geändert\n\nIch habe die Freigabevorschau-Videos angepasst:\n\n- **Freigabe-/Approval-Preview jetzt standardmässig Deutsch**\n- **Finale Videos bleiben später Englisch**\n- Zeilenumbrüche verbessert:\n - vorher: grob nach Zeichenanzahl\n - jetzt: **pixelbreiten-basiertes Wrapping**\n- deutscher Preview-Text pro Scene:\n - Hook\n - Problem\n - Kernidee\n - Beispiel\n - Freigabe/CTA\n- CLI zeigt jetzt explizit:\n\n```text\nApproval language: de\n```\n\n## Neuer Preview\n\nHier ist die neu gerenderte deutsche Freigabevorschau:\n\nMEDIA:/tmp/autoshorts-demo-local-preview-de/preview.mp4\n\n## Visueller Check\n\nIch habe die erste Frame-Card geprüft:\n\n- Text ist Deutsch\n- Hook ist deutlich lesbar\n- Zeilenumbrüche sind sinnvoll\n- Body-Text bleibt innerhalb der Card\n- keine abgeschnittenen Wörter\n\nAlso: wesentlich weniger “Excel-Präsentation im Fluchtmodus”. Sehr gut.\n\n## Tests\n\n```text\n112 passed in 8.64s\n```\n\n## Commit\n\n```text\nf9f737f fix: improve German approval preview readability\n```\n\n## Zusätzlich gespeichert\n\nIch habe deine Präferenz dauerhaft vermerkt:\n\n```text\nApproval previews in German; final videos in English.\n```\n\nUnd die Social-Media-Shorts-Skill-Referenz entsprechend erweitert, damit diese Regel künftig nicht wieder in der Pipeline verloren geht.", "reasoning": "**Finalizing preferences in German**\n\nI need to finalize the output in concise German while mentioning that I’ve saved the user’s preferences in memory. It’ll include an MP4 format for media. I might want to reference any tests or checks that were done, like a vision check. The user also noted that the final videos should be in English, so I’ll make sure to address that too. I think I’ve got everything needed!", "reasoning_content": "**Finalizing preferences in German**\n\nI need to finalize the output in concise German while mentioning that I’ve saved the user’s preferences in memory. It’ll include an MP4 format for media. I might want to reference any tests or checks that were done, like a vision check. The user also noted that the final videos should be in English, so I’ll make sure to address that too. I think I’ve got everything needed!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqExy0fzWkp7HxpMbyq1t14ejPNHjG8OecZn0_7BAbdhC6AnTKrFA3qJQtaYr-rwUCn6jCYRJpsBO43b5jwCwVNDSbnCxPOyaPs2iGwQFCdC-cDoGhDbXw6jxiBNhnGpwl6nJz9s4Dvc3nqE4NjyYUp5yJfIksylMhTT1f5LvylqDBP_snw7EuGv2lrCLJ4unaUt-bNj5WspaymO-n_Sdza-e2sfQLSnZvAQymQg1J6wAQiFWQ4sMImnI4IOjnkYettKjA0DiQRVZIqKABMvpN0PQspkRDym_amJ7pshhv5n6WeKCF9OVmiZ3RWB4FvOhocaZhGMCeE_BPIpc5EPFw0VjcocRQVGPyIFJJARIZZOXiCZTCnDJBFFXvL_tJvg-UxrAMfU0A7i6EDmCBXqbE7lG16Cwi1DfjpViS_dc5-YnXCE-6H_cJ2HYH8w7LDCxFNj5LifQKc_N6vxqnTfMVkBC_v15xbifMxNoDck8F_QY5WVlDGnUWJ7bQ7IR671xBzBsnsMcDDIuiRvqTwjHfWmTJLVOBl41wOso79nnYl2WjZacKUCIgadjsaav3-hStR5oPIa9Jo6zX8jzzJ94wl7tQYBc3uWE8uhqgBXH0CPvnfJtw2vEbIwtI2vmgP8y8j2ErKZkOdeHSSIOltM6aylF3Oq7OvNsqAQBOyXt1-J4JADsbJ8_naCkQieRfMB8laqDyCaTYTyC-CEQ_IMTBY7H4RVK6NEHT258ci38-FLTVmGNfC9Rlm1jXQkn_zf7q0tOvXuLRiFZ4U3oiaMEmPwYEPErjbheAybQIC9NT7xH6dWwzZiRw_CxnQmroAIgtl9U3XyEGgAMVOwm5R3c8NY79-5GpANGIspoKRk_IftsJn5FUyAItsl5uvvzqhRQrsyMKvFIuWGcR46pMaY28CmfQ1lwOTcxQ1VvhEomzU3Z1s13rOLkrsphNb4iq5zrAZtve0pWhdSp2Uf9G5On17WwQBCUA4V9e87P5dZNuWkB-nCrp88c0hSVRWUcpnfle8gpkezd0NzuY8ObDYqcZaGAreQu9_aUROCabT7107EGEQm42EGajWiW_0wdmcQeO2V_jCytqKjSrkv6SSYJNLzWS7tqaeoXmtLP46atGVqH3jb9R1H30nNSqu-9JWdeTmSTu", "id": "rs_060035eb87c40e14016a131cb210e8819183750ab80b026b32", "summary": [ { "type": "summary_text", "text": "**Finalizing preferences in German**\n\nI need to finalize the output in concise German while mentioning that I’ve saved the user’s preferences in memory. It’ll include an MP4 format for media. I might want to reference any tests or checks that were done, like a vision check. The user also noted that the final videos should be in English, so I’ll make sure to address that too. I think I’ve got everything needed!" } ] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir.\n\n## Geändert\n\nIch habe die Freigabevorschau-Videos angepasst:\n\n- **Freigabe-/Approval-Preview jetzt standardmässig Deutsch**\n- **Finale Videos bleiben später Englisch**\n- Zeilenumbrüche verbessert:\n - vorher: grob nach Zeichenanzahl\n - jetzt: **pixelbreiten-basiertes Wrapping**\n- deutscher Preview-Text pro Scene:\n - Hook\n - Problem\n - Kernidee\n - Beispiel\n - Freigabe/CTA\n- CLI zeigt jetzt explizit:\n\n```text\nApproval language: de\n```\n\n## Neuer Preview\n\nHier ist die neu gerenderte deutsche Freigabevorschau:\n\nMEDIA:/tmp/autoshorts-demo-local-preview-de/preview.mp4\n\n## Visueller Check\n\nIch habe die erste Frame-Card geprüft:\n\n- Text ist Deutsch\n- Hook ist deutlich lesbar\n- Zeilenumbrüche sind sinnvoll\n- Body-Text bleibt innerhalb der Card\n- keine abgeschnittenen Wörter\n\nAlso: wesentlich weniger “Excel-Präsentation im Fluchtmodus”. Sehr gut.\n\n## Tests\n\n```text\n112 passed in 8.64s\n```\n\n## Commit\n\n```text\nf9f737f fix: improve German approval preview readability\n```\n\n## Zusätzlich gespeichert\n\nIch habe deine Präferenz dauerhaft vermerkt:\n\n```text\nApproval previews in German; final videos in English.\n```\n\nUnd die Social-Media-Shorts-Skill-Referenz entsprechend erweitert, damit diese Regel künftig nicht wieder in der Pipeline verloren geht." } ], "id": "msg_060035eb87c40e14016a131cb41b108191a9b650626cdea817", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] Ok, passt. Mach weiter. Recherchiere im netz zudem wie andere personen content erfolgreich automatisiert generieren und bewerte die inputs; danach schauen wir was wir mit implementieren." }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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\"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## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - 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: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft approval package before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - Add a local demo CLI that prints metadata first, then the draft review text; it must not write files, call renderers, call Telegram, or access credentials.\\n - See `references/social-draft-approval-package.md` for the concrete artifact sequence and guardrails.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.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_QoIyfvWjrFqmzOGzav2ah2F1" }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"shortform-growth-automation\", \"description\": \"Design and operate semi-automatic pipelines for YouTube Shorts, TikTok, Instagram Reels, and similar shortform growth: niche strategy, test batches, human review loops, monetization constraints, and analytics feedback.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"instagram-reels\", \"growth\", \"social-media\", \"content-automation\", \"monetization\"], \"related_skills\": [\"youtube-content\", \"google-workspace\", \"github-repo-management\"], \"content\": \"---\\nname: shortform-growth-automation\\ndescription: \\\"Design and operate semi-automatic pipelines for YouTube Shorts, TikTok, Instagram Reels, and similar shortform growth: niche strategy, test batches, human review loops, monetization constraints, and analytics feedback.\\\"\\nversion: 1.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\nmetadata:\\n hermes:\\n tags: [shorts, tiktok, youtube-shorts, instagram-reels, growth, social-media, content-automation, monetization]\\n related_skills: [youtube-content, google-workspace, github-repo-management]\\n---\\n\\n# Shortform Growth Automation\\n\\nUse this skill when building, reviewing, or improving systems that generate shortform social-media videos, especially semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, or repurposed longform content.\\n\\n## Core principle\\n\\nTreat the system as a **growth engine**, not merely a video generator. A working renderer is only useful if it produces content with strong hooks, retention, identity, and feedback loops.\\n\\n## Recommended workflow\\n\\n1. **Define content pillars**\\n - Start with 2-4 clear pillars, not a random topic list.\\n - For each pillar define: audience, promise, tone, no-go topics, hook patterns, CTA patterns, visual rules.\\n - Prefer identity-rich pillars such as `AI as a servant`, `Faith and focus`, `Life systems for overloaded adults` over generic advice like “health tips”.\\n\\n2. **Build a test batch before automation**\\n - Create a small batch, e.g. 9 ideas: 3 per pillar.\\n - Include a mix of short growth candidates and longer monetization candidates.\\n - Each idea should include: id, pillar, title, platform target, duration, hook, script outline, visual concept, caption, CTA, quality score, and risk notes.\\n\\n3. **Score scripts before rendering**\\n - Hook in the first 1-2 seconds.\\n - Clear emotional or practical pain point.\\n - Novelty: avoid “4 tips everyone has heard”.\\n - Retention path: pattern interrupt every 2-3 seconds.\\n - Comment/share trigger.\\n - Platform/policy risk.\\n - AI-slop risk: visual gibberish, generic stock-feel, impossible UI/text.\\n\\n4. **Use a human approval loop first**\\n - Semi-automatic beats fully automatic at the start.\\n - Send drafts/previews to Telegram with: video or script, title, caption, hashtags, duration, target platforms, quality score, and risk notes.\\n - When attaching local preview files through Telegram/Hermes, use `MEDIA:/absolute/path/to/file.mp4`; relative `MEDIA:` paths are not reliable for native delivery.\\n - Do not post publicly without explicit approval commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n\\n5. **Render only after tone is validated**\\n - First validate the script voice and niche fit.\\n - Then build rendering templates.\\n - Otherwise you risk creating polished clips with mediocre text — a beautifully engineered waste of time.\\n\\n6. **Close the analytics loop**\\n - Store per-video metadata and performance: platform, pillar, format, hook, duration, views after 2h/24h/7d, likes, comments, shares, follows, retention if available.\\n - Use this to choose the next batch; do not produce blind forever.\\n\\n## Platform strategy\\n\\n- **YouTube Shorts:** good primary channel; supports vertical/square Shorts up to 3 minutes. For growth, test 20-45s and selected 60-75s pieces.\\n- **TikTok:** growth can come from short videos, but Creator Rewards candidates must be 1 minute or longer. Test both short growth clips and 61-75s monetization candidates.\\n- **Instagram Reels:** strong secondary distribution for the same approved assets; especially good for lifestyle, faith, and save/share formats.\\n- **Facebook Reels:** useful later for faith/family/life content and older audiences.\\n- **LinkedIn:** consider only for AI/productivity content, not general FaithTok unless carefully framed.\\n- **Pinterest:** useful later for evergreen checklist-style content and indirect monetization.\\n\\nSee `references/platform-monetization.md` for concise platform requirements captured from official sources during setup work.\\n\\n## Visual rules\\n\\n- Avoid AI-generated images with readable text, fake app UIs, logos, or interface details; these often produce gibberish and reduce trust.\\n- Prefer real B-roll, screen recordings, kinetic captions, simple cards, and recurring templates.\\n- First frame must stop the swipe: large hook text, concrete visual tension, or a recognizable human/scene.\\n- Keep a consistent brand texture: fonts, caption style, color, recurring intro logic, and thumbnail-like first frame.\\n\\n## Repo setup pattern\\n\\nFor a new shortform automation repo, scaffold around separable concerns:\\n\\n```text\\nautoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\nconfigs/\\n niches/\\n platforms/\\ndata/\\n drafts/\\n performance/\\ndocs/\\ntests/\\n```\\n\\nCommit configs, validation code, draft JSON, and documentation. Do **not** commit `.env`, OAuth tokens, generated credentials, raw downloaded secrets, or generated media outputs.\\n\\nSee `references/autoshortsbot-initialization.md` for a concrete project bootstrap pattern and testbatch schema.\\n\\n## Pitfalls\\n\\n- Generic “tips” content is usually too weak unless it has a sharp hook, specific audience, and strong identity.\\n- 90-second explainer videos often underperform for new accounts; prefer 20-45s growth tests and selected 61-75s monetization tests.\\n- Do not optimize only for platform payouts early; first find repeatable follower growth.\\n- TikTok Creator Rewards eligibility can vary by country/account and should be verified in-app before depending on it.\\n- Do not auto-post during early exploration; use approval-gated previews until the user trusts the channel voice and quality.\\n\", \"path\": \"social-media/shortform-growth-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/shortform-growth-automation\", \"linked_files\": {\"references\": [\"references/autoshortsbot-initialization.md\", \"references/platform-monetization.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\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"instagram-reels\", \"growth\", \"social-media\", \"content-automation\", \"monetization\"], \"related_skills\": [\"youtube-content\", \"google-workspace\", \"github-repo-management\"]}}}", "tool_call_id": "call_ikWoBIfcWZ0gh363Po44dH7z" }, { "role": "assistant", "content": "", "reasoning": "**Researching web tools**\n\nI need to conduct some research online. I think it makes sense to use a web search engine for this task. Maybe I could also delegate the research to work in parallel, which could help me save context along the way. I’m considering that I could handle up to three tasks simultaneously by using the various web toolsets available. It's all about making the process efficient and fruitful!", "finish_reason": "tool_calls", "reasoning_content": "**Researching web tools**\n\nI need to conduct some research online. I think it makes sense to use a web search engine for this task. Maybe I could also delegate the research to work in parallel, which could help me save context along the way. I’m considering that I could handle up to three tasks simultaneously by using the various web toolsets available. It's all about making the process efficient and fruitful!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEx1x-oiZYIcnch6rdys0XH-U9jtRQXN8kid3ag8ctybWOtggVBEJOfoskgSbOdvVyX3Zsrc9Alsd4ESvKof_Z0mchUWv-LrkQXSgPR6QZEztvqaKK5QQPdTqB443UFaa5LMQq0Z7gEKB9e1ZbarI66sWHPKrHpNHwwQGukmEOJPggvDe60Lc8XXUCshf9JtSHZW2zOGL8icwKuchUCjo0Xb2k1hYTvatr25opC4-8h8IlBg7hEv_hq8JfFfsgs-R0FuEahoJi18MgDNzeiFRp4LXeS6KH2z4ZaxyMKvX-c9SI9UA3LPOM7AsINKjuN9mqkk94yG2DobA-Vve00cVn6pHdOjBl9m5Lmt7r_-ZwGk1zFvc9hgGFgDqN0kUZQRh01SwBOh8anxO78hOER11Pyjve_BaAlII_9byfFjg5ynraTcsTpQBl1pbzUZf2BLMqRAslwuuNrN4YZro71O1y6LfrwyTl1L-yCmNSPl6hKanIe_Uqrc6Hxl9EMtZNwqpOT5Huqt1SQKVW93kUezxkdB0nA90pf-dxtdbhkFaZWIu-YdjRNrAwzLzV6kfPWkqK5Ei7L-he5-U_1WqkAanKRElE6DWfQDH0oiDqB-NG-TL013J895h7uJscMveoZIiQUPUG1qYR2AEaewH8n3e_Tv3eVazw8mJ9Qt94-HGc-d89Jo1flyNZ2y_4tghwB-peQM0ISPdzXB-YpUq_0ujZ30mHSd6OeME_UhFUlUSgz-G7KxPXAh7qAqVUxs2cQZzjaQhFDsxna6h4PYsOa71YisGO8RVbyJ-1EsuY4303-Vaet7kWcU9KyQeOLMBuvcdk3tPg1NIkoouvZZ0eXf8LgOuC-HTiIcDxq0N02VhEulcRK9On4ovuJvs1V6iiu0NCMJ_uquGKE7i6kxBl5Lq9JBT3CVaRTGrGS_2Ry_-WhMbtZ209kt7xl2EK7Ne8K0KTEdWHJk4OFLrDA3IS5DEtclZeCIxpkVqYOCWXlB8oV-sD2mcqKByiWTfZBe2L-_dnXONgJCTV3Ztc3GDJ4VYsijaWtDP9JhAWJRvS22KpVTZgGxyz83nU-yvwpJYZ5BCDu4Q", "id": "rs_060035eb87c40e14016a131d6c14808191b34f1fac25b9493a", "summary": [ { "type": "summary_text", "text": "**Researching web tools**\n\nI need to conduct some research online. I think it makes sense to use a web search engine for this task. Maybe I could also delegate the research to work in parallel, which could help me save context along the way. I’m considering that I could handle up to three tasks simultaneously by using the various web toolsets available. It's all about making the process efficient and fruitful!" } ] } ], "tool_calls": [ { "id": "call_rxlWB968v4mzWJM35iVWTbwf", "call_id": "call_rxlWB968v4mzWJM35iVWTbwf", "response_item_id": "fc_060035eb87c40e14016a131d71af84819181ba14ab1cfde51d", "type": "function", "function": { "name": "delegate_task", "arguments": "{\"tasks\":[{\"goal\":\"Research current public examples of successful automated or semi-automated short-form content generation workflows by creators/indie hackers/agencies. Focus on concrete workflows, tools, approval loops, performance claims, and pitfalls. Return concise source-backed findings with URLs and evaluate which inputs are useful for a local approval-first AutoShorts pipeline.\",\"context\":\"User wants German response. Project context: AutoShortsBot approval-first pipeline; German approval previews, final videos English; no auto-posting until human approval; free/local where possible. Need practical inputs to consider implementing, not hype. Use web research; cite URLs.\",\"toolsets\":[\"browser\",\"web\",\"terminal\"]},{\"goal\":\"Research official/platform and creator best-practice guidance for YouTube Shorts, TikTok, Instagram Reels automation/AI content: retention, disclosure, monetization constraints, AI-generated content risks, repurposing/duplicate content issues. Return concise source-backed findings with URLs and implementation implications.\",\"context\":\"User wants German response. Project context: AutoShortsBot approval-first short-form automation. Need evaluate what to implement or avoid. Focus on durable official or high-quality sources; cite URLs.\",\"toolsets\":[\"browser\",\"web\",\"terminal\"]},{\"goal\":\"Research tools/patterns for automated content generation pipelines (e.g. scripting, templates, batch rendering, n8n/Zapier/Make, Airtable/Notion, ffmpeg, Remotion, Creatomate, Bannerbear, Runway/Kling/Pika/CapCut) used successfully. Evaluate free/local vs paid and what fits our current Python/Pillow/FFmpeg approval-first pipeline.\",\"context\":\"User wants German response. Project: local Python AutoShortsBot with typed artifacts, Pillow/FFmpeg preview, future Qwen/Wan/ComfyUI. Need practical implementable patterns and risks. Cite URLs.\",\"toolsets\":[\"browser\",\"web\",\"terminal\"]}]}" } } ] }, { "role": "tool", "name": "delegate_task", "content": "{\"results\": [{\"task_index\": 0, \"status\": \"completed\", \"summary\": \"## Recherche-Ergebnis: öffentliche Short-Form-Automation-Workflows\\n\\nIch habe öffentliche Quellen zu automatisierten/semi-automatisierten Shorts/Reels/TikTok-Workflows geprüft, mit Fokus auf konkrete Pipelines, Tools, Approval-Loops, Performance-Claims und Risiken.\\n\\n### 1) AutoShorts.ai — Indie-SaaS für faceless Serien\\n\\nQuelle: \\nhttps://autoshorts.ai/ \\nhttps://www.starterstory.com/stories/autoshorts-ai-breakdown\\n\\n- **Workflow:** „Series“ anlegen → Thema/Prompt wählen → KI erzeugt Video → **Preview & Customize**: Script, Titel, Bilder, Musik editieren → Posting-Schedule/Channels verbinden → Auto-Posting.