{
"session_id": "20260523_074449_3d9cee59",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-23T07:56:27.243722",
"last_updated": "2026-05-23T08:11:17.513722",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [99% — 2,191/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; use `github_pat_`/`ghp_` cell; never quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [99% — 1,371/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nUser wants JARVIS to act as a comprehensive health-management advisor: consolidate lab reports, clinical reports, nutrition/YAZIO, daily health email data, symptoms and events; make insights visible via dashboards/reports; use a 4-eyes review workflow for extracted lab values before treating them as confirmed.\n§\nTelegram Topic „Gesundheit – Medizin & Strategie“ ist ausschließlich für medizinische Strategie, Morbus Behçet, Major Vessel Disease, Thromboserisiko, Medikamente, Ernährung, Symptome, Laborwerte, Arzttermine und Fragen an Rheumatologie, Dermatologie, Hämatologie oder Augenklinik; technische Health-Systemarchitektur, Skripte und Datenmodelle gehören in „Health System – Architektur & Scripts“.\n§\nFinance: no real amounts/IDs/API keys in chat; credentials via terminal/runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book. Grocery: mappings learned; provider calls explicit/cache-first.\n\nConversation started: Saturday, May 23, 2026 07:44 AM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_doc_read",
"description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
"parameters": {
"type": "object",
"properties": {
"doc_token": {
"type": "string",
"description": "The document token (from the document URL or comment context)."
}
},
"required": [
"doc_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_add_comment",
"description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"content": {
"type": "string",
"description": "The comment text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comment_replies",
"description": "List all replies in a comment thread on a Feishu document.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to list replies for."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"page_size": {
"type": "integer",
"description": "Number of replies per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token",
"comment_id"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comments",
"description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"is_whole": {
"type": "boolean",
"description": "If true, only return whole-document comments.",
"default": false
},
"page_size": {
"type": "integer",
"description": "Number of comments per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_reply_comment",
"description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to reply to."
},
"content": {
"type": "string",
"description": "The reply text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"comment_id",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown  and the gateway will deliver it.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"aspect_ratio": {
"type": "string",
"enum": [
"landscape",
"square",
"portrait"
],
"description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
"default": "landscape"
}
},
"required": [
"prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
"default": "replace"
},
"path": {
"type": "string",
"description": "REQUIRED when mode='replace'. File path to edit."
},
"old_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit",
"close"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "send_message",
"description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"send",
"list"
],
"description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
},
"target": {
"type": "string",
"description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)"
},
"message": {
"type": "string",
"description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "session_search",
"description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
},
"role_filter": {
"type": "string",
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
},
"limit": {
"type": "integer",
"description": "Max sessions to summarize (default: 3, max: 5).",
"default": 3
}
}
}
}
},
{
"type": "function",
"function": {
"name": "skill_manage",
"description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"create",
"patch",
"edit",
"delete",
"write_file",
"remove_file"
],
"description": "The action to perform."
},
"name": {
"type": "string",
"description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
},
"content": {
"type": "string",
"description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
},
"category": {
"type": "string",
"description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
},
"file_path": {
"type": "string",
"description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
},
"file_content": {
"type": "string",
"description": "Content for the file. Required for 'write_file'."
},
"absorbed_into": {
"type": "string",
"description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."
}
},
"required": [
"action",
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skill_view",
"description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')."
},
"file_path": {
"type": "string",
"description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
}
},
"required": [
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skills_list",
"description": "List available skills (name + description). Use skill_view(name) to load full content.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional category filter to narrow results"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "The command to execute on the VM"
},
"background": {
"type": "boolean",
"description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"pty": {
"type": "boolean",
"description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
"default": false
},
"notify_on_complete": {
"type": "boolean",
"description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
"default": false
},
"watch_patterns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "video_analyze",
"description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
"parameters": {
"type": "object",
"properties": {
"video_url": {
"type": "string",
"description": "Video URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
}
},
"required": [
"video_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https), local file path, or data: URL to load."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
}
},
"required": [
"image_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "write_file",
"description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
},
"content": {
"type": "string",
"description": "Complete content to write to the file"
}
},
"required": [
"path",
"content"
]
}
}
}
],
"message_count": 177,
"messages": [
{
"role": "user",
"content": "[M G] Ok, nächster schritt"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_8oTsPqSJeb2Lxwhp3grsOkmr",
"call_id": "call_8oTsPqSJeb2Lxwhp3grsOkmr",
"response_item_id": "fc_0ebac54e0d9e8557016a113ed59c24819190c070e8f2345add",
"type": "function",
"function": {
"name": "session_search",
"arguments": "{}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Clarifying next steps**\n\nI’m trying to understand if I need context for the answer, especially previous information. The phrase “nächster schritt” likely refers to past actions. I think I should use a recent search to gather fresh information. Also, is the project context Hermes relevant here? It seems like there might be no specific skill needed, but things feel a bit ambiguous. I definitely need to run a recent session search to clarify this.",
"reasoning_content": "**Clarifying next steps**\n\nI’m trying to understand if I need context for the answer, especially previous information. The phrase “nächster schritt” likely refers to past actions. I think I should use a recent search to gather fresh information. Also, is the project context Hermes relevant here? It seems like there might be no specific skill needed, but things feel a bit ambiguous. I definitely need to run a recent session search to clarify this.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"content": "{\"success\": true, \"query\": \"AutoShortsBot OR \\\"nächster schritt\\\" OR Topic\", \"results\": [{\"session_id\": \"20260522_230108_c351cf3a\", \"when\": \"May 22, 2026 at 11:01 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nay/Telegram deaktiviert; nur explizit/manuell nutzen.\\\", \\\"Health 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.\\\", \\\"API-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\\\", \\\"Google Workspace (gog): **Credentials-Datei:** `~/.\\n…[truncated]\"}, {\"session_id\": \"20260516_090919_6b84354e\", \"when\": \"May 16, 2026 at 09:09 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The user asked where to store an OpenFIGI API key and requested terminal commands. The conversation was in German via Telegram.\\n\\nActions taken and outcome:\\n- The assistant first loaded the `finance-system-architecture` skill because the request related to the user’s JARVIS Finance System and provider/API-key handling.\\n- A file search was attempted and returned no results (`total_count: 0`), so no existing project-specific secret-file path was found from search.\\n- The assistant instructed the user **not to post the API key in Telegram**.\\n- The assistant recommended storing the key in an external runtime secrets directory outside Git:\\n\\n```text\\n~/jarvis_runtime/finance-system/secrets/openfigi.env\\n```\\n\\nKey commands provided:\\n```bash\\nmkdir -p ~/jarvis_runtime/finance-system/secrets\\nchmod 700 ~/jarvis_runtime/finance-system/secrets\\n```\\n\\nTo enter the key without putting it into shell history:\\n```bash\\nread -rsp \\\"OpenFIGI API key: \\\" OPENFIGI_API_KEY; echo\\ncat > ~/jarvis_runtime/finance-system/secrets/openfigi.env <1200: line=line[:1200]+'...'\\n print(line)\\nPY\", \"timeout\": 60}"
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[100%]\\\\\\\\n12 passed in 0.06s\\\\\\\\nGenerated 9 review drafts in data/reviews/testbatch_002\\\\\\\\nValidation: {'name': 'testbatch_002_retention_probe_no_faith', 'total_ideas': 9, 'pillars': {'ai_life_systems': 3, 'attention_design': 3, 'life_optimization': 3}, 'duration_buckets': {'growth': 6, 'tiktok_rewards_candidate': 3}, 'missing_fields': [], 'minimum_quality_score': 9}\\\\\\\\nRetention: {'life-004': {'score': 10, 'signals': ['comment_trigger', 'concrete_payoff', 'direct_address', 'growth_duration', 'open_loop', 'short_hook', 'specificity', 'tension', 'visual_energy'], 'risks': []}, 'life-005': {'score': 10, 'signals': ['concrete_payoff', 'direct_address', 'growth_duration', 'open_loop', 'short_hook', 'tension', 'visual_energy'], 'risks': []}, 'life-006': {'score': 10, 'signals': ['concrete_payoff', 'direct_address', 'growth_duration', 'open_loop', 'short_hook', 'tension', 'visual_energy'], 'risks': []}, 'ai-004': {'score': 10, 'signals': ['comment_trigger', 'concrete_payoff', 'direct_address', 'growth_duration', 'short_hook', 'specificity', 'tension', 'visu...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_8uN8SuFN4GoAiNECVaLOCgDq\\\", \\\"call_id\\\": \\\"call_8uN8SuFN4GoAiNECVaLOCgDq\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d1f9c0508191b943d30395e55fe7\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Retention-Scoring/Validation implementieren und Review-Drafts neu generieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"3\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Tests ausführen, committen und pushen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"4\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"in_progress\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Recherche-Erkenntnisse zu Retention/Monetarisierung in Projektregeln übersetzen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"FaithTok pausieren und Testbatch v2 ohne religiöse Themen modellieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Retention-Scoring/Validation implementieren und Review-Drafts neu generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 3, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_8uN8SuFN4GoAiNECVaLOCgDq\\\", \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_34qIbiRc0yVJEh52Tot7OzB9\\\", \\\"call_id\\\": \\\"call_34qIbiRc0yVJEh52Tot7OzB9\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d1fd66bc8191a263a98fb1bba287\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"git status --short\\\\\\\\ngit diff --stat\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"M autoshorts/__init__.py\\\\\\\\n M autoshorts/growth_plan.py\\\\\\\\n M autoshorts/script_drafts.py\\\\\\\\n M configs/niches/faithtok.json\\\\\\\\n M scripts/generate_review_drafts.py\\\\\\\\n M tests/test_script_drafts.py\\\\\\\\n?? autoshorts/retention.py\\\\\\\\n?? configs/niches/attention_design.json\\\\\\\\n?? data/drafts/testbatch_002.json\\\\\\\\n?? data/reviews/testbatch_002/\\\\\\\\n?? docs/retention_research.md\\\\\\\\n?? tests/test_retention.py\\\\\\\\n?? tests/test_testbatch_002.py\\\\\\\\n autoshorts/__init__.py | 2 +-\\\\\\\\n autoshorts/growth_plan.py | 3 +--\\\\\\\\n autoshorts/script_drafts.py | 12 ++++++++++++\\\\\\\\n configs/niches/faithtok.json | 27 +++------------------------\\\\\\\\n scripts/generate_review_drafts.py | 3 +++\\\\\\\\n tests/test_script_drafts.py | 1 +\\\\\\\\n 6 files changed, 21 insertions(+), 27 deletions(-)\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_34qIbiRc0yVJEh52Tot7OzB9\\\", \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_QLFDGEuWk5Xw0DuSCQo9Fc7U\\\", \\\"call_id\\\": \\\"call_QLFDGEuWk5Xw0DuSCQo9Fc7U\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d200fbcc8191b3e63b392295eb5a\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"set -euo pipefail\\\\\\\\npython3 -m pytest -q\\\\\\\\ngit add autoshorts configs data docs scripts tests\\\\\\\\ngit commit -m \\\\\\\\\\\\\\\"feat: add retention-led no-faith testbatch\\\\\\\\\\\\\\\"\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"............ [100%]\\\\\\\\n12 passed in 0.04s\\\\\\\\n[feat/initial-growth-engine 5799d16] feat: add retention-led no-faith testbatch\\\\\\\\n 21 files changed, 728 insertions(+), 27 deletions(-)\\\\\\\\n create mode 100644 autoshorts/retention.py\\\\\\\\n create mode 100644 configs/niches/attention_design.json\\\\\\\\n create mode 100644 data/drafts/testbatch_002.json\\\\\\\\n create mode 100644 data/reviews/testbatch_002/ai-004-stop-asking-ai-to-think-for-you.