{
"session_id": "20260525_170059_c017b0",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-25T19:17:42.666196",
"last_updated": "2026-05-25T20:13:01.506662",
"system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format  and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [98% — 2,175/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: Qwen TS:11435; gpt-5.5; AutoShorts GPU RTX A5000 16GB.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [99% — 1,370/1,375 chars]\n══════════════════════════════════════════════\n**Notes:** Mein Herr. Loyaler Partner.\n§\n**Name:** Sir\n§\n**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\n§\n**Language:** Deutsch mit englischen Fachbegriffen\n§\nKommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\n§\nKommunikationspräferenzen: **Textnachricht → Antwort per Text**\n§\nHealth advisor role: consolidate labs/clinical reports/YAZIO/daily health email/symptoms/events into dashboards/reports; lab extraction requires 4-eyes review before values are confirmed.\n§\nTelegram topic Health-Med: only medical strategy/Behçet/Major Vessel Disease/thrombosis/meds/nutrition/symptoms/labs/doctor questions; health system scripts/data models go to Health-Tech.\n§\nFinance/Grocery: no real amounts/IDs/API keys in chat; credentials via runtime files; no DB/CSV/keys/reports in Git/frontend; backups+audit; Preview→Confirm→Audit; no fake prices/buttons; OpenFIGI never show/store; never auto-book; grocery provider calls explicit/cache-first.\n§\nAutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN. Positioning: hidden mechanisms, not AI tips; AI 30–40%. Format: fail→mechanism→fix→takeaway; captions static 3–7w phrase with active word highlight. Visuals: layouts=blueprints; premium text-free AI styleframes; renderer owns labels/arrows/facts; Wan only after approval.\n\nConversation started: Monday, May 25, 2026 05:00 PM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_doc_read",
"description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.",
"parameters": {
"type": "object",
"properties": {
"doc_token": {
"type": "string",
"description": "The document token (from the document URL or comment context)."
}
},
"required": [
"doc_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_add_comment",
"description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"content": {
"type": "string",
"description": "The comment text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comment_replies",
"description": "List all replies in a comment thread on a Feishu document.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to list replies for."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"page_size": {
"type": "integer",
"description": "Number of replies per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token",
"comment_id"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_list_comments",
"description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
},
"is_whole": {
"type": "boolean",
"description": "If true, only return whole-document comments.",
"default": false
},
"page_size": {
"type": "integer",
"description": "Number of comments per page (max 100).",
"default": 100
},
"page_token": {
"type": "string",
"description": "Pagination token for next page."
}
},
"required": [
"file_token"
]
}
}
},
{
"type": "function",
"function": {
"name": "feishu_drive_reply_comment",
"description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.",
"parameters": {
"type": "object",
"properties": {
"file_token": {
"type": "string",
"description": "The document file token."
},
"comment_id": {
"type": "string",
"description": "The comment ID to reply to."
},
"content": {
"type": "string",
"description": "The reply text content (plain text only, no markdown)."
},
"file_type": {
"type": "string",
"description": "File type (default: docx).",
"default": "docx"
}
},
"required": [
"file_token",
"comment_id",
"content"
]
}
}
},
{
"type": "function",
"function": {
"name": "image_generate",
"description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown  and the gateway will deliver it.",
"parameters": {
"type": "object",
"properties": {
"prompt": {
"type": "string",
"description": "The text prompt describing the desired image. Be detailed and descriptive."
},
"aspect_ratio": {
"type": "string",
"enum": [
"landscape",
"square",
"portrait"
],
"description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.",
"default": "landscape"
}
},
"required": [
"prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "memory",
"description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"add",
"replace",
"remove"
],
"description": "The action to perform."
},
"target": {
"type": "string",
"enum": [
"memory",
"user"
],
"description": "Which memory store: 'memory' for personal notes, 'user' for user profile."
},
"content": {
"type": "string",
"description": "The entry content. Required for 'add' and 'replace'."
},
"old_text": {
"type": "string",
"description": "Short unique substring identifying the entry to replace or remove."
}
},
"required": [
"action",
"target"
]
}
}
},
{
"type": "function",
"function": {
"name": "mixture_of_agents",
"description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.",
"parameters": {
"type": "object",
"properties": {
"user_prompt": {
"type": "string",
"description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning."
}
},
"required": [
"user_prompt"
]
}
}
},
{
"type": "function",
"function": {
"name": "patch",
"description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.",
"parameters": {
"type": "object",
"properties": {
"mode": {
"type": "string",
"enum": [
"replace",
"patch"
],
"description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.",
"default": "replace"
},
"path": {
"type": "string",
"description": "REQUIRED when mode='replace'. File path to edit."
},
"old_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "Replace all occurrences instead of requiring a unique match (default: false)",
"default": false
},
"patch": {
"type": "string",
"description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
}
},
"required": [
"mode"
]
}
}
},
{
"type": "function",
"function": {
"name": "process",
"description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"list",
"poll",
"log",
"wait",
"kill",
"write",
"submit",
"close"
],
"description": "Action to perform on background processes"
},
"session_id": {
"type": "string",
"description": "Process session ID (from terminal background output). Required for all actions except 'list'."
},
"data": {
"type": "string",
"description": "Text to send to process stdin (for 'write' and 'submit' actions)"
},
"timeout": {
"type": "integer",
"description": "Max seconds to block for 'wait' action. Returns partial output on timeout.",
"minimum": 1
},
"offset": {
"type": "integer",
"description": "Line offset for 'log' action (default: last 200 lines)"
},
"limit": {
"type": "integer",
"description": "Max lines to return for 'log' action",
"minimum": 1
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to read (absolute, relative, or ~/path)"
},
"offset": {
"type": "integer",
"description": "Line number to start reading from (1-indexed, default: 1)",
"default": 1,
"minimum": 1
},
"limit": {
"type": "integer",
"description": "Maximum number of lines to read (default: 500, max: 2000)",
"default": 500,
"maximum": 2000
}
},
"required": [
"path"
]
}
}
},
{
"type": "function",
"function": {
"name": "search_files",
"description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
"parameters": {
"type": "object",
"properties": {
"pattern": {
"type": "string",
"description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
},
"target": {
"type": "string",
"enum": [
"content",
"files"
],
"description": "'content' searches inside file contents, 'files' searches for files by name",
"default": "content"
},
"path": {
"type": "string",
"description": "Directory or file to search in (default: current working directory)",
"default": "."
},
"file_glob": {
"type": "string",
"description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results to return (default: 50)",
"default": 50
},
"offset": {
"type": "integer",
"description": "Skip first N results for pagination (default: 0)",
"default": 0
},
"output_mode": {
"type": "string",
"enum": [
"content",
"files_only",
"count"
],
"description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
"default": "content"
},
"context": {
"type": "integer",
"description": "Number of context lines before and after each match (grep mode only)",
"default": 0
}
},
"required": [
"pattern"
]
}
}
},
{
"type": "function",
"function": {
"name": "send_message",
"description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"send",
"list"
],
"description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."
},
"target": {
"type": "string",
"description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)"
},
"message": {
"type": "string",
"description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "session_search",
"description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."
},
"role_filter": {
"type": "string",
"description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."
},
"limit": {
"type": "integer",
"description": "Max sessions to summarize (default: 3, max: 5).",
"default": 3
}
}
}
}
},
{
"type": "function",
"function": {
"name": "skill_manage",
"description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": [
"create",
"patch",
"edit",
"delete",
"write_file",
"remove_file"
],
"description": "The action to perform."
},
"name": {
"type": "string",
"description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."
},
"content": {
"type": "string",
"description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."
},
"old_string": {
"type": "string",
"description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."
},
"new_string": {
"type": "string",
"description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."
},
"replace_all": {
"type": "boolean",
"description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."
},
"category": {
"type": "string",
"description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."
},
"file_path": {
"type": "string",
"description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."
},
"file_content": {
"type": "string",
"description": "Content for the file. Required for 'write_file'."
},
"absorbed_into": {
"type": "string",
"description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."
}
},
"required": [
"action",
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skill_view",
"description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.",
"parameters": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')."
},
"file_path": {
"type": "string",
"description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."
}
},
"required": [
"name"
]
}
}
},
{
"type": "function",
"function": {
"name": "skills_list",
"description": "List available skills (name + description). Use skill_view(name) to load full content.",
"parameters": {
"type": "object",
"properties": {
"category": {
"type": "string",
"description": "Optional category filter to narrow results"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "terminal",
"description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n",
"parameters": {
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "The command to execute on the VM"
},
"background": {
"type": "boolean",
"description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.",
"default": false
},
"timeout": {
"type": "integer",
"description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.",
"minimum": 1
},
"workdir": {
"type": "string",
"description": "Working directory for this command (absolute path). Defaults to the session working directory."
},
"pty": {
"type": "boolean",
"description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.",
"default": false
},
"notify_on_complete": {
"type": "boolean",
"description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.",
"default": false
},
"watch_patterns": {
"type": "array",
"items": {
"type": "string"
},
"description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."
}
},
"required": [
"command"
]
}
}
},
{
"type": "function",
"function": {
"name": "text_to_speech",
"description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."
},
"output_path": {
"type": "string",
"description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3"
}
},
"required": [
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "todo",
"description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.",
"parameters": {
"type": "object",
"properties": {
"todos": {
"type": "array",
"description": "Task items to write. Omit to read current list.",
"items": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Unique item identifier"
},
"content": {
"type": "string",
"description": "Task description"
},
"status": {
"type": "string",
"enum": [
"pending",
"in_progress",
"completed",
"cancelled"
],
"description": "Current status"
}
},
"required": [
"id",
"content",
"status"
]
}
},
"merge": {
"type": "boolean",
"description": "true: update existing items by id, add new ones. false (default): replace the entire list.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "video_analyze",
"description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.",
"parameters": {
"type": "object",
"properties": {
"video_url": {
"type": "string",
"description": "Video URL (http/https) or local file path to analyze."
},
"question": {
"type": "string",
"description": "Your specific question about the video. The AI will describe what happens in the video and answer your question."
}
},
"required": [
"video_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "vision_analyze",
"description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.",
"parameters": {
"type": "object",
"properties": {
"image_url": {
"type": "string",
"description": "Image URL (http/https), local file path, or data: URL to load."
},
"question": {
"type": "string",
"description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
}
},
"required": [
"image_url",
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "write_file",
"description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).",
"parameters": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
},
"content": {
"type": "string",
"description": "Complete content to write to the file"
}
},
"required": [
"path",
"content"
]
}
}
}
],
"message_count": 139,
"messages": [
{
"role": "user",
"content": "[M G] Die subtitles sind nicht genau gleichzeitig mit dem voiceover. Dies ist irritierend beim hören und mitlesen. Harmozi aufleuchten der einzelnen wörter genau im zeitpunkt des sprechens (wie es Tiktok pber seinen integrierten subtitles-generator macht) wirkt angenehmer und professioneller. Zudem frage ich mich, ob es am ende des video nicht eine zusammenfassung oder fazit braucht. Eine wirkliche lösung wird einem nicht angeboten. Nach dem schauen des videos denkt sich der nitzer jetzt nicht; ,wow, wie interessant, das löst nun mein problem…‘\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. 177 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": "Need find example workflows. Search."
},
{
"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. 21 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 ---"
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"content": "Sprint 3.0 — Real Viewer Problem Reset / Proof Short 1\n\nPreview MP4:\n\nContact Sheet / QA Frames:\n\nReviewPackageV2 JSON:"
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"role": "user",
"content": "[M G] JARVIS, wir machen jetzt einen klaren strategischen Reset.\n\nDer bisherige Pilot “AI Summary / Decision Target” wird pausiert und als Lernfall archiviert. Nicht weiter daran optimieren, nicht weiter rendern, nicht posten.\n\nGrund:\nDas Video sieht zwar zunehmend sauberer aus, aber es löst das zentrale Problem nicht: Es zeigt keinen sofort sichtbaren realen Schmerz. Die Bilder sind hübsch, aber semantisch leer. Das schaut sich niemand lange an.\n\nNeue Grundregel:\nWir fokussieren ab jetzt auf wirklichen Nutzen und reale Probleme der Viewer.\n\nNicht mehr zuerst fragen:\n“Wie machen wir ein schönes KI-Video?”\n\nSondern zuerst:\n“Welches echte Problem erkennt der Zuschauer in Sekunde 1 — und warum hilft ihm dieses Video sofort?”\n\nNeuer Sprint:\n\nSprint 3.0 — Real Viewer Problem Reset / Proof Short 1\n\nZiel:\nBaue einen neuen ersten Pilot als Proof Short, nicht als Premium-KI-Film.\n\nFokus:\n\n* reales Problem\n* sichtbarer Fail\n* sofortiger Nutzen\n* starke Retention\n* klare Regel\n* hoher Save-/Share-Wert\n* keine abstrakten Glaswolken\n* kein generisches AI-B-Roll\n* kein Wan-Final\n* keine Styleframe-Orgie\n\n1. Alten Pilot archivieren\n\nBitte archiviere den bisherigen Kandidaten:\n\nai-output-autopsy-decision-summary-38s\n\nStatus:\n\npaused_learning_case\n\nReason:\n“Pretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.”\n\nSpeichere ein kurzes Postmortem:\n\n* Was funktionierte:\n\n * Approval-Gates\n * Captions ohne Box\n * Delivery\n * Styleframe-Workflow-Lernen\n * I2V-Sanity-Test\n* Was nicht funktionierte:\n\n * Thema zu abstrakt für ersten Pilot\n * Bilder zeigten keinen sichtbaren Fail\n * zu viel Produktionsaufwand vor validiertem Format\n * zu viele visuelle Metaphern\n * Wan/Bild-KI wurde zu früh als Story-Träger benutzt\n* Neue Regel:\n\n * Pretty Empty Visual Blocker\n\n2. Pretty Empty Visual Blocker einführen\n\nBitte ergänze als Quality Gate:\n\npretty_empty_visual_blocker\n\nBlockiert einen Kandidaten oder Visual-Plan, wenn:\n\n* das Bild hochwertig aussieht, aber ohne Voiceover kein klares Problem zeigt\n* kein sichtbarer Fail vorhanden ist\n* das Visual nur Mood/B-Roll ist\n* der Zuschauer nicht innerhalb von 1 Sekunde erkennt, worum es geht\n* der Nutzen nur durch abstrakte Erklärung entsteht\n* die Szene austauschbar ist\n* das Bild auch für 50 andere AI-/Productivity-Videos passen würde\n\nEin guter Visual-Plan muss beantworten:\n\n* Was ist konkret kaputt?\n* Wo sieht man den Fehler?\n* Was steht für den Zuschauer auf dem Spiel?\n* Welche einfache Regel repariert es?\n* Warum sollte man es speichern oder teilen?\n\n3. Neuer Content-Modus: Proof Short\n\nBitte lege einen neuen Produktionsmodus an oder dokumentiere ihn:\n\nProofShortMode\n\nEigenschaften:\n\n* 25–35 Sekunden\n* screen/mockup-led oder real-problem-led\n* kein Wan-Final\n* keine aufwendigen AI-Styleframes\n* keine abstrakten Metaphern\n* keine Premium-Episode-Produktion\n* schnelle, klare, nützliche Preview\n* Ziel: Format und Thema validieren\n\nProof Shorts sind für die ersten 10–20 Videos der Default.\n\nPremium Episodes kommen erst später, wenn ein Thema/Format über echte Zuschauerreaktionen bewiesen ist.\n\n4. Neues Thema für Pilot 1 wählen\n\nBitte erstelle als neuen Hauptpilot:\n\nDigital Red Flags — Fake Invoice Payment Route\n\nArbeitsname:\n\ndigital-red-flags-fake-invoice-payment-route-30s\n\nWarum dieses Thema:\n\n* reales Problem\n* sofort sichtbar\n* hoher Nutzwert\n* hoher Share-Wert\n* klarer Schmerz\n* leicht visuell darstellbar\n* kein abstraktes AI-Thema\n* kein Wan nötig\n* sehr gut als Proof Short\n\n5. Neues Skript bauen\n\nBitte erstelle einen neuen ScriptDraft für ca. 28–35 Sekunden.\n\nHook-Vorschlag:\n\n“This invoice looks normal. The trap is not the logo — it’s the payment route.”\n\nStruktur:\n\n0–2s:\nSichtbare Rechnung / E-Mail / Zahlungsaufforderung.\nHook.\n\n2–7s:\n“Same layout. Same professional tone. Same urgency.”\n\n7–14s:\n“But one thing changed: where the money goes.”\n\n14–24s:\n“Before you pay, check three things: sender domain, urgency language, and payment route.”\n\n24–31s:\n“If the route changed, don’t pay from the email. Verify through a second\nchannel.”\n\nTakeaway:\n“Urgency is not proof. It’s the red flag.”\n\nBitte mache 3 Hook-Varianten:\n\n* Problem Hook\n* Red Flag Hook\n* Save/Share Hook\n\nAber wähle danach eine Hauptversion aus.\n\n6. Visual-Ansatz\n\nNicht:\n\n* abstrakte AI-Bilder\n* Glaswolken\n* generische Laptop-B-Roll\n* Wan als Hauptvisual\n* pseudo-realistische Fake-Rechnung mit echten Details\n* echte Marken, IBANs, QR-Codes, Logos\n\nSondern:\nkontrolliertes, fiktives Mockup.\n\nRenderer-owned:\n\n* Fake-Invoice-Mockup ohne echte Marke\n* Fake-E-Mail-Screen ohne echte Adresse\n* fiktive Zahlungskarte\n* sichtbarer Unterschied: payment route changed\n* drei Checks:\n\n * sender\n * urgency\n * payment route\n* roter/gelber Red-Flag-Moment\n* keine realen Bankdaten\n* keine echten Firmen\n* keine echten Logos\n* keine echten QR-Codes\n* keine echten Personen\n\nVisual-Format:\n\n* screen/mockup-led motion short\n* 9:16\n* klare mobile Lesbarkeit\n* hochwertige UI-/Dokument-Mockups\n* ruhige Animation\n* gezielte Highlights\n* keine überladenen Textflächen\n* keine AI-Bildgenerierung nötig, ausser optional für neutralen Hintergrund\n\n7. Preview bauen, nicht Final\n\nBitte erstelle zuerst nur ein Preview / Proof Short MP4:\n\n* Voiceover\n* Captions ohne Box\n* screen/mockup visuals\n* simple motion\n* no Wan\n* no AI image generation unless strictly necessary\n* no platform API\n* no posting\n\nOutput:\ndigital-red-flags-fake-invoice-payment-route-30s_preview_v1.mp4\n\n8. Captions\n\nNutze die inzwischen bewährte Caption-Regel:\n\n* static phrase segments\n* active-word highlight\n* keine Rahmenbox\n* keine Vollbreiten-Bar\n* keine dunkle Fläche\n* weisse Wörter\n* aktives Wort gelb\n* subtiler Schatten/Outline\n* mobile-safe\n\n9. Claims / Safety\n\nBitte Claim Gate sauber setzen.\n\nClaim-Typ:\ngeneral safety advice / low-to-medium risk.\n\nNicht behaupten:\n\n* dass alle Fake-Rechnungen so funktionieren\n* dass diese drei Checks alle Scams verhindern\n* dass dies rechtliche/finanzielle Beratung ist\n\nBesser formulieren:\n\n* “A common red flag is…”\n* “Before you pay, check…”\n* “Verify through a second channel.”\n\nKeine echten Betrugsanleitungen.\nKeine echten Bankdaten.\nKeine echten Firmen.\nKeine echten Scam-Vorlagen, die missbraucht werden können.\n\n10. Review Package\n\nBitte liefere ein ReviewPackageV2 mit:\n\n* Candidate ID\n* Hook\n* Script\n* Visual Plan\n* Pretty Empty Visual Blocker Result\n* Claim Gate\n* AI Disclosure\n* ProofShortMode = true\n* Save Reason\n* Share Reason\n* Follow Reason\n* Platform Fit\n* Preview MP4 path\n* SHA256\n* Telegram Delivery Status\n\n11. Telegram Delivery\n\nBitte Preview-MP4 senden und verifizieren:\n\n* telegram_delivery_attempted\n* telegram_delivery_verified\n* telegram_message_id\n* file_path\n* file_size\n* sha256\n* duration\n* resolution\n\nWenn Telegram nicht klappt:\nklar sagen: “lokal erzeugt, Telegram-Versand nicht erfolgt.”\n\n12. Kein Full-Final\n\nNicht ausführen:\n\n* kein Wan\n* kein I2V\n* kein Full-Final\n* keine Plattform-API\n* kein Auto-Posting\n* kein Scheduling\n* keine Render-Serie\n\n13. Danach mögliche Kommandos\n\nWenn Preview passt:\n\nAPPROVED_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s\n\nWenn überarbeiten:\n\nREVISE_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n\nWenn ablehnen:\n\nREJECT_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Grund\"\n\nWenn manuelle Upload-Vorbereitung später erlaubt:\n\nAPPROVED_FOR_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s\n\nWichtigster Grundsatz:\nWir bauen jetzt keinen schönen AI-Film. Wir bauen ein nützliches Short, das ein echtes Problem sichtbar macht und dem Zuschauer eine sofort brauchbare Regel gibt."
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"content": "Sir, **Sprint 3.0 — Real Viewer Problem Reset / Proof Short 1 ist umgesetzt.**\n\nStrategischer Reset wurde angewendet:\n\n- Der bisherige Pilot **`ai-output-autopsy-decision-summary-38s`** ist pausiert.\n- Neuer Hauptpilot: **`digital-red-flags-fake-invoice-payment-route-30s`**\n- Modus: **ProofShortMode**\n- Visual-Ansatz: **screen/mockup-led**, reales Problem, sichtbarer Fail, kein Premium-KI-Film.\n- Preview wurde gerendert und per Telegram geliefert.\n\nNicht ausgeführt:\n\n- kein Wan\n- kein I2V\n- kein Full-Final\n- keine AI-Bildgenerierung\n- keine Plattform-API\n- kein Posting\n- kein Scheduling\n- keine Render-Serie\n\n## 1. Alter Pilot archiviert\n\nArchiv/Postmortem:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/archive/ai-output-autopsy-decision-summary-38s_postmortem.json\n```\n\nStatus:\n\n```text\npaused_learning_case\n```\n\nReason:\n\n```text\nPretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.\n```\n\nWas funktionierte:\n\n- Approval-Gates\n- Captions ohne Box\n- Delivery\n- Styleframe-Workflow-Lernen\n- I2V-Sanity-Test\n\nWas nicht funktionierte:\n\n- Thema zu abstrakt für ersten Pilot\n- Bilder zeigten keinen sichtbaren Fail\n- zu viel Produktionsaufwand vor validiertem Format\n- zu viele visuelle Metaphern\n- Wan/Bild-KI wurde zu früh als Story-Träger benutzt\n\nNeue Regel:\n\n```text\nPretty Empty Visual Blocker\n```\n\n## 2. Pretty Empty Visual Blocker + ProofShortMode\n\nIm Repo ergänzt und committed:\n\n```text\nautoshorts/strategy/proof_short.py\ntests/test_proof_short_mode.py\n```\n\nCommit:\n\n```text\ndac8906 Add proof short mode and pretty empty visual gate\n```\n\nDer Gate blockiert Kandidaten/Visual-Pläne, wenn:\n\n- hochwertiges Bild, aber kein klares Problem ohne Voiceover\n- kein sichtbarer Fail\n- nur Mood/B-Roll\n- Zuschauer erkennt in Sekunde 1 nicht, worum es geht\n- Nutzen nur abstrakt erklärt wird\n- Szene austauschbar wirkt\n- Visual für 50 andere AI-/Productivity-Videos passen würde\n\nProofShortMode Default:\n\n```text\nduration: 25–35s\nscreen_or_real_problem_led: true\nwan_final_allowed: false\npremium_styleframe_pass_allowed: false\nabstract_metaphors_allowed: false\n```\n\nTests:\n\n```text\n10 passed in 0.08s\n```\n\nGeprüft:\n\n```text\ntests/test_proof_short_mode.py\ntests/test_pilot_production_v2.py\n```\n\n## 3. Neuer Pilot\n\nCandidate ID:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nArbeitstitel:\n\n```text\nDigital Red Flags — Fake Invoice Payment Route\n```\n\nWarum stark:\n\n- reales Problem\n- sofort sichtbar\n- hoher Nutzwert\n- hoher Share-Wert\n- klarer Schmerz\n- visuell kontrollierbar über Mockups\n- kein Wan nötig\n- kein AI-B-Roll nötig\n\n## 4. Hook-Varianten\n\nGespeichert:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/script_draft_v1.json\n```\n\nProblem Hook:\n\n```text\nThis invoice looks normal. The trap is not the logo — it’s the payment route.\n```\n\nRed Flag Hook:\n\n```text\nA fake invoice does not need a fake logo. It only needs a changed payment route.\n```\n\nSave/Share Hook:\n\n```text\nBefore anyone on your team pays an invoice, check this one red flag.\n```\n\nGewählte Hauptversion:\n\n```text\nThis invoice looks normal. The trap is not the logo — it’s the payment route.\n```\n\n## 5. ScriptDraft\n\nScript:\n\n```text\nThis invoice looks normal. The trap is not the logo — it’s the payment route. Same layout. Same professional tone. Same urgency. But one thing changed: where the money goes. Before you pay, check three things: sender domain, urgency language, and payment route. If the route changed, don’t pay from the email. Verify through a second channel. Urgency is not proof. It’s the red flag.\n```\n\nDauer:\n\n```text\n31.29s MP4\n31.32s Voiceover\n```\n\n## 6. Visual Plan\n\nGespeichert:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_plan_v1.json\n```\n\nFormat:\n\n```text\nscreen/mockup-led motion short\n9:16\ncontrolled fictional UI/document mockup\n```\n\nSzenen:\n\n```text\ns01_visible_fail:\nfake email + invoice mockup; payment route visible\n\ns02_same_layout:\nsame layout / same tone / same urgency\n\ns03_route_changed:\npayment route changes; red-flag moment\n\ns04_three_checks:\nsender domain / urgency language / payment route\n\ns05_second_channel:\ndo not pay from email; verify through second channel\n\ns06_takeaway:\nUrgency is not proof. It is the red flag.\n```\n\nSafety:\n\n- keine echten Marken\n- keine echten Bankdaten\n- keine IBANs\n- keine QR-Codes\n- keine echten Personen\n- keine echten Firmen\n- keine echten Scam-Vorlagen\n- keine Plattform-API\n\n## 7. Pretty Empty Visual Blocker Result\n\n```text\npretty_empty_visual_blocker_result:\npass\n```\n\nWarum:\n\n- konkreter sichtbarer Fail im ersten Frame\n- echte Viewer-Pain: Fake Invoice / Payment Route Change\n- klarer Stakes: Geld geht an falsche Route\n- einfache Regel: Sender, Urgency, Payment Route, Second Channel\n- Save/Share-Grund klar\n\n## 8. Claim Gate\n\nClaim-Typ:\n\n```text\ngeneral safety advice / low-to-medium risk\n```\n\nRisk:\n\n```text\nmedium\n```\n\nResult:\n\n```text\npass\n```\n\nSichere Formulierungen:\n\n```text\nA common red flag is…\nBefore you pay, check…\nVerify through a second channel.\n```\n\nNicht behauptet:\n\n- dass alle Fake-Rechnungen so funktionieren\n- dass drei Checks alle Scams verhindern\n- dass dies rechtliche oder finanzielle Beratung ist\n\nAbuse Controls:\n\n- keine echten Bankdaten\n- keine echten Firmen\n- keine Logos\n- keine QR-Codes\n- keine operationalisierbare Scam-Vorlage\n\n## 9. ReviewPackageV2\n\nFinales ReviewPackage:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/review_package_v2.json\n```\n\nEnthält:\n\n- Candidate ID\n- Hook\n- Script\n- Visual Plan\n- Pretty Empty Visual Blocker Result\n- Claim Gate\n- AI Disclosure\n- ProofShortMode = true\n- Save Reason\n- Share Reason\n- Follow Reason\n- Platform Fit\n- Preview MP4 path\n- SHA256\n- Telegram Delivery Status\n\nScoreV2:\n\n```text\n87/100\nrender_allowed: true\nreject_reasons: []\n```\n\nAI Disclosure:\n\n```text\nno: deterministic mockups and TTS preview; no realistic AI-generated people/scenes\n```\n\n## 10. Preview MP4\n\nPer Telegram gesendet.\n\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1.mp4\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1.mp4\n\nfile_exists:\ntrue\n\nfile_size:\n1270875 bytes\n\nsha256:\nbc8256104d1db71de6277b64db351ac3e1282d33396015ef09439aff033c47ba\n\nmedia_type:\nvideo/mp4\n\nduration:\n31.291667s\n\nresolution:\n1080x1920\n\nfps:\n24/1\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5951\n```\n\n## 11. Contact Sheet / QA Frames\n\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags_contact_sheet_v1.jpg\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags_contact_sheet_v1.jpg\n\nfile_exists:\ntrue\n\nfile_size:\n79590 bytes\n\nsha256:\nbf2d7131fb10d2065221d41f3a2abdd0b6e61bdb5832fa7e9b5dc7fd0a9f888e\n\nmedia_type:\nimage/jpeg\n\nresolution:\n1080x720\n\ntelegram_delivery_attempted:\ntrue\n\ntelegram_delivery_verified:\ntrue\n\ntelegram_message_id:\n5951\n```\n\nQA:\n\n```text\npass\n```\n\nBeobachtung:\n\n- sichtbare Invoice-/Payment-Route-Situation\n- kein abstraktes B-Roll\n- keine echten Logos/Brands\n- keine realen Bankdaten\n- keine QR-Codes\n- Captions white/yellow, ohne Box/Bar\n- Problem ist visuell deutlich schneller erkennbar als beim alten Pilot\n\n## 12. Voiceover\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/audio/voiceover.mp3\n\nfile_exists:\ntrue\n\nfile_size:\n187920 bytes\n\nsha256:\n4e9da013d7c4baf1ddddbba9d0900471a0ffea243325b52bf02cf0f56f643bf5\n\nduration:\n31.32s\n```\n\nVoice:\n\n```text\nen-US-GuyNeural\n```\n\n## 13. Caption Style\n\nVerwendet:\n\n```text\nstatic phrase segments\nactive-word highlight\nwhite inactive words\nyellow active word\nsubtle shadow/outline\nno box\nno full-width bar\nno dark rectangle\nmobile-safe bottom\n```\n\n## 14. Einschätzung\n\nDas ist strategisch deutlich richtiger als der alte Pilot.\n\nWarum:\n\n- Erster Frame zeigt ein reales Problem.\n- Das Thema ist sofort verständlich.\n- Der Viewer erkennt sich oder sein Team wieder.\n- Die Regel ist konkret und speicherbar.\n- Das Short braucht keine KI-Ästhetik, um zu funktionieren.\n- Der Nutzen ist nicht abhängig von abstrakter Voiceover-Erklärung.\n\nKurz: **weniger hübscher Nebel, mehr echter Schmerz.** Welch radikale Innovation.\n\n## 15. Nicht ausgeführt\n\n```text\nwan_used:\nfalse\n\ni2v_used:\nfalse\n\nfull_final_render:\nfalse\n\nposting:\nfalse\n\nplatform_api:\nfalse\n\nscheduling:\nfalse\n\nnew_ai_images:\nfalse\n```\n\n## 16. Nächste erlaubte Kommandos\n\nWenn Preview passt:\n\n```text\nAPPROVED_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s\n```\n\nWenn überarbeiten:\n\n```text\nREVISE_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n```\n\nWenn ablehnen:\n\n```text\nREJECT_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Grund\"\n```\n\nWenn später manuelle Upload-Vorbereitung erlaubt ist:\n\n```text\nAPPROVED_FOR_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s\n```\n\nPipeline bleibt blockiert für Full-Final, Wan/I2V, Posting und Plattform-APIs."
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"content": "Sprint 3.1 Artefakte — Preview v2 Caption Safe\n\nPreview MP4:\n\nQA Frame Sheet:\n\nSubtitle Style Preview:"
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"reasoning_content": "**Considering skills for automation**\n\nI see that I need to take action and assess the skills required for this project. It sounds like I should look into relevant social automations, perhaps using something like the hermes-agent. I think I’ll need to focus on configuring the Hermes image generator and ensure it has the proper loading capabilities. Alright, let's see how I can get started with these configurations!",
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{
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"encrypted_content": "gAAAAABqFIQ7okwQpQUSF92m0BkoUuwbCKRqbiYmCByN59nn4a2MQ2KH5jDyeArp3EpqlTdSKllbCeo1rY8TnC4u_6c8JtYBx4lbCgeQI1om5RjWm8uAYNVD2fWIeOhMkErPVT3puc3dEj_M4BLrcQ8jXMxDP4bHpycpgzx1qzfn1cQ5BAGYYEC-6hEOZzW_Ut88woeLMzyy9cZwUjSgnJoDU9O2ysqCFWGjWvjimcUlmQgKH2FnaWK2oYzYGpf4qcaxM2-Gzpj2ZPQJmfNVcaPm8Vq-bj2Tk_G3tbzIlAkEV4p0Vw8zTeI1vJdhm2ennHH-Qz72CqrzG1elmHKaaRVf54CKHHHQm3QZsOiz8Cl87a5IsCQjAgkLBu3942Au8KRe1MAHMxrVmep6FFaIl5md7Cu2wjEzjHXp_d1N1Ggl6MOHrGa3mGSeCpOyH-Y9VXp8kmoN5CVTMqpDJqwHTI8Cuw1CcVqKuf-VLsOBF7T0BTxoJVGkEmW7YVQsbqQrBp8g1rrjpvsodSG_rozO2bUEFVlTaZqFJ2PeAJTIWDFAXmqnntnRRpZsIwhwW4Y0DLMq_42G_1asi7AN8UmzO7Xe9Y8WL3kOihUfyqpVfcO8eAjnlOW__hEwHLBsb9ZwPccB2F-G9HmIdyhgOAwSr4689eMslV33lufYFYfPF6M-6pXA1sz4U-SqRnB7rmKgx6HsSLkJMaHf3ogCoXaGrOxNjR0H2RH9yHSpOK53SJks5c1jp5k59WhbuRw_WHB2gH3-ufvx_nSPCifNrIxd8Z2BxAcSBmafnSpLlgZyLjWBDIQFIKJAkAaPWtwzcrSHAO1F6uu8SFzrw2E3R0PrDxmvW1tNrCjYB9eFJBxD4VPIiE1ej6MfnmeaaNUNyAtfAyUZA4W3-wAkzmQmb76WrYGVRjVx9fkd9_lMXuMWN5-Vo5SnRUDUYMrzyxTa3AjG81Tj3pITmkOBLpShAYn6HmjaFTXvUTbw1PxqqQA3CTZ1xtoTvYEWns4YqAGnBt36S7sc3OdqrsmNCzpkXcjaEpJuQ9hRjEz2KdfzwJDCmNuxP_FfobCMXYNlNUZIKp4WajmGhfKfCYL5",
"id": "rs_06f2edf006b6bde1016a148437c3848191909bdcdff47190d2",
"summary": [
{
"type": "summary_text",
"text": "**Considering skills for automation**\n\nI see that I need to take action and assess the skills required for this project. It sounds like I should look into relevant social automations, perhaps using something like the hermes-agent. I think I’ll need to focus on configuring the Hermes image generator and ensure it has the proper loading capabilities. Alright, let's see how I can get started with these configurations!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nReference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected caption style and controlled final pilot render pattern: static 3–7 word phrase segments with only the active spoken word highlighted, hash-bound final render gates, one Wan text-free background layer, deterministic overlays, and draft-only manual posting packages. If a final render is rejected as preview-overlay-dominated or not visual-first, follow `references/visual-first-final-render-repair.md`: mark the render rejected/not-postable, fix Wan output parsing, enforce final overlay limits, produce only a short visual proof under `APPROVED_FOR_VISUAL_PROOF`, and do not run another full final until the visual direction is accepted. If even the visual proof is rejected as abstract/generic/dark or “blind T2V”, switch to `references/keyframe-first-final-visuals.md`: plan and approve start/end/style keyframes first, then run only a short I2V/first-last-frame proof with minimal renderer-owned overlays. If the user says the production method itself is wrong, keyframes are not I2V-ready, or an animatic was incorrectly substituted for a failed Wan proof, follow `references/visual-production-reset-i2v-first.md`: reject/audit the proof, stop content rendering, run technical I2V sanity first, then generate at most three styleframes for direction review. If deterministic layout proofs are useful as composition blueprints but rejected as too wireframe/PowerPoint for final style, switch to `references/premium-ai-styleframe-direction.md`: use the layouts only as mechanism blueprints, generate at most 3 tightly briefed premium text-free AI styleframes, deliver a contact sheet with verified media metadata, and keep Wan/I2V/endframes/full-final blocked until style direction approval. If those premium styleframes are better quality but still too abstract, switch to `references/real-life-premium-styleframes.md`: anchor the mechanism in a realistic work/meeting/desk scene, keep all readable overlays renderer-owned, generate at most 3 new real-life styleframes plus contact sheet, and still block Wan/I2V/endframes/full-final until explicit approval. If the user approves one or more real-life styleframe directions but wants to compare A/B/C before Wan, use `references/prerelease-animatic-preview.md`: build only a small AI-keyframe + deterministic-motion animatic per approved direction, reuse existing voiceover/captions, deliver MP4/contact/QA sheets with metadata, and keep Wan/I2V/final/posting blocked. If the user then rejects the renderer-added circles/arrows/labels/boxes/decision-target markers as destroying the premium look, switch to `references/clean-image-cut-no-overlay-pilot.md`: render only AI images + voiceover + clean subtitles, with a hard subtitles-only NoOverlayPolicy and no Wan/I2V/full-final/posting. If the user performs a strategic reset because the pilot is pretty but semantically weak, stop optimizing that candidate and follow `references/proof-short-real-problem-reset.md`: archive the old pilot as a learning case, enforce the Pretty Empty Visual Blocker, and build a screen/mockup-led Proof Short around an immediately visible real viewer problem before any premium/Wan production.\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\nFor the user's TrueTraceShorts / AutoShortsBot strategy, do **not** default to a narrow AI/prompt-tips channel. The current strategic direction is broader: explain hidden digital everyday mechanisms — AI, work, attention, decisions, scams, productivity, workflows, social search — and end each video with one small system rule that fixes the outcome. Use the frame `Trace the hidden system. Fix the outcome.` AI should remain an important pillar, but normally only about 30–40% of the topic mix, not the whole product. See `references/truetrace-strategic-neuausrichtung.md`.\\n\\nWhen the user asks to start implementation work on this strategy, especially “Sprint 1: Strategic Spine Refactor”, follow `references/truetrace-strategic-spine-refactor-v2.md`: implement a side-effect-free V2 spine in parallel to existing V1 models, with mandatory strategic fields, hard quality gates, anti-AI-slop blockers, hash/version-bound ReviewPackageV2, and tests before integration with renderers or platform workflows.\\n\\nWhen the user asks for “Sprint 1.5” or to make the V2 spine operational without production, follow `references/truetrace-v2-operational-bridge.md`: first secure/commit the Sprint-1 baseline, then add only minimal side-effect-free bridges (`to_candidate_v2`, `workflow_v2`, `demo_review_batch_v2`), deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated non-generous scoring, anti-example/human-texture gates, minimal manual claim/disclosure stubs, LinkedIn manual metadata, and a Remotion-as-later-spike note. Keep it capped at 3 demo candidates and avoid rendering, platform APIs, dashboards, auto-posting, or 30-day batches.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - For TrueTraceShorts, prefer the structure `visible fail → identification → mechanism → fix → before/after → memorable takeaway` over generic AI-tip narration. Every video must answer: what goes wrong, why it happens, what rule fixes it, what sentence sticks, why viewers save/share it, and why they follow the series.\\n - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n - first-frame clarity\\n - mechanism value\\n - practical payoff\\n - save/share/follow reason\\n - trust/human texture\\n - series fit and saturation defense\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n For TrueTraceShorts, use the 100-point scoring and hard blockers in `references/truetrace-strategic-neuausrichtung.md`: block generic AI hooks, pure text first frames, unsourced claims, missing before/after, missing human texture, unclear AI disclosure, and candidates too similar to recent videos.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - For final renders, Wan must carry the visual scene, not merely provide a blurred/looped background underneath preview cards. Use full-bleed, scene-specific Wan clips, minimal renderer-owned overlays, and a final overlay policy that blocks preview frames, large UI cards/textboxes, debug bars, and overlay overload. If architecture is rejected, create a short visual proof first; see `references/visual-first-final-render-repair.md`.\\n - If a visual proof still looks like blind text-to-video or random abstract AI output, stop T2V and move to a keyframe-first workflow: generate/approve 1–2 text-free start/end/style keyframes for the scene, then attempt only a short Wan I2V/first-last-frame proof. See `references/keyframe-first-final-visuals.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or simplify captions before sending another final-near clip. For this user's AutoShorts/TrueTraceShorts default, do **not** show one word alone and do **not** slide a changing word-window every word. Use static phrase segments: keep 3–7 words fixed on screen while only the currently spoken word highlights/pulses left-to-right; replace the full segment only at a sentence/phrase boundary. See `references/static-active-word-captions-and-final-pilot-render.md`.\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style. Treat them as composition blueprints and run a small, tightly prompted premium AI styleframe pass instead: AI supplies glassmorphism/product-film quality and atmosphere; the renderer still owns labels, arrows, captions, facts, and final assembly. See `references/premium-ai-styleframe-direction.md`.\\n - If premium AI styleframes improve quality but still feel like abstract SaaS symbolism, do not continue iterating abstract funnels/rings. Move to real-life anchored work scenes: desk/laptop, empty meeting room/glass board, or project table/work evidence, with only subtle glass/holographic mechanism overlays. See `references/real-life-premium-styleframes.md`.\\n - If real-life styleframe directions are accepted but the user has not approved Wan/I2V yet, insert a pre-release animatic comparison step rather than jumping to endframes or video generation. Create small per-direction AI keyframe sets, deterministic push-ins/crossfades, existing voiceover, static active-word captions, contact sheets, and QA frame sheets; report Telegram delivery metadata. See `references/prerelease-animatic-preview.md`.\\n - If the user rejects added visual explanation overlays as making the result look like a technical explainer board, treat this as a hard style failure. For the clean pilot, use only AI images, voiceover, and clean subtitles; block circles, arrows, labels, boxes, connector lines, decision-target markers, debug/diagram elements, and caption boxes/bars. See `references/clean-image-cut-no-overlay-pilot.md`.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/truetrace-strategic-spine-refactor-v2.md` for implementing the new TrueTraceShorts strategy in code: V2 strategic models, hard gates, anti-AI-slop blockers, SeriesRegistry, 100-point scoring, hash-bound ReviewPackageV2, and deterministic five-candidate examples without rendering or platform side effects. See `references/truetrace-v2-operational-bridge.md` for the next implementation step: secure the Sprint-1 commit, add minimal side-effect-free bridge/workflow/CLI modules, deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated scoring, anti-example/human-texture gates, manual claim/disclosure stubs, LinkedIn manual metadata, and Remotion-as-later-spike documentation without production expansion.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected Hormozi-style caption behaviour and the one-render final pilot review-package checklist.\\n\\nSee `references/final-motion-hybrid-rendering.md` for the final-near hybrid renderer pattern: ChatGPT Image/Codex or other AI tools provide text-free background/keyframe texture while deterministic renderer overlays own text, arrows, labels, captions, and final assembly. For quick prototypes, prefer ChatGPT Image/Codex backgrounds before local Qwen/ComfyUI unless privacy/cost/local-only constraints dominate. After a 1080 export, create a side-effect-free final approval package with media path, SHA256, platform metadata, social copy, quality notes, and approval commands before any delivery or platform step.\\n\\nSee `references/visual-first-final-render-repair.md` for the repaired final-render architecture after a rejected pilot: mark rejected renders as not-postable, avoid preview-board overlays, enforce `FinalOverlayPolicy`, validate ComfyUI/Wan output histories robustly, and use `APPROVED_FOR_VISUAL_PROOF` for one short visual proof before any second full final.\\n\\nSee `references/hybrid-scene-backgrounds-codex.md` for the concrete ChatGPT Image/Codex scene-background workflow: side-effect-free `SceneBackgroundPlan`, per-scene text-free backgrounds, renderer-owned overlays, and visual QA before review delivery. Prefer this over local Qwen/ComfyUI when the user allows Codex image generation and local GPU should be reserved for video/model work.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not assume local Qwen/ComfyUI is the best first image step for every final-near short. If the user permits ChatGPT Image/Codex, use it for text-free scene backgrounds/keyframes when the deterministic renderer owns labels/captions and local GPU should be saved for video/model work.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not treat a keyframe animatic as a successful Wan/I2V proof. If ComfyUI/Wan I2V fails or history contains no valid video output, label any fallback MP4 as `not_wan` / composition-review-only and block full-final progression.\\n- Do not treat deterministic layout proofs as the final premium visual style when the user asked for a high-end social-video look. Layouts are for mechanism composition; after approval, use a tightly scoped premium AI styleframe pass with strict negative prompts and verified delivery before endframes/Wan.\\n- Do not mistake premium abstract glassmorphism for an approved final direction if the user wants a credible everyday work scene. When the user says the motif is still too abstract, switch to real-life work-scene styleframes with laptop/tablet/meeting-room/project-table anchors; keep motion graphics subtle and renderer-owned.\\n- If the user rejects the production method itself, do not keep iterating content visuals. Reject/audit the proof, run a technical I2V sanity test with a trivial non-content image, and only after it passes generate a small set of styleframes for direction review; see `references/visual-production-reset-i2v-first.md`.\\n- Do not keep trying blind text-to-video after the user rejects a proof as abstract, dark, generic, or semantically ungrounded. Move to keyframe-first planning, review the keyframes, then run at most one short I2V/first-last-frame proof.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/premium-ai-styleframe-direction.md\", \"references/social-draft-approval-package.md\", \"references/static-active-word-captions-and-final-pilot-render.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/visual-first-final-render-repair.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/proof-short-real-problem-reset.md\", \"references/truetrace-strategic-neuausrichtung.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/controlled-pilot-production-v2.md\", \"references/keyframe-first-final-visuals.md\", \"references/clean-image-cut-no-overlay-pilot.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/pilot-preview-render-v2.md\", \"references/truetrace-strategic-spine-refactor-v2.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/real-life-premium-styleframes.md\", \"references/monetization-and-approval-notes.md\", \"references/visual-production-reset-i2v-first.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/final-motion-hybrid-rendering.md\", \"references/prerelease-animatic-preview.md\", \"references/imported-ai-clip-assembly.md\", \"references/hybrid-scene-backgrounds-codex.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\", \"references/truetrace-v2-operational-bridge.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}",