\\n- **Tools/Features:** eigener Generator, Serienlogik, Custom Prompts, HD, Background Music, Voice Cloning, Auto-Post.\\n- **Approval-Loop:** explizit vorhanden: Nutzer kann Videos vor Posting prüfen und ändern.\\n- **Performance-Claim:** StarterStory nennt „How Eric Smith Grew AutoShorts.ai To $92K MRR“; AutoShorts selbst wirbt mit automatisierten faceless Channels.\\n- **Pitfalls:** stark auf Auto-Posting ausgelegt; Risiko von massenhaft generischem Content.\\n- **Nützlich für AutoShortsBot:** Sehr relevant:\\n - Serien-/Topic-Modell übernehmen.\\n - Preview-first statt Auto-post.\\n - editierbare Felder: Script, Titel, Visuals, Musik.\\n - Posting bewusst deaktiviert lassen, bis Human Approval erfolgt.\\n\\n---\\n\\n### 2) OpusClip + Audacy — Long-form/live content zu Shorts\\n\\nQuelle: \\nhttps://www.opus.pro/how-audacy-drove-1b-views-using-opusclip \\nhttps://www.opus.pro/customer-stories\\n\\n- **Workflow:** Live-/Radio-/Podcast-Momente → AI-Clipping → social-ready Clips → Publishing über mehrere Plattformen.\\n- **Tools/Features:** OpusClip, AI-Auswahl von Momenten, Clipping, Captions, Social Formatting.\\n- **Approval-Loop:** nicht detailliert als Human Approval beschrieben, aber bei Audacy Business-Kontext plausibel redaktionell eingebettet.\\n- **Performance-Claim:** Audacy habe mit OpusClip über **1B+ Views** über teilnehmende Stationen generiert; 50 Stationen produzieren täglich Content; 12+ Plattformen wurden evaluiert.\\n- **Pitfalls:** funktioniert besonders gut, wenn hochwertiger Long-form-Input existiert. Für reine generative Faceless-Videos weniger direkt übertragbar.\\n- **Nützlich für AutoShortsBot:**\\n - Priorisiere „source-backed“ Shorts aus vorhandenem Material, nicht nur synthetische Ideen.\\n - Baue Clip-/Hook-Scoring ein.\\n - Approval-Preview sollte Quelle, Hook, Transkript und vorgeschlagenen Cut zeigen.\\n\\n---\\n\\n### 3) n8n Template: vollautomatische AI Video Generation + Multi-Platform Publishing\\n\\nQuelle: \\nhttps://n8n.io/workflows/3442-fully-automated-ai-video-generation-and-multi-platform-publishing/\\n\\n- **Workflow:** Google Sheet mit Ideen → AI generiert Konzepte/Scripts/Prompts → Bilder/Clips/Voiceover → Creatomate rendert Video → Whisper/GPT erzeugt Beschreibung → Upload-Post.com publiziert auf TikTok, Instagram, YouTube, Facebook, LinkedIn → Google Sheet bekommt Status, Kosten, Token, Links → Discord Notification.\\n- **Tools:** Google Sheets, OpenAI/Whisper/GPT, Creatomate, Google Drive, upload-post.com, Discord, n8n.\\n- **Approval-Loop:** Template ist auf Auto-Publishing ausgelegt; Approval fehlt.\\n- **Performance-Claim:** kein echter Erfolgs-Case, aber sehr konkrete öffentliche Architektur.\\n- **Pitfalls:** viele externe APIs, Kosten/Token, viele Failure Points, Auto-Posting-Risiko.\\n- **Nützlich für AutoShortsBot:**\\n - Status-/Kosten-/Token-Tracking übernehmen.\\n - Google Sheet/Airtable-ähnliche Queue ist praktisch.\\n - Discord/Telegram Notification als Approval Trigger.\\n - Publishing-Schritt ersetzen durch „send German preview to Telegram, wait for Approve/Edit/Reject“.\\n\\n---\\n\\n### 4) n8n Template: Faceless Shorts mit OpenAI, RunwayML, ElevenLabs\\n\\nQuelle: \\nhttps://n8n.io/workflows/8290-automate-faceless-shorts-with-openai-runwayml-and-elevenlabs-script-to-social-media/\\n\\n- **Workflow:** Google Sheets Webhook → Anthropic schreibt 213–223 Zeichen Script für ca. 20s Hook → Szenen splitten → OpenAI Bilder → RunwayML Gen3a Turbo Animation → ElevenLabs Voiceover → Creatomate Merge → Replicate Captions → Google Drive/YouTube Upload → Sheet-Status.\\n- **Tools:** Google Sheets, Anthropic, OpenAI, RunwayML, ElevenLabs, Creatomate, Replicate, Google Drive, YouTube, n8n.\\n- **Approval-Loop:** nicht eingebaut; „ready to post“/Upload-orientiert.\\n- **Performance-Claim:** kein validierter Erfolg; Template verkauft als skalierbare Faceless-Fabrik.\\n- **Pitfalls:** teuer durch Runway/ElevenLabs/Creatomate; „viral potential“ ist Marketing, nicht belegt.\\n- **Nützlich für AutoShortsBot:**\\n - Sehr gute Sequenz: short script → scenes → visuals → voice → captions → render.\\n - Für local/free Pipeline externe Teile ersetzen:\\n - Runway → lokale Bild-/Video-Tools oder stock/B-roll.\\n - ElevenLabs → Piper/Coqui/edge-tts.\\n - Creatomate → ffmpeg/MoviePy/Remotion.\\n - Replicate Captions → Whisper + ffmpeg subtitles.\\n\\n---\\n\\n### 5) Make.com Community: „1,000 YouTube Shorts“ Workflow\\n\\nQuelle: \\nhttps://community.make.com/t/make-1-000-youtube-shorts-with-this-ai-automation-make-com/54511\\n\\n- **Workflow:** ChatGPT schreibt Scripts in Batches → Canva Bulk Create generiert Shorts mit Templates/Text Overlays → Videos in Dropbox/Google Drive → Make Repeater lädt sequenziell zu YouTube hoch → dynamische Titel/Privacy/Schedule.\\n- **Tools:** ChatGPT, Canva, Make.com, Dropbox/Google Drive, YouTube Module.\\n- **Approval-Loop:** Review der ChatGPT-Scripts wird erwähnt; finaler Approval-Prozess nicht stark ausgeprägt.\\n- **Performance-Claim:** Titel/Anspruch „1,000 Shorts“, aber keine belastbaren Views/Revenue-Zahlen.\\n- **Pitfalls:** ChatGPT Context Window → in kleinen Batches arbeiten; Gefahr von Template-Spam; braucht Error Handling und Upload Confirmation.\\n- **Nützlich für AutoShortsBot:**\\n - Batch-Größe begrenzen.\\n - „Master spreadsheet“/Queue mit Status verwenden.\\n - Canva-Bulk-Prinzip lokal als Template-System nachbauen.\\n - Vor Upload immer Preview + Confirmation.\\n\\n---\\n\\n### 6) Creatomate Tutorial: AI zu Shorts/Reels/Stories\\n\\nQuelle: \\nhttps://creatomate.com/blog/how-to-use-ai-to-automate-videos-for-shorts-stories-and-reels\\n\\n- **Workflow:** Slack-Input/Topic → Zapier → ChatGPT Script → DALL·E Visuals → ElevenLabs Voiceover → Creatomate Template → YouTube/TikTok/Instagram Posting.\\n- **Tools:** Slack, Zapier, ChatGPT, DALL·E, ElevenLabs, Creatomate, Social Platforms.\\n- **Approval-Loop:** Tutorial zeigt eher Automation; Approval müsste ergänzt werden.\\n- **Wichtiger Pitfall aus Quelle:** AI-generierter Content kann „unpredictable and chaotic“ sein; Templates und klare Prompt-Struktur sind nötig.\\n- **Nützlich für AutoShortsBot:**\\n - Template-first Rendering ist zentral.\\n - Input sollte nicht nur „Topic“ sein, sondern: Zielgruppe, Tone, Struktur, Szenenformat, Länge.\\n - Gute Stelle für German approval preview: Script + Szenen + Audio + Thumbnail vor Render/Finalisierung.\\n\\n---\\n\\n### 7) MindStudio Workflow: Script-to-MP4 Pipeline\\n\\nQuelle: \\nhttps://www.mindstudio.ai/blog/ai-short-form-video-creation-workflow-script-to-mp4\\n\\n- **Workflow:** Claude Script → ElevenLabs Voiceover → HyperFrames/API Visuals → Timeline/Render → Subtitles/Music/Branding.\\n- **Empfohlene Script-Struktur:** Hook 0–3s, Setup 3–15s, Main Content 15–50s, CTA 50–60s.\\n- **Tools:** Claude, ElevenLabs, HyperFrames, optional Claude Code/Batching.\\n- **Approval-Loop:** nicht zentral, aber Struktur ist gut reviewbar.\\n- **Pitfalls:** Quelle betont: viele AI-Videos scheitern bereits am Script; Hook, klare POV und Struktur sind wichtiger als nur Visuals.\\n- **Nützlich für AutoShortsBot:**\\n - Approval-Preview sollte Hook separat hervorheben.\\n - Script-Checker vor Video-Render: Hook unter 10 Wörter, eine klare These, keine Füllwörter.\\n - Für finale Videos English, aber Approval Deutsch: Preview kann Script auf Englisch + deutsche Zusammenfassung/Begründung zeigen.\\n\\n---\\n\\n### 8) Approval-Prozess aus Social-Media-Teams\\n\\nQuelle: \\nhttps://planable.io/blog/social-media-approval-process/\\n\\n- **Workflow-Stufen:** Ideation → Copywriting → Design → Internal Review → Client/Stakeholder Approval → Scheduling/Publishing.\\n- **Pitfalls:** verstreutes Feedback in Slack/E-Mail/Docs, unklare Reviewer, Änderungen nach Sign-off, Version-Chaos bei Skalierung.\\n- **Nützlich für AutoShortsBot:**\\n - Approval muss versioniert sein.\\n - Nach Approval Video/Metadata einfrieren oder neue Version erzeugen.\\n - Telegram Buttons: Approve / Reject / Edit Script / Edit Title / Regenerate Visuals / Render Again.\\n - Jede Entscheidung mit Timestamp, Reviewer, Version speichern.\\n\\n---\\n\\n### 9) Plattform-Risiko: YouTube Monetization Policies\\n\\nQuelle: \\nhttps://support.google.com/youtube/answer/1311392?hl=en\\n\\n- **Wichtiger Hinweis:** YouTube hat 2025 „repetitious content“ klarer als „inauthentic content“ beschrieben; repetitive oder mass-produced Inhalte sind nicht monetarisierbar.\\n- **Bedeutung:** Automatisierte Shorts können Reichweite bekommen, aber Monetarisierung/Account Health leiden, wenn Content generisch, massenhaft oder nicht originell wirkt.\\n- **Nützlich für AutoShortsBot:**\\n - Kein blindes Massenposting.\\n - Originalität prüfen: eigener Angle, Quellen, unique script, nicht nur templated facts.\\n - Human Approval als Safety Gate ist sinnvoll und sollte Kernfeature bleiben.\\n\\n---\\n\\n## Praktische Inputs für eine lokale approval-first AutoShorts-Pipeline\\n\\n**Empfohlene Pipeline:**\\n\\n1. **Input Queue**\\n - YAML/SQLite/Google Sheet lokal möglich.\\n - Felder: topic, audience, angle, source_url, language_final=`en`, approval_language=`de`, status.\\n\\n2. **Script Generation**\\n - Struktur nach MindStudio: Hook, Setup, Main, CTA.\\n - Script final Englisch.\\n - Deutsche Approval-Zusammenfassung automatisch erzeugen.\\n\\n3. **Preflight Checks**\\n - Hook-Qualität.\\n - Länge/Sprechdauer.\\n - Originalitäts-/Reused-Content-Risiko.\\n - Keine verbotenen Claims.\\n - Quellen vorhanden, wenn factual.\\n\\n4. **Asset Generation**\\n - Local/free bevorzugt:\\n - Voice: Piper, Coqui, edge-tts.\\n - Captions: Whisper/faster-whisper.\\n - Render: ffmpeg, MoviePy, Remotion.\\n - Visuals: lokale Bildmodelle, lizenzfreie B-roll, eigene Templates.\\n - Externe APIs optional als Plug-ins.\\n\\n5. **German Approval Preview**\\n - Telegram-Nachricht mit:\\n - Deutscher Kurzbeschreibung.\\n - Englischem Script.\\n - Hook-Bewertung.\\n - Szenenliste.\\n - Thumbnail/Frame Preview.\\n - Audio/Video Preview.\\n - geschätzte Kosten/Renderzeit.\\n - Buttons: Approve, Reject, Edit, Regenerate, Render Final.\\n\\n6. **Versionierung**\\n - Jede Preview bekommt version_id.\\n - Approval gilt nur für exakt diese Script-/Asset-/Metadata-Version.\\n - Nach Änderung neue Preview erzwingen.\\n\\n7. **No Auto-Posting**\\n - Nach Approval: final render/export.\\n - Optional „ready_to_upload“ Ordner.\\n - Posting erst später bewusst/extern.\\n\\n---\\n\\n## Wichtigste Umsetzungsideen\\n\\n- Von AutoShorts.ai übernehmen: Series, Preview & Customize.\\n- Von OpusClip übernehmen: bestehende hochwertige Inputs stärker nutzen als reine KI-Fantasie.\\n- Von n8n/Make übernehmen: Queue, Status, Kosten, Error Handling.\\n- Von Creatomate übernehmen: Template-first Rendering.\\n- Von Planable übernehmen: klare Approval-Stufen und Version Lock.\\n- Von YouTube Policies ableiten: kein massenhaft generischer Content; Originalität als Gate.\\n\\n## Dateien\\n\\nKeine Dateien erstellt oder geändert.\\n\\n## Issues / Einschränkungen\\n\\n- Einige Quellen sind Vendor-/Marketing-Claims; Performance-Zahlen wie „1B+ Views“ oder „$92K MRR“ sind öffentlich genannt, aber nicht unabhängig verifiziert.\\n- Viele öffentliche Workflows sind Auto-Publishing-orientiert. 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Consider upgrading Browserbase plan for proxy support.\", \"stealth_features\": [\"local\"], \"snapshot\": \"- generic\\n - link \\\"Skip to main content\\\" [ref=e1]\\n - generic\\n - banner\\n - generic\\n - button \\\"Main menu\\\" [expanded=false, ref=e8]\\n - image\\n - link \\\"YouTube Help\\\" [ref=e17]\\n - search\\n - combobox \\\"Describe your issue\\\" [expanded=false, ref=e23]\\n - button \\\"Search Help Center\\\" [ref=e18]\\n - image\\n - generic\\n - button \\\"Google apps\\\" [expanded=false, ref=e27]\\n - StaticText \\\" \\\"\\n - link \\\"Sign in\\\" [ref=e19]\\n - StaticText \\\" \\\"\\n - StaticText \\\"Sign in\\\"\\n - navigation \\\"Support page navigation bar\\\" [ref=e2]\\n - list\\n - listitem [level=1]\\n - link \\\"Help Center\\\" [ref=e24]\\n - listitem [level=1]\\n - link \\\"Community\\\" [ref=e25]\\n - listitem [level=1]\\n - link \\\"Creator Tips\\\" [ref=e26]\\n - link \\\"YouTube (Open in a new window)\\\" [ref=e6]\\n - StaticText \\\"YouTube\\\"\\n - image\\n - navigation \\\"Top navigation\\\" [ref=e3]\\n - tablist\\n - tab \\\"Fix a problem\\\" [ref=e9]\\n - tab \\\"Watch videos\\\" [ref=e10]\\n - tab \\\"Manage your account & settings\\\" [ref=e11]\\n - tab \\\"Supervised experiences on YouTube\\\" [ref=e12]\\n - tab \\\"YouTube Premium\\\" [ref=e13]\\n - tab \\\"Create & grow your channel\\\" [ref=e14]\\n - tab \\\"Monetize with the YouTube Partner Program\\\" [selected, ref=e15]\\n - tab \\\"Policy, safety, & copyright\\\" [ref=e16]\\n - generic \\\"Notification\\\" [ref=e4] focusable [tabindex]\\n - generic [ref=e7] focusable [tabindex]\\n - paragraph\\n - StaticText \\\"To receive the latest updates on our Advertiser-friendly content guidelines, please check out our \\\"\\n - link \\\"Advertiser-friendly content guidelines\\\" [ref=e20]\\n - StaticText \\\" posts in the YouTube Help Center.\\\"\\n - generic\\n - main\\n - generic\\n - generic\\n - article\\n - generic\\n - heading \\\"YouTube channel monetization policies\\\" [level=1, ref=e35]\\n - generic\\n - region \\\"alert\\\" [ref=e41]\\n - generic\\n - strong\\n - StaticText \\\" We’re making a minor update to our “repetitious content” policy to better clarify this includes content that is repetitive or mass-produced. We are also renaming this policy from “repetitious content” to “inauthentic content.” This type of content has always been ineligible for monetization under our existing policies, where creators are rewarded for original and authentic content. There is no change to our reused content policy which reviews content like commentary, clips, compilations, and reaction videos.\\\"\\n - generic\\n - strong\\n - StaticText \\\"March 10, 2025: \\\"\\n - link \\\"Learn more\\\" [ref=e74]\\n - paragraph\\n - link \\\"Learn more\\\" [ref=e75]\\n - paragraph\\n - StaticText \\\"If you’re monetizing on YouTube, it’s important that your channel follows YouTube monetization policies. These include policies described below, as well as YouTube’s \\\"\\n - link \\\"Community Guidelines Opens in new window\\\" [ref=e47]\\n - StaticText \\\", \\\"\\n - link \\\"Terms of Service Opens in new window\\\" [ref=e48]\\n - StaticText \\\", \\\"\\n - link \\\"Copyright Opens in new window\\\" [ref=e49]\\n - StaticText \\\", \\\"\\n - link \\\"Rights Clearance Adjustment policies\\\" [ref=e50]\\n - StaticText \\\", and our\\\"\\n - link \\\" program policies\\\" [ref=e51]\\n - StaticText \\\".\\\"\\n - paragraph\\n - StaticText \\\"These policies apply to anyone in, or looking to apply to, the YouTube Partner Program. The \\\"\\n - link \\\"YouTube Shorts monetization policies\\\" [ref=e52]\\n - StaticText \\\" also apply if you’re monetizing Shorts on YouTube.\\\"\\n - paragraph\\n - StaticText \\\"All content monetizing with ads must follow our\\\"\\n - link \\\" advertiser-friendly content guidelines\\\" [ref=e53]\\n - StaticText \\\". To earn revenue from fan funding features, first time users must accept the Commerce Product Module (CPM) before turning on the individual features. You must also follow the \\\"\\n - link \\\"Commerce Products monetization policies\\\" [ref=e54]\\n - StaticText \\\" when monetizing with fan funding features.\\\"\\n - paragraph\\n - StaticText \\\"Here’s a quick overview of each major policy. Make sure you read each policy thoroughly, as these policies are used to check if a channel is suitable to monetize. Our reviewers regularly check to see whether monetizing channels follow these policies. \\\"\\n - link \\\"Learn more about how we enforce our policies\\\" [ref=e55]\\n - StaticText \\\".\\\"\\n - paragraph\\n - StaticText \\\"Keep in mind that when we use the term \\\"\\n - strong\\n - StaticText \\\" on this page, it refers to Shorts, long-form videos, and live streaming. These policies apply wherever videos are viewed including the Watch Page (pages within YouTube, YouTube Music, or YouTube Kids), the YouTube Video Player (player that embeds YouTube content on other sites), and the YouTube Shorts Player (player that makes Shorts available).\\\"\\n - heading \\\"What we check when we review your channel\\\" [level=2, ref=e42]\\n - paragraph\\n - StaticText \\\"If you’re \\\"\\n - link \\\"making money on YouTube\\\" [ref=e56]\\n - StaticText \\\", your content should be original and \\\\\\\"authentic.\\\\\\\" This means that we expect your content to:\\\"\\n - list\\n - listitem [level=1]\\n - listitem [level=1]\\n - paragraph\\n - StaticText \\\"Our reviewers will check your channel and content against our policies. Since they can’t check every video, our reviewers may focus on your channel’s:\\\"\\n - list\\n - listitem [level=1]\\n - listitem [level=1]\\n - listitem [level=1]\\n - listitem [level=1]\\n - listitem [level=1]\\n - listitem [level=1]\\n - paragraph\\n - StaticText \\\"The above are just examples of content our reviewers may assess. 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At the same time, we recognize that viewers want to know if what they’re watching or listening to is real.\\\"\\n - paragraph\\n - StaticText \\\"To help keep viewers informed about the content they're viewing, we require creators to disclose content that is meaningfully altered or synthetically generated when it seems realistic.\\\"\\n - paragraph\\n - StaticText \\\"Creators must disclose content that:\\\"\\n - list\\n - listitem [level=1]\\n - StaticText \\\"•\\\"\\n - StaticText \\\"Makes a real person appear to say or do something they didn’t do.\\\"\\n - listitem [level=1]\\n - StaticText \\\"•\\\"\\n - StaticText \\\"Alters footage of a real event or place.\\\"\\n - listitem [level=1]\\n - StaticText \\\"•\\\"\\n - StaticText \\\"Generates a realistic-looking scene that didn’t actually occur.\\\"\\n - paragraph\\n - StaticText \\\"This could include content that is fully or partially altered or created using audio, video or image creation or editing tools.\\\"\\n - heading \\\"Disclose using the ‘altered content’ setting in YouTube Studio\\\" [level=2, ref=e45]\\n - paragraph\\n - StaticText \\\"To disclose content that is meaningfully altered or synthetically generated, the ‘altered content’ setting is available to creators using YouTube Studio on a computer or mobile device. We’ll expand this setting to other devices and YouTube apps in the future.\\\"\\n - paragraph\\n - StaticText \\\"After a creator selects this field and uploads content, a label will appear in their \\\"\\n - link \\\"video’s expanded description\\\" [ref=e56]\\n - StaticText \\\".\\\"\\n - paragraph\\n - StaticText \\\"Creators who make a post or YouTube Short using one of YouTube's generative artificial intelligence (AI) tools don't need to take extra steps to disclose. The tool will automatically disclose the use of AI for creators. For other AI tools, creators need to disclose their use during the upload flow.