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/ai-005-the-4-box-prompt-for-mental-overload.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/ai-006-a-real-ai-assistant-needs-an-approval-gate.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/attention-001-your-phone-wins-before-breakfast.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/attention-002-the-algorithm-does-not-need-you-happy.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/attention-003-the-5-second-rule-for-opening-any-app.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/life-004-you-do-not-need-more-discipline.md\\\\\\\\n create mode 100644 data/reviews/testbatch_002/l...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_d9IogglDnQXyfTzLrG9J30Ph\\\", \\\"call_id\\\": \\\"call_d9IogglDnQXyfTzLrG9J30Ph\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d207a4608191b57e6afea0d90174\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"set -euo pipefail\\\\\\\\ncd /home/agent/jarvis_runtime/AutoShortsBot\\\\\\\\nexport GITHUB_TOKEN=*** -d '\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\\\\\\\nASKPASS=$(mktemp)\\\\\\\\ncat > \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" <<'SH'\\\\\\\\n#!/bin/sh\\\\\\\\ncase \\\\\\\\\\\\\\\"$1\\\\\\\\\\\\\\\" in\\\\\\\\n *Username*) printf '%s\\\\\\\\\\\\\\\\n' x-access-token ;;\\\\\\\\n *Password*) printf '%s\\\\\\\\\\\\\\\\n' \\\\\\\\\\\\\\\"$GITHUB_TOKEN\\\\\\\\\\\\\\\" ;;\\\\\\\\n *) printf '\\\\\\\\\\\\\\\\n' ;;\\\\\\\\nesac\\\\\\\\nSH\\\\\\\\nchmod 700 \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\"\\\\\\\\ntrap 'rm -f \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\"' EXIT\\\\\\\\nGIT_ASKPASS=\\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" GIT_TERMINAL_PROMPT=0 git push >/tmp/autoshorts_push3.log 2>&1 || { sed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push3.log; exit 1; }\\\\\\\\nBRANCH=$(git branch --show-current)\\\\\\\\nLOCAL=$(git rev-parse HEAD)\\\\\\\\nREMOTE=$(GIT_ASKPASS=\\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\\\\\\\\\\\\\"refs/heads/$BRANCH\\\\\\\\\\\\\\\" | awk '{print $1}')\\\\\\\\np...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"branch=feat/initial-growth-engine\\\\\\\\nlocal=5799d16982686932f1e2c78b0524f4c8cad1c1e5\\\\\\\\nremote=5799d16982686932f1e2c78b0524f4c8cad1c1e5\\\\\\\\nTo https://github.com/Gamexgit/AutoShortsBot.git\\\\\\\\n 2100bfd..5799d16 feat/initial-growth-engine -> feat/initial-growth-engine\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_d9IogglDnQXyfTzLrG9J30Ph\\\", \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_krSUIzLnVWJvdHz4h8D9XxUF\\\", \\\"call_id\\\": \\\"call_krSUIzLnVWJvdHz4h8D9XxUF\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d2120eec8191a3de0abf3d72ee1b\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"git status --short --branch && python3 -m pytest -q\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\\\\\\\n............ [100%]\\\\\\\\n12 passed in 0.05s\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_krSUIzLnVWJvdHz4h8D9XxUF\\\", \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_vh4oVxICrxWjVuyMUanEnCOE\\\", \\\"call_id\\\": \\\"call_vh4oVxICrxWjVuyMUanEnCOE\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d216cbe48191acd52b57608b5fdd\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Tests ausführen, committen und pushen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"4\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Recherche-Erkenntnisse zu Retention/Monetarisierung in Projektregeln übersetzen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"FaithTok pausieren und Testbatch v2 ohne religiöse Themen modellieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Retention-Scoring/Validation implementieren und Review-Drafts neu generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 0, \\\\\\\"completed\\\\\\\": 4, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_vh4oVxICrxWjVuyMUanEnCOE\\\", \\\"timestamp\\\": \\\"2026-05-23T00:01:37.103777\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"Erledigt, Sir.\\\\n\\\\nIch habe die religiösen Themen **vorerst sauber pausiert** und den nächsten Entwicklungsschritt umgesetzt: weg von „Themen sammeln“, hin zu **Retention-led Content Engineering**.\\\\n\\\\n## Recherche-Kernaussagen\\\\n\\\\nIch habe offizielle/nahe Quellen geprüft, u.a.:\\\\n\\\\n- TikTok Creator Academy / Creator Rewards\\\\n- TikTok Creator Rewards Terms\\\\n- YouTube Help / YPP / Performance & Discovery FAQs\\\\n\\\\n## Was Menschen eher hängen lässt\\\\n\\\\nDie wichtigen Muster für Shorts sind ziemlich klar:\\\\n\\\\n- **Immediate self-relevance** \\\\n Der Zuschauer muss in Sekunde 0–2 denken: „Das bin ich.“\\\\n\\\\n- **Open loop** \\\\n Nicht: „Hier sind Tipps.“ \\\\n Sondern: „Wenn X passiert, mach Y bevor Z.“\\\\n\\\\n- **Specificity** \\\\n Zahlen, konkrete Situationen, bekannte Tools/Plattformen. \\\\n Beispiel: „47 Tabs im Kopf“ schlägt „mental overload“.\\\\n\\\\n- **Tension / Contrarian Angle** \\\\n „Du brauchst nicht mehr Disziplin. Du brauchst einen kleineren ersten Schritt.“\\\\n\\\\n- **Concrete payoff** \\\\n Template, Prompt, 2-Minuten-System, Vorher/Nachher.\\\\n\\\\n- **Visual pattern interrupt** \\\\n Kein statisches KI-Bild. Bewegung, Freeze-frame, Before/After, Screen Recording, Time...\\n{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"[M G] Ok mach das.\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...4Lxr...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Voiceover-Finalizer implementieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 3, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 0, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_eyNbaY8QuGvIvzfzyMl1yaFv\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_x6VkrZKnIOFUwG4farpXbu5G\\\", \\\"call_id\\\": \\\"call_x6VkrZKnIOFUwG4farpXbu5G\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d2e1434081919fab0905b1049bb9\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"write_file\\\", \\\"arguments\\\": \\\"{\\\\\\\"content\\\\\\\":\\\\\\\"from pathlib import Path\\\\\\\\n\\\\\\\\nfrom autoshorts.growth_plan import load_testbatch\\\\\\\\nfrom autoshorts.voiceover_finalizer import finalize_voiceover, write_final_scripts\\\\\\\\n\\\\\\\\n\\\\\\\\ndef test_finalize_voiceover_keeps_hook_first_and_estimates_duration():\\\\\\\\n batch = load_testbatch(Path(\\\\\\\\\\\\\\\"data/drafts/testbatch_002.json\\\\\\\\\\\\\\\"))\\\\\\\\n idea = next(item for item in batch[\\\\\\\\\\\\\\\"ideas\\\\\\\\\\\\\\\"] if item[\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\"] == \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\")\\\\\\\\n\\\\\\\\n final = finalize_voiceover(idea)\\\\\\\\n\\\\\\\\n assert final[\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\"] == \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\"\\\\\\\\n assert final[\\\\\\\\\\\\\\\"voiceover\\\\\\\\\\\\\\\"].startswith(idea[\\\\\\\\\\\\\\\"hook\\\\\\\\\\\\\\\"])\\\\\\\\n assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] <= idea[\\\\\\\\\\\\\\\"duration_seconds\\\\\\\\\\\\\\\"] + 4\\\\\\\\n assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] >= idea[\\\\\\\\\\\\\\\"duration_seconds\\\\\\\\\\\\\\\"] - 12\\\\\\\\n assert final[\\\\\\\\\\\\\\\"word_count\\\\\\\\\\\\\\\"] > 70\\\\\\\\n assert final[\\\\\\\\\\\\\\\"caption_beats\\\\\\\\\\\\\\\"][0][\\\\\\\\\\\\\\\"start\\\\\\\\\\\\\\\"] == ...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"write_file\\\", \\\"content\\\": \\\"{\\\\\\\"bytes_written\\\\\\\": 1704, \\\\\\\"dirs_created\\\\\\\": true, \\\\\\\"lint\\\\\\\": {\\\\\\\"status\\\\\\\": \\\\\\\"ok\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"\\\\\\\"}, \\\\\\\"lsp_diagnostics\\\\\\\": \\\\\\\"LSP diagnostics introduced by this edit:\\\\\\\\n\\\\\\\\nERROR [4:6] Import \\\\\\\\\\\\\\\"autoshorts.voiceover_finalizer\\\\\\\\\\\\\\\" could not be resolved [reportMissingImports] (Pyright)\\\\\\\\n\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_x6VkrZKnIOFUwG4farpXbu5G\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_w8FoWWINtFK2JBXnLbTvV0Od\\\", \\\"call_id\\\": \\\"call_w8FoWWINtFK2JBXnLbTvV0Od\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d2ee90b481918549ac6bbe50164b\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python3 -m pytest tests/test_voiceover_finalizer.py -q\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"==================================== ERRORS ====================================\\\\\\\\n______________ ERROR collecting tests/test_voiceover_finalizer.py ______________\\\\\\\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_voiceover_finalizer.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_voiceover_finalizer.py:4: in \\\\\\\\n from autoshorts.voiceover_finalizer import finalize_voiceover, write_final_scripts\\\\\\\\nE ModuleNotFoundError: No module named 'autoshorts.voiceover_finalizer'\\\\\\\\n=========================== short test summary info ============================\\\\\\\\nERROR tests/test_voiceover_finalizer.py\\\\\\\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\\\\\\\n1 error in 0.10s\\\\\\\", \\\\\\\"exit_code\\\\\\\": 2, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_w8FoWWINt...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_jfC5PaDDzndpPwV1AgsdyOWv\\\", \\\"call_id\\\": \\\"call_jfC5PaDDzndpPwV1AgsdyOWv\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d2f2bac88191b59820140f026949\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"1\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Voiceover-Finalizer implementieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"2\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"in_progress\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Voiceover-Finalizer implementieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 2, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 1, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_jfC5PaDDzndpPwV1AgsdyOWv\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_gDdsMASZD1UC3xybGGS6WBSM\\\", \\\"call_id\\\": \\\"call_gDdsMASZD1UC3xybGGS6WBSM\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d2f7fa048191b0f84d6200020af2\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"write_file\\\", \\\"arguments\\\": \\\"{\\\\\\\"content\\\\\\\":\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Deterministic voiceover finalizer for curated AutoShortsBot ideas.\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\\nfrom __future__ import annotations\\\\\\\\n\\\\\\\\nimport re\\\\\\\\nfrom pathlib import Path\\\\\\\\nfrom typing import Any\\\\\\\\n\\\\\\\\nfrom autoshorts.script_drafts import _slug\\\\\\\\n\\\\\\\\nTOP3_CUSTOM_VOICEOVERS = {\\\\\\\\n \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Stop asking AI to think for you. Ask it to argue with you. Most people use AI like a vending machine: type a question, get a confident answer, move on. That is the weakest use case. The better move is this: paste your idea and ask, what assumptions am I making? What is the weakest part? What would a smart critic disagree with? What are three alternatives I have not considered? Now AI is not replacing your thinking. It is putting resistance against lazy thinking. You still decide. You still own the consequence. But you arrive sharper, b...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"write_file\\\", \\\"content\\\": \\\"{\\\\\\\"bytes_written\\\\\\\": 6706, \\\\\\\"dirs_created\\\\\\\": true, \\\\\\\"lint\\\\\\\": {\\\\\\\"status\\\\\\\": \\\\\\\"ok\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"\\\\\\\"}}\\\", \\\"tool_call_id\\\": \\\"call_gDdsMASZD1UC3xybGGS6WBSM\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_QMD7hI3OspK2CbmFHrWovzBd\\\", \\\"call_id\\\": \\\"call_QMD7hI3OspK2CbmFHrWovzBd\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d31e81c081919fe0c5cb85ba7145\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"patch\\\", \\\"arguments\\\": \\\"{\\\\\\\"mode\\\\\\\":\\\\\\\"replace\\\\\\\",\\\\\\\"new_string\\\\\\\":\\\\\\\"__all__ = [\\\\\\\\\\\\\\\"growth_plan\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"retention\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"script_drafts\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"voiceover_finalizer\\\\\\\\\\\\\\\"]\\\\\\\",\\\\\\\"old_string\\\\\\\":\\\\\\\"__all__ = [\\\\\\\\\\\\\\\"growth_plan\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"retention\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"script_drafts\\\\\\\\\\\\\\\"]\\\\\\\",\\\\\\\"path\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"patch\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"diff\\\\\\\": \\\\\\\"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\\\\\\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\\\\\\\n@@ -1,3 +1,3 @@\\\\\\\\n \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"AutoShortsBot growth engine package.\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\n \\\\\\\\n-__all__ = [\\\\\\\\\\\\\\\"growth_plan\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"retention\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"script_drafts\\\\\\\\\\\\\\\"]\\\\\\\\n+__all__ = [\\\\\\\\\\\\\\\"growth_plan\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"retention\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"script_drafts\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"voiceover_finalizer\\\\\\\\\\\\\\\"]\\\\\\\\n\\\\\\\", \\\\\\\"files_modified\\\\\\\": [\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/__init__.py\\\\\\\"], \\\\\\\"lint\\\\\\\": {\\\\\\\"status\\\\\\\": \\\\\\\"ok\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"\\\\\\\"}}\\\", \\\"tool_call_id\\\": \\\"call_QMD7hI3OspK2CbmFHrWovzBd\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_eEe4PMTKzfbmd7chThnnfzzr\\\", \\\"call_id\\\": \\\"call_eEe4PMTKzfbmd7chThnnfzzr\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d323da608191805190110ceb5d3d\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python3 -m pytest tests/test_voiceover_finalizer.py -q\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"F.. [100%]\\\\\\\\n=================================== FAILURES ===================================\\\\\\\\n_______ test_finalize_voiceover_keeps_hook_first_and_estimates_duration ________\\\\\\\\n\\\\\\\\n def test_finalize_voiceover_keeps_hook_first_and_estimates_duration():\\\\\\\\n batch = load_testbatch(Path(\\\\\\\\\\\\\\\"data/drafts/testbatch_002.json\\\\\\\\\\\\\\\"))\\\\\\\\n idea = next(item for item in batch[\\\\\\\\\\\\\\\"ideas\\\\\\\\\\\\\\\"] if item[\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\"] == \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\")\\\\\\\\n \\\\\\\\n final = finalize_voiceover(idea)\\\\\\\\n \\\\\\\\n assert final[\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\"] == \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\"\\\\\\\\n assert final[\\\\\\\\\\\\\\\"voiceover\\\\\\\\\\\\\\\"].startswith(idea[\\\\\\\\\\\\\\\"hook\\\\\\\\\\\\\\\"])\\\\\\\\n> assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] <= idea[\\\\\\\\\\\\\\\"duration_seconds\\\\\\\\\\\\\\\"] + 4\\\\\\\\nE assert 48 <= (41 + 4)\\\\\\\\n\\\\\\\\ntests/test_voiceover_finalizer.py:15: AssertionError\\\\\\\\n=========================== short test summary info ============================\\\\\\\\nFAILED tests/test_voiceover_finalizer.py::test_finalize_voiceover_keeps_hook_first_and_estimates_duration\\\\\\\\n1 failed, 2 passed in 0.05s\\\\\\\", \\\\\\\"exit_code\\\\\\\": 1, \\\\\\\"error\\\\\\\": null}\\\\n\\\\n[Tool loo...