"tool_call_id": "call_XeQMurYyidXcJrGcxtOQTtES"
},
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"role": "tool",
"content": "{\"success\": true, \"name\": \"hermes-agent\", \"description\": \"Configure, extend, or contribute to Hermes Agent.\", \"tags\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"], \"content\": \"---\\nname: hermes-agent\\ndescription: \\\"Configure, extend, or contribute to Hermes Agent.\\\"\\nversion: 2.1.0\\nauthor: Hermes Agent + Teknium\\nlicense: MIT\\nmetadata:\\n hermes:\\n tags: [hermes, setup, configuration, multi-agent, spawning, cli, gateway, development]\\n homepage: https://github.com/NousResearch/hermes-agent\\n related_skills: [claude-code, codex, opencode]\\n---\\n\\n# Hermes Agent\\n\\nHermes Agent is an open-source AI agent framework by Nous Research that runs in your terminal, messaging platforms, and IDEs. It belongs to the same category as Claude Code (Anthropic), Codex (OpenAI), and OpenClaw — autonomous coding and task-execution agents that use tool calling to interact with your system. Hermes works with any LLM provider (OpenRouter, Anthropic, OpenAI, DeepSeek, local models, and 15+ others) and runs on Linux, macOS, and WSL.\\n\\nWhat makes Hermes different:\\n\\n- **Self-improving through skills** — Hermes learns from experience by saving reusable procedures as skills. When it solves a complex problem, discovers a workflow, or gets corrected, it can persist that knowledge as a skill document that loads into future sessions. Skills accumulate over time, making the agent better at your specific tasks and environment.\\n- **Persistent memory across sessions** — remembers who you are, your preferences, environment details, and lessons learned. Pluggable memory backends (built-in, Honcho, Mem0, and more) let you choose how memory works.\\n- **Multi-platform gateway** — the same agent runs on Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, and 10+ other platforms with full tool access, not just chat.\\n- **Provider-agnostic** — swap models and providers mid-workflow without changing anything else. Credential pools rotate across multiple API keys automatically.\\n- **Profiles** — run multiple independent Hermes instances with isolated configs, sessions, skills, and memory.\\n- **Extensible** — plugins, MCP servers, custom tools, webhook triggers, cron scheduling, and the full Python ecosystem.\\n\\nPeople use Hermes for software development, research, system administration, data analysis, content creation, home automation, and anything else that benefits from an AI agent with persistent context and full system access.\\n\\n**This skill helps you work with Hermes Agent effectively** — setting it up, configuring features, spawning additional agent instances, troubleshooting issues, finding the right commands and settings, and understanding how the system works when you need to extend or contribute to it.\\n\\n**Docs:** https://hermes-agent.nousresearch.com/docs/\\n\\n## Quick Start\\n\\n```bash\\n# Install\\ncurl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash\\n\\n# Interactive chat (default)\\nhermes\\n\\n# Single query\\nhermes chat -q \\\"What is the capital of France?\\\"\\n\\n# Setup wizard\\nhermes setup\\n\\n# Change model/provider\\nhermes model\\n\\n# Check health\\nhermes doctor\\n```\\n\\n---\\n\\n## CLI Reference\\n\\n### Global Flags\\n\\n```\\nhermes [flags] [command]\\n\\n --version, -V Show version\\n --resume, -r SESSION Resume session by ID or title\\n --continue, -c [NAME] Resume by name, or most recent session\\n --worktree, -w Isolated git worktree mode (parallel agents)\\n --skills, -s SKILL Preload skills (comma-separate or repeat)\\n --profile, -p NAME Use a named profile\\n --yolo Skip dangerous command approval\\n --pass-session-id Include session ID in system prompt\\n```\\n\\nNo subcommand defaults to `chat`.\\n\\n### Chat\\n\\n```\\nhermes chat [flags]\\n -q, --query TEXT Single query, non-interactive\\n -m, --model MODEL Model (e.g. anthropic/claude-sonnet-4)\\n -t, --toolsets LIST Comma-separated toolsets\\n --provider PROVIDER Force provider (openrouter, anthropic, nous, etc.)\\n -v, --verbose Verbose output\\n -Q, --quiet Suppress banner, spinner, tool previews\\n --checkpoints Enable filesystem checkpoints (/rollback)\\n --source TAG Session source tag (default: cli)\\n```\\n\\n### Configuration\\n\\n```\\nhermes setup [section] Interactive wizard (model|terminal|gateway|tools|agent)\\nhermes model Interactive model/provider picker\\nhermes config View current config\\nhermes config edit Open config.yaml in $EDITOR\\nhermes config set KEY VAL Set a config value\\nhermes config path Print config.yaml path\\nhermes config env-path Print .env path\\nhermes config check Check for missing/outdated config\\nhermes config migrate Update config with new options\\nhermes login [--provider P] OAuth login (nous, openai-codex)\\nhermes logout Clear stored auth\\nhermes doctor [--fix] Check dependencies and config\\nhermes status [--all] Show component status\\n```\\n\\n### Tools & Skills\\n\\n```\\nhermes tools Interactive tool enable/disable (curses UI)\\nhermes tools list Show all tools and status\\nhermes tools enable NAME Enable a toolset\\nhermes tools disable NAME Disable a toolset\\n\\nhermes skills list List installed skills\\nhermes skills search QUERY Search the skills hub\\nhermes skills install ID Install a skill (ID can be a hub identifier OR a direct https://…/SKILL.md URL; pass --name to override when frontmatter has no name)\\nhermes skills inspect ID Preview without installing\\nhermes skills config Enable/disable skills per platform\\nhermes skills check Check for updates\\nhermes skills update Update outdated skills\\nhermes skills uninstall N Remove a hub skill\\nhermes skills publish PATH Publish to registry\\nhermes skills browse Browse all available skills\\nhermes skills tap add REPO Add a GitHub repo as skill source\\n```\\n\\n#### Image generation via ChatGPT/Codex OAuth\\n\\nWhen the user asks to use ChatGPT Images / GPT Image from Hermes without a separate OpenAI API key, prefer the bundled `openai-codex` image-generation backend. It uses the existing Hermes-managed ChatGPT/Codex OAuth token and routes image generation through the Responses `image_generation` tool.\\n\\nSetup:\\n\\n```bash\\n# Ensure image_gen is enabled for the platform, then select the Codex-backed provider.\\nhermes config set image_gen.provider openai-codex\\nhermes config set image_gen.model gpt-image-2-medium\\n```\\n\\nAvailable practical tiers:\\n\\n```bash\\nhermes config set image_gen.model gpt-image-2-low # quick drafts / cheaper iteration\\nhermes config set image_gen.model gpt-image-2-medium # balanced default\\nhermes config set image_gen.model gpt-image-2-high # final keyframes / highest fidelity\\n```\\n\\nVerify from Python if needed:\\n\\n```bash\\npython - <<'PY'\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom agent.image_gen_registry import get_provider\\n_ensure_plugins_discovered(force=True)\\np = get_provider('openai-codex')\\nprint(bool(p and p.is_available()), p.default_model() if p else None)\\nPY\\n```\\n\\nIf the provider is unavailable, sign in with Codex/ChatGPT OAuth (`hermes auth add` / `hermes login --provider openai-codex`, depending on the installed Hermes version) rather than creating a new API-key workflow. Config changes may require `/reset` or `/restart` in gateway sessions before the tool schema/availability updates.\\n\\n### MCP Servers\\n\\n```\\nhermes mcp serve Run Hermes as an MCP server\\nhermes mcp add NAME Add an MCP server (--url or --command)\\nhermes mcp remove NAME Remove an MCP server\\nhermes mcp list List configured servers\\nhermes mcp test NAME Test connection\\nhermes mcp configure NAME Toggle tool selection\\n```\\n\\n### Gateway (Messaging Platforms)\\n\\n```\\nhermes gateway run Start gateway foreground\\nhermes gateway install Install as background service\\nhermes gateway start/stop Control the service\\nhermes gateway restart Restart the service\\nhermes gateway status Check status\\nhermes gateway setup Configure platforms\\n```\\n\\nSupported platforms: Telegram, Discord, Slack, WhatsApp, Signal, Email, SMS, Matrix, Mattermost, Home Assistant, DingTalk, Feishu, WeCom, BlueBubbles (iMessage), Weixin (WeChat), API Server, Webhooks. Open WebUI connects via the API Server adapter.\\n\\nPlatform docs: https://hermes-agent.nousresearch.com/docs/user-guide/messaging/\\n\\n**Telegram setup:** See `references/telegram-gateway-setup.md` for the complete step-by-step configuration workflow.\\n\\n### Sessions\\n\\n```\\nhermes sessions list List recent sessions\\nhermes sessions browse Interactive picker\\nhermes sessions export OUT Export to JSONL\\nhermes sessions rename ID T Rename a session\\nhermes sessions delete ID Delete a session\\nhermes sessions prune Clean up old sessions (--older-than N days)\\nhermes sessions stats Session store statistics\\n```\\n\\n### Cron Jobs\\n\\n```\\nhermes cron list List jobs (--all for disabled)\\nhermes cron create SCHED Create: '30m', 'every 2h', '0 9 * * *'\\nhermes cron edit ID Edit schedule, prompt, delivery\\nhermes cron pause/resume ID Control job state\\nhermes cron run ID Trigger on next tick\\nhermes cron remove ID Delete a job\\nhermes cron status Scheduler status\\n```\\n\\n### Webhooks\\n\\n```\\nhermes webhook subscribe N Create route at /webhooks/\\nhermes webhook list List subscriptions\\nhermes webhook remove NAME Remove a subscription\\nhermes webhook test NAME Send a test POST\\n```\\n\\n### Profiles\\n\\n```\\nhermes profile list List all profiles\\nhermes profile create NAME Create (--clone, --clone-all, --clone-from)\\nhermes profile use NAME Set sticky default\\nhermes profile delete NAME Delete a profile\\nhermes profile show NAME Show details\\nhermes profile alias NAME Manage wrapper scripts\\nhermes profile rename A B Rename a profile\\nhermes profile export NAME Export to tar.gz\\nhermes profile import FILE Import from archive\\n```\\n\\n### Credential Pools\\n\\n```\\nhermes auth add Interactive credential wizard\\nhermes auth list [PROVIDER] List pooled credentials\\nhermes auth remove P INDEX Remove by provider + index\\nhermes auth reset PROVIDER Clear exhaustion status\\n```\\n\\n### Other\\n\\n```\\nhermes insights [--days N] Usage analytics\\nhermes update Update to latest version\\nhermes pairing list/approve/revoke DM authorization\\nhermes plugins list/install/remove Plugin management\\nhermes honcho setup/status Honcho memory integration (requires honcho plugin)\\nhermes memory setup/status/off Memory provider config\\nhermes completion bash|zsh Shell completions\\nhermes acp ACP server (IDE integration)\\nhermes claw migrate Migrate from OpenClaw\\nhermes uninstall Uninstall Hermes\\n```\\n\\n---\\n\\n## Slash Commands (In-Session)\\n\\nType these during an interactive chat session. New commands land fairly\\noften; if something below looks stale, run `/help` in-session for the\\nauthoritative list or see the [live slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands).\\nThe registry of record is `hermes_cli/commands.py` — every consumer\\n(autocomplete, Telegram menu, Slack mapping, `/help`) derives from it.\\n\\n### Session Control\\n```\\n/new (/reset) Fresh session\\n/clear Clear screen + new session (CLI)\\n/retry Resend last message\\n/undo Remove last exchange\\n/title [name] Name the session\\n/compress Manually compress context\\n/stop Kill background processes\\n/rollback [N] Restore filesystem checkpoint\\n/snapshot [sub] Create or restore state snapshots of Hermes config/state (CLI)\\n/background Run prompt in background\\n/queue Queue for next turn\\n/steer Inject a message after the next tool call without interrupting\\n/agents (/tasks) Show active agents and running tasks\\n/resume [name] Resume a named session\\n/goal [text|sub] Set a standing goal Hermes works on across turns until achieved\\n (subcommands: status, pause, resume, clear)\\n/redraw Force a full UI repaint (CLI)\\n```\\n\\n### Configuration\\n```\\n/config Show config (CLI)\\n/model [name] Show or change model\\n/personality [name] Set personality\\n/reasoning [level] Set reasoning (none|minimal|low|medium|high|xhigh|show|hide)\\n/verbose Cycle: off → new → all → verbose\\n/voice [on|off|tts] Voice mode\\n/yolo Toggle approval bypass\\n/busy [sub] Control what Enter does while Hermes is working (CLI)\\n (subcommands: queue, steer, interrupt, status)\\n/indicator [style] Pick the TUI busy-indicator style (CLI)\\n (styles: kaomoji, emoji, unicode, ascii)\\n/footer [on|off] Toggle gateway runtime-metadata footer on final replies\\n/skin [name] Change theme (CLI)\\n/statusbar Toggle status bar (CLI)\\n```\\n\\n### Tools & Skills\\n```\\n/tools Manage tools (CLI)\\n/toolsets List toolsets (CLI)\\n/skills Search/install skills (CLI)\\n/skill Load a skill into session\\n/reload-skills Re-scan ~/.hermes/skills/ for added/removed skills\\n/reload Reload .env variables into the running session (CLI)\\n/reload-mcp Reload MCP servers\\n/cron Manage cron jobs (CLI)\\n/curator [sub] Background skill maintenance (status, run, pin, archive, …)\\n/kanban [sub] Multi-profile collaboration board (tasks, links, comments)\\n/plugins List plugins (CLI)\\n```\\n\\n### Gateway\\n```\\n/approve Approve a pending command (gateway)\\n/deny Deny a pending command (gateway)\\n/restart Restart gateway (gateway)\\n/sethome Set current chat as home channel (gateway)\\n/update Update Hermes to latest (gateway)\\n/topic [sub] Enable or inspect Telegram DM topic sessions (gateway)\\n/platforms (/gateway) Show platform connection status (gateway)\\n```\\n\\n### Utility\\n```\\n/branch (/fork) Branch the current session\\n/fast Toggle priority/fast processing\\n/browser Open CDP browser connection\\n/history Show conversation history (CLI)\\n/save Save conversation to file (CLI)\\n/copy [N] Copy the last assistant response to clipboard (CLI)\\n/paste Attach clipboard image (CLI)\\n/image Attach local image file (CLI)\\n```\\n\\n### Info\\n```\\n/help Show commands\\n/commands [page] Browse all commands (gateway)\\n/usage Token usage\\n/insights [days] Usage analytics\\n/gquota Show Google Gemini Code Assist quota usage (CLI)\\n/status Session info (gateway)\\n/profile Active profile info\\n/debug Upload debug report (system info + logs) and get shareable links\\n```\\n\\n### Exit\\n```\\n/quit (/exit, /q) Exit CLI\\n```\\n\\n---\\n\\n## Key Paths & Config\\n\\n```\\n~/.hermes/config.yaml Main configuration\\n~/.hermes/.env API keys and secrets\\n$HERMES_HOME/skills/ Installed skills\\n~/.hermes/sessions/ Session transcripts\\n~/.hermes/logs/ Gateway and error logs\\n~/.hermes/auth.json OAuth tokens and credential pools\\n~/.hermes/hermes-agent/ Source code (if git-installed)\\n```\\n\\nProfiles use `~/.hermes/profiles//` with the same layout.\\n\\n### Config Sections\\n\\nEdit with `hermes config edit` or `hermes config set section.key value`.\\n\\n| Section | Key options |\\n|---------|-------------|\\n| `model` | `default`, `provider`, `base_url`, `api_key`, `context_length` |\\n| `agent` | `max_turns` (90), `tool_use_enforcement` |\\n| `terminal` | `backend` (local/docker/ssh/modal), `cwd`, `timeout` (180) |\\n| `compression` | `enabled`, `threshold` (0.50), `target_ratio` (0.20) |\\n| `display` | `skin`, `tool_progress`, `show_reasoning`, `show_cost` |\\n| `stt` | `enabled`, `provider` (local/groq/openai/mistral) |\\n| `tts` | `provider` (edge/elevenlabs/openai/minimax/mistral/neutts) |\\n| `memory` | `memory_enabled`, `user_profile_enabled`, `provider` |\\n| `security` | `tirith_enabled`, `website_blocklist` |\\n| `delegation` | `model`, `provider`, `base_url`, `api_key`, `max_iterations` (50), `reasoning_effort` |\\n| `checkpoints` | `enabled`, `max_snapshots` (50) |\\n\\nFull config reference: https://hermes-agent.nousresearch.com/docs/user-guide/configuration\\n\\n### Providers\\n\\n20+ providers supported. Set via `hermes model` or `hermes setup`.\\n\\n| Provider | Auth | Key env var |\\n|----------|------|-------------|\\n| OpenRouter | API key | `OPENROUTER_API_KEY` |\\n| Anthropic | API key | `ANTHROPIC_API_KEY` |\\n| Nous Portal | OAuth | `hermes auth` |\\n| OpenAI Codex | OAuth | `hermes auth` |\\n| GitHub Copilot | Token | `COPILOT_GITHUB_TOKEN` |\\n| Google Gemini | API key | `GOOGLE_API_KEY` or `GEMINI_API_KEY` |\\n| DeepSeek | API key | `DEEPSEEK_API_KEY` |\\n| xAI / Grok | API key | `XAI_API_KEY` |\\n| Hugging Face | Token | `HF_TOKEN` |\\n| Z.AI / GLM | API key | `GLM_API_KEY` |\\n| MiniMax | API key | `MINIMAX_API_KEY` |\\n| MiniMax CN | API key | `MINIMAX_CN_API_KEY` |\\n| Kimi / Moonshot | API key | `KIMI_API_KEY` |\\n| Alibaba / DashScope | API key | `DASHSCOPE_API_KEY` |\\n| Xiaomi MiMo | API key | `XIAOMI_API_KEY` |\\n| Kilo Code | API key | `KILOCODE_API_KEY` |\\n| AI Gateway (Vercel) | API key | `AI_GATEWAY_API_KEY` |\\n| OpenCode Zen | API key | `OPENCODE_ZEN_API_KEY` |\\n| OpenCode Go | API key | `OPENCODE_GO_API_KEY` |\\n| Qwen OAuth | OAuth | `hermes login --provider qwen-oauth` |\\n| Custom endpoint | Config | `model.base_url` + `model.api_key` in config.yaml |\\n| GitHub Copilot ACP | External | `COPILOT_CLI_PATH` or Copilot CLI |\\n\\nFull provider docs: https://hermes-agent.nousresearch.com/docs/integrations/providers\\n\\n### Toolsets\\n\\nEnable/disable via `hermes tools` (interactive) or `hermes tools enable/disable NAME`.\\n\\n| Toolset | What it provides |\\n|---------|-----------------|\\n| `web` | Web search and content extraction |\\n| `search` | Web search only (subset of `web`) |\\n| `browser` | Browser automation (Browserbase, Camofox, or local Chromium) |\\n| `terminal` | Shell commands and process management |\\n| `file` | File read/write/search/patch |\\n| `code_execution` | Sandboxed Python execution |\\n| `vision` | Image analysis |\\n| `image_gen` | AI image generation |\\n| `video` | Video analysis and generation |\\n\\n**Image generation via Codex OAuth:** If the user wants ChatGPT/Codex-backed image generation without a separate OpenAI API key, configure `image_gen.provider=openai-codex` and choose a `gpt-image-2-*` tier. See `references/openai-codex-image-generation.md` for the setup, verification probe, and AutoShortsBot keyframe pipeline notes.\\n| `tts` | Text-to-speech |\\n\\n#### Image generation via OpenAI/Codex OAuth\\n\\nWhen the user wants GPT Image 2 / ChatGPT Images generation inside Hermes without a separate OpenAI API key, configure the bundled `openai-codex` image backend. It uses the user's existing Codex/ChatGPT OAuth credentials and keeps image generation off the local GPU, which is useful when ComfyUI/Wan video workloads need the VRAM.\\n\\n```bash\\nhermes config set image_gen.provider openai-codex\\nhermes config set image_gen.model gpt-image-2-medium\\n```\\n\\nUse `gpt-image-2-low` for quick drafts, `gpt-image-2-medium` as the normal default, and `gpt-image-2-high` for final/high-fidelity keyframes or thumbnails. After changing this for the Telegram gateway, use `/restart` or a fresh `/reset` if the old config appears cached. See `references/openai-codex-image-generation.md` for verification commands and operational notes.\\n| `skills` | Skill browsing and management |\\n| `memory` | Persistent cross-session memory |\\n| `session_search` | Search past conversations |\\n| `delegation` | Subagent task delegation |\\n| `cronjob` | Scheduled task management |\\n| `clarify` | Ask user clarifying questions |\\n| `messaging` | Cross-platform message sending |\\n| `todo` | In-session task planning and tracking |\\n| `kanban` | Multi-agent work-queue tools (gated to workers) |\\n| `debugging` | Extra introspection/debug tools (off by default) |\\n| `safe` | Minimal, low-risk toolset for locked-down sessions |\\n| `spotify` | Spotify playback and playlist control |\\n| `homeassistant` | Smart home control (off by default) |\\n| `discord` | Discord integration tools |\\n| `discord_admin` | Discord admin/moderation tools |\\n| `feishu_doc` | Feishu (Lark) document tools |\\n| `feishu_drive` | Feishu (Lark) drive tools |\\n| `yuanbao` | Yuanbao integration tools |\\n| `rl` | Reinforcement learning tools (off by default) |\\n| `moa` | Mixture of Agents (off by default) |\\n\\nFull enumeration lives in `toolsets.py` as the `TOOLSETS` dict; `_HERMES_CORE_TOOLS` is the default bundle most platforms inherit from.\\n\\nTool changes take effect on `/reset` (new session). They do NOT apply mid-conversation to preserve prompt caching.\\n\\n---\\n\\n## Security & Privacy Toggles\\n\\nCommon \\\"why is Hermes doing X to my output / tool calls / commands?\\\" toggles — and the exact commands to change them. Most of these need a fresh session (`/reset` in chat, or start a new `hermes` invocation) because they're read once at startup.\\n\\n### Secret redaction in tool output\\n\\nSecret redaction is **off by default** — tool output (terminal stdout, `read_file`, web content, subagent summaries, etc.) passes through unmodified. If the user wants Hermes to auto-mask strings that look like API keys, tokens, and secrets before they enter the conversation context and logs:\\n\\n```bash\\nhermes config set security.redact_secrets true # enable globally\\n```\\n\\n**Restart required.** `security.redact_secrets` is snapshotted at import time — toggling it mid-session (e.g. via `export HERMES_REDACT_SECRETS=true` from a tool call) will NOT take effect for the running process. Tell the user to run `hermes config set security.redact_secrets true` in a terminal, then start a new session. This is deliberate — it prevents an LLM from flipping the toggle on itself mid-task.\\n\\nDisable again with:\\n```bash\\nhermes config set security.redact_secrets false\\n```\\n\\n### PII redaction in gateway messages\\n\\nSeparate from secret redaction. When enabled, the gateway hashes user IDs and strips phone numbers from the session context before it reaches the model:\\n\\n```bash\\nhermes config set privacy.redact_pii true # enable\\nhermes config set privacy.redact_pii false # disable (default)\\n```\\n\\n### Command approval prompts\\n\\nBy default (`approvals.mode: manual`), Hermes prompts the user before running shell commands flagged as destructive (`rm -rf`, `git reset --hard`, etc.). The modes are:\\n\\n- `manual` — always prompt (default)\\n- `smart` — use an auxiliary LLM to auto-approve low-risk commands, prompt on high-risk\\n- `off` — skip all approval prompts (equivalent to `--yolo`)\\n\\n```bash\\nhermes config set approvals.mode smart # recommended middle ground\\nhermes config set approvals.mode off # bypass everything (not recommended)\\n```\\n\\nPer-invocation bypass without changing config:\\n- `hermes --yolo …`\\n- `export HERMES_YOLO_MODE=1`\\n\\nNote: YOLO / `approvals.mode: off` does NOT turn off secret redaction. They are independent.\\n\\n### Shell hooks allowlist\\n\\nSome shell-hook integrations require explicit allowlisting before they fire. Managed via `~/.hermes/shell-hooks-allowlist.json` — prompted interactively the first time a hook wants to run.\\n\\n### Disabling the web/browser/image-gen tools\\n\\nTo keep the model away from network or media tools entirely, open `hermes tools` and toggle per-platform. Takes effect on next session (`/reset`). See the Tools & Skills section above.\\n\\n---\\n\\n### Voice & Transcription\\n\\n### STT (Voice → Text)\\n\\nVoice messages from messaging platforms are auto-transcribed.\\n\\n**⚠️ Pitfall: faster-whisper must be installed for local STT to work.** The config may show `provider: local` but if `faster-whisper` is not installed in the venv, STT silently fails. Always verify with `pip show faster-whisper` after configuring local STT.\\n\\nProvider priority (auto-detected):\\n1. **Local faster-whisper** — free, no API key: `pip install faster-whisper`\\n2. **Groq Whisper** — free tier: set `GROQ_API_KEY`\\n3. **OpenAI Whisper** — paid: set `VOICE_TOOLS_OPENAI_KEY`\\n4. **Mistral Voxtral** — set `MISTRAL_API_KEY`\\n\\nConfig:\\n```yaml\\nstt:\\n enabled: true\\n provider: local # local, groq, openai, mistral\\n local:\\n model: small # tiny, base, small, medium, large-v3 — use 'small' or higher for German\\n language: de # always set explicitly for German — auto-detect is unreliable\\n```\\n\\n### Pitfalls\\n- **faster-whisper must be installed** — config shows `provider: local` but STT silently fails if the package is missing\\n- **Use `small` model minimum for German** — `base` is too weak\\n- **Set `language: de` explicitly** — auto-detect is unreliable\\n- **Swiss German (dialect) is NOT supported** — Whisper transcribes dialect poorly; user should speak Standard German\\n\\n### TTS (Text → Voice)\\n\\n| Provider | Env var | Free? |\\n|----------|---------|-------|\\n| Edge TTS | None | Yes (default) |\\n| ElevenLabs | `ELEVENLABS_API_KEY` | Free tier |\\n| OpenAI | `VOICE_TOOLS_OPENAI_KEY` | Paid |\\n| MiniMax | `MINIMAX_API_KEY` | Paid |\\n| Mistral (Voxtral) | `MISTRAL_API_KEY` | Paid |\\n| NeuTTS (local) | None (`pip install neutts[all]` + `espeak-ng`) | Free |\\n\\nVoice commands: `/voice on` (voice-to-voice), `/voice tts` (always voice), `/voice off`.\\n\\n---\\n\\n## Spawning Additional Hermes Instances\\n\\nRun additional Hermes processes as fully independent subprocesses — separate sessions, tools, and environments.\\n\\n### When to Use This vs delegate_task\\n\\n| | `delegate_task` | Spawning `hermes` process |\\n|-|-----------------|--------------------------|\\n| Isolation | Separate conversation, shared process | Fully independent process |\\n| Duration | Minutes (bounded by parent loop) | Hours/days |\\n| Tool access | Subset of parent's tools | Full tool access |\\n| Interactive | No | Yes (PTY mode) |\\n| Use case | Quick parallel subtasks | Long autonomous missions |\\n\\n### One-Shot Mode\\n\\n```\\nterminal(command=\\\"hermes chat -q 'Research GRPO papers and write summary to ~/research/grpo.md'\\\", timeout=300)\\n\\n# Background for long tasks:\\nterminal(command=\\\"hermes chat -q 'Set up CI/CD for ~/myapp'\\\", background=true)\\n```\\n\\n### Interactive PTY Mode (via tmux)\\n\\nHermes uses prompt_toolkit, which requires a real terminal. Use tmux for interactive spawning:\\n\\n```\\n# Start\\nterminal(command=\\\"tmux new-session -d -s agent1 -x 120 -y 40 'hermes'\\\", timeout=10)\\n\\n# Wait for startup, then send a message\\nterminal(command=\\\"sleep 8 && tmux send-keys -t agent1 'Build a FastAPI auth service' Enter\\\", timeout=15)\\n\\n# Read output\\nterminal(command=\\\"sleep 20 && tmux capture-pane -t agent1 -p\\\", timeout=5)\\n\\n# Send follow-up\\nterminal(command=\\\"tmux send-keys -t agent1 'Add rate limiting middleware' Enter\\\", timeout=5)\\n\\n# Exit\\nterminal(command=\\\"tmux send-keys -t agent1 '/exit' Enter && sleep 2 && tmux kill-session -t agent1\\\", timeout=10)\\n```\\n\\n### Multi-Agent Coordination\\n\\n```\\n# Agent A: backend\\nterminal(command=\\\"tmux new-session -d -s backend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t backend 'Build REST API for user management' Enter\\\", timeout=15)\\n\\n# Agent B: frontend\\nterminal(command=\\\"tmux new-session -d -s frontend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t frontend 'Build React dashboard for user management' Enter\\\", timeout=15)\\n\\n# Check progress, relay context between them\\nterminal(command=\\\"tmux capture-pane -t backend -p | tail -30\\\", timeout=5)\\nterminal(command=\\\"tmux send-keys -t frontend 'Here is the API schema from the backend agent: ...' Enter\\\", timeout=5)\\n```\\n\\n### Session Resume\\n\\n```\\n# Resume most recent session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --continue'\\\", timeout=10)\\n\\n# Resume specific session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --resume 20260225_143052_a1b2c3'\\\", timeout=10)\\n```\\n\\n### Tips\\n\\n- **Prefer `delegate_task` for quick subtasks** — less overhead than spawning a full process\\n- **Use `-w` (worktree mode)** when spawning agents that edit code — prevents git conflicts\\n- **Set timeouts** for one-shot mode — complex tasks can take 5-10 minutes\\n- **Use `hermes chat -q` for fire-and-forget** — no PTY needed\\n- **Use tmux for interactive sessions** — raw PTY mode has `\\\\r` vs `\\\\n` issues with prompt_toolkit\\n- **For scheduled tasks**, use the `cronjob` tool instead of spawning — handles delivery and retry\\n\\n---\\n\\n## Durable & Background Systems\\n\\nFour systems run alongside the main conversation loop. Quick reference\\nhere; full developer notes live in `AGENTS.md`, user-facing docs under\\n`website/docs/user-guide/features/`.\\n\\n### Delegation (`delegate_task`)\\n\\nSynchronous subagent spawn — the parent waits for the child's summary\\nbefore continuing its own loop. Isolated context + terminal session.\\n\\n- **Single:** `delegate_task(goal, context, toolsets)`.\\n- **Batch:** `delegate_task(tasks=[{goal, ...}, ...])` runs children in\\n parallel, capped by `delegation.max_concurrent_children` (default 3).\\n- **Roles:** `leaf` (default; cannot re-delegate) vs `orchestrator`\\n (can spawn its own workers, bounded by `delegation.max_spawn_depth`).\\n- **Not durable.** If the parent is interrupted, the child is\\n cancelled. For work that must outlive the turn, use `cronjob` or\\n `terminal(background=True, notify_on_complete=True)`.\\n\\nConfig: `delegation.*` in `config.yaml`.\\n\\n### Cron (scheduled jobs)\\n\\nDurable scheduler — `cron/jobs.py` + `cron/scheduler.py`. Drive it via\\nthe `cronjob` tool, the `hermes cron` CLI (`list`, `add`, `edit`,\\n`pause`, `resume`, `run`, `remove`), or the `/cron` slash command.\\n\\n- **Schedules:** duration (`\\\"30m\\\"`, `\\\"2h\\\"`), \\\"every\\\" phrase\\n (`\\\"every monday 9am\\\"`), 5-field cron (`\\\"0 9 * * *\\\"`), or ISO timestamp.\\n- **Per-job knobs:** `skills`, `model`/`provider` override, `script`\\n (pre-run data collection; `no_agent=True` makes the script the whole\\n job), `context_from` (chain job A's output into job B), `workdir`\\n (run in a specific dir with its `AGENTS.md` / `CLAUDE.md` loaded),\\n multi-platform delivery.\\n- **Invariants:** 3-minute hard interrupt per run, `.tick.lock` file\\n prevents duplicate ticks across processes, cron sessions pass\\n `skip_memory=True` by default, and cron deliveries are framed with a\\n header/footer instead of being mirrored into the target gateway\\n session (keeps role alternation intact).\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/cron\\n\\n### Curator (skill lifecycle)\\n\\nBackground maintenance for agent-created skills. Tracks usage, marks\\nidle skills stale, archives stale ones, keeps a pre-run tar.gz backup\\nso nothing is lost.\\n\\n- **CLI:** `hermes curator ` — `status`, `run`, `pause`, `resume`,\\n `pin`, `unpin`, `archive`, `restore`, `prune`, `backup`, `rollback`.\\n- **Slash:** `/curator ` mirrors the CLI.\\n- **Scope:** only touches skills with `created_by: \\\"agent\\\"` provenance.\\n Bundled + hub-installed skills are off-limits. **Never deletes** —\\n max destructive action is archive. Pinned skills are exempt from\\n every auto-transition and every LLM review pass.\\n- **Telemetry:** sidecar at `~/.hermes/skills/.usage.json` holds\\n per-skill `use_count`, `view_count`, `patch_count`,\\n `last_activity_at`, `state`, `pinned`.\\n\\nConfig: `curator.*` (`enabled`, `interval_hours`, `min_idle_hours`,\\n`stale_after_days`, `archive_after_days`, `backup.*`).\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/curator\\n\\n### Kanban (multi-agent work queue)\\n\\nDurable SQLite board for multi-profile / multi-worker collaboration.\\nUsers drive it via `hermes kanban `; dispatcher-spawned workers\\nsee a focused `kanban_*` toolset gated by `HERMES_KANBAN_TASK` so the\\nschema footprint is zero outside worker processes.\\n\\n- **CLI verbs (common):** `init`, `create`, `list` (alias `ls`),\\n `show`, `assign`, `link`, `unlink`, `comment`, `complete`, `block`,\\n `unblock`, `archive`, `tail`. Less common: `watch`, `stats`, `runs`,\\n `log`, `dispatch`, `daemon`, `gc`.\\n- **Worker toolset:** `kanban_show`, `kanban_complete`, `kanban_block`,\\n `kanban_heartbeat`, `kanban_comment`, `kanban_create`, `kanban_link`.\\n- **Dispatcher** runs inside the gateway by default\\n (`kanban.dispatch_in_gateway: true`) — reclaims stale claims,\\n promotes ready tasks, atomically claims, spawns assigned profiles.\\n Auto-blocks a task after ~5 consecutive spawn failures.\\n- **Isolation:** board is the hard boundary (workers get\\n `HERMES_KANBAN_BOARD` pinned in env); tenant is a soft namespace\\n within a board for workspace-path + memory-key isolation.\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/kanban\\n\\n---\\n\\n## Troubleshooting\\n\\n### Voice not working\\n1. Check `stt.enabled: true` in config.yaml\\n2. Verify provider: `pip install faster-whisper` or set API key\\n3. In gateway: `/restart`. In CLI: exit and relaunch.\\n\\n### Tool not available\\n1. `hermes tools` — check if toolset is enabled for your platform\\n2. Some tools need env vars (check `.env`)\\n3. `/reset` after enabling tools\\n\\n### Image generation via ChatGPT/Codex OAuth\\n\\nWhen the user wants Hermes/Telegram image generation using ChatGPT/Codex rather than local ComfyUI or a separate OpenAI API key, prefer the bundled `openai-codex` image-gen backend if available. It routes GPT Image generation through the existing Codex/ChatGPT OAuth session and avoids consuming local GPU/ComfyUI VRAM.\\n\\nSetup/check pattern:\\n```bash\\n# Codex/ChatGPT OAuth must already be available; otherwise sign in first.\\nhermes login --provider openai-codex\\n\\n# Route Hermes' image_generate tool to the Codex-backed GPT Image backend.\\nhermes config set image_gen.provider openai-codex\\nhermes config set image_gen.model gpt-image-2-medium\\n\\n# Alternatives for cost/quality tradeoff:\\nhermes config set image_gen.model gpt-image-2-low # fast iteration\\nhermes config set image_gen.model gpt-image-2-high # final keyframes/assets\\n```\\n\\nVerify without printing secrets:\\n```bash\\npython - <<'PY'\\nfrom hermes_cli.config import load_config\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom agent.image_gen_registry import get_provider\\ncfg = load_config().get('image_gen', {})\\n_ensure_plugins_discovered(force=True)\\np = get_provider('openai-codex')\\nprint('image_gen.provider =', cfg.get('provider'))\\nprint('image_gen.model =', cfg.get('model'))\\nprint('openai-codex provider available =', bool(p and p.is_available()))\\nPY\\n```\\n\\nIf Telegram/gateway keeps old tool config, start a fresh session or use `/restart`/`/reset`. For content pipelines, use this as a cloud-side keyframe/thumbnail generator; add captions, numbers, UI labels, and audit-sensitive facts later in deterministic rendering rather than trusting baked image text.\\n\\nWhen Hermes provider calls hang for many minutes (for example non-streaming GPT calls aborting after 900s), apply the bounded policy in `references/provider-call-timeout-policy.md`: cap ordinary non-streaming calls around 300s, use at most 2 retries with short backoff, checkpoint requests before calls, prefer streaming for long calls, log provider/model/attempt/duration, then split the task or use fallback/partial failure after repeated timeouts.\\n\\n### Model/provider issues\\n1. `hermes doctor` — check config and dependencies\\n2. `hermes login` — re-authenticate OAuth providers\\n3. Check `.env` has the right API key\\n4. **Copilot 403**: `gh auth login` tokens do NOT work for Copilot API. You must use the Copilot-specific OAuth device code flow via `hermes model` → GitHub Copilot.\\n\\n### Changes not taking effect\\n- **Tools/skills:** `/reset` starts a new session with updated toolset\\n- **Config changes:** In gateway: `/restart`. In CLI: exit and relaunch.\\n- **Code changes:** Restart the CLI or gateway process\\n\\n### Image generation via ChatGPT/Codex OAuth\\n\\nHermes can route the `image_generate` tool through ChatGPT/Codex OAuth instead of requiring a separate `OPENAI_API_KEY`, when the bundled `openai-codex` image-gen backend is available and the user is already signed in with Codex/ChatGPT.\\n\\nSetup:\\n```bash\\nhermes config set image_gen.provider openai-codex\\nhermes config set image_gen.model gpt-image-2-medium\\n```\\n\\nUseful model tiers:\\n```bash\\nhermes config set image_gen.model gpt-image-2-low # fast iteration\\nhermes config set image_gen.model gpt-image-2-medium # balanced default\\nhermes config set image_gen.model gpt-image-2-high # final keyframes/assets\\n```\\n\\nVerification pattern:\\n```bash\\npython - <<'PY'\\nfrom hermes_cli.config import load_config\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom agent.image_gen_registry import get_provider\\ncfg = load_config().get('image_gen', {})\\n_ensure_plugins_discovered(force=True)\\np = get_provider('openai-codex')\\nprint('image_gen.provider =', cfg.get('provider'))\\nprint('image_gen.model =', cfg.get('model'))\\nprint('openai-codex provider available =', bool(p and p.is_available()))\\nPY\\n```\\n\\nIf it does not appear in a long-lived gateway session, restart or reset the session so config/tool availability is reloaded. The backend saves generated images under `$HERMES_HOME/cache/images/` and is useful for cloud-side keyframes without local GPU/VRAM contention.\\n\\n### Tool / iteration budget\\nWhen the user asks to increase the \\\"tool budget\\\", \\\"turn budget\\\", or max tool-calling iterations, set the config key, not a transient shell variable:\\n```bash\\nhermes config set agent.max_turns 250\\n```\\nThen verify by reading `~/.hermes/config.yaml` or using a safe config inspection path. Avoid piping `hermes config` into `python` or another interpreter; security policy may flag `command | python` as high-risk even when the intent is only parsing. For gateway runs, `agent.max_turns` is the authoritative setting and is bridged internally to `HERMES_MAX_ITERATIONS`; config changes apply to fresh agent runs, while long-lived gateway/config changes may still need `/restart` if behavior appears stale.\\n\\n### Skills not showing\\n1. `hermes skills list` — verify installed\\n2. `hermes skills config` — check platform enablement\\n3. Load explicitly: `/skill name` or `hermes -s name`\\n\\n### Gateway issues\\nCheck logs first:\\n```bash\\ngrep -i \\\"failed to send\\\\|error\\\" ~/.hermes/logs/gateway.log | tail -20\\n```\\n\\nCommon gateway problems:\\n- **Gateway dies on SSH logout**: Enable linger: `sudo loginctl enable-linger $USER`\\n- **Gateway dies on WSL2 close**: WSL2 requires `systemd=true` in `/etc/wsl.conf` for systemd services to work. Without it, gateway falls back to `nohup` (dies when session closes).\\n- **Gateway crash loop**: Reset the failed state: `systemctl --user reset-failed hermes-gateway`\\n\\n### Platform-specific issues\\n- **Discord bot silent**: Must enable **Message Content Intent** in Bot → Privileged Gateway Intents.\\n- **Slack bot only works in DMs**: Must subscribe to `message.channels` event. Without it, the bot ignores public channels.\\n- **Windows HTTP 400 \\\"No models provided\\\"**: Config file encoding issue (BOM). Ensure `config.yaml` is saved as UTF-8 without BOM.\\n\\n### Auxiliary models not working\\nIf `auxiliary` tasks (vision, compression, session_search) fail silently, the `auto` provider can't find a backend. Either set `OPENROUTER_API_KEY` or `GOOGLE_API_KEY`, or explicitly configure each auxiliary task's provider:\\n```bash\\nhermes config set auxiliary.vision.provider \\nhermes config set auxiliary.vision.model \\n```\\n\\n---\\n\\n## Where to Find Things\\n\\n| Looking for... | Location |\\n|----------------|----------|\\n| Config options | `hermes config edit` or [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) |\\n| Available tools | `hermes tools list` or [Tools reference](https://hermes-agent.nousresearch.com/docs/reference/tools-reference) |\\n| Slash commands | `/help` in session or [Slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands) |\\n| Skills catalog | `hermes skills browse` or [Skills catalog](https://hermes-agent.nousresearch.com/docs/reference/skills-catalog) |\\n| Provider setup | `hermes model` or [Providers guide](https://hermes-agent.nousresearch.com/docs/integrations/providers) |\\n| Platform setup | `hermes gateway setup` or [Messaging docs](https://hermes-agent.nousresearch.com/docs/user-guide/messaging/) |\\n| MCP servers | `hermes mcp list` or [MCP guide](https://hermes-agent.nousresearch.com/docs/user-guide/features/mcp) |\\n| Profiles | `hermes profile list` or [Profiles docs](https://hermes-agent.nousresearch.com/docs/user-guide/profiles) |\\n| Cron jobs | `hermes cron list` or [Cron docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/cron) |\\n| Memory | `hermes memory status` or [Memory docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory) |\\n| Env variables | `hermes config env-path` or [Env vars reference](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) |\\n| CLI commands | `hermes --help` or [CLI reference](https://hermes-agent.nousresearch.com/docs/reference/cli-commands) |\\n| Gateway logs | `~/.hermes/logs/gateway.log` |\\n| Session files | `~/.hermes/sessions/` or `hermes sessions browse` |\\n| Source code | `~/.hermes/hermes-agent/` |\\n\\n---\\n\\n## Contributor Quick Reference\\n\\nFor occasional contributors and PR authors. Full developer docs: https://hermes-agent.nousresearch.com/docs/developer-guide/\\n\\n### Project Layout\\n\\n```\\nhermes-agent/\\n├── run_agent.py # AIAgent — core conversation loop\\n├── model_tools.py # Tool discovery and dispatch\\n├── toolsets.py # Toolset definitions\\n├── cli.py # Interactive CLI (HermesCLI)\\n├── hermes_state.py # SQLite session store\\n├── agent/ # Prompt builder, context compression, memory, model routing, credential pooling, skill dispatch\\n├── hermes_cli/ # CLI subcommands, config, setup, commands\\n│ ├── commands.py # Slash command registry (CommandDef)\\n│ ├── config.py # DEFAULT_CONFIG, env var definitions\\n│ └── main.py # CLI entry point and argparse\\n├── tools/ # One file per tool\\n│ └── registry.py # Central tool registry\\n├── gateway/ # Messaging gateway\\n│ └── platforms/ # Platform adapters (telegram, discord, etc.)\\n├── cron/ # Job scheduler\\n├── tests/ # ~3000 pytest tests\\n└── website/ # Docusaurus docs site\\n```\\n\\nConfig: `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys).\\n\\n### Adding a Tool (3 files)\\n\\n**1. Create `tools/your_tool.py`:**\\n```python\\nimport json, os\\nfrom tools.registry import registry\\n\\ndef check_requirements() -> bool:\\n return bool(os.getenv(\\\"EXAMPLE_API_KEY\\\"))\\n\\ndef example_tool(param: str, task_id: str = None) -> str:\\n return json.dumps({\\\"success\\\": True, \\\"data\\\": \\\"...\\\"})\\n\\nregistry.register(\\n name=\\\"example_tool\\\",\\n toolset=\\\"example\\\",\\n schema={\\\"name\\\": \\\"example_tool\\\", \\\"description\\\": \\\"...\\\", \\\"parameters\\\": {...}},\\n handler=lambda args, **kw: example_tool(\\n param=args.get(\\\"param\\\", \\\"\\\"), task_id=kw.get(\\\"task_id\\\")),\\n check_fn=check_requirements,\\n requires_env=[\\\"EXAMPLE_API_KEY\\\"],\\n)\\n```\\n\\n**2. Add to `toolsets.py`** → `_HERMES_CORE_TOOLS` list.\\n\\nAuto-discovery: any `tools/*.py` file with a top-level `registry.register()` call is imported automatically — no manual list needed.\\n\\nAll handlers must return JSON strings. Use `get_hermes_home()` for paths, never hardcode `~/.hermes`.\\n\\n### Adding a Slash Command\\n\\n1. Add `CommandDef` to `COMMAND_REGISTRY` in `hermes_cli/commands.py`\\n2. Add handler in `cli.py` → `process_command()`\\n3. (Optional) Add gateway handler in `gateway/run.py`\\n\\nAll consumers (help text, autocomplete, Telegram menu, Slack mapping) derive from the central registry automatically.\\n\\n### Agent Loop (High Level)\\n\\n```\\nrun_conversation():\\n 1. Build system prompt\\n 2. Loop while iterations < max:\\n a. Call LLM (OpenAI-format messages + tool schemas)\\n b. If tool_calls → dispatch each via handle_function_call() → append results → continue\\n c. If text response → return\\n 3. Context compression triggers automatically near token limit\\n```\\n\\n### Testing\\n\\n```bash\\npython -m pytest tests/ -o 'addopts=' -q # Full suite\\npython -m pytest tests/tools/ -q # Specific area\\n```\\n\\n- Tests auto-redirect `HERMES_HOME` to temp dirs — never touch real `~/.hermes/`\\n- Run full suite before pushing any change\\n- Use `-o 'addopts='` to clear any baked-in pytest flags\\n\\n### Commit Conventions\\n\\n```\\ntype: concise subject line\\n\\nOptional body.\\n```\\n\\nTypes: `fix:`, `feat:`, `refactor:`, `docs:`, `chore:`\\n\\n### Key Rules\\n\\n- **Never break prompt caching** — don't change context, tools, or system prompt mid-conversation\\n- **Message role alternation** — never two assistant or two user messages in a row\\n- Use `get_hermes_home()` from `hermes_constants` for all paths (profile-safe)\\n- Config values go in `config.yaml`, secrets go in `.env`\\n- New tools need a `check_fn` so they only appear when requirements are met\\n\", \"path\": \"autonomous-ai-agents/hermes-agent/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/autonomous-ai-agents/hermes-agent\", \"linked_files\": {\"references\": [\"references/telegram-gateway-setup.md\", \"references/openai-codex-image-generation.md\", \"references/provider-call-timeout-policy.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\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"homepage\": \"https://github.com/NousResearch/hermes-agent\", \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"]}}}",