\\\"\\n - heading \\\"Examples of altered or synthetic content\\\" [level=3, ref=e46]\\n - StaticText \\\"Examples of altered or synthetic content\\\"\\n - paragraph\\n - StaticText \\\"The following list includes examples of altered or synthetic content. Altered or synthetic content can include content that is fully or partially altered or created using any audio, video, image creation or editing tools. Realistic content and meaningful changes require disclosure, while unrealistic or minor edits don’t. Keep in mind, this isn’t a complete list.\\\"\\n - table\\n - row\\n - cell \\\"Doesn’t require disclosure by creators\\\" [ref=e62]\\n - link \\\"Doesn’t require disclosure by creators\\\" [ref=e73]\\n - cell \\\"Does require disclosure by creators\\\" [ref=e63]\\n - link \\\"Does require disclosure by creators\\\" [ref=e74]\\n - row\\n - cell \\\"• Applying beauty filters\\\" [ref=e64]\\n - cell \\\"• Digitally generating or altering content to replace the face of one individual with another's\\\" [ref=e65]\\n - row\\n - cell \\\"• Synthetically generating or extending a backdrop to simulate a moving car\\\" [ref=e66]\\n - cell \\\"• Digitally altering a famous car chase scene to include a celebrity who wasn’t in the original movie\\\" [ref=e67]\\n - row\\n - cell \\\"• Using effects to enhance previously recorded audio\\\" [ref=e68]\\n - cell \\\"• Simulating audio to make it sound as if a medical professional gave advice when the professional did not actually give that advice\\\" [ref=e69]\\n - row\\n - cell \\\"• Using an AI-generated animation of a missile in a video\\\" [ref=e70]\\n - cell \\\"• Showing a realistic depiction of a missile fired toward a real city\\\" [ref=e71]\\n - button \\\"Examples of content creators don’t have to disclose\\\" [expanded=false, ref=e57]\\n - StaticText \\\"Examples of content creators don’t have to disclose\\\"\\n - button \\\"Examples of content creators need to disclose\\\" [expanded=false, ref=e58]\\n - StaticText \\\"Examples of content creators need to disclose\\\"\\n - heading \\\"Disclose altered or synthetic content\\\" [level=2, ref=e47]\\n - StaticText \\\"Disclose altered or synthetic content\\\"\\n - paragraph\\n - StaticText \\\"We require creators to disclose meaningfully altered or synthetically generated content that seems realistic. Creators can make this disclosure during the upload process.\\\"\\n - tablist\\n - tab \\\"Computer\\\" [selected, ref=e75]\\n - tab \\\"Android\\\" [ref=e76]\\n - tab \\\"iPhone & iPad\\\" [ref=e77]\\n - list\\n - listitem [level=1]\\n - link \\\"YouTube Studio Opens in new window\\\" [ref=e72]\\n - paragraph\\n - paragraph\\n - paragraph\\n - heading \\\"What happens after creators disclose\\\" [level=3, ref=e48]\\n - link \\\"What happens after creators disclose\\\" [ref=e59]\\n - paragraph\\n\\n[... 62 more lines truncated, use browser_snapshot for full content]\", \"element_count\": 77}", "tool_call_id": "call_keabyEey3G22pGov02sbP3Ig" }, { "role": "tool", "name": "browser_navigate", "content": "{\"success\": true, \"url\": \"https://www.tiktok.com/support/faq_detail?id=7581821550694013452\", \"title\": \"TikTok Support\", \"snapshot\": \"- generic [ref=e1] clickable [onclick]\", \"element_count\": 1}", "tool_call_id": "call_7tOsBrzAedZm4vu43XJV9nVq" }, { "role": "tool", "name": "browser_navigate", "content": "{\"success\": true, \"url\": \"https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/\", \"title\": \"Our Approach to Labeling AI-Generated Content and Manipulated Media\", \"snapshot\": \"- generic\\n - link \\\"Skip to content\\\" [ref=e1]\\n - complementary\\n - LabelText\\n - StaticText \\\"Diese Seite anzeigen auf\\\"\\n - generic\\n - combobox \\\"Englisch\\\" [expanded=false, ref=e37]\\n - StaticText \\\"Englisch\\\"\\n - button \\\"Dismiss locale prompt\\\" [expanded=true, ref=e2]\\n - image \\\"Dismiss locale prompt\\\"\\n - banner\\n - link \\\"Meta\\\" [ref=e5]\\n - StaticText \\\"Meta\\\"\\n - image\\n - generic\\n - navigation \\\"Main\\\" [ref=e6]\\n - list\\n - listitem [level=1]\\n - link \\\"AI glasses\\\" [expanded=false, ref=e96]\\n - listitem [level=1]\\n - link \\\"Meta Quest\\\" [expanded=false, ref=e97]\\n - listitem [level=1]\\n - link \\\"Explore Meta\\\" [expanded=false, ref=e98]\\n - list\\n - listitem [level=1]\\n - link \\\"Support\\\" [expanded=false, ref=e99]\\n - listitem [level=1]\\n - link \\\"Newsroom\\\" [ref=e100]\\n - generic\\n - button \\\"Toggle site search\\\" [ref=e17]\\n - image\\n - generic\\n - navigation \\\"Main\\\" [ref=e18]\\n - list\\n - listitem [level=1]\\n - link \\\"Technologies\\\" [expanded=false, ref=e114]\\n - listitem [level=1]\\n - link \\\"Product News\\\" [ref=e115]\\n - listitem [level=1]\\n - link \\\"Innovation\\\" [ref=e116]\\n - listitem [level=1]\\n - link \\\"Public Policy\\\" [ref=e117]\\n - listitem [level=1]\\n - link \\\"Youth Well-Being\\\" [ref=e118]\\n - listitem [level=1]\\n - link \\\"Privacy\\\" [ref=e119]\\n - listitem [level=1]\\n - link \\\"Security\\\" [ref=e120]\\n - generic\\n - generic\\n - image\\n - LabelText\\n - StaticText \\\"Search News\\\"\\n - generic\\n - generic\\n - combobox \\\"Search News\\\" [expanded=false, ref=e150]\\n - generic\\n - main\\n - link \\\" Back to Newsroom\\\" [ref=e38]\\n - image\\n - StaticText \\\" Back to Newsroom\\\"\\n - link \\\"META\\\" [ref=e39]\\n - sectionheader\\n - heading \\\"Our Approach to Labeling AI-Generated Content and Manipulated Media\\\" [level=1, ref=e40]\\n - StaticText \\\"Our Approach to Labeling AI-Generated Content and Manipulated Media\\\"\\n - time\\n - StaticText \\\"April 5, 2024\\\"\\n - StaticText \\\"By Monika Bickert, Vice President of Content Policy\\\"\\n - button \\\"Listen to Article\\\" [ref=e19]\\n - StaticText \\\"LISTEN TO ARTICLE\\\"\\n - generic\\n - generic\\n - image \\\"\\\\\\\"AI Info\\\\\\\" label on images.\\\"\\n - button \\\"Download image\\\" [ref=e41]\\n - StaticText \\\"Download image\\\"\\n - heading \\\"Takeaways\\\" [level=2, ref=e20]\\n - list\\n - listitem [level=1]\\n - ListMarker \\\"• \\\"\\n - StaticText \\\"We are making changes to the way we handle manipulated media based on \\\"\\n - link \\\"feedback\\\" [ref=e101]\\n - StaticText \\\" from the Oversight Board and our policy review process with public opinion surveys and expert consultations.\\\"\\n - listitem [level=1]\\n - ListMarker \\\"• \\\"\\n - StaticText \\\"We will begin adding “AI info” labels to a wider range of video, audio and image content when we detect industry standard AI image indicators or when people disclose that they’re uploading AI-generated content. (\\\"\\n - emphasis\\n - strong\\n - StaticText \\\")\\\"\\n - listitem [level=1]\\n - ListMarker \\\"• \\\"\\n - StaticText \\\"We agree with the Oversight Board’s recommendation that providing transparency and additional context is now the better way to address manipulated media and avoid the risk of unnecessarily restricting freedom of speech, so we’ll keep this content on our platforms so we can add labels and context.\\\"\\n - article\\n - paragraph\\n - emphasis\\n - strong\\n - paragraph\\n - StaticText \\\"Soon we’re rolling out a change to the “AI info” labels on our platforms so they better reflect the extent of AI used in content. Our intent has always been to help people know when they see content that was made with AI, and we’ve continued to work with companies across the industry to improve our labeling process so that labels on our platforms are more in line with peoples’ expectations.\\\"\\n - paragraph\\n - StaticText \\\"For content that we detect was only modified or edited by AI tools, we are moving the “AI info” label to the post’s menu. We will still display the “AI info” label for content we detect was generated by an AI tool and share whether the content is labeled because of industry-shared signals or because someone self-disclosed. These changes will begin next week.\\\"\\n - generic\\n - generic\\n - image \\\"Phone screen showing image of a cat posted on Facebook\\\"\\n - button \\\"Download slideshow images\\\" [ref=e102]\\n - StaticText \\\"Download slideshow images\\\"\\n - generic\\n - button \\\"Previous Slide\\\" [ref=e121]\\n - button \\\"Pause Slideshow\\\" [ref=e122]\\n - button \\\"Next Slide\\\" [ref=e123]\\n - paragraph\\n - emphasis\\n - strong\\n - paragraph\\n - StaticText \\\"We want people to know when they see posts that have been made with AI. Earlier this year, we announced a new approach for labeling AI-generated content. An important part of this approach relies on industry standard indicators that other companies include in content created using their tools, which help us assess whether something is created using AI.\\\"\\n - paragraph\\n - StaticText \\\"Like others across the industry, we’ve found that our labels based on these indicators weren’t always aligned with people’s expectations and didn’t always provide enough context. For example, some content that included minor modifications using AI, such as retouching tools, included industry standard indicators that were then labeled “Made with AI.” While we work with companies across the industry to improve the process so our labeling approach better matches our intent, we’re updating the “Made with AI” label to “AI info” across our apps, which people can click for more information.\\\"\\n - generic\\n - generic\\n - image \\\"AI content label on Facebook\\\"\\n - button \\\"Download slideshow images\\\" [ref=e103]\\n - StaticText \\\"Download slideshow images\\\"\\n - generic\\n - button \\\"Previous Slide\\\" [ref=e124]\\n - button \\\"Pause Slideshow\\\" [ref=e125]\\n - button \\\"Next Slide\\\" [ref=e126]\\n - paragraph\\n - emphasis\\n - strong\\n - paragraph\\n - StaticText \\\"We are making changes to the way we handle manipulated media on Facebook, Instagram and Threads based on \\\"\\n - link \\\"feedback\\\" [ref=e104]\\n - StaticText \\\" from the Oversight Board that we should update our approach to reflect a broader range of content that exists today and provide context about the content through labels. These changes are also informed by Meta’s policy review process that included extensive public opinion surveys and consultations with academics, civil society organizations and others.\\\"\\n - paragraph\\n - StaticText \\\"We agree with the Oversight Board’s argument that our existing approach is too narrow since it only covers videos that are created or altered by AI to make a person appear to say something they didn’t say. Our manipulated media policy was written in 2020 when realistic AI-generated content was rare and the overarching concern was about videos. In the last four years, and particularly in the last year, people have developed other kinds of realistic AI-generated content like audio and photos, and this technology is quickly evolving. As the Board noted, it’s equally important to address manipulation that shows a person doing something they didn’t do.\\\"\\n - paragraph\\n\\n[... 263 more lines truncated, use browser_snapshot for full content]\", \"element_count\": 154}", "tool_call_id": "call_TQ3QUziIYzJBVeE4INqjFEbD" }, { "role": "assistant", "content": "", "reasoning": "**Organizing research and implementation**\n\nI need to handle the browser parallel clobbers, but I think I have enough information now. It makes sense to mark the research as complete and create a concise research file, perhaps a document named `content_automation_research.md`. After that, I should focus on the package implementation using Test-Driven Development (TDD). Writing the research document could happen if I decide to \"take action.\" I’ll also want to commit everything and inspect the social draft for review.", "finish_reason": "tool_calls", "reasoning_content": "**Organizing research and implementation**\n\nI need to handle the browser parallel clobbers, but I think I have enough information now. It makes sense to mark the research as complete and create a concise research file, perhaps a document named `content_automation_research.md`. After that, I should focus on the package implementation using Test-Driven Development (TDD). Writing the research document could happen if I decide to \"take action.\" I’ll also want to commit everything and inspect the social draft for review.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEx_8RsgbLZDlY4np31AdlwDk9Q9FfEwdF-ENgIPp2DjL5pZQvN7zQZ3NRqJVPsCXJvPmkGqCtvj20D77NKRHyufRxE_P35ytkoc6y-u7zqyQepQIeCH9DWZ1UCHT58fQfJ2WLkn1uT1X_pR5zV36fn4XwQxV9RypdZYCJhYtGF4plReRsOj610HDx86_AaMVFuFVRqkIOazWRfl0PbFzcUfx7k1PkKM6U_rjoBKC38uy9ZkSRlpilYq-X2OzCVgsUhggqNy9brHpZBUqUyIBiFdDJlXdQDUwJjNL2MoaLnyk19NpPK5hNZTIXZls0B04nBWu1XVTdEFmRXCjhTlUQU3b5IUlAv9tU7XdsDtSUYeU9h4cF0aN59jXtEQgEheLKwwId2xzqQUnIY7Jpm2unzF88yVQV56qo47PrC_AWqEjF7Vo4SSVygOmndXBiDhRbSwKHvbW7LaNzeEC-cO2SE-fp-kbgJEstV0HnoT0pMY9FsBANGYiaNFmt9kDXedC21rIpD_2ijNL0mPnqFJVWhKod0QoDSCJ-vSY67UqN0fC_nVgM_hthd_3kh2f4LxllzcysCLm9tjwCrkuWPR_K8dmmwaM_zChRcu_oHcofGsOer20JdIq4GjD6s9NpCjosXWcqllM_O666fjlDvfiwN6jDTwF2XkhLSyoU5ucvxS__DCj-2PdkRF3Lh8-f1A2HrUOEnPUkiv4DXw3V4vZAD_bqIJYvDdBtLvgG1vt_ijdUH0dXQnF0Uotc7E1CX-G_icChC1C2TvXABQQeTt91oviK8eNNowGyGhVN9nFjDwbChzeke1wzM4OqUgiVN6zokIoPPFxwXdxsledAdhhjCqrF-qIqDhH8imBzhdy5XWCcCG9s4trww-XYAsIeKI6zObseCNQWC1jTythjmaXcU3jFgn3B79cDyzjKvAWbjQWWodfMCZs53PdQX3zQKkWkjk3tJSNNtJzQN3unHre5LkpbumdVQHywUXLC7UMaNC3jcKezEDSt3-PeJLjaK-0jhmjF08kzcu3gy4r2Gq4MdiWD5qQPY7eluZXoYscIy9HoFz0vAF5dRl_WlzwM9k3CKJ2FcdKxqaGycHaapX_0QMGlKo8JnJlUMP6SHdtOvcuAuScwh0CpbldN6YDNSjf6AFnk4hgmUZwKsY3sPv7IvHM0HKKIDQx5tTLS2gl_vTz2QV7j2DrKPlDZpA2-kBHY9Orwntgbm8Zp6DrfQ4NmxKcYFub2mkhM4Iy7jCnE-2q9Y4I3IQFJt5CHsq7HdZt5UDiQfnKy4FQLQXcHFvIYEybS6h7Ud_FDto6KHSKy7wbqQg9mvq77iC44FCS5XQYaR5dHYpS_pgEgw6_zEAzcHovytlp9K0OretVVs0BRI8gU2-E4l6gjEnA_rq3lZRJtVkOt1afNP_CixLV9sUNImnCzGBG5aSsaH8WpUz2fvOPOrTqORCzklSIBOJ4w-HyIABEjjcpGLmX7st6gst5ngBJAWYWjtiQJUWgWrWr4R1_Q9XhjH_-nR-foCChWbw6uBxwzKu6U4R7AnNYpEVn034NOhZpbVoXbumi8N2_h9YBEmtrr0V1on8yC_1ktIYUj6-whp6UKRHZSh7tOpDNhP4QQaTnazbjFbvegjHNvjXenqU_4_tDplMnBLC0QVZ1W9EE", "id": "rs_060035eb87c40e14016a131ff7c0148191913e5b412eef1adc", "summary": [ { "type": "summary_text", "text": "**Organizing research and implementation**\n\nI need to handle the browser parallel clobbers, but I think I have enough information now. It makes sense to mark the research as complete and create a concise research file, perhaps a document named `content_automation_research.md`. After that, I should focus on the package implementation using Test-Driven Development (TDD). Writing the research document could happen if I decide to \"take action.\" I’ll also want to commit everything and inspect the social draft for review." } ] } ], "tool_calls": [ { "id": "call_XqnZBTyeDDkH1t0JoN1Hqjvg", "call_id": "call_XqnZBTyeDDkH1t0JoN1Hqjvg", "response_item_id": "fc_060035eb87c40e14016a131ffcb1f08191904c8fa8e5c64203", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Im Netz recherchieren, wie andere Personen Content-Automation erfolgreich aufbauen, und Inputs bewerten\",\"id\":\"research\",\"status\":\"completed\"},{\"content\":\"ContentPreviewApprovalPackage als nächstes Approval-Preview-Bindeglied per TDD bauen\",\"id\":\"package\",\"status\":\"in_progress\"}]}" } } ] }, { "role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"research\", \"content\": \"Im Netz recherchieren, wie andere Personen Content-Automation erfolgreich aufbauen, und Inputs bewerten\", \"status\": \"completed\"}, {\"id\": \"package\", \"content\": \"ContentPreviewApprovalPackage als nächstes Approval-Preview-Bindeglied per TDD bauen\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 2, \"pending\": 0, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_XqnZBTyeDDkH1t0JoN1Hqjvg" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_TMQiDum7YgclhhSo2RZ2d2F2", "call_id": "call_TMQiDum7YgclhhSo2RZ2d2F2", "response_item_id": "fc_060035eb87c40e14016a132005f9008191a405fc4b7134ba05", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":240,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/draft.py\"}" } }, { "id": "call_Syvne0gdgTOAwl2IpqRo15xm", "call_id": "call_Syvne0gdgTOAwl2IpqRo15xm", "response_item_id": "fc_060035eb87c40e14016a13200600a88191912a032a24c0b933", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":200,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/review.py\"}" } }, { "id": "call_eSmwaDSLTVIw1gCuYtLsr2Wa", "call_id": "call_eSmwaDSLTVIw1gCuYtLsr2Wa", "response_item_id": "fc_060035eb87c40e14016a13200604dc81918575519dfb704c19", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":160,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py\"}" } }, { "id": "call_K3lKw1TJXHV16oel6FWltiYN", "call_id": "call_K3lKw1TJXHV16oel6FWltiYN", "response_item_id": "fc_060035eb87c40e14016a132006052c8191acf64c9a37f0eff4", "type": "function", "function": { "name": "search_files", "arguments": "{\"limit\":50,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"pattern\":\"docs\",\"target\":\"files\"}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Approval-first social media draft artifacts.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import SUPPORTED_PLATFORMS, ValidationResult, VideoCandidate, validate_candidate\\n 8|from autoshorts.rendering.preview import RenderPreview, validate_render_preview\\n 9|\\n 10|_PLATFORM_LABELS = {\\n 11| \\\"youtube\\\": \\\"YouTube Shorts\\\",\\n 12| \\\"tiktok\\\": \\\"TikTok\\\",\\n 13| \\\"instagram\\\": \\\"Instagram Reels\\\",\\n 14|}\\n 15|\\n 16|\\n 17|@dataclass(frozen=True)\\n 18|class SocialMediaDraft:\\n 19| candidate_id: str\\n 20| platforms: tuple[str, ...]\\n 21| platform_labels: tuple[str, ...]\\n 22| title_variants: tuple[str, ...]\\n 23| caption: str\\n 24| hashtags: tuple[str, ...]\\n 25| cta: str\\n 26| hook: str\\n 27| planned_duration_seconds: int\\n 28| quality_risk_summary: str\\n 29| preview_reference: str\\n 30| preview_type: str\\n 31| requires_human_approval: bool = True\\n 32| side_effects: tuple[str, ...] = ()\\n 33|\\n 34|\\n 35|def _is_blank(value: str) -> bool:\\n 36| return not (value or \\\"\\\").strip()\\n 37|\\n 38|\\n 39|def _quality_risk_summary(candidate: VideoCandidate) -> str:\\n 40| score = candidate.quality_score\\n 41| return (\\n 42| f\\\"Quality average: {score.average_quality:.1f}; \\\"\\n 43| f\\\"AI-slop risk: {score.ai_slop_risk}; \\\"\\n 44| f\\\"Policy risk: {score.policy_risk}\\\"\\n 45| )\\n 46|\\n 47|\\n 48|def _source_brief_id(candidate: VideoCandidate) -> str | None:\\n 49| prefix = \\\"Source brief: \\\"\\n 50| for note in candidate.policy_notes:\\n 51| if note.startswith(prefix):\\n 52| return note.removeprefix(prefix).strip()\\n 53| return None\\n 54|\\n 55|\\n 56|def build_social_media_draft(candidate: VideoCandidate, preview: RenderPreview) -> SocialMediaDraft:\\n 57| \\\"\\\"\\\"Build a side-effect-free platform draft from a candidate and preview.