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...R4V4...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"patch\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"diff\\\\\\\": \\\\\\\"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/voiceover_finalizer.py\\\\\\\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/voiceover_finalizer.py\\\\\\\\n@@ -9,7 +9,7 @@\\\\\\\\n from autoshorts.script_drafts import _slug\\\\\\\\n \\\\\\\\n TOP3_CUSTOM_VOICEOVERS = {\\\\\\\\n- \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Stop asking AI to think for you. Ask it to argue with you. Most people use AI like a vending machine: type a question, get a confident answer, move on. That is the weakest use case. The better move is this: paste your idea and ask, what assumptions am I making? What is the weakest part? What would a smart critic disagree with? What are three alternatives I have not considered? Now AI is not replacing your thinking. It is putting resistance against lazy thinking. You still decide. You still own the consequence. But you arrive sharper, because the first version of your idea had to survive a fight. Comment CRITIC if you want the exact prompt.\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\",\\\\\\\\n+ \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Stop asking AI to think for you. Ask it to argue with you. Most people use AI like a vending machine: type a question, get a conf...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_gvnAPU7DfXLQNU80sukakNin\\\", \\\"call_id\\\": \\\"call_gvnAPU7DfXLQNU80sukakNin\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d333f9d08191839e9ffc1169af4e\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python3 -m pytest tests/test_voiceover_finalizer.py -q\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"... [100%]\\\\\\\\n3 passed in 0.04s\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_gvnAPU7DfXLQNU80sukakNin\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_B6ZpmFkBxgP6BfDYlntOosjz\\\", \\\"call_id\\\": \\\"call_B6ZpmFkBxgP6BfDYlntOosjz\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d33991f48191b93f1225ad598955\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Voiceover-Finalizer implementieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"2\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"3\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"in_progress\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Voiceover-Finalizer implementieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 1, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 2, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_B6ZpmFkBxgP6BfDYlntOosjz\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_KSZtpMwWkuhcs4nH6IMiDiJA\\\", \\\"call_id\\\": \\\"call_KSZtpMwWkuhcs4nH6IMiDiJA\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d33d92048191b295e7545c3143b7\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"write_file\\\", \\\"arguments\\\": \\\"{\\\\\\\"content\\\\\\\":\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Generate final voiceover scripts for selected AutoShortsBot ideas.\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\n\\\\\\\\nfrom __future__ import annotations\\\\\\\\n\\\\\\\\nimport argparse\\\\\\\\nimport sys\\\\\\\\nfrom pathlib import Path\\\\\\\\n\\\\\\\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\\\\\\\nif str(PROJECT_ROOT) not in sys.path:\\\\\\\\n sys.path.insert(0, str(PROJECT_ROOT))\\\\\\\\n\\\\\\\\nfrom autoshorts.growth_plan import load_testbatch\\\\\\\\nfrom autoshorts.voiceover_finalizer import write_final_scripts\\\\\\\\n\\\\\\\\n\\\\\\\\nDEFAULT_SELECTED_IDS = [\\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"ai-005\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"attention-002\\\\\\\\\\\\\\\"]\\\\\\\\n\\\\\\\\n\\\\\\\\ndef main() -> None:\\\\\\\\n parser = argparse.ArgumentParser(description=\\\\\\\\\\\\\\\"Generate final voiceover scripts.\\\\\\\\\\\\\\\")\\\\\\\\n parser.add_argument(\\\\\\\\\\\\\\\"--batch\\\\\\\\\\\\\\\", default=\\\\\\\\\\\\\\\"data/drafts/testbatch_002.json\\\\\\\\\\\\\\\")\\\\\\\\n parser.add_argument(\\\\\\\\\\\\\\\"--out\\\\\\\\\\\\\\\", default=\\\\\\\\\\\\\\\"data/final_scripts/testbatch_002_top3\\\\\\\\\\\\\\\")\\\\\\\\n ...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"write_file\\\", \\\"content\\\": \\\"{\\\\\\\"bytes_written\\\\\\\": 1115, \\\\\\\"dirs_created\\\\\\\": true, \\\\\\\"lint\\\\\\\": {\\\\\\\"status\\\\\\\": \\\\\\\"ok\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"\\\\\\\"}}\\\", \\\"tool_call_id\\\": \\\"call_KSZtpMwWkuhcs4nH6IMiDiJA\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_MIV5gAcfQuiZ1PK5iP1ZKHb6\\\", \\\"call_id\\\": \\\"call_MIV5gAcfQuiZ1PK5iP1ZKHb6\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d34c347c8191be0d8bd0a80a90d6\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python3 scripts/generate_final_scripts.py --batch data/drafts/testbatch_002.json --out data/final_scripts/testbatch_002_top3\\\\\\\\npython3 - <<'PY'\\\\\\\\nfrom pathlib import Path\\\\\\\\nfiles=sorted(Path('data/final_scripts/testbatch_002_top3').glob('*.md'))\\\\\\\\nprint('final_files', len(files))\\\\\\\\nfor p in files:\\\\\\\\n print('\\\\\\\\\\\\\\\\n---', p.name, '---')\\\\\\\\n text=p.read_text(encoding='utf-8')\\\\\\\\n print('\\\\\\\\\\\\\\\\n'.join(text.splitlines()[:18]))\\\\\\\\nPY\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"Generated 3 final scripts in data/final_scripts/testbatch_002_top3\\\\\\\\ndata/final_scripts/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.md\\\\\\\\ndata/final_scripts/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.md\\\\\\\\ndata/final_scripts/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.md\\\\\\\\nfinal_files 3\\\\\\\\n\\\\\\\\n--- ai-004-stop-asking-ai-to-think-for-you.md ---\\\\\\\\n# Stop asking AI to think for you\\\\\\\\n\\\\\\\\n- ID: `ai-004`\\\\\\\\n- Pillar: `ai_life_systems`\\\\\\\\n- Target duration: 41s\\\\\\\\n- Estimated voiceover duration: 43s\\\\\\\\n- Word count: 104\\\\\\\\n- Platform note: Growth Short\\\\\\\\n\\\\\\\\n## Voiceover\\\\\\\\n\\\\\\\\nStop asking AI to think for you. Ask it to argue with you. Most people use AI like a vending machine: type a question, get a confident answer, move on. That is the weakest use case. Try this instead: paste your idea and ask, what assumptions am I making? What is the weakest part? What would a smart critic disagree with? Now AI is not replacing your thinking. It is putting resistance against lazy thinking. You still decide. You still own the consequence. But you arrive sharper, because the first version ha...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Adjusting criteria for AI tests**\\\\n\\\\nI need to consider the issue with the AI005 candidate. It’s estimated that a score of 58 is below the 60 threshold. The tests currently allow scores down to 55, but for production, I might want to aim for over 60. I should add a sentence emphasizing that the target is now 61, and make sure to include a few additional words to clarify that the tests may still be acceptable.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Adjusting criteria for AI tests**\\\\n\\\\nI need to consider the issue with the AI005 candidate. It’s estimated that a score of 58 is below the 60 threshold. The tests currently allow scores down to 55, but for production, I might want to aim for over 60. I should add a sentence emphasizing that the target is now 61, and make sure to include a few additional words to clarify that the tests may still be acceptable.\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...2Jqu...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"patch\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"diff\\\\\\\": \\\\\\\"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/voiceover_finalizer.py\\\\\\\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/voiceover_finalizer.py\\\\\\\\n@@ -10,7 +10,7 @@\\\\\\\\n \\\\\\\\n TOP3_CUSTOM_VOICEOVERS = {\\\\\\\\n \\\\\\\\\\\\\\\"ai-004\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"Stop asking AI to think for you. Ask it to argue with you. Most people use AI like a vending machine: type a question, get a confident answer, move on. That is the weakest use case. Try this instead: paste your idea and ask, what assumptions am I making? What is the weakest part? What would a smart critic disagree with? Now AI is not replacing your thinking. It is putting resistance against lazy thinking. You still decide. You still own the consequence. But you arrive sharper, because the first version had to survive a fight. Comment CRITIC if you want the exact prompt.\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\",\\\\\\\\n- \\\\\\\\\\\\\\\"ai-005\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\"If your brain has 47 tabs open, paste the mess into this four-box prompt. Do not ask AI to motivate you. Ask it to sort the noise. First, dump everything: tasks, worries, random reminders, half-decisions, all of it. Then ask AI: split this into...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_vJ39v6GpqqVArpzJmT9XnIQs\\\", \\\"call_id\\\": \\\"call_vJ39v6GpqqVArpzJmT9XnIQs\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d361d85481919ea1f00aef2802ca\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"patch\\\", \\\"arguments\\\": \\\"{\\\\\\\"mode\\\\\\\":\\\\\\\"replace\\\\\\\",\\\\\\\"new_string\\\\\\\":\\\\\\\" assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] >= 60\\\\\\\",\\\\\\\"old_string\\\\\\\":\\\\\\\" assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] >= 55\\\\\\\",\\\\\\\"path\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_voiceover_finalizer.py\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"patch\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"diff\\\\\\\": \\\\\\\"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_voiceover_finalizer.py\\\\\\\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_voiceover_finalizer.py\\\\\\\\n@@ -27,7 +27,7 @@\\\\\\\\n \\\\\\\\n assert final[\\\\\\\\\\\\\\\"platform_note\\\\\\\\\\\\\\\"] == \\\\\\\\\\\\\\\"TikTok Rewards Candidate\\\\\\\\\\\\\\\"\\\\\\\\n assert final[\\\\\\\\\\\\\\\"word_count\\\\\\\\\\\\\\\"] >= 120\\\\\\\\n- assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] >= 55\\\\\\\\n+ assert final[\\\\\\\\\\\\\\\"estimated_seconds\\\\\\\\\\\\\\\"] >= 60\\\\\\\\n assert \\\\\\\\\\\\\\\"comment\\\\\\\\\\\\\\\" in final[\\\\\\\\\\\\\\\"voiceover\\\\\\\\\\\\\\\"].lower()\\\\\\\\n \\\\\\\\n \\\\\\\\n\\\\\\\", \\\\\\\"files_modified\\\\\\\": [\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_voiceover_finalizer.py\\\\\\\"], \\\\\\\"lint\\\\\\\": {\\\\\\\"status\\\\\\\": \\\\\\\"ok\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"\\\\\\\"}}\\\", \\\"tool_call_id\\\": \\\"call_vJ39v6GpqqVArpzJmT9XnIQs\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_BUdQV1PB9Dt6XeE3NbAGyzw2\\\", \\\"call_id\\\": \\\"call_BUdQV1PB9Dt6XeE3NbAGyzw2\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d36930f88191b76bf5425c5dc3cc\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"rm -rf data/final_scripts/testbatch_002_top3\\\\\\\\npython3 scripts/generate_final_scripts.py --batch data/drafts/testbatch_002.json --out data/final_scripts/testbatch_002_top3\\\\\\\\npython3 -m pytest tests/test_voiceover_finalizer.py -q\\\\\\\\npython3 - <<'PY'\\\\\\\\nfrom pathlib import Path\\\\\\\\nfor p in sorted(Path('data/final_scripts/testbatch_002_top3').glob('*.md')):\\\\\\\\n for line in p.read_text(encoding='utf-8').splitlines():\\\\\\\\n if line.startswith('- Estimated') or line.startswith('- Word') or line.startswith('- ID'):\\\\\\\\n print(p.name, line)\\\\\\\\nPY\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"Generated 3 final scripts in data/final_scripts/testbatch_002_top3\\\\\\\\ndata/final_scripts/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.md\\\\\\\\ndata/final_scripts/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.md\\\\\\\\ndata/final_scripts/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.md\\\\\\\\n... [100%]\\\\\\\\n3 passed in 0.04s\\\\\\\\nai-004-stop-asking-ai-to-think-for-you.md - ID: `ai-004`\\\\\\\\nai-004-stop-asking-ai-to-think-for-you.md - Estimated voiceover duration: 43s\\\\\\\\nai-004-stop-asking-ai-to-think-for-you.md - Word count: 104\\\\\\\\nai-005-the-4-box-prompt-for-mental-overload.md - ID: `ai-005`\\\\\\\\nai-005-the-4-box-prompt-for-mental-overload.md - Estimated voiceover duration: 61s\\\\\\\\nai-005-the-4-box-prompt-for-mental-overload.md - Word count: 148\\\\\\\\nattention-002-the-algorithm-does-not-need-you-happy.md - ID: `attention-002`\\\\\\\\nattention-002-the-algorithm-does-not-need-you-happy.md - Estimated voiceover duration: 61s\\\\\\\\nattention-002-the-algorithm-does-not-need-you-happy.md - Word count: 148\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\",...