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"content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### 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. 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\\\"https://chatgpt.com/backend-api/codex\\\",\\n \\\"default\\\": \\\"gpt-5.5\\\",\\n \\\"provider\\\": \\\"openai-codex\\\"\\n}\\nproviders {\\n \\\"openai-codex\\\": {\\n \\\"request_timeout_seconds\\\": 1800,\\n \\\"stale_timeout_seconds\\\": 900,\\n \\\"models\\\": {\\n \\\"gpt-5.5\\\": {\\n \\\"timeout_seconds\\\": 1800,\\n \\\"stale_timeout_seconds\\\": 900\\n }\\n }\\n }\\n}\\n\", \"tool_calls_made\": 3, \"duration_seconds\": 19.07}",
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"reasoning": "**Exploring image generation tools**\n\nI need to set up a working directory and check the files related to the image generation registry and logs. I might want to use the image_generate tool for testing capabilities, but I noticed the backend is currently configured for gpt-image-2-medium. I’m considering whether I can change the config to high. It seems that testing capabilities through the tool might use the current config, but I can't adjust the model with each call. Maybe I should look into the provider code directly to see what's possible.",
"reasoning_content": "**Exploring image generation tools**\n\nI need to set up a working directory and check the files related to the image generation registry and logs. I might want to use the image_generate tool for testing capabilities, but I noticed the backend is currently configured for gpt-image-2-medium. I’m considering whether I can change the config to high. It seems that testing capabilities through the tool might use the current config, but I can't adjust the model with each call. Maybe I should look into the provider code directly to see what's possible.",
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{
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"content": "{\"total_count\": 0}\n\n[Subdirectory context discovered: .hermes/hermes-agent/AGENTS.md]\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\n# Prefer .venv; fall back to venv if that's what your checkout has.\nsource .venv/bin/activate # or: source venv/bin/activate\n```\n\n`scripts/run_tests.sh` probes `.venv` first, then `venv`, then\n`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the\nmain checkout).\n\n## Project Structure\n\nFile counts shift constantly — don't treat the tree below as exhaustive.\nThe canonical source is the filesystem. The notes call out the load-bearing\nentry points you'll actually edit.\n\n```\nhermes-agent/\n├── run_agent.py # AIAgent class — core conversation loop (~12k LOC)\n├── model_tools.py # Tool orchestration, discover_builtin_tools(), handle_function_call()\n├── toolsets.py # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py # HermesCLI class — interactive CLI orchestrator (~11k LOC)\n├── hermes_state.py # SessionDB — SQLite session store (FTS5 search)\n├── hermes_constants.py # get_hermes_home(), display_hermes_home() — profile-aware paths\n├── hermes_logging.py # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)\n├── batch_runner.py # Parallel batch processing\n├── agent/ # Agent internals (provider adapters, memory, caching, compression, etc.)\n├── hermes_cli/ # CLI subcommands, setup wizard, plugins loader, skin engine\n├── tools/ # Tool implementations — auto-discovered via tools/registry.py\n│ └── environments/ # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/ # Messaging gateway — run.py + session.py + platforms/\n│ ├── platforms/ # Adapter per platform (telegram, discord, slack, whatsapp,\n│ │ # homeassistant, signal, matrix, mattermost, email, sms,\n│ │ # dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,\n│ │ # yuanbao, webhook, api_server, ...). See ADDING_A_PLATFORM.md.\n│ └── builtin_hooks/ # Extension point for always-registered gateway hooks (none shipped)\n├── plugins/ # Plugin system (see \"Plugins\" section below)\n│ ├── memory/ # Memory-provider plugins (honcho, mem0, supermemory, ...)\n│ ├── context_engine/ # Context-engine plugins\n│ ├── model-providers/ # Inference backend plugins (openrouter, anthropic, gmi, ...)\n│ ├── kanban/ # Multi-agent board dispatcher + worker plugin\n│ ├── hermes-achievements/ # Gamified achievement tracking\n│ ├── observability/ # Metrics / traces / logs plugin\n│ ├── image_gen/ # Image-generation providers\n│ └── / # disk-cleanup, example-dashboard, google_meet, platforms,\n│ # spotify, strike-freedom-cockpit, ...\n├── optional-skills/ # Heavier/niche skills shipped but NOT active by default\n├── skills/ # Built-in skills bundled with the repo\n├── ui-tui/ # Ink (React) terminal UI — `hermes --tui`\n│ └── src/ # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib\n├── tui_gateway/ # Python JSON-RPC backend for the TUI\n├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/ # Scheduler — jobs.py, scheduler.py\n├── scripts/ # run_tests.sh, release.py, auxiliary scripts\n├── website/ # Docusaurus docs site\n└── tests/ # Pytest suite (~17k tests across ~900 files as of May 2026)\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).\n**Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),\n`gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.\nBrowse with `hermes logs [--follow] [--level ...] [--session ...]`.\n\n## File Dependency Chain\n\n```\ntools/registry.py (no deps — imported by all tool files)\n ↑\ntools/*.py (each calls registry.register() at import time)\n ↑\nmodel_tools.py (imports tools/registry + triggers tool discovery)\n ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\nThe real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,\nsession context, budget, credential pool, etc.). The signature below is the\nminimum subset you'll usually touch — read `run_agent.py` for the full list.\n\n```python\nclass AIAgent:\n def __init__(self,\n base_url: str = None,\n api_key: str = None,\n provider: str = None,\n api_mode: str = None, # \"chat_completions\" | \"codex_responses\" | ...\n model: str = \"\", # empty → resolved from config/provider later\n max_iterations: int = 90, # tool-calling iterations (shared with subagents)\n enabled_toolsets: list = None,\n disabled_toolsets: list = None,\n quiet_mode: bool = False,\n save_trajectories: bool = False,\n platform: str = None, # \"cli\", \"telegram\", etc.\n session_id: str = None,\n skip_context_files: bool = False,\n skip_memory: bool = False,\n credential_pool=None,\n # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,\n # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.\n ): ...\n\n def chat(self, message: str) -> str:\n \"\"\"Simple interface — returns final response string.\"\"\"\n\n def run_conversation(self, user_message: str, system_message: str = None,\n conversation_history: list = None, task_id: str = None) -> dict:\n \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous, with\ninterrupt checks, budget tracking, and a one-turn grace call:\n\n```python\nwhile (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \\\n or self._budget_grace_call:\n if self._interrupt_requested: break\n response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n if response.tool_calls:\n for tool_call in response.tool_calls:\n result = handle_function_call(tool_call.name, tool_call.args, task_id)\n messages.append(tool_result_message(result))\n api_call_count += 1\n else:\n return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`.\nReasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch\n- **Gateway help** — `gateway_help_lines()` generat\n\n[...truncated AGENTS.md: 50,982 chars total]",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Static active-word caption segment logic.\\n 2|\\n 3|Caption rule for TrueTraceShorts previews/finals:\\n 4|- A subtitle phrase segment stays fixed on screen.\\n 5|- At least three words are visible per default segment whenever possible.\\n 6|- Only the active spoken word changes highlight.\\n 7|- The highlight moves left-to-right until the next phrase segment replaces it.\\n 8|\\\"\\\"\\\"\\n 9|\\n 10|from __future__ import annotations\\n 11|\\n 12|from dataclasses import dataclass\\n 13|from typing import Iterable, Sequence\\n 14|\\n 15|\\n 16|@dataclass(frozen=True)\\n 17|class WordCue:\\n 18| start: float\\n 19| end: float\\n 20| word: str\\n 21| sentence: str = \\\"\\\"\\n 22|\\n 23|\\n 24|@dataclass(frozen=True)\\n 25|class StaticCaptionSegment:\\n 26| start: float\\n 27| end: float\\n 28| word_indices: tuple[int, ...]\\n 29| words: tuple[str, ...]\\n 30| sentence: str = \\\"\\\"\\n 31|\\n 32| @property\\n 33| def text(self) -> str:\\n 34| return \\\" \\\".join(self.words)\\n 35|\\n 36|\\n 37|def build_static_caption_segments(\\n 38| word_cues: Sequence[WordCue],\\n 39| *,\\n 40| min_words: int = 3,\\n 41| target_words: int = 6,\\n 42| max_words: int = 7,\\n 43|) -> tuple[StaticCaptionSegment, ...]:\\n 44| if min_words < 2:\\n 45| raise ValueError(\\\"min_words must be >= 2\\\")\\n 46| if target_words < min_words:\\n 47| raise ValueError(\\\"target_words must be >= min_words\\\")\\n 48| if max_words < target_words:\\n 49| raise ValueError(\\\"max_words must be >= target_words\\\")\\n 50|\\n 51| grouped: list[list[tuple[int, WordCue]]] = []\\n 52| current_sentence: str | None = None\\n 53| current: list[tuple[int, WordCue]] = []\\n 54| for index, cue in enumerate(word_cues):\\n 55| sentence_key = cue.sentence or \\\"__no_sentence__\\\"\\n 56| if current and sentence_key != current_sentence:\\n 57| grouped.append(current)\\n 58| current = []\\n 59| current_sentence = sentence_key\\n 60| current.append((index, cue))\\n 61| if current:\\n 62| grouped.append(current)\\n 63|\\n 64| segments: list[StaticCaptionSegment] = []\\n 65| for group in grouped:\\n 66| chunks: list[list[tuple[int, WordCue]]] = []\\n 67| i = 0\\n 68| while i < len(group):\\n 69| remaining = len(group) - i\\n 70| size = min(target_words, remaining)\\n 71| if remaining < min_words and chunks:\\n 72| chunks[-1].extend(group[i:])\\n 73| break\\n 74| if size < min_words and len(group) >= min_words:\\n 75| start = max(0, len(group) - min_words)\\n 76| chunks.append(group[start:])\\n 77| break\\n 78| chunks.append(group[i : i + size])\\n 79| i += size\\n 80| for chunk in chunks:\\n 81| if not chunk:\\n 82| continue\\n 83| if len(chunk) > max_words:\\n 84| # Split oversized merged chunks conservatively while preserving min_words.\\n 85| while len(chunk) > max_words:\\n 86| head = chunk[:max_words]\\n 87| segments.append(_segment_from_chunk(head))\\n 88| chunk = chunk[max_words:]\\n 89| if 0 < len(chunk) < min_words and segments:\\n 90| previous = segments.pop()\\n 91| prev_pairs = [(idx, word_cues[idx]) for idx in previous.word_indices]\\n 92| chunk = prev_pairs + chunk\\n 93| segments.append(_segment_from_chunk(chunk))\\n 94| return tuple(segments)\\n 95|\\n 96|\\n 97|def _segment_from_chunk(chunk: Sequence[tuple[int, WordCue]]) -> StaticCaptionSegment:\\n 98| return StaticCaptionSegment(\\n 99| start=chunk[0][1].start,\\n 100| end=chunk[-1][1].end,\\n 101| word_indices=tuple(index for index, _ in chunk),\\n 102| words=tuple(cue.word for _, cue in chunk),\\n 103| sentence=chunk[0][1].sentence,\\n 104| )\\n 105|\\n 106|\\n 107|def active_word_index_at(word_cues: Sequence[WordCue], timestamp: float) -> int:\\n 108| for index, cue in enumerate(word_cues):\\n 109| if cue.start <= timestamp < cue.end:\\n 110| return index\\n 111| previous = [index for index, cue in enumerate(word_cues) if cue.start <= timestamp]\\n 112| return previous[-1] if previous else 0\\n 113|\\n 114|\\n 115|def segment_for_active_word(segments: Sequence[StaticCaptionSegment], active_word_index: int) -> StaticCaptionSegment:\\n 116| for segment in segments:\\n 117| if active_word_index in segment.word_indices:\\n 118| return segment\\n 119| raise ValueError(f\\\"active word index {active_word_index} is not present in any segment\\\")\\n 120|\\n 121|\\n 122|def caption_state_at(word_cues: Sequence[WordCue], segments: Sequence[StaticCaptionSegment], timestamp: float) -> dict[str, object]:\\n 123| active_index = active_word_index_at(word_cues, timestamp)\\n 124| segment = segment_for_active_word(segments, active_index)\\n 125| return {\\n 126| \\\"segment_text\\\": segment.text,\\n 127| \\\"segment_words\\\": segment.words,\\n 128| \\\"word_indices\\\": segment.word_indices,\\n 129| \\\"active_word_index\\\": active_index,\\n 130| \\\"active_word\\\": word_cues[active_index].word,\\n 131| }\\n 132|\", \"total_lines\": 131, \"file_size\": 4603, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"limit\":240,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_static_caption_segments.py\"}"
}
}
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"role": "tool",
"content": "{\"content\": \" 1|from autoshorts.rendering.static_captions import (\\n 2| WordCue,\\n 3| active_word_index_at,\\n 4| build_static_caption_segments,\\n 5| caption_state_at,\\n 6| segment_for_active_word,\\n 7|)\\n 8|\\n 9|\\n 10|def cues():\\n 11| words = \\\"Your AI summary is useless because it has no decision target\\\".split()\\n 12| return tuple(\\n 13| WordCue(start=i * 0.4, end=(i + 1) * 0.4, word=word, sentence=\\\"Your AI summary is useless because it has no decision target\\\")\\n 14| for i, word in enumerate(words)\\n 15| )\\n 16|\\n 17|\\n 18|def test_static_caption_segment_contains_multiple_words():\\n 19| segments = build_static_caption_segments(cues())\\n 20|\\n 21| assert segments\\n 22| assert all(len(segment.words) >= 3 for segment in segments)\\n 23| assert segments[0].text == \\\"Your AI summary is useless because\\\"\\n 24|\\n 25|\\n 26|def test_active_word_changes_within_same_static_segment():\\n 27| word_cues = cues()\\n 28| segments = build_static_caption_segments(word_cues)\\n 29|\\n 30| early = caption_state_at(word_cues, segments, 0.1)\\n 31| later = caption_state_at(word_cues, segments, 1.1)\\n 32|\\n 33| assert early[\\\"segment_text\\\"] == later[\\\"segment_text\\\"]\\n 34| assert early[\\\"active_word\\\"] == \\\"Your\\\"\\n 35| assert later[\\\"active_word\\\"] == \\\"summary\\\"\\n 36|\\n 37|\\n 38|def test_segment_text_remains_stable_while_highlight_moves_left_to_right():\\n 39| word_cues = cues()\\n 40| segments = build_static_caption_segments(word_cues)\\n 41| state_a = caption_state_at(word_cues, segments, 0.5)\\n 42| state_b = caption_state_at(word_cues, segments, 1.7)\\n 43|\\n 44| assert state_a[\\\"segment_words\\\"] == state_b[\\\"segment_words\\\"]\\n 45| assert state_a[\\\"active_word_index\\\"] < state_b[\\\"active_word_index\\\"]\\n 46|\\n 47|\\n 48|def test_no_one_word_only_caption_mode_as_default_when_enough_words_exist():\\n 49| segments = build_static_caption_segments(cues())\\n 50|\\n 51| assert min(len(segment.words) for segment in segments) >= 3\\n 52|\", \"total_lines\": 51, \"file_size\": 1741, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "",
"tool_calls": [
{
"id": "call_Va7VRePbvMdoi9NH8bDyIHlE",
"call_id": "call_Va7VRePbvMdoi9NH8bDyIHlE",
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"type": "function",
"function": {
"name": "read_file",
"arguments": "{\"path\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/render_metadata_v1.json\",\"limit\":200}"
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"reasoning": "**Looking for render script**\n\nI need to find the render script in the /tmp directory, which probably requires some manual work. I’m considering searching for the preview_v1 code. It could be possible that the render_metadata has the script path I'm looking for. I’ll need to check that out to see if it leads me to the right spot. Let's see if I can make this process smoother!",
"reasoning_content": "**Looking for render script**\n\nI need to find the render script in the /tmp directory, which probably requires some manual work. I’m considering searching for the preview_v1 code. It could be possible that the render_metadata has the script path I'm looking for. I’ll need to check that out to see if it leads me to the right spot. Let's see if I can make this process smoother!",
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"summary": [
{
"type": "summary_text",
"text": "**Looking for render script**\n\nI need to find the render script in the /tmp directory, which probably requires some manual work. I’m considering searching for the preview_v1 code. It could be possible that the render_metadata has the script path I'm looking for. I’ll need to check that out to see if it leads me to the right spot. Let's see if I can make this process smoother!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"content\": \" 1|{\\n 2| \\\"candidate_id\\\": \\\"digital-red-flags-fake-invoice-payment-route-30s\\\",\\n 3| \\\"version\\\": \\\"preview_v1\\\",\\n 4| \\\"new_ai_images\\\": false,\\n 5| \\\"wan_used\\\": false,\\n 6| \\\"i2v_used\\\": false,\\n 7| \\\"platform_api\\\": false,\\n 8| \\\"artifacts\\\": {\\n 9| \\\"preview_mp4\\\": {\\n 10| \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1.mp4\\\",\\n 11| \\\"file_exists\\\": true,\\n 12| \\\"file_size\\\": 1270875,\\n 13| \\\"sha256\\\": \\\"bc8256104d1db71de6277b64db351ac3e1282d33396015ef09439aff033c47ba\\\",\\n 14| \\\"media_type\\\": \\\"video/mp4\\\",\\n 15| \\\"telegram_delivery_attempted\\\": true,\\n 16| \\\"telegram_delivery_verified\\\": true,\\n 17| \\\"telegram_message_id\\\": \\\"5951\\\",\\n 18| \\\"duration\\\": 31.291667,\\n 19| \\\"resolution\\\": \\\"1080x1920\\\",\\n 20| \\\"fps\\\": \\\"24/1\\\",\\n 21| \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n 22| },\\n 23| \\\"silent_mp4\\\": {\\n 24| \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags-fake-invoice-payment-route-30s_preview_v1_silent.mp4\\\",\\n 25| \\\"file_exists\\\": true,\\n 26| \\\"file_size\\\": 930007,\\n 27| \\\"sha256\\\": \\\"eec0f95eccc788fb9a8b9811d0b5175304fd135fe3d7d145fa49488714a5b85e\\\",\\n 28| \\\"media_type\\\": \\\"video/mp4\\\",\\n 29| \\\"telegram_delivery_attempted\\\": true,\\n 30| \\\"telegram_delivery_verified\\\": true,\\n 31| \\\"telegram_message_id\\\": \\\"5951\\\",\\n 32| \\\"duration\\\": 31.291667,\\n 33| \\\"resolution\\\": \\\"1080x1920\\\",\\n 34| \\\"fps\\\": \\\"24/1\\\",\\n 35| \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n 36| },\\n 37| \\\"contact_sheet\\\": {\\n 38| \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v1/digital-red-flags_contact_sheet_v1.jpg\\\",\\n 39| \\\"file_exists\\\": true,\\n 40| \\\"file_size\\\": 79590,\\n 41| \\\"sha256\\\": \\\"bf2d7131fb10d2065221d41f3a2abdd0b6e61bdb5832fa7e9b5dc7fd0a9f888e\\\",\\n 42| \\\"media_type\\\": \\\"image/jpeg\\\",\\n 43| \\\"telegram_delivery_attempted\\\": true,\\n 44| \\\"telegram_delivery_verified\\\": true,\\n 45| \\\"telegram_message_id\\\": \\\"5951\\\",\\n 46| \\\"duration\\\": \\\"n/a\\\",\\n 47| \\\"resolution\\\": \\\"1080x720\\\",\\n 48| \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n 49| },\\n 50| \\\"voiceover\\\": {\\n 51| \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/audio/voiceover.mp3\\\",\\n 52| \\\"file_exists\\\": true,\\n 53| \\\"file_size\\\": 187920,\\n 54| \\\"sha256\\\": \\\"4e9da013d7c4baf1ddddbba9d0900471a0ffea243325b52bf02cf0f56f643bf5\\\",\\n 55| \\\"media_type\\\": \\\"audio/mpeg\\\",\\n 56| \\\"telegram_delivery_attempted\\\": true,\\n 57| \\\"telegram_delivery_verified\\\": true,\\n 58| \\\"telegram_message_id\\\": \\\"5951\\\",\\n 59| \\\"duration\\\": 31.32,\\n 60| \\\"resolution\\\": \\\"n/a\\\",\\n 61| \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n 62| }\\n 63| },\\n 64| \\\"caption_style\\\": \\\"static phrase segments; white words; yellow active word; no box/bar; subtle outline\\\",\\n 65| \\\"thread_delivery\\\": {\\n 66| \\\"telegram_delivery_attempted\\\": true,\\n 67| \\\"telegram_delivery_verified\\\": true,\\n 68| \\\"telegram_message_id\\\": \\\"5951\\\",\\n 69| \\\"chat_id\\\": \\\"-1003907117629\\\",\\n 70| \\\"target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n 71| }\\n 72|}\", \"total_lines\": 71, \"file_size\": 3059, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_Va7VRePbvMdoi9NH8bDyIHlE"
},
{
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"tool_calls": [
{
"id": "call_ZeXENOs1emqaxhfi22UaLwYL",
"call_id": "call_ZeXENOs1emqaxhfi22UaLwYL",
"response_item_id": "fc_0fbc2865f3cea890016a14847990e881918f4362dbdd39e5d9",
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"arguments": "{\"code\":\"from hermes_tools import terminal, search_files\\nprint('Hermes image provider files')\\nprint(terminal('python - <<\\\\'PY\\\\'\\\\nfrom pathlib import Path\\\\nfor root in [Path.home()/\\\\'.hermes/hermes-agent/plugins/image_gen\\\\', Path.home()/\\\\'.hermes/hermes-agent/agent\\\\', Path.home()/\\\\'.hermes/hermes-agent/tools\\\\']:\\\\n print(\\\\'ROOT\\\\', root)\\\\n if root.exists():\\\\n for p in root.rglob(\\\\'*.py\\\\'):\\\\n txt=p.read_text(errors=\\\\'ignore\\\\')\\\\n if \\\\'gpt-image\\\\' in txt or \\\\'openai-codex\\\\' in txt or \\\\'image_gen\\\\' in txt:\\\\n print(p)\\\\nPY', timeout=30)['output'])\\nprint('Logs relevant')\\nprint(terminal('python - <<\\\\'PY\\\\'\\\\nfrom pathlib import Path\\\\nfor p in (Path.home()/\\\\'.hermes/logs\\\\').glob(\\\\'*.log\\\\'):\\\\n print(\\\\'---\\\\', p)\\\\n try:\\\\n lines=[l for l in p.read_text(errors=\\\\'ignore\\\\').splitlines() if any(s in l.lower() for s in [\\\\'image\\\\',\\\\'gpt-image\\\\',\\\\'openai-codex\\\\',\\\\'styleframe\\\\'])]\\\\n for l in lines[-30:]: print(l[:1000])\\\\n except Exception as e: print(e)\\\\nPY', timeout=30)['output'][:12000])\\nprint('Image cache recent')\\nprint(terminal('python - <<\\\\'PY\\\\'\\\\nfrom pathlib import Path\\\\nfor root in [Path.home()/\\\\'.hermes/cache/images\\\\', Path.home()/\\\\'.hermes/cache\\\\', Path(\\\\'/tmp/autoshorts_proof_short_sprint30\\\\'), Path.home()/\\\\'jarvis_runtime/autoshortsbot/AutoShortsBot/data\\\\']:\\\\n print(\\\\'ROOT\\\\', root)\\\\n if root.exists():\\\\n files=[p for p in root.rglob(\\\\'*\\\\') if p.suffix.lower() in [\\\\'.png\\\\',\\\\'.jpg\\\\',\\\\'.jpeg\\\\',\\\\'.webp\\\\',\\\\'.json\\\\']]\\\\n files=sorted(files, key=lambda p:p.stat().st_mtime, reverse=True)[:30]\\\\n for p in files:\\\\n print(p, p.stat().st_size)\\\\nPY', timeout=30)['output'][:12000])\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning image rendering**\n\nI’m thinking I might need to generate something with a script, but I don’t want to keep the old one. I'll create a new rendering script using PIL. First, though, I need to set up TDD tests and conduct an image audit. I should search the cached images to check if any are missing. I wonder if the search didn’t find them because they are hidden or ignored. I'll use the terminal with Python to list files.",
"reasoning_content": "**Planning image rendering**\n\nI’m thinking I might need to generate something with a script, but I don’t want to keep the old one. I'll create a new rendering script using PIL. First, though, I need to set up TDD tests and conduct an image audit. I should search the cached images to check if any are missing. I wonder if the search didn’t find them because they are hidden or ignored. I'll use the terminal with Python to list files.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_0fbc2865f3cea890016a1484759ab88191a013ec7ab3417a96",
"summary": [
{
"type": "summary_text",
"text": "**Planning image rendering**\n\nI’m thinking I might need to generate something with a script, but I don’t want to keep the old one. I'll create a new rendering script using PIL. First, though, I need to set up TDD tests and conduct an image audit. I should search the cached images to check if any are missing. I wonder if the search didn’t find them because they are hidden or ignored. I'll use the terminal with Python to list files."
}
]
}
]
},
{
"role": "tool",
"content": "{\"status\": \"success\", \"output\": \"Hermes image provider files\\nROOT /home/agent/.hermes/hermes-agent/plugins/image_gen\\n/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/__init__.py\\n/home/agent/.hermes/hermes-agent/plugins/image_gen/xai/__init__.py\\n/home/agent/.hermes/hermes-agent/plugins/image_gen/openai/__init__.py\\nROOT /home/agent/.hermes/hermes-agent/agent\\n/home/agent/.hermes/hermes-agent/agent/image_gen_provider.py\\n/home/agent/.hermes/hermes-agent/agent/usage_pricing.py\\n/home/agent/.hermes/hermes-agent/agent/auxiliary_client.py\\n/home/agent/.hermes/hermes-agent/agent/models_dev.py\\n/home/agent/.hermes/hermes-agent/agent/credential_sources.py\\n/home/agent/.hermes/hermes-agent/agent/credential_pool.py\\n/home/agent/.hermes/hermes-agent/agent/video_gen_registry.py\\n/home/agent/.hermes/hermes-agent/agent/account_usage.py\\n/home/agent/.hermes/hermes-agent/agent/image_gen_registry.py\\n/home/agent/.hermes/hermes-agent/agent/model_metadata.py\\n/home/agent/.hermes/hermes-agent/agent/video_gen_provider.py\\n/home/agent/.hermes/hermes-agent/agent/display.py\\n/home/agent/.hermes/hermes-agent/agent/prompt_builder.py\\n/home/agent/.hermes/hermes-agent/agent/transports/hermes_tools_mcp_server.py\\nROOT /home/agent/.hermes/hermes-agent/tools\\n/home/agent/.hermes/hermes-agent/tools/image_generation_tool.py\\n/home/agent/.hermes/hermes-agent/tools/delegate_tool.py\\n/home/agent/.hermes/hermes-agent/tools/debug_helpers.py\\n/home/agent/.hermes/hermes-agent/tools/video_generation_tool.py\\n/home/agent/.hermes/hermes-agent/tools/vision_tools.py\\nLogs relevant\\n--- /home/agent/.hermes/logs/gateway.log\\n2026-05-09 22:23:51,057 INFO gateway.platforms.telegram: [Telegram] Cached user photo at /home/agent/.hermes/image_cache/img_708f0a539545.jpg\\n2026-05-09 22:23:51,217 INFO gateway.platforms.telegram: [Telegram] Cached user photo at /home/agent/.hermes/image_cache/img_d2cda0fb3e48.jpg\\n2026-05-09 22:23:52,052 INFO gateway.run: Image routing: text (mode=text). Pre-analyzing 2 image(s) via vision_analyze.\\n2026-05-10 22:25:34,419 INFO gateway.run: Image cache cleanup: removed 2 stale file(s)\\n2026-05-12 17:07:50,371 WARNING gateway.platforms.telegram: [Telegram] send_media_group failed (chunk 1/1), falling back to per-image: Failed to send message #1 with the error message \\\"photo_invalid_dimensions\\\"\\n File \\\"/home/agent/.hermes/hermes-agent/gateway/platforms/telegram.py\\\", line 2266, in send_multiple_images\\n2026-05-12 17:07:50,375 INFO gateway.platforms.base: [Telegram] Sending image: file:///home/agent/.hermes/cache/screenshots/browser_screenshot_9e7d3bcb51604... (alt=)\\n2026-05-12 17:07:50,717 INFO gateway.platforms.telegram: [Telegram] Image dimensions exceed Telegram photo limits, sending as document: /home/agent/.hermes/cache/screenshots/browser_screenshot_9e7d3bcb51604f9eb357e734b8d2eea0.png\\n2026-05-23 19:59:28,911 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='Nutze es nun per ,image to video‘. Image kannst du per chatgpt pro erstellen las'\\n2026-05-24 09:12:19,045 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='co/Civitai/Qwen-Image-2512-GGUF \\\"Civitai/Qwen-Image-2512-GGUF · Hugging Face\\\" So'\\n2026-05-24 23:52:33,586 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='Ok, leg los. Bedenke: du kannst ChatGPT Image nutzen über codex anstelle eines l'\\n2026-05-25 14:18:02,944 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='JARVIS, die drei Styleframe-Richtungen sind abgelehnt. REJECT_STYLEFRAME_DIRECT'\\n2026-05-25 16:34:57,085 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='JARVIS, die Premium-Styleframes A/B/C sind qualitativ deutlich besser als die Pi'\\n2026-05-25 16:58:58,723 INFO gateway.run: inbound message: platform=telegram user=M G chat=-1003907117629 msg='JARVIS, wir gehen jetzt in den nächsten kontrollierten Schritt. Die Styleframe-'\\n--- /home/agent/.hermes/logs/agent.log\\n2026-05-25 19:17:42,665 INFO [20260525_170059_c017b0] run_agent: OpenAI client created (agent_init, shared=True) thread=asyncio_0:135595230156480 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:42,674 INFO [20260525_170059_c017b0] agent.auxiliary_client: Auxiliary auto-detect: using main provider openai-codex (gpt-5.5)\\n2026-05-25 19:17:42,676 INFO [20260525_170059_c017b0] run_agent: conversation turn: session=20260525_170059_c017b0 model=gpt-5.5 provider=openai-codex platform=telegram history=7 msg='[M G] JARVIS, das neue Fake-Invoice-Proof-Short-Konzept ist strategisch deutlich...'\\n2026-05-25 19:17:42,684 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-372 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:47,410 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-372 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:47,410 INFO [20260525_170059_c017b0] run_agent: API call #1: model=gpt-5.5 provider=openai-codex in=22924 out=102 total=23026 latency=4.7s cache=15872/22924 (69%)\\n2026-05-25 19:17:47,540 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-373 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:52,404 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-373 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:52,404 INFO [20260525_170059_c017b0] run_agent: API call #2: model=gpt-5.5 provider=openai-codex in=46965 out=136 total=47101 latency=4.9s cache=22528/46965 (48%)\\n2026-05-25 19:17:52,443 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-374 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:57,941 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-374 (_call):135594390841024 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:17:57,941 INFO [20260525_170059_c017b0] run_agent: API call #3: model=gpt-5.5 provider=openai-codex in=47296 out=208 total=47504 latency=5.5s cache=46592/47296 (99%)\\n2026-05-25 19:18:17,054 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-385 (_call):135595087087296 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:23,253 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-385 (_call):135595087087296 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:23,253 INFO [20260525_170059_c017b0] run_agent: API call #4: model=gpt-5.5 provider=openai-codex in=50463 out=255 total=50718 latency=6.2s cache=47104/50463 (93%)\\n2026-05-25 19:18:26,863 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-394 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:30,280 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-394 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:30,280 INFO [20260525_170059_c017b0] run_agent: API call #5: model=gpt-5.5 provider=openai-codex in=53386 out=84 total=53470 latency=3.4s cache=50176/53386 (94%)\\n2026-05-25 19:18:30,817 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-397 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:33,247 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-397 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:33,248 INFO [20260525_170059_c017b0] run_agent: API call #6: model=gpt-5.5 provider=openai-codex in=55592 out=48 total=55640 latency=2.4s cache=53248/55592 (96%)\\n2026-05-25 19:18:34,260 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-402 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:36,859 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-402 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:36,859 INFO [20260525_170059_c017b0] run_agent: API call #7: model=gpt-5.5 provider=openai-codex in=57375 out=44 total=57419 latency=2.6s cache=55296/57375 (96%)\\n2026-05-25 19:18:37,868 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-407 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:42,923 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-407 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:42,924 INFO [20260525_170059_c017b0] run_agent: API call #8: model=gpt-5.5 provider=openai-codex in=58161 out=84 total=58245 latency=5.1s cache=56832/58161 (98%)\\n2026-05-25 19:18:43,929 INFO run_agent: OpenAI client created (codex_stream_request, shared=False) thread=Thread-412 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:57,379 INFO run_agent: OpenAI client closed (request_complete, shared=False, tcp_force_closed=0) thread=Thread-412 (_call):135594365662912 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5\\n2026-05-25 19:18:57,380 INFO [20260525_170059_c017b0] run_agent: API call #9: model=gpt-5.5 provider=openai-codex in=59610 out=637 total=60247 latency=13.5s cache=57856/59610 (97%)\\n--- /home/agent/.hermes/logs/mcp-stderr.log\\n--- /home/agent/.hermes/logs/gateway-exit-diag.log\\n--- /home/agent/.hermes/logs/gateway-shutdown-diag.log\\n--- /home/agent/.hermes/logs/errors.log\\n2026-05-24 17:40:29,128 WARNING [20260524_120031_1cfb85] run_agent: API call failed (attempt 1/3) error_type=TimeoutError thread=asyncio_2:125596109629120 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 summary=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 17:40:29,129 WARNING [20260524_120031_1cfb85] run_agent: Retrying API call in 2.388389115417217s (attempt 1/3) thread=asyncio_2:125596109629120 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 error=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 18:55:16,868 WARNING [20260524_171930_769d7a] run_agent: API call failed (attempt 1/3) error_type=TimeoutError thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 summary=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 18:55:16,869 WARNING [20260524_171930_769d7a] run_agent: Retrying API call in 2.324133697850045s (attempt 1/3) thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 error=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 20:09:48,031 WARNING [20260524_171930_769d7a] run_agent: API call failed (attempt 1/3) error_type=ReadError thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 summary=[SSL] record layer failure (_ssl.c:2590)\\n2026-05-24 20:09:48,032 WARNING [20260524_171930_769d7a] run_agent: Retrying API call in 2.319087862098872s (attempt 1/3) thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 error=[SSL] record layer failure (_ssl.c:2590)\\n2026-05-24 20:24:52,640 WARNING [20260524_171930_769d7a] run_agent: API call failed (attempt 2/3) error_type=TimeoutError thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 summary=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 20:24:52,641 WARNING [20260524_171930_769d7a] run_agent: Retrying API call in 4.059938401004144s (attempt 2/3) thread=asyncio_1:125596118021824 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 error=Non-streaming API call timed out after 900s with no response (threshold: 900s)\\n2026-05-24 21:11:13,493 WARNING [20260524_171930_769d7a] run_agent: API call failed (attempt 1/3) error_type=InternalServerError thread=asyncio_0:125596214941376 provider=openai-codex base_url=https://chatgpt.com/backend-api/codex model=gpt-5.5 summary=HTTP 503: upstream connect error or disconnect/reset before headers. retried and the latest reset reason: connection timeout\\n2026-05-24 21:11:13,495 WARNING [20260524_171930\\nImage cache recent\\nROOT /home/agent/.hermes/cache/images\\n/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171425_7e5c5015.png 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"content": "{\"content\": \" 1|\\\"\\\"\\\"OpenAI image generation backend — ChatGPT/Codex OAuth variant.\\n 2|\\n 3|Identical model catalog and tier semantics to the ``openai`` image-gen plugin\\n 4|(``gpt-image-2`` at low/medium/high quality), but routes the request through\\n 5|the Codex Responses API ``image_generation`` tool instead of the\\n 6|``images.generate`` REST endpoint. This lets users who are already\\n 7|authenticated with Codex/ChatGPT generate images without configuring a\\n 8|separate ``OPENAI_API_KEY``.\\n 9|\\n 10|Selection precedence for the tier (first hit wins):\\n 11|\\n 12|1. ``OPENAI_IMAGE_MODEL`` env var (escape hatch for scripts / tests)\\n 13|2. ``image_gen.openai-codex.model`` in ``config.yaml``\\n 14|3. ``image_gen.model`` in ``config.yaml`` (when it's one of our tier IDs)\\n 15|4. :data:`DEFAULT_MODEL` — ``gpt-image-2-medium``\\n 16|\\n 17|Output is saved as PNG under ``$HERMES_HOME/cache/images/``.\\n 18|\\\"\\\"\\\"\\n 19|\\n 20|from __future__ import annotations\\n 21|\\n 22|import logging\\n 23|from typing import Any, Dict, List, Optional, Tuple\\n 24|\\n 25|from agent.image_gen_provider import (\\n 26| DEFAULT_ASPECT_RATIO,\\n 27| ImageGenProvider,\\n 28| error_response,\\n 29| resolve_aspect_ratio,\\n 30| save_b64_image,\\n 31| success_response,\\n 32|)\\n 33|\\n 34|logger = logging.getLogger(__name__)\\n 35|\\n 36|\\n 37|# ---------------------------------------------------------------------------\\n 38|# Model catalog — mirrors the ``openai`` plugin so the picker UX is identical.\\n 39|# ---------------------------------------------------------------------------\\n 40|\\n 41|API_MODEL = \\\"gpt-image-2\\\"\\n 42|\\n 43|_MODELS: Dict[str, Dict[str, Any]] = {\\n 44| \\\"gpt-image-2-low\\\": {\\n 45| \\\"display\\\": \\\"GPT Image 2 (Low)\\\",\\n 46| \\\"speed\\\": \\\"~15s\\\",\\n 47| \\\"strengths\\\": \\\"Fast iteration, lowest cost\\\",\\n 48| \\\"quality\\\": \\\"low\\\",\\n 49| },\\n 50| \\\"gpt-image-2-medium\\\": {\\n 51| \\\"display\\\": \\\"GPT Image 2 (Medium)\\\",\\n 52| \\\"speed\\\": \\\"~40s\\\",\\n 53| \\\"strengths\\\": \\\"Balanced — default\\\",\\n 54| \\\"quality\\\": \\\"medium\\\",\\n 55| },\\n 56| \\\"gpt-image-2-high\\\": {\\n 57| \\\"display\\\": \\\"GPT Image 2 (High)\\\",\\n 58| \\\"speed\\\": \\\"~2min\\\",\\n 59| \\\"strengths\\\": \\\"Highest fidelity, strongest prompt adherence\\\",\\n 60| \\\"quality\\\": \\\"high\\\",\\n 61| },\\n 62|}\\n 63|\\n 64|DEFAULT_MODEL = \\\"gpt-image-2-medium\\\"\\n 65|\\n 66|_SIZES = {\\n 67| \\\"landscape\\\": \\\"1536x1024\\\",\\n 68| \\\"square\\\": \\\"1024x1024\\\",\\n 69| \\\"portrait\\\": \\\"1024x1536\\\",\\n 70|}\\n 71|\\n 72|# Codex Responses surface used for the request. The chat model itself is only\\n 73|# the host that calls the ``image_generation`` tool; the actual image work is\\n 74|# done by ``API_MODEL``.\\n 75|_CODEX_CHAT_MODEL = \\\"gpt-5.4\\\"\\n 76|_CODEX_BASE_URL = \\\"https://chatgpt.com/backend-api/codex\\\"\\n 77|_CODEX_INSTRUCTIONS = (\\n 78| \\\"You are an assistant that must fulfill image generation requests by \\\"\\n 79| \\\"using the image_generation tool when provided.\\\"\\n 80|)\\n 81|\\n 82|\\n 83|# ---------------------------------------------------------------------------\\n 84|# Config + auth helpers\\n 85|# ---------------------------------------------------------------------------\\n 86|\\n 87|\\n 88|def _load_image_gen_config() -> Dict[str, Any]:\\n 89| \\\"\\\"\\\"Read ``image_gen`` from config.yaml (returns {} on any failure).\\\"\\\"\\\"\\n 90| try:\\n 91| from hermes_cli.config import load_config\\n 92|\\n 93| cfg = load_config()\\n 94| section = cfg.get(\\\"image_gen\\\") if isinstance(cfg, dict) else None\\n 95| return section if isinstance(section, dict) else {}\\n 96| except Exception as exc:\\n 97| logger.debug(\\\"Could not load image_gen config: %s\\\", exc)\\n 98| return {}\\n 99|\\n 100|\\n 101|def _resolve_model() -> Tuple[str, Dict[str, Any]]:\\n 102| \\\"\\\"\\\"Decide which tier to use and return ``(model_id, meta)``.\\\"\\\"\\\"\\n 103| import os\\n 104|\\n 105| env_override = os.environ.get(\\\"OPENAI_IMAGE_MODEL\\\")\\n 106| if env_override and env_override in _MODELS:\\n 107| return env_override, _MODELS[env_override]\\n 108|\\n 109| cfg = _load_image_gen_config()\\n 110| sub = cfg.get(\\\"openai-codex\\\") if isinstance(cfg.get(\\\"openai-codex\\\"), dict) else {}\\n 111| candidate: Optional[str] = None\\n 112| if isinstance(sub, dict):\\n 113| value = sub.get(\\\"model\\\")\\n 114| if isinstance(value, str) and value in _MODELS:\\n 115| candidate = value\\n 116| if candidate is None:\\n 117| top = cfg.get(\\\"model\\\")\\n 118| if isinstance(top, str) and top in _MODELS:\\n 119| candidate = top\\n 120|\\n 121| if candidate is not None:\\n 122| return candidate, _MODELS[candidate]\\n 123|\\n 124| return DEFAULT_MODEL, _MODELS[DEFAULT_MODEL]\\n 125|\\n 126|\\n 127|def _read_codex_access_token() -> Optional[str]:\\n 128| \\\"\\\"\\\"Return a usable Codex OAuth token, or None.\\n 129|\\n 130| Delegates to the canonical reader in ``agent.auxiliary_client`` so token\\n 131| expiry, credential pool selection, and JWT decoding stay in one place.\\n 132| \\\"\\\"\\\"\\n 133| try:\\n 134| from agent.auxiliary_client import _read_codex_access_token as _reader\\n 135|\\n 136| token = _reader()\\n 137| if isinstance(token, str) and token.strip():\\n 138| return token.strip()\\n 139| return None\\n 140| except Exception as exc:\\n 141| logger.debug(\\\"Could not resolve Codex access token: %s\\\", exc)\\n 142| return None\\n 143|\\n 144|\\n 145|def _build_codex_client():\\n 146| \\\"\\\"\\\"Return an OpenAI client pointed at the ChatGPT/Codex backend, or None.\\\"\\\"\\\"\\n 147| token = _read_codex_access_token()\\n 148| if not token:\\n 149| return None\\n 150| try:\\n 151| import openai\\n 152| from agent.auxiliary_client import _codex_cloudflare_headers\\n 153|\\n 154| return openai.OpenAI(\\n 155| api_key=token,\\n 156| base_url=_CODEX_BASE_URL,\\n 157| default_headers=_codex_cloudflare_headers(token),\\n 158| )\\n 159| except Exception as exc:\\n 160| logger.debug(\\\"Could not build Codex image client: %s\\\", exc)\\n 161| return None\\n 162|\\n 163|\\n 164|def _collect_image_b64(client: Any, *, prompt: str, size: str, quality: str) -> Optional[str]:\\n 165| \\\"\\\"\\\"Stream a Codex Responses image_generation call and return the b64 image.\\\"\\\"\\\"\\n 166| image_b64: Optional[str] = None\\n 167|\\n 168| with client.responses.stream(\\n 169| model=_CODEX_CHAT_MODEL,\\n 170| store=False,\\n 171| instructions=_CODEX_INSTRUCTIONS,\\n 172| input=[{\\n 173| \\\"type\\\": \\\"message\\\",\\n 174| \\\"role\\\": \\\"user\\\",\\n 175| \\\"content\\\": [{\\\"type\\\": \\\"input_text\\\", \\\"text\\\": prompt}],\\n 176| }],\\n 177| tools=[{\\n 178| \\\"type\\\": \\\"image_generation\\\",\\n 179| \\\"model\\\": API_MODEL,\\n 180| \\\"size\\\": size,\\n 181| \\\"quality\\\": quality,\\n 182| \\\"output_format\\\": \\\"png\\\",\\n 183| \\\"background\\\": \\\"opaque\\\",\\n 184| \\\"partial_images\\\": 1,\\n 185| }],\\n 186| tool_choice={\\n 187| \\\"type\\\": \\\"allowed_tools\\\",\\n 188| \\\"mode\\\": \\\"required\\\",\\n 189| \\\"tools\\\": [{\\\"type\\\": \\\"image_generation\\\"}],\\n 190| },\\n 191| ) as stream:\\n 192| for event in stream:\\n 193| event_type = getattr(event, \\\"type\\\", \\\"\\\")\\n 194| if event_type == \\\"response.output_item.done\\\":\\n 195| item = getattr(event, \\\"item\\\", None)\\n 196| if getattr(item, \\\"type\\\", None) == \\\"image_generation_call\\\":\\n 197| result = getattr(item, \\\"result\\\", None)\\n 198| if isinstance(result, str) and result:\\n 199| image_b64 = result\\n 200| elif event_type == \\\"response.image_generation_call.partial_image\\\":\\n 201| partial = getattr(event, \\\"partial_image_b64\\\", None)\\n 202| if isinstance(partial, str) and partial:\\n 203| image_b64 = partial\\n 204| final = stream.get_final_response()\\n 205|\\n 206| # Final-response sweep covers the case where the stream finished before\\n 207| # we observed the ``output_item.done`` event for the image call.\\n 208| for item in getattr(final, \\\"output\\\", None) or []:\\n 209| if getattr(item, \\\"type\\\", None) == \\\"image_generation_call\\\":\\n 210| result = getattr(item, \\\"result\\\", None)\\n 211| if isinstance(result, str) and result:\\n 212| image_b64 = result\\n 213|\\n 214| return image_b64\\n 215|\\n 216|\\n 217|# ---------------------------------------------------------------------------\\n 218|# Provider\\n 219|# ---------------------------------------------------------------------------\\n 220|\\n 221|\\n 222|class OpenAICodexImageGenProvider(ImageGenProvider):\\n 223| \\\"\\\"\\\"gpt-image-2 routed through ChatGPT/Codex OAuth instead of an API key.\\\"\\\"\\\"\\n 224|\\n 225| @property\\n 226| def name(self) -> str:\\n 227| return \\\"openai-codex\\\"\\n 228|\\n 229| @property\\n 230| def display_name(self) -> str:\\n 231| return \\\"OpenAI (Codex auth)\\\"\\n 232|\\n 233| def is_available(self) -> bool:\\n 234| if not _read_codex_access_token():\\n 235| return False\\n 236| try:\\n 237| import openai # noqa: F401\\n 238| except ImportError:\\n 239| return False\\n 240| return True\\n 241|\\n 242| def list_models(self) -> List[Dict[str, Any]]:\\n 243| return [\\n 244| {\\n 245| \\\"id\\\": model_id,\\n 246| \\\"display\\\": meta[\\\"display\\\"],\\n 247| \\\"speed\\\": meta[\\\"speed\\\"],\\n 248| \\\"strengths\\\": meta[\\\"strengths\\\"],\\n 249| \\\"price\\\": \\\"varies\\\",\\n 250| }\\n 251| for model_id, meta in _MODELS.items()\\n 252| ]\\n 253|\\n 254| def default_model(self) -> Optional[str]:\\n 255| return DEFAULT_MODEL\\n 256|\\n 257| def get_setup_schema(self) -> Dict[str, Any]:\\n 258| return {\\n 259| \\\"name\\\": \\\"OpenAI (Codex auth)\\\",\\n 260| \\\"badge\\\": \\\"free\\\",\\n 261|\", \"total_lines\": 378, \"file_size\": 12605, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 378 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|#!/usr/bin/env python3\\n 2|\\\"\\\"\\\"\\n 3|Image Generation Tools Module\\n 4|\\n 5|Provides image generation via FAL.ai. Multiple FAL models are supported and\\n 6|selectable via ``hermes tools`` → Image Generation; the active model is\\n 7|persisted to ``image_gen.model`` in ``config.yaml``.\\n 8|\\n 9|Architecture:\\n 10|- ``FAL_MODELS`` is a catalog of supported models with per-model metadata\\n 11| (size-style family, defaults, ``supports`` whitelist, upscaler flag).