\\\"\\\"\\\"\\n 58|\\n 59| candidate_validation = validate_candidate(candidate)\\n 60| if not candidate_validation.is_valid:\\n 61| raise ValueError(\\\"; \\\".join(candidate_validation.errors))\\n 62|\\n 63| preview_validation = validate_render_preview(preview)\\n 64| if not preview_validation.is_valid:\\n 65| raise ValueError(\\\"; \\\".join(preview_validation.errors))\\n 66|\\n 67| source_brief_id = _source_brief_id(candidate)\\n 68| if source_brief_id is not None and preview.script_id != source_brief_id:\\n 69| raise ValueError(\\\"preview script_id must match candidate source brief\\\")\\n 70|\\n 71| draft = SocialMediaDraft(\\n 72| candidate_id=candidate.candidate_id,\\n 73| platforms=candidate.platforms,\\n 74| platform_labels=tuple(_PLATFORM_LABELS[platform] for platform in candidate.platforms),\\n 75| title_variants=(candidate.title, candidate.hook),\\n 76| caption=candidate.caption,\\n 77| hashtags=candidate.hashtags,\\n 78| cta=candidate.cta,\\n 79| hook=candidate.hook,\\n 80| planned_duration_seconds=candidate.duration_seconds,\\n 81| quality_risk_summary=_quality_risk_summary(candidate),\\n 82| preview_reference=\\\"HTML timeline preview available\\\",\\n 83| preview_type=preview.preview_type,\\n 84| requires_human_approval=True,\\n 85| side_effects=(),\\n 86| )\\n 87| validation = validate_social_media_draft(draft)\\n 88| if not validation.is_valid:\\n 89| raise ValueError(\\\"; \\\".join(validation.errors))\\n 90| return draft\\n 91|\\n 92|\\n 93|def validate_social_media_draft(draft: SocialMediaDraft) -> ValidationResult:\\n 94| errors: list[str] = []\\n 95| for field_name, value in {\\n 96| \\\"candidate_id\\\": draft.candidate_id,\\n 97| \\\"caption\\\": draft.caption,\\n 98| \\\"cta\\\": draft.cta,\\n 99| \\\"hook\\\": draft.hook,\\n 100| \\\"quality_risk_summary\\\": draft.quality_risk_summary,\\n 101| \\\"preview_reference\\\": draft.preview_reference,\\n 102| \\\"preview_type\\\": draft.preview_type,\\n 103| }.items():\\n 104| if _is_blank(value):\\n 105| errors.append(f\\\"{field_name} is required\\\")\\n 106|\\n 107| if not draft.platforms:\\n 108| errors.append(\\\"at least one platform is required\\\")\\n 109| for platform in draft.platforms:\\n 110| if platform not in SUPPORTED_PLATFORMS:\\n 111| errors.append(f\\\"unsupported platform: {platform}\\\")\\n 112|\\n 113| if len(draft.platform_labels) != len(draft.platforms):\\n 114| errors.append(\\\"platform_labels must match platforms\\\")\\n 115| if not draft.title_variants:\\n 116| errors.append(\\\"at least one title variant is required\\\")\\n 117| elif any(_is_blank(title) for title in draft.title_variants):\\n 118| errors.append(\\\"title variants must not be blank\\\")\\n 119| if not draft.hashtags:\\n 120| errors.append(\\\"at least one hashtag is required\\\")\\n 121| elif any(_is_blank(hashtag) for hashtag in draft.hashtags):\\n 122| errors.append(\\\"hashtags must not be blank\\\")\\n 123| if not 10 <= draft.planned_duration_seconds <= 180:\\n 124| errors.append(\\\"planned_duration_seconds must be between 10 and 180\\\")\\n 125| if not draft.requires_human_approval:\\n 126| errors.append(\\\"human approval is required\\\")\\n 127| if draft.side_effects:\\n 128| errors.append(\\\"social media drafts must not have side effects\\\")\\n 129|\\n 130| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 131|\", \"total_lines\": 130, \"file_size\": 4730, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_TMQiDum7YgclhhSo2RZ2d2F2" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Telegram-safe social media draft review rendering.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from autoshorts.social.draft import SocialMediaDraft, validate_social_media_draft\\n 6|\\n 7|\\n 8|def _platform_approval_command(platform: str) -> str:\\n 9| if platform == \\\"youtube\\\":\\n 10| return \\\"Freigabe YouTube\\\"\\n 11| if platform == \\\"tiktok\\\":\\n 12| return \\\"Freigabe TikTok\\\"\\n 13| if platform == \\\"instagram\\\":\\n 14| return \\\"Freigabe Instagram\\\"\\n 15| return f\\\"Freigabe {platform}\\\"\\n 16|\\n 17|\\n 18|def render_social_media_draft_review(draft: SocialMediaDraft) -> str:\\n 19| \\\"\\\"\\\"Render a social-media draft as a Telegram-safe approval message.\\\"\\\"\\\"\\n 20|\\n 21| validation = validate_social_media_draft(draft)\\n 22| if not validation.is_valid:\\n 23| raise ValueError(\\\"; \\\".join(validation.errors))\\n 24|\\n 25| approval_commands = tuple(_platform_approval_command(platform) for platform in draft.platforms)\\n 26| title_lines = \\\"\\\\n\\\".join(f\\\"- {title}\\\" for title in draft.title_variants)\\n 27| command_lines = \\\"\\\\n\\\".join(\\n 28| f\\\"- {command}\\\"\\n 29| for command in (\\n 30| *approval_commands,\\n 31| \\\"Freigabe alle\\\",\\n 32| \\\"Ablehnen\\\",\\n 33| \\\"Ändern: <requested change>\\\",\\n 34| )\\n 35| )\\n 36| side_effects = \\\", \\\".join(draft.side_effects) if draft.side_effects else \\\"none\\\"\\n 37| approval = \\\"yes\\\" if draft.requires_human_approval else \\\"no\\\"\\n 38|\\n 39| return \\\"\\\\n\\\".join(\\n 40| (\\n 41| \\\"## Social Media Draft Review\\\",\\n 42| \\\"\\\",\\n 43| f\\\"Candidate: {draft.candidate_id}\\\",\\n 44| f\\\"Platforms: {', '.join(draft.platform_labels)}\\\",\\n 45| f\\\"Duration: {draft.planned_duration_seconds}s\\\",\\n 46| \\\"\\\",\\n 47| \\\"Hook:\\\",\\n 48| draft.hook,\\n 49| \\\"\\\",\\n 50| \\\"Title variants:\\\",\\n 51| title_lines,\\n 52| \\\"\\\",\\n 53| \\\"Caption:\\\",\\n 54| draft.caption,\\n 55| \\\"\\\",\\n 56| f\\\"Hashtags: {' '.join(draft.hashtags)}\\\",\\n 57| f\\\"CTA: {draft.cta}\\\",\\n 58| f\\\"Preview: {draft.preview_reference} ({draft.preview_type})\\\",\\n 59| f\\\"Quality/Risk: {draft.quality_risk_summary}\\\",\\n 60| f\\\"Requires human approval: {approval}\\\",\\n 61| f\\\"Side effects: {side_effects}\\\",\\n 62| \\\"\\\",\\n 63| \\\"Approval commands:\\\",\\n 64| command_lines,\\n 65| )\\n 66| )\\n 67|\", \"total_lines\": 66, \"file_size\": 2182, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_Syvne0gdgTOAwl2IpqRo15xm" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"FFmpeg rendering boundary for local preview videos.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|from pathlib import Path\\n 7|import subprocess\\n 8|\\n 9|from autoshorts.rendering.ffmpeg_plan import FFmpegConcatPlan, validate_ffmpeg_concat_plan\\n 10|\\n 11|\\n 12|@dataclass(frozen=True)\\n 13|class FFmpegRenderResult:\\n 14| script_id: str\\n 15| output_path: str\\n 16| concat_file_path: str\\n 17| returncode: int\\n 18| stdout: str\\n 19| stderr: str\\n 20| side_effects: tuple[str, ...] = (\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n 21| external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n 22|\\n 23|\\n 24|def render_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult:\\n 25| \\\"\\\"\\\"Write the concat file and invoke FFmpeg to create a local MP4 preview.\\\"\\\"\\\"\\n 26|\\n 27| validation = validate_ffmpeg_concat_plan(plan)\\n 28| if not validation.is_valid:\\n 29| raise ValueError(\\\"; \\\".join(validation.errors))\\n 30|\\n 31| concat_path = Path(plan.concat_file_path)\\n 32| output_path = Path(plan.output_path)\\n 33| concat_path.parent.mkdir(parents=True, exist_ok=True)\\n 34| output_path.parent.mkdir(parents=True, exist_ok=True)\\n 35| concat_path.write_text(plan.concat_file_content)\\n 36|\\n 37| command = [\\n 38| \\\"ffmpeg\\\",\\n 39| \\\"-y\\\",\\n 40| \\\"-f\\\",\\n 41| \\\"concat\\\",\\n 42| \\\"-safe\\\",\\n 43| \\\"0\\\",\\n 44| \\\"-i\\\",\\n 45| str(concat_path),\\n 46| \\\"-vf\\\",\\n 47| f\\\"fps={plan.fps},format=yuv420p\\\",\\n 48| \\\"-movflags\\\",\\n 49| \\\"+faststart\\\",\\n 50| str(output_path),\\n 51| ]\\n 52| completed = subprocess.run(command, capture_output=True, text=True, check=False)\\n 53| if completed.returncode != 0:\\n 54| raise RuntimeError(completed.stderr or \\\"ffmpeg failed\\\")\\n 55|\\n 56| return FFmpegRenderResult(\\n 57| script_id=plan.script_id,\\n 58| output_path=str(output_path),\\n 59| concat_file_path=str(concat_path),\\n 60| returncode=completed.returncode,\\n 61| stdout=completed.stdout,\\n 62| stderr=completed.stderr,\\n 63| side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\"),\\n 64| external_calls=(\\\"ffmpeg\\\",),\\n 65| )\\n 66|\", \"total_lines\": 65, \"file_size\": 1952, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_eSmwaDSLTVIw1gCuYtLsr2Wa" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 0}", "tool_call_id": "call_K3lKw1TJXHV16oel6FWltiYN" }, { "role": "assistant", "content": "", "reasoning": "**Planning package tests**\n\nI need to focus on creating package tests, but it seems there might not be an existing documentation directory. Instead, I could create a \"docs/research\" folder as a separate effort. My main goal is to concentrate on the package itself. I'll need some helpers to create candidate drafts or previews, and I think I can just use the existing tests that are already there. That should streamline the process a bit!", "finish_reason": "tool_calls", "reasoning_content": "**Planning package tests**\n\nI need to focus on creating package tests, but it seems there might not be an existing documentation directory. Instead, I could create a \"docs/research\" folder as a separate effort. My main goal is to concentrate on the package itself. I'll need some helpers to create candidate drafts or previews, and I think I can just use the existing tests that are already there. That should streamline the process a bit!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyA1ZhzfrZZ8Q02nLaKIbtauyx3kZbIpKOSJWGH9ilcoPYougGxd3byuhiHfqQ0plc6MfrFY4JfpXU8EKfffjG8o5x2vz84Z-6q_bJmzVlrymgKWvkOsH9AwjgD_caLOLmcIPRyziSa3qdWAEjguBkLQJ8Hx9k5vxBJiFgRS7WytrkzS-IaHMVbT238qQgB_Eo85UCfcBkQjsjKLBlmPl-N5oyb9mgu6Zgv9FLWpwNP6jos9MxDE-SDAIb3yvGF-LYTOI4BH564qeRzlJp8RxwfeRsw8seb7tG8xjXdIUKYv5Zs9zqvNFX_jZD_-pe-fehCzCSXtVQII208F_QIUU_JI_Ypi1RGWwCh6VQNagedRh0z9m3AK1GLfiW26NTdmdM2HJpQSemKKj2OyGZfVzfH4w6ebVHcbOSWJyUJLvvM9uU0IjsweWI-GduznU3J7mLNqcCCNpUhrrDsnuX6J7l7HFG5DPji0P7SjTqUdbTitQz5J1LQkhoIYy5l-6SgFGqiHlYjkiHyWnYcjqiuNEV2vto8I5oEnrxpR3QqHkGwUzzIzTA8cIiudMOKH7iW6373ZsSripv-qifljJuocwKyKMJDsym1TnjnMrILoDKayvflh2UjD-WLYFUr2tLKjfQfu4jVwmaS9A2VNFq-KO4mG23L61hdiqaL1jYSsQsADRTaEXyNw2i1AoJXe2awIRroIIbUrw86wRF6EZbySf6syxADjC8Xu02uPhQGB-yvfGdJSIvYr5lszCglMBAq_ZezYFRB6nBrhaTg57Y1Dbd_fCL80lUKJchsQaOXk3XcdW0v0s2SSH9KNKVHDhU1hXQmGP66q5wdUevI0gb65bCI13Yzo0Ia4LoDnSDBNbvUJ1c5jitmZSyE4vHQULCrCFBxWO8GVu9hdaO11GGC9ZOmZHXQEKJjlA1h67PJephjKwGrl5aowTiWRg-T8wFhtlrKxayy7NoYsUpp4NBs9nDmelQ0_ZhZa1mSgYF65i1iRmzqLZ6bZ9ZnYWpIbpGf0Qq0UW9CNksg1nCDcVn6LpKTPPR6uikT_dQSo-7mmiY3X44BbtDX-Rpl9ksBrPY11ZMlSGtn8bwxa_Kh53zj6mgT69tBz7_iufW85f0v31kuYzjnVCzZEKsaeYX0w-isDFDcjZMlVmqo8Pj-P4pzzR0fW-Q==", "id": "rs_060035eb87c40e14016a13202861708191a5c6b2be99b5be19", "summary": [ { "type": "summary_text", "text": "**Planning package tests**\n\nI need to focus on creating package tests, but it seems there might not be an existing documentation directory. Instead, I could create a \"docs/research\" folder as a separate effort. My main goal is to concentrate on the package itself. I'll need some helpers to create candidate drafts or previews, and I think I can just use the existing tests that are already there. That should streamline the process a bit!" } ] } ], "tool_calls": [ { "id": "call_bNe0Ag2BnRGXXvbkJ9eSuwMH", "call_id": "call_bNe0Ag2BnRGXXvbkJ9eSuwMH", "response_item_id": "fc_060035eb87c40e14016a132035a6a48191828b17857a9ab1c4", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"from pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.preview.package import (\\n ContentPreviewApprovalPackage,\\n build_content_preview_approval_package,\\n render_content_preview_approval_text,\\n validate_content_preview_approval_package,\\n)\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.social.draft import build_social_media_draft\\nfrom autoshorts.social.review import render_social_media_draft_review\\n\\n\\ndef _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n candidate = convert_script_draft_to_candidate(script)\\n manifest = build_render_manifest(create_shot_plan(script))\\n preview = build_html_preview(manifest)\\n draft = build_social_media_draft(candidate, preview)\\n return draft, render_social_media_draft_review(draft)\\n\\n\\ndef test_build_content_preview_approval_package_combines_video_and_social_review(tmp_path):\\n draft, review_text = _draft_and_review()\\n video_path = tmp_path / \\\"preview.mp4\\\"\\n video_path.write_bytes(b\\\"fake mp4\\\")\\n\\n package = build_content_preview_approval_package(\\n draft=draft,\\n preview_video_path=video_path,\\n social_review_text=review_text,\\n )\\n\\n assert isinstance(package, ContentPreviewApprovalPackage)\\n assert package.candidate_id == \\\"ai_life_systems-ai-weekly-review-routine-35s\\\"\\n assert package.preview_video_path == str(video_path)\\n assert package.social_review_text == review_text\\n assert package.approval_language == \\\"de\\\"\\n assert package.final_video_language == \\\"en\\\"\\n assert package.approval_commands == (\\n \\\"Freigabe YouTube\\\",\\n \\\"Freigabe TikTok\\\",\\n \\\"Freigabe Instagram\\\",\\n \\\"Freigabe alle\\\",\\n \\\"Ablehnen\\\",\\n \\\"Ändern: <requested change>\\\",\\n )\\n assert package.requires_human_approval is True\\n assert package.side_effects == ()\\n assert package.external_calls == ()\\n\\n\\ndef test_validate_content_preview_approval_package_accepts_valid_package(tmp_path):\\n draft, review_text = _draft_and_review()\\n video_path = tmp_path / \\\"preview.mp4\\\"\\n video_path.write_bytes(b\\\"fake mp4\\\")\\n\\n result = validate_content_preview_approval_package(\\n build_content_preview_approval_package(\\n draft=draft,\\n preview_video_path=video_path,\\n social_review_text=review_text,\\n )\\n )\\n\\n assert result.is_valid is True\\n assert result.errors == ()\\n\\n\\ndef test_build_content_preview_approval_package_rejects_missing_video(tmp_path):\\n draft, review_text = _draft_and_review()\\n\\n with pytest.raises(ValueError, match=\\\"preview video path must exist\\\"):\\n build_content_preview_approval_package(\\n draft=draft,\\n preview_video_path=tmp_path / \\\"missing.mp4\\\",\\n social_review_text=review_text,\\n )\\n\\n\\ndef test_validate_content_preview_approval_package_rejects_side_effects_and_action_language(tmp_path):\\n draft, review_text = _draft_and_review()\\n video_path = tmp_path / \\\"preview.mp4\\\"\\n video_path.write_bytes(b\\\"fake mp4\\\")\\n package = build_content_preview_approval_package(\\n draft=draft,\\n preview_video_path=video_path,\\n social_review_text=review_text,\\n )\\n unsafe = ContentPreviewApprovalPackage(\\n candidate_id=package.candidate_id,\\n preview_video_path=package.preview_video_path,\\n social_review_text=\\\"Post this now\\\",\\n approval_commands=package.approval_commands,\\n approval_language=package.approval_language,\\n final_video_language=package.final_video_language,\\n requires_human_approval=False,\\n side_effects=(\\\"send_telegram\\\",),\\n external_calls=(\\\"telegram\\\",),\\n )\\n\\n result = validate_content_preview_approval_package(unsafe)\\n\\n assert result.is_valid is False\\n assert \\\"human approval is required\\\" in result.errors\\n assert \\\"content preview packages must not have side effects\\\" in result.errors\\n assert \\\"content preview packages must not call external tools\\\" in result.errors\\n assert \\\"social_review_text must not contain publish/post action language\\\" in result.errors\\n\\n\\ndef test_render_content_preview_approval_text_is_telegram_safe_and_references_video(tmp_path):\\n draft, review_text = _draft_and_review()\\n video_path = tmp_path / \\\"preview.mp4\\\"\\n video_path.write_bytes(b\\\"fake mp4\\\")\\n package = build_content_preview_approval_package(\\n draft=draft,\\n preview_video_path=video_path,\\n social_review_text=review_text,\\n )\\n\\n text = render_content_preview_approval_text(package)\\n\\n assert text.startswith(\\\"## Content Preview Review\\\")\\n assert f\\\"Video preview: {video_path}\\\" in text\\n assert \\\"Approval preview language: de\\\" in text\\n assert \\\"Final video language: en\\\" in text\\n assert \\\"Freigabe alle\\\" in text\\n assert \\\"## Social Media Draft Review\\\" in text\\n assert \\\"publish\\\" not in text.casefold()\\n assert \\\"post\\\" not in text.casefold()\\n assert len(text) < 4096\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5501, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\\\">\\nERROR [8:6] Import \\\"autoshorts.preview.package\\\" could not be resolved [reportMissingImports] (Pyright)\\nERROR [29:17] Argument missing for parameter \\\"script\\\" [reportCallIssue] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_bNe0Ag2BnRGXXvbkJ9eSuwMH" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_MrtNNUAJPixhK1XwL6ao0gsz", "call_id": "call_MrtNNUAJPixhK1XwL6ao0gsz", "response_item_id": "fc_060035eb87c40e14016a132066cc288191804c76e3dc9d3d27", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_preview_package.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n____________ ERROR collecting tests/test_content_preview_package.py ____________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.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_content_preview_package.py:8: in <module>\\n from autoshorts.preview.package import (\\nE ModuleNotFoundError: No module named 'autoshorts.preview'\\n=========================== short test summary info ============================\\nERROR tests/test_content_preview_package.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.12s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_MrtNNUAJPixhK1XwL6ao0gsz" }, { "role": "assistant", "content": "", "reasoning": "**Implementing script draft**\n\nI need to focus on implementing this draft. There’s a parameter for LSP that might require a script, but I think I can ignore that for now. Actually, the create_script_draft function worked when I used it with just the brief. So I’ll go ahead and implement it now! It’s all about ensuring that everything runs smoothly and efficiently. Let’s keep moving forward and get this done!", "finish_reason": "tool_calls", "reasoning_content": "**Implementing script draft**\n\nI need to focus on implementing this draft. There’s a parameter for LSP that might require a script, but I think I can ignore that for now. Actually, the create_script_draft function worked when I used it with just the brief. So I’ll go ahead and implement it now! It’s all about ensuring that everything runs smoothly and efficiently. Let’s keep moving forward and get this done!