\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_FXPyxIzMczJmAqVTk3CNL94o\\\", \\\"call_id\\\": \\\"call_FXPyxIzMczJmAqVTk3CNL94o\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d36e8a0c81919a578f17ed459288\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"3\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Tests ausführen, committen und pushen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"4\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"in_progress\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Voiceover-Finalizer implementieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 3, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_FXPyxIzMczJmAqVTk3CNL94o\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_MxpkFlBzaArQ0A3g1iz6hmsE\\\", \\\"call_id\\\": \\\"call_MxpkFlBzaArQ0A3g1iz6hmsE\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d375078081919692b73e9e58c52c\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python3 -m pytest -q\\\\\\\\ngit status --short\\\\\\\\ngit diff --stat\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"............... [100%]\\\\\\\\n15 passed in 0.05s\\\\\\\\n M autoshorts/__init__.py\\\\\\\\n?? autoshorts/voiceover_finalizer.py\\\\\\\\n?? data/final_scripts/\\\\\\\\n?? scripts/generate_final_scripts.py\\\\\\\\n?? tests/test_voiceover_finalizer.py\\\\\\\\n autoshorts/__init__.py | 2 +-\\\\\\\\n 1 file changed, 1 insertion(+), 1 deletion(-)\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_MxpkFlBzaArQ0A3g1iz6hmsE\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_EmnbleEFwbHn9InDWBzAwHwB\\\", \\\"call_id\\\": \\\"call_EmnbleEFwbHn9InDWBzAwHwB\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d37a73d881918f9882d5ddbe3ea5\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"set -euo pipefail\\\\\\\\ngit add autoshorts data/final_scripts scripts/generate_final_scripts.py tests/test_voiceover_finalizer.py\\\\\\\\ngit commit -m \\\\\\\\\\\\\\\"feat: add voiceover finalizer for top retention scripts\\\\\\\\\\\\\\\"\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"[feat/initial-growth-engine f73ddef] feat: add voiceover finalizer for top retention scripts\\\\\\\\n 7 files changed, 371 insertions(+), 1 deletion(-)\\\\\\\\n create mode 100644 autoshorts/voiceover_finalizer.py\\\\\\\\n create mode 100644 data/final_scripts/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.md\\\\\\\\n create mode 100644 data/final_scripts/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.md\\\\\\\\n create mode 100644 data/final_scripts/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.md\\\\\\\\n create mode 100644 scripts/generate_final_scripts.py\\\\\\\\n create mode 100644 tests/test_voiceover_finalizer.py\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_EmnbleEFwbHn9InDWBzAwHwB\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_PVS8069zH9wYIrL3TfZEBNUx\\\", \\\"call_id\\\": \\\"call_PVS8069zH9wYIrL3TfZEBNUx\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d37ea4a08191961875b311dbeb4a\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"set -euo pipefail\\\\\\\\ncd /home/agent/jarvis_runtime/AutoShortsBot\\\\\\\\nexport GITHUB_TOKEN=*** -d '\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\r' < /home/agent/.hermes/secrets/github_autoshortsbot_token)\\\\\\\\nASKPASS=$(mktemp)\\\\\\\\ncat > \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" <<'SH'\\\\\\\\n#!/bin/sh\\\\\\\\ncase \\\\\\\\\\\\\\\"$1\\\\\\\\\\\\\\\" in\\\\\\\\n *Username*) printf '%s\\\\\\\\\\\\\\\\n' x-access-token ;;\\\\\\\\n *Password*) printf '%s\\\\\\\\\\\\\\\\n' \\\\\\\\\\\\\\\"$GITHUB_TOKEN\\\\\\\\\\\\\\\" ;;\\\\\\\\n *) printf '\\\\\\\\\\\\\\\\n' ;;\\\\\\\\nesac\\\\\\\\nSH\\\\\\\\nchmod 700 \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\"\\\\\\\\ntrap 'rm -f \\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\"' EXIT\\\\\\\\nGIT_ASKPASS=\\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" GIT_TERMINAL_PROMPT=0 git push >/tmp/autoshorts_push4.log 2>&1 || { sed -E 's/(github_pat_|ghp_)[A-Za-z0-9_]+/[REDACTED]/g' /tmp/autoshorts_push4.log; exit 1; }\\\\\\\\nBRANCH=$(git branch --show-current)\\\\\\\\nLOCAL=$(git rev-parse HEAD)\\\\\\\\nREMOTE=$(GIT_ASKPASS=\\\\\\\\\\\\\\\"$ASKPASS\\\\\\\\\\\\\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\\\\\\\\\\\\\"refs/heads/$BRANCH\\\\\\\\\\\\\\\" | awk '{print $1}')\\\\\\\\np...\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"branch=feat/initial-growth-engine\\\\\\\\nlocal=f73ddef6f49aff7472fafed67558db01baf2706c\\\\\\\\nremote=f73ddef6f49aff7472fafed67558db01baf2706c\\\\\\\\nTo https://github.com/Gamexgit/AutoShortsBot.git\\\\\\\\n 5799d16..f73ddef feat/initial-growth-engine -> feat/initial-growth-engine\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_PVS8069zH9wYIrL3TfZEBNUx\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_tJlqGOMfqL7AbWetxDAMIEjY\\\", \\\"call_id\\\": \\\"call_tJlqGOMfqL7AbWetxDAMIEjY\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d38acae88191b3fee2b135d79e84\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"git status --short --branch && python3 -m pytest -q\\\\\\\",\\\\\\\"timeout\\\\\\\":60,\\\\\\\"workdir\\\\\\\":\\\\\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"## feat/initial-growth-engine...origin/feat/initial-growth-engine\\\\\\\\n............... [100%]\\\\\\\\n15 passed in 0.05s\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_tJlqGOMfqL7AbWetxDAMIEjY\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_XjjHVsVDSGi8fWbUksnNNE07\\\", \\\"call_id\\\": \\\"call_XjjHVsVDSGi8fWbUksnNNE07\\\", \\\"response_item_id\\\": \\\"fc_049e6b492e9f3d48016a10d38f50148191863ca821865c5a0a\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Tests ausführen, committen und pushen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"4\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests für Voiceover-Finalizer schreiben\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Voiceover-Finalizer implementieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"3\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Finale Skripte für Top-3 generieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"4\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Tests ausführen, committen und pushen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 0, \\\\\\\"completed\\\\\\\": 4, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_XjjHVsVDSGi8fWbUksnNNE07\\\", \\\"timestamp\\\": \\\"2026-05-23T00:07:38.450685\\\"}\\n{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"Erledigt, Sir.\\\\n\\\\n## Implementiert\\\\n\\\\nIch habe den **Voiceover-Finalizer v1** gebaut und für die Top-3-Kandidaten finale Voiceover-Skripte generiert.\\\\n\\\\nGepusht auf:\\\\n\\\\n```text\\\\nfeat/initial-growth-engine\\\\n```\\\\n\\\\nNeuer Commit:\\\\n\\\\n```text\\\\nf73ddef feat: add voiceover finalizer for top retention scripts\\\\n```\\\\n\\\\nRemote-Verifikation:\\\\n\\\\n```text\\\\nlocal == remote\\\\n```\\\\n\\\\nTests:\\\\n\\\\n```text\\\\n15 passed in 0.05s\\\\n```\\\\n\\\\nPR-Link bleibt:\\\\n\\\\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\\\\n\\\\n---\\\\n\\\\n## Neue Dateien\\\\n\\\\n### Code\\\\n\\\\n```text\\\\nautoshorts/voiceover_finalizer.py\\\\nscripts/generate_final_scripts.py\\\\ntests/test_voiceover_finalizer.py\\\\n```\\\\n\\\\n### Finale Top-3-Skripte\\\\n\\\\n```text\\\\ndata/final_scripts/testbatch_002_top3/\\\\n ai-004-stop-asking-ai-to-think-for-you.md\\\\n ai-005-the-4-box-prompt-for-mental-overload.md\\\\n attention-002-the-algorithm-does-not-need-you-happy.md\\\\n```\\\\n\\\\n---\\\\n\\\\n## Finalisierte Top-3\\\\n\\\\n### 1. `ai-004` — Stop asking AI to think for you\\\\n\\\\n- **Ziel:** Growth Short\\\\n- **Target:** 41s\\\\n- **Estimated Voiceover:** 43s\\\\n- **Word Count:** 104\\\\n\\\\n**Voiceover:**\\\\n\\\\n> Stop asking AI to think for you. Ask it to argue ...\", \"exit_code\": 0, \"error\": null}",
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"content": "{\"output\": \"mp everything: tasks, worries, random reminders, half-decisions, all of it. Then ask AI: split this into four boxes. Box one: tasks I can actually do. Box two: worries I cannot solve right now. Box three: decisions I need to make. Box four: later, not today. Then ask one final question: what is the next action that takes less than two minutes? That is the whole trick. You are not looking for a perfect life plan. You are looking for one clean handle on the chaos. When the output appears, do only the two-minute action first. If it still feels too big, shrink it again. Momentum beats clarity. Comment RESET if you want me to turn this into a copy-paste template.\\n\\n---\\n\\n### 3. `attention-002` — The algorithm does not need you happy\\n\\n- **Ziel:** TikTok Rewards Candidate\\n- **Target:** 67s\\n- **Estimated Voiceover:** 61s\\n- **Word Count:** 148\\n\\n**Voiceover:**\\n\\n> The algorithm does not need you happy. It needs you to stay. That does not mean every app is evil. It means the incentives are not the same as yours. You want focus, sleep, actual progress, maybe a quiet brain for once. The feed wants one more swipe. So stop fighting a machine with vibes. Change the design. First, turn your phone to greyscale when you need deep work. Second, remove your worst app from the home screen. Do not delete it if that feels dramatic. Just make it less automatic. Third, before you open a feed, set a leaving cue: one search, three videos, or five minutes. If there is no exit rule, the app owns the exit. This is not about becoming a monk. It is about making your phone slightly less talented at stealing your day. Which app gets moved off your home screen today?\\n\\n---\\n\\n## Was der Finalizer jetzt erzeugt\\n\\nFür jedes finale Skript:\\n\\n- finales Voiceover\\n- Ziel-/Schätzlänge\\n- Word Count\\n- Caption Beats mit Timing\\n- Visual Concept\\n- Caption\\n- CTA\\n- Production Notes\\n\\nBeispiel Production Notes:\\n\\n- First frame must show the hook as large kinetic caption text.\\n- Use motion or a visual state change in the first 1–2 seconds.\\n- Avoid static AI slideshow visuals.\\n- For AI clips: show human decision/approval moment.\\n- For Attention Design: avoid moral panic; frame as defaults/design.\\n\\n---\\n\\n## Nächster Entwicklungsschritt\\n\\nJetzt ist der Text stark genug für den ersten Rendering-Schritt.\\n\\nIch würde als Nächstes bauen:\\n\\n## Render Plan v1\\n\\nNoch nicht direkt voll rendern, sondern zuerst pro Video einen **Shot Plan** generieren:\\n\\nFür jedes Script:\\n\\n- Szene 1: First-frame hook\\n- Szene 2: Pattern interrupt\\n- Szene 3: Core explanation\\n- Szene 4: Screen/card visual\\n- Szene 5: CTA\\n- Caption-Beats\\n- Asset-Typ:\\n - Screen recording\\n - generated background\\n - card animation\\n - stock/B-roll\\n - kinetic text\\n\\nDanach erst Renderer anpassen.\\n\\nWarum? Weil die bisherigen Videos vor allem am **generischen KI-Bild/Slideshow-Look** leiden. Wenn wir jetzt blind rendern, macht der Bot wieder hübschen, aber scrollbaren Tapetenwechsel. Das wäre ästhetisch sauber und strategisch dumm — also exakt das, was wir vermeiden.\", \"exit_code\": 0, \"error\": null}",
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"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\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**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention, platform fit, and analytics feedback.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n\\n6. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n7. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/monetization-and-approval-notes.md\", \"references/retention-led-shorts.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}",
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"content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}",
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It needs you to stay. That does not mean every app is evil. It means the incentives are not the same as yours. You want focus, sleep, actual progress, maybe a quiet brain for once. The feed wants one more swipe. So stop fighting a machine with vibes. Change the design. First, turn your phone to greyscale when you need deep work. Second, remove your worst app from the home screen. Do not delete it if that feels dramatic. Just make it less automatic. Third, before you open a feed, set a leaving cue: one search, three videos, or five minutes. If there is no exit rule, the app owns the exit. This is not about becoming a monk. It is about making your phone slightly less talented at stealing your day. Which app gets moved off your home screen today?\\\"\\\"\\\",\\n 15|}\\n 16|\\n 17|\\n 18|def _word_count(text: str) -> int:\\n 19| return len(re.findall(r\\\"\\\\b[\\\\w'-]+\\\\b\\\", text))\\n 20|\\n 21|\\n 22|def _estimate_seconds(word_count: int, words_per_minute: int = 145) -> int:\\n 23| return round(word_count / words_per_minute * 60)\\n 24|\\n 25|\\n 26|def _generic_voiceover(idea: dict[str, Any]) -> str:\\n 27| \\\"\\\"\\\"Fallback finalizer for non-custom ideas.\\\"\\\"\\\"\\n 28| return (\\n 29| f\\\"{idea['hook']} \\\"\\n 30| f\\\"Here is the useful part. {idea['script_outline']} \\\"\\n 31| f\\\"Do not turn this into another idea you save and forget. Try the smallest version today. \\\"\\n 32| f\\\"{idea['cta']}\\\"\\n 33| )\\n 34|\\n 35|\\n 36|def _caption_beats(voiceover: str, duration: int) -> list[dict[str, Any]]:\\n 37| sentences = [part.strip() for part in re.split(r\\\"(?<=[.!?])\\\\s+\\\", voiceover) if part.strip()]\\n 38| if not sentences:\\n 39| return []\\n 40| step = max(2, round(duration / len(sentences)))\\n 41| beats: list[dict[str, Any]] = []\\n 42| current = 0\\n 43| for sentence in sentences:\\n 44| beats.append({\\\"start\\\": current, \\\"text\\\": sentence})\\n 45| current += step\\n 46| return beats\\n 47|\\n 48|\\n 49|def _production_notes(idea: dict[str, Any], estimated_seconds: int) -> list[str]:\\n 50| notes = [\\n 51| \\\"First frame must show the hook as large kinetic caption text.\\\",\\n 52| \\\"Use motion or a visual state change in the first 1-2 seconds.\\\",\\n 53| \\\"Avoid static AI slideshow visuals; use screen recordings, cards, timer, or before/after cuts.\\\",\\n 54| ]\\n 55| if estimated_seconds >= 60:\\n 56| notes.append(\\\"Keep this version above 60 seconds for TikTok Creator Rewards testing.\\\")\\n 57| if idea[\\\"pillar\\\"] == \\\"ai_life_systems\\\":\\n 58| notes.append(\\\"Show the human approval/decision moment; do not imply AI has final authority.\\\")\\n 59| if idea[\\\"pillar\\\"] == \\\"attention_design\\\":\\n 60| notes.append(\\\"Avoid moral panic; frame as attention design and defaults.\\\")\\n 61| return notes\\n 62|\\n 63|\\n 64|def finalize_voiceover(idea: dict[str, Any]) -> dict[str, Any]:\\n 65| \\\"\\\"\\\"Create a final spoken script and production metadata for one idea.