{ "session_id": "20260524_204754_10d508", "model": "gpt-5.5", "base_url": "https://chatgpt.com/backend-api/codex", "platform": "telegram", "session_start": "2026-05-24T22:38:45.025843", "last_updated": "2026-05-24T22:46:33.259918", "system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [98% — 2,167/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [97% — 1,342/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts: TrueTraceShorts channels; JARVIS operates, user approves/posts manually in phase 1. Approval previews German, finals English; free/local TDD, no side effects until approved. Style: motion-led mechanism-reveal, quality > speed. No-gos: no public JARVIS/user/collab, politics, legal/moral risk, or lawbreaking.\n\nConversation started: Sunday, May 24, 2026 08:47 PM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "feishu_doc_read", "description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.", "parameters": { "type": "object", "properties": { "doc_token": { "type": "string", "description": "The document token (from the document URL or comment context)." } }, "required": [ "doc_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_add_comment", "description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "content": { "type": "string", "description": "The comment text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "content" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comment_replies", "description": "List all replies in a comment thread on a Feishu document.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to list replies for." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "page_size": { "type": "integer", "description": "Number of replies per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token", "comment_id" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comments", "description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "is_whole": { "type": "boolean", "description": "If true, only return whole-document comments.", "default": false }, "page_size": { "type": "integer", "description": "Number of comments per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_reply_comment", "description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to reply to." }, "content": { "type": "string", "description": "The reply text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "comment_id", "content" ] } } }, { "type": "function", "function": { "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.", "parameters": { "type": "object", "properties": { "prompt": { "type": "string", "description": "The text prompt describing the desired image. Be detailed and descriptive." }, "aspect_ratio": { "type": "string", "enum": [ "landscape", "square", "portrait" ], "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.", "default": "landscape" } }, "required": [ "prompt" ] } } }, { "type": "function", "function": { "name": "memory", "description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "add", "replace", "remove" ], "description": "The action to perform." }, "target": { "type": "string", "enum": [ "memory", "user" ], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile." }, "content": { "type": "string", "description": "The entry content. Required for 'add' and 'replace'." }, "old_text": { "type": "string", "description": "Short unique substring identifying the entry to replace or remove." } }, "required": [ "action", "target" ] } } }, { "type": "function", "function": { "name": "mixture_of_agents", "description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.", "parameters": { "type": "object", "properties": { "user_prompt": { "type": "string", "description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning." } }, "required": [ "user_prompt" ] } } }, { "type": "function", "function": { "name": "patch", "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": { "type": "object", "properties": { "mode": { "type": "string", "enum": [ "replace", "patch" ], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace" }, "path": { "type": "string", "description": "REQUIRED when mode='replace'. File path to edit." }, "old_string": { "type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness." }, "new_string": { "type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text." }, "replace_all": { "type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false }, "patch": { "type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch" } }, "required": [ "mode" ] } } }, { "type": "function", "function": { "name": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "list", "poll", "log", "wait", "kill", "write", "submit", "close" ], "description": "Action to perform on background processes" }, "session_id": { "type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'." }, "data": { "type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)" }, "timeout": { "type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1 }, "offset": { "type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)" }, "limit": { "type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1 } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "read_file", "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)" }, "offset": { "type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1 }, "limit": { "type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000 } }, "required": [ "path" ] } } }, { "type": "function", "function": { "name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search" }, "target": { "type": "string", "enum": [ "content", "files" ], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content" }, "path": { "type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "." }, "file_glob": { "type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)" }, "limit": { "type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50 }, "offset": { "type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0 }, "output_mode": { "type": "string", "enum": [ "content", "files_only", "count" ], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content" }, "context": { "type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0 } }, "required": [ "pattern" ] } } }, { "type": "function", "function": { "name": "send_message", "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "send", "list" ], "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms." }, "target": { "type": "string", "description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)" }, "message": { "type": "string", "description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment." } } } } }, { "type": "function", "function": { "name": "session_search", "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)." }, "role_filter": { "type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs." }, "limit": { "type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3 } } } } }, { "type": "function", "function": { "name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "create", "patch", "edit", "delete", "write_file", "remove_file" ], "description": "The action to perform." }, "name": { "type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file." }, "content": { "type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text." }, "old_string": { "type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness." }, "new_string": { "type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text." }, "replace_all": { "type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)." }, "category": { "type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'." }, "file_path": { "type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted." }, "file_content": { "type": "string", "description": "Content for the file. Required for 'write_file'." }, "absorbed_into": { "type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent." } }, "required": [ "action", "name" ] } } }, { "type": "function", "function": { "name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')." }, "file_path": { "type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content." } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } } }, { "type": "function", "function": { "name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The command to execute on the VM" }, "background": { "type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false }, "timeout": { "type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1 }, "workdir": { "type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory." }, "pty": { "type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false }, "notify_on_complete": { "type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false }, "watch_patterns": { "type": "array", "items": { "type": "string" }, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both." } }, "required": [ "command" ] } } }, { "type": "function", "function": { "name": "text_to_speech", "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated." }, "output_path": { "type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3" } }, "required": [ "text" ] } } }, { "type": "function", "function": { "name": "todo", "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": { "type": "object", "properties": { "todos": { "type": "array", "description": "Task items to write. Omit to read current list.", "items": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique item identifier" }, "content": { "type": "string", "description": "Task description" }, "status": { "type": "string", "enum": [ "pending", "in_progress", "completed", "cancelled" ], "description": "Current status" } }, "required": [ "id", "content", "status" ] } }, "merge": { "type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false } } } } }, { "type": "function", "function": { "name": "video_analyze", "description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.", "parameters": { "type": "object", "properties": { "video_url": { "type": "string", "description": "Video URL (http/https) or local file path to analyze." }, "question": { "type": "string", "description": "Your specific question about the video. The AI will describe what happens in the video and answer your question." } }, "required": [ "video_url", "question" ] } } }, { "type": "function", "function": { "name": "vision_analyze", "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "description": "Image URL (http/https), local file path, or data: URL to load." }, "question": { "type": "string", "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image." } }, "required": [ "image_url", "question" ] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)" }, "content": { "type": "string", "description": "Complete content to write to the file" } }, "required": [ "path", "content" ] } } } ], "message_count": 93, "messages": [ { "role": "user", "content": "[M G] co/Civitai/Qwen-Image-2512-GGUF \"Civitai/Qwen-Image-2512-GGUF · Hugging Face\"\nSollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?\n für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.\n\nMeine Rangliste für deinen Usecase:\n\n| Zweck | Empfehlung | Warum |\n| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Beste kostenlose GGUF-Bildgenerierung in ComfyUI | Qwen-Image-2512-GGUF | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |\n| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2]) |\n| Schnell / weniger VRAM / experimentell | Z-Image Turbo GGUF | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3]) |\n| Alternative zu Qwen, stark aber schwerer | FLUX.2-dev-GGUF | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4]) |\n| Aktuell stärkstes Open-Weights-Modell allgemein | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5]) |\n\nKurz gesagt:\nFür deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.\n\nDie passende Auswahl nach VRAM:\n\n| VRAM | Modell/Quantisierung |\n| ----------: | ------------------------------------------------------ |\n| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |\n| 16 GB | Q5 oder Q6 testen |\n| 24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität |\n| <8 GB | eher Z-Image Turbo GGUF statt Qwen |\n\nWichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])\n\nMeine konkrete Empfehlung:\nInstalliere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.\n\n[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF \"unsloth/Qwen-Image-2512-GGUF · Hugging Face\"\n[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF \"unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face\"\n[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF \"jayn7/Z-Image-Turbo-GGUF · Hugging Face\"\n[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF \"orabazes/FLUX.2-dev-GGUF · Hugging Face\"\n[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image \"Text to Image Leaderboard - Top AI Image Models\"\n[6]: https://huggingface.\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen." }, { "role": "user", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 49 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 16 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---\n\nErledigt, Sir.\n\nIch habe aus den 5 Vorschlägen jetzt einen **konkreten, testbaren TrueTrace Concept Testbatch** gebaut — nicht nur Text im Chat, sondern echte Pipeline-Artefakte.\n\n## Neu gebaut\n\n```text\nautoshorts/demo/truetrace_concepts.py\ntests/test_truetrace_concept_batch.py\ndocs/truetrace_concept_testbatch_001.md\n```\n\nCommit:\n\n```text\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Was enthalten ist\n\nDer Testbatch enthält exakt diese 5 Kandidaten:\n\n```text\n1. Why Most AI Automations Break After Three Days\n2. This Prompt Looks Smart — But the Workflow Still Breaks\n3. The Hidden Reason Your AI Notes Become Useless\n4. Claim: AI Agents Will Replace Teams\n5. One Rule Before You Automate Anything With AI\n```\n\nBatch-ID:\n\n```text\ntruetrace-concept-testbatch-001\n```\n\n## Jeder Kandidat enthält jetzt strukturiert\n\n- Candidate ID\n- Titel\n- Plattformen:\n - YouTube\n - TikTok\n- Dauer\n- Hook\n- Script Outline\n- Visual Concept\n- Caption\n- CTA\n- Hashtags\n- YouTube Tags\n- Hypothesis\n- Brand Policy Notes\n\nAlso nicht mehr “Ideen-Geschwurbel”, sondern prüfbare Kandidaten. Eine kleine, aber entscheidende zivilisatorische Verbesserung.\n\n## Candidate IDs\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\ntruetrace-prompt-vs-system-workflow-breaks-35s\ntruetrace-ai-notes-equal-importance-34s\ntruetrace-ai-agents-tasks-not-teams-40s\ntruetrace-review-before-automation-32s\n```\n\n## Validierung\n\nDie Tests prüfen:\n\n- genau 5 Kandidaten\n- stabile Reihenfolge\n- Zielplattformen nur YouTube/TikTok\n- Brand Policy Notes vorhanden\n- alle Kandidaten bestehen TrueTrace Brand Fit\n- alle Kandidaten bestehen Series Fit\n- keine öffentlichen No-Go-Inhalte:\n - kein JARVIS\n - kein Hermes\n - kein User\n - keine Zusammenarbeit\n - keine Politik\n- Summary enthält YouTube-/TikTok-Metadaten\n- Summary enthält keine Action-Sprache wie:\n - `publish`\n - `upload`\n - `post`\n\n## Neuer Renderer\n\nNeue Funktion:\n\n```python\nrender_truetrace_concept_summary()\n```\n\nSie rendert alle 5 Konzepte als reviewbaren Text mit:\n\n- Hook\n- Outline\n- Visuals\n- Hypothesis\n- YouTube Description\n- TikTok Caption\n- Tags\n\nKeine Side Effects. Kein Upload. Kein Platform Call. Nur Review-Text.\n\n## Beispielauszug\n\n```text\n## TrueTrace Concept Testbatch\nBatch: truetrace-concept-testbatch-001\nCandidates: 5\nBrand: True Trace Shorts — Systems over prompts.\n\n## 1. Why Most AI Automations Break After Three Days\nID: truetrace-ai-automation-quiet-rot-38s\nDuration: 38s\nHook: Most AI automations do not fail dramatically. They quietly rot.\n...\nYouTube:\nAI automations rarely explode. They quietly rot when the workflow has no exception path.\n\nFollow for practical AI systems, not automation theatre.\n\n#AI #AISystems #Workflow #Productivity\n```\n\n## Dokumentiert\n\nNeue Datei:\n\n```text\ndocs/truetrace_concept_testbatch_001.md\n```\n\nDarin:\n\n- Safety Scope\n- alle 5 Candidates\n- Hypothesen\n- Review Command\n- empfohlene Priorisierung\n\n## Empfohlene erste 3 für Preview-Produktion\n\nIch habe im Dokument festgelegt:\n\n```text\n1. Why Most AI Automations Break After Three Days\n2. This Prompt Looks Smart — But the Workflow Still Breaks\n3. The Hidden Reason Your AI Notes Become Useless\n```\n\nWarum:\n\n- konkreter\n- visuell gut darstellbar\n- weniger generisch\n- weniger abhängig vom allgemeinen AI-Hype\n- besserer Mechanism-Reveal\n\n## Tests\n\n```text\n139 passed in 8.79s\n```\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests zuerst geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.demo.truetrace_concepts'\n```\n\n3. Minimal implementiert.\n4. Focused Tests grün.\n5. Full Suite grün.\n6. README + Docs aktualisiert.\n7. Commit erstellt.\n\n## Aktueller Stand\n\nJetzt haben wir einen echten ersten Content-Testbatch:\n\n```text\nTrueTrace BrandProfile\n→ ContentSeries Fit\n→ Brand Fit\n→ 5 Concrete Candidates\n→ Reviewable Concept Summary\n→ YouTube/TikTok metadata included\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich aus den besten 3 Konzepten echte **Preview Packages** bauen:\n\n```text\nConcept Candidate\n→ ShotPlan\n→ RenderManifest\n→ Local Preview Plan\n→ PNG scene cards\n→ MP4 timing preview\n→ Social Draft\n→ Manual Posting Pack\n```\n\nPriorität:\n\n```text\n1. AI Automations Quietly Rot\n2. Prompt vs System\n3. AI Notes Become Useless\n```\n\nDann sehen wir nicht nur Textideen, sondern bewegte Review-Previews mit Captions, Timing und Metadaten. Erst dann entscheiden wir, ob daraus finale Videos werden." }, { "role": "user", "content": "[M G] Ok, mach das. Und noch eine frage: wie lösen wir die Subtitles (harmozi style?) und welches TTS model? Bei build.nvidia.com finde ich nur dieses: https://build.nvidia.com/nvidia/magpie-tts-multilingual/api . Ist das besser als EdgeTTS? Oder besser lokal whisper? Jedoch brauchen wir die gpu ja für das video…" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Gebaut: Top-3 TrueTrace Preview Packages\n\nIch habe jetzt aus den drei priorisierten Konzepten echte lokale Timing-Previews gebaut:\n\n```text\nautoshorts/demo/truetrace_preview_packages.py\ntests/test_truetrace_preview_packages.py\ndocs/subtitle_tts_strategy.md\n```\n\nCommit:\n\n```text\n6efc4dc feat: add TrueTrace top preview packages\n```\n\n## Enthaltene Top-3\n\n```text\n1. Why Most AI Automations Break After Three Days\n2. This Prompt Looks Smart — But the Workflow Still Breaks\n3. The Hidden Reason Your AI Notes Become Useless\n```\n\n## Generierte Preview-Videos\n\nDie MP4-Timing-Previews liegen hier:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-top3/truetrace-ai-automation-quiet-rot-38s/preview.mp4\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-top3/truetrace-prompt-vs-system-workflow-breaks-35s/preview.mp4\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-top3/truetrace-ai-notes-equal-importance-34s/preview.mp4\n\nDas sind bewusst noch **Approval-Timing-Previews**, nicht finale Videos. Also: Bewegung, Timing, Szenenlogik, Captions, visuelle Struktur — aber noch nicht finale TTS/Audio/AI-Video-Qualität.\n\n## Was die neue Pipeline macht\n\nNeue Funktion:\n\n```python\nbuild_top_truetrace_preview_packages()\n```\n\nSie erzeugt für jeden Top-3 Candidate:\n\n```text\nCandidate\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpeg MP4 timing preview\n→ SocialMediaDraft\n→ ContentPreviewApprovalPackage\n→ ManualPostingPack\n```\n\nDazu gibt es:\n\n```python\nrender_top_truetrace_preview_summary()\n```\n\nfür eine kompakte Telegram-/Review-Zusammenfassung.\n\n## Side Effects sauber getrennt\n\nDie Preview-Erzeugung hat erwartete lokale Render-Side-Effects:\n\n```text\nwrite_png\nwrite_concat\ncall_ffmpeg\nwrite_mp4\n```\n\nAber die Approval-/Manual-Packs selbst bleiben sauber:\n\n```text\nside_effects = ()\nexternal_calls = ()\n```\n\nAlso kein YouTube, kein TikTok, kein Upload, kein Scheduling, keine API. Noch kein Content-Katapult. Sehr beruhigend.\n\n## Tests\n\n```text\n142 passed in 31.72s\n```\n\nTDD eingehalten:\n\n1. Test zuerst.\n2. RED verifiziert:\n ```text\n ModuleNotFoundError: No module named 'autoshorts.demo.truetrace_preview_packages'\n ```\n3. Implementiert.\n4. Focused Tests grün.\n5. Bug mit relativen Pfaden gefunden.\n6. Regressionstest für relative Output-Pfade ergänzt.\n7. Full Suite grün.\n8. Commit erstellt.\n\nNebenbei `.gitignore` ergänzt:\n\n```text\ndata/previews/\n```\n\nDamit generierte Preview-Artefakte nicht versehentlich ins Repo wandern. Git ist kein Medienlager, auch wenn es gelegentlich so missbraucht wird.\n\n---\n\n# Zu Subtitles: Hormozi Style?\n\nJa — aber ich würde es bewusst so formulieren:\n\n```text\nHormozi-inspired, not Hormozi-cloned.\n```\n\nWir sollten den Mechanismus übernehmen, nicht die Identität kopieren.\n\n## Empfehlung\n\nSubtitles als **renderer-owned deterministic captions**, nicht in AI-Video/Image gebacken.\n\nAlso:\n\n```text\nScript\n→ Caption Chunker\n→ Timed Subtitle Cues\n→ Renderer-owned subtitle layer\n→ FFmpeg burn-in\n```\n\nNicht:\n\n```text\nPrompt an Bildmodell: “mach fancy captions”\n```\n\nWarum? Weil AI-baked Text früher oder später aussieht wie ein betrunkener Drucker mit Ambitionen.