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyBxSJhKnJXMJSl7ryExFytSuNa76ghO3JhLS2_4xO0GSfFyxiXFTY-fT8A5Rj7-XMiAoxaPHBdPHqcJl3VNc1yCjyoIBaUFN3yeRJPa-yBWQaWvlBk7cszusVbL5zqRqSOZ4tgY1rVJiCy3iUFQotA0AOlrgKBt8kqfR3mvqhFG1V8NvEX7dKuDJLq7G-mdF9S9eNIgXevs8dubGT4Tp7j5tgpYOLnhNJ59Vfrlmp0BpN6nyrPsFSpzDJryqtgSupmrKBeEksbnBgH0R7h8gtL9-Np0nBSXnWic1j7j_aNfIhzDHEUu2MsMjWvBdHE-BMZJmXSiGv7O_15Fyg3gDQP265v2-UNADnnxTOn-ZP5DqWPbAfl22S-Ik1qxwLvWYPaKtYEahLasIFMNjq668hAdgWuTnzIgo-ZzeLV4rrz5oKzCcftIZJ0dkFpNEoWDTVUP5Vvt6sNjt8MCmoOunteE7S6rPFphxO952D3x1wzJ_6V-HH3zFkc_AOaGVsTV3bV3GeV_B9O61oVkSPmmM9p3bQnL4YHGeVUscE13vqNv5Wm5djLp44vdKvWQEW03toimaAet6JinOiXskRXXvImRIJDvlctOqiiLRHrWcwZI0BMwuQ8pRwavK8fsLn4cXF1iUV7kJqOggljJ7yNwo2D7oYdrHydnYJ9nS3CbgP9jxefKGfRvxSiDWfCnP5JbQgMM2yTMPBmYAfFQVH8imz_O7dRf3eqmLJ9zrFU5nv7WhxcbwqgASly4HTf0gwtEUNAg5RgCyvQ5EkfRcTdmQfWUzgL4HQALmEj2VBM-dK_UixKVkWTQdut2FQjsZSKu8MDtc7hTtD6KKJZ4buO9pa6dSK9wLiOGyCuq0rWj69G-N3xTV14KF7iCRdDsolj6EjVhowlXZalBctd82tKdVFBFgfi7JxTqqU7PNDTnsEDcrFDUwh6NprSrKQqx_DICsmdQu3J7ymvuKEG9VqujgxC-MpKbv0iQa6W-gIVjM-JxRGmc4P3edCkrCeK4r7Gt-VXcNocpGewoiziEJ7uhNZOFWEGmtEBksKuGmMt-Y_2e1JC0sye399yy8qxl3Os0kOPrjIr60n81221iOHZsulD_iEyPgnV-FZT4Ben3NxLMhUg=", "id": "rs_060035eb87c40e14016a13206b4ab48191b5372e37f1070014", "summary": [ { "type": "summary_text", "text": "**Implementing script draft**\n\nI need to focus on implementing this draft. There’s a parameter for LSP that might require a script, but I think I can ignore that for now. Actually, the create_script_draft function worked when I used it with just the brief. So I’ll go ahead and implement it now! It’s all about ensuring that everything runs smoothly and efficiently. Let’s keep moving forward and get this done!" } ] } ], "tool_calls": [ { "id": "call_IMi5jhVzPFaUxYBoVN3SF8mQ", "call_id": "call_IMi5jhVzPFaUxYBoVN3SF8mQ", "response_item_id": "fc_060035eb87c40e14016a1320715ccc8191a628c2a7c3661c4b", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Content preview approval package combining MP4 preview and social draft review.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.social.draft import SocialMediaDraft, validate_social_media_draft\\n\\n_FORBIDDEN_ACTION_WORDS = (\\\"publish\\\", \\\"post\\\")\\n_TELEGRAM_MESSAGE_LIMIT = 4096\\n\\n\\n@dataclass(frozen=True)\\nclass ContentPreviewApprovalPackage:\\n candidate_id: str\\n preview_video_path: str\\n social_review_text: str\\n approval_commands: tuple[str, ...]\\n approval_language: str = \\\"de\\\"\\n final_video_language: str = \\\"en\\\"\\n requires_human_approval: bool = True\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef _platform_approval_command(platform: str) -> str:\\n if platform == \\\"youtube\\\":\\n return \\\"Freigabe YouTube\\\"\\n if platform == \\\"tiktok\\\":\\n return \\\"Freigabe TikTok\\\"\\n if platform == \\\"instagram\\\":\\n return \\\"Freigabe Instagram\\\"\\n return f\\\"Freigabe {platform}\\\"\\n\\n\\ndef _approval_commands(draft: SocialMediaDraft) -> tuple[str, ...]:\\n return (\\n *(tuple(_platform_approval_command(platform) for platform in draft.platforms)),\\n \\\"Freigabe alle\\\",\\n \\\"Ablehnen\\\",\\n \\\"Ändern: <requested change>\\\",\\n )\\n\\n\\ndef build_content_preview_approval_package(\\n *,\\n draft: SocialMediaDraft,\\n preview_video_path: str | Path,\\n social_review_text: str,\\n approval_language: str = \\\"de\\\",\\n final_video_language: str = \\\"en\\\",\\n) -> ContentPreviewApprovalPackage:\\n \\\"\\\"\\\"Build a side-effect-free approval package from MP4 preview plus social review text.\\\"\\\"\\\"\\n\\n draft_validation = validate_social_media_draft(draft)\\n if not draft_validation.is_valid:\\n raise ValueError(\\\"; \\\".join(draft_validation.errors))\\n if _is_blank(social_review_text):\\n raise ValueError(\\\"social_review_text is required\\\")\\n\\n video_path = Path(preview_video_path)\\n if not video_path.exists():\\n raise ValueError(\\\"preview video path must exist\\\")\\n if video_path.suffix != \\\".mp4\\\":\\n raise ValueError(\\\"preview video path must be .mp4\\\")\\n\\n package = ContentPreviewApprovalPackage(\\n candidate_id=draft.candidate_id,\\n preview_video_path=str(video_path),\\n social_review_text=social_review_text,\\n approval_commands=_approval_commands(draft),\\n approval_language=approval_language,\\n final_video_language=final_video_language,\\n requires_human_approval=True,\\n side_effects=(),\\n external_calls=(),\\n )\\n validation = validate_content_preview_approval_package(package)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return package\\n\\n\\ndef validate_content_preview_approval_package(package: ContentPreviewApprovalPackage) -> ValidationResult:\\n errors: list[str] = []\\n for field_name, value in {\\n \\\"candidate_id\\\": package.candidate_id,\\n \\\"preview_video_path\\\": package.preview_video_path,\\n \\\"social_review_text\\\": package.social_review_text,\\n \\\"approval_language\\\": package.approval_language,\\n \\\"final_video_language\\\": package.final_video_language,\\n }.items():\\n if _is_blank(value):\\n errors.append(f\\\"{field_name} is required\\\")\\n\\n if package.preview_video_path and not package.preview_video_path.endswith(\\\".mp4\\\"):\\n errors.append(\\\"preview_video_path must end with .mp4\\\")\\n if package.preview_video_path and not Path(package.preview_video_path).exists():\\n errors.append(\\\"preview video path must exist\\\")\\n if not package.approval_commands:\\n errors.append(\\\"at least one approval command is required\\\")\\n elif any(_is_blank(command) for command in package.approval_commands):\\n errors.append(\\\"approval commands must not be blank\\\")\\n if \\\"Freigabe alle\\\" not in package.approval_commands:\\n errors.append(\\\"Freigabe alle command is required\\\")\\n if not package.requires_human_approval:\\n errors.append(\\\"human approval is required\\\")\\n if package.side_effects:\\n errors.append(\\\"content preview packages must not have side effects\\\")\\n if package.external_calls:\\n errors.append(\\\"content preview packages must not call external tools\\\")\\n if any(word in package.social_review_text.casefold() for word in _FORBIDDEN_ACTION_WORDS):\\n errors.append(\\\"social_review_text must not contain publish/post action language\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef render_content_preview_approval_text(package: ContentPreviewApprovalPackage) -> str:\\n \\\"\\\"\\\"Render the combined preview package as a Telegram-safe text block.\\\"\\\"\\\"\\n\\n validation = validate_content_preview_approval_package(package)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n command_lines = \\\"\\\\n\\\".join(f\\\"- {command}\\\" for command in package.approval_commands)\\n text = \\\"\\\\n\\\".join(\\n (\\n \\\"## Content Preview Review\\\",\\n \\\"\\\",\\n f\\\"Candidate: {package.candidate_id}\\\",\\n f\\\"Video preview: {package.preview_video_path}\\\",\\n f\\\"Approval preview language: {package.approval_language}\\\",\\n f\\\"Final video language: {package.final_video_language}\\\",\\n \\\"Requires human approval: yes\\\",\\n \\\"Side effects: none\\\",\\n \\\"External calls: none\\\",\\n \\\"\\\",\\n \\\"Approval commands:\\\",\\n command_lines,\\n \\\"\\\",\\n package.social_review_text,\\n )\\n )\\n if len(text) > _TELEGRAM_MESSAGE_LIMIT:\\n raise ValueError(\\\"content preview approval text exceeds Telegram message limit\\\")\\n return text\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/preview/package.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5747, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_IMi5jhVzPFaUxYBoVN3SF8mQ" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_oEZVfKl0YrIe7kUqYXiGxDrd", "call_id": "call_oEZVfKl0YrIe7kUqYXiGxDrd", "response_item_id": "fc_060035eb87c40e14016a13208ea00c8191a391c1cdcf4f2bb1", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Preview approval package artifacts.\\\"\\\"\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/preview/__init__.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 42, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_oEZVfKl0YrIe7kUqYXiGxDrd" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_jceOjPFLmtKEorJ2SbDNioM1", "call_id": "call_jceOjPFLmtKEorJ2SbDNioM1", "response_item_id": "fc_060035eb87c40e14016a13209bba64819195cc818c5a91710a", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_preview_package.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"FFFFF [100%]\\n=================================== FAILURES ===================================\\n_ test_build_content_preview_approval_package_combines_video_and_social_review _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-99/test_build_content_preview_app0')\\n\\n def test_build_content_preview_approval_package_combines_video_and_social_review(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:37: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n> candidate = convert_script_draft_to_candidate(script)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: convert_script_draft_to_candidate() missing 1 required positional argument: 'script'\\n\\ntests/test_content_preview_package.py:29: TypeError\\n_____ test_validate_content_preview_approval_package_accepts_valid_package _____\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-99/test_validate_content_preview_0')\\n\\n def test_validate_content_preview_approval_package_accepts_valid_package(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:67: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n> candidate = convert_script_draft_to_candidate(script)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: convert_script_draft_to_candidate() missing 1 required positional argument: 'script'\\n\\ntests/test_content_preview_package.py:29: TypeError\\n______ test_build_content_preview_approval_package_rejects_missing_video _______\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-99/test_build_content_preview_app1')\\n\\n def test_build_content_preview_approval_package_rejects_missing_video(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:84: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n> candidate = convert_script_draft_to_candidate(script)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: convert_script_draft_to_candidate() missing 1 required positional argument: 'script'\\n\\ntests/test_content_preview_package.py:29: TypeError\\n_ test_validate_content_preview_approval_package_rejects_side_effects_and_action_language _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-99/test_validate_content_preview_1')\\n\\n def test_validate_content_preview_approval_package_rejects_side_effects_and_action_language(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:95: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n> candidate = convert_script_draft_to_candidate(script)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: convert_script_draft_to_candidate() missing 1 required positional argument: 'script'\\n\\ntests/test_content_preview_package.py:29: TypeError\\n_ test_render_content_preview_approval_text_is_telegram_safe_and_references_video _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-99/test_render_content_preview_ap0')\\n\\n def test_render_content_preview_approval_text_is_telegram_safe_and_references_video(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:125: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def _draft_and_review():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n> candidate = convert_script_draft_to_candidate(script)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: convert_script_draft_to_candidate() missing 1 required positional argument: 'script'\\n\\ntests/test_content_preview_package.py:29: TypeError\\n=========================== short test summary info ============================\\nFAILED tests/test_content_preview_package.py::test_build_content_preview_approval_package_combines_video_and_social_review\\nFAILED tests/test_content_preview_package.py::test_validate_content_preview_approval_package_accepts_valid_package\\nFAILED tests/test_content_preview_package.py::test_build_content_preview_approval_package_rejects_missing_video\\nFAILED tests/test_content_preview_package.py::test_validate_content_preview_approval_package_rejects_side_effects_and_action_language\\nFAILED tests/test_content_preview_package.py::test_render_content_preview_approval_text_is_telegram_safe_and_references_video\\n5 failed in 0.10s\", \"exit_code\": 1, \"error\": null}\n\n[Tool loop warning: repeated_exact_failure_warning; count=2; terminal has failed 2 times with identical arguments. This looks like a loop; inspect the error and change strategy instead of retrying it unchanged.]", "tool_call_id": "call_jceOjPFLmtKEorJ2SbDNioM1" }, { "role": "assistant", "content": "", "reasoning": "**Fixing test helper signature**\n\nI’m thinking about the need to fix the test helper signature. Should I read the file I looked at earlier? It seems like to convert it, I might need a brief or maybe a script. I need to ensure I’m clear on what needs to be changed and how to implement that. It feels a bit tricky, but I’m committed to getting it right!", "finish_reason": "tool_calls", "reasoning_content": "**Fixing test helper signature**\n\nI’m thinking about the need to fix the test helper signature. Should I read the file I looked at earlier? It seems like to convert it, I might need a brief or maybe a script. I need to ensure I’m clear on what needs to be changed and how to implement that. It feels a bit tricky, but I’m committed to getting it right!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyCoQVvLhMycmcb7s_2r2MExB6G5ZjnYvLRnDMoRc8OeVmfyi4B43u_LJGIBET_yE1G3tge5gA2nAMdpZlNhg8yWYzWvFPwskD_H-y-fTaUrQUx5XPfDdAW8QXBzDahXi0x3xoBejlRC_sNVm4yXkW0gbTGw9ZgelXFei-WVSPky52GVFyVpYuh18XV_nS3Yc9j06CsVATeWVd4tO1i6fiAzD3Y9QL2E-8VMKo2Ue4yk2n4nmvVzdTNM5ZDY0fdQwHMyO-mB0IdowxwsEVJqZR39wJODMy8WNIjjxgl99lLTnYU_LD-3y815gNP6pXtNa6t7AVA1Dmzy9O8P4vV7dflxgCbqLJ-pPH0N1bFHSkxr2xELz5KlIzphzHuLuhQ7unzPDQSqlR-2kBEmkvkYKNVg4HAMEkYkinSoLHalzPg9Hk1QMTeKsm3Tab0wY4GOz-61Fee1Oy2HMFMi3BVXLwl7aFZ48lJu1029Zf6pp6ORMzpqHZH1IM4lgJzaWkiOmE_-MSONlEirrmWP1qwUMeE83ADU3pkbef-q5VPXzLfaiw5ySa9Ol8ZuKPkCEBeRYC_f9QU16gJwkZtU6oBefM9SW1mYJfBRcQlkE4Ic3bcfuEPlmfv8K6m1Z2rpzIFdu2C2XWuPYEplH8VWopMoyviuIHHinChf8_cbYbIdU8G4vKI0_WowxjqMe_GIC6nq6ooHDNCb1CmEG0CJ0KJDsLAQQMfCP0-Gg1jjjOXSdGeYVtVCCzm3yC1x4VsPyt39jDZGU6FwsKZShI_qH9eGQ79ggygosSy74xpnEnOh0qm094AoFygh_dkexeum3WO1XS0zSW6geiiM0SusUS084hOW57srP2tQ1IbxZPXvchuJHWammJ1MiJ8aRrEhGZcPvNSCiTr58_4cdqEvKteDQC2ZZELGYhwM1_Sf6ciS0j_ooKi6sAKGwv5ERgd0uW1C_2pvt3VEUAOg5jT3QLLkR03Y8zKeWSl6wU73IHMjZ41SnqhvtucpecoChMRJPxQUQ3EDJCN63ZafIPagmvEe4j_RfA==", "id": "rs_060035eb87c40e14016a1320a5227c8191b354b349d8aad0b3", "summary": [ { "type": "summary_text", "text": "**Fixing test helper signature**\n\nI’m thinking about the need to fix the test helper signature. Should I read the file I looked at earlier? It seems like to convert it, I might need a brief or maybe a script. I need to ensure I’m clear on what needs to be changed and how to implement that. It feels a bit tricky, but I’m committed to getting it right!" } ] } ], "tool_calls": [ { "id": "call_0fL8RGlz3EtyoeEWB0JmJqR0", "call_id": "call_0fL8RGlz3EtyoeEWB0JmJqR0", "response_item_id": "fc_060035eb87c40e14016a1320a8e2dc81918fae0b4bfd7a2978", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":80,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/to_candidate.py\"}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Convert content-development artifacts into video candidates.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from autoshorts.config.pillars import DEFAULT_PILLARS, ContentPillar\\n 6|from autoshorts.content.brief import ContentBrief\\n 7|from autoshorts.content.script_draft import ScriptDraft, validate_script_draft\\n 8|from autoshorts.ideas.candidate import VideoCandidate\\n 9|from autoshorts.ideas.factory import CandidateDraft, create_candidate\\n 10|from autoshorts.scripting.quality import ScriptQualityScore\\n 11|\\n 12|_DEFAULT_INITIAL_QUALITY_SCORE = ScriptQualityScore(\\n 13| hook_strength=8,\\n 14| novelty=8,\\n 15| clarity=8,\\n 16| retention=8,\\n 17| ai_slop_risk=3,\\n 18| policy_risk=\\\"low\\\",\\n 19|)\\n 20|\\n 21|_DEFAULT_HYPOTHESIS = (\\n 22| \\\"A human-approval AI routine creates saves because it gives viewers control without hype.\\\"\\n 23|)\\n 24|\\n 25|\\n 26|def _policy_notes_from_brief(brief: ContentBrief) -> tuple[str, ...]:\\n 27| return (\\n 28| f\\\"Source brief: {brief.brief_id}\\\",\\n 29| \\\"Human approval remains explicit\\\",\\n 30| *(f\\\"No-go inherited: {item}\\\" for item in brief.no_go),\\n 31| )\\n 32|\\n 33|\\n 34|def _ensure_script_matches_brief(brief: ContentBrief, script: ScriptDraft) -> None:\\n 35| if script.brief_id != brief.brief_id:\\n 36| raise ValueError(\\\"script draft does not belong to content brief\\\")\\n 37|\\n 38|\\n 39|def convert_script_draft_to_candidate_draft(\\n 40| brief: ContentBrief,\\n 41| script: ScriptDraft,\\n 42| *,\\n 43| quality_score: ScriptQualityScore = _DEFAULT_INITIAL_QUALITY_SCORE,\\n 44| hypothesis: str = _DEFAULT_HYPOTHESIS,\\n 45|) -> CandidateDraft:\\n 46| \\\"\\\"\\\"Translate a script draft into the existing candidate-factory input.\\\"\\\"\\\"\\n 47|\\n 48| _ensure_script_matches_brief(brief, script)\\n 49| validation = validate_script_draft(script)\\n 50| if not validation.is_valid:\\n 51| raise ValueError(\\\"; \\\".join(validation.errors))\\n 52|\\n 53| return CandidateDraft(\\n 54| pillar=brief.pillar_id,\\n 55| title=script.title,\\n 56| platforms=brief.platforms,\\n 57| duration_seconds=script.estimated_duration_seconds,\\n 58| hook=script.hook,\\n 59| script_outline=script.script_outline,\\n 60| visual_concept=script.visual_concept,\\n 61| caption=script.caption,\\n 62| cta=script.cta,\\n 63| quality_score=quality_score,\\n 64| policy_notes=_policy_notes_from_brief(brief),\\n 65| hypothesis=hypothesis,\\n 66| )\\n 67|\\n 68|\\n 69|def convert_script_draft_to_candidate(\\n 70| brief: ContentBrief,\\n 71| script: ScriptDraft,\\n 72| *,\\n 73| registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n 74| quality_score: ScriptQualityScore = _DEFAULT_INITIAL_QUALITY_SCORE,\\n 75| hypothesis: str = _DEFAULT_HYPOTHESIS,\\n 76|) -> VideoCandidate:\\n 77| \\\"\\\"\\\"Create a validated VideoCandidate from content-development artifacts.\\\"\\\"\\\"\\n 78|\\n 79| candidate_draft = convert_script_draft_to_candidate_draft(\\n 80| brief,\\n 81|\", \"total_lines\": 85, \"file_size\": 2809, \"truncated\": true, \"hint\": \"Use offset=81 to continue reading (showing 1-80 of 85 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_0fL8RGlz3EtyoeEWB0JmJqR0" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_ZWgRVx6U2DsAccqtH3GSORii", "call_id": "call_ZWgRVx6U2DsAccqtH3GSORii", "response_item_id": "fc_060035eb87c40e14016a1320b0bee48191b585b5f99e847de1", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" candidate = convert_script_draft_to_candidate(brief, script)\\n\",\"old_string\":\" candidate = convert_script_draft_to_candidate(script)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\\n@@ -26,7 +26,7 @@\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n script = create_script_draft(brief)\\n- candidate = convert_script_draft_to_candidate(script)\\n+ candidate = convert_script_draft_to_candidate(brief, script)\\n manifest = build_render_manifest(create_shot_plan(script))\\n preview = build_html_preview(manifest)\\n draft = build_social_media_draft(candidate, preview)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_preview_package.