\\\"\\\"\\\"\\n 66| voiceover = TOP3_CUSTOM_VOICEOVERS.get(idea[\\\"id\\\"], _generic_voiceover(idea)).strip()\\n 67| word_count = _word_count(voiceover)\\n 68| estimated_seconds = _estimate_seconds(word_count)\\n 69| platform_note = \\\"TikTok Rewards Candidate\\\" if int(idea[\\\"duration_seconds\\\"]) >= 60 else \\\"Growth Short\\\"\\n 70|\\n 71| return {\\n 72| \\\"id\\\": idea[\\\"id\\\"],\\n 73| \\\"pillar\\\": idea[\\\"pillar\\\"],\\n 74| \\\"title\\\": idea[\\\"title\\\"],\\n 75| \\\"target_seconds\\\": int(idea[\\\"duration_seconds\\\"]),\\n 76| \\\"estimated_seconds\\\": estimated_seconds,\\n 77| \\\"platform_note\\\": platform_note,\\n 78| \\\"word_count\\\": word_count,\\n 79| \\\"voiceover\\\": voiceover,\\n 80| \\\"caption_beats\\\": _caption_beats(voiceover, estimated_seconds),\\n 81| \\\"visual_concept\\\": idea[\\\"visual_concept\\\"],\\n 82| \\\"caption\\\": idea[\\\"caption\\\"],\\n 83| \\\"cta\\\": idea[\\\"cta\\\"],\\n 84| \\\"production_notes\\\": _production_notes(idea, estimated_seconds),\\n 85| }\\n 86|\\n 87|\\n 88|def format_final_script(final: dict[str, Any]) -> str:\\n 89| \\\"\\\"\\\"Format final voiceover package as Markdown.\\\"\\\"\\\"\\n 90| beats = \\\"\\\\n\\\".join(f\\\"- {beat['start']:02d}s: {beat['text']}\\\" for beat in final[\\\"caption_beats\\\"])\\n 91| notes = \\\"\\\\n\\\".join(f\\\"- {note}\\\" for note in final[\\\"production_notes\\\"])\\n 92| return f\\\"\\\"\\\"# {final['title']}\\n 93|\\n 94|- ID: `{final['id']}`\\n 95|- Pillar: `{final['pillar']}`\\n 96|- Target duration: {final['target_seconds']}s\\n 97|- Estimated voiceover duration: {final['estimated_seconds']}s\\n 98|- Word count: {final['word_count']}\\n 99|- Platform note: {final['platform_note']}\\n 100|\\n 101|## Voiceover\\n 102|\\n 103|{final['voiceover']}\\n 104|\\n 105|## Caption beats\\n 106|\\n 107|{beats}\\n 108|\\n 109|## Visual concept\\n 110|\\n 111|{final['visual_concept']}\\n 112|\\n 113|## Caption\\n 114|\\n 115|{final['caption']}\\n 116|\\n 117|## CTA\\n 118|\\n 119|{final['cta']}\\n 120|\\n 121|## Production notes\\n 122|\\n 123|{notes}\\n 124|\\\"\\\"\\\"\\n 125|\\n 126|\\n 127|def write_final_scripts(\\n 128| batch: dict[str, Any], output_dir: Path, selected_ids: list[str] | None = None\\n 129|) -> list[Path]:\\n 130| \\\"\\\"\\\"Write final voiceover scripts for selected ideas.\\\"\\\"\\\"\\n 131| selected = set(selected_ids or [])\\n 132| output_dir.mkdir(parents=True, exist_ok=True)\\n 133| paths: list[Path] = []\\n 134| for idea in batch.get(\\\"ideas\\\", []):\\n 135| if selected and idea[\\\"id\\\"] not in selected:\\n 136| continue\\n 137| final = finalize_voiceover(idea)\\n 138| path = output_dir / f\\\"{final['id']}-{_slug(final['title'])}.md\\\"\\n 139| path.write_text(format_final_script(final), encoding=\\\"utf-8\\\")\\n 140| paths.append(path)\\n 141| return paths\\n 142|\", \"total_lines\": 141, \"file_size\": 6679, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|from pathlib import Path\\n 2|\\n 3|from autoshorts.growth_plan import load_testbatch\\n 4|from autoshorts.voiceover_finalizer import finalize_voiceover, write_final_scripts\\n 5|\\n 6|\\n 7|def test_finalize_voiceover_keeps_hook_first_and_estimates_duration():\\n 8| batch = load_testbatch(Path(\\\"data/drafts/testbatch_002.json\\\"))\\n 9| idea = next(item for item in batch[\\\"ideas\\\"] if item[\\\"id\\\"] == \\\"ai-004\\\")\\n 10|\\n 11| final = finalize_voiceover(idea)\\n 12|\\n 13| assert final[\\\"id\\\"] == \\\"ai-004\\\"\\n 14| assert final[\\\"voiceover\\\"].startswith(idea[\\\"hook\\\"])\\n 15| assert final[\\\"estimated_seconds\\\"] <= idea[\\\"duration_seconds\\\"] + 4\\n 16| assert final[\\\"estimated_seconds\\\"] >= idea[\\\"duration_seconds\\\"] - 12\\n 17| assert final[\\\"word_count\\\"] > 70\\n 18| assert final[\\\"caption_beats\\\"][0][\\\"start\\\"] == 0\\n 19| assert final[\\\"production_notes\\\"]\\n 20|\\n 21|\\n 22|def test_finalize_voiceover_uses_longer_script_for_rewards_candidate():\\n 23| batch = load_testbatch(Path(\\\"data/drafts/testbatch_002.json\\\"))\\n 24| idea = next(item for item in batch[\\\"ideas\\\"] if item[\\\"id\\\"] == \\\"ai-005\\\")\\n 25|\\n 26| final = finalize_voiceover(idea)\\n 27|\\n 28| assert final[\\\"platform_note\\\"] == \\\"TikTok Rewards Candidate\\\"\\n 29| assert final[\\\"word_count\\\"] >= 120\\n 30| assert final[\\\"estimated_seconds\\\"] >= 60\\n 31| assert \\\"comment\\\" in final[\\\"voiceover\\\"].lower()\\n 32|\\n 33|\\n 34|def test_write_final_scripts_only_writes_selected_ids(tmp_path):\\n 35| batch = load_testbatch(Path(\\\"data/drafts/testbatch_002.json\\\"))\\n 36|\\n 37| paths = write_final_scripts(batch, tmp_path, selected_ids=[\\\"ai-004\\\", \\\"ai-005\\\", \\\"attention-002\\\"])\\n 38|\\n 39| assert len(paths) == 3\\n 40| names = {path.name for path in paths}\\n 41| assert any(name.startswith(\\\"ai-004\\\") for name in names)\\n 42| assert all(path.suffix == \\\".md\\\" for path in paths)\\n 43| assert \\\"Voiceover\\\" in paths[0].read_text(encoding=\\\"utf-8\\\")\\n 44|\", \"total_lines\": 43, \"file_size\": 1704, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_shot_planner.py __________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_shot_planner.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_shot_planner.py:4: in \\n from autoshorts.shot_planner import build_shot_plan, write_shot_plans\\nE ModuleNotFoundError: No module named 'autoshorts.shot_planner'\\n=========================== short test summary info ============================\\nERROR tests/test_shot_planner.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.10s\", \"exit_code\": 2, \"error\": null}",
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immediate zoom-in on the contradiction.\\\",\\n \\\"Fast cut to a real prompt being rewritten from passive question to critic prompt.\\\",\\n \\\"Use simple cards for the key questions; one card changes at a time, no dense paragraphs.\\\",\\n \\\"Screen recording of the final copy-paste prompt with a visible human approval/decision moment.\\\",\\n \\\"End card with the comment keyword and one clear action; keep the CTA legible for one beat.\\\",\\n ]\\n if final[\\\"pillar\\\"] == \\\"attention_design\\\":\\n return [\\n \\\"Large hook text over a phone/feed freeze-frame; make the first frame readable without audio.\\\",\\n \\\"Relatable stock/B-roll: thumb hovering over an endless feed, then hard cut to greyscale.\\\",\\n \\\"Card animation comparing your incentive vs. the feed incentive; two columns, very little text.\\\",\\n \\\"Screen-style visual showing home screen cleanup, app moved away, timer/exit rule set.\\\",\\n \\\"End on the phone home screen question; highlight the app moved off the home screen today.\\\",\\n ]\\n return [\\n \\\"Large kinetic hook text; visible movement in the first second.\\\",\\n \\\"Pattern interrupt with a fast before/after visual state change.\\\",\\n \\\"Use cards to explain the core idea one beat at a time.\\\",\\n \\\"Support the payoff with B-roll or a concrete screen/card visual.\\\",\\n \\\"End with the CTA as readable kinetic text.\\\",\\n ]\\n\\n\\ndef _retention_goals() -> list[str]:\\n return [\\n \\\"Stop the scroll with self-relevance and tension in the first two seconds.\\\",\\n \\\"Reset attention before the viewer predicts a generic slideshow.\\\",\\n \\\"Deliver the main mental model with low cognitive load.\\\",\\n \\\"Show the concrete payoff so the advice feels usable, not ornamental.\\\",\\n \\\"Convert attention into a comment/save action without adding a second idea.\\\",\\n ]\\n\\n\\ndef build_shot_plan(final: dict[str, Any]) -> dict[str, Any]:\\n \\\"\\\"\\\"Build a five-scene, retention-led shot plan from a finalized voiceover package.\\\"\\\"\\\"\\n windows = _scene_windows(int(final[\\\"estimated_seconds\\\"]))\\n asset_types = _asset_sequence(final)\\n directions = _visual_directions(final)\\n goals = _retention_goals()\\n beats = final.get(\\\"caption_beats\\\", [])\\n hook_text = final[\\\"voiceover\\\"].split(\\\".\\\", 1)[0].strip()\\n\\n scenes: list[dict[str, Any]] = []\\n for index, purpose in enumerate(SCENE_PURPOSES):\\n start, end = windows[index]\\n beat_text = beats[min(index, len(beats) - 1)][\\\"text\\\"] if beats else final[\\\"voiceover\\\"]\\n on_screen = hook_text if index == 0 else _split_text(beat_text)\\n scenes.append(\\n {\\n \\\"scene\\\": index + 1,\\n \\\"purpose\\\": purpose,\\n \\\"start\\\": start,\\n \\\"end\\\": end,\\n \\\"asset_type\\\": asset_types[index],\\n \\\"on_screen_text\\\": on_screen,\\n \\\"visual_direction\\\": directions[index],\\n \\\"retention_goal\\\": goals[index],\\n }\\n )\\n\\n return {\\n \\\"id\\\": final[\\\"id\\\"],\\n \\\"title\\\": final[\\\"title\\\"],\\n \\\"pillar\\\": final[\\\"pillar\\\"],\\n \\\"estimated_seconds\\\": final[\\\"estimated_seconds\\\"],\\n \\\"caption_beats\\\": final.get(\\\"caption_beats\\\", []),\\n \\\"scenes\\\": scenes,\\n \\\"production_notes\\\": final.get(\\\"production_notes\\\", []),\\n }\\n\\n\\ndef format_shot_plan(plan: dict[str, Any]) -> str:\\n \\\"\\\"\\\"Format a shot plan as Markdown for human review and future renderer input.\\\"\\\"\\\"\\n scene_blocks = []\\n for scene in plan[\\\"scenes\\\"]:\\n scene_blocks.append(\\n f\\\"### Scene {scene['scene']} — {scene['purpose']}\\\\n\\\\n\\\"\\n f\\\"- Timing: {scene['start']:02d}s–{scene['end']:02d}s\\\\n\\\"\\n f\\\"- Asset type: {scene['asset_type']}\\\\n\\\"\\n f\\\"- On-screen text: {scene['on_screen_text']}\\\\n\\\"\\n f\\\"- Visual direction: {scene['visual_direction']}\\\\n\\\"\\n f\\\"- Retention goal: {scene['retention_goal']}\\\"\\n )\\n scenes = \\\"\\\\n\\\\n\\\".join(scene_blocks)\\n beats = \\\"\\\\n\\\".join(f\\\"- {beat['start']:02d}s: {beat['text']}\\\" for beat in plan[\\\"caption_beats\\\"])\\n notes = \\\"\\\\n\\\".join(f\\\"- {note}\\\" for note in plan[\\\"production_notes\\\"])\\n return f\\\"\\\"\\\"# {plan['title']}\\n\\n- ID: `{plan['id']}`\\n- Pillar: `{plan['pillar']}`\\n- Estimated duration: {plan['estimated_seconds']}s\\n\\n## Shot plan\\n\\n{scenes}\\n\\n## Caption beats\\n\\n{beats}\\n\\n## Production notes\\n\\n{notes}\\n\\\"\\\"\\\"\\n\\n\\ndef write_shot_plans(finals: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write one Markdown shot plan per finalized script.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for final in finals:\\n plan = build_shot_plan(final)\\n path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.md\\\"\\n path.write_text(format_shot_plan(plan), encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/shot_planner.py\"}"
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"content": "Erledigt, Sir.\n\n## Nächster Schritt umgesetzt: Render Plan v1 / Shot Planner\n\nIch habe den **Shot-Plan-Generator v1** gebaut — also bewusst noch keinen Blind-Renderer, sondern die saubere Zwischenstufe gegen generischen KI-Slideshow-Tapetenwechsel. Kleine Kunstpause für strategische Vernunft.\n\n## Implementiert\n\nNeue Komponente:\n\n```text\nautoshorts/shot_planner.py\n```\n\nNeues CLI-Script:\n\n```text\nscripts/generate_shot_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_shot_planner.py\n```\n\nGenerierte Shot Plans:\n\n```text\ndata/shot_plans/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.md\n ai-005-the-4-box-prompt-for-mental-overload.md\n attention-002-the-algorithm-does-not-need-you-happy.md\n```\n\n## Was der Shot Planner pro Video erzeugt\n\nJe finalem Script jetzt:\n\n- 5 Szenen:\n - `first_frame_hook`\n - `pattern_interrupt`\n - `core_explanation`\n - `screen_or_card_visual`\n - `cta`\n- Timing-Fenster\n- Asset Type:\n - `kinetic_text`\n - `screen_recording`\n - `card_animation`\n - `stock_broll`\n- On-screen text\n- Visual Direction\n- Retention Goal\n- Caption Beats\n- Production Notes\n\n## Verifikation\n\nTDD sauber durchgeführt:\n\n- Erst Test geschrieben\n- RED verifiziert: fehlendes Modul\n- Implementiert\n- GREEN verifiziert\n\nFinal:\n\n```text\n18 passed in 0.06s\n```\n\nGit:\n\n```text\nbranch=feat/initial-growth-engine\nlocal == remote\n```\n\nNeuer Commit:\n\n```text\n673595a feat: add shot planner for top retention scripts\n```\n\nGepusht nach:\n\n```text\nfeat/initial-growth-engine\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir **Renderer v1** bauen, aber nur gegen diese Shot Plans:\n\n1. Markdown Shot Plan → structured render manifest, z.B. JSON\n2. Renderer liest Manifest statt nur Script-Text\n3. Erste Ausgabe: 1 Testvideo, vermutlich `ai-004`\n4. Danach Preview/Approval-Loop in Telegram\n\nKurz: Nicht „mach Video hübsch“, sondern „mach Video nach Retention-Regieplan“. Subtiler Unterschied. Entscheidend.",
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"text": "Erledigt, Sir.\n\n## Nächster Schritt umgesetzt: Render Plan v1 / Shot Planner\n\nIch habe den **Shot-Plan-Generator v1** gebaut — also bewusst noch keinen Blind-Renderer, sondern die saubere Zwischenstufe gegen generischen KI-Slideshow-Tapetenwechsel. Kleine Kunstpause für strategische Vernunft.\n\n## Implementiert\n\nNeue Komponente:\n\n```text\nautoshorts/shot_planner.py\n```\n\nNeues CLI-Script:\n\n```text\nscripts/generate_shot_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_shot_planner.py\n```\n\nGenerierte Shot Plans:\n\n```text\ndata/shot_plans/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.md\n ai-005-the-4-box-prompt-for-mental-overload.md\n attention-002-the-algorithm-does-not-need-you-happy.md\n```\n\n## Was der Shot Planner pro Video erzeugt\n\nJe finalem Script jetzt:\n\n- 5 Szenen:\n - `first_frame_hook`\n - `pattern_interrupt`\n - `core_explanation`\n - `screen_or_card_visual`\n - `cta`\n- Timing-Fenster\n- Asset Type:\n - `kinetic_text`\n - `screen_recording`\n - `card_animation`\n - `stock_broll`\n- On-screen text\n- Visual Direction\n- Retention Goal\n- Caption Beats\n- Production Notes\n\n## Verifikation\n\nTDD sauber durchgeführt:\n\n- Erst Test geschrieben\n- RED verifiziert: fehlendes Modul\n- Implementiert\n- GREEN verifiziert\n\nFinal:\n\n```text\n18 passed in 0.06s\n```\n\nGit:\n\n```text\nbranch=feat/initial-growth-engine\nlocal == remote\n```\n\nNeuer Commit:\n\n```text\n673595a feat: add shot planner for top retention scripts\n```\n\nGepusht nach:\n\n```text\nfeat/initial-growth-engine\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir **Renderer v1** bauen, aber nur gegen diese Shot Plans:\n\n1. Markdown Shot Plan → structured render manifest, z.B. JSON\n2. Renderer liest Manifest statt nur Script-Text\n3. Erste Ausgabe: 1 Testvideo, vermutlich `ai-004`\n4. Danach Preview/Approval-Loop in Telegram\n\nKurz: Nicht „mach Video hübsch“, sondern „mach Video nach Retention-Regieplan“. Subtiler Unterschied. Entscheidend."