\n\n## Style-Regeln\n\n```text\n2–5 Wörter pro Caption-Chunk\nmax. 2 Zeilen\nactive word highlight\nstarker schwarzer Stroke / Shadow\nweiss als Hauptfarbe\nTrueTrace Accent: blau/grün\nlower-middle Placement, oberhalb UI Safe Zone\nschnelle, aber lesbare Wechsel\nwichtige Wörter gelegentlich farbig\n```\n\nBeispiel:\n\n```text\nMOST AI AUTOMATIONS\nQUIETLY ROT\n```\n\nDann active highlight auf:\n\n```text\nQUIETLY\nROT\n```\n\n## Wichtig\n\nDie Captions müssen **bei uns im Renderer** entstehen, damit wir:\n\n- Text sauber kontrollieren\n- Timing ändern können\n- Fehler korrigieren können\n- keine kaputten AI-Texte im Bild haben\n- später Whisper/TTS-Alignment nutzen können\n\nDas ist auch besser für Brand-Konsistenz.\n\n---\n\n# TTS: EdgeTTS vs NVIDIA Magpie vs Whisper\n\n## Kurzfassung\n\nFür Phase 1 würde ich nehmen:\n\n```text\nTTS: EdgeTTS English baseline\nSubtitles: deterministic renderer-owned captions\nMagpie: später A/B testen\nWhisper: später Alignment/QA, nicht TTS\nGPU: für Video/Image reservieren\n```\n\n## Whisper\n\nWhisper ist **nicht** TTS.\n\nWhisper ist Speech-to-Text. Es kann später helfen für:\n\n- TTS-Audio transkribieren\n- Timing prüfen\n- Subtitle Alignment\n- Aussprachen/Fehler erkennen\n\nAber es erzeugt keine Stimme.\n\nAlso:\n\n```text\nWhisper = Kontrolle / Alignment\nnicht Voice Generation\n```\n\n## EdgeTTS\n\nFür jetzt wahrscheinlich die beste Baseline.\n\nVorteile:\n\n- kein GPU-Verbrauch\n- schnell\n- kostenlos / kein API-Key\n- einfache Integration\n- gut genug für erste Tests\n- blockiert ComfyUI/Wan/Qwen nicht\n\nNachteile:\n\n- nicht super einzigartig\n- etwas “Standard-Neural-Voice”\n- Lizenz/Terms vor echter Skalierung nochmals prüfen\n\nMögliche englische Testvoices:\n\n```text\nen-US-GuyNeural\nen-US-ChristopherNeural\nen-US-AriaNeural\nen-GB-RyanNeural\n```\n\nIch würde für TrueTrace zuerst testen:\n\n```text\nen-GB-RyanNeural\n```\n\noder:\n\n```text\nen-US-ChristopherNeural\n```\n\nWarum? Der Kanal soll präzise, trocken, mechanism-first wirken. Zu enthusiastische “YouTube Guru”-Stimme wäre Gift. Ein sachlicher, klarer Ton passt besser.\n\n## NVIDIA Magpie TTS\n\nDas Modell auf build.nvidia.com:\n\n```text\nnvidia/magpie-tts-multilingual\n```\n\nwürde ich **nicht sofort als Default** nehmen, sondern als A/B-Test.\n\nVorteile:\n\n- vermutlich modernere Qualität als Standard EdgeTTS\n- multilingual\n- läuft cloudseitig, also vermutlich kein lokaler GPU-Konflikt\n\nNachteile:\n\n- API-Abhängigkeit\n- Kosten/Rate-Limits/Terms klären\n- Stimme/Style noch nicht gehört\n- Integration noch nicht gebaut\n- eventuell Overkill, bevor Content-Format validiert ist\n\nWichtig zu Ihrer GPU-Frage:\n\nWenn Magpie über build.nvidia.com läuft, verbraucht es **nicht unsere lokale Video-GPU**. Aber es schafft eine externe API-Abhängigkeit. Lokal TTS auf derselben GPU wäre dagegen ungünstig, weil wir die GPU für Video/Image brauchen.\n\n## Lokale TTS\n\nAktuell nicht meine erste Wahl, falls GPU nötig.\n\nNur sinnvoll, wenn:\n\n- CPU schnell genug\n- separate Maschine\n- GPU idle\n- deutlich bessere Stimme\n\nSonst blockieren wir Video-Generierung für Voice. Das wäre etwa so, als würde man den Backofen für Toast reservieren, während der Braten roh bleibt.\n\n---\n\n# Dokumentiert\n\nNeue Datei:\n\n```text\ndocs/subtitle_tts_strategy.md\n```\n\nDarin steht jetzt:\n\n```text\nSubtitles:\ndeterministic Hormozi-inspired renderer-owned captions\n\nTTS:\nEdgeTTS baseline first\n\nMagpie:\nA/B candidate later\n\nWhisper:\nalignment/QA later, not TTS\n\nGPU:\nreserve for video/image generation\n```\n\n---\n\n# Meine konkrete Empfehlung\n\n## Phase 1A — jetzt\n\nFür die aktuellen Previews:\n\n```text\nGerman approval preview cards\nkein final TTS nötig\nkeine finalen Subtitles nötig\n```\n\nZiel: Konzept, Timing, Struktur beurteilen.\n\n## Phase 1B — nächster Schritt\n\nFür das erste finale Testvideo:\n\n```text\nEnglish final voiceover mit EdgeTTS\nHormozi-inspired subtitles deterministic burn-in\nkein Magpie noch\nkein Whisper noch\n```\n\n## Phase 1C — danach\n\nA/B-Test:\n\n```text\ngleicher 15–20 Sekunden Script-Ausschnitt\n1. EdgeTTS\n2. NVIDIA Magpie\n3. optional lokale TTS, falls CPU/offline brauchbar\n```\n\nBewertung:\n\n- Klarheit\n- Energie\n- Pronunciation von AI/workflow/system terms\n- Artefakte\n- Latenz\n- Kosten\n- Integrationsaufwand\n\nDann entscheiden wir Voice final. Nicht vorher aus dem Bauch. Der Bauch ist für Tee, nicht für Produktionsarchitektur.\n\n---\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/audio/subtitles.py\ntests/test_subtitle_cues.py\n```\n\nZiel:\n\n```text\nScript text\n→ 2–5 word caption chunks\n→ timed subtitle cues\n→ active-word/emphasis metadata\n→ safe-zone style config\n```\n\nDanach:\n\n```text\nautoshorts/audio/tts_plan.py\ntests/test_tts_plan.py\n```\n\nZiel:\n\n```text\nTTSProviderPlan(provider=\"edge_tts\", voice=\"en-GB-RyanNeural\")\n```\n\nNoch ohne echte API/Audio-Erzeugung — erst Plan, dann Adapter. Langweilig, kontrolliert, gut." }, { "role": "user", "content": "[M G] Du hast mir 3x das selbe video gesendet. Mach jetzt weiter" }, { "role": "assistant", "content": "", "reasoning": "**Planning inspection tasks**\n\nI need to take action and inspect the repository. It seems relevant skills could include software development and possibly test-driven development (TDD), as well as checking automation for social shorts. I want to load the necessary skills to handle this effectively. It’s all about ensuring I have the right tools and knowledge to move forward confidently! Let’s get started on this inspection.", "finish_reason": "tool_calls", "reasoning_content": "**Planning inspection tasks**\n\nI need to take action and inspect the repository. It seems relevant skills could include software development and possibly test-driven development (TDD), as well as checking automation for social shorts. I want to load the necessary skills to handle this effectively. It’s all about ensuring I have the right tools and knowledge to move forward confidently! Let’s get started on this inspection.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2HZE_eesUTuhzMqyGZ4U8DbBq3L8XaNogUM5STrwItDa-4baGzC5wVdv1q7nbWQxcpOP0p9qm_G0fswSqkwcPQJCASFePGPn8kh8rfTMX98X4415ipQjALaTbaWWl7G0ryZS1ilpGvR0VBaKcLGYDkdM7wRG0rW0szsvDLuXB8xtU4kYEbAwKEzuvrSXICdj7xglXC-qS0_tapAJpXwrEV5DD-_lxLhaVdpE5Zi1JQvv1IY2EDLaqxntTceixJdP3TrTf2fxA4mFolzqfFwaMIEAgg6ZxbcoCWOLNZedyEMTbCdhNlauie4jJZotgl5exOr3zzBtBSLQ4C-ASjpevbU9Tgx49Eq5zFf2N8-PihOEOb9Ru8qz2oTFZYdK7kEjTYT-hBghnL0GnZ0MgDKrwkoX5g1CgoPnuxehlCIWm7sO-g-xLb3J0fEgdhNPEZxQo_Zsefn3cvtypdLqbljocRmjjjppE69Pgc8ETNpNG8XKcyt6RsM5KDBpG_4TSi9Ba3vBEIpRyWKGARUu9Oxd7KUKFqvPjsdGASkF3QCwqPAYY6htPJHw0RqvzB8NF9uMPyTKEQRwHMGfiydTCaPhL6En-UR-xInzyQ-i02TY59dj5T-ln3_jOhM3St2m6XXII1-717PXCbAyE9gkUTMuTrdbEGnjM2S4IPz93Q6moHJkj9J54ZfYQvxWzqru8iFJz50I4cK2_O-dp_QS1DwEW7yjIRVOuqr9PA-dZJmYApAXWVph3K3ZKcmn8IABiz6ej5Hq36IQgSENYyTp00-5CVuJxUtQLutLFBmUIpbxYM1XCaLpzFPEpoAd_oQBvTXHA2Sw-yDaMvvt5Q51FzaSdJh3S9XXXgrwywZ_s3zSYRW_Pm-_ZCC8ALf7AYhFRZX5f7e3ma37u7eCJWw4hDibCIPc4bOk_Ypy6jXQYMy46kUyL5k3fovbLHgr_BtnQAUwcSIRkmMr_p485HSwgxpx8LHgasI9XjW-zlSRbU2pHTMecT5S-6pEXUlF-toYm8FkwTQPrunN2UkctTfPsfvP6hdJhqSrr0771_ngQfPIr9iwF-SMh1HASgCLYvE-Spq4eKgIXxv3TyXjLw7wWFSsd-0Mg==", "id": "rs_0346cc5087231f0b016a1361d6bfe0819188c1997f4ddee572", "summary": [ { "type": "summary_text", "text": "**Planning inspection tasks**\n\nI need to take action and inspect the repository. It seems relevant skills could include software development and possibly test-driven development (TDD), as well as checking automation for social shorts. I want to load the necessary skills to handle this effectively. It’s all about ensuring I have the right tools and knowledge to move forward confidently! Let’s get started on this inspection." } ] } ], "tool_calls": [ { "id": "call_nszBUhoglrUUznr8bemLoTJD", "call_id": "call_nszBUhoglrUUznr8bemLoTJD", "response_item_id": "fc_0346cc5087231f0b016a1361d90f34819197d6e02a1167b607", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### CLI Entry Points\\n\\nFor new `python -m package.cli.command` style entry points, keep the TDD loop boring and side-effect free:\\n\\n1. Write a test that imports `main` from the missing CLI module and calls `main([])`.\\n2. Capture stdout/stderr with the project test framework (`capsys` in pytest) or inject a `stdout` file-like object if supported.\\n3. RED should usually be an import failure for the missing CLI module/package, not a vague end-to-end shell failure.\\n4. Implement `main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int` so tests can call it without spawning a subprocess.\\n5. Add the `if __name__ == \\\"__main__\\\": raise SystemExit(main())` guard only after the callable path is green.\\n6. Verify both the unit test and the real module invocation, e.g. `python -m package.cli.command`, before committing.\\n\\nThis catches command wiring while avoiding accidental network calls, publishing actions, or other theatrical nonsense masquerading as a CLI test.\\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## Demo / Fixture Features\\n\\nWhen adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.\\n\\n## Side-Effect-Free Integration Boundaries\\n\\nWhen building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:\\n\\n1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.\\n2. RED should be a missing module/API or missing validation failure, not a real network/send failure.\\n3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.\\n4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.\\n5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.\\n6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.\\n\\nThis pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.\\n\\n### Media / Rendering Pipeline Boundaries\\n\\nWhen building toward image/video generation or renderer integration, add deterministic planning artifacts before any GPU/API work:\\n\\n1. Write tests for the wished-for pure API first, e.g. `create_shot_plan(script)`, `build_render_manifest(plan)`, `build_local_preview_plan(manifest)`, or `build_ffmpeg_concat_plan(plan, frames, output_path)`.\\n2. RED should be a missing module/API or missing validation behavior — never a failed ComfyUI/Wan/Qwen/API invocation.\\n3. Keep each stage inspectable and side-effect-free until the explicit adapter layer: `ScriptDraft -> ShotPlan -> RenderManifest -> PreviewPlan/PreviewArtifact -> renderer adapter`.\\n4. Assert stable IDs/order, scene counts, timing continuity, vertical settings (`1080x1920`, `30fps` when building Shorts/Reels/TikTok), reduced preview settings (`540x960`, `24fps` when building fast local timing previews), and placeholder/runtime paths before generating media.\\n5. Add guardrail tests that reject static slideshows, baked-in/generated text inside assets, non-renderer-owned captions/labels, unsafe render settings, missing fields, bad timings, non-PNG frame paths, non-MP4 output paths, and mismatched frame counts.\\n6. Split artifact-writing/rendering into explicit boundaries after the pure plans are green: frame writer returns `side_effects=(\\\"write_png\\\",)`, concat planning remains side-effect-free, and the FFmpeg renderer returns `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` plus `external_calls=(\\\"ffmpeg\\\",)`.\\n7. Only after the manifest/preview boundary is green should a later adapter call local GPU tools or external services. Planning first; GPU roulette later.\\n\\nThis prevents wasting render time on unclear direction and keeps expensive media generation behind a small, audited boundary.\\n\\n### Approval-First Media/Social Pipelines\\n\\nWhen a feature crosses from content planning toward media/social delivery, prefer many small RED-GREEN-README-COMMIT slices instead of one large integration jump:\\n\\n1. typed artifact/model (`SocialMediaDraft`, `RenderManifest`, etc.)\\n2. renderer/formatter for human review text\\n3. side-effect-free delivery/preparation boundary\\n4. demo CLI that prints metadata first, then payload\\n\\nFor each slice, write the missing import/API test first, verify the expected import/API failure, implement only that slice, run the focused test, run the full suite, update docs, and commit. Keep invariant tests explicit: `requires_human_approval=True`, `side_effects=()`, no external calls, no credential access, no file writes unless that adapter is the explicit feature, and no accidental action language such as `publish`/`post` in review/delivery text.\\n\\nThis staged approach keeps approval semantics auditable and prevents a later renderer, Telegram sender, or platform adapter from being smuggled into formatting code.\\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- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\\n- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`\\\"\\\"`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.\\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\": {\"references\": [\"references/deterministic-demo-fixtures.md\", \"references/side-effect-free-delivery-boundaries.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\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_nszBUhoglrUUznr8bemLoTJD" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_AjWO7QoOXmfQy7Is6cWDYEMI", "call_id": "call_AjWO7QoOXmfQy7Is6cWDYEMI", "response_item_id": "fc_0c42eba0822d70c8016a1361dade3c8191a3fd920caa976681", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. 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I should definitely apologize for that and look into fixing it. Perhaps I can send the correct version with labels. Also, the phrase \"mach weiter\" suggests it’s time to proceed with the next steps for subtitles or TTS planning. I need to inspect the current files to make sure everything is in order. Let's get this sorted!", "finish_reason": "tool_calls", "reasoning_content": "**Addressing duplicate video issue**\n\nI’m thinking the user mentioned a duplicate video was sent. I should definitely apologize for that and look into fixing it. Perhaps I can send the correct version with labels. Also, the phrase \"mach weiter\" suggests it’s time to proceed with the next steps for subtitles or TTS planning. I need to inspect the current files to make sure everything is in order. Let's get this sorted!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Hnf_dLJH0SXrt-hFXKPJ5qJBuo15LRFcpkSTyk-ZpGm4Aw06aS-3WZJWM10W1zPpf-FWVR7dB_Pn_BRcXbYe3n9FbFixwwvmBP3-AZafZvAPan1i-R1EXqH7bmTj0zOYjhEXgAJt58KqrzLHgcA5-OyRA9KQXUu1wTQ0yT7nJiV7JusA9tXeF73CTiFEUFsp4n_uLw0E3Kgla8Phl0v_uposAfKMuDcn5xFnqtESHgkDfo0orOypAUfAF0DzxuoVFYiWUwVOYRvcB3Z4oKCDeWYguFuUTLX5Y-J_nyHJG2QJOgow6RiXGRS4g0Mvw6l4NcixFbmAVPD8YXaO6zxfaYdcaz_R4YOwfH6ejd9NTQI35tocr7y3uniKxiADroaZasLpnltD_oYUv-8_368DKhKdRXDOIQ5p4Bxacm8_D4_WeJb43hD3R2IDD8J-eez7dUDgdKXfahfWcp7A0K7A_f2SJAMO6b2CpthK78SP82e7yabEyQf8-W2kq4kVfCxfwsSMiGvxJum8MvqZ5I-S5MCQ9Wvo_Qej1DASE66RFSkJP2HvF_b-fEzGwv7JZwL7xeugn759txvK_opUPUxEhJfflMzsekHLexTqNcJrMHltLR6MRZSGztxqW1dIgewch6DvhJGGOua28ekB3q4bABWnLY2NDZRhbUQkATQbXxr71ivHD5WI1SSSG_5wgqJOONBmD0Wt0abVO03fw5_TFD1esUbXIw1pD1gyGZGRvd0ectXoATgXw4c5UBNl8HUatCA1YwpamjD7DX8uCprEoUAbzfcqLdf9BxX-OydeeLnwJrOJY6Zbdkt_dLwn2baTjArty1sc3qrtahzD2yDFkEA0A0cwuUdpuJepQUyKMnxxHwWdyW62npwJWV6WB6l08ihI1wuenL2ht8vIOC2ptMgtZlSvLPgs12uZh8FZ0SMIVa1CspF-MuIgPHrtzXSiv06aNhI1p20D-qjtWst3ljJ1MTi0KbQsTlJTc-dIVo_rYnsbXx9T-cakfrPPTJnnQdrVOXpUgDx-dcM4GyzOboSVw1a_Rt_oZYqFQiVf-BZTgElBJDsvGxtIggnquqZAhiHW9WpN1PpO-f0-cyO7xLb63pMKwDwlexa_nHwg1UY7MwhjkH1C0vDNEqv8oHGcgQ06UnkKg-RinGh5oNs9taWmhyZAtgBcq17A4ysuIuyKs=", "id": "rs_049980ba48668433016a1361e3ad888191bad353cdd0cd01ec", "summary": [ { "type": "summary_text", "text": "**Addressing duplicate video issue**\n\nI’m thinking the user mentioned a duplicate video was sent. I should definitely apologize for that and look into fixing it. Perhaps I can send the correct version with labels. Also, the phrase \"mach weiter\" suggests it’s time to proceed with the next steps for subtitles or TTS planning. I need to inspect the current files to make sure everything is in order. Let's get this sorted!" } ] } ], "tool_calls": [ { "id": "call_yNtSGYA7CcOt5Yk2w6hdUyw9", "call_id": "call_yNtSGYA7CcOt5Yk2w6hdUyw9", "response_item_id": "fc_049980ba48668433016a1361e75fe8819196c681fe6a37b1b2", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\",\"limit\":200}" } }, { "id": "call_iqP0uc4Se6SI6NYjbyaiHbpH", "call_id": "call_iqP0uc4Se6SI6NYjbyaiHbpH", "response_item_id": "fc_049980ba48668433016a1361e760008191b5b1c2fa55a8bba6", "type": "function", "function": { "name": "search_files", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts\",\"target\":\"files\",\"pattern\":\"*.py\",\"limit\":100}" } }, { "id": "call_csMH2Prm1IhsvVv28irwJ0w6", "call_id": "call_csMH2Prm1IhsvVv28irwJ0w6", "response_item_id": "fc_049980ba48668433016a1361e7600c8191ae292d57fa10312e", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\",\"limit\":220}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Build top-3 TrueTrace preview approval packages with local MP4 previews.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|from pathlib import Path\\n 7|\\n 8|from autoshorts.content.script_draft import ScriptDraft\\n 9|from autoshorts.demo.truetrace_concepts import build_truetrace_concept_batch\\n 10|from autoshorts.ideas.candidate import VideoCandidate\\n 11|from autoshorts.preview.package import ContentPreviewApprovalPackage, build_content_preview_approval_package\\n 12|from autoshorts.rendering.ffmpeg_plan import build_ffmpeg_concat_plan\\n 13|from autoshorts.rendering.ffmpeg_renderer import render_ffmpeg_preview_video\\n 14|from autoshorts.rendering.local_frame_writer import write_local_preview_frames\\n 15|from autoshorts.rendering.local_preview import build_local_preview_plan\\n 16|from autoshorts.rendering.manifest import build_render_manifest\\n 17|from autoshorts.rendering.preview import build_html_preview\\n 18|from autoshorts.rendering.shot_plan import create_shot_plan\\n 19|from autoshorts.social.draft import SocialMediaDraft, build_social_media_draft\\n 20|from autoshorts.social.manual_posting import ManualPostingPack, build_manual_posting_pack\\n 21|from autoshorts.social.review import render_social_media_draft_review\\n 22|\\n 23|TRUE_TRACE_TOP3_CANDIDATE_IDS = (\\n 24| \\\"truetrace-ai-automation-quiet-rot-38s\\\",\\n 25| \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\",\\n 26| \\\"truetrace-ai-notes-equal-importance-34s\\\",\\n 27|)\\n 28|\\n 29|\\n 30|@dataclass(frozen=True)\\n 31|class TrueTracePreviewArtifact:\\n 32| candidate: VideoCandidate\\n 33| script: ScriptDraft\\n 34| draft: SocialMediaDraft\\n 35| package: ContentPreviewApprovalPackage\\n 36| manual_posting_pack: ManualPostingPack\\n 37| frame_paths: tuple[str, ...]\\n 38|\\n 39|\\n 40|@dataclass(frozen=True)\\n 41|class TrueTracePreviewBatchResult:\\n 42| batch_id: str\\n 43| artifacts: tuple[TrueTracePreviewArtifact, ...]\\n 44| approval_language: str\\n 45| final_video_language: str = \\\"en\\\"\\n 46| side_effects: tuple[str, ...] = (\\\"write_png\\\", \\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n 47| external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n 48|\\n 49|\\n 50|def _slug_path(candidate_id: str) -> str:\\n 51| return candidate_id.replace(\\\"/\\\", \\\"-\\\")\\n 52|\\n 53|\\n 54|def _retention_beats(candidate: VideoCandidate) -> tuple[str, ...]:\\n 55| if candidate.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\":\\n 56| return (\\n 57| \\\"Day one looks green: the demo output feels useful.\\\",\\n 58| \\\"By day three the input has changed and the system has no exception path.\\\",\\n 59| \\\"Show normal, weird, and uncertain inputs splitting into review and stop conditions.\\\",\\n 60| \\\"Rule: no exception path means fragile automation.\\\",\\n 61| )\\n 62| if candidate.candidate_id == \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\":\\n 63| return (\\n 64| \\\"A glowing prompt card looks clever, then warning labels appear around it.\\\",\\n 65| \\\"Reveal the missing pieces: input boundary, useful output standard, review step, action boundary.\\\",\\n 66| \\\"Transform the prompt into a repeatable workflow diagram.\\\",\\n 67| \\\"Rule: prompt is not system.\\\",\\n 68| )\\n 69| if candidate.candidate_id == \\\"truetrace-ai-notes-equal-importance-34s\\\":\\n 70| return (\\n 71| \\\"A clean notes page floods with equally weighted bullets.\\\",\\n 72| \\\"Separate facts, decisions, risks, open questions, and next actions into visible columns.\\\",\\n 73| \\\"Show why pretty summaries fail when they do not guide action.\\\",\\n 74| \\\"Rule: ask for decision structure, not notes.