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ZWgRVx6U2DsAccqtH3GSORii" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_C5OonQt4IWMM6dJxX11qEn49", "call_id": "call_C5OonQt4IWMM6dJxX11qEn49", "response_item_id": "fc_060035eb87c40e14016a1320b6fc588191910d6d4623d1cb99", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_content_preview_package.py -q && python -m pytest tests -q\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"..... [100%]\\n5 passed in 0.09s\\n........................................................................ [ 61%]\\n............................................. [100%]\\n117 passed in 8.54s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_C5OonQt4IWMM6dJxX11qEn49" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyDI3VuCAmHyGOo_2s231UjCNi5ymxYiAi71lsZUG0yt-7_hux4LrboJ7dhyd36Xo5LZ5MjNumZrWugKvTIPH_XwNOBqQn7GkQoa0UoySF9zTpARWQCCOaFaMsvGib0-39-RE-qeh512whRia-dfB1umXdofvWs3x0SQuFXlNZJK7g1p-RhItbwN5P7qPYJ-uxSPQ3rb9-kCf4ucvscz1kOUHDT9n8Ya1wA94AX8uxRhSMKBZT77JXvtiTxL9TdwsD4i6HThYNL7S0CTmFguOH3oXfvE0xFmOrSpLrXSp32ltMpsAFzSm8h7DDW8bHzebux5lC5TDdDwXRxs0NQKbLQKgABZLfW7_vIwL1bYvJl4FY2SfdlMVqamPf_QgMWaVsHjiU8HkQDMOkjx6jmuxuV6HgLJa0QWM_6Kg_hq6sQnfFFXrbCZR5DA57gxTx-qACEQaFwGTrCNbzUGE3lhf0A7WbRobkrLEzAfu5rNhqg-N1G2oGa-kIMU5sNf3N8HrxSQMQUlII3sw9Pvwduu-5yZNX6n5nUHg5HPMgjp9CFy4ufZ8zJxARb0-V_TpWFDHTbQA1tcuWBFOv1EYdNsqDfVQy3Alv2kWzrA6uDxO2N5ySRpWS9MJDaFpy60dTqVTDqAAtuUMygnvNpeAI1HVU7mge2itPbmBfuQp1b5Iy591QozgJF4kMH4tddQowiwcVbigfv9IaNNo8Q4twqkd0BE_JL2G1ahJ0TTTdMJS51vVCbCTu0Savcl7QZRGtjFt6yVAM_GxDCYNE5aGKXmqIYzoHF-u5zQRlolsEDOPH4otXtMJdnPPIDb5xqD5bWgJLjyEMwLhS6-OxC5FLBagLBBOyjkc222spO35ObYM4Fdyphepnh2wD-ubB8xTceRBCAV7S8dXtylA06cIPb4trla8xNh49fMyzZGJ_Q88bHfqpShzRyZRLdvKfFWpSYRIJllxnGDQsYdQGkDI9D-QlKrLY_dReFsyfWWC40fEW3ANgE=", "id": "rs_060035eb87c40e14016a1320c6d3208191aae832835cd932c5", "summary": [] } ], "tool_calls": [ { "id": "call_SPaIPBHDxd5TLDoZqUlFGH7A", "call_id": "call_SPaIPBHDxd5TLDoZqUlFGH7A", "response_item_id": "fc_060035eb87c40e14016a1320c8c10c8191a9bc34766f4ee49b", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"# Content Automation Research Notes\\n\\nDate: 2026-05-24\\n\\n## What successful/public workflows have in common\\n\\n### 1. Series + editable preview beats blind generation\\n\\nSources:\\n- AutoShorts.ai: https://autoshorts.ai/\\n- StarterStory AutoShorts.ai breakdown: https://www.starterstory.com/stories/autoshorts-ai-breakdown\\n\\nPublic faceless-video tools such as AutoShorts.ai are built around repeatable series, scheduled generation, and a preview/customization step for script, title, images, music, and channels before distribution.\\n\\nImplementation implication: keep `Series` / content-pillar state and make every generated asset editable before final render. Do not jump from topic to upload.\\n\\n### 2. Source-backed clipping works when raw input is strong\\n\\nSources:\\n- OpusClip Audacy case: https://www.opus.pro/how-audacy-drove-1b-views-using-opusclip\\n- OpusClip customer stories: https://www.opus.pro/customer-stories\\n\\nOpusClip-style workflows succeed when they start from high-quality long-form/live/podcast content and identify strong moments for short-form distribution.\\n\\nImplementation implication: add a future source-backed mode: transcript/imported source -> candidate moments -> hook scoring -> approval preview. Pure synthetic topics should not be the only path.\\n\\n### 3. Automation templates are useful, but auto-publishing is the dangerous part\\n\\nSources:\\n- n8n multi-platform AI video workflow: https://n8n.io/workflows/3442-fully-automated-ai-video-generation-and-multi-platform-publishing/\\n- n8n faceless shorts workflow: https://n8n.io/workflows/8290-automate-faceless-shorts-with-openai-runwayml-and-elevenlabs-script-to-social-media/\\n- Make community 1000 Shorts workflow: https://community.make.com/t/make-1-000-youtube-shorts-with-this-ai-automation-make-com/54511\\n\\nCommon pattern: Google Sheets/Airtable queue -> script -> scene prompts -> images/video -> voiceover -> captions -> renderer -> drive/upload -> status notifications.\\n\\nImplementation implication: copy the queue/status/cost/error-tracking model, not blind auto-posting. For this project: all publishing steps stay behind explicit human approval.\\n\\n### 4. Template-first rendering is more robust than free-form generation\\n\\nSources:\\n- Creatomate AI video automation tutorial: https://creatomate.com/blog/how-to-use-ai-to-automate-videos-for-shorts-stories-and-reels\\n- MindStudio script-to-MP4 workflow: https://www.mindstudio.ai/blog/ai-short-form-video-creation-workflow-script-to-mp4\\n\\nThe durable pattern is structured script -> scenes -> template renderer. Creatomate and similar services separate content fields from render templates; MindStudio highlights Hook/Setup/Main/CTA structure.\\n\\nImplementation implication: continue the typed artifact chain. Use German approval previews for concept review, but keep final social-media script/video language English.\\n\\n### 5. Approval process must be versioned\\n\\nSource:\\n- Planable social media approval process: https://planable.io/blog/social-media-approval-process/\\n\\nCommon operational failures: scattered feedback, unclear reviewers, changes after sign-off, and version chaos.\\n\\nImplementation implication: every preview package should get a version/fingerprint later. Approval applies only to the exact package version.\\n\\n## Platform and policy constraints\\n\\n### YouTube synthetic/altered content disclosure\\n\\nSource:\\n- YouTube Help, altered or synthetic content: https://support.google.com/youtube/answer/14328491?hl=en\\n\\nYouTube requires disclosure for meaningfully altered or synthetically generated realistic content, including making a real person appear to say/do something they did not, altering real events/places, or generating realistic scenes that did not occur.\\n\\nImplementation implication: add future metadata field `requires_synthetic_content_disclosure`. AI visuals that look realistic should trigger review.\\n\\n### YouTube monetization: inauthentic / mass-produced content risk\\n\\nSource:\\n- YouTube channel monetization policies: https://support.google.com/youtube/answer/1311392?hl=en\\n\\nYouTube explicitly says repetitive or mass-produced inauthentic content is ineligible for monetization. Original/authentic content matters.\\n\\nImplementation implication: add originality/inauthentic-content gate before final render. Avoid template spam.\\n\\n### Meta AI labels\\n\\nSource:\\n- Meta AI-generated content labeling approach: https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/\\n\\nMeta labels AI-generated content when it detects industry indicators or when users self-disclose.\\n\\nImplementation implication: preserve source/tool metadata for final exports; future platform adapter should expose disclosure state rather than hiding AI use.\\n\\n## Inputs worth implementing next\\n\\n1. `ContentPreviewApprovalPackage` combining MP4 preview + social draft review.\\n2. `PreviewVersion` / package fingerprint so approvals lock to a specific artifact.\\n3. Queue/status model: idea -> draft -> preview -> approved -> final-render-ready.\\n4. Cost/runtime metadata fields even for local renders: render seconds, external calls, tokens/credits if any.\\n5. Originality/inauthentic-content gate.\\n6. Synthetic-content disclosure metadata.\\n7. Source-backed mode for clips/transcripts later.\\n\\n## Inputs to avoid for now\\n\\n- Full auto-posting workflows.\\n- Paid-first API chains before the local approval loop is trusted.\\n- Mass-generation targets such as “1000 Shorts” without quality gates.\\n- AI-generated images/video containing baked-in text, fake UI labels, logos, or factual claims.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/research/content_automation_research.md\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5532, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_SPaIPBHDxd5TLDoZqUlFGH7A" }, { "role": "assistant", "content": "Need README update package + research doc.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Need README update package + research doc." } ], "id": "msg_060035eb87c40e14016a1320e59d148191a7b82a13a8a2ccf5", "phase": "commentary" } ], "tool_calls": [ { "id": "call_f7DOsSxgnnpwaWl5YrKwsm60", "call_id": "call_f7DOsSxgnnpwaWl5YrKwsm60", "response_item_id": "fc_060035eb87c40e14016a1320e608888191ac403baaea334010", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.preview.package` — combines MP4 timing preview plus social draft review into an approval package.\\n- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n\",\"old_string\":\"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -22,6 +22,7 @@\\n - `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n - `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n+- `autoshorts.preview.package` — combines MP4 timing preview plus social draft review into an approval package.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n - `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}, \"_warning\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md was modified by sibling subagent '20260524_120031_1cfb85' but this agent never read it. Read the file before writing to avoid overwriting the sibling's changes.\"}", "tool_call_id": "call_f7DOsSxgnnpwaWl5YrKwsm60" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_tOqhsoflH9nTkREbEvtONqnT", "call_id": "call_tOqhsoflH9nTkREbEvtONqnT", "response_item_id": "fc_060035eb87c40e14016a1320f0e40081918fc43e820522e354", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"## Social media draft\\n\\n`autoshorts.preview.package.build_content_preview_approval_package()` combines an existing MP4 timing preview with the social media draft review text into a side-effect-free `ContentPreviewApprovalPackage`. It records approval language `de`, final video language `en`, approval commands, preview video path, and requires human approval.\\n\\n`render_content_preview_approval_text()` creates a Telegram-safe text block that references the MP4 preview and embeds the social media draft review. The package layer does not send Telegram messages, publish to platforms, call renderers, or access credentials.\\n\\nResearch notes for external content-automation patterns are tracked in `docs/research/content_automation_research.md`. They emphasize series/queues, editable previews, versioned approval, originality gates, synthetic-content disclosure metadata, and avoiding blind auto-posting.\\n\\n## Social media draft\",\"old_string\":\"## Social media draft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -144,6 +144,14 @@\\n \\n ## Social media draft\\n \\n+`autoshorts.preview.package.build_content_preview_approval_package()` combines an existing MP4 timing preview with the social media draft review text into a side-effect-free `ContentPreviewApprovalPackage`. It records approval language `de`, final video language `en`, approval commands, preview video path, and requires human approval.\\n+\\n+`render_content_preview_approval_text()` creates a Telegram-safe text block that references the MP4 preview and embeds the social media draft review. The package layer does not send Telegram messages, publish to platforms, call renderers, or access credentials.\\n+\\n+Research notes for external content-automation patterns are tracked in `docs/research/content_automation_research.md`. They emphasize series/queues, editable previews, versioned approval, originality gates, synthetic-content disclosure metadata, and avoiding blind auto-posting.\\n+\\n+## Social media draft\\n+\\n `autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n \\n - candidate id;\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_tOqhsoflH9nTkREbEvtONqnT" }, { "role": "assistant", "content": "", "reasoning": "**Correcting headings**\n\nI noticed that we've duplicated the heading. We should fix it to say \"Content preview package\" instead. It's a small detail, but getting headings right is essential for clarity. Maybe I overlooked that earlier, but I'm glad I'm catching it now! Let’s make sure to keep everything organized so that it looks neat and professional. Updating this will help present the information clearly to the user. I'll go ahead and make that change!", "finish_reason": "tool_calls", "reasoning_content": "**Correcting headings**\n\nI noticed that we've duplicated the heading. We should fix it to say \"Content preview package\" instead. It's a small detail, but getting headings right is essential for clarity. Maybe I overlooked that earlier, but I'm glad I'm catching it now! Let’s make sure to keep everything organized so that it looks neat and professional. Updating this will help present the information clearly to the user. I'll go ahead and make that change!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyD_9MguIBYtRDYxqzRSDUbrGWlOCnUcApz7_91gYWUgbsdSK1RwbKHhLUWyRZSHfg4ORxD9jYloPxiOeaUQT4wy-z-hvCd-ROe3NySBZjlxU-nC7fL_fYnboT9WxvvrtYZWQu-5KQR0U7Nd_F2i6MihOqmjb_XILZJ1nFtLuYkNBdW4doCIObk1qXggl5tP4wJBrqCRXUrWm7Id3Cg86klkSEJCC72o-Yj55qRxLW1jm9hhHGLuWAu4akLUuyeA2GOpr81QIyGoegDVm7eV123xkVoqJ7VWHVrKI-sU3Fo6wlfbh3vSYINu5GZe9cll96u50SHpQkvgg1CrSn6zRHrHit8-tANbIBsN7J-KbdMbkJ9UbeNmeuaSYHyLXsMlS_qOP-maf6DzckNyGi-LnjiqZXsnqmBlQdTRoDU5kTnQf5Q__Wva-O2w3_h9dorodzhX36cBMI-BJf3Iq5YQ03ncjP4hLTNHVGXiDY4RU5i4DR4BpgtNmZbZ4Vrdet2ioJigUYq5An9azS48_5pLQX_7WPTHh9o22o_xaFGu_aIfzpywuM9vpZX4a7fUXHqZyb32aoJJSJPqU0D7FYuK_cBTogPEsne-NyId6pqNUZ5MrAztNhG6qhAKR7qMdXYed-nDgyDYsGv2I6sZID-1gPC9sLf_-0Sh42X8d3tWONScEo2aU2mXwEzhIeWyDSwZrJAqMReI5zAvY36NKc1YfoUL6sDt2hkNWwF4r9Acoe5ByZoiZeq6O3BoPOnNeAh5OiCzeLhTiou2Rw8BV65-uMmBXdLHF8yfJ76r100OUnxtl-PXJ71uixAFWwX-VQy0zSAy2LQXSsuUyq2nphC4bkyxt4syQy-yrsUQ694kuzrAd7e1dh4BU7jKO1l2cehlyD--V1ljIIkarc3R3rvH1TDARfZ0vB9eEO1nwBXzETLpCuhplJ9tMQWobPV-gxbOi043p1LC0mez3WmERFx7pt__jygW83rai2XkDyUyE2dwxkk=", "id": "rs_060035eb87c40e14016a1320fdfe9c81918ed66e3cd22af792", "summary": [ { "type": "summary_text", "text": "**Correcting headings**\n\nI noticed that we've duplicated the heading. We should fix it to say \"Content preview package\" instead. It's a small detail, but getting headings right is essential for clarity. Maybe I overlooked that earlier, but I'm glad I'm catching it now! Let’s make sure to keep everything organized so that it looks neat and professional. Updating this will help present the information clearly to the user. I'll go ahead and make that change!" } ] } ], "tool_calls": [ { "id": "call_wgubhT3jWUaIlPFHJWg4o0Pi", "call_id": "call_wgubhT3jWUaIlPFHJWg4o0Pi", "response_item_id": "fc_060035eb87c40e14016a1320fffdb88191ad4ab1a8cb610f2f", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"## Content preview package\\n\\n`autoshorts.preview.package.build_content_preview_approval_package()`\",\"old_string\":\"## Social media draft\\n\\n`autoshorts.preview.package.build_content_preview_approval_package()`\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -142,7 +142,7 @@\\n \\n The command creates German approval-preview PNG scene cards under `frames/`, writes `preview.concat.txt`, renders `preview.mp4`, and prints script/output metadata. It performs no Telegram calls, no platform calls, and no AI/API calls. Final social-media videos can still be produced in English later; this preview layer is German for concept review.\\n \\n-## Social media draft\\n+## Content preview package\\n \\n `autoshorts.preview.package.build_content_preview_approval_package()` combines an existing MP4 timing preview with the social media draft review text into a side-effect-free `ContentPreviewApprovalPackage`. It records approval language `de`, final video language `en`, approval commands, preview video path, and requires human approval.\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_wgubhT3jWUaIlPFHJWg4o0Pi" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_i1k6r2mQ9xkfYtqOAt5x97iX", "call_id": "call_i1k6r2mQ9xkfYtqOAt5x97iX", "response_item_id": "fc_060035eb87c40e14016a132105fa208191927f7d9c0b5360d8", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom pathlib import Path\\nfrom tempfile import TemporaryDirectory\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.social.draft import build_social_media_draft\\nfrom autoshorts.social.review import render_social_media_draft_review\\nfrom autoshorts.preview.package import build_content_preview_approval_package, render_content_preview_approval_text\\nbrief=create_content_brief(pillar_id='ai_life_systems',topic='AI weekly review routine',audience_problem='I use AI tools but still lose control of priorities.',payoff='a 10-minute Sunday review that keeps AI as assistant, not boss')\\nscript=create_script_draft(brief)\\ncandidate=convert_script_draft_to_candidate(brief,script)\\npreview=build_html_preview(build_render_manifest(create_shot_plan(script)))\\ndraft=build_social_media_draft(candidate,preview)\\nreview=render_social_media_draft_review(draft)\\nwith TemporaryDirectory() as tmp:\\n p=Path(tmp)/'preview.mp4'; p.write_bytes(b'fake mp4')\\n package=build_content_preview_approval_package(draft=draft,preview_video_path=p,social_review_text=review)\\n text=render_content_preview_approval_text(package)\\n print(package.candidate_id)\\n print(package.approval_language, package.final_video_language)\\n print(package.side_effects, package.external_calls)\\n print(len(text), 'Freigabe alle' in text)\\nPY\\ngit status --short\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 61%]\\n............................................. [100%]\\n117 passed in 9.11s\\nai_life_systems-ai-weekly-review-routine-35s\\nde en\\n() ()\\n1162 True\\n M README.md\\n?? autoshorts/preview/\\n?? docs/\\n?? tests/test_content_preview_package.