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Shot plan generation for finalized AutoShortsBot voiceover scripts.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from pathlib import Path\\n 6|from typing import Any\\n 7|\\n 8|from autoshorts.script_drafts import _slug\\n 9|\\n 10|SCENE_PURPOSES = [\\n 11| \\\"first_frame_hook\\\",\\n 12| \\\"pattern_interrupt\\\",\\n 13| \\\"core_explanation\\\",\\n 14| \\\"screen_or_card_visual\\\",\\n 15| \\\"cta\\\",\\n 16|]\\n 17|\\n 18|\\n 19|def _split_text(text: str, limit: int = 74) -> str:\\n 20| \\\"\\\"\\\"Return a compact on-screen phrase, preserving strong hooks.\\\"\\\"\\\"\\n 21| cleaned = \\\" \\\".join(text.split())\\n 22| if len(cleaned) <= limit:\\n 23| return cleaned\\n 24| return cleaned[:limit].rsplit(\\\" \\\", 1)[0].rstrip(\\\".,;:\\\")\\n 25|\\n 26|\\n 27|def _scene_windows(duration: int) -> list[tuple[int, int]]:\\n 28| \\\"\\\"\\\"Create five coarse timing windows for a short-form shot plan.\\\"\\\"\\\"\\n 29| if duration <= 45:\\n 30| cuts = [0, 3, 8, max(16, duration - 16), max(24, duration - 7), duration]\\n 31| else:\\n 32| cuts = [0, 3, 9, max(22, duration - 28), max(42, duration - 10), duration]\\n 33| windows: list[tuple[int, int]] = []\\n 34| for start, end in zip(cuts, cuts[1:]):\\n 35| windows.append((start, max(start + 2, end)))\\n 36| return windows[:5]\\n 37|\\n 38|\\n 39|def _asset_sequence(final: dict[str, Any]) -> list[str]:\\n 40| pillar = final[\\\"pillar\\\"]\\n 41| if pillar == \\\"ai_life_systems\\\":\\n 42| return [\\\"kinetic_text\\\", \\\"screen_recording\\\", \\\"card_animation\\\", \\\"screen_recording\\\", \\\"kinetic_text\\\"]\\n 43| if pillar == \\\"attention_design\\\":\\n 44| return [\\\"kinetic_text\\\", \\\"stock_broll\\\", \\\"card_animation\\\", \\\"screen_recording\\\", \\\"kinetic_text\\\"]\\n 45| return [\\\"kinetic_text\\\", \\\"generated_background\\\", \\\"card_animation\\\", \\\"stock_broll\\\", \\\"kinetic_text\\\"]\\n 46|\\n 47|\\n 48|def _visual_directions(final: dict[str, Any]) -> list[str]:\\n 49| if final[\\\"pillar\\\"] == \\\"ai_life_systems\\\":\\n 50| return [\\n 51| \\\"Large kinetic hook text over a messy prompt window; immediate zoom-in on the contradiction.\\\",\\n 52| \\\"Fast cut to a real prompt being rewritten from passive question to critic prompt.\\\",\\n 53| \\\"Use simple cards for the key questions; one card changes at a time, no dense paragraphs.\\\",\\n 54| \\\"Screen recording of the final copy-paste prompt with a visible human approval/decision moment.\\\",\\n 55| \\\"End card with the comment keyword and one clear action; keep the CTA legible for one beat.\\\",\\n 56| ]\\n 57| if final[\\\"pillar\\\"] == \\\"attention_design\\\":\\n 58| return [\\n 59| \\\"Large hook text over a phone/feed freeze-frame; make the first frame readable without audio.\\\",\\n 60| \\\"Relatable stock/B-roll: thumb hovering over an endless feed, then hard cut to greyscale.\\\",\\n 61| \\\"Card animation comparing your incentive vs. the feed incentive; two columns, very little text.\\\",\\n 62| \\\"Screen-style visual showing home screen cleanup, app moved away, timer/exit rule set.\\\",\\n 63| \\\"End on the phone home screen question; highlight the app moved off the home screen today.\\\",\\n 64| ]\\n 65| return [\\n 66| \\\"Large kinetic hook text; visible movement in the first second.\\\",\\n 67| \\\"Pattern interrupt with a fast before/after visual state change.\\\",\\n 68| \\\"Use cards to explain the core idea one beat at a time.\\\",\\n 69| \\\"Support the payoff with B-roll or a concrete screen/card visual.\\\",\\n 70| \\\"End with the CTA as readable kinetic text.\\\",\\n 71| ]\\n 72|\\n 73|\\n 74|def _retention_goals() -> list[str]:\\n 75| return [\\n 76| \\\"Stop the scroll with self-relevance and tension in the first two seconds.\\\",\\n 77| \\\"Reset attention before the viewer predicts a generic slideshow.\\\",\\n 78| \\\"Deliver the main mental model with low cognitive load.\\\",\\n 79| \\\"Show the concrete payoff so the advice feels usable, not ornamental.\\\",\\n 80| \\\"Convert attention into a comment/save action without adding a second idea.\\\",\\n 81| ]\\n 82|\\n 83|\\n 84|def build_shot_plan(final: dict[str, Any]) -> dict[str, Any]:\\n 85| \\\"\\\"\\\"Build a five-scene, retention-led shot plan from a finalized voiceover package.\\\"\\\"\\\"\\n 86| windows = _scene_windows(int(final[\\\"estimated_seconds\\\"]))\\n 87| asset_types = _asset_sequence(final)\\n 88| directions = _visual_directions(final)\\n 89| goals = _retention_goals()\\n 90| beats = final.get(\\\"caption_beats\\\", [])\\n 91| hook_sentences = [part.strip() for part in final[\\\"voiceover\\\"].split(\\\".\\\") if part.strip()]\\n 92| hook_text = \\\". \\\".join(hook_sentences[:2]).strip()\\n 93|\\n 94| scenes: list[dict[str, Any]] = []\\n 95| for index, purpose in enumerate(SCENE_PURPOSES):\\n 96| start, end = windows[index]\\n 97| beat_text = beats[min(index, len(beats) - 1)][\\\"text\\\"] if beats else final[\\\"voiceover\\\"]\\n 98| on_screen = hook_text if index == 0 else _split_text(beat_text)\\n 99| scenes.append(\\n 100| {\\n 101| \\\"scene\\\": index + 1,\\n 102| \\\"purpose\\\": purpose,\\n 103| \\\"start\\\": start,\\n 104| \\\"end\\\": end,\\n 105| \\\"asset_type\\\": asset_types[index],\\n 106| \\\"on_screen_text\\\": on_screen,\\n 107| \\\"visual_direction\\\": directions[index],\\n 108| \\\"retention_goal\\\": goals[index],\\n 109| }\\n 110| )\\n 111|\\n 112| return {\\n 113| \\\"id\\\": final[\\\"id\\\"],\\n 114| \\\"title\\\": final[\\\"title\\\"],\\n 115| \\\"pillar\\\": final[\\\"pillar\\\"],\\n 116| \\\"estimated_seconds\\\": final[\\\"estimated_seconds\\\"],\\n 117| \\\"caption_beats\\\": final.get(\\\"caption_beats\\\", []),\\n 118| \\\"scenes\\\": scenes,\\n 119| \\\"production_notes\\\": final.get(\\\"production_notes\\\", []),\\n 120| }\\n 121|\\n 122|\\n 123|def format_shot_plan(plan: dict[str, Any]) -> str:\\n 124| \\\"\\\"\\\"Format a shot plan as Markdown for human review and future renderer input.\\\"\\\"\\\"\\n 125| scene_blocks = []\\n 126| for scene in plan[\\\"scenes\\\"]:\\n 127| scene_blocks.append(\\n 128| f\\\"### Scene {scene['scene']} — {scene['purpose']}\\\\n\\\\n\\\"\\n 129| f\\\"- Timing: {scene['start']:02d}s–{scene['end']:02d}s\\\\n\\\"\\n 130| f\\\"- Asset type: {scene['asset_type']}\\\\n\\\"\\n 131| f\\\"- On-screen text: {scene['on_screen_text']}\\\\n\\\"\\n 132| f\\\"- Visual direction: {scene['visual_direction']}\\\\n\\\"\\n 133| f\\\"- Retention goal: {scene['retention_goal']}\\\"\\n 134| )\\n 135| scenes = \\\"\\\\n\\\\n\\\".join(scene_blocks)\\n 136| beats = \\\"\\\\n\\\".join(f\\\"- {beat['start']:02d}s: {beat['text']}\\\" for beat in plan[\\\"caption_beats\\\"])\\n 137| notes = \\\"\\\\n\\\".join(f\\\"- {note}\\\" for note in plan[\\\"production_notes\\\"])\\n 138| return f\\\"\\\"\\\"# {plan['title']}\\n 139|\\n 140|- ID: `{plan['id']}`\\n 141|- Pillar: `{plan['pillar']}`\\n 142|- Estimated duration: {plan['estimated_seconds']}s\\n 143|\\n 144|## Shot plan\\n 145|\\n 146|{scenes}\\n 147|\\n 148|## Caption beats\\n 149|\\n 150|{beats}\\n 151|\\n 152|## Production notes\\n 153|\\n 154|{notes}\\n 155|\\\"\\\"\\\"\\n 156|\\n 157|\\n 158|def write_shot_plans(finals: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n 159| \\\"\\\"\\\"Write one Markdown shot plan per finalized script.\\\"\\\"\\\"\\n 160| output_dir.mkdir(parents=True, exist_ok=True)\\n 161| paths: list[Path] = []\\n 162| for final in finals:\\n 163| plan = build_shot_plan(final)\\n 164| path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.md\\\"\\n 165| path.write_text(format_shot_plan(plan), encoding=\\\"utf-8\\\")\\n 166| paths.append(path)\\n 167| return paths\\n 168|\", \"total_lines\": 167, \"file_size\": 6678, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.py\",\"content\":\"import json\\nfrom pathlib import Path\\n\\nfrom autoshorts.growth_plan import load_testbatch\\nfrom autoshorts.render_manifest import build_render_manifest, write_render_manifests\\nfrom autoshorts.shot_planner import build_shot_plan\\nfrom autoshorts.voiceover_finalizer import finalize_voiceover\\n\\n\\ndef _shot_plan(script_id: str):\\n batch = load_testbatch(Path(\\\"data/drafts/testbatch_002.json\\\"))\\n idea = next(item for item in batch[\\\"ideas\\\"] if item[\\\"id\\\"] == script_id)\\n return build_shot_plan(finalize_voiceover(idea))\\n\\n\\ndef test_build_render_manifest_maps_shot_plan_to_renderer_contract():\\n plan = _shot_plan(\\\"ai-004\\\")\\n\\n manifest = build_render_manifest(plan)\\n\\n assert manifest[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n assert manifest[\\\"video\\\"][\\\"id\\\"] == \\\"ai-004\\\"\\n assert manifest[\\\"video\\\"][\\\"aspect_ratio\\\"] == \\\"9:16\\\"\\n assert manifest[\\\"video\\\"][\\\"resolution\\\"] == {\\\"width\\\": 1080, \\\"height\\\": 1920}\\n assert manifest[\\\"timeline\\\"][0][\\\"track\\\"] == \\\"visual\\\"\\n assert manifest[\\\"timeline\\\"][0][\\\"start_ms\\\"] == 0\\n assert manifest[\\\"timeline\\\"][0][\\\"duration_ms\\\"] > 0\\n assert manifest[\\\"timeline\\\"][0][\\\"type\\\"] == \\\"kinetic_text\\\"\\n assert manifest[\\\"timeline\\\"][0][\\\"text\\\"] == plan[\\\"scenes\\\"][0][\\\"on_screen_text\\\"]\\n assert manifest[\\\"timeline\\\"][0][\\\"style\\\"][\\\"safe_area_pct\\\"] == 8\\n assert any(item[\\\"track\\\"] == \\\"captions\\\" for item in manifest[\\\"timeline\\\"])\\n assert manifest[\\\"render_constraints\\\"][\\\"avoid_static_slideshow\\\"] is True\\n\\n\\ndef test_build_render_manifest_uses_asset_placeholders_by_type():\\n plan = _shot_plan(\\\"attention-002\\\")\\n\\n manifest = build_render_manifest(plan)\\n\\n visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n assert any(item[\\\"type\\\"] == \\\"stock_broll\\\" for item in visual_items)\\n assert any(item[\\\"type\\\"] == \\\"card_animation\\\" for item in visual_items)\\n assert all(item[\\\"asset_ref\\\"].startswith(\\\"placeholder://\\\") for item in visual_items)\\n assert manifest[\\\"asset_requirements\\\"][\\\"stock_broll\\\"]\\n assert manifest[\\\"asset_requirements\\\"][\\\"screen_recording\\\"]\\n\\n\\ndef test_write_render_manifests_outputs_valid_json_files(tmp_path):\\n plans = [_shot_plan(\\\"ai-004\\\"), _shot_plan(\\\"ai-005\\\"), _shot_plan(\\\"attention-002\\\")]\\n\\n paths = write_render_manifests(plans, tmp_path)\\n\\n assert len(paths) == 3\\n assert all(path.suffix == \\\".json\\\" for path in paths)\\n payload = json.loads(paths[0].read_text(encoding=\\\"utf-8\\\"))\\n assert payload[\\\"schema_version\\\"] == \\\"render_manifest.v1\\\"\\n assert \\\"timeline\\\" in payload\\n assert len(payload[\\\"timeline\\\"]) >= 10\\n\"}"
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_render_manifest.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_render_manifest.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_render_manifest.py:5: in \\n from autoshorts.render_manifest import build_render_manifest, write_render_manifests\\nE ModuleNotFoundError: No module named 'autoshorts.render_manifest'\\n=========================== short test summary info ============================\\nERROR tests/test_render_manifest.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.10s\", \"exit_code\": 2, \"error\": null}",
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"arguments": "{\"content\":\"\\\"\\\"\\\"Renderer manifest generation for retention-led AutoShortsBot videos.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nfrom autoshorts.script_drafts import _slug\\n\\nRESOLUTION = {\\\"width\\\": 1080, \\\"height\\\": 1920}\\nASPECT_RATIO = \\\"9:16\\\"\\nFPS = 30\\n\\n\\nASSET_REQUIREMENT_HINTS = {\\n \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n}\\n\\n\\ndef _duration_ms(start: int, end: int) -> int:\\n return max(500, (int(end) - int(start)) * 1000)\\n\\n\\ndef _placeholder(asset_type: str, video_id: str, scene: int) -> str:\\n return f\\\"placeholder://{video_id}/scene-{scene:02d}/{asset_type}\\\"\\n\\n\\ndef _visual_style(scene: dict[str, Any]) -> dict[str, Any]:\\n base = {\\n \\\"safe_area_pct\\\": 8,\\n \\\"font\\\": \\\"bold_sans\\\",\\n \\\"caption_case\\\": \\\"sentence\\\",\\n \\\"motion\\\": \\\"push_zoom\\\" if scene[\\\"scene\\\"] == 1 else \\\"cut_or_slide\\\",\\n \\\"contrast\\\": \\\"high\\\",\\n }\\n if scene[\\\"asset_type\\\"] == \\\"card_animation\\\":\\n base.update({\\\"layout\\\": \\\"minimal_cards\\\", \\\"max_words_on_screen\\\": 18})\\n elif scene[\\\"asset_type\\\"] == \\\"screen_recording\\\":\\n base.update({\\\"layout\\\": \\\"screen_plus_caption\\\", \\\"blur_sensitive_ui\\\": True})\\n elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n return base\\n\\n\\ndef _visual_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n video_id = plan[\\\"id\\\"]\\n items: list[dict[str, Any]] = []\\n for scene in plan[\\\"scenes\\\"]:\\n items.append(\\n {\\n \\\"id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n \\\"track\\\": \\\"visual\\\",\\n \\\"type\\\": scene[\\\"asset_type\\\"],\\n \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n \\\"start_ms\\\": int(scene[\\\"start\\\"]) * 1000,\\n \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n \\\"style\\\": _visual_style(scene),\\n }\\n )\\n return items\\n\\n\\ndef _caption_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n beats = plan.get(\\\"caption_beats\\\", [])\\n total_ms = int(plan[\\\"estimated_seconds\\\"]) * 1000\\n items: list[dict[str, Any]] = []\\n for index, beat in enumerate(beats):\\n start_ms = int(beat[\\\"start\\\"]) * 1000\\n next_start_ms = int(beats[index + 1][\\\"start\\\"]) * 1000 if index + 1 < len(beats) else total_ms\\n items.append(\\n {\\n \\\"id\\\": f\\\"caption_{index + 1:02d}\\\",\\n \\\"track\\\": \\\"captions\\\",\\n \\\"type\\\": \\\"kinetic_caption\\\",\\n \\\"start_ms\\\": start_ms,\\n \\\"duration_ms\\\": max(750, next_start_ms - start_ms),\\n \\\"text\\\": beat[\\\"text\\\"],\\n \\\"style\\\": {\\n \\\"safe_area_pct\\\": 8,\\n \\\"position\\\": \\\"lower_third\\\",\\n \\\"max_chars_per_line\\\": 28,\\n \\\"highlight_keywords\\\": True,\\n },\\n }\\n )\\n return items\\n\\n\\ndef _asset_requirements(plan: dict[str, Any]) -> dict[str, list[dict[str, Any]]]:\\n requirements: dict[str, list[dict[str, Any]]] = {key: [] for key in ASSET_REQUIREMENT_HINTS}\\n for scene in plan[\\\"scenes\\\"]:\\n asset_type = scene[\\\"asset_type\\\"]\\n requirements.setdefault(asset_type, []).append(\\n {\\n \\\"scene\\\": scene[\\\"scene\\\"],\\n \\\"asset_ref\\\": _placeholder(asset_type, plan[\\\"id\\\"], scene[\\\"scene\\\"]),\\n \\\"brief\\\": scene[\\\"visual_direction\\\"],\\n \\\"hint\\\": ASSET_REQUIREMENT_HINTS.get(asset_type, \\\"Provide asset matching the visual direction.\\\"),\\n }\\n )\\n return requirements\\n\\n\\ndef build_render_manifest(plan: dict[str, Any]) -> dict[str, Any]:\\n \\\"\\\"\\\"Convert a human shot plan into a structured renderer contract.\\\"\\\"\\\"\\n timeline = _visual_timeline_items(plan) + _caption_timeline_items(plan)\\n timeline.sort(key=lambda item: (item[\\\"start_ms\\\"], item[\\\"track\\\"], item[\\\"id\\\"]))\\n return {\\n \\\"schema_version\\\": \\\"render_manifest.v1\\\",\\n \\\"video\\\": {\\n \\\"id\\\": plan[\\\"id\\\"],\\n \\\"title\\\": plan[\\\"title\\\"],\\n \\\"pillar\\\": plan[\\\"pillar\\\"],\\n \\\"duration_ms\\\": int(plan[\\\"estimated_seconds\\\"]) * 1000,\\n \\\"aspect_ratio\\\": ASPECT_RATIO,\\n \\\"resolution\\\": RESOLUTION,\\n \\\"fps\\\": FPS,\\n },\\n \\\"timeline\\\": timeline,\\n \\\"asset_requirements\\\": _asset_requirements(plan),\\n \\\"render_constraints\\\": {\\n \\\"avoid_static_slideshow\\\": True,\\n \\\"pattern_interrupt_every_seconds\\\": 3,\\n \\\"first_frame_hook_required\\\": True,\\n \\\"max_words_per_visual_card\\\": 18,\\n \\\"human_approval_required_before_publish\\\": True,\\n },\\n \\\"production_notes\\\": plan.get(\\\"production_notes\\\", []),\\n }\\n\\n\\ndef write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for plan in plans:\\n manifest = build_render_manifest(plan)\\n path = output_dir / f\\\"{plan['id']}-{_slug(plan['title'])}.json\\\"\\n path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n paths.append(path)\\n return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py\"}"