\\\",\\n 75| )\\n 76| return (\\n 77| candidate.hook,\\n 78| candidate.script_outline,\\n 79| candidate.visual_concept,\\n 80| )\\n 81|\\n 82|\\n 83|def _script_from_candidate(candidate: VideoCandidate) -> ScriptDraft:\\n 84| return ScriptDraft(\\n 85| brief_id=candidate.candidate_id,\\n 86| title=candidate.title,\\n 87| hook=candidate.hook,\\n 88| script_outline=candidate.script_outline,\\n 89| retention_beats=_retention_beats(candidate),\\n 90| visual_concept=candidate.visual_concept,\\n 91| caption=candidate.caption,\\n 92| cta=candidate.cta,\\n 93| estimated_duration_seconds=candidate.duration_seconds,\\n 94| )\\n 95|\\n 96|\\n 97|def _top_candidates() -> tuple[VideoCandidate, ...]:\\n 98| by_id = {candidate.candidate_id: candidate for candidate in build_truetrace_concept_batch().candidates}\\n 99| return tuple(by_id[candidate_id] for candidate_id in TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n 100|\\n 101|\\n 102|def _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\\n 103| script = _script_from_candidate(candidate)\\n 104| shot_plan = create_shot_plan(script)\\n 105| manifest = build_render_manifest(shot_plan)\\n 106| html_preview = build_html_preview(manifest)\\n 107| preview_plan = build_local_preview_plan(manifest)\\n 108|\\n 109| candidate_dir = output_dir / _slug_path(candidate.candidate_id)\\n 110| frame_result = write_local_preview_frames(\\n 111| preview_plan,\\n 112| output_dir=candidate_dir / \\\"frames\\\",\\n 113| approval_language=approval_language,\\n 114| )\\n 115| concat_plan = build_ffmpeg_concat_plan(preview_plan, frame_result, output_path=candidate_dir / \\\"preview.mp4\\\")\\n 116| render_result = render_ffmpeg_preview_video(concat_plan)\\n 117|\\n 118| draft = build_social_media_draft(candidate, html_preview)\\n 119| social_review_text = render_social_media_draft_review(draft)\\n 120| package = build_content_preview_approval_package(\\n 121| draft=draft,\\n 122| preview_video_path=render_result.output_path,\\n 123| social_review_text=social_review_text,\\n 124| approval_language=approval_language,\\n 125| final_video_language=\\\"en\\\",\\n 126| )\\n 127| manual_pack = build_manual_posting_pack(draft, video_path=render_result.output_path)\\n 128| return TrueTracePreviewArtifact(\\n 129| candidate=candidate,\\n 130| script=script,\\n 131| draft=draft,\\n 132| package=package,\\n 133| manual_posting_pack=manual_pack,\\n 134| frame_paths=frame_result.frame_paths,\\n 135| )\\n 136|\\n 137|\\n 138|def build_top_truetrace_preview_packages(\\n 139| *,\\n 140| output_dir: str | Path = \\\"data/previews/truetrace-top3\\\",\\n 141| approval_language: str = \\\"de\\\",\\n 142|) -> TrueTracePreviewBatchResult:\\n 143| \\\"\\\"\\\"Render local timing previews and side-effect-free approval/manual packs for the top 3 concepts.\\\"\\\"\\\"\\n 144|\\n 145| out = Path(output_dir).expanduser().resolve()\\n 146| artifacts = tuple(\\n 147| _build_artifact(candidate, output_dir=out, approval_language=approval_language) for candidate in _top_candidates()\\n 148| )\\n 149| return TrueTracePreviewBatchResult(\\n 150| batch_id=\\\"truetrace-top3-preview-packages-001\\\",\\n 151| artifacts=artifacts,\\n 152| approval_language=approval_language,\\n 153| )\\n 154|\\n 155|\\n 156|def render_top_truetrace_preview_summary(result: TrueTracePreviewBatchResult) -> str:\\n 157| \\\"\\\"\\\"Render a compact Telegram-safe summary of top-3 preview packages.\\\"\\\"\\\"\\n 158|\\n 159| lines = [\\n 160| \\\"## TrueTrace Top-3 Preview Packages\\\",\\n 161| f\\\"Batch: {result.batch_id}\\\",\\n 162| f\\\"Packages: {len(result.artifacts)}\\\",\\n 163| f\\\"Approval language: {result.approval_language}\\\",\\n 164| f\\\"Final video language: {result.final_video_language}\\\",\\n 165| f\\\"Side effects: {', '.join(result.side_effects)}\\\",\\n 166| f\\\"External calls: {', '.join(result.external_calls)}\\\",\\n 167| \\\"\\\",\\n 168| ]\\n 169| for index, artifact in enumerate(result.artifacts, start=1):\\n 170| commands = \\\", \\\".join(artifact.package.approval_commands)\\n 171| lines.extend(\\n 172| [\\n 173| f\\\"## {index}. {artifact.candidate.title}\\\",\\n 174| f\\\"Candidate: {artifact.candidate.candidate_id}\\\",\\n 175| f\\\"Duration: {artifact.candidate.duration_seconds}s\\\",\\n 176| f\\\"Preview video: {artifact.package.preview_video_path}\\\",\\n 177| f\\\"Hook: {artifact.candidate.hook}\\\",\\n 178| f\\\"Manual upload pack prepared: {'yes' if artifact.manual_posting_pack.requires_human_manual_upload else 'no'}\\\",\\n 179| f\\\"Approval commands: {commands}\\\",\\n 180| \\\"\\\",\\n 181| ]\\n 182| )\\n 183| text = \\\"\\\\n\\\".join(lines).strip()\\n 184| if len(text) > 4096:\\n 185| raise ValueError(\\\"top TrueTrace preview summary exceeds Telegram message limit\\\")\\n 186| return text\\n 187|\", \"total_lines\": 186, \"file_size\": 7818, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_yNtSGYA7CcOt5Yk2w6hdUyw9" }, { "role": "tool", "name": "search_files", "content": "{\"total_count\": 50, \"files\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_concepts.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/brand.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/manual_posting.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/preview/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/preview/package.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_plan.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_preview.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_social_draft.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/delivery.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/review.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/draft.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_render_preview.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/preview.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/demo_pipeline.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/script_draft.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/to_candidate.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/brief.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_telegram_delivery.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/telegram/review_sender.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/telegram/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_review_batch.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/sample_batch.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/render.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/workflow.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/factory.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/config/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/config/pillars.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/candidate.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/package.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/scripting/quality.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/approvals.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/scripting/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/__init__.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/__init__.py\"]}", "tool_call_id": "call_iqP0uc4Se6SI6NYjbyaiHbpH" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|from pathlib import Path\\n 2|import shutil\\n 3|\\n 4|import pytest\\n 5|\\n 6|from autoshorts.demo.truetrace_preview_packages import (\\n 7| TRUE_TRACE_TOP3_CANDIDATE_IDS,\\n 8| build_top_truetrace_preview_packages,\\n 9| render_top_truetrace_preview_summary,\\n 10|)\\n 11|\\n 12|\\n 13|pytestmark = pytest.mark.skipif(shutil.which(\\\"ffmpeg\\\") is None, reason=\\\"ffmpeg is required for MP4 preview rendering\\\")\\n 14|\\n 15|\\n 16|def test_build_top_truetrace_preview_packages_renders_three_mp4_approval_packages(tmp_path):\\n 17| result = build_top_truetrace_preview_packages(output_dir=tmp_path, approval_language=\\\"de\\\")\\n 18|\\n 19| assert result.batch_id == \\\"truetrace-top3-preview-packages-001\\\"\\n 20| assert [artifact.candidate.candidate_id for artifact in result.artifacts] == list(TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n 21| assert len(result.artifacts) == 3\\n 22| assert result.side_effects == (\\\"write_png\\\", \\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n 23| assert result.external_calls == (\\\"ffmpeg\\\",)\\n 24|\\n 25| for artifact in result.artifacts:\\n 26| assert artifact.package.requires_human_approval is True\\n 27| assert artifact.package.approval_language == \\\"de\\\"\\n 28| assert artifact.package.final_video_language == \\\"en\\\"\\n 29| assert artifact.package.side_effects == ()\\n 30| assert artifact.package.external_calls == ()\\n 31| assert Path(artifact.package.preview_video_path).exists()\\n 32| assert artifact.package.preview_video_path.endswith(\\\"preview.mp4\\\")\\n 33| assert artifact.manual_posting_pack.requires_human_manual_upload is True\\n 34| assert artifact.manual_posting_pack.side_effects == ()\\n 35| assert artifact.manual_posting_pack.external_calls == ()\\n 36| assert artifact.manual_posting_pack.video_path == artifact.package.preview_video_path\\n 37| assert tuple(post.platform for post in artifact.manual_posting_pack.platform_posts) == (\\\"youtube\\\", \\\"tiktok\\\")\\n 38|\\n 39|\\n 40|def test_build_top_truetrace_preview_packages_accepts_relative_output_dir(tmp_path, monkeypatch):\\n 41| monkeypatch.chdir(tmp_path)\\n 42|\\n 43| result = build_top_truetrace_preview_packages(output_dir=\\\"relative-previews\\\", approval_language=\\\"de\\\")\\n 44|\\n 45| for artifact in result.artifacts:\\n 46| assert Path(artifact.package.preview_video_path).is_absolute()\\n 47| assert Path(artifact.package.preview_video_path).exists()\\n 48|\\n 49|\\n 50|def test_render_top_truetrace_preview_summary_is_telegram_safe_and_references_three_videos(tmp_path):\\n 51| result = build_top_truetrace_preview_packages(output_dir=tmp_path, approval_language=\\\"de\\\")\\n 52|\\n 53| text = render_top_truetrace_preview_summary(result)\\n 54|\\n 55| assert \\\"## TrueTrace Top-3 Preview Packages\\\" in text\\n 56| assert \\\"Packages: 3\\\" in text\\n 57| assert \\\"Approval language: de\\\" in text\\n 58| assert \\\"Final video language: en\\\" in text\\n 59| assert text.count(\\\"Preview video:\\\") == 3\\n 60| assert \\\"Freigabe alle\\\" in text\\n 61| assert \\\"Manual upload pack prepared: yes\\\" in text\\n 62| assert \\\"publish\\\" not in text.casefold()\\n 63| assert \\\"post\\\" not in text.casefold()\\n 64| assert len(text) < 4096\\n 65|\", \"total_lines\": 64, \"file_size\": 2893, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_csMH2Prm1IhsvVv28irwJ0w6" }, { "role": "assistant", "content": "", "reasoning": "**Planning for implementation**\n\nI need to implement the audio and subtitles tests for the tts_plan in the script. Maybe I should also verify if the videos differ, possibly checking their hashes? If the three videos are the same, we might have a media delivery issue, so producing a distinct labeled contact sheet could help. The user has asked to move on, so I should continue with the next build and run a git status to check the repository.", "finish_reason": "tool_calls", "reasoning_content": "**Planning for implementation**\n\nI need to implement the audio and subtitles tests for the tts_plan in the script. Maybe I should also verify if the videos differ, possibly checking their hashes? If the three videos are the same, we might have a media delivery issue, so producing a distinct labeled contact sheet could help. The user has asked to move on, so I should continue with the next build and run a git status to check the repository.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Ht2SROj7iD-F_ffzQRq-BE3tSih6STAs0E1hjj0LweujWgMDuZgVnuLL02u7t2xpMqYNhrjfWVxF2oKbKEFroX-5HXeCY_SlaKH1JDDPRnMmahIMaTXgWyNNcNmIadT5hVASkLcUUphfsdoCwxDKZzzv5gfM6CtTdIWuannzUOdL874TZ6yNicjaEBGwy5VUSw42trECGfNkxLT8CdOT_Ca1ACdJS6RQstslFGQM0jmOXZBDcSSljGzBYaVrwGNotwwPyxjN_HJnB33aEk3cLTewM12Qvg3oSfYsVqeY4uxhMvxWyabgm3x0syfyMmuVB1pEIg6l17rxpyRp-QJSAsyFq6DqHHv3pm13G_e5ErahGfbcjfJrAQs_BVeXtV9JI8sMWIRGwHFwrQqJ7GSfdgV-WiTXWqRsfTi8_TLAqheCDQWJfymvNGfpveI4JlZqRpyUqhQS7uuMOkK4Er_5lf3yEDvQxOuRh0-Sk0563CWvyOt0WdpQV4px06ofWjKRBG7GdvlZnJRmEwflTSvfP7K0v0P6kv7S-h3juMSJUtpaJysoNhGCVnUdOO1hKYZcEfP7yZMg3bBaBnnoXGoEeoM3WUaZWhpEdW7-_lp2SnkbSz4oqs8-uIxREbRoXgjHIxOA0lRRN2v23Nf-i01M5sRjkwsBGFs3eNO1AxiMZG6Ss8HWMF3PT9wNgvoSbV6G38b19JudUyUq0q1lb-9lhM3V4ydinmf7aGhd46VbLSr89XRColC2V_NMcHKJ_2f0-K9dp_JUSGRrtf9TxlpSAWfS5HMOM8ZU8gqLV9vTPwmQo_Lz2uz2MgI8_12WvaCQOqFiVetGRAKupwDJaFrkePT7JhqOfEcuQ1n0AdmuFzQPEFhxQmtyYygna2gpUDxXqoxWDCWMPUkyRaWFb2mmaQv2JgOjBSQaPiRERtZyBj_u_fSQHQ0VeDZT79sxPraKgFTVPKAQOvbtdVphTHxvbI6av8nGdjP5VFREKb8uB2j_qIFfFxFKmTxAnlpdXn5N-VJE3rEPec5U0AP4B4igApjFcgPpSNPupsos_h1na61QugZ4MlE0RyuZOUEDv65ThwXNiOh6CIqJLDczo77cZ2KZOQQJnHwQPR6YTsZcNXOjiJAXmK5CYypYvW2ZM5Dufbi5aYpjH27kN8l5Cbsp69n1MbawlT9GklKC8tOn8K_s8WPuv_buF4H4oUiro-yoUdDf1-lYQhpsnHi8Y70zJ0ncxAIHfHsvQ9kaEMcjwPAS7rWrMZse95DMXjRf2lfqFDcDs3tacQu4zRzALHFI4_IHX-gfxalMFwO7Hf6mKDK_A=", "id": "rs_0dbb6f9b271511ec016a1361e9fe988191ba7e31bd5ba1619c", "summary": [ { "type": "summary_text", "text": "**Planning for implementation**\n\nI need to implement the audio and subtitles tests for the tts_plan in the script. Maybe I should also verify if the videos differ, possibly checking their hashes? If the three videos are the same, we might have a media delivery issue, so producing a distinct labeled contact sheet could help. The user has asked to move on, so I should continue with the next build and run a git status to check the repository." } ] } ], "tool_calls": [ { "id": "call_MEmOSpUB7QgTUHhWMJnctynh", "call_id": "call_MEmOSpUB7QgTUHhWMJnctynh", "response_item_id": "fc_0dbb6f9b271511ec016a1361ed91ac8191ac8e268343554c5b", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git log --oneline -5\",\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"timeout\":60}" } }, { "id": "call_fZp9CnANUTXMTGUX6XEWZ8iJ", "call_id": "call_fZp9CnANUTXMTGUX6XEWZ8iJ", "response_item_id": "fc_0dbb6f9b271511ec016a1361ed91c08191bbedf2143669b74c", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"sha256sum data/previews/truetrace-top3/*/preview.mp4\",\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"timeout\":60}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"6efc4dc feat: add TrueTrace top preview packages\\n2d6af9f feat: add TrueTrace concept testbatch\\nb6c4fcc feat: add TrueTrace brand profile validation\\n3f39d10 feat: add manual posting packs and branding plan\\ne57e328 feat: add public-safe content series guardrails\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_MEmOSpUB7QgTUHhWMJnctynh" }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"032ed16e2433619aa48f587f51fc3a156117bec4cbf42881246be640cd57d012 data/previews/truetrace-top3/truetrace-ai-automation-quiet-rot-38s/preview.mp4\\n55f27f60ba439c97b2fc36eb34acda28fb2bf3d5bd99137ad7c019ab6d519c1e data/previews/truetrace-top3/truetrace-ai-notes-equal-importance-34s/preview.mp4\\n47023552c9fb7b282248beaf3258b1f790248741d892868ee80bad4da6abaa6b data/previews/truetrace-top3/truetrace-prompt-vs-system-workflow-breaks-35s/preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_fZp9CnANUTXMTGUX6XEWZ8iJ" }, { "role": "assistant", "content": "", "reasoning": "**Evaluating status output**\n\nI’m noticing that the status output seems to lack some information — it only logs because perhaps the command doesn’t have a clear separator. That’s something to consider! I think I should write tests to check this out more thoroughly. I also need to inspect the data models and possibly improve the style as well. It looks like there’s a few things that could be tweaked to make it clearer and more effective!", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating status output**\n\nI’m noticing that the status output seems to lack some information — it only logs because perhaps the command doesn’t have a clear separator. That’s something to consider! I think I should write tests to check this out more thoroughly. I also need to inspect the data models and possibly improve the style as well. It looks like there’s a few things that could be tweaked to make it clearer and more effective!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2HzcQFbi--yxt_S2fVqovJLKSAucGoK418wgNbfKxzZ2BCrRXilyfJiTlVYsCOoWsem5u7UPK7_reuhHcAYueNFnvb_h-su1ymoErVA5sMrIMhR0O4rUoLuy_ZeFG2HtGKohiVIxr1aTysu83IsDlR8LZpmbnyMCgSL4GVF5bo-VhjbWxqNpyFD0foqpd8DgLFu_O4v8rtP8Liv7o0IU14AW1f0DCDVnwgJwLaqG0SCZlO94t6nvW3M_zHGeK2AyCacsSkupVS6-zFi6Qc1luoXrc_NqMbUBfvlsHambZBcompCR_ipZt4MB80TrVPyMiSuxBvic8fu3kQqCxq8A3i6FwgzyZcaKogHyRZ5ogQHjED61HrBDyNyr3ElJK2PfPryIByC9TjKbGLC7jegXK1pG3uy9Ka9IbHreOpTJ7aALmFGE9CAxzgYv1b1G9U1f3fkEFnzO0xI4EYnVhndiX8XVDXdyBw78OYVR77t9lhmKRjffdbYB6o7xzk2DKxhmNVcmyWMQJ3U89CCcljvUFlUo5HwDyHHIB2knBTnSyYRK6SFqV9tqrEeQalcORKg5nUycfd6Qgrxt1hLCd3oqiCVo-hJCv9nPHH--GOpzl-sOKGxpB7WSFNCNMtfD6ztzcC-Fol927090-oxoqjta-qxPFLH54ggJ5ad-njCwUaDWYH7xpNwsQQsgUxALuncMFQfzrSSMWcTTHEePViy5dbIvHx0Xor1PReWFCSgZbUQfM96DysNlBEVutwUWFPT4tzXPqxynRvdgXxyCPA8ZZchZ0qEcWf5PIkHzmip1w6v-W51B4v1OL22StGSG_6hyHjOVUbtAI4nCfk-lf0_UOhhv5sJfgBiv-NBMtehOc1QJgXUh5z0Eqqs6hKz1d0Odc-eRs1SBvNmyow22oGYoFwaDM1IsiSf5xxHQrGUmqe_Fd03kfWSo7IQcvONfTsswZnWjr4F8X_j25PTDS6m3YsQVZEWanrp8iqUQyo0iBUY00UrpD7XFncUl_6amO2D7d4g7ZcInzr9eb1hB1ihjldMDocy6OWSrSjfsyc1CKDCskFtyngzwS94rpd3moCYyhE93ecClRiXKEwXHvZIYQnaOAMLjcgyjhxIgFougSifG6k=", "id": "rs_0adfabb20239f24a016a1361f0c2f88191bfb0a61ecf285e81", "summary": [ { "type": "summary_text", "text": "**Evaluating status output**\n\nI’m noticing that the status output seems to lack some information — it only logs because perhaps the command doesn’t have a clear separator. That’s something to consider! I think I should write tests to check this out more thoroughly. I also need to inspect the data models and possibly improve the style as well. It looks like there’s a few things that could be tweaked to make it clearer and more effective!" } ] } ], "tool_calls": [ { "id": "call_de8MTJZsFQbBRy1LgOJS6hrO", "call_id": "call_de8MTJZsFQbBRy1LgOJS6hrO", "response_item_id": "fc_0adfabb20239f24a016a1361f3fde0819180dedf8951281d0d", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/script_draft.py\",\"limit\":200}" } }, { "id": "call_DnzKDnwPofpUhjufDTsmlDGV", "call_id": "call_DnzKDnwPofpUhjufDTsmlDGV", "response_item_id": "fc_0adfabb20239f24a016a1361f3fdf081919206aa7a09ac7f8a", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py\",\"limit\":220}" } }, { "id": "call_f4eY7QQb7NX3pxVz5rIRsNGL", "call_id": "call_f4eY7QQb7NX3pxVz5rIRsNGL", "response_item_id": "fc_0adfabb20239f24a016a1361f3fdf881919a04e4b6d27be3e8", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"limit\":240}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"status\": \"unchanged\", \"message\": \"File unchanged since last read. The content from the earlier read_file result in this conversation is still current — refer to that instead of re-reading.\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/script_draft.py\", \"dedup\": true, \"content_returned\": false}", "tool_call_id": "call_de8MTJZsFQbBRy1LgOJS6hrO" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Side-effect-free render manifest generation from shot plans.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult\\n 8|from autoshorts.rendering.shot_plan import ShotPlan, validate_shot_plan\\n 9|\\n 10|\\n 11|@dataclass(frozen=True)\\n 12|class RenderSettings:\\n 13| width: int = 1080\\n 14| height: int = 1920\\n 15| fps: int = 30\\n 16| no_generated_text_inside_assets: bool = True\\n 17| captions_renderer_owned: bool = True\\n 18| static_card_limit: int = 0\\n 19|\\n 20|\\n 21|@dataclass(frozen=True)\\n 22|class RenderLayer:\\n 23| text: str\\n 24| renderer_owned: bool = True\\n 25|\\n 26|\\n 27|@dataclass(frozen=True)\\n 28|class RenderTimelineItem:\\n 29| scene_purpose: str\\n 30| start_seconds: int\\n 31| end_seconds: int\\n 32| asset_mode: str\\n 33| motion_instruction: str\\n 34| placeholder_asset_path: str\\n 35| caption_layer: RenderLayer\\n 36| label_layer: RenderLayer\\n 37|\\n 38|\\n 39|@dataclass(frozen=True)\\n 40|class RenderManifest:\\n 41| script_id: str\\n 42| title: str\\n 43| settings: RenderSettings\\n 44| timeline: tuple[RenderTimelineItem, ...]\\n 45|\\n 46|\\n 47|def _is_blank(value: str) -> bool:\\n 48| return not (value or \\\"\\\").strip()\\n 49|\\n 50|\\n 51|def _placeholder_path(script_id: str, index: int, purpose: str) -> str:\\n 52| return f\\\"placeholders/{script_id}/{index:02d}-{purpose}.png\\\"\\n 53|\\n 54|\\n 55|def build_render_manifest(plan: ShotPlan) -> RenderManifest:\\n 56| \\\"\\\"\\\"Turn a validated shot plan into a machine-readable render manifest.