\\n 12|- ``_build_fal_payload()`` translates the agent's unified inputs (prompt +\\n 13| aspect_ratio) into the model-specific payload and filters to the\\n 14| ``supports`` whitelist so models never receive rejected keys.\\n 15|- Upscaling via FAL's Clarity Upscaler is gated per-model via the ``upscale``\\n 16| flag — on for FLUX 2 Pro (backward-compat), off for all faster/newer models\\n 17| where upscaling would either hurt latency or add marginal quality.\\n 18|\\n 19|Pricing shown in UI strings is as-of the initial commit; we accept drift and\\n 20|update when it's noticed.\\n 21|\\\"\\\"\\\"\\n 22|\\n 23|import json\\n 24|import logging\\n 25|import os\\n 26|import datetime\\n 27|import threading\\n 28|import uuid\\n 29|from typing import Any, Dict, Optional, Union\\n 30|from urllib.parse import urlencode\\n 31|\\n 32|# fal_client is imported lazily — see _load_fal_client(). Pulling it\\n 33|# eagerly added ~64 ms to every CLI cold start because\\n 34|# discover_builtin_tools() imports this module unconditionally during\\n 35|# the registry walk, even when image generation is never used.\\n 36|#\\n 37|# Tests that monkeypatch this attribute (e.g.\\n 38|# ``monkeypatch.setattr(image_tool, \\\"fal_client\\\", fake_fal_client)``)\\n 39|# still work: _load_fal_client() short-circuits when the attribute is\\n 40|# anything truthy, so a test-installed mock is not overwritten by a\\n 41|# subsequent real import.\\n 42|fal_client: Any = None\\n 43|\\n 44|\\n 45|def _load_fal_client() -> Any:\\n 46| \\\"\\\"\\\"Lazily import fal_client and rebind the module global on first use.\\n 47|\\n 48| Idempotent. Returns the (now-loaded) ``fal_client`` module reference.\\n 49| Skips the import if the global is already truthy — this preserves the\\n 50| test pattern of monkeypatching the module global to install a mock.\\n 51| \\\"\\\"\\\"\\n 52| global fal_client\\n 53| if fal_client is not None:\\n 54| return fal_client\\n 55| try:\\n 56| from tools.lazy_deps import ensure as _lazy_ensure\\n 57| _lazy_ensure(\\\"image.fal\\\", prompt=False)\\n 58| except ImportError:\\n 59| pass\\n 60| except Exception as e:\\n 61| raise ImportError(str(e))\\n 62| import fal_client as _fal_client # noqa: F811 — module-global rebind\\n 63| fal_client = _fal_client\\n 64| return fal_client\\n 65|\\n 66|\\n 67|from tools.debug_helpers import DebugSession\\n 68|from tools.managed_tool_gateway import resolve_managed_tool_gateway\\n 69|from tools.tool_backend_helpers import (\\n 70| fal_ke...ured,\\n 71| managed_nous_tools_enabled,\\n 72| prefers_gateway,\\n 73|)\\n 74|\\n 75|logger = logging.getLogger(__name__)\\n 76|\\n 77|\\n 78|# ---------------------------------------------------------------------------\\n 79|# FAL model catalog\\n 80|# ---------------------------------------------------------------------------\\n 81|#\\n 82|# Each entry declares how to translate our unified inputs into the model's\\n 83|# native payload shape. Size specification falls into three families:\\n 84|#\\n 85|# \\\"image_size_preset\\\" — preset enum (\\\"square_hd\\\", \\\"landscape_16_9\\\", ...)\\n 86|# used by the flux family, z-image, qwen, recraft,\\n 87|# ideogram.\\n 88|# \\\"aspect_ratio\\\" — aspect ratio enum (\\\"16:9\\\", \\\"1:1\\\", ...) used by\\n 89|# nano-banana (Gemini).\\n 90|# \\\"gpt_literal\\\" — literal dimension strings (\\\"1024x1024\\\", etc.)\\n 91|# used by gpt-image-1.5.\\n 92|#\\n 93|# ``supports`` is a whitelist of keys allowed in the outgoing payload — any\\n 94|# key outside this set is stripped before submission so models never receive\\n 95|# rejected parameters (each FAL model rejects unknown keys differently).\\n 96|#\\n 97|# ``upscale`` controls whether to chain Clarity Upscaler after generation.\\n 98|\\n 99|FAL_MODELS: Dict[str, Dict[str, Any]] = {\\n 100| \\\"fal-ai/flux-2/klein/9b\\\": {\\n 101| \\\"display\\\": \\\"FLUX 2 Klein 9B\\\",\\n 102| \\\"speed\\\": \\\"<1s\\\",\\n 103| \\\"strengths\\\": \\\"Fast, crisp text\\\",\\n 104| \\\"price\\\": \\\"$0.006/MP\\\",\\n 105| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 106| \\\"sizes\\\": {\\n 107| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 108| \\\"square\\\": \\\"square_hd\\\",\\n 109| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 110| },\\n 111| \\\"defaults\\\": {\\n 112| \\\"num_inference_steps\\\": 4,\\n 113| \\\"output_format\\\": \\\"png\\\",\\n 114| \\\"enable_safety_checker\\\": False,\\n 115| },\\n 116| \\\"supports\\\": {\\n 117| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"seed\\\",\\n 118| \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 119| },\\n 120| \\\"upscale\\\": False,\\n 121| },\\n 122| \\\"fal-ai/flux-2-pro\\\": {\\n 123| \\\"display\\\": \\\"FLUX 2 Pro\\\",\\n 124| \\\"speed\\\": \\\"~6s\\\",\\n 125| \\\"strengths\\\": \\\"Studio photorealism\\\",\\n 126| \\\"price\\\": \\\"$0.03/MP\\\",\\n 127| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 128| \\\"sizes\\\": {\\n 129| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 130| \\\"square\\\": \\\"square_hd\\\",\\n 131| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 132| },\\n 133| \\\"defaults\\\": {\\n 134| \\\"num_inference_steps\\\": 50,\\n 135| \\\"guidance_scale\\\": 4.5,\\n 136| \\\"num_images\\\": 1,\\n 137| \\\"output_format\\\": \\\"png\\\",\\n 138| \\\"enable_safety_checker\\\": False,\\n 139| \\\"safety_tolerance\\\": \\\"5\\\",\\n 140| \\\"sync_mode\\\": True,\\n 141| },\\n 142| \\\"supports\\\": {\\n 143| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"guidance_scale\\\",\\n 144| \\\"num_images\\\", \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 145| \\\"safety_tolerance\\\", \\\"sync_mode\\\", \\\"seed\\\",\\n 146| },\\n 147| \\\"upscale\\\": True, # Backward-compat: current default behavior.\\n 148| },\\n 149| \\\"fal-ai/z-image/turbo\\\": {\\n 150| \\\"display\\\": \\\"Z-Image Turbo\\\",\\n 151| \\\"speed\\\": \\\"~2s\\\",\\n 152| \\\"strengths\\\": \\\"Bilingual EN/CN, 6B\\\",\\n 153| \\\"price\\\": \\\"$0.005/MP\\\",\\n 154| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 155| \\\"sizes\\\": {\\n 156| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 157| \\\"square\\\": \\\"square_hd\\\",\\n 158| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 159| },\\n 160| \\\"defaults\\\": {\\n 161| \\\"num_inference_steps\\\": 8,\\n 162| \\\"num_images\\\": 1,\\n 163| \\\"output_format\\\": \\\"png\\\",\\n 164| \\\"enable_safety_checker\\\": False,\\n 165| \\\"enable_prompt_expansion\\\": False, # avoid the extra per-request charge\\n 166| },\\n 167| \\\"supports\\\": {\\n 168| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"num_images\\\",\\n 169| \\\"seed\\\", \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 170| \\\"enable_prompt_expansion\\\",\\n 171| },\\n 172| \\\"upscale\\\": False,\\n 173| },\\n 174| \\\"fal-ai/nano-banana-pro\\\": {\\n 175| \\\"display\\\": \\\"Nano Banana Pro (Gemini 3 Pro Image)\\\",\\n 176| \\\"speed\\\": \\\"~8s\\\",\\n 177| \\\"strengths\\\": \\\"Gemini 3 Pro, reasoning depth, text rendering\\\",\\n 178| \\\"price\\\": \\\"$0.15/image (1K)\\\",\\n 179| \\\"size_style\\\": \\\"aspect_ratio\\\",\\n 180| \\\"sizes\\\": {\\n 181| \\\"landscape\\\": \\\"16:9\\\",\\n 182| \\\"square\\\": \\\"1:1\\\",\\n 183| \\\"portrait\\\": \\\"9:16\\\",\\n 184| },\\n 185| \\\"defaults\\\": {\\n 186| \\\"num_images\\\": 1,\\n 187| \\\"output_format\\\": \\\"png\\\",\\n 188| \\\"safety_tolerance\\\": \\\"5\\\",\\n 189| # \\\"1K\\\" is the cheapest tier; 4K doubles the per-image cost.\\n 190| # Users on Nous Subscription should stay at 1K for predictable billing.\\n 191| \\\"resolution\\\": \\\"1K\\\",\\n 192| },\\n 193| \\\"supports\\\": {\\n 194| \\\"prompt\\\", \\\"aspect_ratio\\\", \\\"num_images\\\", \\\"output_format\\\",\\n 195| \\\"safety_tolerance\\\", \\\"seed\\\", \\\"sync_mode\\\", \\\"resolution\\\",\\n 196| \\\"enable_web_search\\\", \\\"limit_generations\\\",\\n 197| },\\n 198| \\\"upscale\\\": False,\\n 199| },\\n 200| \\\"fal-ai/gpt-image-1.5\\\": {\\n 201| \\\"display\\\": \\\"GPT Image 1.5\\\",\\n 202| \\\"speed\\\": \\\"~15s\\\",\\n 203| \\\"strengths\\\": \\\"Prompt adherence\\\",\\n 204| \\\"price\\\": \\\"$0.034/image\\\",\\n 205| \\\"size_style\\\": \\\"gpt_literal\\\",\\n 206| \\\"sizes\\\": {\\n 207| \\\"landscape\\\": \\\"1536x1024\\\",\\n 208| \\\"square\\\": \\\"1024x1024\\\",\\n 209| \\\"portrait\\\": \\\"1024x1536\\\",\\n 210| },\\n 211| \\\"defaults\\\": {\\n 212| # Quality is pinned to medium to keep portal billing predictable\\n 213| # across all users (low is too rough, high is 4-6x more expensive).\\n 214| \\\"quality\\\": \\\"medium\\\",\\n 215| \\\"num_images\\\": 1,\\n 216| \\\"output_format\\\": \\\"png\\\",\\n 217| },\\n 218| \\\"supports\\\": {\\n 219| \\\"prompt\\\", \\\"image_size\\\", \\\"quality\\\", \\\"num_images\\\", \\\"output_format\\\",\\n 220| \\\"background\\\", \\\"sync_mode\\\",\\n 221|\", \"total_lines\": 1098, \"file_size\": 41478, \"truncated\": true, \"hint\": \"Use offset=221 to continue reading (showing 1-220 of 1098 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"\\n 2|Image Generation Provider Registry\\n 3|==================================\\n 4|\\n 5|Central map of registered providers. Populated by plugins at import-time via\\n 6|``PluginContext.register_image_gen_provider()``; consumed by the\\n 7|``image_generate`` tool to dispatch each call to the active backend.\\n 8|\\n 9|Active selection\\n 10|----------------\\n 11|The active provider is chosen by ``image_gen.provider`` in ``config.yaml``.\\n 12|If unset, :func:`get_active_provider` applies fallback logic:\\n 13|\\n 14|1. If exactly one provider is registered, use it.\\n 15|2. Otherwise if a provider named ``fal`` is registered, use it (legacy\\n 16| default — matches pre-plugin behavior).\\n 17|3. Otherwise return ``None`` (the tool surfaces a helpful error pointing\\n 18| the user at ``hermes tools``).\\n 19|\\\"\\\"\\\"\\n 20|\\n 21|from __future__ import annotations\\n 22|\\n 23|import logging\\n 24|import threading\\n 25|from typing import Dict, List, Optional\\n 26|\\n 27|from agent.image_gen_provider import ImageGenProvider\\n 28|\\n 29|logger = logging.getLogger(__name__)\\n 30|\\n 31|\\n 32|_providers: Dict[str, ImageGenProvider] = {}\\n 33|_lock = threading.Lock()\\n 34|\\n 35|\\n 36|def register_provider(provider: ImageGenProvider) -> None:\\n 37| \\\"\\\"\\\"Register an image generation provider.\\n 38|\\n 39| Re-registration (same ``name``) overwrites the previous entry and logs\\n 40| a debug message — this makes hot-reload scenarios (tests, dev loops)\\n 41| behave predictably.\\n 42| \\\"\\\"\\\"\\n 43| if not isinstance(provider, ImageGenProvider):\\n 44| raise TypeError(\\n 45| f\\\"register_provider() expects an ImageGenProvider instance, \\\"\\n 46| f\\\"got {type(provider).__name__}\\\"\\n 47| )\\n 48| name = provider.name\\n 49| if not isinstance(name, str) or not name.strip():\\n 50| raise ValueError(\\\"Image gen provider .name must be a non-empty string\\\")\\n 51| with _lock:\\n 52| existing = _providers.get(name)\\n 53| _providers[name] = provider\\n 54| if existing is not None:\\n 55| logger.debug(\\\"Image gen provider '%s' re-registered (was %r)\\\", name, type(existing).__name__)\\n 56| else:\\n 57| logger.debug(\\\"Registered image gen provider '%s' (%s)\\\", name, type(provider).__name__)\\n 58|\\n 59|\\n 60|def list_providers() -> List[ImageGenProvider]:\\n 61| \\\"\\\"\\\"Return all registered providers, sorted by name.\\\"\\\"\\\"\\n 62| with _lock:\\n 63| items = list(_providers.values())\\n 64| return sorted(items, key=lambda p: p.name)\\n 65|\\n 66|\\n 67|def get_provider(name: str) -> Optional[ImageGenProvider]:\\n 68| \\\"\\\"\\\"Return the provider registered under *name*, or None.\\\"\\\"\\\"\\n 69| if not isinstance(name, str):\\n 70| return None\\n 71| with _lock:\\n 72| return _providers.get(name.strip())\\n 73|\\n 74|\\n 75|def get_active_provider() -> Optional[ImageGenProvider]:\\n 76| \\\"\\\"\\\"Resolve the currently-active provider.\\n 77|\\n 78| Reads ``image_gen.provider`` from config.yaml; falls back per the\\n 79| module docstring.\\n 80|\\n 81| **Availability semantics** (mirrors :mod:`agent.web_search_registry`):\\n 82|\\n 83| - When ``image_gen.provider`` is explicitly set, the configured\\n 84| provider is returned even if :meth:`ImageGenProvider.is_available`\\n 85| reports False — the dispatcher surfaces a precise \\\"X_API_KEY is not\\n 86| set\\\" error rather than silently switching backends.\\n 87| - When ``image_gen.provider`` is unset, the fallback path (single-\\n 88| provider shortcut and the FAL legacy preference) is filtered by\\n 89| ``is_available()`` so we don't pick a provider the user has no\\n 90| credentials for.\\n 91| \\\"\\\"\\\"\\n 92| configured: Optional[str] = None\\n 93| try:\\n 94| from hermes_cli.config import load_config\\n 95|\\n 96| cfg = load_config()\\n 97| section = cfg.get(\\\"image_gen\\\") if isinstance(cfg, dict) else None\\n 98| if isinstance(section, dict):\\n 99| raw = section.get(\\\"provider\\\")\\n 100| if isinstance(raw, str) and raw.strip():\\n 101| configured = raw.strip()\\n 102| except Exception as exc:\\n 103| logger.debug(\\\"Could not read image_gen.provider from config: %s\\\", exc)\\n 104|\\n 105| with _lock:\\n 106| snapshot = dict(_providers)\\n 107|\\n 108| def _is_available_safe(p: ImageGenProvider) -> bool:\\n 109| \\\"\\\"\\\"Wrap ``is_available()`` so a buggy provider doesn't kill resolution.\\\"\\\"\\\"\\n 110| try:\\n 111| return bool(p.is_available())\\n 112| except Exception as exc: # noqa: BLE001\\n 113| logger.debug(\\\"image_gen provider %s.is_available() raised %s\\\", p.name, exc)\\n 114| return False\\n 115|\\n 116| # 1. Explicit config wins — return regardless of is_available() so the\\n 117| # user gets a precise downstream error message rather than a silent\\n 118| # backend switch.\\n 119| if configured:\\n 120| provider = snapshot.get(configured)\\n 121| if provider is not None:\\n 122| return provider\\n 123| logger.debug(\\n 124| \\\"image_gen.provider='%s' configured but not registered; falling back\\\",\\n 125| configured,\\n 126| )\\n 127|\\n 128| # 2. Fallback: single registered provider — but only if it's actually\\n 129| # available (no credentials = don't surface it as \\\"active\\\").\\n 130| available = [p for p in snapshot.values() if _is_available_safe(p)]\\n 131| if len(available) == 1:\\n 132| return available[0]\\n 133|\\n 134| # 3. Fallback: prefer legacy FAL for backward compat, when available.\\n 135| fal = snapshot.get(\\\"fal\\\")\\n 136| if fal is not None and _is_available_safe(fal):\\n 137| return fal\\n 138|\\n 139| return None\\n 140|\\n 141|\\n 142|def _reset_for_tests() -> None:\\n 143| \\\"\\\"\\\"Clear the registry. **Test-only.**\\\"\\\"\\\"\\n 144| with _lock:\\n 145| _providers.clear()\\n 146|\", \"total_lines\": 145, \"file_size\": 5158, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 260| \\\"badge\\\": \\\"free\\\",\\n 261| \\\"tag\\\": \\\"gpt-image-2 via ChatGPT/Codex OAuth — no API key required\\\",\\n 262| \\\"env_vars\\\": [],\\n 263| \\\"post_setup_hint\\\": (\\n 264| \\\"Sign in with `hermes auth codex` (or `hermes setup` → Codex) \\\"\\n 265| \\\"if you haven't already. No API key needed.\\\"\\n 266| ),\\n 267| }\\n 268|\\n 269| def generate(\\n 270| self,\\n 271| prompt: str,\\n 272| aspect_ratio: str = DEFAULT_ASPECT_RATIO,\\n 273| **kwargs: Any,\\n 274| ) -> Dict[str, Any]:\\n 275| prompt = (prompt or \\\"\\\").strip()\\n 276| aspect = resolve_aspect_ratio(aspect_ratio)\\n 277|\\n 278| if not prompt:\\n 279| return error_response(\\n 280| error=\\\"Prompt is required and must be a non-empty string\\\",\\n 281| error_type=\\\"invalid_argument\\\",\\n 282| provider=\\\"openai-codex\\\",\\n 283| aspect_ratio=aspect,\\n 284| )\\n 285|\\n 286| if not _read_codex_access_token():\\n 287| return error_response(\\n 288| error=(\\n 289| \\\"No Codex/ChatGPT OAuth credentials available. Run \\\"\\n 290| \\\"`hermes auth codex` (or `hermes setup` → Codex) to sign in.\\\"\\n 291| ),\\n 292| error_type=\\\"auth_required\\\",\\n 293| provider=\\\"openai-codex\\\",\\n 294| aspect_ratio=aspect,\\n 295| )\\n 296|\\n 297| try:\\n 298| import openai # noqa: F401\\n 299| except ImportError:\\n 300| return error_response(\\n 301| error=\\\"openai Python package not installed (pip install openai)\\\",\\n 302| error_type=\\\"missing_dependency\\\",\\n 303| provider=\\\"openai-codex\\\",\\n 304| aspect_ratio=aspect,\\n 305| )\\n 306|\\n 307| tier_id, meta = _resolve_model()\\n 308| size = _SIZES.get(aspect, _SIZES[\\\"square\\\"])\\n 309|\\n 310| client = _build_codex_client()\\n 311| if client is None:\\n 312| return error_response(\\n 313| error=\\\"Could not initialize Codex image client\\\",\\n 314| error_type=\\\"auth_required\\\",\\n 315| provider=\\\"openai-codex\\\",\\n 316| model=tier_id,\\n 317| prompt=prompt,\\n 318| aspect_ratio=aspect,\\n 319| )\\n 320|\\n 321| try:\\n 322| b64 = _collect_image_b64(\\n 323| client,\\n 324| prompt=prompt,\\n 325| size=size,\\n 326| quality=meta[\\\"quality\\\"],\\n 327| )\\n 328| except Exception as exc:\\n 329| logger.debug(\\\"Codex image generation failed\\\", exc_info=True)\\n 330| return error_response(\\n 331| error=f\\\"OpenAI image generation via Codex auth failed: {exc}\\\",\\n 332| error_type=\\\"api_error\\\",\\n 333| provider=\\\"openai-codex\\\",\\n 334| model=tier_id,\\n 335| prompt=prompt,\\n 336| aspect_ratio=aspect,\\n 337| )\\n 338|\\n 339| if not b64:\\n 340| return error_response(\\n 341| error=\\\"Codex response contained no image_generation_call result\\\",\\n 342| error_type=\\\"empty_response\\\",\\n 343| provider=\\\"openai-codex\\\",\\n 344| model=tier_id,\\n 345| prompt=prompt,\\n 346| aspect_ratio=aspect,\\n 347| )\\n 348|\\n 349| try:\\n 350| saved_path = save_b64_image(b64, prefix=f\\\"openai_codex_{tier_id}\\\")\\n 351| except Exception as exc:\\n 352| return error_response(\\n 353| error=f\\\"Could not save image to cache: {exc}\\\",\\n 354| error_type=\\\"io_error\\\",\\n 355| provider=\\\"openai-codex\\\",\\n 356| model=tier_id,\\n 357| prompt=prompt,\\n 358| aspect_ratio=aspect,\\n 359| )\\n 360|\\n 361| return success_response(\\n 362| image=str(saved_path),\\n 363| model=tier_id,\\n 364| prompt=prompt,\\n 365| aspect_ratio=aspect,\\n 366| provider=\\\"openai-codex\\\",\\n 367| extra={\\\"size\\\": size, \\\"quality\\\": meta[\\\"quality\\\"]},\\n 368| )\\n 369|\\n 370|\\n 371|# ---------------------------------------------------------------------------\\n 372|# Plugin entry point\\n 373|# ---------------------------------------------------------------------------\\n 374|\\n 375|\\n 376|def register(ctx) -> None:\\n 377| \\\"\\\"\\\"Plugin entry point — register the Codex-backed image-gen provider.\\\"\\\"\\\"\\n 378| ctx.register_image_gen_provider(OpenAICodexImageGenProvider())\\n 379|\", \"total_lines\": 378, \"file_size\": 12605, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"command\":\"python - <<'PY'\\nimport json, os, time, hashlib\\nfrom pathlib import Path\\nimport sys\\nsys.path.insert(0, str(Path.home()/'.hermes/hermes-agent'))\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom hermes_cli.config import load_config\\nfrom agent.image_gen_registry import get_provider, list_providers\\n_ensure_plugins_discovered(force=True)\\ncfg=load_config().get('image_gen',{})\\nproviders=[]\\nfor p in list_providers():\\n try: available=p.is_available()\\n except Exception as e: available=f'error:{e}'\\n models=[]\\n try: models=p.list_models()\\n except Exception as e: models=f'error:{e}'\\n providers.append({'name':p.name,'display':getattr(p,'display_name',''),'available':available,'default_model':p.default_model(),'models':models})\\nprov=get_provider('openai-codex')\\nprint(json.dumps({'config':cfg,'providers':providers,'openai_codex_available': bool(prov and prov.is_available())}, indent=2)[:12000])\\n# capability: single high quality image if provider available\\noutdir=Path('/tmp/autoshorts_proof_short_sprint30/image_provider_audit')\\noutdir.mkdir(parents=True, exist_ok=True)\\nresult={'attempted': False}\\nif prov and prov.is_available():\\n os.environ['OPENAI_IMAGE_MODEL']='gpt-image-2-high'\\n prompt=(\\\"Neutral controlled styleframe capability test for AutoShortsBot: vertical 9:16 social-video keyframe, \\\"\\n \\\"clean modern desk with a generic blank document mockup and soft studio light, no readable text, no logos, \\\"\\n \\\"no brands, no QR codes, no people, safe margins, realistic product-film composition.\\\")\\n t=time.time()\\n result=prov.generate(prompt, aspect_ratio='portrait')\\n result['attempted']=True\\n result['elapsed_seconds']=round(time.time()-t,2)\\n if result.get('success') and result.get('image'):\\n p=Path(result['image'])\\n copy=outdir/('capability_test_'+p.name)\\n copy.write_bytes(p.read_bytes())\\n result['audit_copy']=str(copy)\\n result['sha256']=hashlib.sha256(copy.read_bytes()).hexdigest()\\n result['file_size']=copy.stat().st_size\\n try:\\n from PIL import Image\\n im=Image.open(copy)\\n result['resolution']=f'{im.width}x{im.height}'\\n result['png_info_keys']=list(getattr(im,'info',{}).keys())\\n result['png_info']={k: str(v)[:500] for k,v in im.info.items()}\\n except Exception as e: result['pil_error']=str(e)\\n(Path(outdir/'capability_test_result.json')).write_text(json.dumps(result, indent=2), encoding='utf-8')\\nprint('CAPABILITY_RESULT')\\nprint(json.dumps(result, indent=2)[:12000])\\nPY\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
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"content": "{\"output\": \"{\\n \\\"config\\\": {\\n \\\"provider\\\": \\\"openai-codex\\\",\\n \\\"model\\\": \\\"gpt-image-2-medium\\\"\\n },\\n \\\"providers\\\": [\\n {\\n \\\"name\\\": \\\"openai\\\",\\n \\\"display\\\": \\\"OpenAI\\\",\\n \\\"available\\\": false,\\n \\\"default_model\\\": \\\"gpt-image-2-medium\\\",\\n \\\"models\\\": [\\n {\\n \\\"id\\\": \\\"gpt-image-2-low\\\",\\n \\\"display\\\": \\\"GPT Image 2 (Low)\\\",\\n \\\"speed\\\": \\\"~15s\\\",\\n \\\"strengths\\\": \\\"Fast iteration, lowest cost\\\",\\n \\\"price\\\": \\\"varies\\\"\\n },\\n {\\n \\\"id\\\": \\\"gpt-image-2-medium\\\",\\n \\\"display\\\": \\\"GPT Image 2 (Medium)\\\",\\n \\\"speed\\\": \\\"~40s\\\",\\n \\\"strengths\\\": \\\"Balanced \\\\u2014 default\\\",\\n \\\"price\\\": \\\"varies\\\"\\n },\\n {\\n \\\"id\\\": \\\"gpt-image-2-high\\\",\\n \\\"display\\\": \\\"GPT Image 2 (High)\\\",\\n \\\"speed\\\": \\\"~2min\\\",\\n \\\"strengths\\\": \\\"Highest fidelity, strongest prompt adherence\\\",\\n \\\"price\\\": \\\"varies\\\"\\n }\\n ]\\n },\\n {\\n \\\"name\\\": \\\"openai-codex\\\",\\n \\\"display\\\": \\\"OpenAI (Codex auth)\\\",\\n \\\"available\\\": true,\\n \\\"default_model\\\": \\\"gpt-image-2-medium\\\",\\n \\\"models\\\": [\\n {\\n \\\"id\\\": \\\"gpt-image-2-low\\\",\\n \\\"display\\\": \\\"GPT Image 2 (Low)\\\",\\n \\\"speed\\\": \\\"~15s\\\",\\n \\\"strengths\\\": \\\"Fast iteration, lowest cost\\\",\\n \\\"price\\\": \\\"varies\\\"\\n },\\n {\\n \\\"id\\\": \\\"gpt-image-2-medium\\\",\\n \\\"display\\\": \\\"GPT Image 2 (Medium)\\\",\\n \\\"speed\\\": \\\"~40s\\\",\\n \\\"strengths\\\": \\\"Balanced \\\\u2014 default\\\",\\n \\\"price\\\": \\\"varies\\\"\\n },\\n {\\n \\\"id\\\": \\\"gpt-image-2-high\\\",\\n \\\"display\\\": \\\"GPT Image 2 (High)\\\",\\n \\\"speed\\\": \\\"~2min\\\",\\n \\\"strengths\\\": \\\"Highest fidelity, strongest prompt adherence\\\",\\n \\\"price\\\": \\\"varies\\\"\\n }\\n ]\\n },\\n {\\n \\\"name\\\": \\\"xai\\\",\\n \\\"display\\\": \\\"xAI (Grok)\\\",\\n \\\"available\\\": false,\\n \\\"default_model\\\": \\\"grok-imagine-image\\\",\\n \\\"models\\\": [\\n {\\n \\\"id\\\": \\\"grok-imagine-image\\\",\\n \\\"display\\\": \\\"Grok Imagine Image\\\",\\n \\\"speed\\\": \\\"~5-10s\\\",\\n \\\"strengths\\\": \\\"Fast, high-quality\\\"\\n },\\n {\\n \\\"id\\\": \\\"grok-imagine-image-quality\\\",\\n \\\"display\\\": \\\"Grok Imagine Image (Quality)\\\",\\n \\\"speed\\\": \\\"~10-20s\\\",\\n \\\"strengths\\\": \\\"Higher fidelity / detail; slower than the standard model.\\\"\\n }\\n ]\\n }\\n ],\\n \\\"openai_codex_available\\\": true\\n}\\nCAPABILITY_RESULT\\n{\\n \\\"success\\\": true,\\n \\\"image\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\\\",\\n \\\"model\\\": \\\"gpt-image-2-high\\\",\\n \\\"prompt\\\": \\\"Neutral controlled styleframe capability test for AutoShortsBot: vertical 9:16 social-video keyframe, clean modern desk with a generic blank document mockup and soft studio light, no readable text, no logos, no brands, no QR codes, no people, safe margins, realistic product-film composition.\\\",\\n \\\"aspect_ratio\\\": \\\"portrait\\\",\\n \\\"provider\\\": \\\"openai-codex\\\",\\n \\\"size\\\": \\\"1024x1536\\\",\\n \\\"quality\\\": \\\"high\\\",\\n \\\"attempted\\\": true,\\n \\\"elapsed_seconds\\\": 60.93,\\n \\\"audit_copy\\\": \\\"/tmp/autoshorts_proof_short_sprint30/image_provider_audit/capability_test_openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\\\",\\n \\\"sha256\\\": \\\"bdcbc300f918c07ebe4cb5620afa633ff7f8e02184f3d4f6d43c522745465baf\\\",\\n \\\"file_size\\\": 2029976,\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_info_keys\\\": [],\\n \\\"png_info\\\": {}\\n}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_UFlpszFyldL4RplGF1P5Vpyp"
},
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"arguments": "{\"limit\":80,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"pattern\":\"*config*\",\"target\":\"files\"}"
}
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},
{
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"content": "{\"total_count\": 0}",
"tool_call_id": "call_e8GrbcqWHLqLGlgaDAqF3cfl"
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"tool_calls": [
{
"id": "call_KpUjZul7psPxw7d0n7ChQmvZ",
"call_id": "call_KpUjZul7psPxw7d0n7ChQmvZ",
"response_item_id": "fc_058b7e1607c20939016a1484e6021c8191812f5e9e7320c5fc",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_caption_safe_zone.py\",\"content\":\"from autoshorts.rendering.static_captions import (\\n CaptionLayoutConfig,\\n WordCue,\\n build_safe_caption_segments,\\n layout_caption_segment,\\n)\\n\\n\\ndef _cue_words(text: str):\\n words = text.split()\\n return tuple(\\n WordCue(start=i * 0.3, end=(i + 1) * 0.3, word=word, sentence=text)\\n for i, word in enumerate(words)\\n )\\n\\n\\ndef test_long_phrase_wraps_into_two_lines_within_safe_width():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, horizontal_margin_px=60)\\n segment = build_safe_caption_segments(_cue_words(\\\"Before you pay check three important things\\\"), layout=cfg)[0]\\n\\n layout = layout_caption_segment(segment.words, active_word=segment.words[0], config=cfg)\\n\\n assert 1 <= len(layout.lines) <= 2\\n assert layout.width_px <= cfg.safe_width_px\\n\\n\\ndef test_caption_and_active_word_highlight_never_exceed_safe_bounds():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, horizontal_margin_px=60)\\n segment = build_safe_caption_segments(_cue_words(\\\"sender domain urgency language payment route\\\"), layout=cfg)[0]\\n\\n for word in segment.words:\\n layout = layout_caption_segment(segment.words, active_word=word, config=cfg)\\n assert layout.left_px >= cfg.horizontal_margin_px\\n assert layout.right_px <= cfg.video_width - cfg.horizontal_margin_px\\n assert layout.active_word_box is not None\\n assert layout.active_word_box[0] >= cfg.horizontal_margin_px\\n assert layout.active_word_box[2] <= cfg.video_width - cfg.horizontal_margin_px\\n\\n\\ndef test_no_word_clipped_at_left_or_right_edge():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, horizontal_margin_px=60)\\n segment = build_safe_caption_segments(_cue_words(\\\"This invoice looks normal\\\"), layout=cfg)[0]\\n\\n layout = layout_caption_segment(segment.words, active_word=\\\"invoice\\\", config=cfg)\\n\\n assert all(box[0] >= cfg.horizontal_margin_px for box in layout.word_boxes)\\n assert all(box[2] <= cfg.video_width - cfg.horizontal_margin_px for box in layout.word_boxes)\\n\\n\\ndef test_max_two_lines_enforced_by_splitting_earlier():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, horizontal_margin_px=60)\\n text = \\\"Before you pay check sender domain urgency language and payment route carefully\\\"\\n segments = build_safe_caption_segments(_cue_words(text), layout=cfg)\\n\\n assert len(segments) > 1\\n for segment in segments:\\n layout = layout_caption_segment(segment.words, active_word=segment.words[0], config=cfg)\\n assert len(layout.lines) <= 2\\n assert layout.width_px <= cfg.safe_width_px\\n\\n\\ndef test_mobile_bottom_safe_zone_respected():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, bottom_margin_px=260)\\n segment = build_safe_caption_segments(_cue_words(\\\"Urgency is not proof\\\"), layout=cfg)[0]\\n\\n layout = layout_caption_segment(segment.words, active_word=\\\"not\\\", config=cfg)\\n\\n assert layout.bottom_px <= cfg.video_height - cfg.bottom_margin_px\\n\\n\\ndef test_domain_strings_wrap_or_shrink_safely():\\n cfg = CaptionLayoutConfig(video_width=1080, video_height=1920, horizontal_margin_px=60)\\n segment = build_safe_caption_segments(_cue_words(\\\"verify example-company.com before paying\\\"), layout=cfg)[0]\\n\\n layout = layout_caption_segment(segment.words, active_word=\\\"example-company.com\\\", config=cfg)\\n\\n assert layout.width_px <= cfg.safe_width_px\\n assert layout.right_px <= cfg.video_width - cfg.horizontal_margin_px\\n assert layout.font_size_px >= cfg.min_font_size_px\\n\"}"
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],
"finish_reason": "tool_calls",
"reasoning": "**Planning tests for modules**\n\nI need to write tests for the new policy module in `autoshorts/production/image_generation_policy.py` that includes a dataclass. I'll focus on testing the caption layout module since the current `static_captions` doesn’t have a proper layout. It’s important to add `CaptionLayoutConfig` and `layout_caption_segment` as well. I think starting with tests will help ensure everything works well, then I can move on to the rendering script, possibly rewriting it from scratch.",
"reasoning_content": "**Planning tests for modules**\n\nI need to write tests for the new policy module in `autoshorts/production/image_generation_policy.py` that includes a dataclass. I'll focus on testing the caption layout module since the current `static_captions` doesn’t have a proper layout. It’s important to add `CaptionLayoutConfig` and `layout_caption_segment` as well. I think starting with tests will help ensure everything works well, then I can move on to the rendering script, possibly rewriting it from scratch.",
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"text": "**Planning tests for modules**\n\nI need to write tests for the new policy module in `autoshorts/production/image_generation_policy.py` that includes a dataclass. I'll focus on testing the caption layout module since the current `static_captions` doesn’t have a proper layout. It’s important to add `CaptionLayoutConfig` and `layout_caption_segment` as well. I think starting with tests will help ensure everything works well, then I can move on to the rendering script, possibly rewriting it from scratch."
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"arguments": "{\"content\":\"from autoshorts.production.image_generation_policy import (\\n ImageGenerationPolicy,\\n image_policy_as_config,\\n)\\n\\n\\ndef test_image_policy_pins_openai_codex_and_best_available_model():\\n policy = ImageGenerationPolicy()\\n\\n assert policy.allowed_image_provider == \\\"openai-codex\\\"\\n assert policy.preferred_image_model == \\\"best_available_gpt_image_model\\\"\\n assert policy.preferred_quality == \\\"highest_available_reasonable\\\"\\n\\n\\ndef test_image_policy_limits_styleframe_spend_and_random_batches():\\n policy = ImageGenerationPolicy()\\n\\n assert policy.max_styleframes_per_scene_without_approval == 3\\n assert policy.max_retries_per_styleframe == 1\\n assert policy.no_random_batches is True\\n\\n\\ndef test_image_policy_prefers_deterministic_mockups_for_invoice_ui():\\n policy = ImageGenerationPolicy()\\n\\n assert policy.no_ai_images_for_mockups_when_deterministic_renderer_is_better is True\\n assert policy.requires_ai_for_mockup(\\\"fake_invoice\\\") is False\\n assert policy.requires_ai_for_mockup(\\\"ui_mockup\\\") is False\\n\\n\\ndef test_image_policy_config_is_serializable():\\n cfg = image_policy_as_config()\\n\\n assert cfg[\\\"allowed_image_provider\\\"] == \\\"openai-codex\\\"\\n assert cfg[\\\"max_styleframes_per_scene_without_approval\\\"] == 3\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_image_generation_policy.py\"}"
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"content": "{\"output\": \"==================================== ERRORS ====================================\\n_______________ ERROR collecting tests/test_caption_safe_zone.py _______________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_caption_safe_zone.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_caption_safe_zone.py:1: in \\n from autoshorts.rendering.static_captions import (\\nE ImportError: cannot import name 'CaptionLayoutConfig' from 'autoshorts.rendering.static_captions' (/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/static_captions.py)\\n____________ ERROR collecting tests/test_image_generation_policy.py ____________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_image_generation_policy.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_image_generation_policy.py:1: in \\n from autoshorts.production.image_generation_policy import (\\nE ModuleNotFoundError: No module named 'autoshorts.production.image_generation_policy'\\n=========================== short test summary info ============================\\nERROR tests/test_caption_safe_zone.py\\nERROR tests/test_image_generation_policy.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.14s\", \"exit_code\": 2, \"error\": null}",
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{
"id": "call_hHII5ovUp4RP6zwq06YKVYeH",
"call_id": "call_hHII5ovUp4RP6zwq06YKVYeH",
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"arguments": "{\"content\":\"\\\"\\\"\\\"Static active-word caption segment logic.\\n\\nCaption rule for TrueTraceShorts previews/finals:\\n- A subtitle phrase segment stays fixed on screen.\\n- At least three words are visible per default segment whenever possible.\\n- Only the active spoken word changes highlight.\\n- The highlight moves left-to-right until the next phrase segment replaces it.\\n- Caption layout must remain inside the mobile safe zone.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom typing import Sequence\\n\\n\\n@dataclass(frozen=True)\\nclass WordCue:\\n start: float\\n end: float\\n word: str\\n sentence: str = \\\"\\\"\\n\\n\\n@dataclass(frozen=True)\\nclass StaticCaptionSegment:\\n start: float\\n end: float\\n word_indices: tuple[int, ...]\\n words: tuple[str, ...]\\n sentence: str = \\\"\\\"\\n\\n @property\\n def text(self) -> str:\\n return \\\" \\\".join(self.words)\\n\\n\\n@dataclass(frozen=True)\\nclass CaptionLayoutConfig:\\n video_width: int = 1080\\n video_height: int = 1920\\n horizontal_margin_px: int = 60\\n bottom_margin_px: int = 260\\n max_width_ratio: float = 0.88\\n max_lines: int = 2\\n font_size_px: int = 68\\n min_font_size_px: int = 46\\n line_gap_px: int = 14\\n word_gap_px: int = 18\\n highlight_pad_x_px: int = 10\\n highlight_pad_y_px: int = 5\\n\\n @property\\n def safe_width_px(self) -> int:\\n ratio_width = int(self.video_width * self.max_width_ratio)\\n margin_width = self.video_width - 2 * self.horizontal_margin_px\\n return min(ratio_width, margin_width)\\n\\n\\n@dataclass(frozen=True)\\nclass CaptionLayout:\\n lines: tuple[tuple[str, ...], ...]\\n font_size_px: int\\n left_px: int\\n right_px: int\\n top_px: int\\n bottom_px: int\\n width_px: int\\n height_px: int\\n word_boxes: tuple[tuple[int, int, int, int], ...]\\n active_word_box: tuple[int, int, int, int] | None\\n\\n\\ndef build_static_caption_segments(\\n word_cues: Sequence[WordCue],\\n *,\\n min_words: int = 3,\\n target_words: int = 6,\\n max_words: int = 7,\\n) -> tuple[StaticCaptionSegment, ...]:\\n if min_words < 2:\\n raise ValueError(\\\"min_words must be >= 2\\\")\\n if target_words < min_words:\\n raise ValueError(\\\"target_words must be >= min_words\\\")\\n if max_words < target_words:\\n raise ValueError(\\\"max_words must be >= target_words\\\")\\n\\n grouped: list[list[tuple[int, WordCue]]] = []\\n current_sentence: str | None = None\\n current: list[tuple[int, WordCue]] = []\\n for index, cue in enumerate(word_cues):\\n sentence_key = cue.sentence or \\\"__no_sentence__\\\"\\n if current and sentence_key != current_sentence:\\n grouped.append(current)\\n current = []\\n current_sentence = sentence_key\\n current.append((index, cue))\\n if current:\\n grouped.append(current)\\n\\n segments: list[StaticCaptionSegment] = []\\n for group in grouped:\\n chunks: list[list[tuple[int, WordCue]]] = []\\n i = 0\\n while i < len(group):\\n remaining = len(group) - i\\n size = min(target_words, remaining)\\n if remaining < min_words and chunks:\\n chunks[-1].extend(group[i:])\\n break\\n if size < min_words and len(group) >= min_words:\\n start = max(0, len(group) - min_words)\\n chunks.append(group[start:])\\n break\\n chunks.append(group[i : i + size])\\n i += size\\n for chunk in chunks:\\n if not chunk:\\n continue\\n if len(chunk) > max_words:\\n # Split oversized merged chunks conservatively while preserving min_words.\\n while len(chunk) > max_words:\\n head = chunk[:max_words]\\n segments.append(_segment_from_chunk(head))\\n chunk = chunk[max_words:]\\n if 0 < len(chunk) < min_words and segments:\\n previous = segments.pop()\\n prev_pairs = [(idx, word_cues[idx]) for idx in previous.word_indices]\\n chunk = prev_pairs + chunk\\n segments.append(_segment_from_chunk(chunk))\\n return tuple(segments)\\n\\n\\ndef build_safe_caption_segments(\\n word_cues: Sequence[WordCue],\\n *,\\n layout: CaptionLayoutConfig | None = None,\\n min_words: int = 3,\\n target_words: int = 6,\\n max_words: int = 7,\\n) -> tuple[StaticCaptionSegment, ...]:\\n \\\"\\\"\\\"Build phrase segments that are pre-split to fit the caption safe zone.\\n\\n The function keeps the established 3–7 word phrase behavior, but refuses to\\n create a segment that would need more than two lines or exceed the safe\\n width at the minimum allowed font size. Very long tokens are handled by\\n dynamic shrink in layout, and if a phrase is still too wide the segment is\\n split earlier.\\n \\\"\\\"\\\"\\n cfg = layout or CaptionLayoutConfig()\\n base_segments = build_static_caption_segments(\\n word_cues,\\n min_words=min_words,\\n target_words=target_words,\\n max_words=max_words,\\n )\\n safe_segments: list[StaticCaptionSegment] = []\\n for segment in base_segments:\\n pairs = [(idx, word_cues[idx]) for idx in segment.word_indices]\\n current: list[tuple[int, WordCue]] = []\\n for pair in pairs:\\n candidate = current + [pair]\\n candidate_words = tuple(cue.word for _, cue in candidate)\\n if len(candidate_words) <= max_words and _fits_caption_layout(candidate_words, cfg):\\n current = candidate\\n continue\\n if current:\\n safe_segments.append(_segment_from_chunk(current))\\n current = [pair]\\n else:\\n current = [pair]\\n if current:\\n # Try to merge a too-small tail back only when still safe.\\n if (\\n len(current) < min_words\\n and safe_segments\\n and len(safe_segments[-1].words) + len(current) <= max_words\\n ):\\n prev = safe_segments[-1]\\n merged_pairs = [(idx, word_cues[idx]) for idx in prev.word_indices] + current\\n merged_words = tuple(cue.word for _, cue in merged_pairs)\\n if _fits_caption_layout(merged_words, cfg):\\n safe_segments[-1] = _segment_from_chunk(merged_pairs)\\n else:\\n safe_segments.append(_segment_from_chunk(current))\\n else:\\n safe_segments.append(_segment_from_chunk(current))\\n return tuple(safe_segments)\\n\\n\\ndef _segment_from_chunk(chunk: Sequence[tuple[int, WordCue]]) -> StaticCaptionSegment:\\n return StaticCaptionSegment(\\n start=chunk[0][1].start,\\n end=chunk[-1][1].end,\\n word_indices=tuple(index for index, _ in chunk),\\n words=tuple(cue.word for _, cue in chunk),\\n sentence=chunk[0][1].sentence,\\n )\\n\\n\\ndef active_word_index_at(word_cues: Sequence[WordCue], timestamp: float) -> int:\\n for index, cue in enumerate(word_cues):\\n if cue.start <= timestamp < cue.end:\\n return index\\n previous = [index for index, cue in enumerate(word_cues) if cue.start <= timestamp]\\n return previous[-1] if previous else 0\\n\\n\\ndef segment_for_active_word(segments: Sequence[StaticCaptionSegment], active_word_index: int) -> StaticCaptionSegment:\\n for segment in segments:\\n if active_word_index in segment.word_indices:\\n return segment\\n raise ValueError(f\\\"active word index {active_word_index} is not present in any segment\\\")\\n\\n\\ndef caption_state_at(word_cues: Sequence[WordCue], segments: Sequence[StaticCaptionSegment], timestamp: float) -> dict[str, object]:\\n active_index = active_word_index_at(word_cues, timestamp)\\n segment = segment_for_active_word(segments, active_index)\\n return {\\n \\\"segment_text\\\": segment.text,\\n \\\"segment_words\\\": segment.words,\\n \\\"word_indices\\\": segment.word_indices,\\n \\\"active_word_index\\\": active_index,\\n \\\"active_word\\\": word_cues[active_index].word,\\n }\\n\\n\\ndef layout_caption_segment(\\n words: Sequence[str],\\n *,\\n active_word: str | None = None,\\n config: CaptionLayoutConfig | None = None,\\n) -> CaptionLayout:\\n cfg = config or CaptionLayoutConfig()\\n if not words:\\n raise ValueError(\\\"words must not be empty\\\")\\n\\n for size in range(cfg.font_size_px, cfg.min_font_size_px - 1, -2):\\n lines = _wrap_words(words, cfg, size)\\n metrics = _measure_lines(lines, cfg, size, active_word=active_word)\\n if len(lines) <= cfg.max_lines and metrics[\\\"width\\\"] <= cfg.safe_width_px:\\n return _layout_from_metrics(lines, cfg, size, metrics)\\n\\n # Last resort: return minimum-size layout; callers can split earlier.\\n lines = _wrap_words(words, cfg, cfg.min_font_size_px)\\n if len(lines) > cfg.max_lines:\\n lines = lines[: cfg.max_lines - 1] + (tuple(word for line in lines[cfg.max_lines - 1 :] for word in line),)\\n metrics = _measure_lines(lines, cfg, cfg.min_font_size_px, active_word=active_word)\\n return _layout_from_metrics(lines, cfg, cfg.min_font_size_px, metrics)\\n\\n\\ndef _fits_caption_layout(words: Sequence[str], cfg: CaptionLayoutConfig) -> bool:\\n layout = layout_caption_segment(words, active_word=words[0] if words else None, config=cfg)\\n return len(layout.lines) <= cfg.max_lines and layout.width_px <= cfg.safe_width_px\\n\\n\\ndef _font(size: int):\\n from PIL import ImageFont\\n\\n for path in (\\n \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\",\\n \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\",\\n ):\\n try:\\n return ImageFont.truetype(path, size=size)\\n except OSError:\\n continue\\n return ImageFont.load_default()\\n\\n\\ndef _word_width(word: str, size: int) -> int:\\n from PIL import Image, ImageDraw\\n\\n image = Image.new(\\\"RGB\\\", (10, 10))\\n draw = ImageDraw.Draw(image)\\n bbox = draw.textbbox((0, 0), word, font=_font(size), stroke_width=2)\\n return bbox[2] - bbox[0]\\n\\n\\ndef _line_width(line: Sequence[str], cfg: CaptionLayoutConfig, size: int) -> int:\\n if not line:\\n return 0\\n return sum(_word_width(word, size) for word in line) + cfg.word_gap_px * (len(line) - 1)\\n\\n\\ndef _wrap_words(words: Sequence[str], cfg: CaptionLayoutConfig, size: int) -> tuple[tuple[str, ...], ...]:\\n lines: list[list[str]] = [[]]\\n for word in words:\\n current = lines[-1]\\n candidate = current + [word]\\n if current and _line_width(candidate, cfg, size) > cfg.safe_width_px:\\n lines.append([word])\\n else:\\n current.append(word)\\n return tuple(tuple(line) for line in lines if line)\\n\\n\\ndef _measure_lines(\\n lines: Sequence[Sequence[str]],\\n cfg: CaptionLayoutConfig,\\n size: int,\\n *,\\n active_word: str | None,\\n) -> dict[str, object]:\\n line_height = int(size * 1.15)\\n widths = [_line_width(line, cfg, size) for line in lines]\\n width = max(widths) if widths else 0\\n height = len(lines) * line_height + max(0, len(lines) - 1) * cfg.line_gap_px\\n left = max(cfg.horizontal_margin_px, (cfg.video_width - width) // 2)\\n top = cfg.video_height - cfg.bottom_margin_px - height\\n word_boxes: list[tuple[int, int, int, int]] = []\\n active_box = None\\n for line_index, line in enumerate(lines):\\n line_width = _line_width(line, cfg, size)\\n x = max(cfg.horizontal_margin_px, (cfg.video_width - line_width) // 2)\\n y = top + line_index * (line_height + cfg.line_gap_px)\\n for word in line:\\n ww = _word_width(word, size)\\n box = (x, y, x + ww, y + line_height)\\n word_boxes.append(box)\\n if active_box is None and active_word == word:\\n active_box = (\\n max(cfg.horizontal_margin_px, box[0] - cfg.highlight_pad_x_px),\\n box[1] - cfg.highlight_pad_y_px,\\n min(cfg.video_width - cfg.horizontal_margin_px, box[2] + cfg.highlight_pad_x_px),\\n box[3] + cfg.highlight_pad_y_px,\\n )\\n x += ww + cfg.word_gap_px\\n return {\\n \\\"width\\\": width,\\n \\\"height\\\": height,\\n \\\"left\\\": left,\\n \\\"top\\\": top,\\n \\\"word_boxes\\\": tuple(word_boxes),\\n \\\"active_word_box\\\": active_box,\\n }\\n\\n\\ndef _layout_from_metrics(\\n lines: Sequence[Sequence[str]],\\n cfg: CaptionLayoutConfig,\\n size: int,\\n metrics: dict[str, object],\\n) -> CaptionLayout:\\n left = int(metrics[\\\"left\\\"])\\n top = int(metrics[\\\"top\\\"])\\n width = int(metrics[\\\"width\\\"])\\n height = int(metrics[\\\"height\\\"])\\n return CaptionLayout(\\n lines=tuple(tuple(line) for line in lines),\\n font_size_px=size,\\n left_px=left,\\n right_px=left + width,\\n top_px=top,\\n bottom_px=top + height,\\n width_px=width,\\n height_px=height,\\n word_boxes=metrics[\\\"word_boxes\\\"], # type: ignore[arg-type]\\n active_word_box=metrics[\\\"active_word_box\\\"], # type: ignore[arg-type]\\n )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/static_captions.py\"}"
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"text": "**Implementing layout features**\n\nI'm focusing on using the PIL ImageFont to measure text properly. I need to add optional layouts that include line splitting for active word boxes. The font I want to use is DejaVu. I'm planning to use ImageDraw's textbbox for this. My goal is to build safe segments iteratively by trying to add words while keeping the layout within a maximum limit. Ideally, I also want to ensure a minimum number of words."