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"call_id": "call_8JeJsCHRKgHLTjpelinECAWQ",
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"function": {
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"arguments": "{\"content\":\"\\\"\\\"\\\"Generate renderer-ready JSON manifests for selected AutoShortsBot scripts.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.growth_plan import load_testbatch\\nfrom autoshorts.render_manifest import write_render_manifests\\nfrom autoshorts.shot_planner import build_shot_plan\\nfrom autoshorts.voiceover_finalizer import finalize_voiceover\\n\\nDEFAULT_SELECTED_IDS = [\\\"ai-004\\\", \\\"ai-005\\\", \\\"attention-002\\\"]\\n\\n\\ndef main() -> None:\\n parser = argparse.ArgumentParser(description=\\\"Generate renderer manifest JSON files.\\\")\\n parser.add_argument(\\\"--batch\\\", default=\\\"data/drafts/testbatch_002.json\\\")\\n parser.add_argument(\\\"--out\\\", default=\\\"data/render_manifests/testbatch_002_top3\\\")\\n parser.add_argument(\\\"--ids\\\", nargs=\\\"*\\\", default=DEFAULT_SELECTED_IDS)\\n args = parser.parse_args()\\n\\n batch = load_testbatch(Path(args.batch))\\n selected = set(args.ids)\\n plans = [\\n build_shot_plan(finalize_voiceover(idea))\\n for idea in batch.get(\\\"ideas\\\", [])\\n if idea[\\\"id\\\"] in selected\\n ]\\n paths = write_render_manifests(plans, Path(args.out))\\n\\n print(f\\\"Generated {len(paths)} render manifests in {args.out}\\\")\\n for path in paths:\\n print(path)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_render_manifests.py\"}"
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"content": "Erledigt, Sir.\n\n## Weitergebaut: Renderer-Manifest v1\n\nIch habe jetzt die nächste Stufe nach dem Shot Plan umgesetzt: **renderer-ready JSON manifests**.\n\nDas ist noch kein finaler Video-Renderer, sondern der saubere Contract zwischen:\n\n```text\nFinal Script → Shot Plan → Render Manifest → später Renderer\n```\n\nDamit der Renderer nicht wieder „irgendwas Hübsches mit KI-Hintergrund“ produziert. Wir bleiben also erfreulich unromantisch und bauen zuerst die Leitplanken.\n\n## Implementiert\n\nNeue Komponente:\n\n```text\nautoshorts/render_manifest.py\n```\n\nNeues CLI-Script:\n\n```text\nscripts/generate_render_manifests.py\n```\n\nNeue Tests:\n\n```text\ntests/test_render_manifest.py\n```\n\nGenerierte Manifeste:\n\n```text\ndata/render_manifests/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.json\n ai-005-the-4-box-prompt-for-mental-overload.json\n attention-002-the-algorithm-does-not-need-you-happy.json\n```\n\n## Was im Render Manifest steht\n\nPro Video jetzt maschinenlesbar:\n\n- `schema_version`: `render_manifest.v1`\n- Video-Metadaten:\n - ID\n - Titel\n - Pillar\n - Dauer\n - `9:16`\n - `1080x1920`\n - `30fps`\n- Timeline Items:\n - Visual Scene Items\n - Caption Items\n- Asset Requirements:\n - `kinetic_text`\n - `screen_recording`\n - `card_animation`\n - `stock_broll`\n- Render Constraints:\n - keine statische Slideshow\n - Pattern Interrupt ca. alle 3 Sekunden\n - First-frame hook required\n - max. 18 Wörter pro Visual Card\n - Human approval before publish\n\n## Generierte Top-3-Manifeste\n\nKurzprüfung:\n\n```text\nai-004:\n duration: 43000ms\n visual items: 5\n caption items: 13\n asset types: card_animation, kinetic_text, screen_recording\n\nai-005:\n duration: 61000ms\n visual items: 5\n caption items: 17\n asset types: card_animation, kinetic_text, screen_recording\n\nattention-002:\n duration: 61000ms\n visual items: 5\n caption items: 17\n asset types: card_animation, kinetic_text, screen_recording, stock_broll\n```\n\n## Tests / Push\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.render_manifest`\n- GREEN: Manifest Builder implementiert\n- Full suite:\n\n```text\n21 passed in 0.05s\n```\n\nNeuer Commit:\n\n```text\ne1444ea feat: add renderer manifests for top scripts\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt tatsächlich **Renderer v1**:\n\n- nimmt ein Render Manifest JSON\n- rendert zunächst ein simples, aber nicht-generisches Vertical MP4\n- Fokus auf:\n - kinetic hook text\n - caption timing\n - card scenes\n - placeholder screen/card visuals\n- erstes Testvideo: `ai-004`\n\nDas wird noch kein Pixar-Kurzfilm. Aber es wird ein kontrollierter erster Video-Prototyp statt KI-Tapete mit Untertiteln — und das ist der Punkt.",
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"text": "Erledigt, Sir.\n\n## Weitergebaut: Renderer-Manifest v1\n\nIch habe jetzt die nächste Stufe nach dem Shot Plan umgesetzt: **renderer-ready JSON manifests**.\n\nDas ist noch kein finaler Video-Renderer, sondern der saubere Contract zwischen:\n\n```text\nFinal Script → Shot Plan → Render Manifest → später Renderer\n```\n\nDamit der Renderer nicht wieder „irgendwas Hübsches mit KI-Hintergrund“ produziert. Wir bleiben also erfreulich unromantisch und bauen zuerst die Leitplanken.\n\n## Implementiert\n\nNeue Komponente:\n\n```text\nautoshorts/render_manifest.py\n```\n\nNeues CLI-Script:\n\n```text\nscripts/generate_render_manifests.py\n```\n\nNeue Tests:\n\n```text\ntests/test_render_manifest.py\n```\n\nGenerierte Manifeste:\n\n```text\ndata/render_manifests/testbatch_002_top3/\n ai-004-stop-asking-ai-to-think-for-you.json\n ai-005-the-4-box-prompt-for-mental-overload.json\n attention-002-the-algorithm-does-not-need-you-happy.json\n```\n\n## Was im Render Manifest steht\n\nPro Video jetzt maschinenlesbar:\n\n- `schema_version`: `render_manifest.v1`\n- Video-Metadaten:\n - ID\n - Titel\n - Pillar\n - Dauer\n - `9:16`\n - `1080x1920`\n - `30fps`\n- Timeline Items:\n - Visual Scene Items\n - Caption Items\n- Asset Requirements:\n - `kinetic_text`\n - `screen_recording`\n - `card_animation`\n - `stock_broll`\n- Render Constraints:\n - keine statische Slideshow\n - Pattern Interrupt ca. alle 3 Sekunden\n - First-frame hook required\n - max. 18 Wörter pro Visual Card\n - Human approval before publish\n\n## Generierte Top-3-Manifeste\n\nKurzprüfung:\n\n```text\nai-004:\n duration: 43000ms\n visual items: 5\n caption items: 13\n asset types: card_animation, kinetic_text, screen_recording\n\nai-005:\n duration: 61000ms\n visual items: 5\n caption items: 17\n asset types: card_animation, kinetic_text, screen_recording\n\nattention-002:\n duration: 61000ms\n visual items: 5\n caption items: 17\n asset types: card_animation, kinetic_text, screen_recording, stock_broll\n```\n\n## Tests / Push\n\nTDD durchgeführt:\n\n- RED: fehlendes `autoshorts.render_manifest`\n- GREEN: Manifest Builder implementiert\n- Full suite:\n\n```text\n21 passed in 0.05s\n```\n\nNeuer Commit:\n\n```text\ne1444ea feat: add renderer manifests for top scripts\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt tatsächlich **Renderer v1**:\n\n- nimmt ein Render Manifest JSON\n- rendert zunächst ein simples, aber nicht-generisches Vertical MP4\n- Fokus auf:\n - kinetic hook text\n - caption timing\n - card scenes\n - placeholder screen/card visuals\n- erstes Testvideo: `ai-004`\n\nDas wird noch kein Pixar-Kurzfilm. Aber es wird ein kontrollierter erster Video-Prototyp statt KI-Tapete mit Untertiteln — und das ist der Punkt."
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"content": "[M G] Erklär mir kurz wie er das video ,rendert‘? Mit einem LLM? Oder wie macht er das?"
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"content": "Kurz: **Nein, das Video wird nicht von einem LLM „gerendert“.**\n\nEin LLM hilft höchstens bei **Planung und Text/Struktur**. Das eigentliche Rendering macht ein klassischer Video-Renderer: Code, der Texte, Szenen, Bilder/Clips, Animationen, Captions und Audio auf eine Timeline legt und als MP4 exportiert.\n\n## Unsere aktuelle Pipeline\n\nAktuell haben wir gebaut:\n\n```text\nIdee\n→ Voiceover Script\n→ Shot Plan\n→ Render Manifest JSON\n→ später: Renderer\n→ MP4\n```\n\n## Rolle des LLM\n\nDas LLM macht Dinge wie:\n\n- Idee formulieren\n- Hook verbessern\n- Voiceover schreiben\n- Szenen planen\n- Asset-Typen bestimmen\n- Caption Beats erzeugen\n- Render Manifest strukturieren\n\nAlso quasi:\n\n> „Was soll im Video wann passieren?“\n\nAber es erzeugt nicht Frame für Frame das Video.\n\n## Rolle des Renderers\n\nDer Renderer nimmt das Manifest und baut daraus technisch das Video.\n\nBeispiel aus dem Manifest:\n\n```json\n{\n \"track\": \"visual\",\n \"type\": \"kinetic_text\",\n \"start_ms\": 0,\n \"duration_ms\": 3000,\n \"text\": \"Stop asking AI to think for you. Ask it to argue with you\"\n}\n```\n\nDer Renderer sagt dann sinngemäß:\n\n> Von 0 bis 3 Sekunden: zeige diesen Text groß, animiert, vertikal in 1080x1920.\n\nTechnisch kann das z.B. über eines dieser Systeme laufen:\n\n- **MoviePy** in Python\n- **FFmpeg** direkt\n- **Remotion** mit React/Node\n- **Manim**\n- später evtl. Kombination mit Bild-/Video-Generatoren\n\n## Was wir wahrscheinlich zuerst machen\n\nFür AutoShortsBot wäre ein erster Renderer v1 vermutlich:\n\n- Python + MoviePy oder FFmpeg\n- 1080x1920 Vertical Video\n- farbiger/dynamischer Hintergrund\n- große Hook-Texte\n- animierte Cards\n- Captions\n- ggf. später TTS-Audio\n- Export als `.mp4`\n\nDas wäre deterministisch und kontrollierbar.\n\n## Was ein Bild-/Video-KI-Modell machen könnte\n\nEin KI-Video/Bildmodell könnte später Assets liefern, z.B.:\n\n- Hintergrundbild\n- B-roll-artiger Clip\n- abstrakte Animation\n- Illustrationen\n\nAber das wäre dann nur ein **Asset-Lieferant**, nicht der eigentliche Orchestrator.\n\nDer eigentliche Ablauf bleibt:\n\n```text\nManifest entscheidet was/wann\nRenderer baut Timeline\nFFmpeg/MoviePy exportiert MP4\n```\n\n## Wichtigster Punkt\n\nDas LLM ist hier der **Regisseur/Storyboarder**.\n\nDer Renderer ist die **Kamera, Schnittsoftware und Exportmaschine**.\n\nWenn wir das LLM direkt „mach mir ein Video“ spielen lassen, bekommen wir hübsche Zufallssuppe. Wenn wir stattdessen Manifest → Renderer nutzen, bekommen wir reproduzierbare Videos, die wir testen, verbessern und automatisieren können.",
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"text": "Kurz: **Nein, das Video wird nicht von einem LLM „gerendert“.**\n\nEin LLM hilft höchstens bei **Planung und Text/Struktur**. Das eigentliche Rendering macht ein klassischer Video-Renderer: Code, der Texte, Szenen, Bilder/Clips, Animationen, Captions und Audio auf eine Timeline legt und als MP4 exportiert.\n\n## Unsere aktuelle Pipeline\n\nAktuell haben wir gebaut:\n\n```text\nIdee\n→ Voiceover Script\n→ Shot Plan\n→ Render Manifest JSON\n→ später: Renderer\n→ MP4\n```\n\n## Rolle des LLM\n\nDas LLM macht Dinge wie:\n\n- Idee formulieren\n- Hook verbessern\n- Voiceover schreiben\n- Szenen planen\n- Asset-Typen bestimmen\n- Caption Beats erzeugen\n- Render Manifest strukturieren\n\nAlso quasi:\n\n> „Was soll im Video wann passieren?“\n\nAber es erzeugt nicht Frame für Frame das Video.\n\n## Rolle des Renderers\n\nDer Renderer nimmt das Manifest und baut daraus technisch das Video.\n\nBeispiel aus dem Manifest:\n\n```json\n{\n \"track\": \"visual\",\n \"type\": \"kinetic_text\",\n \"start_ms\": 0,\n \"duration_ms\": 3000,\n \"text\": \"Stop asking AI to think for you. Ask it to argue with you\"\n}\n```\n\nDer Renderer sagt dann sinngemäß:\n\n> Von 0 bis 3 Sekunden: zeige diesen Text groß, animiert, vertikal in 1080x1920.\n\nTechnisch kann das z.B. über eines dieser Systeme laufen:\n\n- **MoviePy** in Python\n- **FFmpeg** direkt\n- **Remotion** mit React/Node\n- **Manim**\n- später evtl. Kombination mit Bild-/Video-Generatoren\n\n## Was wir wahrscheinlich zuerst machen\n\nFür AutoShortsBot wäre ein erster Renderer v1 vermutlich:\n\n- Python + MoviePy oder FFmpeg\n- 1080x1920 Vertical Video\n- farbiger/dynamischer Hintergrund\n- große Hook-Texte\n- animierte Cards\n- Captions\n- ggf. später TTS-Audio\n- Export als `.mp4`\n\nDas wäre deterministisch und kontrollierbar.\n\n## Was ein Bild-/Video-KI-Modell machen könnte\n\nEin KI-Video/Bildmodell könnte später Assets liefern, z.B.:\n\n- Hintergrundbild\n- B-roll-artiger Clip\n- abstrakte Animation\n- Illustrationen\n\nAber das wäre dann nur ein **Asset-Lieferant**, nicht der eigentliche Orchestrator.\n\nDer eigentliche Ablauf bleibt:\n\n```text\nManifest entscheidet was/wann\nRenderer baut Timeline\nFFmpeg/MoviePy exportiert MP4\n```\n\n## Wichtigster Punkt\n\nDas LLM ist hier der **Regisseur/Storyboarder**.\n\nDer Renderer ist die **Kamera, Schnittsoftware und Exportmaschine**.\n\nWenn wir das LLM direkt „mach mir ein Video“ spielen lassen, bekommen wir hübsche Zufallssuppe. Wenn wir stattdessen Manifest → Renderer nutzen, bekommen wir reproduzierbare Videos, die wir testen, verbessern und automatisieren können."
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"text": "**Planning coding tasks**\n\nI need to continue coding and ensure I load the relevant skills. They seem already loaded, but I must scan for new turns, focusing on TDD and maybe social media and GitHub. I’ll also need to use tools effectively. I think I should check the availability of MoviePy while building Renderer v1. It’s important to inspect the dependencies as well. Let's get organized and make sure everything's in place!"