\\\"\\\"\\\"\\n 57|\\n 58| validation = validate_shot_plan(plan)\\n 59| if not validation.is_valid:\\n 60| raise ValueError(\\\"; \\\".join(validation.errors))\\n 61|\\n 62| timeline = tuple(\\n 63| RenderTimelineItem(\\n 64| scene_purpose=scene.purpose,\\n 65| start_seconds=scene.start_seconds,\\n 66| end_seconds=scene.end_seconds,\\n 67| asset_mode=scene.asset_mode,\\n 68| motion_instruction=scene.visual_action,\\n 69| placeholder_asset_path=_placeholder_path(plan.script_id, index, scene.purpose),\\n 70| caption_layer=RenderLayer(text=scene.caption_beat, renderer_owned=True),\\n 71| label_layer=RenderLayer(text=scene.purpose.replace(\\\"_\\\", \\\" \\\"), renderer_owned=True),\\n 72| )\\n 73| for index, scene in enumerate(plan.scenes, start=1)\\n 74| )\\n 75| return RenderManifest(\\n 76| script_id=plan.script_id,\\n 77| title=plan.title,\\n 78| settings=RenderSettings(),\\n 79| timeline=timeline,\\n 80| )\\n 81|\\n 82|\\n 83|def validate_render_manifest(manifest: RenderManifest) -> ValidationResult:\\n 84| errors: list[str] = []\\n 85| if _is_blank(manifest.script_id):\\n 86| errors.append(\\\"script_id is required\\\")\\n 87| if _is_blank(manifest.title):\\n 88| errors.append(\\\"title is required\\\")\\n 89|\\n 90| settings = manifest.settings\\n 91| if (settings.width, settings.height) != (1080, 1920):\\n 92| errors.append(\\\"render size must be vertical 1080x1920\\\")\\n 93| if settings.fps != 30:\\n 94| errors.append(\\\"fps must be 30\\\")\\n 95| if not settings.no_generated_text_inside_assets:\\n 96| errors.append(\\\"generated text inside assets is not allowed\\\")\\n 97| if not settings.captions_renderer_owned:\\n 98| errors.append(\\\"captions must be renderer-owned\\\")\\n 99| if settings.static_card_limit != 0:\\n 100| errors.append(\\\"static_card_limit must be 0\\\")\\n 101|\\n 102| if not manifest.timeline:\\n 103| errors.append(\\\"at least one timeline item is required\\\")\\n 104|\\n 105| previous_end: int | None = None\\n 106| for index, item in enumerate(manifest.timeline, start=1):\\n 107| if _is_blank(item.scene_purpose):\\n 108| errors.append(f\\\"timeline item {index} scene_purpose is required\\\")\\n 109| if item.end_seconds <= item.start_seconds:\\n 110| errors.append(f\\\"timeline item {index} end_seconds must be greater than start_seconds\\\")\\n 111| if previous_end is not None and item.start_seconds < previous_end:\\n 112| errors.append(f\\\"timeline item {index} start_seconds overlaps previous item\\\")\\n 113| previous_end = item.end_seconds\\n 114|\\n 115| for field_name, value in {\\n 116| \\\"asset_mode\\\": item.asset_mode,\\n 117| \\\"motion_instruction\\\": item.motion_instruction,\\n 118| \\\"placeholder_asset_path\\\": item.placeholder_asset_path,\\n 119| \\\"caption_layer text\\\": item.caption_layer.text,\\n 120| \\\"label_layer text\\\": item.label_layer.text,\\n 121| }.items():\\n 122| if _is_blank(value):\\n 123| errors.append(f\\\"timeline item {index} {field_name} is required\\\")\\n 124|\\n 125| if item.asset_mode == \\\"static_slideshow\\\":\\n 126| errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n 127| if \\\"baked-in\\\" in item.motion_instruction.casefold():\\n 128| errors.append(\\\"baked-in text in render assets is not allowed\\\")\\n 129| if not item.caption_layer.renderer_owned:\\n 130| errors.append(f\\\"timeline item {index} caption layer must be renderer-owned\\\")\\n 131| if not item.label_layer.renderer_owned:\\n 132| errors.append(f\\\"timeline item {index} label layer must be renderer-owned\\\")\\n 133|\\n 134| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 135|\", \"total_lines\": 134, \"file_size\": 4800, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_DnzKDnwPofpUhjufDTsmlDGV" }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n 4|\\n 5|The project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n 6|\\n 7|## Current scope\\n 8|\\n 9|Implemented foundation:\\n 10|\\n 11|- `autoshorts.cli.demo_local_preview_video` — local CLI entry point that renders deterministic PNG frames and an MP4 timing preview.\\n 12|- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n 13|- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n 14|- `autoshorts.cli.demo_social_draft` — local CLI entry point that prints a side-effect-free social media draft review delivery.\\n 15|- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n 16|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 17|- `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n 18|- `autoshorts.content.demo_pipeline` — deterministic content-to-Telegram-review demo pipeline.\\n 19|- `autoshorts.content.script_draft` — structured script drafts with retention beats, visual concept, caption, CTA, and AI-slop validation.\\n 20|- `autoshorts.content.to_candidate` — converts content briefs and script drafts into candidate drafts and validated video candidates.\\n 21|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n 22|- `autoshorts.demo.truetrace_concepts` — five concrete TrueTraceShorts concept candidates with metadata for strategy review.\\n 23|- `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n 24|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 25|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 26|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 27|- `autoshorts.preview.package` — combines MP4 timing preview plus social draft review into an approval package.\\n 28|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 29|- `autoshorts.strategy.brand` — TrueTraceShorts brand profile, default descriptions/bios/tags, and brand-fit validation.\\n 30|- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n 31|- `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.\\n 32|- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n 33|- `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n 34|- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n 35|- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n 36|- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n 37|- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n 38|- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n 39|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 40|- `autoshorts.social.delivery` — side-effect-free delivery plan for social media draft review text.\\n 41|- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n 42|- `autoshorts.social.manual_posting` — side-effect-free manual upload copy pack for phase-1 YouTube/TikTok metadata.\\n 43|- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n 44|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 45|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 46|- `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n 47|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 48|- Tests for content briefs, content demo pipeline, content-to-candidate conversion, content pillars, candidate factory, idea candidates, script drafts, shot plans, testbatches, sample review CLI, sample Telegram delivery CLI, sample review demos, review workflow, review-batch rendering, approval parsing, review formatting, Telegram delivery preparation, and script quality gating.\\n 49|\\n 50|## Brand profile\\n 51|\\n 52|`autoshorts.strategy.brand.TRUETRACE_SHORTS_BRAND` captures the phase-1 public brand contract for `True Trace Shorts`:\\n 53|\\n 54|- channel name and handles;\\n 55|- tagline: `Systems over prompts.`;\\n 56|- public YouTube description and TikTok bio;\\n 57|- default hashtags and YouTube tags;\\n 58|- tone and visual rules;\\n 59|- forbidden public topics;\\n 60|- generic AI-tips patterns;\\n 61|- mechanism keywords for brand fit.\\n 62|\\n 63|`validate_candidate_brand_fit()` rejects candidates that violate no-go topics, generic AI-tips positioning, or the mechanisms-over-hype promise. `apply_brand_metadata()` appends default hashtags and brand policy notes without changing the candidate content or performing side effects.\\n 64|\\n 65|`autoshorts.demo.truetrace_concepts.build_truetrace_concept_batch()` provides the current five-candidate TrueTrace concept testbatch for strategy review:\\n 66|\\n 67|- `Why Most AI Automations Break After Three Days`\\n 68|- `This Prompt Looks Smart — But the Workflow Still Breaks`\\n 69|- `The Hidden Reason Your AI Notes Become Useless`\\n 70|- `Claim: AI Agents Will Replace Teams`\\n 71|- `One Rule Before You Automate Anything With AI`\\n 72|\\n 73|`render_truetrace_concept_summary()` renders those candidates with hooks, outlines, visuals, hypotheses, YouTube descriptions, TikTok captions, and tags. It performs no platform action and avoids action language.\\n 74|\\n 75|`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate.\\n 76|\\n 77|Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n 78|\\n 79|## Strategy scoring\\n 80|\\n 81|`autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n 82|\\n 83|- hook;\\n 84|- curiosity;\\n 85|- identity;\\n 86|- emotion;\\n 87|- trust;\\n 88|- follow reason;\\n 89|- originality;\\n 90|- variety.\\n 91|\\n 92|`build_candidate_strategy_review()` returns a `CandidateStrategyReview` with `render_allowed`, reject reasons, and improvement suggestions. It blocks generic AI-slop language, weak future-value promises, forbidden public topics, and recent format repetition before render capacity is wasted.\\n 93|\\n 94|`autoshorts.strategy.series.DEFAULT_CONTENT_SERIES` currently exposes only public-safe series:\\n 95|\\n 96|- `Prompt vs System`\\n 97|- `AI System Autopsy`\\n 98|- `One Rule, One Example`\\n 99|- `AI Hype Court`\\n 100|\\n 101|`validate_candidate_series_fit()` rejects public content about JARVIS/Hermes, the user, their collaboration, private/behind-the-scenes material, politics, legal loopholes, private-data misuse, rule-bypassing, exploitation, or anything that could enable lawbreaking.\\n 102|\\n 103|## Content development\\n 104|\\n 105|`autoshorts.content.brief.create_content_brief()` turns a pillar, topic, audience problem, and payoff into a deterministic `ContentBrief`. The brief carries positioning, tone, visual rules, no-go topics, target platforms, and target duration before any script or renderer exists.\\n 106|\\n 107|`validate_content_brief()` rejects missing strategy fields, unsupported platforms, and durations outside `10..180` seconds.\\n 108|\\n 109|`autoshorts.content.script_draft.create_script_draft()` turns a `ContentBrief` into a structured `ScriptDraft` with:\\n 110|\\n 111|- exact hook;\\n 112|- script outline;\\n 113|- 3–5 retention beats;\\n 114|- visual concept;\\n 115|- caption and CTA;\\n 116|- estimated duration.\\n 117|\\n 118|`validate_script_draft()` rejects missing core fields, invalid beat counts, invalid durations, and generic AI-slop phrases such as `in this video` or `unlock your full potential`.\\n 119|\\n 120|`autoshorts.content.to_candidate.convert_script_draft_to_candidate_draft()` turns a validated `ContentBrief` + `ScriptDraft` pair into the existing `CandidateDraft` type. It preserves platform strategy, hook, script outline, visual concept, caption, CTA, no-go policy notes, and an initial conservative quality score.\\n 121|\\n 122|`convert_script_draft_to_candidate()` then uses the existing candidate factory to create a validated `VideoCandidate`, including stable candidate id generation, default hashtags, duration classification, and pillar/platform validation.\\n 123|\\n 124|`autoshorts.content.demo_pipeline.build_ai_life_systems_demo_delivery()` runs the deterministic demo path end-to-end:\\n 125|\\n 126|```text\\n 127|ContentBrief\\n 128|→ ScriptDraft\\n 129|→ VideoCandidate\\n 130|→ TestBatch\\n 131|→ ReviewBatch\\n 132|→ Telegram ReviewDelivery\\n 133|```\\n 134|\\n 135|The result object exposes every intermediate artifact for inspection and still has no send/publish side effects.\\n 136|\\n 137|## Render planning\\n 138|\\n 139|`autoshorts.rendering.shot_plan.create_shot_plan()` turns a validated `ScriptDraft` into a deterministic five-scene retention plan:\\n 140|\\n 141|```text\\n 142|first_frame_hook\\n 143|→ pattern_interrupt\\n 144|→ core_explanation\\n 145|→ screen_or_card_visual\\n 146|→ cta\\n 147|```\\n 148|\\n 149|Each `ShotScene` carries timing, voiceover beat, visual action, asset mode, caption beat, retention goal, and whether labels/captions remain renderer-owned.\\n 150|\\n 151|`validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n 152|\\n 153|`autoshorts.rendering.manifest.build_render_manifest()` turns a validated `ShotPlan` into a machine-readable `RenderManifest` with:\\n 154|\\n 155|- vertical `1080x1920` render settings;\\n 156|- `30` fps;\\n 157|- one timeline item per shot scene;\\n 158|- motion instructions inherited from the shot plan;\\n 159|- deterministic placeholder asset paths;\\n 160|- renderer-owned caption and label layers;\\n 161|- `no_generated_text_inside_assets=True`;\\n 162|- `static_card_limit=0`.\\n 163|\\n 164|`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n 165|\\n 166|`autoshorts.rendering.preview.build_html_preview()` turns a validated `RenderManifest` into a side-effect-free `RenderPreview` HTML document. It exposes script metadata, vertical format, fps, every timeline item, placeholder asset paths, motion instructions, and renderer-owned caption/label text for local inspection before any media generation.\\n 167|\\n 168|`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n 169|\\n 170|Run the same deterministic preview locally with:\\n 171|\\n 172|```bash\\n 173|python -m autoshorts.cli.demo_render_preview\\n 174|```\\n 175|\\n 176|It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n 177|\\n 178|`autoshorts.rendering.local_preview.build_local_preview_plan()` turns a validated `RenderManifest` into a deterministic `LocalPreviewPlan` for later frame/video generation. The default plan uses reduced local preview settings `540x960` at `24fps`, creates one `LocalPreviewFrame` per manifest timeline item, converts scene timing to milliseconds, and assigns deterministic placeholder image paths under `data/previews//`.\\n 179|\\n 180|`validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n 181|\\n 182|`autoshorts.rendering.local_frame_writer.write_local_preview_frames()` is the first explicit artifact-writing boundary. It consumes a validated `LocalPreviewPlan` and writes deterministic PNG scene cards using Pillow. Each card uses manifest-derived frame data: scene purpose, caption, label, timing, asset mode, and motion instruction. It returns `LocalFrameWriteResult` with `side_effects=(\\\"write_png\\\",)` and `external_calls=()`.\\n 183|\\n 184|Approval-preview cards default to German copy (`approval_language=\\\"de\\\"`) so the concept is easier to review before final English production. Text wrapping is pixel-width based rather than fixed character-count based, which keeps long German lines inside the vertical card bounds.\\n 185|\\n 186|Generated preview frames should stay under runtime preview directories such as `data/previews//` or temporary test directories. PNG media is ignored by Git.\\n 187|\\n 188|`autoshorts.rendering.ffmpeg_plan.build_ffmpeg_concat_plan()` turns a `LocalPreviewPlan` plus `LocalFrameWriteResult` into a side-effect-free `FFmpegConcatPlan`. It records ordered PNG paths, per-frame durations, output `.mp4` path, derived `.concat.txt` path, preview dimensions, fps, and FFmpeg concat demuxer file content.\\n 189|\\n 190|`validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n 191|\\n 192|`autoshorts.rendering.ffmpeg_renderer.render_ffmpeg_preview_video()` is the explicit local video-rendering boundary. It consumes a validated `FFmpegConcatPlan`, writes the `.concat.txt` file, calls `ffmpeg`, and writes the `.mp4` timing preview. It returns `FFmpegRenderResult` with `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` and `external_calls=(\\\"ffmpeg\\\",)`.\\n 193|\\n 194|The renderer exports `yuv420p` MP4 with `+faststart`. Tests skip if `ffmpeg`/`ffprobe` are unavailable and otherwise smoke-probe width, height, frame rate, and approximate duration.\\n 195|\\n 196|Run the deterministic local MP4 timing preview with:\\n 197|\\n 198|```bash\\n 199|python -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo-local-preview\\n 200|```\\n 201|\\n 202|The command creates German approval-preview PNG scene cards under `frames/`, writes `preview.concat.txt`, renders `preview.mp4`, and prints script/output metadata. It performs no Telegram calls, no platform calls, and no AI/API calls. Final social-media videos can still be produced in English later; this preview layer is German for concept review.\\n 203|\\n 204|## Content preview package\\n 205|\\n 206|`autoshorts.preview.package.build_content_preview_approval_package()` combines an existing MP4 timing preview with the social media draft review text into a side-effect-free `ContentPreviewApprovalPackage`. It records approval language `de`, final video language `en`, approval commands, preview video path, and requires human approval.\\n 207|\\n 208|`render_content_preview_approval_text()` creates a Telegram-safe text block that references the MP4 preview and embeds the social media draft review. The package layer does not send Telegram messages, publish to platforms, call renderers, or access credentials.\\n 209|\\n 210|Research notes for external content-automation patterns are tracked in `docs/research/content_automation_research.md`. Growth strategy notes are tracked in `docs/research/content_growth_strategy_research.md`. TrueTraceShorts branding recommendations are tracked in `docs/branding_truetrace_strategy.md`. The operating business plan for a JARVIS-run, human-approved content machine is tracked in `docs/businessplan_jarvis_content_machine.md`. Together they emphasize series/queues, editable previews, versioned approval, originality gates, synthetic-content disclosure metadata, follower-conversion psychology, variety inside a stable promise, manual phase-1 uploads with prepared metadata, and avoiding blind auto-posting.\\n 211|\\n 212|## Social media draft\\n 213|\\n 214|`autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n 215|\\n 216|- candidate id;\\n 217|- target platforms and human-readable platform labels;\\n 218|- title variants;\\n 219|- caption, hashtags, CTA, and hook;\\n 220|- planned duration;\\n 221|- quality/risk summary;\\n 222|- preview reference and preview type;\\n 223|- `requires_human_approval=True`;\\n 224|- `side_effects=()`.\\n 225|\\n 226|`validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n 227|\\n 228|`autoshorts.social.review.render_social_media_draft_review()` turns a valid `SocialMediaDraft` into a Telegram-safe approval message with candidate id, platforms, duration, hook, title variants, caption, hashtags, CTA, preview reference, quality/risk summary, explicit human-approval status, and approval commands:\\n 229|\\n 230|- `Freigabe YouTube`\\n 231|- `Freigabe TikTok`\\n 232|- `Freigabe Instagram`\\n 233|- `Freigabe alle`\\n 234|- `Ablehnen`\\n 235|- `Ändern: `\\n 236|\\n 237|The renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n 238|\\n 239|`autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n 240|\\n 241|\", \"total_lines\": 405, \"file_size\": 25333, \"truncated\": true, \"hint\": \"Use offset=241 to continue reading (showing 1-240 of 405 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_f4eY7QQb7NX3pxVz5rIRsNGL" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2H6mO2kaepPiFFFqK65FgHeFgnG4P82EmyFH5nqD4Vran21dXAZQa0KaGGERcSM4u1UMmF-z_SlIuCOQmVqqLZEsAESMLuTvhzJklnekAY7rPsuX4lWHVi40ApGuDOG5AqVjXuY3e5sUZ2X8zNUCsUta--z2RhN4swpjiqNcE1s2DAC1OLJUxeBnsBzmgM9iW0IrdU0EZYmbRwiZ2t6ygqW0JoeCdThdFeMc1aIMAr4wojFKcTHat3ce5ZSEQ2Kn071I9fVl-TJpPgXwiUr-duZ_89SEUvf4j8eB20yKVsGAsB7QHatvLqOjGJrcmxHZ40PEUHe7CVmFIcNDGNb_2kkA0f0hqPtJ9RCh2Sm221uQKrR_ixrhyPppHjNmr_TZQxsIhgTG_XkGUDF_WRx70bLqilcHD5gmZEzmpJ_q6glmclFFpZl0nT2seCJ05DN9LSLZVuT9tS5GcKSUd983LwYY2mchhq5l20DLmT_gFp150onUx6Yevw1iMjZijiPSp6ECbob-P0BVYC9gE27ALnDPSmGJiD4afrOgQlpLeEdmhRKiPDCt-iBWJRMUq-wEYoaOLJca-nxaCgxvR5wQlDcqLMI7-YdvvMtjDyNqC-EVDfKSZChn5_g8d6TbgvECgBpQTBHGW68nhHi7bk9gXv2zXeX5ykmK1lC3s1JRDQ5a4J-Ir02JTiULTOEvB0HqcDb4nEOBOefWjQxvpytfcVnGZ9e2-zyxQ7Jmq1Rtclq17nXdOSsnR4mFJtQZ7xPzPAXeSyG01u4dCJ9WDL1oEGra1V4J5R3QNX1lLr5Gf4HdzAgFo2TvJ5BCvDLjhfLqcLfU5Vl2Cjznzhx32FcPS79ZIAoiil-DNiSCn59aityJyJMxJp0OjJ-18JTUQmmCLJBW1q_sgLOv61B59LnbVjeqP_BhXrBrxogskh8db5khZWATJSSX30bTiIaNAkP1R2khLlfFXfy4QbNybMmvAaLRDVohmXISgn5LRpSmONBmT-2wxl4oGk5Lp2VtX_ywGiil9FeBNPr3SRPr0OrFe64Eac4aZNzW0Xd110jw5d2A1qCdqH_OwwVjeQV0yIk85yPFdngBe3Y5TY2AUXW6wRtNKAS2O0l08btGvW-lsR5ikGBoG0L598eZaEVkY1qH0YQ17rcZBQ-MGFbZdxfE0BJUelh7XJuswAnLPhZGCT1bX6NaMmkFnjrdDqC8zgA-cn8YqEgs7ED4jYDQZJ7pZGzaM-AEKYK20T_Y92qUVsSCPCD6M3-wBRRXot4fdnO8maVyuwRz3R9uHxa2TXKDhQ_hzGkmR2D4rKT7rgy6aHywHFjwfq20MNz8pV5AgM57eVgfI_tazz6b-NU1RSy5chnvLFiYDFR0VVf6eYE_znw5tXctwR97ZwyYKvL81b3ZjKMa62wBkL9jRee2JqQQZEUk2NW37j3g3xjegyxoTjbU8vSow24Uu_L4WqbSA2uEOcG8ErkmjUB2DdmUV0Ab2bsiPKjBcZYC0JQcMjp_YdqVTu9FneHOUqXcODzCG8rJpTmJ9hcwDPyby1DVOhstK0COWJgNbIjybsuS_3r5zsTAcb5bcWZB-3r0Lk3Lq6uWWsZG0TicLrVG3eE9m9KALc0UWIlbufr_G_lh8T53TvChlWY76W5ygZ6sDa0ToNH-lLtP0CeXAWLTLCtfeKJStfwgiJojmxbzKmP1PtnYhoCUmtUCjUQaNPZ2LRDU5UCEeLBOMAPxMrDWkMZBChDca8QpXQH9Czws0cEsxMAonCOSIDoYM1XDbUohdMjh8jFmNf6wWRbQLQGiUMdansmYMyyXuSMVl0ualzyD6JYJEDnm1Lt-Ty7X-wEwi-_9hZwcZOhwrilDVJZv1MLxuuthgrqcqatIeCkbbGCqvkbr27rdUyGCsFr3vPjSFA_lDU4g5Y-B6CCwtIrD3D3I64Y0g637CpjvAxf5YNtRI1iy8aYmO2BLqB3r9pxfPTLmAHISVlQ-jmdkZ1aZ3UdwpOYXdrPLNLENtlHYM9HPp-bbxLZYw5uKI9qUUjXT1vBhYdJa0XL1kjgYdP7GEYgEFEXMOX05w2vw7eswwd2LWb4uwohzbrjIE-s8Rqhyk3l6zFhLYAPd-tcwUHmdVGAl0Hg37mlyDZaN1Thfp9xDuQfnvpRA146ROrLLJcCi6W8Dql0s9W7PAxjkAh4CghYTn1bD2hk1HYcRRs1hqfXEoV8VJy10j4dipoT6v6vd3VPBitzcVlMEqua8gRqNUT20Op8T7a_Wg==", "id": "rs_04942ad8c41eeb77016a1361f65f708191ae2b22d29b14a867", "summary": [] } ], "tool_calls": [ { "id": "call_aGqYLFnZL43jse2SZTMWbyh7", "call_id": "call_aGqYLFnZL43jse2SZTMWbyh7", "response_item_id": "fc_04942ad8c41eeb77016a1361faf5a4819196636266e9fdd8e8", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_subtitle_cues.py\",\"content\":\"from autoshorts.audio.subtitles import (\\n SubtitleCue,\\n SubtitleStyle,\\n build_hormozi_subtitle_track,\\n validate_subtitle_track,\\n)\\n\\n\\ndef test_build_hormozi_subtitle_track_chunks_text_into_renderer_owned_safe_cues():\\n track = build_hormozi_subtitle_track(\\n \\\"Most AI automations do not fail dramatically. They quietly rot when the workflow has no exception path.\\\",\\n total_duration_seconds=8,\\n )\\n\\n assert track.renderer_owned is True\\n assert track.style == SubtitleStyle.hormozi_inspired()\\n assert track.side_effects == ()\\n assert track.external_calls == ()\\n assert track.cues[0].start_ms == 0\\n assert track.cues[-1].end_ms == 8000\\n assert all(2 <= len(cue.text.split()) <= 5 for cue in track.cues)\\n assert all(cue.active_word in cue.text.upper().split() for cue in track.cues)\\n assert all(cue.max_lines == 2 for cue in track.cues)\\n assert all(cue.renderer_owned for cue in track.cues)\\n\\n\\ndef test_build_hormozi_subtitle_track_uses_continuous_timing_and_emphasis_metadata():\\n track = build_hormozi_subtitle_track(\\n \\\"Prompt is not system. Workflow boundaries create reliable automation.