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"reasoning": "**Planning active word highlight**\n\nI need to create an active word highlight, but without using a box—just highlighting the yellow text. The user mentioned that the highlight box cannot overflow, so I’ll stick to drawing white words and making the active ones yellow. I should use word cues from the script and estimate their duration based on the character count, using audio duration data if available. It might be better to use existing sentence timings if they’re available.",
"reasoning_content": "**Planning active word highlight**\n\nI need to create an active word highlight, but without using a box—just highlighting the yellow text. The user mentioned that the highlight box cannot overflow, so I’ll stick to drawing white words and making the active ones yellow. I should use word cues from the script and estimate their duration based on the character count, using audio duration data if available. It might be better to use existing sentence timings if they’re available.",
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},
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"content": "{\"content\": \" 1|1\\n 2|00:00:00,100 --> 00:00:02,462\\n 3|This invoice looks normal.\\n 4|\\n 5|2\\n 6|00:00:02,412 --> 00:00:05,825\\n 7|The trap is not the logo — it’s the payment route.\\n 8|\\n 9|3\\n 10|00:00:05,825 --> 00:00:07,700\\n 11|Same layout.\\n 12|\\n 13|4\\n 14|00:00:07,700 --> 00:00:10,012\\n 15|Same professional tone.\\n 16|\\n 17|5\\n 18|00:00:10,012 --> 00:00:11,962\\n 19|Same urgency.\\n 20|\\n 21|6\\n 22|00:00:11,962 --> 00:00:15,162\\n 23|But one thing changed: where the money goes.\\n 24|\\n 25|7\\n 26|00:00:15,162 --> 00:00:20,837\\n 27|Before you pay, check three things: sender domain, urgency language, and payment route.\\n 28|\\n 29|8\\n 30|00:00:20,837 --> 00:00:24,187\\n 31|If the route changed, don’t pay from the email.\\n 32|\\n 33|9\\n 34|00:00:24,187 --> 00:00:26,887\\n 35|Verify through a second channel.\\n 36|\\n 37|10\\n 38|00:00:26,887 --> 00:00:29,250\\n 39|Urgency is not proof.\\n 40|\\n 41|11\\n 42|00:00:29,250 --> 00:00:31,262\\n 43|It’s the red flag.\\n 44|\\n 45|\", \"total_lines\": 44, \"file_size\": 757, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
"tool_call_id": "call_AUzMusN2rcM9fjKDNeglsN7K"
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"tool_calls": [
{
"id": "call_3Xd6rm9XR2oDw0eOEfmcc1Bo",
"call_id": "call_3Xd6rm9XR2oDw0eOEfmcc1Bo",
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"arguments": "{\"path\":\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\",\"content\":\"from __future__ import annotations\\n\\nimport hashlib, json, math, os, re, subprocess, sys\\nfrom pathlib import Path\\n\\nsys.path.insert(0, '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')\\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter\\nfrom autoshorts.rendering.static_captions import (\\n CaptionLayoutConfig,\\n WordCue,\\n active_word_index_at,\\n build_safe_caption_segments,\\n layout_caption_segment,\\n segment_for_active_word,\\n)\\n\\nBASE = Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s')\\nOUT = BASE / 'preview_v2_caption_safe'\\nFRAMES = OUT / 'frames'\\nQA = OUT / 'qa_frames'\\nfor p in [OUT, FRAMES, QA]: p.mkdir(parents=True, exist_ok=True)\\nAUDIO = BASE / 'audio/voiceover.mp3'\\nSRT = BASE / 'audio/edge_subtitles.srt'\\nCANDIDATE = 'digital-red-flags-fake-invoice-payment-route-30s'\\nW,H,FPS = 1080,1920,24\\nDURATION = 31.291667\\nSAFE = CaptionLayoutConfig(video_width=W, video_height=H, horizontal_margin_px=70, bottom_margin_px=310, max_width_ratio=0.86, font_size_px=66, min_font_size_px=48, word_gap_px=18)\\nFONT_BOLD='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf'\\nFONT_REG='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'\\n\\ndef font(size, bold=True):\\n return ImageFont.truetype(FONT_BOLD if bold else FONT_REG, size)\\n\\ndef parse_time(t):\\n h,m,rest=t.split(':'); s,ms=rest.split(',')\\n return int(h)*3600+int(m)*60+int(s)+int(ms)/1000\\n\\ndef parse_srt(path):\\n txt=path.read_text(encoding='utf-8')\\n blocks=re.split(r'\\\\n\\\\s*\\\\n', txt.strip())\\n out=[]\\n for b in blocks:\\n lines=b.splitlines()\\n if len(lines)>=3:\\n m=re.match(r'(\\\\d\\\\d:\\\\d\\\\d:\\\\d\\\\d,\\\\d\\\\d\\\\d) --> (\\\\d\\\\d:\\\\d\\\\d:\\\\d\\\\d,\\\\d\\\\d\\\\d)', lines[1])\\n if m: out.append((parse_time(m.group(1)), parse_time(m.group(2)), ' '.join(lines[2:])))\\n return out\\n\\ndef clean_words(text):\\n return re.findall(r\\\"[A-Za-z0-9]+(?:[-–—'][A-Za-z0-9]+)*(?:\\\\.com)?|[A-Za-z0-9.-]+\\\\.com\\\", text.replace('’',\\\"'\\\"))\\n\\ndef build_cues():\\n cues=[]\\n for start,end,text in parse_srt(SRT):\\n words=clean_words(text)\\n weights=[max(0.55, len(w)/5) for w in words]\\n total=sum(weights) or 1\\n t=start\\n for w,wt in zip(words,weights):\\n dur=(end-start)*wt/total\\n cues.append(WordCue(start=t, end=t+dur, word=w, sentence=text))\\n t+=dur\\n return tuple(cues)\\n\\nWORD_CUES=build_cues()\\nSEGMENTS=build_safe_caption_segments(WORD_CUES, layout=SAFE, min_words=2, target_words=5, max_words=7)\\n\\ndef rounded(draw, box, r, fill, outline=None, width=1):\\n draw.rounded_rectangle(box, radius=r, fill=fill, outline=outline, width=width)\\n\\ndef text(draw, xy, s, size, fill, bold=True, anchor=None, align='left'):\\n draw.text(xy, s, font=font(size,bold), fill=fill, anchor=anchor, align=align)\\n\\ndef scene_for(t):\\n if t < 5.8: return 'hook'\\n if t < 11.9: return 'same'\\n if t < 15.2: return 'route'\\n if t < 20.9: return 'checks'\\n if t < 26.9: return 'verify'\\n return 'takeaway'\\n\\ndef bg(draw):\\n draw.rectangle((0,0,W,H), fill=(14,18,29))\\n for y in range(0,H,6):\\n c=int(18+18*y/H)\\n draw.line((0,y,W,y), fill=(c,c+4,c+14))\\n draw.rectangle((0,0,W,H), outline=(40,51,73), width=8)\\n\\ndef draw_email_invoice(draw, t, mode):\\n # phone/browser screen card\\n rounded(draw, (82,170,998,1330), 44, (235,239,244), (78,92,122), 3)\\n rounded(draw, (118,220,962,420), 26, (255,255,255), (218,224,234), 2)\\n text(draw,(150,252),'Inbox',38,(39,48,70))\\n text(draw,(150,314),'From: billing@example-company.com',30,(78,88,110),False)\\n urgency=(236,165,37) if mode in ['same','route'] else (113,124,148)\\n text(draw,(150,362),'Subject: Payment due today',32,urgency)\\n # invoice doc\\n rounded(draw, (150,470,930,1198), 24, (250,251,253), (199,207,222), 2)\\n text(draw,(196,522),'INVOICE',52,(38,48,66))\\n text(draw,(196,590),'Generic Services',31,(104,115,136),False)\\n for yy in [670,735,800]:\\n draw.rounded_rectangle((196,yy,730,yy+28),10, fill=(224,230,240))\\n draw.rounded_rectangle((196,872,884,1012),20, fill=(240,244,249), outline=(210,218,231), width=2)\\n text(draw,(224,905),'Payment route',34,(54,64,86))\\n changed = mode in ['route','checks','verify']\\n route_color=(207,54,54) if changed else (60,120,90)\\n route_txt='NEW ROUTE' if changed else 'KNOWN ROUTE'\\n rounded(draw,(224,956,538,1006),16,(255,247,238) if changed else (235,248,241),route_color,3)\\n text(draw,(250,966),route_txt,29,route_color)\\n if changed:\\n draw.line((610,970,830,970), fill=(207,54,54), width=7)\\n draw.polygon([(830,970),(800,950),(800,990)], fill=(207,54,54))\\n text(draw,(224,1060),'Route changed',42,(207,54,54))\\n else:\\n text(draw,(224,1060),'Looks normal',42,(60,120,90))\\n\\ndef draw_checks(draw, t):\\n labels=[('sender domain',15.3),('urgency language',16.9),('payment route',18.4)]\\n y=1190\\n for i,(lab,ts) in enumerate(labels):\\n active=t>=ts\\n col=(247,202,76) if active else (122,133,155)\\n rounded(draw,(155,y+i*92,925,y+64+i*92),22,(35,43,61),(col if active else (74,86,110)),3)\\n text(draw,(205,y+13+i*92),f'{i+1}. {lab}',33,(255,255,255) if active else (188,197,214))\\n\\ndef draw_verify(draw, t):\\n rounded(draw,(150,1190,930,1385),30,(32,42,61),(88,104,137),3)\\n text(draw,(190,1235),'Do not pay from the email',39,(255,255,255))\\n text(draw,(190,1300),'Verify through a second channel',36,(247,202,76))\\n # phone/check icon\\n rounded(draw,(730,1240,862,1342),18,(18,24,36),(247,202,76),3)\\n draw.line((760,1298,790,1325,836,1266), fill=(247,202,76), width=9)\\n\\ndef draw_takeaway(draw):\\n rounded(draw,(92,420,988,1010),44,(24,32,48),(247,202,76),4)\\n text(draw,(540,565),'Urgency is not proof.',64,(255,255,255),anchor='mm')\\n text(draw,(540,700),\\\"It's the red flag.\\\",76,(247,202,76),anchor='mm')\\n text(draw,(540,850),'Save this before paying from email.',36,(198,207,224),anchor='mm')\\n\\ndef draw_caption(draw, t):\\n active=active_word_index_at(WORD_CUES,t)\\n seg=segment_for_active_word(SEGMENTS,active)\\n active_word=WORD_CUES[active].word\\n layout=layout_caption_segment(seg.words, active_word=active_word, config=SAFE)\\n f=font(layout.font_size_px, True)\\n # subtle shadow/outline no box/bar\\n flat_words=[]\\n for line in layout.lines: flat_words.extend(line)\\n boxes=list(layout.word_boxes)\\n for word,box in zip(flat_words, boxes):\\n x,y,_,_=box\\n fill=(247,202,76) if word==active_word else (255,255,255)\\n for dx,dy in [(-2,2),(2,2),(0,3)]:\\n draw.text((x+dx,y+dy), word, font=f, fill=(0,0,0,160), stroke_width=2, stroke_fill=(0,0,0,130))\\n draw.text((x,y), word, font=f, fill=fill, stroke_width=2, stroke_fill=(6,10,18))\\n\\ndef draw_frame(t):\\n img=Image.new('RGB',(W,H),(14,18,29)); draw=ImageDraw.Draw(img,'RGBA')\\n bg(draw)\\n sc=scene_for(t)\\n if sc=='takeaway':\\n draw_takeaway(draw)\\n else:\\n draw_email_invoice(draw,t, 'route' if sc in ['route','checks','verify'] else sc)\\n if sc=='same':\\n rounded(draw,(160,1210,920,1320),28,(30,40,59),(90,103,132),2)\\n text(draw,(540,1265),'Same layout. Same tone. Same urgency.',35,(255,255,255),anchor='mm')\\n if sc in ['checks','verify']: draw_checks(draw,t)\\n if sc=='verify': draw_verify(draw,t)\\n # safe guides very subtle? don't include in final\\n draw_caption(draw,t)\\n return img\\n\\n# render raw frames to ffmpeg\\nsilent=OUT/f'{CANDIDATE}_preview_v2_caption_safe_silent.mp4'\\nfinal=OUT/f'{CANDIDATE}_preview_v2_caption_safe.mp4'\\ncmd=['ffmpeg','-y','-f','rawvideo','-vcodec','rawvideo','-pix_fmt','rgb24','-s',f'{W}x{H}','-r',str(FPS),'-i','-','-an','-c:v','libx264','-pix_fmt','yuv420p','-crf','20',str(silent)]\\nproc=subprocess.Popen(cmd, stdin=subprocess.PIPE)\\nframes=int(math.ceil(DURATION*FPS))\\nfor i in range(frames):\\n t=i/FPS\\n proc.stdin.write(draw_frame(t).tobytes())\\nproc.stdin.close(); proc.wait()\\nif proc.returncode: raise SystemExit(proc.returncode)\\nsubprocess.run(['ffmpeg','-y','-i',str(silent),'-i',str(AUDIO),'-c:v','copy','-c:a','aac','-shortest',str(final)], check=True)\\n# QA frames and contact sheet\\nsample_times=[1,4,10,15.8,17.8,20.2,24.5,28.2,30.2]\\nthumbs=[]\\nfor ts in sample_times:\\n im=draw_frame(ts)\\n p=QA/f'frame_{str(ts).replace(\\\".\\\",\\\"_\\\")}s.jpg'\\n im.save(p, quality=92)\\n thumbs.append((ts, im.resize((270,480))))\\nsheet=Image.new('RGB',(810,1440),(12,16,24)); sd=ImageDraw.Draw(sheet)\\nfor idx,(ts,thumb) in enumerate(thumbs):\\n x=(idx%3)*270; y=(idx//3)*480\\n sheet.paste(thumb,(x,y)); sd.rectangle((x,y,x+269,y+479),outline=(80,90,115),width=2)\\n sd.text((x+12,y+12),f'{ts:.1f}s',font=font(24),fill=(247,202,76))\\ncontact=OUT/'digital-red-flags_caption_safe_contact_sheet_v2.jpg'\\nsheet.save(contact, quality=92)\\n# subtitle style preview\\nsp=OUT/'digital-red-flags_subtitle_style_preview_v2.jpg'\\ndraw_frame(17.8).save(sp, quality=94)\\n# Validate all segment layouts\\nviol=[]\\nfor seg in SEGMENTS:\\n for w in seg.words:\\n lo=layout_caption_segment(seg.words, active_word=w, config=SAFE)\\n if lo.left_px < SAFE.horizontal_margin_px or lo.right_px > W-SAFE.horizontal_margin_px or lo.width_px > SAFE.safe_width_px or len(lo.lines)>2 or lo.bottom_px>H-SAFE.bottom_margin_px:\\n viol.append({'segment':seg.text,'active':w,'layout':lo.__dict__})\\n\\ndef sha(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest()\\ndef probe(p):\\n r=subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration:stream=width,height,r_frame_rate','-of','json',str(p)], text=True)\\n return json.loads(r)\\ndef meta(p, media_type):\\n p=Path(p); d={'file_path':str(p),'file_exists':p.exists(),'file_size':p.stat().st_size,'sha256':sha(p),'media_type':media_type}\\n if media_type.startswith('video'):\\n pr=probe(p); vs=next((s for s in pr.get('streams',[]) if 'width' in s),{})\\n d.update({'duration':round(float(pr['format']['duration']),6),'resolution':f\\\"{vs.get('width')}x{vs.get('height')}\\\",'fps':vs.get('r_frame_rate')})\\n elif media_type.startswith('image'):\\n im=Image.open(p); d.update({'duration':'n/a','resolution':f'{im.width}x{im.height}'})\\n return d\\nmanifest={\\n 'candidate_id':CANDIDATE,\\n 'version':'preview_v2_caption_safe',\\n 'changes':['caption safe-zone wrap/shrink/split only','same voiceover','deterministic renderer-owned mockups'],\\n 'wan_used':False,'i2v_used':False,'t2v_used':False,'new_ai_images':False,'platform_api':False,'posting':False,\\n 'caption_rules': {'max_width_ratio':SAFE.max_width_ratio,'horizontal_margin_px':SAFE.horizontal_margin_px,'bottom_margin_px':SAFE.bottom_margin_px,'max_lines':SAFE.max_lines,'font_size_px':SAFE.font_size_px,'min_font_size_px':SAFE.min_font_size_px,'preferred_words_per_segment':'4-7'},\\n 'caption_layout_validation': {'passed': len(viol)==0, 'violations': viol, 'segments':[{'text':s.text,'words':s.words,'start':s.start,'end':s.end} for s in SEGMENTS]},\\n 'artifacts': {'preview_mp4':meta(final,'video/mp4'), 'silent_mp4':meta(silent,'video/mp4'), 'qa_frame_sheet':meta(contact,'image/jpeg'), 'subtitle_style_preview':meta(sp,'image/jpeg')},\\n}\\n(OUT/'manifest_preview_v2_caption_safe.json').write_text(json.dumps(manifest,indent=2),encoding='utf-8')\\nprint(json.dumps(manifest, indent=2)[:12000])\\n\"}"
}
}
],
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"reasoning": "**Creating render script**\n\nI need to write a render script, and I’ll start by importing the necessary modules, including PIL and ffmpeg. I’ll use regex for handling words and assign a time per word within the SRT segment. I plan to draw scenes using shapes, but I suspect there might be a bug relating to the layout of word boxes, particularly concerning the first occurrence of active_word. I’ll need to calculate x positions and mimic metrics for drawing with lines. Let's get the script written and execute it!",
"reasoning_content": "**Creating render script**\n\nI need to write a render script, and I’ll start by importing the necessary modules, including PIL and ffmpeg. I’ll use regex for handling words and assign a time per word within the SRT segment. I plan to draw scenes using shapes, but I suspect there might be a bug relating to the layout of word boxes, particularly concerning the first occurrence of active_word. I’ll need to calculate x positions and mimic metrics for drawing with lines. Let's get the script written and execute it!",
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"summary": [
{
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}
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}
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"arguments": "{\"command\":\"python /tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
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{
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"content": "{\"output\": \"ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, rawvideo, from 'fd:':\\n Duration: N/A, start: 0.000000, bitrate: 1194393 kb/s\\n Stream #0:0: Video: rawvideo (RGB[24] / 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512kB time=00:00:27.29 bitrate= 153.7kbits/s speed=0.724x \\rframe= 670 fps= 18 q=25.0 size= 512kB time=00:00:27.83 bitrate= 150.7kbits/s speed=0.729x \\rframe= 684 fps= 18 q=25.0 size= 512kB time=00:00:28.41 bitrate= 147.6kbits/s speed=0.734x \\rframe= 697 fps= 18 q=25.0 size= 512kB time=00:00:28.95 bitrate= 144.9kbits/s speed=0.739x \\r[out#0/mp4 @ 0x60b95b1acd00] video:704kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.368665%\\nframe= 752 fps= 19 q=-1.0 Lsize= 714kB time=00:00:31.20 bitrate= 187.4kbits/s speed=0.79x \\n[libx264 @ 0x60b95b1ae440] frame I:4 Avg QP:12.66 size: 49860\\n[libx264 @ 0x60b95b1ae440] frame P:190 Avg QP:17.17 size: 2277\\n[libx264 @ 0x60b95b1ae440] frame B:558 Avg QP:23.81 size: 159\\n[libx264 @ 0x60b95b1ae440] consecutive B-frames: 1.1% 0.0% 0.0% 98.9%\\n[libx264 @ 0x60b95b1ae440] mb I I16..4: 72.2% 10.3% 17.5%\\n[libx264 @ 0x60b95b1ae440] mb P I16..4: 0.8% 0.1% 0.3% P16..4: 0.5% 0.1% 0.1% 0.0% 0.0% skip:98.2%\\n[libx264 @ 0x60b95b1ae440] mb B I16..4: 0.1% 0.0% 0.0% B16..8: 1.2% 0.0% 0.0% direct: 0.0% skip:98.6% L0:49.2% L1:50.8% BI: 0.0%\\n[libx264 @ 0x60b95b1ae440] 8x8 transform intra:11.1% inter:15.0%\\n[libx264 @ 0x60b95b1ae440] coded y,uvDC,uvAC intra: 11.3% 11.5% 8.6% inter: 0.0% 0.1% 0.1%\\n[libx264 @ 0x60b95b1ae440] i16 v,h,dc,p: 26% 74% 0% 0%\\n[libx264 @ 0x60b95b1ae440] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 30% 11% 59% 0% 0% 0% 0% 0% 0%\\n[libx264 @ 0x60b95b1ae440] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 29% 46% 9% 2% 3% 3% 3% 2% 3%\\n[libx264 @ 0x60b95b1ae440] i8c dc,h,v,p: 76% 13% 10% 1%\\n[libx264 @ 0x60b95b1ae440] Weighted P-Frames: Y:0.0% UV:0.0%\\n[libx264 @ 0x60b95b1ae440] ref P L0: 71.4% 7.6% 16.8% 4.1%\\n[libx264 @ 0x60b95b1ae440] ref B L0: 58.5% 40.6% 0.9%\\n[libx264 @ 0x60b95b1ae440] ref B L1: 96.9% 3.1%\\n[libx264 @ 0x60b95b1ae440] kb/s:184.00\\nffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Duration: 00:00:31.33, start: 0.000000, bitrate: 186 kb/s\\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 184 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n[mp3 @ 0x652be8a01840] Estimating duration from bitrate, this may be inaccurate\\nInput #1, mp3, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/audio/voiceover.mp3':\\n Duration: 00:00:31.32, start: 0.000000, bitrate: 48 kb/s\\n Stream #1:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s\\nStream mapping:\\n Stream #0:0 -> #0:0 (copy)\\n Stream #1:0 -> #0:1 (mp3 (mp3float) -> aac (native))\\nPress [q] to stop, [?] for help\\nOutput #0, mp4, to '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, q=2-31, 184 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 24000 Hz, mono, fltp, 69 kb/s\\n Metadata:\\n encoder : Lavc60.31.102 aac\\nsize= 0kB time=N/A bitrate=N/A speed=N/A \\r[out#0/mp4 @ 0x652be8974fc0] video:704kB audio:242kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.030851%\\nsize= 965kB time=00:00:31.31 bitrate= 252.5kbits/s speed=78.7x \\n[aac @ 0x652be8a78740] Qavg: 18380.342\\n{\\n \\\"candidate_id\\\": \\\"digital-red-flags-fake-invoice-payment-route-30s\\\",\\n \\\"version\\\": \\\"preview_v2_caption_safe\\\",\\n \\\"changes\\\": [\\n \\\"caption safe-zone wrap/shrink/split only\\\",\\n \\\"same voiceover\\\",\\n \\\"deterministic renderer-owned mockups\\\"\\n ],\\n \\\"wan_used\\\": false,\\n \\\"i2v_used\\\": false,\\n \\\"t2v_used\\\": false,\\n \\\"new_ai_images\\\": false,\\n \\\"platform_api\\\": false,\\n \\\"posting\\\": false,\\n \\\"caption_rules\\\": {\\n \\\"max_width_ratio\\\": 0.86,\\n \\\"horizontal_margin_px\\\": 70,\\n \\\"bottom_margin_px\\\": 310,\\n \\\"max_lines\\\": 2,\\n \\\"font_size_px\\\": 66,\\n \\\"min_font_size_px\\\": 48,\\n \\\"preferred_words_per_segment\\\": \\\"4-7\\\"\\n },\\n \\\"caption_layout_validation\\\": {\\n \\\"passed\\\": true,\\n \\\"violations\\\": [],\\n \\\"segments\\\": [\\n {\\n \\\"text\\\": \\\"This invoice looks normal\\\",\\n \\\"words\\\": [\\n \\\"This\\\",\\n \\\"invoice\\\",\\n \\\"looks\\\",\\n \\\"normal\\\"\\n ],\\n \\\"start\\\": 0.1,\\n \\\"end\\\": 2.4619999999999997\\n },\\n {\\n \\\"text\\\": \\\"The trap is not the\\\",\\n \\\"words\\\": [\\n \\\"The\\\",\\n \\\"trap\\\",\\n \\\"is\\\",\\n \\\"not\\\",\\n \\\"the\\\"\\n ],\\n \\\"start\\\": 2.412,\\n \\\"end\\\": 3.79921935483871\\n },\\n {\\n \\\"text\\\": \\\"logo it's the payment route\\\",\\n \\\"words\\\": [\\n \\\"logo\\\",\\n \\\"it's\\\",\\n \\\"the\\\",\\n \\\"payment\\\",\\n \\\"route\\\"\\n ],\\n \\\"start\\\": 3.79921935483871,\\n \\\"end\\\": 5.825\\n },\\n {\\n \\\"text\\\": \\\"Same layout\\\",\\n \\\"words\\\": [\\n \\\"Same\\\",\\n \\\"layout\\\"\\n ],\\n \\\"start\\\": 5.825,\\n \\\"end\\\": 7.7\\n },\\n {\\n \\\"text\\\": \\\"Same professional tone\\\",\\n \\\"words\\\": [\\n \\\"Same\\\",\\n \\\"professional\\\",\\n \\\"tone\\\"\\n ],\\n \\\"start\\\": 7.7,\\n \\\"end\\\": 10.012\\n },\\n {\\n \\\"text\\\": \\\"Same urgency\\\",\\n \\\"words\\\": [\\n \\\"Same\\\",\\n \\\"urgency\\\"\\n ],\\n \\\"start\\\": 10.012,\\n \\\"end\\\": 11.962\\n },\\n {\\n \\\"text\\\": \\\"But one thing changed where\\\",\\n \\\"words\\\": [\\n \\\"But\\\",\\n \\\"one\\\",\\n \\\"thing\\\",\\n \\\"changed\\\",\\n \\\"where\\\"\\n ],\\n \\\"start\\\": 11.962,\\n \\\"end\\\": 14.064857142857143\\n },\\n {\\n \\\"text\\\": \\\"the money goes\\\",\\n \\\"words\\\": [\\n \\\"the\\\",\\n \\\"money\\\",\\n \\\"goes\\\"\\n ],\\n \\\"start\\\": 14.064857142857143,\\n \\\"end\\\": 15.162\\n },\\n {\\n \\\"text\\\": \\\"Before you pay check three\\\",\\n \\\"words\\\": [\\n \\\"Before\\\",\\n \\\"you\\\",\\n \\\"pay\\\",\\n \\\"check\\\",\\n \\\"three\\\"\\n ],\\n \\\"start\\\": 15.162,\\n \\\"end\\\": 16.945571428571427\\n },\\n {\\n \\\"text\\\": \\\"things sender domain urgency language\\\",\\n \\\"words\\\": [\\n \\\"things\\\",\\n \\\"sender\\\",\\n \\\"domain\\\",\\n \\\"urgency\\\",\\n \\\"language\\\"\\n ],\\n \\\"start\\\": 16.945571428571427,\\n \\\"end\\\": 19.62092857142857\\n },\\n {\\n \\\"text\\\": \\\"and payment route\\\",\\n \\\"words\\\": [\\n \\\"and\\\",\\n \\\"payment\\\",\\n \\\"route\\\"\\n ],\\n \\\"start\\\": 19.62092857142857,\\n \\\"end\\\": 20.836999999999996\\n },\\n {\\n \\\"text\\\": \\\"If the route changed don't\\\",\\n \\\"words\\\": [\\n \\\"If\\\",\\n \\\"the\\\",\\n \\\"route\\\",\\n \\\"changed\\\",\\n \\\"don't\\\"\\n ],\\n \\\"start\\\": 20.837,\\n \\\"end\\\": 22.855874172185434\\n },\\n {\\n \\\"text\\\": \\\"pay from the email\\\",\\n \\\"words\\\": [\\n \\\"pay\\\",\\n \\\"from\\\",\\n \\\"the\\\",\\n \\\"email\\\"\\n ],\\n \\\"start\\\": 22.855874172185434,\\n \\\"end\\\": 24.18700000000001\\n },\\n {\\n \\\"text\\\": \\\"Verify through a second channel\\\",\\n \\\"words\\\": [\\n \\\"Verify\\\",\\n \\\"through\\\",\\n \\\"a\\\",\\n \\\"second\\\",\\n \\\"channel\\\"\\n ],\\n \\\"start\\\": 24.187,\\n \\\"end\\\": 26.887\\n },\\n {\\n \\\"text\\\": \\\"Urgency is not proof\\\",\\n \\\"words\\\": [\\n \\\"Urgency\\\",\\n \\\"is\\\",\\n \\\"not\\\",\\n \\\"proof\\\"\\n ],\\n \\\"start\\\": 26.887,\\n \\\"end\\\": 29.250000000000004\\n },\\n {\\n \\\"text\\\": \\\"It's the red flag\\\",\\n \\\"words\\\": [\\n \\\"It's\\\",\\n \\\"the\\\",\\n \\\"red\\\",\\n \\\"flag\\\"\\n ],\\n \\\"start\\\": 29.25,\\n \\\"end\\\": 31.262\\n }\\n ]\\n },\\n \\\"artifacts\\\": {\\n \\\"preview_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 988380,\\n \\\"sha256\\\": \\\"d01e0595414f405224b72118a8de7d18e24d71ee461928b16222332d3da555b4\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.32,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"silent_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 731233,\\n \\\"sha256\\\": \\\"b930d5b0b1146035dd95a8aa61cf08a372f372864891707a67d94bf51a72a0e5\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.333333,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"qa_frame_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 235038,\\n \\\"sha256\\\": \\\"eb7f948c87691f4344423efa090bdedae74ab559f36a71e3094e1463621dbaad\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\"\\n },\\n \\\"subtitle_style_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 353885,\\n \\\"sha256\\\": \\\"6ad056f7d8b9d0569313597241f8628cbe7b4b606eac5df3244347ff1e7972e6\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x1920\\\"\\n }\\n }\\n}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_7Y7kr1aaTQr9fVNhtZCcmscp"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_crKMv1W4HB2j0eUIbHB7fVRl",
"call_id": "call_crKMv1W4HB2j0eUIbHB7fVRl",
"response_item_id": "fc_0a6bb421a870f111016a148611d4848191876868f1de82f468",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\",\"question\":\"QA this 9-frame contact sheet for mobile-safe captions: do any subtitles appear clipped or over left/right edges? Note overall readability and obvious issues.\"}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
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]
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA this 9-frame contact sheet for mobile-safe captions: do any subtitles appear clipped or over left/right edges? Note overall readability and obvious issues.\n[screenshot]",
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{
"id": "call_LCqALMmG3TGhT8T1b13phJLi",
"call_id": "call_LCqALMmG3TGhT8T1b13phJLi",
"response_item_id": "fc_095061aaeb5aebee016a14861b4edc8191b0e12773d3bfbac1",
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"name": "patch",
"arguments": "{\"mode\":\"replace\",\"path\":\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\",\"old_string\":\" if sc=='same':\\n rounded(draw,(160,1210,920,1320),28,(30,40,59),(90,103,132),2)\\n text(draw,(540,1265),'Same layout. Same tone. Same urgency.',35,(255,255,255),anchor='mm')\\n\",\"new_string\":\" if sc=='same':\\n # No extra renderer text card here; the safe captions carry the phrase.\\n pass\\n\"}"
}
}
],
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{
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"id": "rs_095061aaeb5aebee016a148617653c81918b4ee11c28812cca",
"summary": []
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"diff\": \"--- a//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n+++ b//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n@@ -163,8 +163,8 @@\\n else:\\n draw_email_invoice(draw,t, 'route' if sc in ['route','checks','verify'] else sc)\\n if sc=='same':\\n- rounded(draw,(160,1210,920,1320),28,(30,40,59),(90,103,132),2)\\n- text(draw,(540,1265),'Same layout. Same tone. Same urgency.',35,(255,255,255),anchor='mm')\\n+ # No extra renderer text card here; the safe captions carry the phrase.\\n+ pass\\n if sc in ['checks','verify']: draw_checks(draw,t)\\n if sc=='verify': draw_verify(draw,t)\\n # safe guides very subtle? don't include in final\\n\", \"files_modified\": [\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_LCqALMmG3TGhT8T1b13phJLi"
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{
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{
"id": "call_VrgmGSFsfGJfb1RKQh7l8ls9",
"call_id": "call_VrgmGSFsfGJfb1RKQh7l8ls9",
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"arguments": "{\"mode\":\"replace\",\"new_string\":\" text(draw,(190,1235),'Use a second channel',39,(255,255,255))\\n text(draw,(190,1300),'before paying',36,(247,202,76))\",\"old_string\":\" text(draw,(190,1235),'Do not pay from the email',39,(255,255,255))\\n text(draw,(190,1300),'Verify through a second channel',36,(247,202,76))\",\"path\":\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\"}"
}
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],
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"content": "{\"success\": true, \"diff\": \"--- a//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n+++ b//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n@@ -125,8 +125,8 @@\\n \\n def draw_verify(draw, t):\\n rounded(draw,(150,1190,930,1385),30,(32,42,61),(88,104,137),3)\\n- text(draw,(190,1235),'Do not pay from the email',39,(255,255,255))\\n- text(draw,(190,1300),'Verify through a second channel',36,(247,202,76))\\n+ text(draw,(190,1235),'Use a second channel',39,(255,255,255))\\n+ text(draw,(190,1300),'before paying',36,(247,202,76))\\n # phone/check icon\\n rounded(draw,(730,1240,862,1342),18,(18,24,36),(247,202,76),3)\\n draw.line((760,1298,790,1325,836,1266), fill=(247,202,76), width=9)\\n\", \"files_modified\": [\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
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{
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--enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, rawvideo, from 'fd:':\\n Duration: N/A, start: 0.000000, bitrate: 1194393 kb/s\\n Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 1080x1920, 1194393 kb/s, 24 tbr, 24 tbn\\nStream mapping:\\n Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))\\n[libx264 @ 0x63d364642480] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\\n[libx264 @ 0x63d364642480] profile High, level 4.0, 4:2:0, 8-bit\\n[libx264 @ 0x63d364642480] 264 - core 164 r3108 31e19f9 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=9 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=24 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=20.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 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512kB time=00:00:29.00 bitrate= 144.6kbits/s speed=0.766x \\r[out#0/mp4 @ 0x63d364640d00] video:684kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.409009%\\nframe= 752 fps= 20 q=-1.0 Lsize= 694kB time=00:00:31.20 bitrate= 182.2kbits/s speed=0.819x \\n[libx264 @ 0x63d364642480] frame I:4 Avg QP:12.71 size: 47589\\n[libx264 @ 0x63d364642480] frame P:190 Avg QP:17.40 size: 2217\\n[libx264 @ 0x63d364642480] frame B:558 Avg QP:23.88 size: 158\\n[libx264 @ 0x63d364642480] consecutive B-frames: 1.1% 0.0% 0.0% 98.9%\\n[libx264 @ 0x63d364642480] mb I I16..4: 71.5% 11.4% 17.1%\\n[libx264 @ 0x63d364642480] mb P I16..4: 0.8% 0.1% 0.3% P16..4: 0.5% 0.1% 0.1% 0.0% 0.0% skip:98.2%\\n[libx264 @ 0x63d364642480] mb B I16..4: 0.2% 0.1% 0.0% B16..8: 1.1% 0.0% 0.0% direct: 0.0% skip:98.7% L0:47.4% L1:52.5% BI: 0.0%\\n[libx264 @ 0x63d364642480] 8x8 transform intra:11.4% inter:14.7%\\n[libx264 @ 0x63d364642480] coded y,uvDC,uvAC intra: 10.7% 10.7% 7.9% inter: 0.0% 0.1% 0.1%\\n[libx264 @ 0x63d364642480] i16 v,h,dc,p: 24% 76% 0% 0%\\n[libx264 @ 0x63d364642480] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 36% 11% 53% 0% 0% 0% 0% 0% 0%\\n[libx264 @ 0x63d364642480] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 29% 47% 9% 2% 3% 3% 3% 2% 3%\\n[libx264 @ 0x63d364642480] i8c dc,h,v,p: 77% 12% 10% 1%\\n[libx264 @ 0x63d364642480] Weighted P-Frames: Y:0.0% UV:0.0%\\n[libx264 @ 0x63d364642480] ref P L0: 73.3% 8.1% 14.3% 4.3%\\n[libx264 @ 0x63d364642480] ref B L0: 58.5% 40.5% 1.0%\\n[libx264 @ 0x63d364642480] ref B L1: 96.5% 3.5%\\n[libx264 @ 0x63d364642480] kb/s:178.73\\nffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Duration: 00:00:31.33, start: 0.000000, bitrate: 181 kb/s\\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 178 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n[mp3 @ 0x652a04181c40] Estimating duration from bitrate, this may be inaccurate\\nInput #1, mp3, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/audio/voiceover.mp3':\\n Duration: 00:00:31.32, start: 0.000000, bitrate: 48 kb/s\\n Stream #1:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s\\nStream mapping:\\n Stream #0:0 -> #0:0 (copy)\\n Stream #1:0 -> #0:1 (mp3 (mp3float) -> aac (native))\\nPress [q] to stop, [?] for help\\nOutput #0, mp4, to '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, q=2-31, 178 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 24000 Hz, mono, fltp, 69 kb/s\\n Metadata:\\n encoder : Lavc60.31.102 aac\\nsize= 0kB time=N/A bitrate=N/A speed=N/A \\r[out#0/mp4 @ 0x652a040f5280] video:684kB audio:242kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.075097%\\nsize= 945kB time=00:00:31.31 bitrate= 247.2kbits/s speed=77.1x \\n[aac @ 0x652a041f9180] Qavg: 18380.342\\n \\\"start\\\": 22.855874172185434,\\n \\\"end\\\": 24.18700000000001\\n },\\n {\\n \\\"text\\\": \\\"Verify through a second channel\\\",\\n \\\"words\\\": [\\n \\\"Verify\\\",\\n \\\"through\\\",\\n \\\"a\\\",\\n \\\"second\\\",\\n \\\"channel\\\"\\n ],\\n \\\"start\\\": 24.187,\\n \\\"end\\\": 26.887\\n },\\n {\\n \\\"text\\\": \\\"Urgency is not proof\\\",\\n \\\"words\\\": [\\n \\\"Urgency\\\",\\n \\\"is\\\",\\n \\\"not\\\",\\n \\\"proof\\\"\\n ],\\n \\\"start\\\": 26.887,\\n \\\"end\\\": 29.250000000000004\\n },\\n {\\n \\\"text\\\": \\\"It's the red flag\\\",\\n \\\"words\\\": [\\n \\\"It's\\\",\\n \\\"the\\\",\\n \\\"red\\\",\\n \\\"flag\\\"\\n ],\\n \\\"start\\\": 29.25,\\n \\\"end\\\": 31.262\\n }\\n ]\\n },\\n \\\"artifacts\\\": {\\n \\\"preview_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 967725,\\n \\\"sha256\\\": \\\"a94893693b02356487773798f3b306394595fd64dac82588b600e1134ee318b3\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.32,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"silent_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 710578,\\n \\\"sha256\\\": \\\"afe1cbe04971df7fa800243c8d684ef192452cc7b11b9bcb8b6529cc03087f05\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.333333,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"qa_frame_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 232446,\\n \\\"sha256\\\": \\\"f84055c92e266fbc07c4bb577bbd0d6765835f46a00c4ef5da2c6de234be67eb\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\"\\n },\\n \\\"subtitle_style_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 353885,\\n \\\"sha256\\\": \\\"6ad056f7d8b9d0569313597241f8628cbe7b4b606eac5df3244347ff1e7972e6\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x1920\\\"\\n }\\n }\\n}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_txG2JS5Zm2rId8wKnsTmQxqs"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_y3sbr6Nwe5tDlbPr1vpo3Na3",
"call_id": "call_y3sbr6Nwe5tDlbPr1vpo3Na3",
"response_item_id": "fc_0f7970fecc37ff75016a148652ef708191acf38b41c6f53f17",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\",\"question\":\"QA final contact sheet: any subtitles or renderer text clipped/off-safe-area? Is bottom safe zone OK?\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA final contact sheet: any subtitles or renderer text clipped/off-safe-area? Is bottom safe zone OK?\n[screenshot]",
"tool_call_id": "call_y3sbr6Nwe5tDlbPr1vpo3Na3"
},
{
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{
"id": "call_mS9aGqZQ4pnKtVHLGQSy0a7Q",
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"type": "function",
"function": {
"name": "send_message",
"arguments": "{\"action\":\"list\"}"
}
}
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{
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"id": "rs_000aa050c3c8a923016a148656a79881918452e32de45ab231",
"summary": []
}
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{
"role": "tool",
"content": "{\"targets\": \"Available messaging targets:\\n\\nTelegram:\\n telegram:M G (dm)\\n telegram:JARVIS Command Center / topic 4 (group)\\n telegram:JARVIS Command Center / topic 6 (group)\\n telegram:JARVIS Command Center / topic 17 (group)\\n telegram:JARVIS Command Center / topic 16 (group)\\n telegram:JARVIS Command Center / topic 11 (group)\\n telegram:JARVIS Command Center / topic 8 (group)\\n telegram:JARVIS Command Center / topic 2 (group)\\n telegram:JARVIS Command Center / topic 3 (group)\\n telegram:JARVIS Command Center / topic 5 (group)\\n telegram:JARVIS Command Center / topic 20 (group)\\n telegram:JARVIS Command Center / topic 19 (group)\\n telegram:JARVIS Command Center / topic 10 (group)\\n telegram:JARVIS Command Center / topic 76 (group)\\n telegram:JARVIS Command Center / topic 80 (group)\\n telegram:JARVIS Command Center / topic 1 (group)\\n telegram:JARVIS Command Center / topic 4850 (group)\\n\\nUse these as the \\\"target\\\" parameter when sending.\\nBare platform name (e.g. \\\"telegram\\\") sends to home channel.\"}",
"tool_call_id": "call_mS9aGqZQ4pnKtVHLGQSy0a7Q"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_sBpc9CcqHH96gJWTLXeJlCAx",
"call_id": "call_sBpc9CcqHH96gJWTLXeJlCAx",
"response_item_id": "fc_0214eb787c200c1b016a14865ecf4081918c7238addbe72575",
"type": "function",
"function": {
"name": "send_message",
"arguments": "{\"action\":\"send\",\"target\":\"telegram:JARVIS Command Center / topic 4850\",\"message\":\"Sprint 3.1 Artefakte — Preview v2 Caption Safe\\n\\nPreview MP4:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\n\\nQA Frame Sheet:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\\n\\nSubtitle Style Preview:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"5969\", \"mirrored\": true}",
"tool_call_id": "call_sBpc9CcqHH96gJWTLXeJlCAx"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ujlD8wMKiIeVeQQm7SVu4ZXf",
"call_id": "call_ujlD8wMKiIeVeQQm7SVu4ZXf",
"response_item_id": "fc_0383ee306159ce19016a148668e5788191af60ca482b256c2e",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from hermes_tools import terminal\\ncmd = r'''python - <<'PY'\\nfrom pathlib import Path\\nfrom PIL import Image\\nimport json, hashlib, struct, os\\ncache=Path.home()/'.hermes/cache/images'\\nfiles=sorted(cache.glob('openai_codex_gpt-image-2-*.png'), key=lambda p:p.stat().st_mtime, reverse=True)\\nrecords=[]\\nfor p in files[:15]:\\n chunks=[]\\n data=p.read_bytes()\\n pos=8\\n while pos+8 <= len(data):\\n ln=int.from_bytes(data[pos:pos+4],'big'); typ=data[pos+4:pos+8].decode('latin1','replace')\\n chunks.append(typ); pos += 12+ln\\n if typ=='IEND': break\\n try:\\n im=Image.open(p); info_keys=list(im.info.keys()); res=f'{im.width}x{im.height}'\\n except Exception as e:\\n info_keys=[]; res=str(e)\\n records.append({'path':str(p),'mtime':p.stat().st_mtime,'size':p.stat().st_size,'sha256':hashlib.sha256(data).hexdigest(),'resolution':res,'png_chunks':chunks,'pil_info_keys':info_keys,'has_c2pa_or_synthid_or_provenance': any('c2pa' in c.lower() or 'synth' in c.lower() or 'prov' in c.lower() for c in chunks+info_keys)})\\nprint(json.dumps(records, indent=2)[:20000])\\nPY'''\\nprint(terminal(cmd, timeout=60)['output'])\"}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFIZoDAkAXGinuET2LznqZwE92dYBpZiCnsCEUpfWTHuqZJN_15ruNzn4copg5s4yHmOBHlmvXcnFEjZeGuK0oSleeYhMMwcmHZAjqqgYTi6XgibIlRRUpHeE763k2NO7KdqUixUFem7y6vCyiUOoudjqxNCyH3pGSCUoYMbBE4jUwxSR_u2na-4HGtNEevAszrAA2IOllDEDfwGufCEpAtu4GDxkzCfeL9skDE7VRAOvSdNDIxQokjvc7DyjK-XUMttgm7xhwOs2tfR2dGQ7XLy5AU61kNYm-Pv8tiEDwkwL5PoZx50xagsyzk4_vLgFavwdQuhwj71Y1eFJ1omEfZfIg_NEBtNj_rUoJW0xOn-Lh17rjPBUegOXu7VQKIe_n74kgF0czSukWiqF0tw9UdYtXOSH1oWQC1CEWRJu2A2sKgI-1AJZmQhncuHGvZ82xNQsoUsYU-GKPGzrlfOfen4Lm81NuuUkRLzTRzJ82c8T1P9hr1jMA_o5De25BoGESHDdMSAmFprwNGFf2rWxTr0qefcswLFmRG8Z3s8iygo59cNpAlDmFbL45m0K9NL6Gdwrzg8Y8RT8kXWUp8-uxCS3iun1mNMtaeZH1BZceeBHr0N6aD7eqybqFrMMYHfotqkDbPy_PET3IKb1PTsnHJCo4-heWUUrlVSkUoHvVcxQtEE4n8DvaG8m9WOp67R0ZfoPabNPDa0OJNQ7q1oPZnpk4jnxDbuz2aO02s6i-83gdzrwFAgYLzFIEoAuheyrz53zYkQy_nJ784wymQ48nRDq71_DJQX5OxKTXqu4v-lNlie6FH9_99I-UwsjDJVx-gep1qYKGh2SEhXqMNsSvo3PzwjqHRAqTuaXZXdPiX1zYuLsKP6orG0nziwBI2MF_pXixy55Q2IyR1gvm6fX-E-ztQ0uRSiU-V3WgIXEnGBAl6FVoRRH-Bb4iz6IEkbLx5Mgi7Tb6LEDNLB_mseh-yA1pSsVNTeWKYphhV1rafq8URkuG1VYTAQNV0gzYokCWpefH3XPkNlAA5bYNSXx5PMGqNV8iJh_UrHXo5QgXSYnZ2gX5to3xUvVEiBhsopmktz-R25lLdS3Tgd56Edu2ykoJFFLZYDTKtLXxpNTGlY9s6GLa4h_k_ocuQgNj-Ty4_vRtmNqmguNXPnie9bGOvKtZE796WF3pSddOjyavZdfA5qjORDHGLyySaMu__uqw8cgdmwpvQNLkquJ37aUhJRHcBsjk7QjZ8kutJilYMsGILClEz4cK6SNLb7Y9rTgzJPAiNR9wc6Nr0zbqdKNgUh2V1nMQaRP7JCLM6kWfUwSPue7gkmxrtFwQ8A1UhSEj5DRNg-FDDsdaF08STUSJojezzDO8DxFfFMMz5SYVRfQv3qI-WIckB0Uwa_zDk4UVISrHKVVrABQuMvjitUT_1IGtB78wpdfOqBYl42EA3LijYWdx_7oa856_QWBqwdDV05kdC84kMrMrLpRpcm7yTurBej8frqVYk2Y2Qk_sBQkMCKQRZLgPtlmzdcQG3ZfmWLnIvLzGjdCwmrh6MMS8oQE4AdwSWT5eEZ5oOQguGPfL3nGGwwSllLWuW1jEqjcJoBxP1oWyo9l3A3L6eOZsSXjetMiRtLhJ2OgQYR8_qXhOYfPpxDlydACkg4_VK-tcCdwjezKmytyRQkVDkujagQztQ==",
"id": "rs_0383ee306159ce19016a148666321c81919237907581c6a1f9",
"summary": []
}
]
},
{
"role": "tool",
"content": "{\"status\": \"success\", \"output\": \"[\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\\\",\\n \\\"mtime\\\": 1779729625.3366475,\\n \\\"size\\\": 2029976,\\n \\\"sha256\\\": \\\"bdcbc300f918c07ebe4cb5620afa633ff7f8e02184f3d4f6d43c522745465baf\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171425_7e5c5015.png\\\",\\n \\\"mtime\\\": 1779722065.1538935,\\n \\\"size\\\": 2342028,\\n \\\"sha256\\\": \\\"9ad93033d1b52a99a00ee402fe0bfd03ca38367020b5cee59325ed660e6b5151\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171324_5e5577a3.png\\\",\\n \\\"mtime\\\": 1779722004.194337,\\n \\\"size\\\": 2409107,\\n \\\"sha256\\\": \\\"759952a3a2e5bd582b86c71a499243a9ed0fecf1fbbda936fd17dbb82d0cca83\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171159_68e5c2da.png\\\",\\n \\\"mtime\\\": 1779721919.6983407,\\n \\\"size\\\": 2502677,\\n \\\"sha256\\\": \\\"5452c1609d4b5467757e457e5b4426a7ffbd218db92c750b29a907f9c8d1bf2b\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171106_30a3b93c.png\\\",\\n \\\"mtime\\\": 1779721866.1875107,\\n \\\"size\\\": 1850047,\\n \\\"sha256\\\": \\\"cc45d1c4dbf591abfad2a060b9065a23b063984b3cbafdcbfeda974d88303c55\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_171011_d48ab227.png\\\",\\n \\\"mtime\\\": 1779721811.1904747,\\n \\\"size\\\": 1846484,\\n \\\"sha256\\\": \\\"fe77a8321cf5108c6891126df944b6bf3c56098904cc18786ca0b38734dddbc5\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_170919_3ff53fa7.png\\\",\\n \\\"mtime\\\": 1779721759.0169618,\\n \\\"size\\\": 1770763,\\n \\\"sha256\\\": \\\"858219ba9dc2356587060a0d317b2068a9af826ec790c12b2ee4007e006df69a\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_170840_142e83a8.png\\\",\\n \\\"mtime\\\": 1779721720.6722121,\\n \\\"size\\\": 2111685,\\n \\\"sha256\\\": \\\"1848b366679952bf38e0d6285554d6c3590d58c6d0f7efb6f102ea1b279db217\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_170731_5038e2ce.png\\\",\\n \\\"mtime\\\": 1779721651.5509584,\\n \\\"size\\\": 1819479,\\n \\\"sha256\\\": \\\"651b0ff2bccdb0041f28a484f9f6357398e34491844c25ce2b652661943f5591\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_170637_53794aad.png\\\",\\n \\\"mtime\\\": 1779721597.7238116,\\n \\\"size\\\": 1696594,\\n \\\"sha256\\\": \\\"7d3c2538b36671dda3c15b4bfacd58eb34deb908fadfc92a461164a0a5a27b24\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_170531_da9173a6.png\\\",\\n \\\"mtime\\\": 1779721531.958457,\\n \\\"size\\\": 1700699,\\n \\\"sha256\\\": \\\"6b674dd1f916fcf257a3f0d10e4988d3508a322097aa1f783aa12e8d0efed047\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_163945_bde6f7ba.png\\\",\\n \\\"mtime\\\": 1779719985.3383734,\\n \\\"size\\\": 2308943,\\n \\\"sha256\\\": \\\"a295e458b6dbf3fa2fa198a325c52a3b46c7054665bb9419234399ceef0235bd\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_163829_a2a038ee.png\\\",\\n \\\"mtime\\\": 1779719909.6382785,\\n \\\"size\\\": 1745510,\\n \\\"sha256\\\": \\\"70da5c3d820c5a1b28e8a6ad6afec09d713ec811a3ea2b7af89af94d38f95d69\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_163710_c68f8acc.png\\\",\\n \\\"mtime\\\": 1779719830.9154792,\\n \\\"size\\\": 1828729,\\n \\\"sha256\\\": \\\"52892b0c366981aba992cf016d55c1b091cb9c024baa34dfd9363cc50cab68aa\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n },\\n {\\n \\\"path\\\": \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_155120_1c40f80e.png\\\",\\n \\\"mtime\\\": 1779717080.070971,\\n \\\"size\\\": 1780097,\\n \\\"sha256\\\": \\\"35e4d6f9b90be70e7b5b4a3c862595b37515cd701a1c5f9d8866afccd936e390\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"pil_info_keys\\\": [],\\n \\\"has_c2pa_or_synthid_or_provenance\\\": false\\n }\\n]\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.44}",
"tool_call_id": "call_ujlD8wMKiIeVeQQm7SVu4ZXf"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_jgjpYRetz4m1Bca0rdiITVwp",
"call_id": "call_jgjpYRetz4m1Bca0rdiITVwp",
"response_item_id": "fc_0890c728d0b832c0016a14867671308191b6ca2797bab78365",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\np=Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_192025_93a777df.png')\\ndata=p.read_bytes(); pos=8\\nwhile pos+8 <= len(data):\\n ln=int.from_bytes(data[pos:pos+4],'big'); typ=data[pos+4:pos+8]\\n payload=data[pos+8:pos+8+ln]\\n print(typ.decode('latin1'), ln, payload[:80])\\n pos += 12+ln\\n if typ==b'IEND': break\\nPY\",\"timeout\":30}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Investigating caBX and C2PA**\n\nI'm exploring the caBX chunk and whether it indicates CAI. I think C2PA might utilize the caBX chunk, possibly related to a \"CAI claim box.\" It's essential to report when caBX is present, as it likely signifies provenance. I need to examine the chunk further. PNG files contain caBX, but PIL doesn’t expose keys. It's intriguing how the generated images retain caBX, even if processed through PIL. The standard metadata isn’t human-readable, but the presence of the provenance chunk is significant.",
"reasoning_content": "**Investigating caBX and C2PA**\n\nI'm exploring the caBX chunk and whether it indicates CAI. I think C2PA might utilize the caBX chunk, possibly related to a \"CAI claim box.\" It's essential to report when caBX is present, as it likely signifies provenance. I need to examine the chunk further. PNG files contain caBX, but PIL doesn’t expose keys. It's intriguing how the generated images retain caBX, even if processed through PIL. The standard metadata isn’t human-readable, but the presence of the provenance chunk is significant.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_0890c728d0b832c0016a148671daec81918f6ab270c1fe6b5d",
"summary": [
{
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"text": "**Investigating caBX and C2PA**\n\nI'm exploring the caBX chunk and whether it indicates CAI. I think C2PA might utilize the caBX chunk, possibly related to a \"CAI claim box.\" It's essential to report when caBX is present, as it likely signifies provenance. I need to examine the chunk further. PNG files contain caBX, but PIL doesn’t expose keys. It's intriguing how the generated images retain caBX, even if processed through PIL. The standard metadata isn’t human-readable, but the presence of the provenance chunk is significant."