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_i1k6r2mQ9xkfYtqOAt5x97iX" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyEd8Nmsr5CE42S0W5QLzM2_NTgfphw1gL6WXHY2IFGCaelJd9wd6c9N56cAFtpPK_FSrN2LB_LgKG5fZlQju2Q8hfc-iMuTdHw1ADEsWbKeB_gesq6BBs-WW8LEiee6mDAs9lEJRBgIUXcg0DC_k7WeVPDL96QXroCWsBE6SohZNEs7i255ZT5JSiqqy2B9xG2XWsi9LyT2vZO1sj3AGiNHrZSfey7Y0RoeN3KTZyn0MIhvVuzYFoyTbHP7YqsaU0tTswzUmN6MAmNmGqaJQOf0B5D73eVrJjyNHW6WdrgVLLI4hzRkdzCf-CqxA_F0AVy4jhrWHStVi2fO-0E5uJ8H6ZmZ0Yw3U_V9NlRJb8fhTTKWaVse7-knCeUqoDHrrPUVFLNU51PkjpPqLf_8-y8_Z7Biffa2U_zY6G6bAInh4zLb_wXADMDmvsTy8PgXo8C94pRkOnD8OWEvEoVyVfrCss3S5QssJNof-oFQB9JKQ9PMGgm_I--Fy7xlYTywVM5wHOKzt5HtHz4il6RqtfQ2cyIc0kC-r7tTBrZbAUdQ2lh08Dd2xNrSwBPzmRQW19bxcFzL4uH3FMKl0kKOeA1v7bME8xEC9GY0tmuBcOUAZ3f9eHMTz3bW-wo5bREgMl87291TRDCIoJfBXDig8TxdA1o7_TDs5uhAHlxxPEIUbikp0oE79N2HvQAb5otcvJ55Z2gqlKd5WZH0SPvyi0980AYnjAHL6O_tD9_fj0CGGBQnCT7T6hD_KNUsW9MfSJj0qNWwxeQ1FZ7R5jUjg3nmW_avAPG2jt9MsA4ODbQ_EQsbmcsgnT0P68sO-0HYQxvBnvJdKOxlGCMVShLLIBgBuFbhJ4vMQbJd65WJjGrFDjEMsZ_VAyFt_pkkSGeXMAp6NJH5pj9MGWoFFZ5EFUSwzYc6gSQBNWdP3nvDFNIam1A=", "id": "rs_060035eb87c40e14016a13211d5d2c819181e88417d32ec041", "summary": [] } ], "tool_calls": [ { "id": "call_CB2nzQ7pPolGYbcWNjKYkRHB", "call_id": "call_CB2nzQ7pPolGYbcWNjKYkRHB", "response_item_id": "fc_060035eb87c40e14016a13211deaa48191b7fadbddc8750499", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/preview/__init__.py autoshorts/preview/package.py docs/research/content_automation_research.md tests/test_content_preview_package.py && git commit -m \\\"feat: add content preview approval package\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"[feat/core-approval-quality-gates d2e6d06] feat: add content preview approval package\\n 5 files changed, 407 insertions(+)\\n create mode 100644 autoshorts/preview/__init__.py\\n create mode 100644 autoshorts/preview/package.py\\n create mode 100644 docs/research/content_automation_research.md\\n create mode 100644 tests/test_content_preview_package.py\\nd2e6d06 feat: add content preview approval package\\nf9f737f fix: improve German approval preview readability\\n496b0ac feat: add demo local preview video cli\\n89a0ebe feat: render local ffmpeg preview videos\\n1128b7c feat: add ffmpeg concat planning\\n7e881b3 feat: write local preview frame cards\\n02ecf59 feat: add local preview frame planning\\n3b387f4 feat: add demo social draft cli\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_CB2nzQ7pPolGYbcWNjKYkRHB" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_tFc0jR1Wb5mzbgs8obolnNFL", "call_id": "call_tFc0jR1Wb5mzbgs8obolnNFL", "response_item_id": "fc_060035eb87c40e14016a132123909881919ce9b9220c9394e9", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ContentPreviewApprovalPackage als nächstes Approval-Preview-Bindeglied per TDD bauen\",\"id\":\"package\",\"status\":\"completed\"}]}" } } ] }, { "role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"research\", \"content\": \"Im Netz recherchieren, wie andere Personen Content-Automation erfolgreich aufbauen, und Inputs bewerten\", \"status\": \"completed\"}, {\"id\": \"package\", \"content\": \"ContentPreviewApprovalPackage als nächstes Approval-Preview-Bindeglied per TDD bauen\", \"status\": \"completed\"}], \"summary\": {\"total\": 2, \"pending\": 0, \"in_progress\": 0, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_tFc0jR1Wb5mzbgs8obolnNFL" }, { "role": "assistant", "content": "Passt, Sir. Ich habe weitergemacht und parallel die Recherche sauber ausgewertet.\n\n## 1. Recherche: Was andere erfolgreiche Content-Automation-Systeme machen\n\nIch habe öffentliche Workflows, Tools und Plattformregeln geprüft und die verwertbaren Inputs dokumentiert:\n\n```text\ndocs/research/content_automation_research.md\n```\n\n### Quellen / Muster\n\n**AutoShorts.ai**\n- Quellen:\n - https://autoshorts.ai/\n - https://www.starterstory.com/stories/autoshorts-ai-breakdown\n- Muster:\n - Series/Topic anlegen\n - Video automatisch erzeugen\n - Preview & Customize\n - danach Scheduling/Posting\n- Bewertung:\n - **Series + editable Preview** ist sehr relevant.\n - Auto-Posting bleibt bei uns bewusst aus. Sonst baut man sehr effizient eine Spammaschine mit hübscher UI. Grossartig, wenn man Ärger liebt.\n\n**OpusClip / Audacy**\n- Quellen:\n - https://www.opus.pro/how-audacy-drove-1b-views-using-opusclip\n - https://www.opus.pro/customer-stories\n- Muster:\n - vorhandene Longform-/Podcast-/Live-Inhalte\n - AI findet starke Clips\n - short-form Distribution\n- Bewertung:\n - Sehr relevant für später:\n ```text\n Source/Transcript → Clip Candidates → Hook Scoring → Preview\n ```\n - Besser als reine KI-Fantasie, wenn wir später echte Quellen/Meetings/Notizen/Longform-Content haben.\n\n**n8n / Make Workflows**\n- Quellen:\n - https://n8n.io/workflows/3442-fully-automated-ai-video-generation-and-multi-platform-publishing/\n - https://n8n.io/workflows/8290-automate-faceless-shorts-with-openai-runwayml-and-elevenlabs-script-to-social-media/\n - https://community.make.com/t/make-1-000-youtube-shorts-with-this-ai-automation-make-com/54511\n- Muster:\n - Sheet/Queue\n - Script Generation\n - Scene Prompts\n - Image/Video Generation\n - Voiceover\n - Captions\n - Render\n - Upload\n - Status Tracking\n- Bewertung:\n - Übernehmen:\n - Queue\n - Status\n - Kosten-/Token-/Renderzeit-Tracking\n - Error Handling\n - Nicht übernehmen:\n - Blindes Auto-Publishing\n - “1000 Shorts” ohne Quality Gate\n\n**Creatomate / MindStudio**\n- Quellen:\n - https://creatomate.com/blog/how-to-use-ai-to-automate-videos-for-shorts-stories-and-reels\n - https://www.mindstudio.ai/blog/ai-short-form-video-creation-workflow-script-to-mp4\n- Muster:\n - Template-first Rendering\n - Hook / Setup / Main / CTA\n - Script-to-scenes-to-render\n- Bewertung:\n - Bestätigung unserer Architektur:\n ```text\n Typed artifacts → Renderer template → Preview → Approval\n ```\n - Kein Freeform-“mach mal ein Video”-Chaos.\n\n**Planable Approval Process**\n- Quelle:\n - https://planable.io/blog/social-media-approval-process/\n- Muster:\n - Ideation\n - Copy\n - Design\n - Internal Review\n - Approval\n - Scheduling\n- Bewertung:\n - Sehr relevant:\n - Approval muss versioniert werden.\n - Freigabe darf nur für exakt diese Preview-Version gelten.\n - Nach Änderung neue Version.\n\n**YouTube / Meta Plattformregeln**\n- Quellen:\n - YouTube synthetic/altered disclosure:\n https://support.google.com/youtube/answer/14328491?hl=en\n - YouTube monetization / inauthentic content:\n https://support.google.com/youtube/answer/1311392?hl=en\n - Meta AI labels:\n https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/\n- Bewertung:\n - Wir brauchen später:\n - `requires_synthetic_content_disclosure`\n - Originality/Inauthentic-Content-Gate\n - Tool-/Source-Metadata\n - Massenhaft generischer Content ist nicht nur hässlich, sondern potentiell schlecht für Monetarisierung und Account Health.\n\n## Wichtigste Implementierungs-Inputs aus der Recherche\n\nIch würde daraus als nächste Architektur-Bausteine ableiten:\n\n1. **PreviewVersion / Fingerprint**\n - Approval gilt nur für exakt diese Version.\n2. **Queue/Status Model**\n - idea → draft → preview → approved → final-render-ready.\n3. **Originality Gate**\n - verhindert Template-Spam / inauthentic content.\n4. **Synthetic Content Disclosure Metadata**\n - für YouTube/Meta Uploads später.\n5. **Cost/Runtime Tracking**\n - auch lokal:\n - render seconds\n - external calls\n - tokens/credits falls externe APIs.\n6. **Source-backed Mode**\n - später:\n - transcript/source URL → clip candidates → approval preview.\n\n---\n\n## 2. Weitergebaut: ContentPreviewApprovalPackage\n\nNeue Dateien:\n\n```text\nautoshorts/preview/__init__.py\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\n## Neue API\n\n```python\nbuild_content_preview_approval_package(...)\nvalidate_content_preview_approval_package(...)\nrender_content_preview_approval_text(...)\n```\n\n## Neuer Baustein\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nDas Package enthält:\n\n```text\ncandidate_id\npreview_video_path\nsocial_review_text\napproval_commands\napproval_language = de\nfinal_video_language = en\nrequires_human_approval = True\nside_effects = ()\nexternal_calls = ()\n```\n\nApproval Commands:\n\n```text\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nFreigabe alle\nAblehnen\nÄndern: <requested change>\n```\n\n## Guardrails\n\nValidierung rejected:\n\n- fehlende Candidate ID\n- fehlenden Preview Video Path\n- nicht existierende MP4\n- Nicht-`.mp4` Preview Path\n- leeren Review Text\n- fehlende Approval Commands\n- fehlendes `Freigabe alle`\n- `requires_human_approval=False`\n- Side Effects\n- External Calls\n- `publish` / `post` Sprache im Review Text\n\nAlso: Dieses Package kann noch nichts senden, nichts posten, nichts rendern. Es bündelt nur das fertige Preview-Artefakt plus Review-Text. Genau richtig.\n\n## Rendered Approval Text\n\n`render_content_preview_approval_text()` erzeugt:\n\n```text\n## Content Preview Review\n\nCandidate: ...\nVideo preview: ...\nApproval preview language: de\nFinal video language: en\nRequires human approval: yes\nSide effects: none\nExternal calls: none\n\nApproval commands:\n...\n\n## Social Media Draft Review\n...\n```\n\nTelegram-safe:\n\n```text\n1162 Zeichen\n```\n\n## Tests\n\n```text\n117 passed in 9.11s\n```\n\n## Commit\n\n```text\nd2e6d06 feat: add content preview approval package\n```\n\nArbeitsverzeichnis sauber.\n\n---\n\n## Aktueller Pipeline-Stand\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt bauen wir daraus den eigentlichen Telegram-/Local-Delivery-Plan:\n\n```text\nContentPreviewApprovalPackage\n→ ContentPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- `MEDIA:/absolute/path/to/preview.mp4`\n- requires human approval\n- no platform upload\n- no Telegram send yet\n- no external calls\n- approval language deutsch\n- final language englisch\n- side-effect-free delivery object\n\nDanach kommt:\n\n```text\nautoshorts/cli/demo_content_preview.py\n```\n\nDann kann ein einziger Command ein echtes Freigabepaket erzeugen:\n\n```text\nMP4 Preview + Review Text + Approval Commands\n```\n\nUnd erst danach hängen wir es an Telegram. Schrittweise, kontrolliert, ohne “oops, es ist live”.", "reasoning": "**Finalizing German Research**\n\nI should create a concise version of the German research without tables. It’s essential to mention subagents and timeouts, ensuring I've noted any sources I researched. I'll also include details about commits, tests, and documentation. It seems I should suggest \"inputs to implement next\" as well. However, I wonder if I really need citations or just URLs will suffice for this final version. I'll sort that out before concluding!", "finish_reason": "stop", "reasoning_content": "**Finalizing German Research**\n\nI should create a concise version of the German research without tables. It’s essential to mention subagents and timeouts, ensuring I've noted any sources I researched. I'll also include details about commits, tests, and documentation. It seems I should suggest \"inputs to implement next\" as well. However, I wonder if I really need citations or just URLs will suffice for this final version. I'll sort that out before concluding!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyExnwI6EvihzbfqpuuCkVfPCNdhVTZ3wcHYqIvHLQkT1TDyBoo2pA5wge5ZZRZO-267iZJi-0u2OjyW2_qSCPM_snL3bwfOta4gr-VOUk7J4Wj-k6G3LT-BZWDttjoLxhHptRjH22mzr5Av1lKVMiGRkTfoPBaj6lcmakegTwyUO2Vh_C3434y4RWKG6Kqx0tSYXsbS0KU-Qj7ceWVOuveambM9y13y-Mpq3BZakBw3WTE8MDuBCCWg8N94eBiPeXFcedFvCu2WRA_lzYkUhtA_6p2lGpiNZF-ZBM9CAtrPWvxc6N5Euw6EvhPYq3RWilZL6sHJsz-2ppDMPqNKztq5MYTDhC4FB8F0rCrmFg8P8eDbIa4RnwjTmvv5wukjJFh76x39VaZRDO1Xx2VtGMIdMIC2H_UP_1eKb643DdT0oI6ZWJLtj3a8UrnDYtB1pgOl_4sIAl2aJaW4AsqQwDzmFlJsJHiBUlCtgpVWCsZ8SOgm27gCvdA1UrF98rKaFXhXHqt0cSycMm8slsZhhFnp9d0gna4Izu_3AjEewpDWbfprIaKThr3nKGzkNZaIHJ1up9COLtAFBHHvNfaDe55F6MUKsrAYVybHtBiU70TAHKbFPZDkdxJ5KHyW791vw9s4Sdxwsm5ORXIAv06S-4zmujYII2V2J8hHLO74-xS_LCtdQHnICzRlRQx7wPXroi4n0tYaN-Yd3x4DM9VaHakbXTBX9WSjabAWmIfMRxtgf6NYsCsq24lB-r2ueWlzoTfiw3N-p_28V1XPQNfXR46RF4Di9UIj_Gp6dpipw4eMaGlGD0IQe-pUuILsYe3DqZJrdspz2F1P-S95tixeJL6WJWWxzQawRBlHmEQ5a13uCvT9qhDP5V4BFqIPt0rh6DVQwr0dZCrRC_au5QAVVkYUGygodMmkN0vTIiP0v951HvkWg2Fesein3w3FYpnwZglmJrO8qKut66RPBefk9R-y9TlN73OSm8hCC5QAib7YdJ6Vn8RexFZFqc-8pbYOiIq010JwcLsvSJwBzkksEeVbzi2xB5fkDPiGrRJXMkDFE8GT_jjhfc7Hpu68Yy3liR-3bM-cV_CP48F_rSY879Osdo_UHnWNd_-Rz7ptvGND5akVnP6RtjMz5yAZoTh2l8iiIuKlf62346Vr17IRNIHwx6IwywY8TOK7caYrWJXrTw78-9zf9zspN1tio8fojuTFI8eTsRoSkNXJ8KEaIHsM-YLokr5zCRGf8vicxhANdH0=", "id": "rs_060035eb87c40e14016a13212e6b48819187b1f52d93ab90db", "summary": [ { "type": "summary_text", "text": "**Finalizing German Research**\n\nI should create a concise version of the German research without tables. It’s essential to mention subagents and timeouts, ensuring I've noted any sources I researched. I'll also include details about commits, tests, and documentation. It seems I should suggest \"inputs to implement next\" as well. However, I wonder if I really need citations or just URLs will suffice for this final version. I'll sort that out before concluding!" } ] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Passt, Sir. Ich habe weitergemacht und parallel die Recherche sauber ausgewertet.\n\n## 1. Recherche: Was andere erfolgreiche Content-Automation-Systeme machen\n\nIch habe öffentliche Workflows, Tools und Plattformregeln geprüft und die verwertbaren Inputs dokumentiert:\n\n```text\ndocs/research/content_automation_research.md\n```\n\n### Quellen / Muster\n\n**AutoShorts.ai**\n- Quellen:\n - https://autoshorts.ai/\n - https://www.starterstory.com/stories/autoshorts-ai-breakdown\n- Muster:\n - Series/Topic anlegen\n - Video automatisch erzeugen\n - Preview & Customize\n - danach Scheduling/Posting\n- Bewertung:\n - **Series + editable Preview** ist sehr relevant.\n - Auto-Posting bleibt bei uns bewusst aus. Sonst baut man sehr effizient eine Spammaschine mit hübscher UI. Grossartig, wenn man Ärger liebt.\n\n**OpusClip / Audacy**\n- Quellen:\n - https://www.opus.pro/how-audacy-drove-1b-views-using-opusclip\n - https://www.opus.pro/customer-stories\n- Muster:\n - vorhandene Longform-/Podcast-/Live-Inhalte\n - AI findet starke Clips\n - short-form Distribution\n- Bewertung:\n - Sehr relevant für später:\n ```text\n Source/Transcript → Clip Candidates → Hook Scoring → Preview\n ```\n - Besser als reine KI-Fantasie, wenn wir später echte Quellen/Meetings/Notizen/Longform-Content haben.\n\n**n8n / Make Workflows**\n- Quellen:\n - https://n8n.io/workflows/3442-fully-automated-ai-video-generation-and-multi-platform-publishing/\n - https://n8n.io/workflows/8290-automate-faceless-shorts-with-openai-runwayml-and-elevenlabs-script-to-social-media/\n - https://community.make.com/t/make-1-000-youtube-shorts-with-this-ai-automation-make-com/54511\n- Muster:\n - Sheet/Queue\n - Script Generation\n - Scene Prompts\n - Image/Video Generation\n - Voiceover\n - Captions\n - Render\n - Upload\n - Status Tracking\n- Bewertung:\n - Übernehmen:\n - Queue\n - Status\n - Kosten-/Token-/Renderzeit-Tracking\n - Error Handling\n - Nicht übernehmen:\n - Blindes Auto-Publishing\n - “1000 Shorts” ohne Quality Gate\n\n**Creatomate / MindStudio**\n- Quellen:\n - https://creatomate.com/blog/how-to-use-ai-to-automate-videos-for-shorts-stories-and-reels\n - https://www.mindstudio.ai/blog/ai-short-form-video-creation-workflow-script-to-mp4\n- Muster:\n - Template-first Rendering\n - Hook / Setup / Main / CTA\n - Script-to-scenes-to-render\n- Bewertung:\n - Bestätigung unserer Architektur:\n ```text\n Typed artifacts → Renderer template → Preview → Approval\n ```\n - Kein Freeform-“mach mal ein Video”-Chaos.\n\n**Planable Approval Process**\n- Quelle:\n - https://planable.io/blog/social-media-approval-process/\n- Muster:\n - Ideation\n - Copy\n - Design\n - Internal Review\n - Approval\n - Scheduling\n- Bewertung:\n - Sehr relevant:\n - Approval muss versioniert werden.\n - Freigabe darf nur für exakt diese Preview-Version gelten.\n - Nach Änderung neue Version.\n\n**YouTube / Meta Plattformregeln**\n- Quellen:\n - YouTube synthetic/altered disclosure:\n https://support.google.com/youtube/answer/14328491?hl=en\n - YouTube monetization / inauthentic content:\n https://support.google.com/youtube/answer/1311392?hl=en\n - Meta AI labels:\n https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/\n- Bewertung:\n - Wir brauchen später:\n - `requires_synthetic_content_disclosure`\n - Originality/Inauthentic-Content-Gate\n - Tool-/Source-Metadata\n - Massenhaft generischer Content ist nicht nur hässlich, sondern potentiell schlecht für Monetarisierung und Account Health.\n\n## Wichtigste Implementierungs-Inputs aus der Recherche\n\nIch würde daraus als nächste Architektur-Bausteine ableiten:\n\n1. **PreviewVersion / Fingerprint**\n - Approval gilt nur für exakt diese Version.\n2. **Queue/Status Model**\n - idea → draft → preview → approved → final-render-ready.\n3. **Originality Gate**\n - verhindert Template-Spam / inauthentic content.\n4. **Synthetic Content Disclosure Metadata**\n - für YouTube/Meta Uploads später.\n5. **Cost/Runtime Tracking**\n - auch lokal:\n - render seconds\n - external calls\n - tokens/credits falls externe APIs.\n6. **Source-backed Mode**\n - später:\n - transcript/source URL → clip candidates → approval preview.\n\n---\n\n## 2. Weitergebaut: ContentPreviewApprovalPackage\n\nNeue Dateien:\n\n```text\nautoshorts/preview/__init__.py\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\n## Neue API\n\n```python\nbuild_content_preview_approval_package(...)\nvalidate_content_preview_approval_package(...)\nrender_content_preview_approval_text(...)\n```\n\n## Neuer Baustein\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nDas Package enthält:\n\n```text\ncandidate_id\npreview_video_path\nsocial_review_text\napproval_commands\napproval_language = de\nfinal_video_language = en\nrequires_human_approval = True\nside_effects = ()\nexternal_calls = ()\n```\n\nApproval Commands:\n\n```text\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nFreigabe alle\nAblehnen\nÄndern: <requested change>\n```\n\n## Guardrails\n\nValidierung rejected:\n\n- fehlende Candidate ID\n- fehlenden Preview Video Path\n- nicht existierende MP4\n- Nicht-`.mp4` Preview Path\n- leeren Review Text\n- fehlende Approval Commands\n- fehlendes `Freigabe alle`\n- `requires_human_approval=False`\n- Side Effects\n- External Calls\n- `publish` / `post` Sprache im Review Text\n\nAlso: Dieses Package kann noch nichts senden, nichts posten, nichts rendern. Es bündelt nur das fertige Preview-Artefakt plus Review-Text. Genau richtig.\n\n## Rendered Approval Text\n\n`render_content_preview_approval_text()` erzeugt:\n\n```text\n## Content Preview Review\n\nCandidate: ...\nVideo preview: ...\nApproval preview language: de\nFinal video language: en\nRequires human approval: yes\nSide effects: none\nExternal calls: none\n\nApproval commands:\n...\n\n## Social Media Draft Review\n...\n```\n\nTelegram-safe:\n\n```text\n1162 Zeichen\n```\n\n## Tests\n\n```text\n117 passed in 9.11s\n```\n\n## Commit\n\n```text\nd2e6d06 feat: add content preview approval package\n```\n\nArbeitsverzeichnis sauber.\n\n---\n\n## Aktueller Pipeline-Stand\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt bauen wir daraus den eigentlichen Telegram-/Local-Delivery-Plan:\n\n```text\nContentPreviewApprovalPackage\n→ ContentPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- `MEDIA:/absolute/path/to/preview.mp4`\n- requires human approval\n- no platform upload\n- no Telegram send yet\n- no external calls\n- approval language deutsch\n- final language englisch\n- side-effect-free delivery object\n\nDanach kommt:\n\n```text\nautoshorts/cli/demo_content_preview.py\n```\n\nDann kann ein einziger Command ein echtes Freigabepaket erzeugen:\n\n```text\nMP4 Preview + Review Text + Approval Commands\n```\n\nUnd erst danach hängen wir es an Telegram. Schrittweise, kontrolliert, ohne “oops, es ist live”." } ], "id": "msg_060035eb87c40e14016a13213126588191bcfff1175edba7a4", "phase": "final_answer" } ] }, { "role": "user", "content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/<topic>.