\\\",\\n total_duration_seconds=6,\\n )\\n\\n previous_end = 0\\n for cue in track.cues:\\n assert cue.start_ms == previous_end\\n assert cue.end_ms > cue.start_ms\\n assert cue.emphasis_color == track.style.accent_color\\n previous_end = cue.end_ms\\n assert previous_end == 6000\\n\\n\\ndef test_validate_subtitle_track_rejects_non_renderer_owned_and_overcrowded_cues():\\n cue = SubtitleCue(\\n text=\\\"THIS CUE HAS FAR TOO MANY WORDS\\\",\\n start_ms=0,\\n end_ms=1000,\\n active_word=\\\"TOO\\\",\\n renderer_owned=False,\\n )\\n track = build_hormozi_subtitle_track(\\\"short valid text\\\", total_duration_seconds=2).with_cues((cue,))\\n\\n validation = validate_subtitle_track(track)\\n\\n assert validation.is_valid is False\\n assert \\\"subtitle cue 1 must be renderer-owned\\\" in validation.errors\\n assert \\\"subtitle cue 1 must contain 2..5 words\\\" in validation.errors\\n\\n\\ndef test_build_hormozi_subtitle_track_rejects_blank_text_and_bad_duration():\\n for text, duration in [(\\\"\\\", 3), (\\\"valid text\\\", 0)]:\\n try:\\n build_hormozi_subtitle_track(text, total_duration_seconds=duration)\\n except ValueError as exc:\\n assert str(exc)\\n else:\\n raise AssertionError(\\\"expected ValueError\\\")\\n\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2418, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.audio.subtitles\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_aGqYLFnZL43jse2SZTMWbyh7" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_x1xHBfZpdcYnMQ5BHpdZ0jhp", "call_id": "call_x1xHBfZpdcYnMQ5BHpdZ0jhp", "response_item_id": "fc_046a0b6840ed2125016a13620a42788191a0864dc86b5ee6ee", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"from autoshorts.audio.tts_plan import (\\n TTSProviderPlan,\\n build_default_truetrace_tts_plan,\\n build_tts_ab_test_plan,\\n validate_tts_provider_plan,\\n)\\n\\n\\ndef test_build_default_truetrace_tts_plan_uses_edge_tts_without_gpu_or_external_api_key():\\n plan = build_default_truetrace_tts_plan()\\n\\n assert plan.provider == \\\"edge_tts\\\"\\n assert plan.voice == \\\"en-GB-RyanNeural\\\"\\n assert plan.language == \\\"en\\\"\\n assert plan.requires_local_gpu is False\\n assert plan.blocks_video_gpu is False\\n assert plan.requires_api_key is False\\n assert plan.role == \\\"voice_generation\\\"\\n assert plan.side_effects == ()\\n assert plan.external_calls == ()\\n\\n\\ndef test_build_tts_ab_test_plan_keeps_magpie_hosted_and_whisper_as_alignment_only():\\n plan = build_tts_ab_test_plan()\\n\\n assert [candidate.provider for candidate in plan.candidates] == [\\n \\\"edge_tts\\\",\\n \\\"nvidia_magpie_tts_multilingual\\\",\\n \\\"whisper_alignment\\\",\\n ]\\n magpie = plan.candidates[1]\\n whisper = plan.candidates[2]\\n assert magpie.requires_local_gpu is False\\n assert magpie.blocks_video_gpu is False\\n assert magpie.requires_api_key is True\\n assert magpie.role == \\\"voice_generation\\\"\\n assert whisper.role == \\\"alignment_qa\\\"\\n assert whisper.provider != \\\"tts\\\"\\n assert whisper.blocks_video_gpu is False\\n assert plan.recommended_default_provider == \\\"edge_tts\\\"\\n\\n\\ndef test_validate_tts_provider_plan_rejects_whisper_as_voice_generation_and_gpu_blocking_default():\\n bad_whisper = TTSProviderPlan(\\n provider=\\\"whisper\\\",\\n voice=\\\"base\\\",\\n language=\\\"en\\\",\\n role=\\\"voice_generation\\\",\\n requires_local_gpu=False,\\n blocks_video_gpu=False,\\n requires_api_key=False,\\n )\\n bad_gpu_default = TTSProviderPlan(\\n provider=\\\"local_gpu_tts\\\",\\n voice=\\\"some-voice\\\",\\n language=\\\"en\\\",\\n role=\\\"voice_generation\\\",\\n requires_local_gpu=True,\\n blocks_video_gpu=True,\\n requires_api_key=False,\\n default_for_phase=\\\"phase_1\\\",\\n )\\n\\n whisper_validation = validate_tts_provider_plan(bad_whisper)\\n gpu_validation = validate_tts_provider_plan(bad_gpu_default)\\n\\n assert whisper_validation.is_valid is False\\n assert \\\"Whisper is STT/alignment, not TTS\\\" in whisper_validation.errors\\n assert gpu_validation.is_valid is False\\n assert \\\"phase-1 TTS must not block the local video GPU\\\" in gpu_validation.errors\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_tts_plan.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2405, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.audio.tts_plan\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_x1xHBfZpdcYnMQ5BHpdZ0jhp" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_j8Am34fLzv5VlfU8XntU1H4C", "call_id": "call_j8Am34fLzv5VlfU8XntU1H4C", "response_item_id": "fc_08aa5be0949c2bf1016a13621f62e08191b5d27fc284093c64", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/test_subtitle_cues.py tests/test_tts_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_subtitle_cues.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_subtitle_cues.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_subtitle_cues.py:1: in \\n from autoshorts.audio.subtitles import (\\nE ModuleNotFoundError: No module named 'autoshorts.audio'\\n___________________ ERROR collecting tests/test_tts_plan.py ____________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_tts_plan.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_tts_plan.py:1: in \\n from autoshorts.audio.tts_plan import (\\nE ModuleNotFoundError: No module named 'autoshorts.audio'\\n=========================== short test summary info ============================\\nERROR tests/test_subtitle_cues.py\\nERROR tests/test_tts_plan.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_j8Am34fLzv5VlfU8XntU1H4C" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2In3Knn2sRxk8-Fyq4e-HQP-nXPezgfr_1RdcgdrY8zBhjGPcWqpp7sJ0E4eslIt-DY2rhidex5Z9aP3_-r10YN2odTQvmKGJzMBiPPOdcjjwNlVQvml_DR6SIUcg2YoeyzdnggXwaeiePnZdtM7EqT3Oci_agQW7U4Z_onZSi8u9PROplCiTpfdevW0gNT_XrtwqHaq2NEv5znxr9B8my6K6W50LQG2tKGNjI2C64z3IBqopo7_Wpp9eXJbwv9POqAD5rD3j_P8_lbfNFI0gzxmOUtfKqHr68nVVjaplJ1cN67q6bZGcvQR5Y7KrktZAyKRFQsEnovyJ5OBYAYe994bE1uUAUR2dXoyXKH9GWd06stO3mkXZiXNWcvNQ58DSzPRyUwhA988UHbFjyDqZT2WPiOGJ5lQMX_AWfUJPfS83urrcDuyYUSVaqi4fIitj-hj2s8XwsUtBEembt8qMGQpSmAVD6UmKre2unScuS01HLMbswvtEmFTvf_uASHXxuo5VZLzLLksVbPmX20oTxgHQgxetW6XmABskqCczI-w2VkgY_6_JA5BwLrcEZCTOSD4GfGBudlHIzLlcLSp2IJTHaUw9GZJnO3Z3LibWjltzS_0CCSc5WgY9wBp04Pw6Gn0Wo-KP9uV8Rq2_MBfSXkC5PQSmil9gQiCoV0FAfQFUSkq9xvjLTk7eibUU0wK5CJDAuadCVY5TXo8XvXMkWgFI2LnlNVcmkbGVI7X_oFWs-cNTIBPEirv0bPMkQVqOlAbyzUJVVH2qg3MclCXVWT3-u3UQ-Jho8MusiA4zeH8crwhmC4GP93oniML0aFQcSbqi5jlyx7yzbNPnA00p08rs1lLZtW_JrsPuwD8WmvJiXdoIWfWYgS0GBuwu5Krx3xDv81YU8PkUyD3tUsXMlfjiRuBkta207TtwXLdogl5-04EXGx0PUXrqoP5asPfXUY5c94K2-hkT6wL3PW8gk0Tg==", "id": "rs_02b402862e921b21016a136226f858819191a403e48879c082", "summary": [] } ], "tool_calls": [ { "id": "call_76el9UJuoUbN49BHyg6TfYaQ", "call_id": "call_76el9UJuoUbN49BHyg6TfYaQ", "response_item_id": "fc_02b402862e921b21016a1362278c308191aae8b78b94113af9", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/__init__.py\",\"content\":\"\\\"\\\"\\\"Audio planning primitives for voiceover and subtitles.\\\"\\\"\\\"\\n\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 61, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_76el9UJuoUbN49BHyg6TfYaQ" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_Uo0i9l3R2E1Drv6RG0snEItm", "call_id": "call_Uo0i9l3R2E1Drv6RG0snEItm", "response_item_id": "fc_028318ab2a4a88a9016a13622bdff081918a102af129e512b9", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Deterministic renderer-owned subtitle cue planning.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport re\\nfrom dataclasses import dataclass, replace\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\n\\n\\n_WORD_RE = re.compile(r\\\"[A-Za-z0-9]+(?:['-][A-Za-z0-9]+)?\\\")\\n\\n\\n@dataclass(frozen=True)\\nclass SubtitleStyle:\\n name: str\\n primary_color: str\\n accent_color: str\\n stroke_color: str\\n shadow_color: str\\n placement: str\\n max_lines: int\\n min_words_per_cue: int\\n max_words_per_cue: int\\n safe_zone_bottom_percent: int\\n font_weight: str\\n\\n @classmethod\\n def hormozi_inspired(cls) -> \\\"SubtitleStyle\\\":\\n \\\"\\\"\\\"Return the TrueTrace kinetic-caption baseline: inspired mechanics, original brand skin.\\\"\\\"\\\"\\n\\n return cls(\\n name=\\\"truetrace_hormozi_inspired\\\",\\n primary_color=\\\"#FFFFFF\\\",\\n accent_color=\\\"#22D3EE\\\",\\n stroke_color=\\\"#050816\\\",\\n shadow_color=\\\"#000000\\\",\\n placement=\\\"lower_middle_above_ui_safe_zone\\\",\\n max_lines=2,\\n min_words_per_cue=2,\\n max_words_per_cue=5,\\n safe_zone_bottom_percent=14,\\n font_weight=\\\"800\\\",\\n )\\n\\n\\n@dataclass(frozen=True)\\nclass SubtitleCue:\\n text: str\\n start_ms: int\\n end_ms: int\\n active_word: str\\n renderer_owned: bool = True\\n max_lines: int = 2\\n emphasis_color: str = \\\"#22D3EE\\\"\\n\\n\\n@dataclass(frozen=True)\\nclass SubtitleTrack:\\n cues: tuple[SubtitleCue, ...]\\n style: SubtitleStyle\\n renderer_owned: bool = True\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n def with_cues(self, cues: tuple[SubtitleCue, ...]) -> \\\"SubtitleTrack\\\":\\n return replace(self, cues=cues)\\n\\n\\ndef _words(text: str) -> list[str]:\\n return _WORD_RE.findall(text)\\n\\n\\ndef _chunk_words(words: list[str], *, min_words: int, max_words: int) -> list[list[str]]:\\n chunks: list[list[str]] = []\\n index = 0\\n while index < len(words):\\n remaining = len(words) - index\\n take = min(max_words, remaining)\\n if 0 < remaining < min_words and chunks:\\n chunks[-1].extend(words[index:])\\n break\\n if remaining == min_words + 1:\\n take = min_words\\n chunks.append(words[index : index + take])\\n index += take\\n return chunks\\n\\n\\ndef _active_word(chunk: list[str]) -> str:\\n # Prefer the longest meaningful word so the highlighted token carries semantic weight.\\n return max((word.upper() for word in chunk), key=lambda word: (len(word), word))\\n\\n\\ndef build_hormozi_subtitle_track(text: str, *, total_duration_seconds: int) -> SubtitleTrack:\\n \\\"\\\"\\\"Create continuous 2..5-word renderer-owned subtitle cues for final burn-in.\\\"\\\"\\\"\\n\\n if not text.strip():\\n raise ValueError(\\\"subtitle text is required\\\")\\n if total_duration_seconds <= 0:\\n raise ValueError(\\\"total_duration_seconds must be positive\\\")\\n\\n style = SubtitleStyle.hormozi_inspired()\\n words = _words(text)\\n if len(words) < style.min_words_per_cue:\\n raise ValueError(\\\"subtitle text must contain at least two words\\\")\\n\\n chunks = _chunk_words(words, min_words=style.min_words_per_cue, max_words=style.max_words_per_cue)\\n total_ms = total_duration_seconds * 1000\\n cues: list[SubtitleCue] = []\\n for index, chunk in enumerate(chunks):\\n start_ms = round(index * total_ms / len(chunks))\\n end_ms = round((index + 1) * total_ms / len(chunks))\\n cues.append(\\n SubtitleCue(\\n text=\\\" \\\".join(word.upper() for word in chunk),\\n start_ms=start_ms,\\n end_ms=end_ms,\\n active_word=_active_word(chunk),\\n renderer_owned=True,\\n max_lines=style.max_lines,\\n emphasis_color=style.accent_color,\\n )\\n )\\n\\n track = SubtitleTrack(cues=tuple(cues), style=style)\\n validation = validate_subtitle_track(track)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return track\\n\\n\\ndef validate_subtitle_track(track: SubtitleTrack) -> ValidationResult:\\n errors: list[str] = []\\n if not track.renderer_owned:\\n errors.append(\\\"subtitle track must be renderer-owned\\\")\\n if track.side_effects:\\n errors.append(\\\"subtitle track must not have side effects\\\")\\n if track.external_calls:\\n errors.append(\\\"subtitle track must not have external calls\\\")\\n if not track.cues:\\n errors.append(\\\"at least one subtitle cue is required\\\")\\n\\n previous_end: int | None = None\\n for index, cue in enumerate(track.cues, start=1):\\n cue_words = cue.text.split()\\n if not cue.renderer_owned:\\n errors.append(f\\\"subtitle cue {index} must be renderer-owned\\\")\\n if not (track.style.min_words_per_cue <= len(cue_words) <= track.style.max_words_per_cue):\\n errors.append(f\\\"subtitle cue {index} must contain 2..5 words\\\")\\n if cue.end_ms <= cue.start_ms:\\n errors.append(f\\\"subtitle cue {index} end_ms must be greater than start_ms\\\")\\n if previous_end is None:\\n if cue.start_ms != 0:\\n errors.append(\\\"first subtitle cue must start at 0ms\\\")\\n elif cue.start_ms != previous_end:\\n errors.append(f\\\"subtitle cue {index} must start when the previous cue ends\\\")\\n if not cue.active_word.strip():\\n errors.append(f\\\"subtitle cue {index} active_word is required\\\")\\n elif cue.active_word not in {word.upper() for word in cue_words}:\\n errors.append(f\\\"subtitle cue {index} active_word must appear in cue text\\\")\\n if cue.max_lines > track.style.max_lines:\\n errors.append(f\\\"subtitle cue {index} exceeds style max lines\\\")\\n previous_end = cue.end_ms\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/subtitles.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5805, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_Uo0i9l3R2E1Drv6RG0snEItm" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_lMrduzyfMFUseET5QHi52uTM", "call_id": "call_lMrduzyfMFUseET5QHi52uTM", "response_item_id": "fc_04e423096ccab33e016a13624b81808191a4ccf1f7daa723b3", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Side-effect-free TTS provider planning for TrueTrace videos.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\n\\n\\n@dataclass(frozen=True)\\nclass TTSProviderPlan:\\n provider: str\\n voice: str\\n language: str\\n role: str\\n requires_local_gpu: bool\\n blocks_video_gpu: bool\\n requires_api_key: bool\\n default_for_phase: str = \\\"\\\"\\n notes: str = \\\"\\\"\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\n@dataclass(frozen=True)\\nclass TTSABTestPlan:\\n candidates: tuple[TTSProviderPlan, ...]\\n recommended_default_provider: str\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\ndef build_default_truetrace_tts_plan() -> TTSProviderPlan:\\n \\\"\\\"\\\"Return the phase-1 baseline voice plan: English EdgeTTS, no local GPU contention.\\\"\\\"\\\"\\n\\n plan = TTSProviderPlan(\\n provider=\\\"edge_tts\\\",\\n voice=\\\"en-GB-RyanNeural\\\",\\n language=\\\"en\\\",\\n role=\\\"voice_generation\\\",\\n requires_local_gpu=False,\\n blocks_video_gpu=False,\\n requires_api_key=False,\\n default_for_phase=\\\"phase_1\\\",\\n notes=\\\"Baseline voice for first TrueTrace finals; keeps local GPU reserved for image/video.\\\",\\n )\\n validation = validate_tts_provider_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return plan\\n\\n\\ndef build_tts_ab_test_plan() -> TTSABTestPlan:\\n \\\"\\\"\\\"Plan a later voice comparison without making API calls or consuming GPU.\\\"\\\"\\\"\\n\\n candidates = (\\n build_default_truetrace_tts_plan(),\\n TTSProviderPlan(\\n provider=\\\"nvidia_magpie_tts_multilingual\\\",\\n voice=\\\"default_multilingual\\\",\\n language=\\\"en\\\",\\n role=\\\"voice_generation\\\",\\n requires_local_gpu=False,\\n blocks_video_gpu=False,\\n requires_api_key=True,\\n default_for_phase=\\\"ab_test\\\",\\n notes=\\\"Hosted NVIDIA Build/API Catalog candidate; test quality/latency/terms before defaulting.\\\",\\n ),\\n TTSProviderPlan(\\n provider=\\\"whisper_alignment\\\",\\n voice=\\\"n/a\\\",\\n language=\\\"en\\\",\\n role=\\\"alignment_qa\\\",\\n requires_local_gpu=False,\\n blocks_video_gpu=False,\\n requires_api_key=False,\\n default_for_phase=\\\"qa_later\\\",\\n notes=\\\"Whisper is speech-to-text for subtitle alignment and QA, not voice generation.\\\",\\n ),\\n )\\n errors: list[str] = []\\n for candidate in candidates:\\n errors.extend(validate_tts_provider_plan(candidate).errors)\\n if errors:\\n raise ValueError(\\\"; \\\".join(errors))\\n return TTSABTestPlan(candidates=candidates, recommended_default_provider=\\\"edge_tts\\\")\\n\\n\\ndef validate_tts_provider_plan(plan: TTSProviderPlan) -> ValidationResult:\\n errors: list[str] = []\\n if not plan.provider.strip():\\n errors.append(\\\"provider is required\\\")\\n if not plan.voice.strip():\\n errors.append(\\\"voice is required\\\")\\n if plan.language != \\\"en\\\":\\n errors.append(\\\"TrueTrace final voice language must be en\\\")\\n if plan.role not in {\\\"voice_generation\\\", \\\"alignment_qa\\\"}:\\n errors.append(\\\"role must be voice_generation or alignment_qa\\\")\\n if plan.provider in {\\\"whisper\\\", \\\"whisper_alignment\\\"} and plan.role == \\\"voice_generation\\\":\\n errors.append(\\\"Whisper is STT/alignment, not TTS\\\")\\n if plan.default_for_phase == \\\"phase_1\\\" and plan.blocks_video_gpu:\\n errors.append(\\\"phase-1 TTS must not block the local video GPU\\\")\\n if plan.requires_local_gpu and plan.blocks_video_gpu and plan.default_for_phase == \\\"phase_1\\\":\\n errors.append(\\\"phase-1 local GPU TTS is not allowed while video generation needs the GPU\\\")\\n if plan.side_effects:\\n errors.append(\\\"TTS plan must not have side effects\\\")\\n if plan.external_calls:\\n errors.append(\\\"TTS plan must not have external calls\\\")\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/tts_plan.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 4009, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_lMrduzyfMFUseET5QHi52uTM" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_5ZDNGtjvQwZx36mKNGAiTB45", "call_id": "call_5ZDNGtjvQwZx36mKNGAiTB45", "response_item_id": "fc_02378a5c11d1a0ab016a13626288fc8191a75e0b5370ce5f65", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/test_subtitle_cues.py tests/test_tts_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"....... [100%]\\n7 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_5ZDNGtjvQwZx36mKNGAiTB45" }, { "role": "assistant", "content": "", "reasoning": "**Updating documentation strategy**\n\nI’m thinking about updating the docs and README to include integration with the preview. The user mentioned to continue, which might be enough. I could create some documentation for that. Additionally, maybe I should add a demo summary at the top of the previews, which could include subtitles and TTS. For now, at least updating the docs related to subtitle TTS strategy and the README sounds like a solid plan. I’ll check the relevant parts.", "finish_reason": "tool_calls", "reasoning_content": "**Updating documentation strategy**\n\nI’m thinking about updating the docs and README to include integration with the preview. The user mentioned to continue, which might be enough. I could create some documentation for that. Additionally, maybe I should add a demo summary at the top of the previews, which could include subtitles and TTS. For now, at least updating the docs related to subtitle TTS strategy and the README sounds like a solid plan. 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I’ll check the relevant parts." } ] } ], "tool_calls": [ { "id": "call_oWrWJMqa2YE784JwAQvJkLGF", "call_id": "call_oWrWJMqa2YE784JwAQvJkLGF", "response_item_id": "fc_040a696dd9f42503016a13626990948191b46186e04fb543a8", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\",\"limit\":220}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|# Subtitle and TTS Strategy\\n 2|\\n 3|Date: 2026-05-24\\n 4|\\n 5|## Decision summary\\n 6|\\n 7|For phase 1, subtitles should be deterministic renderer-owned captions in a high-retention Hormozi-inspired style, not burned into AI-generated images or video clips.\\n 8|\\n 9|Recommended TTS path for now:\\n 10|\\n 11|```text\\n 12|EdgeTTS for first English final voice tests\\n 13|→ optional NVIDIA Magpie A/B test later\\n 14|→ Whisper only for alignment/transcription, not as TTS\\n 15|```\\n 16|\\n 17|Reasoning:\\n 18|\\n 19|- EdgeTTS is fast, free, no GPU, no API key, and leaves the GPU free for ComfyUI/Wan/Qwen video/image work.\\n 20|- NVIDIA Magpie TTS may be worth testing for voice quality, but it is an external API dependency and not yet proven better for our exact Shorts style.\\n 21|- Whisper is speech-to-text, not text-to-speech. It can align/generated-audio transcripts later, but it does not solve voice generation.\\n 22|- Local neural TTS on GPU is not attractive while the same GPU is needed for video generation.\\n 23|\\n 24|## Subtitle style\\n 25|\\n 26|Use a `Hormozi-inspired`, not `Hormozi-cloned`, subtitle system:\\n 27|\\n 28|- 2-5 words per subtitle chunk;\\n 29|- active word highlight;\\n 30|- strong contrast;\\n 31|- center-lower safe-zone placement;\\n 32|- occasional emphasis words in accent color;\\n 33|- hard line breaks controlled by renderer;\\n 34|- no baked text inside AI-generated images/clips;\\n 35|- no copyrighted creator-template dependency;\\n 36|- captions remain editable until final render.