}
]
}
]
},
{
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{
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"content": "",
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"arguments": "{\"code\":\"from hermes_tools import terminal\\nprint(terminal('python - <<\\\\'PY\\\\'\\\\nfrom pathlib import Path\\\\nimport json\\\\nmanifest=Path(\\\\'/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/manifest_preview_v2_caption_safe.json\\\\')\\\\ndata=json.loads(manifest.read_text())\\\\nfor key in [\\\\'preview_mp4\\\\',\\\\'qa_frame_sheet\\\\',\\\\'subtitle_style_preview\\\\']:\\\\n data[\\\\'artifacts\\\\'][key].update({\\\\n \\\\'telegram_delivery_attempted\\\\': True,\\\\n \\\\'telegram_delivery_verified\\\\': True,\\\\n \\\\'telegram_message_id\\\\': \\\\'5969\\\\',\\\\n \\\\'telegram_target\\\\': \\\\'telegram:JARVIS Command Center / topic 4850\\\\'\\\\n })\\\\ndata[\\\\'thread_delivery\\\\']={\\\\'telegram_delivery_attempted\\\\':True,\\\\'telegram_delivery_verified\\\\':True,\\\\'telegram_message_id\\\\':\\\\'5969\\\\',\\\\'chat_id\\\\':\\\\'-1003907117629\\\\',\\\\'target\\\\':\\\\'telegram:JARVIS Command Center / topic 4850\\\\'}\\\\nmanifest.write_text(json.dumps(data,indent=2),encoding=\\\\'utf-8\\\\')\\\\nprint(json.dumps({k:data[\\\\'artifacts\\\\'][k] for k in [\\\\'preview_mp4\\\\',\\\\'qa_frame_sheet\\\\',\\\\'subtitle_style_preview\\\\']}, indent=2))\\\\nPY', timeout=30)['output'])\"}"
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],
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"reasoning": "**Updating delivery manifest**\n\nAlright, I need to update the manifest with the delivery metadata. I think using execute_code could be helpful here. I might need to load the manifest and then add telegram for the artifacts. I just want to make sure I'm following the right steps and capturing everything necessary for a smooth update. Let’s get into it and ensure everything is in order for this task!",
"reasoning_content": "**Updating delivery manifest**\n\nAlright, I need to update the manifest with the delivery metadata. I think using execute_code could be helpful here. I might need to load the manifest and then add telegram for the artifacts. I just want to make sure I'm following the right steps and capturing everything necessary for a smooth update. Let’s get into it and ensure everything is in order for this task!",
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"summary": [
{
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"text": "**Updating delivery manifest**\n\nAlright, I need to update the manifest with the delivery metadata. I think using execute_code could be helpful here. I might need to load the manifest and then add telegram for the artifacts. I just want to make sure I'm following the right steps and capturing everything necessary for a smooth update. Let’s get into it and ensure everything is in order for this task!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"preview_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 967725,\\n \\\"sha256\\\": \\\"a94893693b02356487773798f3b306394595fd64dac82588b600e1134ee318b3\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.32,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5969\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"qa_frame_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 232446,\\n \\\"sha256\\\": \\\"f84055c92e266fbc07c4bb577bbd0d6765835f46a00c4ef5da2c6de234be67eb\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5969\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"subtitle_style_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 353885,\\n \\\"sha256\\\": \\\"6ad056f7d8b9d0569313597241f8628cbe7b4b606eac5df3244347ff1e7972e6\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5969\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n }\\n}\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.45}",
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{
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"reasoning": "**Checking git status and commits**\n\nI’m thinking it’s time to check the git status since the user may want to commit changes. They didn’t specifically ask this time, but they did before, so it seems like a logical step to consider. Since there have been code changes, it’s likely a good idea to commit. I might also want to run all the tests. Let’s check our status first to see what’s up!",
"reasoning_content": "**Checking git status and commits**\n\nI’m thinking it’s time to check the git status since the user may want to commit changes. They didn’t specifically ask this time, but they did before, so it seems like a logical step to consider. Since there have been code changes, it’s likely a good idea to commit. I might also want to run all the tests. Let’s check our status first to see what’s up!",
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"summary": [
{
"type": "summary_text",
"text": "**Checking git status and commits**\n\nI’m thinking it’s time to check the git status since the user may want to commit changes. They didn’t specifically ask this time, but they did before, so it seems like a logical step to consider. Since there have been code changes, it’s likely a good idea to commit. I might also want to run all the tests. Let’s check our status first to see what’s up!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"output\": \"M autoshorts/rendering/static_captions.py\\n?? autoshorts/config/image_generation_policy.json\\n?? autoshorts/production/image_generation_policy.py\\n?? docs/image_generation_guidelines.md\\n?? tests/test_caption_safe_zone.py\\n?? tests/test_image_generation_policy.py\\n........................ [100%]\\n24 passed in 0.41s\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_k95r6BYgEJbXhXHoskOaNuw0"
},
{
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"content": "",
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"content": "{\"output\": \"diff --git a/autoshorts/rendering/static_captions.py b/autoshorts/rendering/static_captions.py\\nindex 5fe273c..5d238fa 100644\\n--- a/autoshorts/rendering/static_captions.py\\n+++ b/autoshorts/rendering/static_captions.py\\n@@ -5,12 +5,13 @@ Caption rule for TrueTraceShorts previews/finals:\\n - At least three words are visible per default segment whenever possible.\\n - Only the active spoken word changes highlight.\\n - The highlight moves left-to-right until the next phrase segment replaces it.\\n+- Caption layout must remain inside the mobile safe zone.\\n \\\"\\\"\\\"\\n \\n from __future__ import annotations\\n \\n from dataclasses import dataclass\\n-from typing import Iterable, Sequence\\n+from typing import Sequence\\n \\n \\n @dataclass(frozen=True)\\n@@ -34,6 +35,42 @@ class StaticCaptionSegment:\\n return \\\" \\\".join(self.words)\\n \\n \\n+@dataclass(frozen=True)\\n+class CaptionLayoutConfig:\\n+ video_width: int = 1080\\n+ video_height: int = 1920\\n+ horizontal_margin_px: int = 60\\n+ bottom_margin_px: int = 260\\n+ max_width_ratio: float = 0.88\\n+ max_lines: int = 2\\n+ font_size_px: int = 68\\n+ min_font_size_px: int = 46\\n+ line_gap_px: int = 14\\n+ word_gap_px: int = 18\\n+ highlight_pad_x_px: int = 10\\n+ highlight_pad_y_px: int = 5\\n+\\n+ @property\\n+ def safe_width_px(self) -> int:\\n+ ratio_width = int(self.video_width * self.max_width_ratio)\\n+ margin_width = self.video_width - 2 * self.horizontal_margin_px\\n+ return min(ratio_width, margin_width)\\n+\\n+\\n+@dataclass(frozen=True)\\n+class CaptionLayout:\\n+ lines: tuple[tuple[str, ...], ...]\\n+ font_size_px: int\\n+ left_px: int\\n+ right_px: int\\n+ top_px: int\\n+ bottom_px: int\\n+ width_px: int\\n+ height_px: int\\n+ word_boxes: tuple[tuple[int, int, int, int], ...]\\n+ active_word_box: tuple[int, int, int, int] | None\\n+\\n+\\n def build_static_caption_segments(\\n word_cues: Sequence[WordCue],\\n *,\\n@@ -94,6 +131,63 @@ def build_static_caption_segments(\\n return tuple(segments)\\n \\n \\n+def build_safe_caption_segments(\\n+ word_cues: Sequence[WordCue],\\n+ *,\\n+ layout: CaptionLayoutConfig | None = None,\\n+ min_words: int = 3,\\n+ target_words: int = 6,\\n+ max_words: int = 7,\\n+) -> tuple[StaticCaptionSegment, ...]:\\n+ \\\"\\\"\\\"Build phrase segments that are pre-split to fit the caption safe zone.\\n+\\n+ The function keeps the established 3–7 word phrase behavior, but refuses to\\n+ create a segment that would need more than two lines or exceed the safe\\n+ width at the minimum allowed font size. Very long tokens are handled by\\n+ dynamic shrink in layout, and if a phrase is still too wide the segment is\\n+ split earlier.\\n+ \\\"\\\"\\\"\\n+ cfg = layout or CaptionLayoutConfig()\\n+ base_segments = build_static_caption_segments(\\n+ word_cues,\\n+ min_words=min_words,\\n+ target_words=target_words,\\n+ max_words=max_words,\\n+ )\\n+ safe_segments: list[StaticCaptionSegment] = []\\n+ for segment in base_segments:\\n+ pairs = [(idx, word_cues[idx]) for idx in segment.word_indices]\\n+ current: list[tuple[int, WordCue]] = []\\n+ for pair in pairs:\\n+ candidate = current + [pair]\\n+ candidate_words = tuple(cue.word for _, cue in candidate)\\n+ if len(candidate_words) <= max_words and _fits_caption_layout(candidate_words, cfg):\\n+ current = candidate\\n+ continue\\n+ if current:\\n+ safe_segments.append(_segment_from_chunk(current))\\n+ current = [pair]\\n+ else:\\n+ current = [pair]\\n+ if current:\\n+ # Try to merge a too-small tail back only when still safe.\\n+ if (\\n+ len(current) < min_words\\n+ and safe_segments\\n+ and len(safe_segments[-1].words) + len(current) <= max_words\\n+ ):\\n+ prev = safe_segments[-1]\\n+ merged_pairs = [(idx, word_cues[idx]) for idx in prev.word_indices] + current\\n+ merged_words = tuple(cue.word for _, cue in merged_pairs)\\n+ if _fits_caption_layout(merged_words, cfg):\\n+ safe_segments[-1] = _segment_from_chunk(merged_pairs)\\n+ else:\\n+ safe_segments.append(_segment_from_chunk(current))\\n+ else:\\n+ safe_segments.append(_segment_from_chunk(current))\\n+ return tuple(safe_segments)\\n+\\n+\\n def _segment_from_chunk(chunk: Sequence[tuple[int, WordCue]]) -> StaticCaptionSegment:\\n return StaticCaptionSegment(\\n start=chunk[0][1].start,\\n@@ -129,3 +223,138 @@ def caption_state_at(word_cues: Sequence[WordCue], segments: Sequence[StaticCapt\\n \\\"active_word_index\\\": active_index,\\n \\\"active_word\\\": word_cues[active_index].word,\\n }\\n+\\n+\\n+def layout_caption_segment(\\n+ words: Sequence[str],\\n+ *,\\n+ active_word: str | None = None,\\n+ config: CaptionLayoutConfig | None = None,\\n+) -> CaptionLayout:\\n+ cfg = config or CaptionLayoutConfig()\\n+ if not words:\\n+ raise ValueError(\\\"words must not be empty\\\")\\n+\\n+ for size in range(cfg.font_size_px, cfg.min_font_size_px - 1, -2):\\n+ lines = _wrap_words(words, cfg, size)\\n+ metrics = _measure_lines(lines, cfg, size, active_word=active_word)\\n+ if len(lines) <= cfg.max_lines and metrics[\\\"width\\\"] <= cfg.safe_width_px:\\n+ return _layout_from_metrics(lines, cfg, size, metrics)\\n+\\n+ # Last resort: return minimum-size layout; callers can split earlier.\\n+ lines = _wrap_words(words, cfg, cfg.min_font_size_px)\\n+ if len(lines) > cfg.max_lines:\\n+ lines = lines[: cfg.max_lines - 1] + (tuple(word for line in lines[cfg.max_lines - 1 :] for word in line),)\\n+ metrics = _measure_lines(lines, cfg, cfg.min_font_size_px, active_word=active_word)\\n+ return _layout_from_metrics(lines, cfg, cfg.min_font_size_px, metrics)\\n+\\n+\\n+def _fits_caption_layout(words: Sequence[str], cfg: CaptionLayoutConfig) -> bool:\\n+ layout = layout_caption_segment(words, active_word=words[0] if words else None, config=cfg)\\n+ return len(layout.lines) <= cfg.max_lines and layout.width_px <= cfg.safe_width_px\\n+\\n+\\n+def _font(size: int):\\n+ from PIL import ImageFont\\n+\\n+ for path in (\\n+ \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\",\\n+ \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\",\\n+ ):\\n+ try:\\n+ return ImageFont.truetype(path, size=size)\\n+ except OSError:\\n+ continue\\n+ return ImageFont.load_default()\\n+\\n+\\n+def _word_width(word: str, size: int) -> int:\\n+ from PIL import Image, ImageDraw\\n+\\n+ image = Image.new(\\\"RGB\\\", (10, 10))\\n+ draw = ImageDraw.Draw(image)\\n+ bbox = draw.textbbox((0, 0), word, font=_font(size), stroke_width=2)\\n+ return bbox[2] - bbox[0]\\n+\\n+\\n+def _line_width(line: Sequence[str], cfg: CaptionLayoutConfig, size: int) -> int:\\n+ if not line:\\n+ return 0\\n+ return sum(_word_width(word, size) for word in line) + cfg.word_gap_px * (len(line) - 1)\\n+\\n+\\n+def _wrap_words(words: Sequence[str], cfg: CaptionLayoutConfig, size: int) -> tuple[tuple[str, ...], ...]:\\n+ lines: list[list[str]] = [[]]\\n+ for word in words:\\n+ current = lines[-1]\\n+ candidate = current + [word]\\n+ if current and _line_width(candidate, cfg, size) > cfg.safe_width_px:\\n+ lines.append([word])\\n+ else:\\n+ current.append(word)\\n+ return tuple(tuple(line) for line in lines if line)\\n+\\n+\\n+def _measure_lines(\\n+ lines: Sequence[Sequence[str]],\\n+ cfg: CaptionLayoutConfig,\\n+ size: int,\\n+ *,\\n+ active_word: str | None,\\n+) -> dict[str, object]:\\n+ line_height = int(size * 1.15)\\n+ widths = [_line_width(line, cfg, size) for line in lines]\\n+ width = max(widths) if widths else 0\\n+ height = len(lines) * line_height + max(0, len(lines) - 1) * cfg.line_gap_px\\n+ left = max(cfg.horizontal_margin_px, (cfg.video_width - width) // 2)\\n+ top = cfg.video_height - cfg.bottom_margin_px - height\\n+ word_boxes: list[tuple[int, int, int, int]] = []\\n+ active_box = None\\n+ for line_index, line in enumerate(lines):\\n+ line_width = _line_width(line, cfg, size)\\n+ x = max(cfg.horizontal_margin_px, (cfg.video_width - line_width) // 2)\\n+ y = top + line_index * (line_height + cfg.line_gap_px)\\n+ for word in line:\\n+ ww = _word_width(word, size)\\n+ box = (x, y, x + ww, y + line_height)\\n+ word_boxes.append(box)\\n+ if active_box is None and active_word == word:\\n+ active_box = (\\n+ max(cfg.horizontal_margin_px, box[0] - cfg.highlight_pad_x_px),\\n+ box[1] - cfg.highlight_pad_y_px,\\n+ min(cfg.video_width - cfg.horizontal_margin_px, box[2] + cfg.highlight_pad_x_px),\\n+ box[3] + cfg.highlight_pad_y_px,\\n+ )\\n+ x += ww + cfg.word_gap_px\\n+ return {\\n+ \\\"width\\\": width,\\n+ \\\"height\\\": height,\\n+ \\\"left\\\": left,\\n+ \\\"top\\\": top,\\n+ \\\"word_boxes\\\": tuple(word_boxes),\\n+ \\\"active_word_box\\\": active_box,\\n+ }\\n+\\n+\\n+def _layout_from_metrics(\\n+ lines: Sequence[Sequence[str]],\\n+ cfg: CaptionLayoutConfig,\\n+ size: int,\\n+ metrics: dict[str, object],\\n+) -> CaptionLayout:\\n+ left = int(metrics[\\\"left\\\"])\\n+ top = int(metrics[\\\"top\\\"])\\n+ width = int(metrics[\\\"width\\\"])\\n+ height = int(metrics[\\\"height\\\"])\\n+ return CaptionLayout(\\n+ lines=tuple(tuple(line) for line in lines),\\n+ font_size_px=size,\\n+ left_px=left,\\n+ right_px=left + width,\\n+ top_px=top,\\n+ bottom_px=top + height,\", \"exit_code\": 0, \"error\": null}",
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"content": "Sir, **Sprint 3.1 — Image Provider Audit + Subtitle Safe-Zone Fix ist ausgeführt.**\n\nKurzfassung: \n**Image Provider ist verifiziert OpenAI/Codex. `gpt-image-2-high` ist verfügbar und funktioniert. Captions sind jetzt layout-getestet und in Preview v2 innerhalb der Safe-Zone.** Kein Wan, kein I2V, kein T2V, kein Posting. Wie gewünscht: nützlich statt hübsch-vernebelt.\n\n## 1. Image Provider Audit Result\n\nAktuelle Hermes-Konfiguration nach Audit/Fix:\n\n```text\nimage_gen.provider = openai-codex\nimage_gen.model = gpt-image-2-high\n```\n\nVor Sprint 3.1 war gesetzt:\n\n```text\nimage_gen.provider = openai-codex\nimage_gen.model = gpt-image-2-medium\n```\n\nIch habe nach erfolgreichem Capability-Test auf **`gpt-image-2-high`** umgestellt, damit künftige Premium-Styleframes standardmässig die beste verfügbare GPT-Image-Stufe nutzen.\n\nMain model/provider der laufenden Hermes-Session:\n\n```text\nmodel.provider = openai-codex\nmodel.default = gpt-5.5\nbase_url = https://chatgpt.com/backend-api/codex\n```\n\n## 2. Verfügbare Image-Provider / Fallbacks\n\nRegistrierte Image-Provider:\n\n```text\nopenai-codex\navailable: true\ndefault_model: gpt-image-2-medium\nmodels:\n- gpt-image-2-low\n- gpt-image-2-medium\n- gpt-image-2-high\n```\n\n```text\nopenai\navailable: false\nreason: kein separater OpenAI API Key aktiv\nmodels:\n- gpt-image-2-low\n- gpt-image-2-medium\n- gpt-image-2-high\n```\n\n```text\nxai\navailable: false\nmodels:\n- grok-imagine-image\n- grok-imagine-image-quality\n```\n\nFallback-Logik im Hermes-Registry-Code:\n\n- Wenn `image_gen.provider` explizit gesetzt ist, gewinnt dieser Provider.\n- Wenn nicht gesetzt, würde Hermes bei mehreren Providern Legacy/Fallback-Logik nutzen.\n- Für uns ist jetzt explizit gepinnt:\n\n```text\nallowed provider: openai-codex\n```\n\n## 3. Wurde `gpt-image-2-medium` wirklich genutzt?\n\nJa.\n\nDie letzten Styleframe-/Image-Runs liegen unter:\n\n```text\n/home/agent/.hermes/cache/images/\n```\n\nBeispiele:\n\n```text\nopenai_codex_gpt-image-2-medium_20260525_171425_7e5c5015.png\nopenai_codex_gpt-image-2-medium_20260525_171324_5e5577a3.png\nopenai_codex_gpt-image-2-medium_20260525_171159_68e5c2da.png\n...\n```\n\nDas belegt:\n\n```text\nprovider: openai-codex\nmodel/tier: gpt-image-2-medium\nstorage: ~/.hermes/cache/images/\n```\n\nKeine Hinweise, dass die letzten Styleframes lokal, über ComfyUI, Wan, Qwen-Image oder andere lokale Generatoren erzeugt wurden.\n\n## 4. Höherwertiger Modus verfügbar?\n\nJa.\n\nVerfügbar laut Provider-Katalog:\n\n```text\ngpt-image-2-low\ngpt-image-2-medium\ngpt-image-2-high\n```\n\n`gpt-image-2-high` ist der höchste verfügbare Modus im Hermes/OpenAI-Codex Image Provider.\n\nKein separates `images-with-thinking` Profil im aktuellen Provider-Code gefunden. Der Codex-Backend-Pfad nutzt intern:\n\n```text\nResponses API image_generation tool\nAPI_MODEL = gpt-image-2\nquality = low | medium | high\nhost chat model = gpt-5.4\n```\n\n## 5. Kontrollierter Capability-Test\n\nAusgeführt: **1 Testbild**, neutral, keine Batch-Orgie.\n\nResult:\n\n```text\nsuccess: true\nprovider: openai-codex\nmodel: gpt-image-2-high\nquality: high\nsize: 1024x1536\naspect_ratio: portrait\nelapsed: 60.93s\n```\n\nTestbild:\n\n```text\n/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\n```\n\nAudit-Kopie:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/image_provider_audit/capability_test_openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\n```\n\nMetadata:\n\n```text\nfile_size: 2029976 bytes\nsha256: bdcbc300f918c07ebe4cb5620afa633ff7f8e02184f3d4f6d43c522745465baf\nresolution: 1024x1536\n```\n\n## 6. Provenance / C2PA / SynthID\n\nPNG-Chunk-Inspection der OpenAI/Codex-Bilder zeigt:\n\n```text\nIHDR\ncaBX\nIDAT\nIEND\n```\n\nDer `caBX` Chunk enthält JUMBF/C2PA-Struktur, u. a.:\n\n```text\njumb\njumdc2pa\nurn:c2pa:\n```\n\nBewertung:\n\n- **C2PA/Content Authenticity Provenance ist im originalen PNG-Cache vorhanden.**\n- PIL zeigt keine normalen EXIF/Text-Keys an.\n- Die Roh-PNGs aus `~/.hermes/cache/images/` behalten den `caBX` Chunk.\n- Abgeleitete JPEGs/MP4s aus Renderer/Contact-Sheets erhalten diese PNG-Provenance-Metadaten nicht zuverlässig. Erwartbar, leider. Medienpipelines sind da selten Gentlemen.\n\n## 7. Image Generation Guidelines erstellt\n\nErstellt:\n\n```text\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/image_generation_guidelines.md\n```\n\nEnthält:\n\n- Prompt Template\n- Quality Checklist\n- Rejection Criteria\n- Styleframe Budget Policy\n- Scene Consistency Rules\n- Text/Logo/Pseudo-Writing Blocker\n\nWichtige Regel:\n\n```text\nFake-Invoice / Email / Payment Route Mockups bleiben deterministisch renderer-owned.\nAI Images nur für Premium-Styleframes/Keyframes, nicht für einfache UI-/Invoice-Mockups.\n```\n\n## 8. Image Generation Policy ergänzt\n\nCode/config ergänzt:\n\n```text\nautoshorts/production/image_generation_policy.py\nautoshorts/config/image_generation_policy.json\n```\n\nPolicy:\n\n```text\nallowed_image_provider = openai-codex\npreferred_image_model = best_available_gpt_image_model\npreferred_quality = highest_available_reasonable\nmax_styleframes_per_scene_without_approval = 3\nmax_retries_per_styleframe = 1\nno_random_batches = true\nno_ai_images_for_mockups_when_deterministic_renderer_is_better = true\n```\n\nCommit:\n\n```text\ncf66672 fix: enforce caption safe zones and image policy\n```\n\n## 9. Subtitle Safe-Zone Fix\n\nCode ergänzt in:\n\n```text\nautoshorts/rendering/static_captions.py\n```\n\nNeue Funktionen/Modelle:\n\n```text\nCaptionLayoutConfig\nCaptionLayout\nbuild_safe_caption_segments()\nlayout_caption_segment()\n```\n\nHarte Caption-Regeln umgesetzt:\n\n```text\nmax_width_ratio: 0.86\nhorizontal_margin_px: 70\nbottom_margin_px: 310\nmax_lines: 2\nfont_size_px: 66\nmin_font_size_px: 48\npreferred segment length: 4–7 words\n```\n\nValidiert:\n\n```text\ncaption_layout_validation.passed = true\nviolations = []\n```\n\nAktives Wort bleibt innerhalb der Bounds. Kein Caption-Text läuft links/rechts aus dem Safe-Bereich.\n\n## 10. Tests ergänzt\n\nNeue Tests:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_image_generation_policy.py\n```\n\nAbgedeckt:\n\n- long phrase wraps into 2 lines\n- caption never exceeds safe width\n- active word highlight never exceeds safe bounds\n- no word clipped left/right\n- max 2 lines enforced\n- phrase split before >2 lines\n- bottom safe-zone respected\n- domain strings like `example-company.com` handled safely\n- image provider policy pins OpenAI/Codex\n- no random batches / deterministic mockups preference\n\nPytest Ergebnis:\n\n```text\n24 passed in 0.41s\n```\n\nGetestet:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_static_caption_segments.py\ntests/test_image_generation_policy.py\ntests/test_proof_short_mode.py\ntests/test_pilot_production_v2.py\n```\n\n## 11. Preview v2 erstellt\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nVersion:\n\n```text\npreview_v2_caption_safe\n```\n\nÄnderungen:\n\n- gleiche Story\n- gleiche Voiceover\n- deterministische screen/mockup visuals\n- Caption Layout gefixt\n- keine AI-Bilder\n- kein Wan\n- kein I2V/T2V\n- kein Posting\n\nManifest:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/manifest_preview_v2_caption_safe.json\n```\n\n## 12. Telegram Delivery\n\nGesendet an:\n\n```text\ntelegram:JARVIS Command Center / topic 4850\n```\n\nTelegram Status:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\n## 13. Artefakte\n\nPreview MP4:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\n\nfile_exists: true\nfile_size: 967725 bytes\nsha256: a94893693b02356487773798f3b306394595fd64dac82588b600e1134ee318b3\nduration: 31.32s\nresolution: 1080x1920\nfps: 24/1\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\nQA Frame Sheet:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\n\nfile_exists: true\nfile_size: 232446 bytes\nsha256: f84055c92e266fbc07c4bb577bbd0d6765835f46a00c4ef5da2c6de234be67eb\nresolution: 810x1440\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\nSubtitle Style Preview:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\n\nfile_exists: true\nfile_size: 353885 bytes\nsha256: 6ad056f7d8b9d0569313597241f8628cbe7b4b606eac5df3244347ff1e7972e6\nresolution: 1080x1920\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\n## 14. QA Ergebnis\n\nCaption Safe-Zone:\n\n```text\npass\n```\n\nVisuelle QA:\n\n- keine Subtitle-Clips links/rechts\n- max. 2 Zeilen\n- aktive Wörter bleiben innerhalb der Safe-Zone\n- Bottom-Safe-Zone eingehalten\n- lange Phrasen wurden gesplittet:\n - `Before you pay check / three`\n - `things sender domain / urgency language`\n - `Verify through a second / channel`\n- kein Caption-Kasten\n- keine Vollbreiten-Bar\n- white/yellow active-word style bleibt erhalten\n\n## 15. Nicht ausgeführt\n\n```text\nwan_used: false\ni2v_used: false\nt2v_used: false\nfull_final: false\nnew_styleframe_batch: false\nnew_ai_images_for_preview: false\nposting: false\nplatform_social_api: false\nscheduling: false\n```\n\nEinzige Bildgeneration in Sprint 3.1 war der erlaubte **1-Bild Capability-Test**.\n\n## 16. Offene Punkte\n\n- Hermes config ist auf `gpt-image-2-high` gesetzt. Falls ein langlebiger Gateway-Prozess alte Image-Tool-Config cached, wäre später `/restart` oder `/reset` sinnvoll.\n- Provenance/C2PA bleibt im originalen PNG-Cache erhalten, aber nicht garantiert in abgeleiteten JPEG/MP4-Artefakten.\n- Für zukünftige Premium-Styleframes sollten wir nur noch mit klarer Scene Role + Rejection Criteria generieren, maximal 3 Varianten pro Szene ohne neue Freigabe.\n\n## 17. Nächste mögliche Kommandos\n\nWenn Preview v2 passt:\n\n```text\nAPPROVED_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s\n```\n\nWenn überarbeiten:\n\n```text\nREVISE_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n```\n\nWenn später manuelle Upload-Vorbereitung erlaubt ist:\n\n```text\nAPPROVED_FOR_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s\n```",
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"text": "Sir, **Sprint 3.1 — Image Provider Audit + Subtitle Safe-Zone Fix ist ausgeführt.**\n\nKurzfassung: \n**Image Provider ist verifiziert OpenAI/Codex. `gpt-image-2-high` ist verfügbar und funktioniert. Captions sind jetzt layout-getestet und in Preview v2 innerhalb der Safe-Zone.** Kein Wan, kein I2V, kein T2V, kein Posting. Wie gewünscht: nützlich statt hübsch-vernebelt.\n\n## 1. Image Provider Audit Result\n\nAktuelle Hermes-Konfiguration nach Audit/Fix:\n\n```text\nimage_gen.provider = openai-codex\nimage_gen.model = gpt-image-2-high\n```\n\nVor Sprint 3.1 war gesetzt:\n\n```text\nimage_gen.provider = openai-codex\nimage_gen.model = gpt-image-2-medium\n```\n\nIch habe nach erfolgreichem Capability-Test auf **`gpt-image-2-high`** umgestellt, damit künftige Premium-Styleframes standardmässig die beste verfügbare GPT-Image-Stufe nutzen.\n\nMain model/provider der laufenden Hermes-Session:\n\n```text\nmodel.provider = openai-codex\nmodel.default = gpt-5.5\nbase_url = https://chatgpt.com/backend-api/codex\n```\n\n## 2. Verfügbare Image-Provider / Fallbacks\n\nRegistrierte Image-Provider:\n\n```text\nopenai-codex\navailable: true\ndefault_model: gpt-image-2-medium\nmodels:\n- gpt-image-2-low\n- gpt-image-2-medium\n- gpt-image-2-high\n```\n\n```text\nopenai\navailable: false\nreason: kein separater OpenAI API Key aktiv\nmodels:\n- gpt-image-2-low\n- gpt-image-2-medium\n- gpt-image-2-high\n```\n\n```text\nxai\navailable: false\nmodels:\n- grok-imagine-image\n- grok-imagine-image-quality\n```\n\nFallback-Logik im Hermes-Registry-Code:\n\n- Wenn `image_gen.provider` explizit gesetzt ist, gewinnt dieser Provider.\n- Wenn nicht gesetzt, würde Hermes bei mehreren Providern Legacy/Fallback-Logik nutzen.\n- Für uns ist jetzt explizit gepinnt:\n\n```text\nallowed provider: openai-codex\n```\n\n## 3. Wurde `gpt-image-2-medium` wirklich genutzt?\n\nJa.\n\nDie letzten Styleframe-/Image-Runs liegen unter:\n\n```text\n/home/agent/.hermes/cache/images/\n```\n\nBeispiele:\n\n```text\nopenai_codex_gpt-image-2-medium_20260525_171425_7e5c5015.png\nopenai_codex_gpt-image-2-medium_20260525_171324_5e5577a3.png\nopenai_codex_gpt-image-2-medium_20260525_171159_68e5c2da.png\n...\n```\n\nDas belegt:\n\n```text\nprovider: openai-codex\nmodel/tier: gpt-image-2-medium\nstorage: ~/.hermes/cache/images/\n```\n\nKeine Hinweise, dass die letzten Styleframes lokal, über ComfyUI, Wan, Qwen-Image oder andere lokale Generatoren erzeugt wurden.\n\n## 4. Höherwertiger Modus verfügbar?\n\nJa.\n\nVerfügbar laut Provider-Katalog:\n\n```text\ngpt-image-2-low\ngpt-image-2-medium\ngpt-image-2-high\n```\n\n`gpt-image-2-high` ist der höchste verfügbare Modus im Hermes/OpenAI-Codex Image Provider.\n\nKein separates `images-with-thinking` Profil im aktuellen Provider-Code gefunden. Der Codex-Backend-Pfad nutzt intern:\n\n```text\nResponses API image_generation tool\nAPI_MODEL = gpt-image-2\nquality = low | medium | high\nhost chat model = gpt-5.4\n```\n\n## 5. Kontrollierter Capability-Test\n\nAusgeführt: **1 Testbild**, neutral, keine Batch-Orgie.\n\nResult:\n\n```text\nsuccess: true\nprovider: openai-codex\nmodel: gpt-image-2-high\nquality: high\nsize: 1024x1536\naspect_ratio: portrait\nelapsed: 60.93s\n```\n\nTestbild:\n\n```text\n/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\n```\n\nAudit-Kopie:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/image_provider_audit/capability_test_openai_codex_gpt-image-2-high_20260525_192025_93a777df.png\n```\n\nMetadata:\n\n```text\nfile_size: 2029976 bytes\nsha256: bdcbc300f918c07ebe4cb5620afa633ff7f8e02184f3d4f6d43c522745465baf\nresolution: 1024x1536\n```\n\n## 6. Provenance / C2PA / SynthID\n\nPNG-Chunk-Inspection der OpenAI/Codex-Bilder zeigt:\n\n```text\nIHDR\ncaBX\nIDAT\nIEND\n```\n\nDer `caBX` Chunk enthält JUMBF/C2PA-Struktur, u. a.:\n\n```text\njumb\njumdc2pa\nurn:c2pa:\n```\n\nBewertung:\n\n- **C2PA/Content Authenticity Provenance ist im originalen PNG-Cache vorhanden.**\n- PIL zeigt keine normalen EXIF/Text-Keys an.\n- Die Roh-PNGs aus `~/.hermes/cache/images/` behalten den `caBX` Chunk.\n- Abgeleitete JPEGs/MP4s aus Renderer/Contact-Sheets erhalten diese PNG-Provenance-Metadaten nicht zuverlässig. Erwartbar, leider. Medienpipelines sind da selten Gentlemen.\n\n## 7. Image Generation Guidelines erstellt\n\nErstellt:\n\n```text\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/image_generation_guidelines.md\n```\n\nEnthält:\n\n- Prompt Template\n- Quality Checklist\n- Rejection Criteria\n- Styleframe Budget Policy\n- Scene Consistency Rules\n- Text/Logo/Pseudo-Writing Blocker\n\nWichtige Regel:\n\n```text\nFake-Invoice / Email / Payment Route Mockups bleiben deterministisch renderer-owned.\nAI Images nur für Premium-Styleframes/Keyframes, nicht für einfache UI-/Invoice-Mockups.\n```\n\n## 8. Image Generation Policy ergänzt\n\nCode/config ergänzt:\n\n```text\nautoshorts/production/image_generation_policy.py\nautoshorts/config/image_generation_policy.json\n```\n\nPolicy:\n\n```text\nallowed_image_provider = openai-codex\npreferred_image_model = best_available_gpt_image_model\npreferred_quality = highest_available_reasonable\nmax_styleframes_per_scene_without_approval = 3\nmax_retries_per_styleframe = 1\nno_random_batches = true\nno_ai_images_for_mockups_when_deterministic_renderer_is_better = true\n```\n\nCommit:\n\n```text\ncf66672 fix: enforce caption safe zones and image policy\n```\n\n## 9. Subtitle Safe-Zone Fix\n\nCode ergänzt in:\n\n```text\nautoshorts/rendering/static_captions.py\n```\n\nNeue Funktionen/Modelle:\n\n```text\nCaptionLayoutConfig\nCaptionLayout\nbuild_safe_caption_segments()\nlayout_caption_segment()\n```\n\nHarte Caption-Regeln umgesetzt:\n\n```text\nmax_width_ratio: 0.86\nhorizontal_margin_px: 70\nbottom_margin_px: 310\nmax_lines: 2\nfont_size_px: 66\nmin_font_size_px: 48\npreferred segment length: 4–7 words\n```\n\nValidiert:\n\n```text\ncaption_layout_validation.passed = true\nviolations = []\n```\n\nAktives Wort bleibt innerhalb der Bounds. Kein Caption-Text läuft links/rechts aus dem Safe-Bereich.\n\n## 10. Tests ergänzt\n\nNeue Tests:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_image_generation_policy.py\n```\n\nAbgedeckt:\n\n- long phrase wraps into 2 lines\n- caption never exceeds safe width\n- active word highlight never exceeds safe bounds\n- no word clipped left/right\n- max 2 lines enforced\n- phrase split before >2 lines\n- bottom safe-zone respected\n- domain strings like `example-company.com` handled safely\n- image provider policy pins OpenAI/Codex\n- no random batches / deterministic mockups preference\n\nPytest Ergebnis:\n\n```text\n24 passed in 0.41s\n```\n\nGetestet:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_static_caption_segments.py\ntests/test_image_generation_policy.py\ntests/test_proof_short_mode.py\ntests/test_pilot_production_v2.py\n```\n\n## 11. Preview v2 erstellt\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nVersion:\n\n```text\npreview_v2_caption_safe\n```\n\nÄnderungen:\n\n- gleiche Story\n- gleiche Voiceover\n- deterministische screen/mockup visuals\n- Caption Layout gefixt\n- keine AI-Bilder\n- kein Wan\n- kein I2V/T2V\n- kein Posting\n\nManifest:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/manifest_preview_v2_caption_safe.json\n```\n\n## 12. Telegram Delivery\n\nGesendet an:\n\n```text\ntelegram:JARVIS Command Center / topic 4850\n```\n\nTelegram Status:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\n## 13. Artefakte\n\nPreview MP4:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\n\nfile_exists: true\nfile_size: 967725 bytes\nsha256: a94893693b02356487773798f3b306394595fd64dac82588b600e1134ee318b3\nduration: 31.32s\nresolution: 1080x1920\nfps: 24/1\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\nQA Frame Sheet:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\n\nfile_exists: true\nfile_size: 232446 bytes\nsha256: f84055c92e266fbc07c4bb577bbd0d6765835f46a00c4ef5da2c6de234be67eb\nresolution: 810x1440\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\nSubtitle Style Preview:\n\n```text\nfile_path:\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\n\nfile_exists: true\nfile_size: 353885 bytes\nsha256: 6ad056f7d8b9d0569313597241f8628cbe7b4b606eac5df3244347ff1e7972e6\nresolution: 1080x1920\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5969\n```\n\n## 14. QA Ergebnis\n\nCaption Safe-Zone:\n\n```text\npass\n```\n\nVisuelle QA:\n\n- keine Subtitle-Clips links/rechts\n- max. 2 Zeilen\n- aktive Wörter bleiben innerhalb der Safe-Zone\n- Bottom-Safe-Zone eingehalten\n- lange Phrasen wurden gesplittet:\n - `Before you pay check / three`\n - `things sender domain / urgency language`\n - `Verify through a second / channel`\n- kein Caption-Kasten\n- keine Vollbreiten-Bar\n- white/yellow active-word style bleibt erhalten\n\n## 15. Nicht ausgeführt\n\n```text\nwan_used: false\ni2v_used: false\nt2v_used: false\nfull_final: false\nnew_styleframe_batch: false\nnew_ai_images_for_preview: false\nposting: false\nplatform_social_api: false\nscheduling: false\n```\n\nEinzige Bildgeneration in Sprint 3.1 war der erlaubte **1-Bild Capability-Test**.\n\n## 16. Offene Punkte\n\n- Hermes config ist auf `gpt-image-2-high` gesetzt. Falls ein langlebiger Gateway-Prozess alte Image-Tool-Config cached, wäre später `/restart` oder `/reset` sinnvoll.\n- Provenance/C2PA bleibt im originalen PNG-Cache erhalten, aber nicht garantiert in abgeleiteten JPEG/MP4-Artefakten.\n- Für zukünftige Premium-Styleframes sollten wir nur noch mit klarer Scene Role + Rejection Criteria generieren, maximal 3 Varianten pro Szene ohne neue Freigabe.\n\n## 17. Nächste mögliche Kommandos\n\nWenn Preview v2 passt:\n\n```text\nAPPROVED_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s\n```\n\nWenn überarbeiten:\n\n```text\nREVISE_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n```\n\nWenn später manuelle Upload-Vorbereitung erlaubt ist:\n\n```text\nAPPROVED_FOR_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s\n```"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nReference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected caption style and controlled final pilot render pattern: static 3–7 word phrase segments with only the active spoken word highlighted, hash-bound final render gates, one Wan text-free background layer, deterministic overlays, and draft-only manual posting packages. If a final render is rejected as preview-overlay-dominated or not visual-first, follow `references/visual-first-final-render-repair.md`: mark the render rejected/not-postable, fix Wan output parsing, enforce final overlay limits, produce only a short visual proof under `APPROVED_FOR_VISUAL_PROOF`, and do not run another full final until the visual direction is accepted. If even the visual proof is rejected as abstract/generic/dark or “blind T2V”, switch to `references/keyframe-first-final-visuals.md`: plan and approve start/end/style keyframes first, then run only a short I2V/first-last-frame proof with minimal renderer-owned overlays. If the user says the production method itself is wrong, keyframes are not I2V-ready, or an animatic was incorrectly substituted for a failed Wan proof, follow `references/visual-production-reset-i2v-first.md`: reject/audit the proof, stop content rendering, run technical I2V sanity first, then generate at most three styleframes for direction review. If deterministic layout proofs are useful as composition blueprints but rejected as too wireframe/PowerPoint for final style, switch to `references/premium-ai-styleframe-direction.md`: use the layouts only as mechanism blueprints, generate at most 3 tightly briefed premium text-free AI styleframes, deliver a contact sheet with verified media metadata, and keep Wan/I2V/endframes/full-final blocked until style direction approval. If those premium styleframes are better quality but still too abstract, switch to `references/real-life-premium-styleframes.md`: anchor the mechanism in a realistic work/meeting/desk scene, keep all readable overlays renderer-owned, generate at most 3 new real-life styleframes plus contact sheet, and still block Wan/I2V/endframes/full-final until explicit approval. If the user approves one or more real-life styleframe directions but wants to compare A/B/C before Wan, use `references/prerelease-animatic-preview.md`: build only a small AI-keyframe + deterministic-motion animatic per approved direction, reuse existing voiceover/captions, deliver MP4/contact/QA sheets with metadata, and keep Wan/I2V/final/posting blocked. If the user then rejects the renderer-added circles/arrows/labels/boxes/decision-target markers as destroying the premium look, switch to `references/clean-image-cut-no-overlay-pilot.md`: render only AI images + voiceover + clean subtitles, with a hard subtitles-only NoOverlayPolicy and no Wan/I2V/full-final/posting. If the user performs a strategic reset because the pilot is pretty but semantically weak, stop optimizing that candidate and follow `references/proof-short-real-problem-reset.md`: archive the old pilot as a learning case, enforce the Pretty Empty Visual Blocker, and build a screen/mockup-led Proof Short around an immediately visible real viewer problem before any premium/Wan production. If the next review focuses on image provider quality/provenance or subtitles running off-screen, follow `references/image-provider-audit-and-caption-safezone.md`: audit the configured image provider/model/tier from config + provider registry + cache metadata, run at most one neutral capability test, keep UI/invoice mockups deterministic, and add tested caption layout safe-zone bounds before re-rendering.\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\nFor the user's TrueTraceShorts / AutoShortsBot strategy, do **not** default to a narrow AI/prompt-tips channel. The current strategic direction is broader: explain hidden digital everyday mechanisms — AI, work, attention, decisions, scams, productivity, workflows, social search — and end each video with one small system rule that fixes the outcome. Use the frame `Trace the hidden system. Fix the outcome.` AI should remain an important pillar, but normally only about 30–40% of the topic mix, not the whole product. See `references/truetrace-strategic-neuausrichtung.md`.\\n\\nWhen the user asks to start implementation work on this strategy, especially “Sprint 1: Strategic Spine Refactor”, follow `references/truetrace-strategic-spine-refactor-v2.md`: implement a side-effect-free V2 spine in parallel to existing V1 models, with mandatory strategic fields, hard quality gates, anti-AI-slop blockers, hash/version-bound ReviewPackageV2, and tests before integration with renderers or platform workflows.\\n\\nWhen the user asks for “Sprint 1.5” or to make the V2 spine operational without production, follow `references/truetrace-v2-operational-bridge.md`: first secure/commit the Sprint-1 baseline, then add only minimal side-effect-free bridges (`to_candidate_v2`, `workflow_v2`, `demo_review_batch_v2`), deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated non-generous scoring, anti-example/human-texture gates, minimal manual claim/disclosure stubs, LinkedIn manual metadata, and a Remotion-as-later-spike note. Keep it capped at 3 demo candidates and avoid rendering, platform APIs, dashboards, auto-posting, or 30-day batches.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - For TrueTraceShorts, prefer the structure `visible fail → identification → mechanism → fix → before/after → memorable takeaway` over generic AI-tip narration. Every video must answer: what goes wrong, why it happens, what rule fixes it, what sentence sticks, why viewers save/share it, and why they follow the series.\\n - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n - first-frame clarity\\n - mechanism value\\n - practical payoff\\n - save/share/follow reason\\n - trust/human texture\\n - series fit and saturation defense\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n For TrueTraceShorts, use the 100-point scoring and hard blockers in `references/truetrace-strategic-neuausrichtung.md`: block generic AI hooks, pure text first frames, unsourced claims, missing before/after, missing human texture, unclear AI disclosure, and candidates too similar to recent videos.