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"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\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**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention, platform fit, and analytics feedback.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n\\n6. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n7. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/render-manifest-pipeline.md\", \"references/monetization-and-approval-notes.md\", \"references/retention-led-shorts.md\", \"references/retention-render-planning.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}",
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"content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/render-manifest-pipeline.md\", \"content\": \"# Render Manifest Pipeline for Retention-Led Shorts\\n\\nUse this when a short-form automation project has finalized scripts and shot plans but no reliable renderer yet.\\n\\n## Durable lesson\\n\\nDo **not** jump directly from script text to video rendering. That tends to recreate generic AI slideshow output: pretty backgrounds, weak timing, and captions pasted over decorative motion. Insert a structured renderer contract first:\\n\\n```text\\nFinal script -> Shot plan -> Render manifest -> Renderer -> Preview/approval\\n```\\n\\n## Recommended sequence\\n\\n1. **Final script**\\n - Voiceover text\\n - Estimated duration\\n - Caption beats\\n - CTA\\n - Production notes\\n\\n2. **Shot plan**\\n - 5 coarse scenes:\\n - `first_frame_hook`\\n - `pattern_interrupt`\\n - `core_explanation`\\n - `screen_or_card_visual`\\n - `cta`\\n - Each scene should include:\\n - timing window\\n - asset type\\n - on-screen text\\n - visual direction\\n - retention goal\\n\\n3. **Render manifest**\\n - Machine-readable JSON contract for the renderer.\\n - Keep it deterministic and testable.\\n - Suggested fields:\\n - `schema_version`, e.g. `render_manifest.v1`\\n - `video`: id, title, pillar, duration, aspect ratio, resolution, fps\\n - `timeline`: visual and caption timeline items in milliseconds\\n - `asset_requirements`: placeholders and briefs for screen recordings, cards, B-roll, kinetic text\\n - `render_constraints`: first-frame hook, no static slideshow, pattern interrupt cadence, approval requirement\\n\\n4. **Renderer v1**\\n - Consume the manifest, not the raw script.\\n - First renderer can use placeholders/cards/kinetic text before real asset generation.\\n - Produce one test MP4 before scaling to a batch.\\n\\n5. **Human approval loop**\\n - Send preview plus title/caption/hashtags/platform recommendation.\\n - Do not publish without explicit user approval.\\n\\n## Testing pattern\\n\\nUse TDD for each pipeline stage:\\n\\n- Write a failing test for the expected contract first.\\n- Verify RED: missing module/function/field.\\n- Implement minimal deterministic builder.\\n- Verify GREEN with targeted tests.\\n- Run full suite before committing.\\n\\nUseful assertions:\\n\\n- render manifest has `schema_version`\\n- video is `9:16`, `1080x1920`, `30fps`\\n- timeline has visual + caption tracks\\n- first visual starts at `0ms`\\n- each visual item has `asset_ref`, `text`, `style`, and retention direction\\n- render constraints include `avoid_static_slideshow` and `human_approval_required_before_publish`\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer everything from prose. Encode scene intent and constraints explicitly.\\n- Do not make generated backgrounds the main visual strategy; they are fallback texture, not content.\\n- Do not build publishing before the preview/approval path exists.\\n- Do not optimize final visual polish before proving hook/caption timing and scene structure.\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/retention-render-planning.md\", \"content\": \"# Retention-led render planning\\n\\nUse this when a shorts automation project has finalized scripts but the renderer still risks producing generic AI slideshow output.\\n\\n## Durable lesson\\n\\nDo not jump directly from script text to rendered video. Insert a deterministic shot-planning layer first. The shot plan should preserve the content strategy and give the renderer specific visual direction.\\n\\n## Recommended sequence\\n\\n1. Finalize the voiceover script.\\n - Keep hook first.\\n - Estimate duration from word count.\\n - Preserve platform intent, e.g. short growth clip vs. 60s+ TikTok Rewards candidate.\\n\\n2. Generate a structured five-scene shot plan.\\n - `first_frame_hook`\\n - `pattern_interrupt`\\n - `core_explanation`\\n - `screen_or_card_visual`\\n - `cta`\\n\\n3. Include renderer-facing fields for each scene.\\n - timing window\\n - asset type\\n - on-screen text\\n - visual direction\\n - retention goal\\n\\n4. Use asset types that prevent generic slideshow output.\\n - `kinetic_text` for hook/CTA\\n - `screen_recording` for AI/tutorial content\\n - `card_animation` for mental models and frameworks\\n - `stock_broll` only where it supports a relatable action or state change\\n - avoid default static AI-image backgrounds unless the visual concept explicitly needs them\\n\\n5. Render only after the shot plan exists.\\n - Prefer generating a structured render manifest from the shot plan before invoking the renderer.\\n - Start with one test video before batch rendering.\\n - Send preview for human approval before publishing.\\n\\n## Testing pattern\\n\\nFor code-based pipelines, use TDD around the shot planner:\\n\\n- Write tests that assert the five scene purposes, asset-type choices, and first-frame hook handling.\\n- Verify RED by importing a missing/new planner module or asserting missing behavior.\\n- Implement the minimal deterministic planner.\\n- Verify generated Markdown or JSON includes shot plan, caption beats, and asset-type fields.\\n- Run the full test suite before commit/push.\\n\\n## Example output shape\\n\\n```yaml\\nid: ai-004\\ntitle: Stop asking AI to think for you\\nestimated_seconds: 43\\nscenes:\\n - purpose: first_frame_hook\\n start: 0\\n end: 3\\n asset_type: kinetic_text\\n on_screen_text: Stop asking AI to think for you. Ask it to argue with you\\n visual_direction: Large kinetic hook text over a messy prompt window; immediate zoom-in on the contradiction.\\n retention_goal: Stop the scroll with self-relevance and tension in the first two seconds.\\n - purpose: pattern_interrupt\\n asset_type: screen_recording\\n - purpose: core_explanation\\n asset_type: card_animation\\n - purpose: screen_or_card_visual\\n asset_type: screen_recording\\n - purpose: cta\\n asset_type: kinetic_text\\n```\\n\\n## Pitfall\\n\\nA visually clean render can still be strategically bad if it looks like generic AI slideshow wallpaper. The renderer should follow a retention shot plan, not merely decorate a script.\", \"file_type\": \".md\"}",
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n_______________ ERROR collecting tests/test_preview_renderer.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_preview_renderer.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_preview_renderer.py:4: in \\n from autoshorts.preview_renderer import (\\nE ModuleNotFoundError: No module named 'autoshorts.preview_renderer'\\n=========================== short test summary info ============================\\nERROR tests/test_preview_renderer.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.09s\", \"exit_code\": 2, \"error\": null}",
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"arguments": "{\"content\":\"\\\"\\\"\\\"Deterministic preview renderer for AutoShortsBot render manifests.\\n\\nThis v1 renderer intentionally uses manifest-driven cards/placeholders instead of\\nAI-generated video. It renders a simple vertical MP4 suitable for timing and\\napproval review before investing in polished assets.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport shutil\\nimport subprocess\\nimport tempfile\\nimport textwrap\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nfrom PIL import Image, ImageDraw, ImageFont\\n\\nBACKGROUND_BY_TYPE = {\\n \\\"kinetic_text\\\": ((19, 23, 38), (79, 70, 229)),\\n \\\"screen_recording\\\": ((15, 23, 42), (14, 165, 233)),\\n \\\"card_animation\\\": ((17, 24, 39), (245, 158, 11)),\\n \\\"stock_broll\\\": ((28, 25, 23), (34, 197, 94)),\\n \\\"generated_background\\\": ((24, 24, 27), (168, 85, 247)),\\n}\\n\\n\\ndef _font(size: int, bold: bool = False) -> ImageFont.FreeTypeFont | ImageFont.ImageFont:\\n candidates = [\\n \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf\\\",\\n ]\\n for path in candidates:\\n if Path(path).exists():\\n return ImageFont.truetype(path, size=size)\\n return ImageFont.load_default()\\n\\n\\ndef _wrap_text(text: str, width_chars: int) -> str:\\n return \\\"\\\\n\\\".join(textwrap.wrap(\\\" \\\".join(text.split()), width=width_chars))\\n\\n\\ndef _gradient(width: int, height: int, start: tuple[int, int, int], end: tuple[int, int, int]) -> Image.Image:\\n image = Image.new(\\\"RGB\\\", (width, height), start)\\n draw = ImageDraw.Draw(image)\\n for y in range(height):\\n ratio = y / max(1, height - 1)\\n color = tuple(round(start[i] * (1 - ratio) + end[i] * ratio) for i in range(3))\\n draw.line([(0, y), (width, y)], fill=color)\\n return image\\n\\n\\ndef _draw_multiline_center(\\n draw: ImageDraw.ImageDraw,\\n xy: tuple[int, int],\\n text: str,\\n font: ImageFont.ImageFont,\\n fill: tuple[int, int, int],\\n spacing: int = 8,\\n) -> None:\\n lines = text.splitlines()\\n line_boxes = [draw.textbbox((0, 0), line, font=font) for line in lines]\\n line_heights = [box[3] - box[1] for box in line_boxes]\\n total_height = sum(line_heights) + spacing * max(0, len(lines) - 1)\\n y = xy[1] - total_height // 2\\n for line, box, line_height in zip(lines, line_boxes, line_heights):\\n line_width = box[2] - box[0]\\n draw.text((xy[0] - line_width // 2, y), line, font=font, fill=fill)\\n y += line_height + spacing\\n\\n\\ndef _render_scene_image(item: dict[str, Any], manifest: dict[str, Any], width: int, height: int) -> Image.Image:\\n start, end = BACKGROUND_BY_TYPE.get(item[\\\"type\\\"], ((24, 24, 27), (64, 64, 64)))\\n image = _gradient(width, height, start, end)\\n draw = ImageDraw.Draw(image)\\n\\n margin = round(width * 0.075)\\n title_font = _font(max(18, round(width * 0.06)), bold=True)\\n label_font = _font(max(12, round(width * 0.032)), bold=False)\\n body_font = _font(max(18, round(width * 0.052)), bold=True)\\n small_font = _font(max(10, round(width * 0.028)), bold=False)\\n\\n # Safe area and content card.\\n card_top = round(height * 0.18)\\n card_bottom = round(height * 0.78)\\n draw.rounded_rectangle(\\n [margin, card_top, width - margin, card_bottom],\\n radius=round(width * 0.04),\\n fill=(255, 255, 255),\\n outline=(226, 232, 240),\\n width=max(2, width // 180),\\n )\\n\\n draw.text((margin, round(height * 0.055)), manifest[\\\"video\\\"][\\\"id\\\"], font=label_font, fill=(226, 232, 240))\\n draw.text((margin, round(height * 0.09)), item[\\\"type\\\"].replace(\\\"_\\\", \\\" \\\").upper(), font=title_font, fill=(255, 255, 255))\\n\\n scene_label = item[\\\"id\\\"].replace(\\\"_\\\", \\\" \\\").upper()\\n draw.text((margin * 1.4, card_top + margin), scene_label, font=small_font, fill=(71, 85, 105))\\n\\n wrapped = _wrap_text(item[\\\"text\\\"], width_chars=22 if width <= 360 else 28)\\n _draw_multiline_center(\\n draw,\\n (width // 2, round(height * 0.45)),\\n wrapped,\\n body_font,\\n (15, 23, 42),\\n spacing=max(5, width // 70),\\n )\\n\\n retention = _wrap_text(item.get(\\\"retention_goal\\\", \\\"\\\"), width_chars=34 if width <= 360 else 46)\\n _draw_multiline_center(\\n draw,\\n (width // 2, round(height * 0.69)),\\n retention,\\n small_font,\\n (71, 85, 105),\\n spacing=max(3, width // 120),\\n )\\n\\n timing = f\\\"{item['start_ms'] / 1000:.1f}s – {(item['start_ms'] + item['duration_ms']) / 1000:.1f}s\\\"\\n draw.text((margin, height - round(height * 0.075)), timing, font=label_font, fill=(226, 232, 240))\\n draw.text((width - margin - draw.textlength(\\\"PREVIEW\\\", font=label_font), height - round(height * 0.075)), \\\"PREVIEW\\\", font=label_font, fill=(226, 232, 240))\\n return image\\n\\n\\ndef build_preview_frames(manifest: dict[str, Any], width: int = 540, height: int = 960) -> list[dict[str, Any]]:\\n \\\"\\\"\\\"Build one representative image per visual timeline item.\\\"\\\"\\\"\\n visual_items = [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n frames: list[dict[str, Any]] = []\\n for item in visual_items:\\n frames.append(\\n {\\n \\\"scene_id\\\": item[\\\"id\\\"],\\n \\\"start_ms\\\": item[\\\"start_ms\\\"],\\n \\\"duration_ms\\\": item[\\\"duration_ms\\\"],\\n \\\"text\\\": item[\\\"text\\\"],\\n \\\"image\\\": _render_scene_image(item, manifest, width, height),\\n }\\n )\\n return frames\\n\\n\\ndef write_frame_images(frames: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n \\\"\\\"\\\"Write preview frames as numbered PNG files.\\\"\\\"\\\"\\n output_dir.mkdir(parents=True, exist_ok=True)\\n paths: list[Path] = []\\n for index, frame in enumerate(frames, start=1):\\n path = output_dir / f\\\"frame_{index:03d}_{frame['scene_id']}.png\\\"\\n frame[\\\"image\\\"].save(path)\\n paths.append(path)\\n return paths\\n\\n\\ndef _write_concat_file(frames: list[dict[str, Any]], image_paths: list[Path], concat_path: Path) -> None:\\n lines: list[str] = []\\n for frame, image_path in zip(frames, image_paths):\\n seconds = max(0.5, frame[\\\"duration_ms\\\"] / 1000)\\n lines.append(f\\\"file '{image_path.as_posix()}'\\\")\\n lines.append(f\\\"duration {seconds:.3f}\\\")\\n if image_paths:\\n lines.append(f\\\"file '{image_paths[-1].as_posix()}'\\\")\\n concat_path.write_text(\\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n\\n\\ndef render_preview_video(\\n manifest: dict[str, Any],\\n output_path: Path,\\n width: int = 540,\\n height: int = 960,\\n fps: int = 24,\\n) -> dict[str, Any]:\\n \\\"\\\"\\\"Render a manifest-driven preview MP4 using Pillow frames and ffmpeg.\\\"\\\"\\\"\\n ffmpeg = shutil.which(\\\"ffmpeg\\\")\\n if not ffmpeg:\\n raise RuntimeError(\\\"ffmpeg is required to render preview MP4 files\\\")\\n\\n output_path.parent.mkdir(parents=True, exist_ok=True)\\n frames = build_preview_frames(manifest, width=width, height=height)\\n with tempfile.TemporaryDirectory(prefix=\\\"autoshorts_preview_\\\") as tmp:\\n tmp_dir = Path(tmp)\\n image_paths = write_frame_images(frames, tmp_dir / \\\"frames\\\")\\n concat_path = tmp_dir / \\\"concat.txt\\\"\\n _write_concat_file(frames, image_paths, concat_path)\\n command = [\\n ffmpeg,\\n \\\"-y\\\",\\n \\\"-f\\\",\\n \\\"concat\\\",\\n \\\"-safe\\\",\\n \\\"0\\\",\\n \\\"-i\\\",\\n str(concat_path),\\n \\\"-vf\\\",\\n f\\\"fps={fps},format=yuv420p\\\",\\n \\\"-movflags\\\",\\n \\\"+faststart\\\",\\n str(output_path),\\n ]\\n completed = subprocess.run(command, check=True, capture_output=True, text=True)\\n return {\\n \\\"output_path\\\": output_path,\\n \\\"frame_count\\\": len(frames),\\n \\\"fps\\\": fps,\\n \\\"stderr\\\": completed.stderr,\\n }\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/preview_renderer.py\"}"
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"arguments": "{\"content\":\"\\\"\\\"\\\"Render preview MP4 videos from AutoShortsBot renderer manifests.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.preview_renderer import render_preview_video\\n\\n\\ndef _default_output_for_manifest(manifest_path: Path, output_dir: Path) -> Path:\\n return output_dir / f\\\"{manifest_path.stem}.mp4\\\"\\n\\n\\ndef main() -> None:\\n parser = argparse.ArgumentParser(description=\\\"Render manifest-driven preview MP4 files.\\\")\\n parser.add_argument(\\\"manifest\\\", help=\\\"Path to a render manifest JSON file.\\\")\\n parser.add_argument(\\\"--out-dir\\\", default=\\\"data/previews/testbatch_002_top3\\\")\\n parser.add_argument(\\\"--width\\\", type=int, default=540)\\n parser.add_argument(\\\"--height\\\", type=int, default=960)\\n parser.add_argument(\\\"--fps\\\", type=int, default=24)\\n args = parser.parse_args()\\n\\n manifest_path = Path(args.manifest)\\n manifest = json.loads(manifest_path.read_text(encoding=\\\"utf-8\\\"))\\n output_path = _default_output_for_manifest(manifest_path, Path(args.out_dir))\\n result = render_preview_video(\\n manifest,\\n output_path,\\n width=args.width,\\n height=args.height,\\n fps=args.fps,\\n )\\n print(f\\\"Rendered preview: {result['output_path']}\\\")\\n print(f\\\"Frames: {result['frame_count']}\\\")\\n print(f\\\"FPS: {result['fps']}\\\")\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/render_preview.py\"}"
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"content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti.",
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"text": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti."
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"content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/manifest-preview-renderer.md\",\"file_content\":\"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)`\\n - one representative image per visual timeline item\\n - uses the manifest text, asset type, timing, and retention goal\\n- `write_frame_images(frames, output_dir)`\\n - writes numbered PNGs for inspection/debugging\\n- `render_preview_video(manifest, output_path, width, height, fps)`\\n - renders frames to temporary PNGs\\n - writes an FFmpeg concat file with per-scene durations\\n - exports `yuv420p` MP4 with `+faststart`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n data/render_manifests//