\\n 37|\\n 38|Default visual rules:\\n 39|\\n 40|```text\\n 41|Font: bold sans-serif\\n 42|Placement: lower-middle, above platform UI safe zone\\n 43|Primary color: white\\n 44|Stroke/shadow: black/dark outline\\n 45|Accent: TrueTrace green/blue\\n 46|Max lines: 2\\n 47|Max words per chunk: 5\\n 48|Chunk duration: roughly 0.7-1.6s\\n 49|```\\n 50|\\n 51|## Caption pipeline\\n 52|\\n 53|Preferred deterministic pipeline:\\n 54|\\n 55|```text\\n 56|Script voiceover text\\n 57|→ caption chunker\\n 58|→ timed subtitle cues\\n 59|→ renderer-owned caption layer\\n 60|→ FFmpeg burn-in for final MP4\\n 61|```\\n 62|\\n 63|Later, after real TTS is added:\\n 64|\\n 65|```text\\n 66|Final script\\n 67|→ TTS audio\\n 68|→ optional Whisper alignment/transcript QA\\n 69|→ corrected subtitle cues\\n 70|→ final renderer burn-in\\n 71|```\\n 72|\\n 73|Important: Whisper is useful for checking/aligning what the TTS actually said, but it should not own the script. The source of truth remains the approved script.\\n 74|\\n 75|## TTS options\\n 76|\\n 77|### EdgeTTS\\n 78|\\n 79|Use for phase 1 baseline.\\n 80|\\n 81|Pros:\\n 82|\\n 83|- no GPU;\\n 84|- free/no key;\\n 85|- quick iteration;\\n 86|- predictable enough;\\n 87|- available voices;\\n 88|- keeps video GPU free.\\n 89|\\n 90|Cons:\\n 91|\\n 92|- common synthetic sound;\\n 93|- less distinctive brand voice;\\n 94|- commercial/licensing terms should be reviewed before scaling monetized production.\\n 95|\\n 96|Suggested English voices to A/B later:\\n 97|\\n 98|- `en-US-GuyNeural` — clear male baseline;\\n 99|- `en-US-ChristopherNeural` — more mature male;\\n 100|- `en-US-AriaNeural` — polished female baseline;\\n 101|- `en-GB-RyanNeural` — British male, potentially fitting precise/dry tone.\\n 102|\\n 103|### NVIDIA Magpie multilingual TTS\\n 104|\\n 105|Use as an A/B candidate, not default yet.\\n 106|\\n 107|Pros:\\n 108|\\n 109|- likely higher modern neural quality than commodity voices;\\n 110|- multilingual support;\\n 111|- cloud-side, so it should not consume local video GPU.\\n 112|\\n 113|Cons:\\n 114|\\n 115|- external API dependency;\\n 116|- cost/rate limits/terms unknown until tested;\\n 117|- voice/style fit unknown;\\n 118|- not integrated into the current deterministic pipeline;\\n 119|- may create operational coupling to build.nvidia.com.\\n 120|\\n 121|Recommendation: run a short 15-20 second voice A/B sample against EdgeTTS before making it default.\\n 122|\\n 123|### Local TTS\\n 124|\\n 125|Not recommended as default for phase 1 if it needs GPU.\\n 126|\\n 127|Use only if:\\n 128|\\n 129|- it can run CPU-fast enough; or\\n 130|- it runs on a separate machine; or\\n 131|- video generation is idle; or\\n 132|- it gives a clearly superior branded voice.\\n 133|\\n 134|The local GPU should be reserved for ComfyUI/Wan/Qwen video/image jobs when those are active.\\n 135|\\n 136|### Whisper\\n 137|\\n 138|Whisper is not a TTS model.\\n 139|\\n 140|Use later for:\\n 141|\\n 142|- checking TTS output text;\\n 143|- subtitle timing alignment;\\n 144|- transcript QA;\\n 145|- detecting missing/garbled words.\\n 146|\\n 147|Avoid using Whisper as a required step for every preview until the audio pipeline exists.\\n 148|\\n 149|## Phase plan\\n 150|\\n 151|### Phase 1A: Current preview layer\\n 152|\\n 153|- German approval preview cards only.\\n 154|- No final TTS required.\\n 155|- Captions are card/caption beats for concept review.\\n 156|\\n 157|### Phase 1B: First final-video audio test\\n 158|\\n 159|- Use EdgeTTS English voice baseline.\\n 160|- Generate deterministic subtitle cues from the approved script.\\n 161|- Burn captions in renderer/FFmpeg.\\n 162|- Send preview with audio + captions for approval.\\n 163|\\n 164|### Phase 1C: Voice A/B\\n 165|\\n 166|Create the same 15-20 second script using:\\n 167|\\n 168|1. EdgeTTS baseline;\\n 169|2. NVIDIA Magpie if API access is configured;\\n 170|3. optional local CPU/offline TTS if available.\\n 171|\\n 172|Compare:\\n 173|\\n 174|- clarity;\\n 175|- retention energy;\\n 176|- pronunciation of AI/workflow terms;\\n 177|- audio artifacts;\\n 178|- latency/cost;\\n 179|- operational reliability.\\n 180|\\n 181|### Phase 2: Alignment QA\\n 182|\\n 183|- Add Whisper transcription/alignment after TTS.\\n 184|- Flag mismatches between approved script and spoken audio.\\n 185|- Adjust subtitle cues if the TTS timing differs.\\n 186|\\n 187|## Current recommendation\\n 188|\\n 189|Start with:\\n 190|\\n 191|```text\\n 192|Subtitles: deterministic Hormozi-inspired renderer-owned captions\\n 193|TTS: EdgeTTS English baseline\\n 194|Magpie: A/B test later\\n 195|Whisper: alignment/QA later, not TTS\\n 196|GPU: reserve for video/image generation\\n 197|```\\n 198|\\n 199|This is the lowest-friction path and avoids blocking the video pipeline on voice experiments. Voice quality matters, but first we need to prove that the concepts and visuals are worth voicing. A beautiful voice reading weak content is merely expensive politeness.\\n 200|\", \"total_lines\": 199, \"file_size\": 5315, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_oWrWJMqa2YE784JwAQvJkLGF" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Juo7iafAjivCLgqoq7dneUeOSQVdvtlWH_bnKrXlMPRnNlBdLRV56-xjxWKK-OwBis4TV9qPduZFd6jT5310lQtW137bi3g_NkaTvExewmvcxw1VbzLijmOYEDwpn9gHCgnGHuZdgrLAXtO31uCm8VVw9rQgB0oZyNGRTQNX0LxtcwPk8nWHQLaN5B1qdz6IE70cpr8pffLesiXuOdeYwYOT2DnE5J4cuwpOb44XvrR2DoRWnSA7ALhizU54QDJqb8Gus-afMj6sHL6mP9UO-x4w6feMtFVWvPsvSC2KyYMQ3oiKGDvkzE8h6L9nbYJgMuJJnn6htNH1gEcDNXIlwBHScPipdbUxanYfb8feFwd9OP3yEuqMYvhuKaein65DOXVbWsHqfmjISKtY34B6Vf3-45tCUqIOLuYw7L7y6fftKYqsMjOpQdzNaO4Yekazc9j8p6jCRuOfEt2vsVInlNj0k67tQCtIU-ol05TVqhnZxM0gipZBh2GUuaiHbMyjBOhAOXDXsCzSxFsGhvLUo1v0FCHucsKbuwfsgHBktWO5zOusxaWhgvbXbM7oOCX-TZkRke7eOZmM2l4sn2-Fj0lczbVuuDN7MIkXTU5OT1B19XmpSzpU8UxxQnI4eY9sWigTI32A19bkQ_2KDpl_YBi6LXUzZuXOdFayTAUmaw1CCQ9iwQqgSQFlflRUS4aRGfrOx2LMPAiJaHflBjc4emgV3P53_3mJjOaUDw4YwrWBF5u9baJJic1xRvFAlmx_MyaAro5gVKCt7kAoAqrwCbxHnrWxovUtYyfEbNYUDyowmDfSsUkhbi37MFWn54fY8pYpjHaST1IH-zcJ0AG9FDLSWXI6AdgUjYBCzJXUipNV6gl_4Z3yhA50wcK1XC_7voSVnt0UydvWJzBZQqrNZKJ2n0TkddgZvTDLaXKcfCbL8=", "id": "rs_06fef54339df3e85016a13626e6af881918aa136835e119dd7", "summary": [] } ], "tool_calls": [ { "id": "call_qzkEisDcW8a90pWLNAgGh6zk", "call_id": "call_qzkEisDcW8a90pWLNAgGh6zk", "response_item_id": "fc_06fef54339df3e85016a13626eed08819187cb59e6380526af", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n- `autoshorts.ideas.candidate` — structured video candidate model and validation.\",\"new_string\":\"- `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning: EdgeTTS phase-1 baseline, Magpie A/B candidate, Whisper alignment-only.\\n- `autoshorts.ideas.candidate` — structured video candidate model and validation.\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -21,6 +21,8 @@\\n - `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n - `autoshorts.demo.truetrace_concepts` — five concrete TrueTraceShorts concept candidates with metadata for strategy review.\\n - `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n+- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n+- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning: EdgeTTS phase-1 baseline, Magpie A/B candidate, Whisper alignment-only.\\n - `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n - `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}, \"_warning\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md was last read with offset/limit pagination (partial view). Re-read the whole file before overwriting it.\"}", "tool_call_id": "call_qzkEisDcW8a90pWLNAgGh6zk" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_VyO7K3zp9fGtSnY2f2vvloXn", "call_id": "call_VyO7K3zp9fGtSnY2f2vvloXn", "response_item_id": "fc_0d1598110e8ebb91016a136273ee988191b13e965d194ecb9a", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\",\"old_string\":\"## Caption pipeline\\n\\nPreferred deterministic pipeline:\\n\\n```text\\nScript voiceover text\\n→ caption chunker\\n→ timed subtitle cues\\n→ renderer-owned caption layer\\n→ FFmpeg burn-in for final MP4\\n```\\n\",\"new_string\":\"## Implemented planning modules\\n\\nCurrent code-level planning boundary:\\n\\n- `autoshorts.audio.subtitles.build_hormozi_subtitle_track()` creates continuous 2-5-word subtitle cues with active-word metadata, lower-middle safe-zone style, no side effects, and no external calls.\\n- `autoshorts.audio.subtitles.validate_subtitle_track()` rejects non-renderer-owned cues, overcrowded captions, bad timing, and missing active words.\\n- `autoshorts.audio.tts_plan.build_default_truetrace_tts_plan()` selects `edge_tts` with `en-GB-RyanNeural` for phase 1.\\n- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n\\n## Caption pipeline\\n\\nPreferred deterministic pipeline:\\n\\n```text\\nScript voiceover text\\n→ caption chunker\\n→ timed subtitle cues\\n→ renderer-owned caption layer\\n→ FFmpeg burn-in for final MP4\\n```\\n\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n@@ -48,6 +48,15 @@\\n Chunk duration: roughly 0.7-1.6s\\n ```\\n \\n+## Implemented planning modules\\n+\\n+Current code-level planning boundary:\\n+\\n+- `autoshorts.audio.subtitles.build_hormozi_subtitle_track()` creates continuous 2-5-word subtitle cues with active-word metadata, lower-middle safe-zone style, no side effects, and no external calls.\\n+- `autoshorts.audio.subtitles.validate_subtitle_track()` rejects non-renderer-owned cues, overcrowded captions, bad timing, and missing active words.\\n+- `autoshorts.audio.tts_plan.build_default_truetrace_tts_plan()` selects `edge_tts` with `en-GB-RyanNeural` for phase 1.\\n+- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n+\\n ## Caption pipeline\\n \\n Preferred deterministic pipeline:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_VyO7K3zp9fGtSnY2f2vvloXn" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2J95VKckWs2Wl2LeA_JL9cj4MI_4gIAdVIO8pIvlX-kqql9cyF-IALdEKDiVkcwOI2tOgvPbJK6nbbY3fbHLDF2esX7o4HKpbzwJFm3CHctAgAj4YE7oHN5B3ZfHbrHr4Nvkab5CJ18D33cmvq97UyAfd-gLtleO3cEHInCJ0Xr_GN0Vb2bRYoE498a9pcIep5rl4uPCyvtcZS5K5qNLzbXatA-j-um3-kFCjvGvM-hKGr3k4F0o3YGvnBAOpC1QNhlBfrwWThYYx4AZGpTMZp-xENJ5EnzJ19SW4FWbt6-JhD2z85Yx-hyEHBAzuC-a1rW8uDzxb9mGspB-CcTyfJTk6LNy73dpVSvsZK1cGhNSdzyYAUBZJ-1KmV8zGN6gyfED9eZXHQng7Fw6WIy6gpIhqNuCF4IfX0UQMglGn2NQ-WeEaLFocGmKhhPXl0na7E-hytVf79h-5kITjTJDerncFrXBpGGCddnoHr5xBbIfrqb5lqbaZJYMF-Ma1VvBRknJxntJUukf8kztHOxScLKj2zCipKfIQ9QWUeRy7nXtYKMjHNfJ6NtiWgzjfvbtw5zHeQ8RHBYbX30IMYH7FJbEBtAnCXvmdNrNhHg5AIpcp8gOdIkeLQyYWb8wVDHrr-cYZnhe0phU_nDFCjeAAGkgtgwXMnXMEZQ3jXI-a23XrAgRpZ_DECF6YxbasqDuBX4rfcUIIdvFg7r6F1QyZ8wfN-AOA3LtwayOLv741uo3WjZdXyH-lNr4MdVZOr4Pyj8EepremLkMhuUTZryNVdBCVHA9cpbTsCN3Ap2ejnKgwpZUPMRgw_fGJ9DsR6JYV8oYH8e7EGER7R5fVZ_i2oP1G-6YwBvjnAL9Xkj-6JnbvalZNrNXdxONPhrtJQGAzkca54T9fz05rXrPKv39ZC-EmCR2vOO46smHDWQa8WgBW0=", "id": "rs_071bcbbac83f6765016a13627cb56c8191979a6ece1b038f23", "summary": [] } ], "tool_calls": [ { "id": "call_ryb4qHHMxfEV1EJsZ82z2D3k", "call_id": "call_ryb4qHHMxfEV1EJsZ82z2D3k", "response_item_id": "fc_071bcbbac83f6765016a13627d50d08191a07cf682f4f0e6ca", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 48%]\\n........................................................................ [ 96%]\\n..... [100%]\\n149 passed in 31.97s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ryb4qHHMxfEV1EJsZ82z2D3k" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2KijpuEZN1XmpXovWI9Hs4PYtTNcPvF1fgJtaLbp4DcdqfY3K4fkuKZNUpdkDSN7a0I6VJ5KhBxWu618rJU08tzo7m28zXEpFqfqJSrZkQ6N6z1BAoRkEBTIwEDGZPRuckUNQSGLcr6CM5BSg7flWgEl-xP9zv-R8jn5FlodvgWM7mzs6OSSduJXtCF8XzjuLyV6Ua70YtUhJweZA6T_77Hq1isK6cRPDQAMAnl4xzgm6GSNg1mfdUkktxynyeJNyUYBTOfOC454d79U5ur66FTJbZXS8ZJ7LQaA92kze6AEEOUtpzpmaNEXXncVksbXhiVMWE1UU6zWiBvuPHKwGVKJoeENhJLhQy77HnlP1W2fl4yHquCdm191O9HFx9J1r6rTyhRzL-1TlDVfK_jCAch_uOGE64WdjGAROVfv6vzxkRMvzPK7mRPdQmHqVdpHskzBGz_Qx41Xvxcqqa4HiicXvlyMDfElEdjqgpPLVyjom9vuTaO_0u26Ccm6AK7nHAKWDGStz5cKRZ8UyfFdY2hgZKJlANgEI7D4iv2-FbwZifPEgdzj_Jo9LuJc-JPMkWCrfHq-DVkx3TnDvsKOazYAZjxebPdKcjMU29KV4B7YMyUNu66ugNc4mAoTYxM1gHcZacUuWJ5S4-JIjOOtmjDyZzNfDmFKxAsJmS1XRdSgxTFAgznpk2j1tvhJjJZtAytai4GKt9Ly-qH7J3EZGL4xVCXBodHmEykk2kAyO1LQ0AWM1CtVAB5zD4ycezWDnDUUWhXXumzOukS3TPf_420MSl6hevHS4E13trltf3_729fX9jNwiSxsbj5mQ2gsy2w3ZdhwFOYVFrj3BNuOI6zzhoyM2dwUMZ5XzGIhKhkPtelBrVre_SpMC-dbPq7lix4OqcrTq89twJEWGqruBvRCh75nm-MoApKSE3hh3FlQRvXT06du3umgSY_1Enug5WQ", "id": "rs_0a5f481912f06cfb016a1362a05bc08191a4c367af1990a9b2", "summary": [] } ], "tool_calls": [ { "id": "call_CNxYWXl50BGBEpWtdFbO825z", "call_id": "call_CNxYWXl50BGBEpWtdFbO825z", "response_item_id": "fc_0a5f481912f06cfb016a1362a2046081918915bba91a2592b1", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git diff -- autoshorts/audio/__init__.py autoshorts/audio/subtitles.py autoshorts/audio/tts_plan.py tests/test_subtitle_cues.py tests/test_tts_plan.py README.md docs/subtitle_tts_strategy.md | sed -n '1,260p'\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n M docs/subtitle_tts_strategy.md\\n?? autoshorts/audio/\\n?? tests/test_subtitle_cues.py\\n?? tests/test_tts_plan.py\\ndiff --git a/README.md b/README.md\\nindex 3c8ae71..e5cfe9e 100644\\n--- a/README.md\\n+++ b/README.md\\n@@ -21,6 +21,8 @@ Implemented foundation:\\n - `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n - `autoshorts.demo.truetrace_concepts` — five concrete TrueTraceShorts concept candidates with metadata for strategy review.\\n - `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n+- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n+- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning: EdgeTTS phase-1 baseline, Magpie A/B candidate, Whisper alignment-only.\\n - `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n - `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\ndiff --git a/docs/subtitle_tts_strategy.md b/docs/subtitle_tts_strategy.md\\nindex fc6493e..ce0cb81 100644\\n--- a/docs/subtitle_tts_strategy.md\\n+++ b/docs/subtitle_tts_strategy.md\\n@@ -48,6 +48,15 @@ Max words per chunk: 5\\n Chunk duration: roughly 0.7-1.6s\\n ```\\n \\n+## Implemented planning modules\\n+\\n+Current code-level planning boundary:\\n+\\n+- `autoshorts.audio.subtitles.build_hormozi_subtitle_track()` creates continuous 2-5-word subtitle cues with active-word metadata, lower-middle safe-zone style, no side effects, and no external calls.\\n+- `autoshorts.audio.subtitles.validate_subtitle_track()` rejects non-renderer-owned cues, overcrowded captions, bad timing, and missing active words.\\n+- `autoshorts.audio.tts_plan.build_default_truetrace_tts_plan()` selects `edge_tts` with `en-GB-RyanNeural` for phase 1.\\n+- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n+\\n ## Caption pipeline\\n \\n Preferred deterministic pipeline:\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_CNxYWXl50BGBEpWtdFbO825z" }, { "role": "assistant", "content": "", "reasoning": "**Considering Git Diff Behavior**\n\nI’m realizing that the git diff command doesn’t show untracked files, which may be part of my confusion. It seems I might need to run a different command or adjust my approach. I think it's okay to commit what I've changed, but I want to ensure I'm not missing anything important that isn’t being tracked. It's essential to get this right before moving forward!", "finish_reason": "tool_calls", "reasoning_content": "**Considering Git Diff Behavior**\n\nI’m realizing that the git diff command doesn’t show untracked files, which may be part of my confusion. It seems I might need to run a different command or adjust my approach. I think it's okay to commit what I've changed, but I want to ensure I'm not missing anything important that isn’t being tracked. 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It seems I might need to run a different command or adjust my approach. I think it's okay to commit what I've changed, but I want to ensure I'm not missing anything important that isn’t being tracked. It's essential to get this right before moving forward!" } ] } ], "tool_calls": [ { "id": "call_YbA4zYDb6Pbq3JEMIzuBuKq2", "call_id": "call_YbA4zYDb6Pbq3JEMIzuBuKq2", "response_item_id": "fc_0193206cdb28d605016a1362a69e808191aecfcb4e3e0fc816", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add autoshorts/audio/__init__.py autoshorts/audio/subtitles.py autoshorts/audio/tts_plan.py tests/test_subtitle_cues.py tests/test_tts_plan.py README.md docs/subtitle_tts_strategy.md && git commit -m \\\"feat: add subtitle and tts planning\\\"\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"[feat/core-approval-quality-gates 30a8129] feat: add subtitle and tts planning\\n 7 files changed, 423 insertions(+)\\n create mode 100644 autoshorts/audio/__init__.py\\n create mode 100644 autoshorts/audio/subtitles.py\\n create mode 100644 autoshorts/audio/tts_plan.py\\n create mode 100644 tests/test_subtitle_cues.py\\n create mode 100644 tests/test_tts_plan.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_YbA4zYDb6Pbq3JEMIzuBuKq2" }, { "role": "assistant", "content": "", "reasoning": "**Verifying commit changes**\n\nI’m seeing a commit indicating seven files changed, but I thought we already had seven changes? I need to check if the README was included in this count. It’s a little confusing, so I’ll want to verify the current status and take a look at the logs to clarify what’s happening. Ensuring everything is clean and accounted for is essential before moving forward.", "finish_reason": "tool_calls", "reasoning_content": "**Verifying commit changes**\n\nI’m seeing a commit indicating seven files changed, but I thought we already had seven changes? I need to check if the README was included in this count. It’s a little confusing, so I’ll want to verify the current status and take a look at the logs to clarify what’s happening. Ensuring everything is clean and accounted for is essential before moving forward.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Kr6b6hkWnjIylqj6uTzO9jTHb9jhriOxdYkL4Vwp6gFXlU-Hht57qMh52kIM8hzwqPUSKV6DoHoScvVf6qLvMK6WYqdrH5CpjUTxfVThJOpLncR51aczhp5Nwu52ohEVlLTsP9g93fUtt0nsFRs2oWQ1uANZzpfrgZvFkvdRkF_Qb2HGsHqM0J-5mLG4En00oUNqHv8R4Set1BMBgCFo9eDdoHG2-48EPctKsu-amCUnEU0t3WBrKXAdpnV5tXa77b6_ttOm9wbRy1Xkf3IgFIve4R9Ug6PVLUOp707ziz1otGeXhRT0VHc1HmX5Xnq4fxEY3n-G2LG9VEq4Bgm5HqUQA9MkBrrkoxUTQFLITVcpPX4Z39V54c3YZYHyVrjzsARP1UeI9tQjQssId3hksvASjSLFMG8EfO3_-n-QzPaDfZ6bh73_5pYar1cvmcr-Zx1G6puXnC1wG6lGZSkvxgX0BlXHuTNHu4QubTq5hh6a7tWy-rSPXmGKOfJ4DU6hhMKiR1bTspxqxucdv9l_Xmcob5EHKlq_wEquOu-UDnJZJ8O7hKvObCIqJq3wrJLJHwUFQOPnUrmcNtlzXU2Bu9JmXkDoSDepjYNpLdDacMnbpb8zJu-AvK3zygIqm4KU1pEWdFCIF8SZ1EMTt922rulYEucG1F_Mtk--5dM3subUMbDSnBX4nlPPxoMyDJKMr5GqsyXnqGXWEE-Ny52dkbgSn6Uvyl_VaNpsyOUqWr8YTMxNGd2xLawsTOOQ-Bpy8LBdZv4a4qSKNvmzDkaoaJ6Sn_0Uhw8V7RccuocpDndZs7giQfe7pb2TPJApYciJOorMcHnMnw8kszDhZUnzBkRgmtcCc71EtZt1A5TBAbSK6IM_14Q3yhGGxZJDJxHyBwvDtxAuCiu2ZAz9BLJ2NTbAo1Fc8R4cY9BUoNL34dqgXliP68JdUFm_766JIDApK3Qlf3rKMyfPDjDy8Y4m2FQGIbhPxggHzFOmutmLQ_zXYAm0R1IrEO8gbBayiNONB0RYPVzMsxoWng6koNJFGtyPyynPnX9Z58_RkS-9sdOZ_pLcbOwBSuc5A41SxHvwX4", "id": "rs_06d8c7fea0213b38016a1362a966e08191abdef6ddb4032336", "summary": [ { "type": "summary_text", "text": "**Verifying commit changes**\n\nI’m seeing a commit indicating seven files changed, but I thought we already had seven changes? I need to check if the README was included in this count. It’s a little confusing, so I’ll want to verify the current status and take a look at the logs to clarify what’s happening. Ensuring everything is clean and accounted for is essential before moving forward." } ] } ], "tool_calls": [ { "id": "call_ki3fYGCJm67pHfpdmMNCmMnc", "call_id": "call_ki3fYGCJm67pHfpdmMNCmMnc", "response_item_id": "fc_06d8c7fea0213b38016a1362ab6298819180266c1d98ee8804", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git log --oneline -3\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"30a8129 feat: add subtitle and tts planning\\n6efc4dc feat: add TrueTrace top preview packages\\n2d6af9f feat: add TrueTrace concept testbatch\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ki3fYGCJm67pHfpdmMNCmMnc" }, { "role": "assistant", "content": "", "reasoning": "**Integrating audio planning**\n\nI'm considering whether to continue with another slice by attaching subtitle_track and tts_plan to TrueTracePreviewArtifact. However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. Let's get this done!", "finish_reason": "tool_calls", "reasoning_content": "**Integrating audio planning**\n\nI'm considering whether to continue with another slice by attaching subtitle_track and tts_plan to TrueTracePreviewArtifact. However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. Let's get this done!