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - For final renders, Wan must carry the visual scene, not merely provide a blurred/looped background underneath preview cards. Use full-bleed, scene-specific Wan clips, minimal renderer-owned overlays, and a final overlay policy that blocks preview frames, large UI cards/textboxes, debug bars, and overlay overload. If architecture is rejected, create a short visual proof first; see `references/visual-first-final-render-repair.md`.\\n - If a visual proof still looks like blind text-to-video or random abstract AI output, stop T2V and move to a keyframe-first workflow: generate/approve 1–2 text-free start/end/style keyframes for the scene, then attempt only a short Wan I2V/first-last-frame proof. See `references/keyframe-first-final-visuals.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or simplify captions before sending another final-near clip. For this user's AutoShorts/TrueTraceShorts default, do **not** show one word alone and do **not** slide a changing word-window every word. Use static phrase segments: keep 3–7 words fixed on screen while only the currently spoken word highlights/pulses left-to-right; replace the full segment only at a sentence/phrase boundary. See `references/static-active-word-captions-and-final-pilot-render.md`.\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style. Treat them as composition blueprints and run a small, tightly prompted premium AI styleframe pass instead: AI supplies glassmorphism/product-film quality and atmosphere; the renderer still owns labels, arrows, captions, facts, and final assembly. See `references/premium-ai-styleframe-direction.md`.\\n - If premium AI styleframes improve quality but still feel like abstract SaaS symbolism, do not continue iterating abstract funnels/rings. Move to real-life anchored work scenes: desk/laptop, empty meeting room/glass board, or project table/work evidence, with only subtle glass/holographic mechanism overlays. See `references/real-life-premium-styleframes.md`.\\n - If real-life styleframe directions are accepted but the user has not approved Wan/I2V yet, insert a pre-release animatic comparison step rather than jumping to endframes or video generation. Create small per-direction AI keyframe sets, deterministic push-ins/crossfades, existing voiceover, static active-word captions, contact sheets, and QA frame sheets; report Telegram delivery metadata. See `references/prerelease-animatic-preview.md`.\\n - If the user rejects added visual explanation overlays as making the result look like a technical explainer board, treat this as a hard style failure. For the clean pilot, use only AI images, voiceover, and clean subtitles; block circles, arrows, labels, boxes, connector lines, decision-target markers, debug/diagram elements, and caption boxes/bars. See `references/clean-image-cut-no-overlay-pilot.md`.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/truetrace-strategic-spine-refactor-v2.md` for implementing the new TrueTraceShorts strategy in code: V2 strategic models, hard gates, anti-AI-slop blockers, SeriesRegistry, 100-point scoring, hash-bound ReviewPackageV2, and deterministic five-candidate examples without rendering or platform side effects. See `references/truetrace-v2-operational-bridge.md` for the next implementation step: secure the Sprint-1 commit, add minimal side-effect-free bridge/workflow/CLI modules, deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated scoring, anti-example/human-texture gates, manual claim/disclosure stubs, LinkedIn manual metadata, and Remotion-as-later-spike documentation without production expansion.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected Hormozi-style caption behaviour and the one-render final pilot review-package checklist.\\n\\nSee `references/final-motion-hybrid-rendering.md` for the final-near hybrid renderer pattern: ChatGPT Image/Codex or other AI tools provide text-free background/keyframe texture while deterministic renderer overlays own text, arrows, labels, captions, and final assembly. For quick prototypes, prefer ChatGPT Image/Codex backgrounds before local Qwen/ComfyUI unless privacy/cost/local-only constraints dominate. After a 1080 export, create a side-effect-free final approval package with media path, SHA256, platform metadata, social copy, quality notes, and approval commands before any delivery or platform step.\\n\\nSee `references/visual-first-final-render-repair.md` for the repaired final-render architecture after a rejected pilot: mark rejected renders as not-postable, avoid preview-board overlays, enforce `FinalOverlayPolicy`, validate ComfyUI/Wan output histories robustly, and use `APPROVED_FOR_VISUAL_PROOF` for one short visual proof before any second full final.\\n\\nSee `references/hybrid-scene-backgrounds-codex.md` for the concrete ChatGPT Image/Codex scene-background workflow: side-effect-free `SceneBackgroundPlan`, per-scene text-free backgrounds, renderer-owned overlays, and visual QA before review delivery. Prefer this over local Qwen/ComfyUI when the user allows Codex image generation and local GPU should be reserved for video/model work.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, visual checks for clipped/overcrowded text, and hard mobile safe-zone tests for captions/highlights before delivery.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not assume local Qwen/ComfyUI is the best first image step for every final-near short. If the user permits ChatGPT Image/Codex, use it for text-free scene backgrounds/keyframes when the deterministic renderer owns labels/captions and local GPU should be saved for video/model work. When provider quality matters, verify the actual `image_gen.provider`/model from config and the image cache filenames/metadata rather than guessing; for premium styleframes prefer the highest available reasonable GPT Image tier, but keep fake invoice/email/UI/payment-route mockups deterministic.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not treat a keyframe animatic as a successful Wan/I2V proof. If ComfyUI/Wan I2V fails or history contains no valid video output, label any fallback MP4 as `not_wan` / composition-review-only and block full-final progression.\\n- Do not treat deterministic layout proofs as the final premium visual style when the user asked for a high-end social-video look. Layouts are for mechanism composition; after approval, use a tightly scoped premium AI styleframe pass with strict negative prompts and verified delivery before endframes/Wan.\\n- Do not mistake premium abstract glassmorphism for an approved final direction if the user wants a credible everyday work scene. When the user says the motif is still too abstract, switch to real-life work-scene styleframes with laptop/tablet/meeting-room/project-table anchors; keep motion graphics subtle and renderer-owned.\\n- If the user rejects the production method itself, do not keep iterating content visuals. Reject/audit the proof, run a technical I2V sanity test with a trivial non-content image, and only after it passes generate a small set of styleframes for direction review; see `references/visual-production-reset-i2v-first.md`.\\n- Do not keep trying blind text-to-video after the user rejects a proof as abstract, dark, generic, or semantically ungrounded. Move to keyframe-first planning, review the keyframes, then run at most one short I2V/first-last-frame proof.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/premium-ai-styleframe-direction.md\", \"references/social-draft-approval-package.md\", \"references/static-active-word-captions-and-final-pilot-render.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/visual-first-final-render-repair.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/proof-short-real-problem-reset.md\", \"references/truetrace-strategic-neuausrichtung.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/controlled-pilot-production-v2.md\", \"references/keyframe-first-final-visuals.md\", \"references/clean-image-cut-no-overlay-pilot.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/pilot-preview-render-v2.md\", \"references/truetrace-strategic-spine-refactor-v2.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/real-life-premium-styleframes.md\", \"references/monetization-and-approval-notes.md\", \"references/visual-production-reset-i2v-first.md\", \"references/image-provider-audit-and-caption-safezone.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/final-motion-hybrid-rendering.md\", \"references/prerelease-animatic-preview.md\", \"references/imported-ai-clip-assembly.md\", \"references/hybrid-scene-backgrounds-codex.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\", \"references/truetrace-v2-operational-bridge.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}",
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"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. 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"summary": []
}
]
},
{
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"content": "{\"status\": \"success\", \"output\": \"ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Duration: 00:00:31.32, start: 0.000000, bitrate: 247 kb/s\\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 179 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n Stream #0:1[0x2](und): Audio: aac (LC) (mp4a / 0x6134706D), 24000 Hz, mono, fltp, 63 kb/s (default)\\n Metadata:\\n handler_name : SoundHandler\\n vendor_id : [0][0][0][0]\\nStream mapping:\\n Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))\\n Stream #0:1 -> #0:1 (aac (native) -> aac (native))\\nPress [q] to stop, [?] for help\\n[libx264 @ 0x59451a96dc40] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\\n[libx264 @ 0x59451a96dc40] profile High, level 4.1, 4:2:0, 8-bit\\n[libx264 @ 0x59451a96dc40] 264 - core 164 r3108 31e19f9 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=4 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=8 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=2 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=9 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=3 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=24 scenecut=40 intra_refresh=0 rc_lookahead=50 rc=crf mbtree=1 crf=18.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\\nOutput #0, mp4, to '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n title : Digital Red Flags - Fake Invoice Payment Route\\n encoder : Lavf60.16.100\\n Stream #0:0(und): Video: h264 (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, q=2-31, 24 fps, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n Side data:\\n cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\\n Stream #0:1(und): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, mono, fltp, 128 kb/s (default)\\n Metadata:\\n handler_name : SoundHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 aac\\nframe= 2 fps=0.0 q=23.0 size= 0kB time=00:00:00.02 bitrate= 18.0kbits/s speed=0.0414x \\rframe= 50 fps= 49 q=23.0 size= 0kB time=00:00:02.02 bitrate= 0.2kbits/s speed=1.98x \\rframe= 101 fps= 66 q=23.0 size= 0kB time=00:00:04.11 bitrate= 0.1kbits/s speed=2.69x \\rframe= 160 fps= 79 q=23.0 size= 0kB time=00:00:06.54 bitrate= 0.1kbits/s speed=3.21x \\rframe= 217 fps= 85 q=23.0 size= 256kB time=00:00:08.95 bitrate= 234.1kbits/s speed=3.53x \\rframe= 269 fps= 88 q=23.0 size= 256kB time=00:00:11.11 bitrate= 188.7kbits/s speed=3.66x \\rframe= 329 fps= 93 q=23.0 size= 256kB time=00:00:13.63 bitrate= 153.9kbits/s speed=3.85x \\rframe= 387 fps= 96 q=23.0 size= 512kB time=00:00:16.02 bitrate= 261.8kbits/s speed=3.96x \\rframe= 449 fps= 99 q=23.0 size= 512kB time=00:00:18.62 bitrate= 225.2kbits/s speed=4.09x \\rframe= 501 fps= 99 q=23.0 size= 768kB time=00:00:20.82 bitrate= 302.2kbits/s speed=4.12x \\rframe= 562 fps=101 q=23.0 size= 768kB time=00:00:23.31 bitrate= 269.8kbits/s speed= 4.2x \\rframe= 624 fps=103 q=23.0 size= 768kB time=00:00:25.92 bitrate= 242.7kbits/s speed=4.28x \\rframe= 670 fps=102 q=23.0 size= 1024kB time=00:00:27.81 bitrate= 301.6kbits/s speed=4.24x \\r[mp4 @ 0x59451a924700] Starting second pass: moving the moov atom to the beginning of the file\\n[out#0/mp4 @ 0x59451a8d35c0] video:730kB audio:410kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.992877%\\nframe= 751 fps=107 q=-1.0 Lsize= 1163kB time=00:00:31.37 bitrate= 303.5kbits/s speed=4.45x \\n[libx264 @ 0x59451a96dc40] frame I:4 Avg QP: 8.99 size: 52335\\n[libx264 @ 0x59451a96dc40] frame P:189 Avg QP:15.20 size: 2395\\n[libx264 @ 0x59451a96dc40] frame B:558 Avg QP:23.81 size: 152\\n[libx264 @ 0x59451a96dc40] consecutive B-frames: 0.9% 0.0% 0.0% 99.1%\\n[libx264 @ 0x59451a96dc40] mb I I16..4: 82.3% 4.6% 13.1%\\n[libx264 @ 0x59451a96dc40] mb P I16..4: 0.5% 0.0% 0.3% P16..4: 0.5% 0.1% 0.1% 0.0% 0.0% skip:98.5%\\n[libx264 @ 0x59451a96dc40] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 1.1% 0.0% 0.0% direct: 0.0% skip:98.8% L0:48.0% L1:51.8% BI: 0.1%\\n[libx264 @ 0x59451a96dc40] 8x8 transform intra:4.5% inter:15.1%\\n[libx264 @ 0x59451a96dc40] direct mvs spatial:99.1% temporal:0.9%\\n[libx264 @ 0x59451a96dc40] coded y,uvDC,uvAC intra: 14.0% 13.7% 10.0% inter: 0.1% 0.1% 0.1%\\n[libx264 @ 0x59451a96dc40] i16 v,h,dc,p: 30% 70% 0% 0%\\n[libx264 @ 0x59451a96dc40] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 43% 18% 39% 0% 0% 0% 0% 0% 0%\\n[libx264 @ 0x59451a96dc40] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 31% 38% 10% 3% 3% 4% 3% 3% 4%\\n[libx264 @ 0x59451a96dc40] i8c dc,h,v,p: 75% 15% 9% 1%\\n[libx264 @ 0x59451a96dc40] Weighted P-Frames: Y:0.0% UV:0.0%\\n[libx264 @ 0x59451a96dc40] ref P L0: 73.5% 7.8% 13.1% 5.6%\\n[libx264 @ 0x59451a96dc40] ref B L0: 57.4% 41.9% 0.8%\\n[libx264 @ 0x59451a96dc40] ref B L1: 96.4% 3.6%\\n[libx264 @ 0x59451a96dc40] kb/s:190.93\\n[aac @ 0x59451a9908c0] Qavg: 17214.582\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 10.28}",
"tool_call_id": "call_ysvY3ARNCFvuUnci07KufETg"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_rIuPwihAe1nGqrTcYrvZcjot",
"call_id": "call_rIuPwihAe1nGqrTcYrvZcjot",
"response_item_id": "fc_051a8a0b2fb58918016a14884237748191b592c1fd523f4312",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\",\"question\":\"Final QA contact sheet for a 9:16 manual upload video. Check if captions are clipped/off safe area, if obvious logos/real bank data/QR codes/people appear, and whether hook/takeaway are visually clear.\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Creating status and packs**\n\nI need to create a status and packs. It looks like I’m dealing with 751 frames with a duration of 31.37. I should probably check that using ffprobe to confirm the duration is accurate. It seems all good so far, but I need the status approved. I also need to make sure to use the Vision QA contact for any additional support or verification. Alright, let’s keep moving forward with this!",
"reasoning_content": "**Creating status and packs**\n\nI need to create a status and packs. It looks like I’m dealing with 751 frames with a duration of 31.37. I should probably check that using ffprobe to confirm the duration is accurate. It seems all good so far, but I need the status approved. I also need to make sure to use the Vision QA contact for any additional support or verification. Alright, let’s keep moving forward with this!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFIhCyPhEk62yb57dJlr1rVt0fKc3e-VTqrAH2bSOmVK8enjXwX-3sipm2jXaSDmzKDTId9X6g1g_CpCxLCsQA0oHLOsxkRSPw_QS22oacY54wNux6guCXw6eHt2Sogph9YRqtXHLPi4LbonBmoInab56js2Hm9875lsAR2Z7qmNMwD_5adbjToM4RZp1u4T8hI8PZtgK4z-X1ANgk_oM3o-KyuIYlm0H_-0yZA6O067hsT98HxZz8TFg7yfNz5mjK4YlfVG0fk2UqqETJtinxWxBpjvdtRFDFx7Ua_xim6EOS88J5bCNqpVfPoRXVgHaoFXcJxKCtKJlxOFqpsNFLmorBBZqv22nYaO0RcD9uhawqJALg49agcvw68bhrBqdm3WgvEuJPw1aQKR5t3mtSVXYJtYE7wFhoEy931i5sRxdIhwHLmDtmuUDQhbqKKWKD8iLtn9qBp_6lHpRt7vlmToPDdqbZAbR39bCXRFx4QtjTW2gcVonCiTLvJU4tfcJ37HWSTmJQtsuIjFWfCx8DQq-8KPmnxmkrYokpCtae-QekrLrAPxyZ7GlgYseksOXjF1Cz580lXmIu-RIRHAz4WZL8SXCuTZpn9QrIFEvMF_2QgKEJ1ROXlB_WZ9tC_cdDfBLzc33E4Upy7libGcCFG8i9gIH__movEJqr42YBdXvfus_G1cX8h8sWXHlb3CmNjpypC9F_mUXXC9AZ6MAAB7s7nJLdSxa0vkDHDkx_vf-P7vCsBUekhz2oe8oBHAdoIGF37FNDe_VzjdQ2tcCr8ozCxMEelS59HL69jdxsS9wmGB-iRSucaxAKNWnxBKG092Gs-GDRQDbAkFC74lYFpy_YYGd8iVSra9GqBTE_bmBb-5PunPEgL-9yV108JG_pJQAdnNzi9JRPfRxbtPw9X-WCvMGmtrzvNAaQKAPb71rUm4Aml47DKomsgBsbugy44x1sX8fsqei8DiAWATTH83644mTny8nxRfMfF4WfA-lRhv6clgTKHEkTfFZSNnnipzbh1jPbuUsMMe1ibsHtIFivStG7pVVU16u1_R5169TCF7JEL-eBVGnxfrWtUvZy-3bgz8RhyiBUPAO8pHINkBHNCo8FYX8tb3m1_pEgAxzzsDMel8ttx4LBmNug49HIzRc",
"id": "rs_051a8a0b2fb58918016a14883f83048191b12b744000c7853a",
"summary": [
{
"type": "summary_text",
"text": "**Creating status and packs**\n\nI need to create a status and packs. It looks like I’m dealing with 751 frames with a duration of 31.37. I should probably check that using ffprobe to confirm the duration is accurate. It seems all good so far, but I need the status approved. I also need to make sure to use the Vision QA contact for any additional support or verification. Alright, let’s keep moving forward with this!"
}
]
}
]
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Final QA contact sheet for a 9:16 manual upload video. Check if captions are clipped/off safe area, if obvious logos/real bank data/QR codes/people appear, and whether hook/takeaway are visually clear.\n[screenshot]",
"tool_call_id": "call_rIuPwihAe1nGqrTcYrvZcjot"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_lOy1d8WcR21Q5wCMTOyhdN59",
"call_id": "call_lOy1d8WcR21Q5wCMTOyhdN59",
"response_item_id": "fc_051a8a0b2fb58918016a1488504d5c81919ef00da76a95c210",
"type": "function",
"function": {
"name": "patch",
"arguments": "{\"mode\":\"replace\",\"path\":\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\",\"old_string\":\" text(draw,(150,314),'From: billing@example-company.com',30,(78,88,110),False)\\n\",\"new_string\":\" text(draw,(150,314),'From: billing@sample.test',30,(78,88,110),False)\\n\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Evaluating mockup changes**\n\nI'm considering a fictitious mockup where \"example-company.com\" serves as a generic placeholder. But using .com may not be ideal, so I'm thinking of changing it to something like billing@sample.test. That doesn't change the story much, though I need to ensure the final upload doesn’t look real. \n\nI might need to adjust the rendering source script and re-run the manual upload with my modifications. Maybe I should regenerate the preview to reflect these changes and ensure it stays generic. Let's aim for a safe re-render using .test.",
"reasoning_content": "**Evaluating mockup changes**\n\nI'm considering a fictitious mockup where \"example-company.com\" serves as a generic placeholder. But using .com may not be ideal, so I'm thinking of changing it to something like billing@sample.test. That doesn't change the story much, though I need to ensure the final upload doesn’t look real. \n\nI might need to adjust the rendering source script and re-run the manual upload with my modifications. Maybe I should regenerate the preview to reflect these changes and ensure it stays generic. Let's aim for a safe re-render using .test.",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFIhQ_L1654diIkyMnceDPaRtdwSK4vY9k0ywGJf2FntAfriLSSfIrR82Qd-KisOqSh2iNOGtfBxb-KCxdDnnz-tL_LVsp_dtTKAaLrs2fKM1vlPlzRF-PGnn4qowb7kNf4fMF-DdZDug9AEyhr4CQJVS07ZEEmbQhQh4yH_sJNoDZW4WakPogUlBaZciUzlWMylC9jGBEc1hq6o-ytqjTT27aZg00XjzRXjBqT2vYtl2ccZSnDvRkoCzdmapogeR2v0hXplp-nvjjhLO6pf7AMMNVHA1kR2DI0ojiOP6kci4I8G5PI6QIpo7xnW9edbQ8XOJ-V3TXMlTOKe2cXkQPstlZtmIZ1dwIJvvkekXlCErsUS3xz-PJE--KZHhl1b6GZBdl8E-hDDQkvAH2VSBWmuujhAnFqWMosjicaU_vGq5qdbhBbIJW1tS30Qd-EnfKZK_5xsxtrVY_G2X8jMPL9MrHfvLTg9BA9VoquKoxiEQNUdGFzzsTkBcDHsgcZGPabysY1bvBCjW3l2KSPlxorOPhnu_V23E3OYaH43WKkQZ71J-sRWZwYnRaCn8phyDCFipMfVkmcwTrg8wxQ4XljtotsRPsjkmkWeW3i_-BeVm0-0nKfk0bDnv-MZSy5RCErBqjCdmINYBKkemgta7njtxSSEueCoOTqPxqoBcNOXC7vya-5OxbJRjMysYfglAD1CEzczF4THjpIhL4v_EykjJm8vGw82Atgpx-1EIbLF973W0qNIMWbKyAYkPvl3W9XWKE3LK-ycCOIsKaLMzcxiJrEDvDfLPnolRsHCjMtHYPH70O96Os8WFhCoKduCt4UaCFbGvwyEfgH3b4xNLXrCJfiWPAkHF3Js7zlIg6LweDwkEqByvnEQSH4cjcOrfWwDj2xrjoAZw2krOa0nuX3RvMAjKeAlMQI9UL0-S72r4gsXL3TN6EQJxOXmY8NonfE8XwN1_ZThXSecBT-5qfVbx04EUzZaFYSx-EIJjZ_J3EDwsXcXo8qPkF1jUDuctGpKqB7QD0fq1EF2AGT5AufwEFj8yr1axQAeJMYRB7SaiXq6sxcllYt8_l-crGBgI5W45kFRkDXo1ivDStKm1OWMSA79Ycvq6AihIZVQSt32Jm7B0GG-v5sqSK_Q51nxEUwMMLqLkpgdaZ_EM2-gGRTZ9IbOAm05VLQ13xLE19MRxFmmKC9Y4jmzYv7vYvxiRRUcmKQYEvEbOQe1010ZhNDjpivcJIos3gmVDyGogAZLbGhZ1MYvLBlqw6N9DmHMmCLyaIJtno1J4psPXIppsHlnZVncHG3obCtRPeKfHNCpYqYX9H3A1qez7-MmeBSXeYddBqFy2TKuaEj4fMOm6WL0HqJgbCecyLbVAYuLskRB3eEdvS5rzbwDSJ4dXWV0-yuOan2N-sqSVO5zwTfrFfu9iIwb_rdeaa9FKSd5debw_a3uHiI8lDjS6k7CBp1ccyR_IAPCn3fUkaRCmNPNMumNaQp38QALynCL1zyt2baL4nDRf_HXNylzAg_OjmylswSX3VgYWWt8aKg9bfjFpwyhNt6mAt5WNPBPSSqmQ7Mo3TW7yijsvM92pafVX9J3Dfz2vG2ulnPvEVS2CTEt1UKujw56ZXwEXmiGHVvL9NeET8gG98YlbDM8z_PrPlSdg1ap7uPIXTFMel9yGBEGQGl-b5MVPJ0i3AzXKLn-rO2Mc8E7XR8rnEapMAEIeSbetPxsm7WJm0sGHUq5QoL0aiBq98gniVXv8FS-SPalfHiBDAi_YYkcTf7Xv8yVFx06GG-TlE7bJ9K2sA3cki06xfKPKyW9A8wkvv5aS4_6kGjXjZEz73fYa-B6Sz5VDcq1oeE00MFxf79P_d4Yj3aZSvrsW551fDbGdj0677BL9-xwwLCBQ_sWs4BjKIQWOMMrvVKNEChX0zbUhm8UNX7hlAer_qz4a9yxEB69YDwkXBcPCXeGljKvrrwWyVY9zcCJ_MusilxV3Lw-3bN_BLFSwSnHn5dyxOAj-EJtVxW88SxuNRVK0cJjLcgQOPhuwlWW65trok-jmbe7FqenA0ml9bs9L-n4BrNaihqNDkW-u96ZfD4l4Rl2mib5tAlUQuTNQHMdVhjLPjH52AtDXXeDBtWJWqzNpgIdhf6KpShmvZvLFooQzaIOF5SY4M3SGkCLiYIDWHijNMhG9g_z1_tRaj5SMj_RnC7nK442k6j1K4mA3B_CSP3T_DFwK2xwZRpFeAAYE5Nj3PnPJwe5wA3l3_vnyXAI5tmDCSH5CxGZB32IR_Pm473pGtj2nVkYXzPmRwOiT7VaiYYOldGKyQTz0je7wURSRyhyIKeiqKpnPVDn_hiQZA07FfOYLFXW4LwmSxNjwwaP4eDytaOPmdTn_B0P7o_UM6rTiCE5-ro6KcjD3_2GYvw4oFOdRqcL7X46EGfV3Fb3Akt9cw37F08QEgaVNMNK2EtCFopwsjsrxJzLy8P-bcwF_EN48mWjbfz4f0OO4NbC2Ehep2-jLkly5NUVxREFO0CPBrSG6x_ggHPhivvgw6J-r1ykWL89B59Ly8ByHU3tM_xVN8DCUUOmGDv3S5exFwh2visLpEh33VdQfASS-BkqMOZgytwciaCF2R33jbZKJm31WwnW3hIVWgLRW-djrcN3E0zn799X6pCG3dFekpeqbxXO1qSd05slZwiTyluZu1Z2BAWNjBx_c4AurwY_FCljqNDJ7H5_o7IiMEZ47PkP1_UVCMbW76UOMlo4u3zygdukyz17b68Z2C5pVHC55eTQEbrUsPOhMxWB6S1Eyde9jtSStG_r75QnB_oRUj3_pCA5hLvsYSDDhUjnwN91H0ZELv976bA9Qj__xUIN4_WcJZ8AkisPFB0V1948CddaDIyNa-_fdEojyYLrjTD3hsT3ytQ4EkgWyxnRcgFUZ7tv1mphTpNbyEP1E0Qq3Z5ZlCTzGpy2H648WHX-8cqlKrb39-7-Qqjq4D1B2dvzjRhnn5l-8UAVAs_x6QKViI7YxrI_-dYcsCQF_2veCNltJBvM9nXaKqoSK4VEusQdDr1iRf_5ikz3L1hwRM2ABRBm_vlD9rGLv8lVgjFpYs7D-XcKgYLHkTHNkFpx0b_7qXGvLDRxU-nc-KkABPrsz4fgcz1lCL15N8J9nO6UmrpO92iyDexalPzqvW8tpX7OFy6Agr5avQRO4NzBp8msBfFcIsWrnYS36A5pg5l5Xss6P69NSAUrnTAsL1o4i8hoQv-3Za0YK8f_ix1Zc7ymCTPg8AtvF2z9CvU5Inr1oDS8tKlkYa4oKIPqjUkrVjaNx4_0_lgm-IhOrr7W1FQdf4iWrTkmmIFW-pFFLRx9m-2jyaaPjaMqzjP6fUNusN3F1PC0tDm4g8jDTjuvqOgEh-WFx7V5TqmewhlF4jmA0dMN_VdJ2KkjTjlto-DEvX5K2j3jLzKvP9T3AA53nC2iR2_awTH7XQQ-N4jBAOzda2U9nTdKueIx8pgS3-MzZ2jiWdbS6fOk6Fklk3Ttb8qYekMhjwkDamEKMIFgeouhEpA==",
"id": "rs_051a8a0b2fb58918016a14884590a08191a5723cee403446be",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating mockup changes**\n\nI'm considering a fictitious mockup where \"example-company.com\" serves as a generic placeholder. But using .com may not be ideal, so I'm thinking of changing it to something like billing@sample.test. That doesn't change the story much, though I need to ensure the final upload doesn’t look real. \n\nI might need to adjust the rendering source script and re-run the manual upload with my modifications. Maybe I should regenerate the preview to reflect these changes and ensure it stays generic. Let's aim for a safe re-render using .test."
}
]
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"diff\": \"--- a//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n+++ b//tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\\n@@ -91,7 +91,7 @@\\n rounded(draw, (82,170,998,1330), 44, (235,239,244), (78,92,122), 3)\\n rounded(draw, (118,220,962,420), 26, (255,255,255), (218,224,234), 2)\\n text(draw,(150,252),'Inbox',38,(39,48,70))\\n- text(draw,(150,314),'From: billing@example-company.com',30,(78,88,110),False)\\n+ text(draw,(150,314),'From: billing@sample.test',30,(78,88,110),False)\\n urgency=(236,165,37) if mode in ['same','route'] else (113,124,148)\\n text(draw,(150,362),'Subject: Payment due today',32,urgency)\\n # invoice doc\\n\", \"files_modified\": [\"/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
"tool_call_id": "call_lOy1d8WcR21Q5wCMTOyhdN59"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_jZY7oXiHUbZEV70VCXcNcREj",
"call_id": "call_jZY7oXiHUbZEV70VCXcNcREj",
"response_item_id": "fc_051a8a0b2fb58918016a14885480548191a84e315ff8552ffb",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python /tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py >/tmp/render_v2_final_rerun.log && tail -60 /tmp/render_v2_final_rerun.log\\nBASE=/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s\\nPREVIEW=$BASE/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\nOUT=$BASE/manual_upload_v1\\nFINAL=$OUT/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\\nffmpeg -y -i \\\"$PREVIEW\\\" -vf \\\"fps=24,scale=1080:1920:flags=lanczos,format=yuv420p\\\" -af \\\"loudnorm=I=-16:TP=-1.5:LRA=11\\\" -c:v libx264 -preset slow -crf 18 -profile:v high -level 4.1 -c:a aac -b:a 128k -ar 48000 -movflags +faststart -metadata title=\\\"Digital Red Flags - Fake Invoice Payment Route\\\" \\\"$FINAL\\\" >/tmp/manual_encode.log 2>&1\\nffprobe -v error -show_entries format=duration:stream=index,codec_type,codec_name,width,height,r_frame_rate,avg_frame_rate,sample_rate,channels -of json \\\"$FINAL\\\" > \\\"$OUT/ffprobe_manual_upload_v1.json\\\"\\nsha256sum \\\"$FINAL\\\" > \\\"$OUT/sha256_manual_upload_v1.txt\\\"\\nrm -f \\\"$OUT/qa_frames\\\"/*.jpg\\nfor t in 1 4 10 15.8 17.8 20.2 24.5 28.2 30.2; do safe=${t//./_}; ffmpeg -y -ss \\\"$t\\\" -i \\\"$FINAL\\\" -frames:v 1 -q:v 2 \\\"$OUT/qa_frames/frame_${safe}s.jpg\\\" >/dev/null 2>&1; done\\npython - <<'PY'\\nfrom pathlib import Path\\nfrom PIL import Image, ImageDraw, ImageFont\\nOUT=Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1')\\nframes=[(1,'1'),(4,'4'),(10,'10'),(15.8,'15_8'),(17.8,'17_8'),(20.2,'20_2'),(24.5,'24_5'),(28.2,'28_2'),(30.2,'30_2')]\\nfont=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',24)\\nsheet=Image.new('RGB',(810,1440),(12,16,24)); d=ImageDraw.Draw(sheet)\\nfor idx,(ts,safe) in enumerate(frames):\\n p=OUT/'qa_frames'/f'frame_{safe}s.jpg'\\n im=Image.open(p).convert('RGB').resize((270,480))\\n x=(idx%3)*270; y=(idx//3)*480\\n sheet.paste(im,(x,y)); d.rectangle((x,y,x+269,y+479),outline=(80,90,115),width=2)\\n d.text((x+12,y+12),f'{ts:.1f}s',font=font,fill=(247,202,76))\\nsheet.save(OUT/'manual_upload_v1_qa_contact_sheet.jpg', quality=92)\\nPY\\ncat \\\"$OUT/sha256_manual_upload_v1.txt\\\" && cat \\\"$OUT/ffprobe_manual_upload_v1.json\\\"\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, rawvideo, from 'fd:':\\n Duration: N/A, start: 0.000000, bitrate: 1194393 kb/s\\n Stream #0:0: Video: rawvideo (RGB[24] / 0x18424752), rgb24, 1080x1920, 1194393 kb/s, 24 tbr, 24 tbn\\nStream mapping:\\n Stream #0:0 -> #0:0 (rawvideo (native) -> h264 (libx264))\\n[libx264 @ 0x63f97dec8480] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\\n[libx264 @ 0x63f97dec8480] profile High, level 4.0, 4:2:0, 8-bit\\n[libx264 @ 0x63f97dec8480] 264 - core 164 r3108 31e19f9 - H.264/MPEG-4 AVC codec - Copyleft 2003-2023 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=9 lookahead_threads=1 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=24 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=20.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\\nOutput #0, mp4, to '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4':\\n Metadata:\\n encoder : Lavf60.16.100\\n Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, progressive), 1080x1920, q=2-31, 24 fps, 12288 tbn\\n Metadata:\\n encoder : Lavc60.31.102 libx264\\n Side data:\\n cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\\nframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 0 fps=0.0 q=0.0 size= 0kB time=N/A bitrate=N/A speed=N/A \\rframe= 7 fps=2.2 q=25.0 size= 0kB time=00:00:00.20 bitrate= 1.8kbits/s speed=0.0664x \\rframe= 18 fps=4.9 q=25.0 size= 0kB time=00:00:00.66 bitrate= 0.6kbits/s speed=0.183x \\rframe= 29 fps=7.0 q=25.0 size= 0kB time=00:00:01.12 bitrate= 0.3kbits/s speed=0.271x \\rframe= 40 fps=8.5 q=25.0 size= 0kB time=00:00:01.58 bitrate= 0.2kbits/s speed=0.338x \\rframe= 50 fps=9.6 q=25.0 size= 0kB time=00:00:02.00 bitrate= 0.2kbits/s speed=0.383x \\rframe= 60 fps= 10 q=25.0 size= 0kB time=00:00:02.41 bitrate= 0.2kbits/s speed=0.419x \\rframe= 70 fps= 11 q=25.0 size= 0kB time=00:00:02.83 bitrate= 0.1kbits/s speed=0.45x \\rframe= 80 fps= 12 q=25.0 size= 0kB time=00:00:03.25 bitrate= 0.1kbits/s speed=0.478x \\rframe= 92 fps= 13 q=25.0 size= 0kB time=00:00:03.70 bitrate= 0.1kbits/s speed=0.506x \\rframe= 105 fps= 13 q=25.0 size= 0kB time=00:00:04.25 bitrate= 0.1kbits/s speed=0.542x \\rframe= 116 fps= 14 q=25.0 size= 0kB time=00:00:04.75 bitrate= 0.1kbits/s speed=0.568x \\rframe= 129 fps= 14 q=25.0 size= 0kB time=00:00:05.25 bitrate= 0.1kbits/s speed=0.59x \\rframe= 139 fps= 15 q=25.0 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time=00:00:28.58 bitrate= 146.8kbits/s speed=0.754x \\r[out#0/mp4 @ 0x63f97dec6d00] video:686kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.405928%\\nframe= 752 fps= 19 q=-1.0 Lsize= 695kB time=00:00:31.20 bitrate= 182.5kbits/s speed=0.809x \\n[libx264 @ 0x63f97dec8480] frame I:4 Avg QP:12.81 size: 46319\\n[libx264 @ 0x63f97dec8480] frame P:190 Avg QP:17.19 size: 2223\\n[libx264 @ 0x63f97dec8480] frame B:558 Avg QP:23.39 size: 168\\n[libx264 @ 0x63f97dec8480] consecutive B-frames: 1.1% 0.0% 0.0% 98.9%\\n[libx264 @ 0x63f97dec8480] mb I I16..4: 70.8% 12.2% 17.0%\\n[libx264 @ 0x63f97dec8480] mb P I16..4: 0.8% 0.1% 0.3% P16..4: 0.5% 0.1% 0.1% 0.0% 0.0% skip:98.2%\\n[libx264 @ 0x63f97dec8480] mb B I16..4: 0.1% 0.0% 0.0% B16..8: 1.6% 0.0% 0.0% direct: 0.0% skip:98.2% L0:53.1% L1:46.9% BI: 0.0%\\n[libx264 @ 0x63f97dec8480] 8x8 transform intra:12.0% inter:14.2%\\n[libx264 @ 0x63f97dec8480] coded y,uvDC,uvAC intra: 11.0% 10.9% 8.2% inter: 0.0% 0.1% 0.1%\\n[libx264 @ 0x63f97dec8480] i16 v,h,dc,p: 26% 74% 0% 0%\\n[libx264 @ 0x63f97dec8480] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 37% 11% 52% 0% 0% 0% 0% 0% 0%\\n[libx264 @ 0x63f97dec8480] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 29% 47% 9% 2% 3% 3% 3% 2% 3%\\n[libx264 @ 0x63f97dec8480] i8c dc,h,v,p: 73% 15% 11% 1%\\n[libx264 @ 0x63f97dec8480] Weighted P-Frames: Y:0.0% UV:0.0%\\n[libx264 @ 0x63f97dec8480] ref P L0: 69.6% 7.3% 18.5% 4.5%\\n[libx264 @ 0x63f97dec8480] ref B L0: 54.3% 45.1% 0.6%\\n[libx264 @ 0x63f97dec8480] ref B L1: 96.5% 3.5%\\n[libx264 @ 0x63f97dec8480] kb/s:179.12\\nffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\n libavutil 58. 29.100 / 58. 29.100\\n libavcodec 60. 31.102 / 60. 31.102\\n libavformat 60. 16.100 / 60. 16.100\\n libavdevice 60. 3.100 / 60. 3.100\\n libavfilter 9. 12.100 / 9. 12.100\\n libswscale 7. 5.100 / 7. 5.100\\n libswresample 4. 12.100 / 4. 12.100\\n libpostproc 57. 3.100 / 57. 3.100\\nInput #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Duration: 00:00:31.33, start: 0.000000, bitrate: 181 kb/s\\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 179 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n[mp3 @ 0x5f3663ac3640] Estimating duration from bitrate, this may be inaccurate\\nInput #1, mp3, from '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/audio/voiceover.mp3':\\n Duration: 00:00:31.32, start: 0.000000, bitrate: 48 kb/s\\n Stream #1:0: Audio: mp3, 24000 Hz, mono, fltp, 48 kb/s\\nStream mapping:\\n Stream #0:0 -> #0:0 (copy)\\n Stream #1:0 -> #0:1 (mp3 (mp3float) -> aac (native))\\nPress [q] to stop, [?] for help\\nOutput #0, mp4, to '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4':\\n Metadata:\\n major_brand : isom\\n minor_version : 512\\n compatible_brands: isomiso2avc1mp41\\n encoder : Lavf60.16.100\\n Stream #0:0(und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, q=2-31, 179 kb/s, 24 fps, 24 tbr, 12288 tbn (default)\\n Metadata:\\n handler_name : VideoHandler\\n vendor_id : [0][0][0][0]\\n encoder : Lavc60.31.102 libx264\\n Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 24000 Hz, mono, fltp, 69 kb/s\\n Metadata:\\n encoder : Lavc60.31.102 aac\\nsize= 0kB time=N/A bitrate=N/A speed=N/A \\r[out#0/mp4 @ 0x5f3663a37280] video:686kB audio:242kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 2.071741%\\nsize= 947kB time=00:00:31.31 bitrate= 247.6kbits/s speed=68.8x \\n[aac @ 0x5f3663b3abc0] Qavg: 18380.342\\n \\\"not\\\",\\n \\\"proof\\\"\\n ],\\n \\\"start\\\": 26.887,\\n \\\"end\\\": 29.250000000000004\\n },\\n {\\n \\\"text\\\": \\\"It's the red flag\\\",\\n \\\"words\\\": [\\n \\\"It's\\\",\\n \\\"the\\\",\\n \\\"red\\\",\\n \\\"flag\\\"\\n ],\\n \\\"start\\\": 29.25,\\n \\\"end\\\": 31.262\\n }\\n ]\\n },\\n \\\"artifacts\\\": {\\n \\\"preview_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 969261,\\n \\\"sha256\\\": \\\"21be01acac26a3eb1d5b51597c9c3195f98169e9efd376e8ead0ca7d5c2f828d\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.32,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"silent_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags-fake-invoice-payment-route-30s_preview_v2_caption_safe_silent.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 712114,\\n \\\"sha256\\\": \\\"2819f5672b990eeafe879ed66415446d46561c383afe137a6e3a7189f2939694\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.333333,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"qa_frame_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_caption_safe_contact_sheet_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 230788,\\n \\\"sha256\\\": \\\"8da27f967519975d12807d117cccc9069139f1a7908016bdeb8e82b72b6db6c9\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\"\\n },\\n \\\"subtitle_style_preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/preview_v2_caption_safe/digital-red-flags_subtitle_style_preview_v2.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 351467,\\n \\\"sha256\\\": \\\"4fba208eb302571ed110f19c3e7b6af3cea66c920b54c657a33b6efa76c2428d\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x1920\\\"\\n }\\n }\\n}\\na9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\\n{\\n \\\"programs\\\": [\\n\\n ],\\n \\\"streams\\\": [\\n {\\n \\\"index\\\": 0,\\n \\\"codec_name\\\": \\\"h264\\\",\\n \\\"codec_type\\\": \\\"video\\\",\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"r_frame_rate\\\": \\\"24/1\\\",\\n \\\"avg_frame_rate\\\": \\\"24/1\\\"\\n },\\n {\\n \\\"index\\\": 1,\\n \\\"codec_name\\\": \\\"aac\\\",\\n \\\"codec_type\\\": \\\"audio\\\",\\n \\\"sample_rate\\\": \\\"48000\\\",\\n \\\"channels\\\": 1,\\n \\\"r_frame_rate\\\": \\\"0/0\\\",\\n \\\"avg_frame_rate\\\": \\\"0/0\\\"\\n }\\n ],\\n \\\"format\\\": {\\n \\\"duration\\\": \\\"31.400000\\\"\\n }\\n}\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_jZY7oXiHUbZEV70VCXcNcREj"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_HrlhEefsL62ImuPTPNRehYvQ",
"call_id": "call_HrlhEefsL62ImuPTPNRehYvQ",
"response_item_id": "fc_051a8a0b2fb58918016a1488996024819193e0a8ce824114da",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\",\"question\":\"Re-check final QA after switching email to reserved sample.test. Any captions clipped/off safe area, real logos/real bank data/QR codes/people, or major upload-blocking visual issues?\"}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFIiZLH8UeaRxuJ7vbDBTvzBI6-fJvHeXPZ07RIHUDs7aa7zvUmis1d3DWGbc1CZumz4zbKEyoUTmQu_ErvTPvQ0EGn5C83BFfoR0-Z-IHTbCKUSyY0JURT_hC6tVjzw-cGGJlAAFLVbcShghyp8cLp_SrNIO2KG4IAeWE8KhfNJudccsjeFfeSmMucfjRFL5xBuPaADS-1qxT8JVpsT0MFUHfFLDXkhPnMELbQeGSuw9HdwugfOnF96PyFBBdWT8xup8a0dJ1jW5a9ka-i9vmIXDUGquqVb6m1Qd7js8RgaOTPc88sFANLxnT8G-Vi7eMoS7wSjrNpwkCXcxTAf8QPL218rFJ_4tFQfVHe-WGVdmrAMx3U4aWGyZDAw0ie5ZG1Gkd8WjwBSWKOC3zVQJqbkAB-J3IIrKDzlT7A-BXLnkHmXtepulCpWb88MTB-VpJ95N0NFvGmT_FoF4vRyEgaArEhynVJMtYfVsb9kShsa14feEFDtxeV243-rs6bJ6EtCG_dC0x1oyD7EIZ-TilrtANz6drnRlYyTjj98ejrZJkr5fsvr-i2bPA7GNJP1hNoJU4_tkwlXp7duGTlGcc073UK_jiC0fGI7HXCYsJAa62W5G0PZzHjSQPNMrsA-BdD3sa9gIH0ZTjxuf_4n2nt3DeNl2mKWYpRcckX48-yfGCK8WTJQ96YNi4YSYT_i9DAFmLN4CRoy4N9rcUqKOKzqdNZEYacIdQbLLWIiELeiWN-yYVMVgGx2OtoFfdcedckbitYkXJ7CtamsmlIZnN3QRPsLZFDbpl8W1Dgzm_IzFcSADJH0RinLEfKC65XJfNMnkvaOI5YEUTO6HWCS40gYSGm-U4LeL_vrwooqc6pVI5paaFsI77DAgsMIm3XPAF-GlEoy2LpDwfyt8FZDHj00I2n8y5KjlJl1VVF7FBL9KCwKhlGQJUfCaWHAgmAwi-HJS",
"id": "rs_051a8a0b2fb58918016a148898aef4819199b8e67881f23246",
"summary": []
}
]
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Re-check final QA after switching email to reserved sample.test. Any captions clipped/off safe area, real logos/real bank data/QR codes/people, or major upload-blocking visual issues?\n[screenshot]",
"tool_call_id": "call_HrlhEefsL62ImuPTPNRehYvQ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_MNNRMnZyoaM8ki8Dad1fdeKP",
"call_id": "call_MNNRMnZyoaM8ki8Dad1fdeKP",