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/<name>.<ext>` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/<name>.<ext>` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them." }, { "role": "assistant", "content": "", "reasoning": "**Updating skills and tools**\n\nI can only access memory and skill management tools right now. I need to update the skill library, particularly for social media automation and TDD. We've already patched some social media references during the session, but I should review and possibly enhance the social-media-shorts-automation skill. It might be helpful to add research references from the relevant documentation. I’ll consider using skill_view since the user mentioned it’s acceptable. I think calling skill_view is the next step!", "finish_reason": "tool_calls", "reasoning_content": "**Updating skills and tools**\n\nI can only access memory and skill management tools right now. I need to update the skill library, particularly for social media automation and TDD. We've already patched some social media references during the session, but I should review and possibly enhance the social-media-shorts-automation skill. It might be helpful to add research references from the relevant documentation. I’ll consider using skill_view since the user mentioned it’s acceptable. I think calling skill_view is the next step!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyHQgIG0WVmWbdozz1hxp4-C8e29ROUHoG1dcgFdIfdJSEoI9DH2uxCTIhC2MkL5U8N7dozEtGnQ4a1Jg5Cjp_X6iXOfI-t4GrFId-Hb2sW8cN7yub45JyACoxoP1PGaIlBxPBvIdLN3JRjN5ZFzHNULAzwhIPUZ33Rhph_nMIKMU0-SulikIT6x6bC-s40HNDDYAe26n3du15gepiFwoGyMJLbv12u8EIj-qtfkRY1b9radHuuNQLVZiJw_Lyfm2R6lSGQIH2fCWTkrT1byhFFEe8jx2ae7h5rNjCVh7kIlwtIr5ZJJTQyIVAw008K47v30pcSqqJwUXQp_zq688doJo3UOM_1dFmj1JnCDsXAMmDDgmB6FwZpq1ONQiHNJ3seANrOUdetf57IgH-APzq1wvlvyWMyzZR9KMaUqjvNCQ_C8FTExos2G6SV0fua6XXy3-5wbJrHFqYyVtZadcMr15Vr5RdfZQBT2JruVRHnYqgSEI1weBHWMXZdKX9nI1TPqMUVM7pbEfci_bMAy02GG-GMGGXsdvLjKWxh8Ilaz4g3h8xfk7L5fxBGRawl-lAKt_Cc6TH-1GSvCSSMcndUuWHro1I5PCIrcMM6qpo5w4lS4QYWcOb_mtTEVKvRf4lZZmYQhmxiZbE5ATa7RLKNSr6nAnrjbaUpVfP2EBMdX7BaaEnFUJ960iweFBPSnXaV4p6tWuPUmvoeVtVTbjUrurJG1v3dm3LzLjbETCkWGE3uE9ZQnAmSfxoh5WnyoQ8AXNZYSdCldkbGIIsFAAarVaglTmws4600lUwlmKKNUfPFhB3JVSC1D_8DfkwCvrkbPp1HwLxZfIbxw6SlIvdadVpjVxwuq3pFFRjq1cA2u7DPn1OEieEqBAWd4Yeu5z8utvrsHQ4u5w1vTz7_a0bGqYFHj2fZJQi49EDo-6ZyDkWUH01c99VC4WNNHdiScddnC3s49tTh0IsEoGF11d2NQFAmsUcZjLS0iC1_q8LPUI1t93yVmfqVZXU1dA_MJYtmz2slIUI05KxOh2QX-Zsett2jCdj889A2F-OZcHie6IOxyoBCk_hNzcw4CGcIoMudpGFi57CIioawLdKSAcrSkMn7MN4SRkYQMkrG8c7ubeCvzMXz-YpD5xLzftjyU1BD2r5kC5fjxAS09k0apRydJQp2BAi8ejVqEbe4xj6Wc43S_VschQuwIpEjSuu7ETLa-DFVOeVwLyRoAX91O1LTyXxpURbD-qV4C4HkZQ5TuC6IQukAFH56iAFs4eAsizEkE3MjAAoj5ng9iGo96wF4brSue0PuJ6YpESdP18LEdBH_S4fSCZ29bLpHVBL_9pYi5KLATDWEMbr9jNp1dOFbtXQrWXhSRwvJ9EIPbCww5hXGcuyAAgXCZbL3Lqk-f4LZ8s5979_EhMqseF_V_Edx2d7U9Sgc-pQNHDrcxE4Qos7CcXX93lUAIuxLver1kZtJkoRPYaSTP1_lRHdscARKV3KOSUcdvFsN4MjWZyc4jQnD21HZwW_AhgW3Yfpahc5Jydnpy8uG3ChjWFemusWsvl3byg-ImYzt-WUyi7gi9VphQAtmML-aomCu6fKy2F1rbXAscS6oGVMgqtDqOwAUSpvgY2nJmYkHAI-V0AWV7VJHBuIC2IdQa1WoSK5OcCCi8", "id": "rs_060035eb87c40e14016a1321cc93f48191a31b0981ef58e398", "summary": [ { "type": "summary_text", "text": "**Updating skills and tools**\n\nI can only access memory and skill management tools right now. I need to update the skill library, particularly for social media automation and TDD. We've already patched some social media references during the session, but I should review and possibly enhance the social-media-shorts-automation skill. It might be helpful to add research references from the relevant documentation. I’ll consider using skill_view since the user mentioned it’s acceptable. I think calling skill_view is the next step!" } ] } ], "tool_calls": [ { "id": "call_qLjhszaXOTuUvMqFNjWUcYbM", "call_id": "call_qLjhszaXOTuUvMqFNjWUcYbM", "response_item_id": "fc_060035eb87c40e14016a1321d0bf848191a3b454636b9c9b45", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}" } } ] }, { "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## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - 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: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips/<slug>/scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft approval package before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - Add a local demo CLI that prints metadata first, then the draft review text; it must not write files, call renderers, call Telegram, or access credentials.\\n - See `references/social-draft-approval-package.md` for the concrete artifact sequence and guardrails.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.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_qLjhszaXOTuUvMqFNjWUcYbM" }, { "role": "assistant", "content": "", "reasoning": "**Planning content updates**\n\nI need to patch my skills with the approval preview for German and focus on legibility, alongside some content automation research for my support file. I should consider writing reference content, but I want to ensure it’s not too extensive. I could use my skill to manage and write the file references in content-automation-patterns.md while also looking into patching workflow steps 6 and 7. Let’s get started on this!", "finish_reason": "tool_calls", "reasoning_content": "**Planning content updates**\n\nI need to patch my skills with the approval preview for German and focus on legibility, alongside some content automation research for my support file. I should consider writing reference content, but I want to ensure it’s not too extensive. I could use my skill to manage and write the file references in content-automation-patterns.md while also looking into patching workflow steps 6 and 7. Let’s get started on this!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEyHcSGrWqrLv8YacrdcLOYhERUeBk_7P6YfGinLWr86YdjJTpwBFopZSUs0XA2FqQa7yy8qadgbJo84TC49iHenL82Sja8FeraNvQRtzun6-cn1ZcEAFAPU_5Qtj7MKva7-HJesaZKpQXCdPwUGLsVvc1XV76Wa6CGGpYsDfH-5CN8tN2_9CgSGwx35XY7F1BgnY1zGAU9QVHxuekQX2rDTLoaC-VfEOl7kwMUPmktnaYTxCKBgTsJCaTpr8rPZImc7b9J1pukWCWaL566LIlTc4aGzaCRbRH_PG40Gb9FzR5eJVO6p1YdZSp7O-uHi2VJy6lWOnBEMKEDp3CKWrIwmFOu-DvfqupPb2LN35NJWA0-F9P667Fo5W7gYutKsiZowY_PheD-MzYOyOjpaP59h_3WRVRo6MnW9DxIR9FfHyx5SuEmxxVDc8IkW4ZRVbLtRk0uF55k9gM68JOVAcp1uZp5fWpz9Bs4lDoW_MJlw_N7hmjQjdkMlHYi4WdBKB7hWOHNNVbMsRtH1ArFiO9XK7gDMQpsZ9AQq4D0KXFUIGVHkNNw6ucEOt2BIkq_QZX8_gaUgyZX38Gx4qGY3C_E9B_6bjnjbOCNjFXJlr6obLDuCq5D8UXNla4iEq4jVjiI6bykVN-U2XdupHR5kXlu9DGXckaW-GsgZFzF4wjPAZJoDNGBb9250yVZqfKYRCRzFsXGv17gFF_X57HnAezAd1OOy8b8u31x2LH2xKP1tTky4qKN2QO2Qpz90S3aS4WWkWOZ3iQH57q8_kxhicdL1NTZ-xzbFto33k-b71e-7HVUfNmGDH0hj0APi_7yFNeFnJcTGLUlq-6dqUZQbj9-jI2c3mYnhyqY55KxvVD-nPKOO-GCat6Xv3XgXV3biypC8L1hfI0YtOOS9ZQk0iMsCtIo1DYUeKIBljvjv_H_cXUdu9LX4WFPSlQvL69HDA5ISg6LG1dPWBaKK_vgLlsK_nhQufxl11bX5et5vjyX7JTCqIuyqRj_9aIpmd4FkTtjEvV3S0ug_elNKqlBGMmQ6UlrMohCY3xp4zBtxzgTAnf0LWa1zCqOuxjNEXAN6j9uMJEGMozwPBS_Tw8olr2P8m7Q3bhoxW_6fv1a4g6MvLY0PjTlNvsyxPDLvSCOsnlBgoixjpiB0ISAPvCdmGX9Em36nhVBmGhPDV_0O9tw6Zlj7T2FXNP6y--jqEQ7xUP3l-am3i1I5S4B1QuAXVLtj7gylucUA557Xem_dlG-MdKIir80aMEgB1xyRdr2VC1sqLllyYzz4q3CCIAyrXRGUGGhGNBFcNkl4Q0OQyfuaQXdU=", "id": "rs_060035eb87c40e14016a1321d9f4988191afa9f44749c09a48", "summary": [ { "type": "summary_text", "text": "**Planning content updates**\n\nI need to patch my skills with the approval preview for German and focus on legibility, alongside some content automation research for my support file. I should consider writing reference content, but I want to ensure it’s not too extensive. I could use my skill to manage and write the file references in content-automation-patterns.md while also looking into patching workflow steps 6 and 7. Let’s get started on this!" } ] } ], "tool_calls": [ { "id": "call_6PSzCwd53F4WlirsvwOWk5lv", "call_id": "call_6PSzCwd53F4WlirsvwOWk5lv", "response_item_id": "fc_060035eb87c40e14016a1321dcda0081919010e85310b6079b", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/content-automation-patterns.md\",\"file_content\":\"# Content Automation Patterns from Public Workflows\\n\\nUse this reference when designing or extending short-form content automation systems.\\n\\n## Useful public patterns\\n\\n### Series + editable preview\\n\\nExamples: AutoShorts.ai-style faceless-video products use repeatable series/topics plus preview/customize before scheduling. The transferable pattern is not blind generation; it is a stable series model with editable script, title, visuals, music, metadata, and target channels before final output.\\n\\nImplementation guidance:\\n- Model repeatable `Series` / content pillars explicitly.\\n- Build editable preview packages before final rendering.\\n- Keep posting disabled until explicit human approval exists.\\n\\n### Source-backed clipping\\n\\nExamples: OpusClip-style workflows work best when strong long-form, podcast, live, or transcript input exists. The durable pattern is source/transcript -> candidate moments -> hook scoring -> short-form preview.\\n\\nImplementation guidance:\\n- Add source-backed mode later; do not rely only on synthetic topics.\\n- Store source URL/transcript reference and hook rationale in the approval preview.\\n- Treat clipping candidates like generated candidates: score, preview, approve, then render.\\n\\n### Queue/status/cost tracking\\n\\nExamples: n8n/Make workflows commonly use Google Sheets/Airtable-style queues: idea -> script -> scene prompts -> assets -> voice -> captions -> render -> status notification.\\n\\nImplementation guidance:\\n- Copy the queue/status/error/cost tracking, not the blind auto-publishing.\\n- Track render seconds, external calls, tokens/credits, output paths, and failure reason.\\n- Prefer deterministic local artifacts until the approval loop is trusted.\\n\\n### Template-first rendering\\n\\nExamples: Creatomate/MindStudio workflows separate structured content fields from render templates and use script structure such as Hook / Setup / Main / CTA.\\n\\nImplementation guidance:\\n- Keep typed artifacts between every stage: brief, script, candidate, shot plan, manifest, preview package.\\n- Avoid free-form “make a video” prompts as production steps.\\n- Captions, numbers, labels, arrows, UI text, and factual claims should remain renderer-owned.\\n\\n### Versioned approval\\n\\nSocial-media approval workflows fail when feedback is scattered, reviewers are unclear, or content changes after sign-off.\\n\\nImplementation guidance:\\n- Every preview package should eventually have a version/fingerprint.\\n- Approval applies only to the exact version shown to the user.\\n- Any script/asset/metadata change requires a new preview version.\\n\\n## Platform-policy implications\\n\\n### Synthetic/altered disclosure\\n\\nYouTube requires disclosure for meaningfully altered or synthetically generated realistic content. Meta may label AI-generated content based on signals or self-disclosure.\\n\\nImplementation guidance:\\n- Add metadata such as `requires_synthetic_content_disclosure` before upload adapters.\\n- Preserve tool/source metadata; do not hide AI use from downstream upload flows.\\n\\n### Inauthentic or mass-produced content risk\\n\\nYouTube monetization policies make repetitive, mass-produced, inauthentic content ineligible for monetization.\\n\\nImplementation guidance:\\n- Add originality/inauthentic-content gates before final render.\\n- Avoid mass-generation targets without quality gates.\\n- Reward unique angles, source-backed value, and clear audience identity.\\n\\n## User-specific operating rules\\n\\n- Approval-preview videos/text should be in German so the user can understand and approve the concept quickly.\\n- Final social-media videos should be in English unless the user explicitly says otherwise.\\n- Approval previews must prioritize legibility: pixel-width-based text wrapping, readable font sizes, no clipped words, no overcrowded cards.\\n- Early local previews may be technical timing previews, but they must not become static final output; final assets should be motion-led with visible changes every 1–2 seconds.\\n\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"File 'references/content-automation-patterns.md' written to skill 'social-media-shorts-automation'.\", \"path\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation/references/content-automation-patterns.md\"}", "tool_call_id": "call_6PSzCwd53F4WlirsvwOWk5lv" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_SUi2PmAfAFJQVPly48XJV7dw", "call_id": "call_SUi2PmAfAFJQVPly48XJV7dw", "response_item_id": "fc_060035eb87c40e14016a1321ede504819194e211d317502f10", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_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`.\",\"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`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_SUi2PmAfAFJQVPly48XJV7dw" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_aIZTyBPILZtm34AlZKOKxMqi", "call_id": "call_aIZTyBPILZtm34AlZKOKxMqi", "response_item_id": "fc_060035eb87c40e14016a1321f62a748191b2083ebf5b165597", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"6. **Social-media draft approval package before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - Add a local demo CLI that prints metadata first, then the draft review text; it must not write files, call renderers, call Telegram, or access credentials.\\n - See `references/social-draft-approval-package.md` for the concrete artifact sequence and guardrails.\",\"new_string\":\"6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_aIZTyBPILZtm34AlZKOKxMqi" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_KWY5q5Kqup4BGDfF4SP37wx4", "call_id": "call_KWY5q5Kqup4BGDfF4SP37wx4", "response_item_id": "fc_060035eb87c40e14016a1322027d98819188e419c3403861cd", "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-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\",\"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-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_KWY5q5Kqup4BGDfF4SP37wx4" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_87PPCgSqdS7iLy2uilglUBoK", "call_id": "call_87PPCgSqdS7iLy2uilglUBoK", "response_item_id": "fc_060035eb87c40e14016a13221136e48191bb6374c76c77ee0f", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\",\"old_string\":\"- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_87PPCgSqdS7iLy2uilglUBoK" }, { "role": "assistant", "content": "Skill-Library aktualisiert, Sir.\n\n## Aktualisierte Skill\n\n```text\nsocial-media-shorts-automation\n```\n\n## Änderungen\n\n### 1. User-spezifische Preview-Regel eingebettet\n\nIn `SKILL.md` ergänzt:\n\n- Freigabe-/Approval-Previews grundsätzlich **Deutsch**\n- finale Social-Media-Videos grundsätzlich **Englisch**\n- Preview-Lesbarkeit ist Quality Gate:\n - pixel-width-aware line wrapping\n - lesbare Fontgrössen\n - keine abgeschnittenen Wörter\n - keine überfüllten Cards\n\n### 2. ContentPreviewApprovalPackage in Workflow aufgenommen\n\nDer Social-Draft-Schritt wurde erweitert zu:\n\n```text\nSocialMediaDraft\n+ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\nMit Guardrails:\n\n- `approval_language=de`\n- `final_video_language=en`\n- explicit approval commands\n- keine Side Effects\n- keine External Calls\n- keine Telegram-/Platform-Aktion im Package selbst\n\n### 3. Neue Referenzdatei angelegt\n\n```text\nreferences/content-automation-patterns.md\n```\n\nEnthält kondensiert:\n\n- Series + editable preview\n- source-backed clipping\n- queue/status/cost tracking\n- template-first rendering\n- versioned approval\n- synthetic-content disclosure\n- inauthentic/mass-produced content risk\n- German-preview / English-final rule\n- legibility requirements für Approval-Previews\n\n### 4. Pitfalls erweitert\n\nNeu festgehalten:\n\n- öffentliche Automation-Workflows nicht blind kopieren\n- Queue/Status/Cost Tracking übernehmen, aber Auto-Publishing entfernen\n- keine massenhaften repetitiven Shorts ohne Originality Gate\n- keine unlesbaren deutschen Preview-Cards\n- Approval-Sprache nicht mit finaler Audience-Sprache verwechseln\n\n## Hinweis auf Überschneidung\n\nEs gibt eine gewisse Überlappung zwischen:\n\n```text\nsocial-media-shorts-automation\nshortform-growth-automation\n```\n\nFür diese Session war `social-media-shorts-automation` die bessere Umbrella-Skill, weil dort bereits die konkrete Retention-Studio-/Preview-Renderer-Pipeline lebt.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Skill-Library aktualisiert, Sir.\n\n## Aktualisierte Skill\n\n```text\nsocial-media-shorts-automation\n```\n\n## Änderungen\n\n### 1. User-spezifische Preview-Regel eingebettet\n\nIn `SKILL.md` ergänzt:\n\n- Freigabe-/Approval-Previews grundsätzlich **Deutsch**\n- finale Social-Media-Videos grundsätzlich **Englisch**\n- Preview-Lesbarkeit ist Quality Gate:\n - pixel-width-aware line wrapping\n - lesbare Fontgrössen\n - keine abgeschnittenen Wörter\n - keine überfüllten Cards\n\n### 2. ContentPreviewApprovalPackage in Workflow aufgenommen\n\nDer Social-Draft-Schritt wurde erweitert zu:\n\n```text\nSocialMediaDraft\n+ MP4 timing preview\n→ ContentPreviewApprovalPackage\n```\n\nMit Guardrails:\n\n- `approval_language=de`\n- `final_video_language=en`\n- explicit approval commands\n- keine Side Effects\n- keine External Calls\n- keine Telegram-/Platform-Aktion im Package selbst\n\n### 3. Neue Referenzdatei angelegt\n\n```text\nreferences/content-automation-patterns.md\n```\n\nEnthält kondensiert:\n\n- Series + editable preview\n- source-backed clipping\n- queue/status/cost tracking\n- template-first rendering\n- versioned approval\n- synthetic-content disclosure\n- inauthentic/mass-produced content risk\n- German-preview / English-final rule\n- legibility requirements für Approval-Previews\n\n### 4. Pitfalls erweitert\n\nNeu festgehalten:\n\n- öffentliche Automation-Workflows nicht blind kopieren\n- Queue/Status/Cost Tracking übernehmen, aber Auto-Publishing entfernen\n- keine massenhaften repetitiven Shorts ohne Originality Gate\n- keine unlesbaren deutschen Preview-Cards\n- Approval-Sprache nicht mit finaler Audience-Sprache verwechseln\n\n## Hinweis auf Überschneidung\n\nEs gibt eine gewisse Überlappung zwischen:\n\n```text\nsocial-media-shorts-automation\nshortform-growth-automation\n```\n\nFür diese Session war `social-media-shorts-automation` die bessere Umbrella-Skill, weil dort bereits die konkrete Retention-Studio-/Preview-Renderer-Pipeline lebt." } ], "id": "msg_060035eb87c40e14016a132218df3081918f2d78d829e33c9c", "phase": "final_answer" } ] } ] }