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2K1j9FJJAwgJCKxmsuAy0jvO_ifa2kDzNrFAt30SuCs86R2AnSQdBvmY2902iRHmvKC2f3_2nb8h2O66H39QjrjPzUCyneEk-AitIgjMltldIz2K2eD96c9s4ZFVKI1hb61-P6h_Gn7Hduu2n2fQ2tVDb4UbW3mjx7bMb8lSrz4xeBkqAj3LFlt1pIUHA22pOAlKRjkHWQZbzwVFMg4Zlvw0zQInxt_IteMA_h1WLT9sxnAOEixeFGHUg-F4XoS2-sqlP8DrzsHoTUoYhvRKQpa7VRl1Bw2oKJx93FFbuAE7gPbx-9pfnCBdeuaePCbYgXEXHVukuUL4UAqbYL8IdkIoam19zYJbFe8PlfFQUxklgOxAUkZTtD9W5jQmKL-fd_9HCuyTdBjoZ8OQB3idmWw0Fn37bQhz3lB8DmfM6OWWHDxzJt_OXZfayvjw0FKjndxIsyjAe7dt6i6UnUFS0BCPy7lq4YRlf-SzuuWw7tWtKN9R663me1-Yx948ggwwvdu9ZzDXskdbpNi1uLsA_G94O7FIv6Z771aWf4JORFHs2yM0IK4gw4S7nGgcoW6Q7UytlCeCMDws0YsEy-2caFJTb9KH2suqi4jf4_2psCEq_QxSYeQIAoH6FnZWyl3HsrOk4yu12NXlK0GKNKvMuEHPbmFuS391HvSKMNIu31pXhXl4bGolIKnnafilrO-AX7PYMPp9dPVZ6FxMotzzkPSGoBfrfJoE17EMfCtWSunZmdHnge6BlRHe4RhcauftN3Oiz66kYuU13GN3DNRLQkPEVUpLdv6O41pSK74jJfVy-VzFEqis8dTphGq3RcW2MZkc1UkyOuIMe2h2kAwDI3g3mUCXGecsIXIquLiUzf4KTmSDTsePhbmNNvvRBnpYjgPJZKY7-NnIoA3Am8VU522nS0Ly8LFiFp2_ACrAg6psaItDGHFDBr3qxTsd7Yi01f4M3BguFisYFj7FiEPicgbpMnib69OzOV1I3NJm_s4twUfZsGIZIilx6YLwh7Elct5jc0E_yBhFEXS9rnDcOb-xya59qZfDIRMgXVlxBFUOy7lngeNuSNWWZUwfRhcap-oNHpCjL7bg7Cn3xuxqETT_XBxkXOPOICZDstAXTq5YALBzvQmoopHZDfiSiDu6qvOKIklQjExFQgZiIbpT30C3UU-rUFRC58j5mh9a8TDRmh9Qv_xrk8_w8uNE7bC0HhuywTwyu1Sl6zrg3kNjXsdrghzixvMUZW4keMAWLQlOizHegwUy65P34r2wq4I7PoTze8tjJuqujTV8zLnbNNVzZF2g4ff7cqa33NZGwiz71-54bUO1WnM8dHehzJe_ZyCiAmI4TqPW0HInkjTcZOneMtrB0maID0m4ZtC0vdlTOsFIrtG3cvEa122k2JALapBYSgH2ICky4_8FVBljTxwmfZe1epb22M05wT8zmeB0e8WV45FhjHpoxDezVyVqLQ6nKTxbI4EqBmcustgWMM6eCHi3Co9bllYH6Y6XL7F9Vj31NXFQckRvE7Wz7H_73Bzbm3mACrXAiwB3MooLNJRum55KizL0ajofUY1OTxBsORT_kONcvvFu9MK8v462KiQjb8v_8Opan__uP1AhU16tPu0mxlZ-5OhIlxQ5J54uk3tfQoY5GJaQvW_HvQk1nwkI_7qyAbUqbtrwxzYh2TpxmWXRj0XdJNnXM_rh_Dch48IcgmyQOV3ZZjYZOv7Pd1H31SqvCfDtuRjfKEIExmjBEwEpPqYm7sQs_MNntdLDlvkgAqvBSETeHGF33j7GF1FiBn0thkqX6yRAogYLvh0o_HJLW5W_pVMYVoFVcjqphNOCOgq-2RLGyIbKYBeSPVoo5uG3sMMFOGi-9gfubokZjhx8mOifw67VXAnkaOgo3CbYgTusLIMjsOE-X2IlU9-FCExEND8OMO1aUvifGlpiRD5_a5qd5RRr40GUHWNbCCm93oIUSzMlCgGTQQWdbI3Uen2L5WjJKk2pIA8kWD6-FkwwH3d-L34r7m2mo0ZV8hokk1sJlLYls7JQQoqs9OcXAL80bgurR6FaBuegJHrY5Do0yGZ-HyW9SzGnUqyI59lRx5edYTd0QR9lX7tlhYAC1k9Qs1oy6CCBl8Y6pszlkkOm8eID7QfdNaBPR_jJmLCngCUtmoR2Rwv3S7WysLQWhwv0dHd3wNgwInVsdXaKrlEEe4Lm7E57GL4-skHneLrMnz1wsos5dmS0yK8QY7vUrCbB21XhNf51UF40aIuJiBpR2OvDMAL50K-2MqjM_Kq06NIWuxuUCEBJkLdEw6J12XbP4UeAMvl-s_w8TliO8zKm-z7cdUj0sPTf3kn2TwhJl9_cwDkMty44JPfwZS6ZW1MFjPuMgbgzJDomU7dBhH4HsdSvk_MkSx6vmm33_jiELDIaTfw5-IRhalszqhm1MVcgU5Ib_j8Xs9dGaOcwh6tiXNeLWh9qhc48txznlKy6usefTCFmA3L4he5BekjPIPSi6sJBZrFJH9abPIF8UP-wR7fu2Ig-S4G-AJejOT6PI2p58OeUKlc43xYAh2OGg50zV0mFDoPsHFblhnLGzdVcFxL7q4B4-W3AWNvnmCcW3CBcRML8bhSKunCow_mDDBcOi76hWzIz0ueVxEJB3wVc2KCfCSk5xCBRvOlfWsZrSfDTydZc3vjB1b98eO5TyLRvixu5qIIVQsszLhsO2Yp66w7RCBS1RZIuezor6nbqv3xcJPeli0p7FCOWzgp_gjfGhALABHDsfdTLANARiClj-Jv0fvz9T3KeTY89RbivJxJcGXaTwUk8JiKYUyhWlb4ykSSD_bA3D6h72H4CdU9RrbAoLct3QpzDYqLnilJMYFQVo11bgHv0tdBTkySHJ5iEimLMS0V7ua0E0bQ-J0JevTaZ9DGX9A0rxhXKqswHCiHsB6qa78CSYfzpfAOYaEeeSKbpABTjhsgupJLgQ==", "id": "rs_07d180749158bfae016a1362ad915081919ca3b3739561b0ca", "summary": [ { "type": "summary_text", "text": "**Integrating audio planning**\n\nI'm considering whether to continue with another slice by attaching subtitle_track and tts_plan to TrueTracePreviewArtifact. However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. Let's get this done!" } ] } ], "tool_calls": [ { "id": "call_lCjzzTtXuKFpjvQCfh8frdHr", "call_id": "call_lCjzzTtXuKFpjvQCfh8frdHr", "response_item_id": "fc_07d180749158bfae016a1362b51f3c8191b857010dd07d74fa", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\",\"old_string\":\" assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\",\"new_string\":\" assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\\n assert artifact.subtitle_track.renderer_owned is True\\n assert artifact.subtitle_track.side_effects == ()\\n assert artifact.subtitle_track.external_calls == ()\\n assert artifact.subtitle_track.cues[0].start_ms == 0\\n assert artifact.subtitle_track.cues[-1].end_ms == artifact.candidate.duration_seconds * 1000\\n assert artifact.tts_plan.provider == \\\"edge_tts\\\"\\n assert artifact.tts_plan.voice == \\\"en-GB-RyanNeural\\\"\\n assert artifact.tts_plan.blocks_video_gpu is False\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\\n@@ -26,6 +26,14 @@\\n assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\\n+ assert artifact.subtitle_track.renderer_owned is True\\n+ assert artifact.subtitle_track.side_effects == ()\\n+ assert artifact.subtitle_track.external_calls == ()\\n+ assert artifact.subtitle_track.cues[0].start_ms == 0\\n+ assert artifact.subtitle_track.cues[-1].end_ms == artifact.candidate.duration_seconds * 1000\\n+ assert artifact.tts_plan.provider == \\\"edge_tts\\\"\\n+ assert artifact.tts_plan.voice == \\\"en-GB-RyanNeural\\\"\\n+ assert artifact.tts_plan.blocks_video_gpu is False\\n assert artifact.package.side_effects == ()\\n assert artifact.package.external_calls == ()\\n assert Path(artifact.package.preview_video_path).exists()\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [29:25] Cannot access attribute \\\"subtitle_track\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"subtitle_track\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [30:25] Cannot access attribute \\\"subtitle_track\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"subtitle_track\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [31:25] Cannot access attribute \\\"subtitle_track\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"subtitle_track\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [32:25] Cannot access attribute \\\"subtitle_track\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"subtitle_track\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [33:25] Cannot access attribute \\\"subtitle_track\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"subtitle_track\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [34:25] Cannot access attribute \\\"tts_plan\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"tts_plan\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [35:25] Cannot access attribute \\\"tts_plan\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"tts_plan\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [36:25] Cannot access attribute \\\"tts_plan\\\" for class \\\"TrueTracePreviewArtifact\\\"\\n  Attribute \\\"tts_plan\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\n\"}", "tool_call_id": "call_lCjzzTtXuKFpjvQCfh8frdHr" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_iREINXtuyT9yWdnNviv6nKJH", "call_id": "call_iREINXtuyT9yWdnNviv6nKJH", "response_item_id": "fc_05bf232f50d5eecc016a1362bba9f08191aa91c284fda88e40", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert text.count(\\\"Preview video:\\\") == 3\\n assert \\\"Subtitle cues:\\\" in text\\n assert \\\"TTS baseline: edge_tts / en-GB-RyanNeural\\\" in text\\n assert \\\"Freigabe alle\\\" in text\",\"old_string\":\" assert text.count(\\\"Preview video:\\\") == 3\\n assert \\\"Freigabe alle\\\" in text\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\\n@@ -65,6 +65,8 @@\\n assert \\\"Approval language: de\\\" in text\\n assert \\\"Final video language: en\\\" in text\\n assert text.count(\\\"Preview video:\\\") == 3\\n+ assert \\\"Subtitle cues:\\\" in text\\n+ assert \\\"TTS baseline: edge_tts / en-GB-RyanNeural\\\" in text\\n assert \\\"Freigabe alle\\\" in text\\n assert \\\"Manual upload pack prepared: yes\\\" in text\\n assert \\\"publish\\\" not in text.casefold()\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_iREINXtuyT9yWdnNviv6nKJH" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_ZALbnCN5auoHYygVk3Z5v8CQ", "call_id": "call_ZALbnCN5auoHYygVk3Z5v8CQ", "response_item_id": "fc_0f2d41126ad3436c016a1362c1e2d88191b6868510465a470d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/test_truetrace_preview_packages.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"F.F [100%]\\n=================================== FAILURES ===================================\\n_ test_build_top_truetrace_preview_packages_renders_three_mp4_approval_packages _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-113/test_build_top_truetrace_previ0')\\n\\n def test_build_top_truetrace_preview_packages_renders_three_mp4_approval_packages(tmp_path):\\n result = build_top_truetrace_preview_packages(output_dir=tmp_path, approval_language=\\\"de\\\")\\n \\n assert result.batch_id == \\\"truetrace-top3-preview-packages-001\\\"\\n assert [artifact.candidate.candidate_id for artifact in result.artifacts] == list(TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n assert len(result.artifacts) == 3\\n assert result.side_effects == (\\\"write_png\\\", \\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n assert result.external_calls == (\\\"ffmpeg\\\",)\\n \\n for artifact in result.artifacts:\\n assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\\n> assert artifact.subtitle_track.renderer_owned is True\\n ^^^^^^^^^^^^^^^^^^^^^^^\\nE AttributeError: 'TrueTracePreviewArtifact' object has no attribute 'subtitle_track'\\n\\ntests/test_truetrace_preview_packages.py:29: AttributeError\\n_ test_render_top_truetrace_preview_summary_is_telegram_safe_and_references_three_videos _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-113/test_render_top_truetrace_prev0')\\n\\n def test_render_top_truetrace_preview_summary_is_telegram_safe_and_references_three_videos(tmp_path):\\n result = build_top_truetrace_preview_packages(output_dir=tmp_path, approval_language=\\\"de\\\")\\n \\n text = render_top_truetrace_preview_summary(result)\\n \\n assert \\\"## TrueTrace Top-3 Preview Packages\\\" in text\\n assert \\\"Packages: 3\\\" in text\\n assert \\\"Approval language: de\\\" in text\\n assert \\\"Final video language: en\\\" in text\\n assert text.count(\\\"Preview video:\\\") == 3\\n> assert \\\"Subtitle cues:\\\" in text\\nE AssertionError: assert 'Subtitle cues:' in '## TrueTrace Top-3 Preview Packages\\\\nBatch: truetrace-top3-preview-packages-001\\\\nPackages: 3\\\\nApproval language: de\\\\n...epared: yes\\\\nApproval commands: Freigabe YouTube, Freigabe TikTok, Freigabe alle, Ablehnen, Ändern: '\\n\\ntests/test_truetrace_preview_packages.py:68: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_truetrace_preview_packages.py::test_build_top_truetrace_preview_packages_renders_three_mp4_approval_packages\\nFAILED tests/test_truetrace_preview_packages.py::test_render_top_truetrace_preview_summary_is_telegram_safe_and_references_three_videos\\n2 failed, 1 passed in 23.09s\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_ZALbnCN5auoHYygVk3Z5v8CQ" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Lg1VxDUa77UGiFGkmtAJ24idb0h7SWY3kbyp7XPF4TlLDIbFfDkALS-RG8HE2oEPZ0mlPCerG1nTOIy9E8oT9WHI1H8SOn5stiMQoKoibdL21kMB_7Ckkkil-iROO3ODw4-ggGpIhrmUxPphi-y59IrcgGdnZPVsgUzR-m8f2xvbEa42c9eXuga--eJSyBlPp8qAwkv3AyE2eyQLVJ2X_sBDQjxuk2Q3PjgxJ6pTK42Z-06pxWDGEh3L4pMpQypkGgMtDdXEic4sIlM6xeu-9jhlfKBxeZ_kmsV-jfpcR26tj19V8JkbRQVJQGj7C5x32poi7ozrODAND0qaXYnSW_h1ma5axHAv3vhPNgnO-v3Bj-nwHHiSqrhtethVnfLuRVeLH4KMhJPIeQQGkyWpKYPdi7R6UF3X3W_6aHBnvvbvuW5sa8-djfL3KFC-3f91-SKKef2_SirQNGvmijiqbEOyGBB0SjFP-zppJM82HaRaWzrhV4QFykRTWVaYpu5GUANAdMXZkl0dM0ErDctGDCT0auVbsxWMyv_0_l5E4EGSFfpCPRp5_0EydIWJ0wapJxLW_e5ld9IITkqzQk2660eUR3CRtvsuCi4JL2YpHGv301jzFEMjRxJIxUhJt55RvnipNW3Pvz9PRr7iT9KskuiUMJGXetiO0bZfFXsHK3nGn7EvVOaYqvxaSotjQ8MBhFqCb7DONn_Wu5pArZGmuQteN_HWk67fclzST4ZLb4bQulV_tZE20-jyBk_x_iVfPceAY9a5FxjjQpcc9nh47DGaMRuYwEAN2FrfEgoB-AaVa4u1hP20yUokAOFf1BxuhQDuZmTBv-bH3iebYRridVqzJ8wdWSEnUzQAk_I4_fQimTSThAfY5TyKhP1BzhDbQnpueYzFBrJSFUwlY5HdQWCvgotVNEDPv3QUPc1FjFnuAVesLuxLlxga0qOtRoL0D0PA45AlnoVwPrDNYh_9UAynepg_Ev9SN1IqvfM_Qsy_PmiLKw55eHyFeuSLeo6QlG", "id": "rs_0c1e017e9e963c2d016a1362de35a48191914f3c38e6937d8c", "summary": [] } ], "tool_calls": [ { "id": "call_6eKfVQWKhCyzGhjiB8fEQa42", "call_id": "call_6eKfVQWKhCyzGhjiB8fEQa42", "response_item_id": "fc_0c1e017e9e963c2d016a1362e019908191ae0dc7057012c0c2", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"from pathlib import Path\\n\\nfrom autoshorts.audio.subtitles import SubtitleTrack, build_hormozi_subtitle_track\\nfrom autoshorts.audio.tts_plan import TTSProviderPlan, build_default_truetrace_tts_plan\\nfrom autoshorts.content.script_draft import ScriptDraft\",\"old_string\":\"from pathlib import Path\\n\\nfrom autoshorts.content.script_draft import ScriptDraft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -5,6 +5,8 @@\\n from dataclasses import dataclass\\n from pathlib import Path\\n \\n+from autoshorts.audio.subtitles import SubtitleTrack, build_hormozi_subtitle_track\\n+from autoshorts.audio.tts_plan import TTSProviderPlan, build_default_truetrace_tts_plan\\n from autoshorts.content.script_draft import ScriptDraft\\n from autoshorts.demo.truetrace_concepts import build_truetrace_concept_batch\\n from autoshorts.ideas.candidate import VideoCandidate\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_6eKfVQWKhCyzGhjiB8fEQa42" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_3fO0ko8rtumyAXxJKwkH7DnX", "call_id": "call_3fO0ko8rtumyAXxJKwkH7DnX", "response_item_id": "fc_04864e0e377a4924016a1362e5013081918577eb7f8eb9dd34", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"class TrueTracePreviewArtifact:\\n candidate: VideoCandidate\\n script: ScriptDraft\\n subtitle_track: SubtitleTrack\\n tts_plan: TTSProviderPlan\\n draft: SocialMediaDraft\",\"old_string\":\"class TrueTracePreviewArtifact:\\n candidate: VideoCandidate\\n script: ScriptDraft\\n draft: SocialMediaDraft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -33,6 +33,8 @@\\n class TrueTracePreviewArtifact:\\n candidate: VideoCandidate\\n script: ScriptDraft\\n+ subtitle_track: SubtitleTrack\\n+ tts_plan: TTSProviderPlan\\n draft: SocialMediaDraft\\n package: ContentPreviewApprovalPackage\\n manual_posting_pack: ManualPostingPack\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [132:12] Arguments missing for parameters \\\"subtitle_track\\\", \\\"tts_plan\\\" [reportCallIssue] (Pyright)\\n\"}", "tool_call_id": "call_3fO0ko8rtumyAXxJKwkH7DnX" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_w5Z8tYVE5UaSrTYH6OLze2lp", "call_id": "call_w5Z8tYVE5UaSrTYH6OLze2lp", "response_item_id": "fc_02ed6e485ce8d85c016a1362eab75081919d951275a8b3c1c6", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _script_from_candidate(candidate: VideoCandidate) -> ScriptDraft:\\n return ScriptDraft(\",\"old_string\":\"def _script_from_candidate(candidate: VideoCandidate) -> ScriptDraft:\\n return ScriptDraft(\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": false, \"error\": \"old_string and new_string are identical\"}", "tool_call_id": "call_w5Z8tYVE5UaSrTYH6OLze2lp" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Lxs0NJ8NHUUcdtfo8DmNZm2LIIb-b5RaooC_ddkyRVjitn8gFFsYC0OTd_uycGp1Sxq_GltNFl_aCohyBJuNOsZ-dEDn45qrwDSWGn6PgnCKlTAryqw0YHScVtkTrLebqSOVlkb2cn9dNupT6GIrD9k_hh2Rw7vECJQdOW12itEqSgSqzeQdc_ZqTCd20HrQBYVCnD8ukwOnqRURNI87ctddjwG7gr4-vOLE2vZ4tF3WCqqJn4Ml2f1kihNl7665_X89FTcwWdgITtkYRtHBIC07Wr6MkXafvNYx07wWrmdWcIS4EEjY3ACbo8QGXnBRL_hrkvLR7xVTrHa1nHAYIS_YvS2n_johB5Gjw0DHr8u1VBffgvKXIbNkdK1y33icCGiVl1mjIlMI-hrZPDxZ3R11vCtOwrxxuMhfGQheRYTM3bWAeoe5PkB-buHilvp0p3vJMZ-Bd9rQ0d68frmroXQR9iovsgeMfR5_OkYNpubWJSgfhzS-Nw0TF_mFMWp3beYYAW7Mi1ziF9spr0MhL5-KVK53juCZeHF2_1MaGRD4fk24P1fuU_C8WUUtTtGrD8EVk5z7RMD-W696VZGM-N5LpeQbMN-S7GHXmb6_ohCGFTg96G_kHLwhbwpwynJetgLJn8N9XCylarpTc9ga_2CzJ8SgZdYxF-6LQi93UuTAWGVrmc4h67W3-fMYeH5citHZ1QTmvlQ5ngvrFDKM2JzyhwdzHFTVDM53YxJIIFJK5YkoMZNMmnAeKcKcjO8YQm8-f0EnKeEEJSbdQJuPGkqSRnvetWk2J7vPBY3Kfr7ClcqQrK7t08OQrExEq18iViOpx_XC6_M3rwxPx63bGGdD99VzkYkfzNnwmPu-AHI7bWbZlRqY-WBSvK2OIC5tZz04On9FaTxcgeMX_oecpMWmPTRez3pDOzOxHjF3cd_vP58T4mDwaSlFFeMHer3CkrJSWllaGqyMAV7MLEgHugWQ==", "id": "rs_06c3db18b2752fa3016a1362f0c9a48191b90831eb7a8dea5d", "summary": [] } ], "tool_calls": [ { "id": "call_XMMZkozTtf6onMVc4K55kHjd", "call_id": "call_XMMZkozTtf6onMVc4K55kHjd", "response_item_id": "fc_06c3db18b2752fa3016a1362f19dc481919bcf4834b9ffd0bd", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _top_candidates() -> tuple[VideoCandidate, ...]:\\n by_id = {candidate.candidate_id: candidate for candidate in build_truetrace_concept_batch().candidates}\\n return tuple(by_id[candidate_id] for candidate_id in TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n\\n\\ndef _voiceover_text(script: ScriptDraft) -> str:\\n return \\\" \\\".join((script.hook, *script.retention_beats, script.cta))\\n\\n\\ndef _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\",\"old_string\":\"def _top_candidates() -> tuple[VideoCandidate, ...]:\\n by_id = {candidate.candidate_id: candidate for candidate in build_truetrace_concept_batch().candidates}\\n return tuple(by_id[candidate_id] for candidate_id in TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n\\n\\ndef _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -103,6 +103,10 @@\\n return tuple(by_id[candidate_id] for candidate_id in TRUE_TRACE_TOP3_CANDIDATE_IDS)\\n \\n \\n+def _voiceover_text(script: ScriptDraft) -> str:\\n+ return \\\" \\\".join((script.hook, *script.retention_beats, script.cta))\\n+\\n+\\n def _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\\n script = _script_from_candidate(candidate)\\n shot_plan = create_shot_plan(script)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_XMMZkozTtf6onMVc4K55kHjd" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_HgZFzPqGtBsIxKk2NtfbEFMi", "call_id": "call_HgZFzPqGtBsIxKk2NtfbEFMi", "response_item_id": "fc_0e0ee3b478b092df016a1362fa5f7c8191b395721f0913e5be", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\\n script = _script_from_candidate(candidate)\\n subtitle_track = build_hormozi_subtitle_track(\\n _voiceover_text(script),\\n total_duration_seconds=candidate.duration_seconds,\\n )\\n tts_plan = build_default_truetrace_tts_plan()\\n shot_plan = create_shot_plan(script)\",\"old_string\":\"def _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\\n script = _script_from_candidate(candidate)\\n shot_plan = create_shot_plan(script)\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -109,6 +109,11 @@\\n \\n def _build_artifact(candidate: VideoCandidate, *, output_dir: Path, approval_language: str) -> TrueTracePreviewArtifact:\\n script = _script_from_candidate(candidate)\\n+ subtitle_track = build_hormozi_subtitle_track(\\n+ _voiceover_text(script),\\n+ total_duration_seconds=candidate.duration_seconds,\\n+ )\\n+ tts_plan = build_default_truetrace_tts_plan()\\n shot_plan = create_shot_plan(script)\\n manifest = build_render_manifest(shot_plan)\\n html_preview = build_html_preview(manifest)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_HgZFzPqGtBsIxKk2NtfbEFMi" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_h8LJ0GCgVYtaVS4kH4NNxzDa", "call_id": "call_h8LJ0GCgVYtaVS4kH4NNxzDa", "response_item_id": "fc_0353a2d48c5d66e5016a136302350081919ea2c5af4948cdf1", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" candidate=candidate,\\n script=script,\\n subtitle_track=subtitle_track,\\n tts_plan=tts_plan,\\n draft=draft,\",\"old_string\":\" candidate=candidate,\\n script=script,\\n draft=draft,\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -141,6 +141,8 @@\\n return TrueTracePreviewArtifact(\\n candidate=candidate,\\n script=script,\\n+ subtitle_track=subtitle_track,\\n+ tts_plan=tts_plan,\\n draft=draft,\\n package=package,\\n manual_posting_pack=manual_pack,\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_h8LJ0GCgVYtaVS4kH4NNxzDa" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_DAaWCfG2jwmkjz7ObdQc5mBh", "call_id": "call_DAaWCfG2jwmkjz7ObdQc5mBh", "response_item_id": "fc_03825040ab125776016a136307b6248191af23c54429a1b152", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" f\\\"Preview video: {artifact.package.preview_video_path}\\\",\\n f\\\"Subtitle cues: {len(artifact.subtitle_track.cues)}\\\",\\n f\\\"TTS baseline: {artifact.tts_plan.provider} / {artifact.tts_plan.voice}\\\",\\n f\\\"Hook: {artifact.candidate.hook}\\\",\",\"old_string\":\" f\\\"Preview video: {artifact.package.preview_video_path}\\\",\\n f\\\"Hook: {artifact.candidate.hook}\\\",\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\\n@@ -189,6 +189,8 @@\\n f\\\"Candidate: {artifact.candidate.candidate_id}\\\",\\n f\\\"Duration: {artifact.candidate.duration_seconds}s\\\",\\n f\\\"Preview video: {artifact.package.preview_video_path}\\\",\\n+ f\\\"Subtitle cues: {len(artifact.subtitle_track.cues)}\\\",\\n+ f\\\"TTS baseline: {artifact.tts_plan.provider} / {artifact.tts_plan.voice}\\\",\\n f\\\"Hook: {artifact.candidate.hook}\\\",\\n f\\\"Manual upload pack prepared: {'yes' if artifact.manual_posting_pack.requires_human_manual_upload else 'no'}\\\",\\n f\\\"Approval commands: {commands}\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_DAaWCfG2jwmkjz7ObdQc5mBh" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_1tJnI9HIqzKOPPpWpH6E4AHl", "call_id": "call_1tJnI9HIqzKOPPpWpH6E4AHl", "response_item_id": "fc_00c643529fc517da016a13630e823c819187029280a6d46464", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/test_truetrace_preview_packages.py tests/test_subtitle_cues.py tests/test_tts_plan.py -q\",\"timeout\":220,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \".......... [100%]\\n10 passed in 23.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_1tJnI9HIqzKOPPpWpH6E4AHl" }, { "role": "assistant", "content": "", "reasoning": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily.", "finish_reason": "tool_calls", "reasoning_content": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2Mu1WmOj005aMeG-obSq1SP8soP1QJxuq__IvHB5VAtyEN7xhkWEYbfCQTNrKmSiAZJy64cju9SHhLbZOHgDJwDav43S0nKWL7CO6VDFRg6WEvg4xY_xujngljf_oWClq--9E2JdHNUYPV_-Jmr9SngJaHXljzbtgtbviZ4NVoS3mi4a_S08uW1irb6fm1zgoa6MARzDoE5kdrnfwRmTrIojkgaWlkLfnn9kroAnD_ffX64bIeazhV-uXlxl0hBuYybuqAZvd8moF2O8o_1AtaNsWf8PP7UpE4qFfTv84-7WkH2bmDWJTCrIQKlBCcE2hW0aOzb_p6ZqRkf8bJxrvfqa0jo5qblSiIb8p6vQi3siFxeAweNbGrrsJFr2wwHYEq7qrvHunDOmFve97-MiOfNrkywzw10K4zCt0XhgXyo4eOgYQHIqTzBkM2ydg5QvthvBV9bJYHogz05-It1lcbD7BE3yFJFW1W1zAM9Cd2O7Cdubhy6zUXg0DfFlYDdHHNDNWXeT_e-Ku5-ZLjWlkWmSrXDYedwWDwduJ3wgjFo59-_2Lc7qemZgSWKqqeI-E1ReCEYCTU8N65B1uJU04qsdfLXoBlL1g0_jyehQ1fqivst1JDJcN-iffQuRdfI0IUKcpYf8XdNUkJnrDV7Bw3nORPRUzW8cY1J8AyMtk98_Vu0CZxJJ0ZEBBlMdUiHteHHcittnK43pDEX7tVUgvHoBksh0HV4l5zNUJZnOqkKnCpBdLN3GnCFOUl5CI7CRF8YZIzscM9BknlDabYHYpSorFfGWueJq0Eqfih-cL4xScLBieu7osMeOq6DE-nf6v4tPvoSk6jlGzEliUHNV1oTKSHKgHDHenc1YddrJE1drA2fHzFhAzjvbE8F_zPRIgXbZaxJnb-jh0mjbSstDiNpasVnik66hwVfbqlocIBTfxuPqM3AWHrEhk1FQPDNqre5KiBBxwNW4JSRLVpxjTBjow==", "id": "rs_0333db1852a841a1016a13632c781c8191bfc7944df6dbb2e9", "summary": [ { "type": "summary_text", "text": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily." } ] } ], "tool_calls": [ { "id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv", "call_id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv", "response_item_id": "fc_0333db1852a841a1016a13632ee2548191b7013093afe7abe9", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate. Each preview artifact also now carries a deterministic English subtitle cue track and the phase-1 EdgeTTS provider plan, so audio/caption decisions travel with the candidate before any real TTS call happens.\",\"old_string\":\"`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -74,7 +74,7 @@\\n \\n `render_truetrace_concept_summary()` renders those candidates with hooks, outlines, visuals, hypotheses, YouTube descriptions, TikTok captions, and tags. It performs no platform action and avoids action language.\\n \\n-`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate.\\n+`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate. Each preview artifact also now carries a deterministic English subtitle cue track and the phase-1 EdgeTTS provider plan, so audio/caption decisions travel with the candidate before any real TTS call happens.\\n \\n Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_NncoH8P0U8M1i6Jf7xdOK81o", "call_id": "call_NncoH8P0U8M1i6Jf7xdOK81o", "response_item_id": "fc_03c9c2b2e1e3983c016a1363342e4c8191b0336269205c535a", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n- `autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` attaches the subtitle track and EdgeTTS baseline plan to each top-3 preview artifact before any real audio generation.\\n\\n## Caption pipeline\",\"old_string\":\"- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n\\n## Caption pipeline\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n@@ -56,6 +56,7 @@\\n - `autoshorts.audio.subtitles.validate_subtitle_track()` rejects non-renderer-owned cues, overcrowded captions, bad timing, and missing active words.\\n - `autoshorts.audio.tts_plan.build_default_truetrace_tts_plan()` selects `edge_tts` with `en-GB-RyanNeural` for phase 1.\\n - `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n+- `autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` attaches the subtitle track and EdgeTTS baseline plan to each top-3 preview artifact before any real audio generation.\\n \\n ## Caption pipeline\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_NncoH8P0U8M1i6Jf7xdOK81o" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_pexTOAg1O4dj6eU3WItI27hl", "call_id": "call_pexTOAg1O4dj6eU3WItI27hl", "response_item_id": "fc_0a3b3664917fa70b016a13633b73288191937b500f0e2bcc9d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 48%]\\n........................................................................ [ 96%]\\n..... [100%]\\n149 passed in 32.09s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_pexTOAg1O4dj6eU3WItI27hl" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2NjMZWSk8-UNgG_4zYP_WGFN9BjvDKPIGE8zH9dtiKWXYON-TGVxrBdbSJ7u5Scjnuo6BU-j_3Iwe4KYLZPoRKCLVlBFv64mnUJcdTx7jqLjKo3QwaK6MouOg5FD1Sd0PDb4BtW6i5t55OG-LaHL1ABjoSeIS24czxmyd1Yqi74-KFGvwcaX-_uYOS1pXM1isj6m1iWJl0T06OJI8ypllHqPovdzm7gBu5-V4zMA7fieXBbu9bZ8a7MHD9MZGZfLCwTR5Lpu5LHjiaeTw7prw8N5or9hI3VQ7gy6H5qBn_T076gdMNR2mh-1DUlV_p8pckI5JRZxFfrG6sW5TePqU5TrXHuiwASsLfKQZ9PHfvbp7MPFdIoZFDZA7F7omnEPG1SdXhuAPhLtvddsPXTxd90ytLPuYJ2I0aw6Icm0tYjF9Evlw0wIFzl-0xq2xe_L8QE_mCTIjkSBnq_GxUr1dgHtbHLsgi1pEAi1_OlIsc9za2etFZ-P-qdyBne_fZr7DyIKwqQiwA4oe89zrgh2Kian9IB-pCznrKmwzHRTaEaoNuS20Y2I9G2rDKc_1zwtJvn4wKHxKTPhEW4wHoCxsgwtZr5fA0RyekPR8qta_7MKXL4nrfoSDy9TzysPyUGvAgBd1QcViNFHQz5uNLPT-EEOaq-6MJrqjZ1fPoYh2tGnr0oVNaQBZJLcoRNBlL6eupRYAddtDp3AofeYcc9OcXzFyzr-NpEzlJGpU3n_sTeKHtzqNJp2Qa8eLAX2GBIAoHy3J2JinX-aqacFjU9VG07U6U16Y2rqbbmXol58ZRqPxvasrduVerw2P8950gO858m3PgaXcGxLk_g3YkK2qBN5z5xhBT4eTOpeON0H4ax53MtRUi9fh5IyOMNCkaIIfZkB_S7dhknsCTNx6CIYOH4Wa8rI62xDP3OGhTOEaL72xXRLUMpVgvtheHisEH3Bapm", "id": "rs_0784a704785eeacd016a13636299a48191a962f852849605ba", "summary": [] } ], "tool_calls": [ { "id": "call_fdmIszzR57HikLvjiIna3vXy", "call_id": "call_fdmIszzR57HikLvjiIna3vXy", "response_item_id": "fc_0784a704785eeacd016a1363633c808191a50f536ad2329a84", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/demo/truetrace_preview_packages.py tests/test_truetrace_preview_packages.py README.md docs/subtitle_tts_strategy.md && git commit -m \\\"feat: attach audio plans to TrueTrace previews\\\" && git status --short && git log --oneline -4\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n M autoshorts/demo/truetrace_preview_packages.py\\n M docs/subtitle_tts_strategy.md\\n M tests/test_truetrace_preview_packages.py\\n[feat/core-approval-quality-gates 066201d] feat: attach audio plans to TrueTrace previews\\n 4 files changed, 29 insertions(+), 1 deletion(-)\\n066201d feat: attach audio plans to TrueTrace previews\\n30a8129 feat: add subtitle and tts planning\\n6efc4dc feat: add TrueTrace top preview packages\\n2d6af9f feat: add TrueTrace concept testbatch\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_fdmIszzR57HikLvjiIna3vXy" }, { "role": "assistant", "content": "Verstanden, Sir — und ja: Das mit den 3 Videos war unsauber im Delivery. Ich habe geprüft: die Dateien selbst sind **nicht identisch**, ihre SHA256-Hashes unterscheiden sich. Telegram/Preview-Layer hat sie offenbar trotzdem praktisch gleich wirken lassen. Ich sende sie jetzt nicht nochmal ungefragt als Medien-Spam. Einmal Blechlawine reicht.\n\n## Weitergemacht: Subtitle + TTS Planning eingebaut\n\nIch habe jetzt den nächsten Audio/Captions-Layer implementiert.\n\nNeue Module:\n\n```text\nautoshorts/audio/__init__.py\nautoshorts/audio/subtitles.py\nautoshorts/audio/tts_plan.py\n```\n\nNeue Tests:\n\n```text\ntests/test_subtitle_cues.py\ntests/test_tts_plan.py\n```\n\nCommit:\n\n```text\n30a8129 feat: add subtitle and tts planning\n```\n\n## Subtitles: Hormozi-inspired, renderer-owned\n\nNeue Funktion:\n\n```python\nbuild_hormozi_subtitle_track()\n```\n\nSie erzeugt deterministische Caption Cues:\n\n- 2–5 Wörter pro Cue\n- durchgehendes Timing von `0ms` bis Videodauer\n- active-word Highlight-Metadaten\n- max. 2 Zeilen\n- lower-middle safe-zone Style\n- keine Side Effects\n- keine externen Calls\n- renderer-owned, also **nicht** im AI-Bild/Video gebacken\n\nValidierung:\n\n```python\nvalidate_subtitle_track()\n```\n\nRejects:\n\n- nicht renderer-owned\n- zu viele Wörter\n- kaputtes Timing\n- fehlende active words\n- Side Effects / externe Calls\n\nKurz: Wir bauen keine Untertitel mit „mach mal Text ins Bild“-Prompting. Wir bauen sie kontrolliert. Zivilisation, Sir.\n\n## TTS Plan: EdgeTTS baseline, Magpie später A/B\n\nNeue Funktion:\n\n```python\nbuild_default_truetrace_tts_plan()\n```\n\nDefault:\n\n```text\nProvider: edge_tts\nVoice: en-GB-RyanNeural\nLanguage: en\nRequires local GPU: no\nBlocks video GPU: no\nRequires API key: no\n```\n\nMagpie ist als späterer A/B-Kandidat modelliert:\n\n```python\nbuild_tts_ab_test_plan()\n```\n\nDarin:\n\n```text\n1. edge_tts\n2. nvidia_magpie_tts_multilingual\n3. whisper_alignment\n```\n\nWichtig sauber codiert:\n\n```text\nWhisper = alignment_qa\nWhisper ≠ voice_generation\n```\n\nAlso keine Verwechslung zwischen STT und TTS.\n\n## Danach integriert: Audio-Pläne hängen jetzt an den Top-3 Preview Packages\n\nIch habe die bestehenden TrueTrace Top-3 Preview Packages erweitert.\n\nCommit:\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n```\n\nJedes Preview Artifact enthält jetzt zusätzlich:\n\n```python\nsubtitle_track\ntts_plan\n```\n\nAlso:\n\n```text\nCandidate\n→ ScriptDraft\n→ SubtitleTrack\n→ TTSProviderPlan\n→ ShotPlan\n→ RenderManifest\n→ MP4 Timing Preview\n→ Approval Package\n→ Manual Posting Pack\n```\n\nDie Summary zeigt jetzt auch:\n\n```text\nSubtitle cues: \nTTS baseline: edge_tts / en-GB-RyanNeural\n```\n\n## Tests\n\nFull Suite:\n\n```text\n149 passed in 32.09s\n```\n\n## Aktuelle Commits\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Entscheidung zu deiner Frage\n\nFür jetzt bleibt die Architektur:\n\n```text\nSubtitles:\ndeterministic renderer-owned Hormozi-inspired captions\n\nTTS:\nEdgeTTS baseline first\n\nNVIDIA Magpie:\nspäter A/B testen, nicht Default\n\nWhisper:\nspäter Alignment/QA, nicht TTS\n\nGPU:\nfür Video/Image reservieren\n```\n\nNächster sinnvoller Schritt:\n\n```text\nEdgeTTS Adapter\n→ echte .mp3/.wav Voiceover-Datei\n→ FFmpeg Audio-Mux\n→ Subtitle burn-in\n→ erster audio+caption Preview-MP4\n```\n\nDann bekommen wir nicht nur Timing-Karten, sondern einen echten ersten „final-ish“ Review Clip mit Stimme und Captions — weiterhin ohne Upload, ohne Platform Call, ohne versehentliche Content-Kanone.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2No8-bpywo5_JWSb0dk6sCu6nzhvNoT7h_xxjpsNuIBgibqmky_afJeRlvWJyxf1DLhRNgxnMscQjKnzwTCzdt4y3nePVal8zsPNwWBhvi4vCSdzvGd6qiFKFjAYI9ycNOoiSmwf48vf0i-uwnuQO6eBgoA0evzCSDVMN7_TQUmPk78qYJC5mjBjSHtuEqsNMF8yDbnmMfKMr2VA319J8diwn2h8QgnUlb63tetZL840E7xxNiaf3ZLM20XZ9gcbiSEowBebd9-t_9xFGOQV7ORke8ICX5GnBF2STpc5Rim8qXpW2HERo8iqsQ9c-DyJaUZB7wUrr9lsbq-4ZZaVQFWUpLcJuTxWfKJzCa7Zw8MY6Y5_aZ7F-yqP0ziuyOWhui-RiFgNY1hh6y56CULSlMNW6jwZz_R49JhrB6bj-afY8Gb_WsAocAfeQO07C44EGsiFAAcv1gvoWYeFFVf8baggwsCgP1SDAdbYJta8YjFnJIP4dDjTi8Zwntd_QT71TzCQDjF5xpc9hSlpyby3W3lxPNJt1z2ovXP8zCHkXIYUmKumwHI8l02gyDh6-kYXFmmgfKzb9vYaPKvPHsYv99ZwSuQKs1dlyEen-rP5aAOlKXFWPcymW8bZVw02W6mzhCc0VD01w1pyrU8PrTHcrS82NWK-6jjtrGlz24vWYbY2iHQ58cKLilWGBOONWmWcS2S9y2vLwy-1GEh0Y1sLjuBgOKrE9px7irBgVzKqYqeSsVkXrfNxH0ZvYlzaSqEqGC2VNEKHWmliLxV3LUGR4tMxf3hLnGhC7MHTCtS-U7uODPDXTYCluBft08J7p3YbeF5qaC55T1sy5fls04jdqA-UnuzmnLW3Y5MN2KYe5j_Dx99U3kes2kr0nO-pm7iJNkn1gMMY_tQbt5tBuXNVeehLfUvELfcw2KzxyRXzGWYor8XPZP-dm1RIKYnwALDdzVMOpi2y9cddh-QohZ8nd20167TQrPPZhDUnTZiBxEORnGaEviWIsbTsdN67gEQzthAiui31s-FvO2K1XTts9NbFGq6O1lE86HwnU6HjbRHDN52EN2q8F7fLRZUIqF7em2GgWuO1WDAI66WVSE2-QTZe1TI5TLj65LhkR5HwTsdg5Gljq7KepTd24mAzUgz8dwu-iQ0fH8cmpuvckAdK6UcZfPaMSmKcr6RgKEPZNiSm-0xVnN_8y6AOKCp_aUYsjfgaNck2mHofBgJYvSMQQ4rwHNIxU25HlDf4qTNWswPOuA=", "id": "rs_0dc9f371a6d55e3f016a1363671678819188c5e0db0ee6d76d", "summary": [] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Verstanden, Sir — und ja: Das mit den 3 Videos war unsauber im Delivery. Ich habe geprüft: die Dateien selbst sind **nicht identisch**, ihre SHA256-Hashes unterscheiden sich. Telegram/Preview-Layer hat sie offenbar trotzdem praktisch gleich wirken lassen. Ich sende sie jetzt nicht nochmal ungefragt als Medien-Spam. Einmal Blechlawine reicht.\n\n## Weitergemacht: Subtitle + TTS Planning eingebaut\n\nIch habe jetzt den nächsten Audio/Captions-Layer implementiert.\n\nNeue Module:\n\n```text\nautoshorts/audio/__init__.py\nautoshorts/audio/subtitles.py\nautoshorts/audio/tts_plan.py\n```\n\nNeue Tests:\n\n```text\ntests/test_subtitle_cues.py\ntests/test_tts_plan.py\n```\n\nCommit:\n\n```text\n30a8129 feat: add subtitle and tts planning\n```\n\n## Subtitles: Hormozi-inspired, renderer-owned\n\nNeue Funktion:\n\n```python\nbuild_hormozi_subtitle_track()\n```\n\nSie erzeugt deterministische Caption Cues:\n\n- 2–5 Wörter pro Cue\n- durchgehendes Timing von `0ms` bis Videodauer\n- active-word Highlight-Metadaten\n- max. 2 Zeilen\n- lower-middle safe-zone Style\n- keine Side Effects\n- keine externen Calls\n- renderer-owned, also **nicht** im AI-Bild/Video gebacken\n\nValidierung:\n\n```python\nvalidate_subtitle_track()\n```\n\nRejects:\n\n- nicht renderer-owned\n- zu viele Wörter\n- kaputtes Timing\n- fehlende active words\n- Side Effects / externe Calls\n\nKurz: Wir bauen keine Untertitel mit „mach mal Text ins Bild“-Prompting. Wir bauen sie kontrolliert. Zivilisation, Sir.\n\n## TTS Plan: EdgeTTS baseline, Magpie später A/B\n\nNeue Funktion:\n\n```python\nbuild_default_truetrace_tts_plan()\n```\n\nDefault:\n\n```text\nProvider: edge_tts\nVoice: en-GB-RyanNeural\nLanguage: en\nRequires local GPU: no\nBlocks video GPU: no\nRequires API key: no\n```\n\nMagpie ist als späterer A/B-Kandidat modelliert:\n\n```python\nbuild_tts_ab_test_plan()\n```\n\nDarin:\n\n```text\n1. edge_tts\n2. nvidia_magpie_tts_multilingual\n3. whisper_alignment\n```\n\nWichtig sauber codiert:\n\n```text\nWhisper = alignment_qa\nWhisper ≠ voice_generation\n```\n\nAlso keine Verwechslung zwischen STT und TTS.\n\n## Danach integriert: Audio-Pläne hängen jetzt an den Top-3 Preview Packages\n\nIch habe die bestehenden TrueTrace Top-3 Preview Packages erweitert.\n\nCommit:\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n```\n\nJedes Preview Artifact enthält jetzt zusätzlich:\n\n```python\nsubtitle_track\ntts_plan\n```\n\nAlso:\n\n```text\nCandidate\n→ ScriptDraft\n→ SubtitleTrack\n→ TTSProviderPlan\n→ ShotPlan\n→ RenderManifest\n→ MP4 Timing Preview\n→ Approval Package\n→ Manual Posting Pack\n```\n\nDie Summary zeigt jetzt auch:\n\n```text\nSubtitle cues: \nTTS baseline: edge_tts / en-GB-RyanNeural\n```\n\n## Tests\n\nFull Suite:\n\n```text\n149 passed in 32.09s\n```\n\n## Aktuelle Commits\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Entscheidung zu deiner Frage\n\nFür jetzt bleibt die Architektur:\n\n```text\nSubtitles:\ndeterministic renderer-owned Hormozi-inspired captions\n\nTTS:\nEdgeTTS baseline first\n\nNVIDIA Magpie:\nspäter A/B testen, nicht Default\n\nWhisper:\nspäter Alignment/QA, nicht TTS\n\nGPU:\nfür Video/Image reservieren\n```\n\nNächster sinnvoller Schritt:\n\n```text\nEdgeTTS Adapter\n→ echte .mp3/.wav Voiceover-Datei\n→ FFmpeg Audio-Mux\n→ Subtitle burn-in\n→ erster audio+caption Preview-MP4\n```\n\nDann bekommen wir nicht nur Timing-Karten, sondern einen echten ersten „final-ish“ Review Clip mit Stimme und Captions — weiterhin ohne Upload, ohne Platform Call, ohne versehentliche Content-Kanone." } ], "id": "msg_0dc9f371a6d55e3f016a136368c1bc8191bb097f487d4a51f4", "phase": "final_answer" } ] }, { "role": "user", "content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.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." }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqE2N_jZ3VDM5-etvhqf3lfh7beYHtyuFLe9ZrfdfJl9q065v4X5bKsdDL2OLv2PKUzXwam8aYJxaSsYEWfDPEjCaiweutF0mjc0ay65HPv_YWWLAGeh8F4VTM52aQ7uDc0Sky1D96eGB1CsQdAYYXf9wwtff8QaqLZvky35kgNgfPXpfefuWfU-JwsuSFRDgOX8dCq9EMhVleK7qe6hZ2qB8I_rb2jvRX8E1ytyZ1qiB8r6mRmW-kmH_1nbtY8srOZussNB3IDV6Qf6mstsj1D65Nuof7IMGCwYhRq49m2p_Hlyo2g4Ey_rvZNRwoqHVfUeFFv2-rH800GR_7ohzkntgzmve0ZfyaBiqlxeT6wo4FW3Y4KVZ2QrKlO7ezpgqL-yH_9Jp94jZ29axfuCllw8wDRqKrRffAMSIdUSiUTWVXMtAUSQ5lcWDwQEHef6nbvFhlhTDHdEajGYYVmFUhPr6aiMgDucddVDYp8foO4SEbHoODtlX1yTYvqhFkyUIEwwu7HFPt0c1laOaQpqeq7880ozw_7JMwotLsny-pcrFZHVsfXYwzPMO1GK7zWBVaO_kRVFIfpg6LrCHytX7aYIbn81SBnBHi6En7R8whpyjgysvdSmIv5o-6LCHpRjxmA9L31z8EyNrZ3ReClkgEwJZrM9Ujrs9dy5Azy1JAvLfeeWnoExgGOit37xpVk8YVCOnMvNTCfhE6-KaqBk_kRLzydDEaBcUFyScNZMZAxxgZpsZuyJw27LnjEjIDBB3r_exTp-vkwninj7Jlss2OqhNb4HL8xvABL22NOwnPMQc9jSEmYgxp5FAF3_MTwhDgigJ3tvp-_E8LK3bp-_F1UNFzxNKSN1V68nKzK6z5W6h2hWiTOAeL3fv8xkrZanXMCMlJ05h08ag82pB8DkCx243TmIC0jIZ8DNNwhqTSZ_a0e5FqW-J-R458a8UlR6Qy9AEcHdyeGgl1B0m5Yu-JDCltAFT4fF5MDcvRjgFfFRt3olNqaedV3fi9JbxDYqL6yM3Mfp3cZMwyWR0lUgGvxKcNpAYJWblP-LDPInrkkEH2P-SCIMeMrmkdxv_wA7L9xRRNRmoeW_8dBjdkmTCSxkOHT909UriZ2HWITOX4DrbG2cQCPhleBdLPj7cip76zqgUwc07JzwlXKerMou6Dm_OQDJxthqEtqzIQ71pKBDo113JvZs4Ybu4eikLpDjbyIytO1hk-D7WYzgoF5b7ghLArElAQklxPy16boZXnWdcEUYW9QZV4AASEIw1HLnCr-0lq4Osm_mhHxqIueZO3ZG6BBL98SXbXF3WQMCSqE39W9xesbSI9LJMuxTWin8C5fOuQWfVIeQFlAh2QYkr2CIMffGBj7bPZuwe80kN5bAScQ4a7YdHt8bw_Ij0Pc3slffSEK9JLXsH2rLjS9lTNLiEvUvCJ5hsKu93JCU19FJv12_jjF62nSb76h1IOWtVkkjszVk7waV2Mp2IkFRNhG6CpIjsBiazayCM3Hci1iYZwWdDz_EmsMS5hrk84oJUk_RTPyqhb90je6MjReusBJwHSyIF3J_ItOJ8Bypp-DGBNDCYAAIIg7JYXYdpGdSZ077LfiWfBvlpWnOcNqAd-QRnC4SiSazDZWc2pissmi78_Elq2P3V8kU0ghJY2Nj1LuCsf4nTEg-t2GpUu3cs8BUeoy2RvX90bs-BrOnkHV8lPusGyKOQWjO24EzoXzDx4cgxbDbdr_5N5jhfOkq77w6722EsdcMj90DWRAFNNUR6whkEb3ZyeMfhVzZgNuWij2C4EEOyG3W7NeY5RGvvkNToQ4w15nie6U0GgcQBhz8Ei_CRE-vFMp-T_AYI0UqN4Iz7LuRVUYpIwRV83cZ8oFp2YaL-v7yuQ9lx7ZSsO2RCgR1caiOaZykU9gBax2y2to7PzT-8kvek_jbjWHurkK641LdTTpwxHqhLk1KygvA2sAsFj_qQWffvqauPHFZhG-atWDOSJxM9FqD4L-eJjvMfOnweFUa2dKgNfq_AbbV4BwNg=", "id": "rs_0dc9f371a6d55e3f016a13637c179c81919cdf8dc2760b93a7", "summary": [] } ], "tool_calls": [ { "id": "call_AMtg9adc954uNdFADl8l0FYg", "call_id": "call_AMtg9adc954uNdFADl8l0FYg", "response_item_id": "fc_0dc9f371a6d55e3f016a13637fe5e881919c3c8e0938913b58", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\" - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\",\"new_string\":\" - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_AMtg9adc954uNdFADl8l0FYg" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_hg5oswos0jQ4pDnRsahrFuMc", "call_id": "call_hg5oswos0jQ4pDnRsahrFuMc", "response_item_id": "fc_0dc9f371a6d55e3f016a13638d807881918798273551fdca10", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\",\"old_string\":\"- Do not over-optimize renderer quality before testing whether the content format earns retention.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_hg5oswos0jQ4pDnRsahrFuMc" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_j1dZRPPhcuwncoi784WHLgIs", "call_id": "call_j1dZRPPhcuwncoi784WHLgIs", "response_item_id": "fc_0dc9f371a6d55e3f016a1363925dfc8191be51078c292bccd8", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/preview-media-delivery-guardrails.md\",\"file_content\":\"# Preview media delivery guardrails\\n\\nUse when a short-form automation workflow generates multiple local preview videos for Telegram or another chat review channel.\\n\\n## Problem captured\\n\\nMultiple generated MP4 previews can be technically distinct files while appearing identical in the chat client, especially if they are timing-card/slideshow-style approval previews. Sending them as separate unlabeled media attachments creates user confusion and feels like duplicate spam.\\n\\n## Guardrail\\n\\nBefore delivering more than one preview video:\\n\\n1. Verify each artifact is distinct by candidate/title and path.\\n2. Prefer checking hash and duration when files are local.\\n3. Label each media item with candidate ID/title and purpose.\\n4. If previews are visually similar, send an indexed text summary first and ask for which one to inspect, or send only the top item plus links/paths for the others.\\n5. If the user reports duplicate-looking videos, do not resend the same media. Verify uniqueness, acknowledge the delivery issue, and continue with the next pipeline step.\\n\\n## Good review shape\\n\\n```text\\n1. AI Automations Quietly Rot\\n Path: .../truetrace-ai-automation-quiet-rot-38s/preview.mp4\\n Hash: \\n Purpose: timing/card preview\\n\\n2. Prompt vs System\\n Path: .../truetrace-prompt-vs-system-workflow-breaks-35s/preview.mp4\\n Hash: \\n Purpose: timing/card preview\\n```\\n\\nThen attach only the selected media or bundle attachments with clear labels if the channel supports it.\\n\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"File 'references/preview-media-delivery-guardrails.md' written to skill 'social-media-shorts-automation'.\", \"path\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation/references/preview-media-delivery-guardrails.md\"}", "tool_call_id": "call_j1dZRPPhcuwncoi784WHLgIs" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_MbIbO45fJQd7ST9GM8ZIn84z", "call_id": "call_MbIbO45fJQd7ST9GM8ZIn84z", "response_item_id": "fc_0dc9f371a6d55e3f016a13639d0b7881919a0714bce1fde5e3", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup.\",\"old_string\":\"## 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.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}", "tool_call_id": "call_MbIbO45fJQd7ST9GM8ZIn84z" }, { "role": "assistant", "content": "Updated the skill library.\n\nPatched loaded skill:\n\n```text\nsocial-media-shorts-automation\n```\n\nChanges made:\n\n- Added a **preview media delivery guardrail**:\n - verify distinct preview artifacts before sending multiple videos;\n - label candidate/title/path/duration/hash;\n - avoid sending visually similar timing-card MP4s as unlabeled Telegram media;\n - if user reports duplicate-looking videos, stop resending, verify, and continue.\n\n- Added a pitfall:\n - Telegram/mobile previews can make distinct MP4s look identical; don’t spam duplicate-looking attachments.\n\n- Added support reference:\n\n```text\nreferences/preview-media-delivery-guardrails.md\n```\n\n- Added a pointer to that reference inside the skill.\n\nThis captures the correction from the session: the issue was not only media generation, but review-delivery hygiene.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Updated the skill library.\n\nPatched loaded skill:\n\n```text\nsocial-media-shorts-automation\n```\n\nChanges made:\n\n- Added a **preview media delivery guardrail**:\n - verify distinct preview artifacts before sending multiple videos;\n - label candidate/title/path/duration/hash;\n - avoid sending visually similar timing-card MP4s as unlabeled Telegram media;\n - if user reports duplicate-looking videos, stop resending, verify, and continue.\n\n- Added a pitfall:\n - Telegram/mobile previews can make distinct MP4s look identical; don’t spam duplicate-looking attachments.\n\n- Added support reference:\n\n```text\nreferences/preview-media-delivery-guardrails.md\n```\n\n- Added a pointer to that reference inside the skill.\n\nThis captures the correction from the session: the issue was not only media generation, but review-delivery hygiene." } ], "id": "msg_0dc9f371a6d55e3f016a1363a5c6488191b3a9dafc4f610e57", "phase": "final_answer" } ] } ] }