"response_item_id": "fc_051a8a0b2fb58918016a14889f985081918f9c91483de833f8",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from hermes_tools import terminal\\nscript = r'''python - <<'PY'\\nfrom pathlib import Path\\nfrom PIL import Image\\nimport json, csv, hashlib, os, subprocess\\nBASE=Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s')\\nOUT=BASE/'manual_upload_v1'\\nOUT.mkdir(parents=True, exist_ok=True)\\nCID='digital-red-flags-fake-invoice-payment-route-30s'\\nFINAL=OUT/f'{CID}_manual_upload_v1.mp4'\\nHOOK='This invoice looks normal. The trap is not the logo — it’s the payment route.'\\nSCRIPT=\\\"This invoice looks normal. The trap is not the logo — it’s the payment route. Same layout. Same professional tone. Same urgency. But one thing changed: where the money goes. Before you pay, check three things: sender domain, urgency language, and payment route. If the route changed, don’t pay from the email. Verify through a second channel. Urgency is not proof. It’s the red flag.\\\"\\n\\ndef sha(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest()\\ndef probe_video(p):\\n data=json.loads(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration:stream=index,codec_type,codec_name,width,height,r_frame_rate,avg_frame_rate,sample_rate,channels','-of','json',str(p)], text=True))\\n v=next(s for s in data['streams'] if s['codec_type']=='video')\\n a=next((s for s in data['streams'] if s['codec_type']=='audio'), None)\\n return {'duration': round(float(data['format']['duration']),3), 'resolution': f\\\"{v['width']}x{v['height']}\\\", 'fps': v.get('avg_frame_rate') or v.get('r_frame_rate'), 'video_codec': v.get('codec_name'), 'audio_codec': a.get('codec_name') if a else None, 'audio_sample_rate': a.get('sample_rate') if a else None, 'audio_channels': a.get('channels') if a else None}\\ndef meta(path, media_type):\\n p=Path(path); d={'file_path':str(p),'file_exists':p.exists(),'file_size':p.stat().st_size if p.exists() else 0,'sha256':sha(p) if p.exists() else None,'media_type':media_type}\\n if p.exists() and media_type.startswith('video'):\\n d.update(probe_video(p))\\n elif p.exists() and media_type.startswith('image'):\\n im=Image.open(p); d.update({'duration':'n/a','resolution':f'{im.width}x{im.height}'})\\n else:\\n d.update({'duration':'n/a','resolution':'n/a'})\\n return d\\nstatus={\\n 'candidate_id':CID,\\n 'status':'approved_proof_short',\\n 'approved_version':'preview_v2_caption_safe',\\n 'manual_upload_version':'manual_upload_v1',\\n 'posted':False,\\n 'auto_upload_allowed':False,\\n 'platform_api_used':False,\\n 'approval_note':'APPROVED_PROOF_SHORT received. This approves proof short quality only, not automatic posting.'\\n}\\n(OUT/'candidate_status_manual_upload_v1.json').write_text(json.dumps(status,indent=2), encoding='utf-8')\\nposting_pack={\\n 'candidate_id':CID,\\n 'version':'manual_upload_v1',\\n 'manual_only':True,\\n 'auto_posting':False,\\n 'platform_api_used':False,\\n 'hook':HOOK,\\n 'safe_claim_language':['A common red flag is…','Before you pay…','Verify through a second channel.'],\\n 'platforms':{\\n 'youtube_shorts':{\\n 'title':'This Invoice Looks Normal — Check the Payment Route',\\n 'description':'A common red flag in invoice fraud is not the logo. It is where the payment route sends the money. Before you pay from an email, check the sender domain, urgency language, and payment route. If the route changed, verify through a second channel.\\\\n\\\\nGeneral safety awareness only — not financial or legal advice.',\\n 'hashtags':['#Shorts','#DigitalSafety','#InvoiceFraud','#CyberAwareness','#SmallBusiness'],\\n 'short_cta':'Save this before paying from an email.',\\n 'shorts_compatible':True,\\n 'notes':'31.4s vertical 1080x1920, safe for Shorts format.'\\n },\\n 'tiktok':{\\n 'caption':'This invoice looks normal. A common red flag is the payment route. Before you pay, verify through a second channel.',\\n 'hashtags':['#DigitalSafety','#ScamAwareness','#InvoiceFraud','#WorkTips','#CyberSafety'],\\n 'cta':'Save this check for your next invoice.',\\n 'pinned_comment_suggestion':'Quick rule: if the payment route changed, do not pay from the email — verify through a second channel.'\\n },\\n 'instagram_reels':{\\n 'caption':'A fake invoice does not need a fake logo. A common red flag is a changed payment route. Before you pay from an email, check sender, urgency, and route — then verify through a second channel.',\\n 'hashtags':['#DigitalSafety','#ScamAwareness','#InvoiceFraud','#BusinessSafety','#CyberTips'],\\n 'cta':'Save this rule and share it with someone who approves invoices.',\\n 'cover_text_suggestion':'Check the payment route'\\n },\\n 'linkedin_manual':{\\n 'hook_line':'The risky part of an invoice is not always the logo.',\\n 'professional_caption':'A common invoice-fraud red flag is a changed payment route. The email may look professional, use familiar formatting, and create urgency — but the key question is where the money is being sent. Before paying from an email request, check the sender domain, urgency language, and payment route. If the route changed, verify through a second channel.\\\\n\\\\nGeneral safety awareness, not financial or legal advice.',\\n 'short_context':'Useful for teams that approve invoices, vendor payments, or urgent payment requests.',\\n 'cta':'Worth adding to your invoice approval checklist — especially for urgent email requests.',\\n 'hashtags':['#CyberSecurity','#FraudPrevention','#DigitalSafety','#BusinessOperations']\\n }\\n },\\n 'ai_disclosure':{\\n 'summary':'Video uses deterministic mockups and TTS. No realistic humans, no real companies, no real events, no realistic synthetic persons.',\\n 'platform_ai_disclosure_likely_required':False,\\n 'manual_review_note':'Because the video uses non-realistic deterministic UI/document mockups and TTS without realistic synthetic people or real-event depiction, platform synthetic media disclosure is probably not required. If a platform UI asks whether AI-assisted media was used, disclose conservatively according to that platform prompt.'\\n },\\n 'claim_gate':{\\n 'claim_type':'general safety advice / low-to-medium risk',\\n 'result':'pass',\\n 'not_claimed':['Does not claim all fake invoices work this way','Does not claim three checks prevent all scams','Does not provide legal/financial advice'],\\n 'safe_phrasing':['A common red flag…','Before you pay…','Verify through a second channel.']\\n }\\n}\\n(OUT/'manual_posting_pack_v1.json').write_text(json.dumps(posting_pack,indent=2), encoding='utf-8')\\n# Markdown pack for Telegram-readable copy\\nmd=[]\\nmd.append('# Manual Posting Pack — Digital Red Flags / Fake Invoice Payment Route\\\\n')\\nfor platform, data in posting_pack['platforms'].items():\\n md.append(f'## {platform}\\\\n')\\n for k,v in data.items():\\n if isinstance(v, list): v=' '.join(v)\\n md.append(f'- {k}: {v}')\\n md.append('')\\nmd.append('## AI Disclosure / Synthetic Media\\\\n')\\nfor k,v in posting_pack['ai_disclosure'].items(): md.append(f'- {k}: {v}')\\nmd.append('\\\\n## Claim Gate\\\\n- result: pass\\\\n- claim type: general safety advice / low-to-medium risk\\\\n')\\n(OUT/'manual_posting_pack_v1.md').write_text('\\\\n'.join(md), encoding='utf-8')\\nfields=['platform','post_url','post_time','candidate_id','version','hook','topic_cluster','format_type','duration','caption_style','visual_style','cta_type','1h_views','1h_likes','1h_comments','1h_shares','1h_saves','1h_follows','24h_views','24h_completion_rate','24h_average_watch_time','24h_shares','24h_saves','24h_follows','72h_views','7d_views','notes','qualitative_comments','followup_decision']\\ndef row(platform):\\n return {k:'' for k in fields} | {'platform':platform,'candidate_id':CID,'version':'manual_upload_v1','hook':HOOK,'topic_cluster':'digital_red_flags / invoice_fraud_awareness','format_type':'proof_short','duration':'31.4','caption_style':'static phrase segments + active-word highlight, no box/bar','visual_style':'deterministic screen/mockup-led','cta_type':'save/share/checklist'}\\nrows=[row(p) for p in ['youtube_shorts','tiktok','instagram_reels','linkedin_manual']]\\nwith (OUT/'analytics_tracking_template_v1.csv').open('w', newline='', encoding='utf-8') as f:\\n w=csv.DictWriter(f, fieldnames=fields); w.writeheader(); w.writerows(rows)\\n(OUT/'analytics_tracking_template_v1.json').write_text(json.dumps({'fields':fields,'rows':rows},indent=2), encoding='utf-8')\\nqa={\\n 'candidate_id':CID,'version':'manual_upload_v1','qa_result':'pass','upload_ready':True,\\n 'checks':{\\n 'video_plays_full_length':'pass','audio_present':'pass','captions_safe_zone':'pass','no_caption_box':'pass','no_full_width_bar':'pass','no_clipped_words':'pass','no_real_logos':'pass','no_real_bank_data':'pass','no_qr_codes':'pass','no_people':'pass','platform_ui_issues_visible':'pass','hook_first_2_seconds_understandable':'pass','takeaway_clear':'pass','claim_gate':'pass','ai_disclosure':'pass'},\\n 'notes':['Email mockup uses reserved .test domain: billing@sample.test.','Invoice/company/payment data are generic deterministic mockups.','No auto-posting or platform API used.'],\\n 'visual_qa_contact_sheet': str(OUT/'manual_upload_v1_qa_contact_sheet.jpg'),\\n 'final_mp4': meta(FINAL,'video/mp4'),\\n 'manual_posting_pack_json': meta(OUT/'manual_posting_pack_v1.json','application/json'),\\n 'manual_posting_pack_md': meta(OUT/'manual_posting_pack_v1.md','text/markdown'),\\n 'analytics_template_csv': meta(OUT/'analytics_tracking_template_v1.csv','text/csv'),\\n 'analytics_template_json': meta(OUT/'analytics_tracking_template_v1.json','application/json'),\\n 'qa_contact_sheet': meta(OUT/'manual_upload_v1_qa_contact_sheet.jpg','image/jpeg'),\\n 'candidate_status': meta(OUT/'candidate_status_manual_upload_v1.json','application/json')\\n}\\n(OUT/'qa_summary_manual_upload_v1.json').write_text(json.dumps(qa,indent=2), encoding='utf-8')\\nqa_md=['# QA Summary — manual_upload_v1','',f'- candidate_id: {CID}','- version: manual_upload_v1','- qa_result: pass','- upload_ready: true','']\\nfor k,v in qa['checks'].items(): qa_md.append(f'- {k}: {v}')\\nqa_md += ['','## Notes'] + [f'- {n}' for n in qa['notes']]\\n(OUT/'qa_summary_manual_upload_v1.md').write_text('\\\\n'.join(qa_md), encoding='utf-8')\\n# manifest of all deliverables\\narts={name: qa[name] for name in ['final_mp4','manual_posting_pack_json','manual_posting_pack_md','analytics_template_csv','analytics_template_json','qa_contact_sheet','candidate_status']}\\narts['qa_summary_json']=meta(OUT/'qa_summary_manual_upload_v1.json','application/json')\\narts['qa_summary_md']=meta(OUT/'qa_summary_manual_upload_v1.md','text/markdown')\\nmanifest={'candidate_id':CID,'version':'manual_upload_v1','status':'approved_proof_short','posting_status':'not_posted','auto_posting':False,'platform_api_used':False,'wan_used':False,'i2v_used':False,'t2v_used':False,'new_ai_images':False,'artifacts':arts}\\n(OUT/'manual_upload_v1_manifest.json').write_text(json.dumps(manifest,indent=2), encoding='utf-8')\\nprint(json.dumps(manifest, indent=2))\\nPY'''\\nprint(terminal(script, timeout=120, workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'])\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Planning artifact creation**\n\nI'm thinking about creating a package, and a Python script could be useful for that. I'll need to gather some artifact metadata and possibly a status file that indicates approval. It seems I'll be using functions like write_file and execute. I'll consider formats like JSON and CSV, but I want to avoid relying on platform APIs if possible. Okay, let's go ahead and generate everything!",
"reasoning_content": "**Planning artifact creation**\n\nI'm thinking about creating a package, and a Python script could be useful for that. I'll need to gather some artifact metadata and possibly a status file that indicates approval. It seems I'll be using functions like write_file and execute. I'll consider formats like JSON and CSV, but I want to avoid relying on platform APIs if possible. Okay, let's go ahead and generate everything!",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_051a8a0b2fb58918016a14889d27d88191ac4266c72ff05b2e",
"summary": [
{
"type": "summary_text",
"text": "**Planning artifact creation**\n\nI'm thinking about creating a package, and a Python script could be useful for that. I'll need to gather some artifact metadata and possibly a status file that indicates approval. It seems I'll be using functions like write_file and execute. I'll consider formats like JSON and CSV, but I want to avoid relying on platform APIs if possible. Okay, let's go ahead and generate everything!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"candidate_id\\\": \\\"digital-red-flags-fake-invoice-payment-route-30s\\\",\\n \\\"version\\\": \\\"manual_upload_v1\\\",\\n \\\"status\\\": \\\"approved_proof_short\\\",\\n \\\"posting_status\\\": \\\"not_posted\\\",\\n \\\"auto_posting\\\": false,\\n \\\"platform_api_used\\\": false,\\n \\\"wan_used\\\": false,\\n \\\"i2v_used\\\": false,\\n \\\"t2v_used\\\": false,\\n \\\"new_ai_images\\\": false,\\n \\\"artifacts\\\": {\\n \\\"final_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1190549,\\n \\\"sha256\\\": \\\"a9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.4,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\",\\n \\\"video_codec\\\": \\\"h264\\\",\\n \\\"audio_codec\\\": \\\"aac\\\",\\n \\\"audio_sample_rate\\\": \\\"48000\\\",\\n \\\"audio_channels\\\": 1\\n },\\n \\\"manual_posting_pack_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4158,\\n \\\"sha256\\\": \\\"e66195587c7bc5a3ba7245cb2c988f9622a8ba62ff50cbe54eb48f7b098f3040\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"manual_posting_pack_md\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.md\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 2976,\\n \\\"sha256\\\": \\\"1dca29b97479cc9afc5f4e37f3d7355fcf0ce0f6a408acf63554f074871cd204\\\",\\n \\\"media_type\\\": \\\"text/markdown\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"analytics_template_csv\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.csv\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1762,\\n \\\"sha256\\\": \\\"d464584149838bd39b2dd51dbc5763ff6143bbedc13c299c91601d53600ab421\\\",\\n \\\"media_type\\\": \\\"text/csv\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"analytics_template_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4792,\\n \\\"sha256\\\": \\\"089f77a681900716dbace91468ef053e0c4b68188688e299c36877e7f6c9a2f1\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"qa_contact_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 232844,\\n \\\"sha256\\\": \\\"869c71f526def74a27723a8efeee2d99f25b8ef041f340a5c6a05c5bc068866e\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\"\\n },\\n \\\"candidate_status\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/candidate_status_manual_upload_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 401,\\n \\\"sha256\\\": \\\"9a6830c68ee2e21ed148582b7de26754f8ed99db118ec993053892b951a0484e\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"qa_summary_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4016,\\n \\\"sha256\\\": \\\"11e076447a1f569ce9e85475da84950a952c2fc9f7b92aa39b12e278273d2b70\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n },\\n \\\"qa_summary_md\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.md\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 731,\\n \\\"sha256\\\": \\\"4f6df0599494a4854bacdc22392eb82d160f4551d5355e440bd2e08fb7cf111a\\\",\\n \\\"media_type\\\": \\\"text/markdown\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\"\\n }\\n }\\n}\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.45}",
"tool_call_id": "call_MNNRMnZyoaM8ki8Dad1fdeKP"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_FIRsH0eWJAbHyI2BycbpWRiP",
"call_id": "call_FIRsH0eWJAbHyI2BycbpWRiP",
"response_item_id": "fc_051a8a0b2fb58918016a1488dc175881919c2dbb26122f168c",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Git status/tests prüfen und Sprint-3.1 Commit sichern\",\"id\":\"secure\",\"status\":\"completed\"},{\"content\":\"Preview v2 als approved_proof_short markieren\",\"id\":\"approve\",\"status\":\"completed\"},{\"content\":\"manual_upload_v1 MP4 technisch finalisieren und Metadaten erzeugen\",\"id\":\"final\",\"status\":\"completed\"},{\"content\":\"Final QA + Claim/AI Disclosure Gate dokumentieren\",\"id\":\"qa\",\"status\":\"completed\"},{\"content\":\"Manual Posting Pack + Analytics Templates erzeugen\",\"id\":\"pack\",\"status\":\"completed\"},{\"content\":\"MP4, Pack, Analytics, QA Summary per Telegram senden/verifizieren\",\"id\":\"deliver\",\"status\":\"in_progress\"}]}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Processing file delivery**\n\nI’m thinking about how to read the posting pack for the final delivery, but not too detailed. First, I need to update my to-do list and possibly run a git clean since there aren’t any repo modifications. I should check the git status later. It seems we modified some files, but that’s fine. I need to send a target with the necessary files and consider using send_message for attachments, especially for media files like MP4.",
"reasoning_content": "**Processing file delivery**\n\nI’m thinking about how to read the posting pack for the final delivery, but not too detailed. First, I need to update my to-do list and possibly run a git clean since there aren’t any repo modifications. I should check the git status later. It seems we modified some files, but that’s fine. I need to send a target with the necessary files and consider using send_message for attachments, especially for media files like MP4.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_051a8a0b2fb58918016a1488d692288191ba9bde1dda80008f",
"summary": [
{
"type": "summary_text",
"text": "**Processing file delivery**\n\nI’m thinking about how to read the posting pack for the final delivery, but not too detailed. First, I need to update my to-do list and possibly run a git clean since there aren’t any repo modifications. I should check the git status later. It seems we modified some files, but that’s fine. I need to send a target with the necessary files and consider using send_message for attachments, especially for media files like MP4."
}
]
}
]
},
{
"role": "tool",
"content": "{\"todos\": [{\"id\": \"secure\", \"content\": \"Git status/tests prüfen und Sprint-3.1 Commit sichern\", \"status\": \"completed\"}, {\"id\": \"approve\", \"content\": \"Preview v2 als approved_proof_short markieren\", \"status\": \"completed\"}, {\"id\": \"final\", \"content\": \"manual_upload_v1 MP4 technisch finalisieren und Metadaten erzeugen\", \"status\": \"completed\"}, {\"id\": \"qa\", \"content\": \"Final QA + Claim/AI Disclosure Gate dokumentieren\", \"status\": \"completed\"}, {\"id\": \"pack\", \"content\": \"Manual Posting Pack + Analytics Templates erzeugen\", \"status\": \"completed\"}, {\"id\": \"deliver\", \"content\": \"MP4, Pack, Analytics, QA Summary per Telegram senden/verifizieren\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 6, \"pending\": 0, \"in_progress\": 1, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_FIRsH0eWJAbHyI2BycbpWRiP"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_P3SzlWEppS8RaUNf2OQUKI6L",
"call_id": "call_P3SzlWEppS8RaUNf2OQUKI6L",
"response_item_id": "fc_051a8a0b2fb58918016a1488df6ee481919035ed5921c57edd",
"type": "function",
"function": {
"name": "send_message",
"arguments": "{\"action\":\"send\",\"target\":\"telegram:JARVIS Command Center / topic 4850\",\"message\":\"Sprint 3.2 — Manual Upload Pack\\n\\nUpload-ready MP4:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\\n\\nManual Posting Pack JSON:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.json\\n\\nManual Posting Pack Markdown:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.md\\n\\nAnalytics Template CSV:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.csv\\n\\nAnalytics Template JSON:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.json\\n\\nQA Summary JSON:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.json\\n\\nQA Contact Sheet:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"5984\", \"mirrored\": true}",
"tool_call_id": "call_P3SzlWEppS8RaUNf2OQUKI6L"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_B8qkcC1brtttNvYATu1fd8tT",
"call_id": "call_B8qkcC1brtttNvYATu1fd8tT",
"response_item_id": "fc_051a8a0b2fb58918016a1488f2467481919efe6f2a48b43660",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from hermes_tools import terminal\\ncmd=r'''python - <<'PY'\\nfrom pathlib import Path\\nimport json\\nOUT=Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1')\\nfor filename in ['manual_upload_v1_manifest.json','qa_summary_manual_upload_v1.json']:\\n p=OUT/filename\\n data=json.loads(p.read_text())\\n if 'artifacts' in data:\\n artifacts=data['artifacts']\\n else:\\n artifacts={k:v for k,v in data.items() if isinstance(v,dict) and 'file_path' in v}\\n for v in artifacts.values():\\n if isinstance(v,dict) and 'file_path' in v:\\n v['telegram_delivery_attempted']=True\\n v['telegram_delivery_verified']=True\\n v['telegram_message_id']='5984'\\n v['telegram_target']='telegram:JARVIS Command Center / topic 4850'\\n data['thread_delivery']={'telegram_delivery_attempted':True,'telegram_delivery_verified':True,'telegram_message_id':'5984','chat_id':'-1003907117629','target':'telegram:JARVIS Command Center / topic 4850'}\\n p.write_text(json.dumps(data,indent=2),encoding='utf-8')\\nprint((OUT/'manual_upload_v1_manifest.json').read_text()[:9000])\\nPY\\n\\ngit status --short && git log -1 --oneline\\npytest tests/test_caption_safe_zone.py tests/test_static_caption_segments.py tests/test_image_generation_policy.py tests/test_proof_short_mode.py tests/test_pilot_production_v2.py -q\\n'''\\nprint(terminal(cmd, timeout=180, workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'])\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Updating delivery info**\n\nI need to update the manifest delivery information for the artifacts first. After that, I'll check the final git status to ensure everything is in order. It’s essential to have that delivery info updated correctly before moving on. Once I have both tasks done, I can feel secure knowing everything is in place. It’s all about keeping things organized and straightforward to avoid any potential hiccups later!",
"reasoning_content": "**Updating delivery info**\n\nI need to update the manifest delivery information for the artifacts first. After that, I'll check the final git status to ensure everything is in order. It’s essential to have that delivery info updated correctly before moving on. Once I have both tasks done, I can feel secure knowing everything is in place. It’s all about keeping things organized and straightforward to avoid any potential hiccups later!",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_051a8a0b2fb58918016a1488f0a2d081918a6f3d4660fde451",
"summary": [
{
"type": "summary_text",
"text": "**Updating delivery info**\n\nI need to update the manifest delivery information for the artifacts first. After that, I'll check the final git status to ensure everything is in order. It’s essential to have that delivery info updated correctly before moving on. Once I have both tasks done, I can feel secure knowing everything is in place. It’s all about keeping things organized and straightforward to avoid any potential hiccups later!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"candidate_id\\\": \\\"digital-red-flags-fake-invoice-payment-route-30s\\\",\\n \\\"version\\\": \\\"manual_upload_v1\\\",\\n \\\"status\\\": \\\"approved_proof_short\\\",\\n \\\"posting_status\\\": \\\"not_posted\\\",\\n \\\"auto_posting\\\": false,\\n \\\"platform_api_used\\\": false,\\n \\\"wan_used\\\": false,\\n \\\"i2v_used\\\": false,\\n \\\"t2v_used\\\": false,\\n \\\"new_ai_images\\\": false,\\n \\\"artifacts\\\": {\\n \\\"final_mp4\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1190549,\\n \\\"sha256\\\": \\\"a9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.4,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\",\\n \\\"video_codec\\\": \\\"h264\\\",\\n \\\"audio_codec\\\": \\\"aac\\\",\\n \\\"audio_sample_rate\\\": \\\"48000\\\",\\n \\\"audio_channels\\\": 1,\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"manual_posting_pack_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4158,\\n \\\"sha256\\\": \\\"e66195587c7bc5a3ba7245cb2c988f9622a8ba62ff50cbe54eb48f7b098f3040\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"manual_posting_pack_md\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.md\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 2976,\\n \\\"sha256\\\": \\\"1dca29b97479cc9afc5f4e37f3d7355fcf0ce0f6a408acf63554f074871cd204\\\",\\n \\\"media_type\\\": \\\"text/markdown\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"analytics_template_csv\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.csv\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 1762,\\n \\\"sha256\\\": \\\"d464584149838bd39b2dd51dbc5763ff6143bbedc13c299c91601d53600ab421\\\",\\n \\\"media_type\\\": \\\"text/csv\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"analytics_template_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4792,\\n \\\"sha256\\\": \\\"089f77a681900716dbace91468ef053e0c4b68188688e299c36877e7f6c9a2f1\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"qa_contact_sheet\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 232844,\\n \\\"sha256\\\": \\\"869c71f526def74a27723a8efeee2d99f25b8ef041f340a5c6a05c5bc068866e\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"candidate_status\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/candidate_status_manual_upload_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 401,\\n \\\"sha256\\\": \\\"9a6830c68ee2e21ed148582b7de26754f8ed99db118ec993053892b951a0484e\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"qa_summary_json\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.json\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 4016,\\n \\\"sha256\\\": \\\"11e076447a1f569ce9e85475da84950a952c2fc9f7b92aa39b12e278273d2b70\\\",\\n \\\"media_type\\\": \\\"application/json\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n },\\n \\\"qa_summary_md\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.md\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 731,\\n \\\"sha256\\\": \\\"4f6df0599494a4854bacdc22392eb82d160f4551d5355e440bd2e08fb7cf111a\\\",\\n \\\"media_type\\\": \\\"text/markdown\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"n/a\\\",\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n }\\n },\\n \\\"thread_delivery\\\": {\\n \\\"telegram_delivery_attempted\\\": true,\\n \\\"telegram_delivery_verified\\\": true,\\n \\\"telegram_message_id\\\": \\\"5984\\\",\\n \\\"chat_id\\\": \\\"-1003907117629\\\",\\n \\\"target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n }\\n}\\ncf66672 fix: enforce caption safe zones and image policy\\n........................ [100%]\\n24 passed in 0.41s\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 1.25}",
"tool_call_id": "call_B8qkcC1brtttNvYATu1fd8tT"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_FctFJglf4gjC4YfyhSGBcv0T",
"call_id": "call_FctFJglf4gjC4YfyhSGBcv0T",
"response_item_id": "fc_051a8a0b2fb58918016a148901e50081919e40537afe1d8b42",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"MP4, Pack, Analytics, QA Summary per Telegram senden/verifizieren\",\"id\":\"deliver\",\"status\":\"completed\"}]}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Finalizing concise report in German**\n\nI need to prepare a final answer in German that is concise yet detailed. I should mention the git status—if it’s clean, it won’t show extra details, and I’ll note the last commit. There may be a need to differentiate any commit messages. I think I’ll include that media has already been sent without resending it. Also, I should outline next commands and open risks, but I have to keep it straightforward without overwhelming details.",
"reasoning_content": "**Finalizing concise report in German**\n\nI need to prepare a final answer in German that is concise yet detailed. I should mention the git status—if it’s clean, it won’t show extra details, and I’ll note the last commit. There may be a need to differentiate any commit messages. I think I’ll include that media has already been sent without resending it. Also, I should outline next commands and open risks, but I have to keep it straightforward without overwhelming details.",
"codex_reasoning_items": [
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"text": "**Finalizing concise report in German**\n\nI need to prepare a final answer in German that is concise yet detailed. I should mention the git status—if it’s clean, it won’t show extra details, and I’ll note the last commit. There may be a need to differentiate any commit messages. I think I’ll include that media has already been sent without resending it. Also, I should outline next commands and open risks, but I have to keep it straightforward without overwhelming details."
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"content": "{\"todos\": [{\"id\": \"secure\", \"content\": \"Git status/tests prüfen und Sprint-3.1 Commit sichern\", \"status\": \"completed\"}, {\"id\": \"approve\", \"content\": \"Preview v2 als approved_proof_short markieren\", \"status\": \"completed\"}, {\"id\": \"final\", \"content\": \"manual_upload_v1 MP4 technisch finalisieren und Metadaten erzeugen\", \"status\": \"completed\"}, {\"id\": \"qa\", \"content\": \"Final QA + Claim/AI Disclosure Gate dokumentieren\", \"status\": \"completed\"}, {\"id\": \"pack\", \"content\": \"Manual Posting Pack + Analytics Templates erzeugen\", \"status\": \"completed\"}, {\"id\": \"deliver\", \"content\": \"MP4, Pack, Analytics, QA Summary per Telegram senden/verifizieren\", \"status\": \"completed\"}], \"summary\": {\"total\": 6, \"pending\": 0, \"in_progress\": 0, \"completed\": 6, \"cancelled\": 0}}",
"tool_call_id": "call_FctFJglf4gjC4YfyhSGBcv0T"
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"content": "Sir, **Sprint 3.2 — Proof Short Finalization + Manual Posting Pack ist abgeschlossen.**\n\nDer Candidate ist jetzt als **approved_proof_short** markiert und als **manual_upload_v1** uploadbereit vorbereitet.\n\nKein Auto-Posting. Keine Plattform-API. Kein Wan. Kein I2V/T2V. Keine neuen AI-Bilder. Also ausnahmsweise kein digitaler Zirkus mit Nebelmaschine.\n\n## Status\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nStatus:\n\n```text\napproved_proof_short\n```\n\nManual Upload Version:\n\n```text\nmanual_upload_v1\n```\n\nPosting Status:\n\n```text\nnot_posted\n```\n\nAuto Upload:\n\n```text\nfalse\n```\n\n## Git / Tests\n\nGit Status:\n\n```text\nclean\n```\n\nLetzter Commit:\n\n```text\ncf66672 fix: enforce caption safe zones and image policy\n```\n\nRelevante Tests:\n\n```text\n24 passed in 0.41s\n```\n\nGetestet:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_static_caption_segments.py\ntests/test_image_generation_policy.py\ntests/test_proof_short_mode.py\ntests/test_pilot_production_v2.py\n```\n\n## Final MP4\n\nUploadbereites MP4:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 1190549 bytes\nsha256: a9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc\nmedia_type: video/mp4\nduration: 31.4s\nresolution: 1080x1920\nfps: 24/1\nvideo_codec: h264\naudio_codec: aac\naudio_sample_rate: 48000\naudio_channels: 1\n```\n\nEncoding:\n\n```text\nH.264 high profile\nAAC audio\n48kHz mono\n+faststart\nloudnorm applied\n1080x1920\n24fps\n```\n\n## Kleine Safety-Korrektur\n\nIch habe für die uploadbereite Version die sichtbare Mockup-Absenderadresse von:\n\n```text\nbilling@example-company.com\n```\n\nauf eine reservierte Test-Domain geändert:\n\n```text\nbilling@sample.test\n```\n\nGrund: weniger Risiko, dass eine scheinbar echte `.com`-Adresse als real interpretiert wird. Gleiche Story, sauberer Mockup-Standard.\n\n## Final QA Result\n\nQA Result:\n\n```text\npass\n```\n\nUpload Ready:\n\n```text\ntrue\n```\n\nGeprüft:\n\n```text\nvideo_plays_full_length: pass\naudio_present: pass\ncaptions_safe_zone: pass\nno_caption_box: pass\nno_full_width_bar: pass\nno_clipped_words: pass\nno_real_logos: pass\nno_real_bank_data: pass\nno_qr_codes: pass\nno_people: pass\nplatform_ui_issues_visible: pass\nhook_first_2_seconds_understandable: pass\ntakeaway_clear: pass\nclaim_gate: pass\nai_disclosure: pass\n```\n\nQA Contact Sheet:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 232844 bytes\nsha256: 869c71f526def74a27723a8efeee2d99f25b8ef041f340a5c6a05c5bc068866e\nmedia_type: image/jpeg\nresolution: 810x1440\n```\n\n## Manual Posting Pack\n\nErzeugt:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.json\n```\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.md\n```\n\nJSON Metadata:\n\n```text\nfile_size: 4158 bytes\nsha256: e66195587c7bc5a3ba7245cb2c988f9622a8ba62ff50cbe54eb48f7b098f3040\nmedia_type: application/json\n```\n\nMarkdown Metadata:\n\n```text\nfile_size: 2976 bytes\nsha256: 1dca29b97479cc9afc5f4e37f3d7355fcf0ce0f6a408acf63554f074871cd204\nmedia_type: text/markdown\n```\n\nEnthalten:\n\n- YouTube Shorts:\n - Titel\n - Beschreibung\n - Hashtags\n - Kurz-CTA\n - Shorts-Kompatibilität\n- TikTok:\n - Caption\n - Hashtags\n - CTA\n - Kommentar-Pin-Vorschlag\n- Instagram Reels:\n - Caption\n - Hashtags\n - CTA\n - Cover-Text-Vorschlag\n- LinkedIn Manual:\n - professionellere Caption\n - Hook-Zeile\n - Kontext\n - CTA ohne Hype\n\nSichere Claim-Sprache ist verwendet:\n\n```text\nA common red flag…\nBefore you pay…\nVerify through a second channel.\n```\n\nNicht enthalten:\n\n```text\nkein “so erkennst du jeden Scam”\nkeine Finanzberatung\nkeine Rechtsberatung\nkeine Panikmache\nkeine übertriebenen Claims\n```\n\n## AI Disclosure / Synthetic Media\n\nDokumentiert:\n\n```text\nVideo nutzt deterministische Mockups und TTS.\nKeine realistischen Menschen.\nKeine realistischen echten Firmen.\nKeine echten Ereignisse.\nKeine realistischen synthetischen Personen.\n```\n\nPlattform-Disclosure-Einschätzung:\n\n```text\nplatform_ai_disclosure_likely_required: false\n```\n\nManual Review Note:\n\n```text\nBecause the video uses non-realistic deterministic UI/document mockups and TTS without realistic synthetic people or real-event depiction, platform synthetic media disclosure is probably not required. If a platform UI asks whether AI-assisted media was used, disclose conservatively according to that platform prompt.\n```\n\n## Analytics Templates\n\nCSV:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.csv\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 1762 bytes\nsha256: d464584149838bd39b2dd51dbc5763ff6143bbedc13c299c91601d53600ab421\nmedia_type: text/csv\n```\n\nJSON:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.json\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 4792 bytes\nsha256: 089f77a681900716dbace91468ef053e0c4b68188688e299c36877e7f6c9a2f1\nmedia_type: application/json\n```\n\nFelder enthalten wie gefordert:\n\n```text\nplatform\npost_url\npost_time\ncandidate_id\nversion\nhook\ntopic_cluster\nformat_type\nduration\ncaption_style\nvisual_style\ncta_type\n1h_views\n1h_likes\n1h_comments\n1h_shares\n1h_saves\n1h_follows\n24h_views\n24h_completion_rate\n24h_average_watch_time\n24h_shares\n24h_saves\n24h_follows\n72h_views\n7d_views\nnotes\nqualitative_comments\nfollowup_decision\n```\n\n## QA Summary / Manifest\n\nQA Summary JSON:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.json\n```\n\nManifest:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_manifest.json\n```\n\nCandidate Status:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/candidate_status_manual_upload_v1.json\n```\n\n## Telegram Delivery\n\nGesendet an:\n\n```text\ntelegram:JARVIS Command Center / topic 4850\n```\n\nTelegram Status:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5984\nchat_id: -1003907117629\n```\n\nGesendete Artefakte:\n\n- Upload-ready MP4\n- Manual Posting Pack JSON\n- Manual Posting Pack Markdown\n- Analytics Template CSV\n- Analytics Template JSON\n- QA Summary JSON\n- QA Contact Sheet\n\n## Nicht ausgeführt\n\nBestätigt nicht ausgeführt:\n\n```text\nYouTube upload: false\nTikTok upload: false\nInstagram upload: false\nLinkedIn posting: false\nplatform_api_used: false\nscheduling: false\nwan_used: false\ni2v_used: false\nt2v_used: false\nnew_ai_images: false\nnew_topic_batches: false\n```\n\n## Offene Risiken\n\n- Plattformen können beim manuellen Upload trotzdem nach AI-/Synthetic-Media-Disclosure fragen. Empfehlung: konservativ nach UI-Frage antworten.\n- TTS-Stimme ist synthetisch, aber keine realistische Personendarstellung.\n- LinkedIn kann bei sehr kurzen Videos anders performen als TikTok/Reels/Shorts; Analytics-Template ist vorbereitet.\n- Es bleibt ein Proof Short, kein Cinematic Final. Genau der Punkt.\n\n## Nächste mögliche Kommandos\n\nWenn du manuell posten möchtest:\n\n```text\nAPPROVED_FOR_MANUAL_UPLOAD digital-red-flags-fake-invoice-payment-route-30s manual_upload_v1\n```\n\nWenn du noch etwas ändern willst:\n\n```text\nREVISE_MANUAL_UPLOAD digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n```\n\nWenn wir weitere Proof Shorts erzeugen sollen:\n\n```text\nGENERATE_NEXT_PROOF_SHORT_CANDIDATES count=3\n```",
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"text": "Sir, **Sprint 3.2 — Proof Short Finalization + Manual Posting Pack ist abgeschlossen.**\n\nDer Candidate ist jetzt als **approved_proof_short** markiert und als **manual_upload_v1** uploadbereit vorbereitet.\n\nKein Auto-Posting. Keine Plattform-API. Kein Wan. Kein I2V/T2V. Keine neuen AI-Bilder. Also ausnahmsweise kein digitaler Zirkus mit Nebelmaschine.\n\n## Status\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nStatus:\n\n```text\napproved_proof_short\n```\n\nManual Upload Version:\n\n```text\nmanual_upload_v1\n```\n\nPosting Status:\n\n```text\nnot_posted\n```\n\nAuto Upload:\n\n```text\nfalse\n```\n\n## Git / Tests\n\nGit Status:\n\n```text\nclean\n```\n\nLetzter Commit:\n\n```text\ncf66672 fix: enforce caption safe zones and image policy\n```\n\nRelevante Tests:\n\n```text\n24 passed in 0.41s\n```\n\nGetestet:\n\n```text\ntests/test_caption_safe_zone.py\ntests/test_static_caption_segments.py\ntests/test_image_generation_policy.py\ntests/test_proof_short_mode.py\ntests/test_pilot_production_v2.py\n```\n\n## Final MP4\n\nUploadbereites MP4:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1.mp4\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 1190549 bytes\nsha256: a9073f09b80b9b43eb421a4c523e05cf745374fbe7ef33176b082e988a220dfc\nmedia_type: video/mp4\nduration: 31.4s\nresolution: 1080x1920\nfps: 24/1\nvideo_codec: h264\naudio_codec: aac\naudio_sample_rate: 48000\naudio_channels: 1\n```\n\nEncoding:\n\n```text\nH.264 high profile\nAAC audio\n48kHz mono\n+faststart\nloudnorm applied\n1080x1920\n24fps\n```\n\n## Kleine Safety-Korrektur\n\nIch habe für die uploadbereite Version die sichtbare Mockup-Absenderadresse von:\n\n```text\nbilling@example-company.com\n```\n\nauf eine reservierte Test-Domain geändert:\n\n```text\nbilling@sample.test\n```\n\nGrund: weniger Risiko, dass eine scheinbar echte `.com`-Adresse als real interpretiert wird. Gleiche Story, sauberer Mockup-Standard.\n\n## Final QA Result\n\nQA Result:\n\n```text\npass\n```\n\nUpload Ready:\n\n```text\ntrue\n```\n\nGeprüft:\n\n```text\nvideo_plays_full_length: pass\naudio_present: pass\ncaptions_safe_zone: pass\nno_caption_box: pass\nno_full_width_bar: pass\nno_clipped_words: pass\nno_real_logos: pass\nno_real_bank_data: pass\nno_qr_codes: pass\nno_people: pass\nplatform_ui_issues_visible: pass\nhook_first_2_seconds_understandable: pass\ntakeaway_clear: pass\nclaim_gate: pass\nai_disclosure: pass\n```\n\nQA Contact Sheet:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_qa_contact_sheet.jpg\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 232844 bytes\nsha256: 869c71f526def74a27723a8efeee2d99f25b8ef041f340a5c6a05c5bc068866e\nmedia_type: image/jpeg\nresolution: 810x1440\n```\n\n## Manual Posting Pack\n\nErzeugt:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.json\n```\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_posting_pack_v1.md\n```\n\nJSON Metadata:\n\n```text\nfile_size: 4158 bytes\nsha256: e66195587c7bc5a3ba7245cb2c988f9622a8ba62ff50cbe54eb48f7b098f3040\nmedia_type: application/json\n```\n\nMarkdown Metadata:\n\n```text\nfile_size: 2976 bytes\nsha256: 1dca29b97479cc9afc5f4e37f3d7355fcf0ce0f6a408acf63554f074871cd204\nmedia_type: text/markdown\n```\n\nEnthalten:\n\n- YouTube Shorts:\n - Titel\n - Beschreibung\n - Hashtags\n - Kurz-CTA\n - Shorts-Kompatibilität\n- TikTok:\n - Caption\n - Hashtags\n - CTA\n - Kommentar-Pin-Vorschlag\n- Instagram Reels:\n - Caption\n - Hashtags\n - CTA\n - Cover-Text-Vorschlag\n- LinkedIn Manual:\n - professionellere Caption\n - Hook-Zeile\n - Kontext\n - CTA ohne Hype\n\nSichere Claim-Sprache ist verwendet:\n\n```text\nA common red flag…\nBefore you pay…\nVerify through a second channel.\n```\n\nNicht enthalten:\n\n```text\nkein “so erkennst du jeden Scam”\nkeine Finanzberatung\nkeine Rechtsberatung\nkeine Panikmache\nkeine übertriebenen Claims\n```\n\n## AI Disclosure / Synthetic Media\n\nDokumentiert:\n\n```text\nVideo nutzt deterministische Mockups und TTS.\nKeine realistischen Menschen.\nKeine realistischen echten Firmen.\nKeine echten Ereignisse.\nKeine realistischen synthetischen Personen.\n```\n\nPlattform-Disclosure-Einschätzung:\n\n```text\nplatform_ai_disclosure_likely_required: false\n```\n\nManual Review Note:\n\n```text\nBecause the video uses non-realistic deterministic UI/document mockups and TTS without realistic synthetic people or real-event depiction, platform synthetic media disclosure is probably not required. If a platform UI asks whether AI-assisted media was used, disclose conservatively according to that platform prompt.\n```\n\n## Analytics Templates\n\nCSV:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.csv\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 1762 bytes\nsha256: d464584149838bd39b2dd51dbc5763ff6143bbedc13c299c91601d53600ab421\nmedia_type: text/csv\n```\n\nJSON:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/analytics_tracking_template_v1.json\n```\n\nMetadata:\n\n```text\nfile_exists: true\nfile_size: 4792 bytes\nsha256: 089f77a681900716dbace91468ef053e0c4b68188688e299c36877e7f6c9a2f1\nmedia_type: application/json\n```\n\nFelder enthalten wie gefordert:\n\n```text\nplatform\npost_url\npost_time\ncandidate_id\nversion\nhook\ntopic_cluster\nformat_type\nduration\ncaption_style\nvisual_style\ncta_type\n1h_views\n1h_likes\n1h_comments\n1h_shares\n1h_saves\n1h_follows\n24h_views\n24h_completion_rate\n24h_average_watch_time\n24h_shares\n24h_saves\n24h_follows\n72h_views\n7d_views\nnotes\nqualitative_comments\nfollowup_decision\n```\n\n## QA Summary / Manifest\n\nQA Summary JSON:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/qa_summary_manual_upload_v1.json\n```\n\nManifest:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/manual_upload_v1_manifest.json\n```\n\nCandidate Status:\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/candidate_status_manual_upload_v1.json\n```\n\n## Telegram Delivery\n\nGesendet an:\n\n```text\ntelegram:JARVIS Command Center / topic 4850\n```\n\nTelegram Status:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 5984\nchat_id: -1003907117629\n```\n\nGesendete Artefakte:\n\n- Upload-ready MP4\n- Manual Posting Pack JSON\n- Manual Posting Pack Markdown\n- Analytics Template CSV\n- Analytics Template JSON\n- QA Summary JSON\n- QA Contact Sheet\n\n## Nicht ausgeführt\n\nBestätigt nicht ausgeführt:\n\n```text\nYouTube upload: false\nTikTok upload: false\nInstagram upload: false\nLinkedIn posting: false\nplatform_api_used: false\nscheduling: false\nwan_used: false\ni2v_used: false\nt2v_used: false\nnew_ai_images: false\nnew_topic_batches: false\n```\n\n## Offene Risiken\n\n- Plattformen können beim manuellen Upload trotzdem nach AI-/Synthetic-Media-Disclosure fragen. Empfehlung: konservativ nach UI-Frage antworten.\n- TTS-Stimme ist synthetisch, aber keine realistische Personendarstellung.\n- LinkedIn kann bei sehr kurzen Videos anders performen als TikTok/Reels/Shorts; Analytics-Template ist vorbereitet.\n- Es bleibt ein Proof Short, kein Cinematic Final. Genau der Punkt.\n\n## Nächste mögliche Kommandos\n\nWenn du manuell posten möchtest:\n\n```text\nAPPROVED_FOR_MANUAL_UPLOAD digital-red-flags-fake-invoice-payment-route-30s manual_upload_v1\n```\n\nWenn du noch etwas ändern willst:\n\n```text\nREVISE_MANUAL_UPLOAD digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"\n```\n\nWenn wir weitere Proof Shorts erzeugen sollen:\n\n```text\nGENERATE_NEXT_PROOF_SHORT_CANDIDATES count=3\n```"
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"content": "[M G] Konzept ist ok, jedoch Wieso verwenden wir keine KI bilder um das aufzupeppen?"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/image-provider-audit-and-caption-safezone.md\", \"content\": \"# Image Provider Audit + Caption Safe-Zone Pattern\\n\\nUse this reference when a social-short pipeline needs to verify AI image provenance/quality tiers and harden mobile subtitle layout before approving a preview/final.\\n\\n## Image provider audit checklist\\n\\n1. Inspect Hermes config, not memory or assumptions:\\n - `image_gen.provider`\\n - `image_gen.model`\\n - provider-specific subconfig if present, e.g. `image_gen.openai-codex.model`\\n2. Inspect registered image providers/capabilities from the provider registry:\\n - available providers\\n - available model tiers\\n - active provider resolution/fallback behavior\\n3. For ChatGPT/Codex image generation in Hermes, the relevant practical tiers are:\\n - `gpt-image-2-low`\\n - `gpt-image-2-medium`\\n - `gpt-image-2-high`\\n4. For premium keyframes/styleframes, prefer the highest available reasonable tier (`gpt-image-2-high` when available). For deterministic UI/mockups, do not use image generation.\\n5. Verify recent image runs by cache filenames and metadata. The Codex backend saves PNGs under `$HERMES_HOME/cache/images/` with names like:\\n - `openai_codex_gpt-image-2-medium_...png`\\n - `openai_codex_gpt-image-2-high_...png`\\n6. If needed, run exactly one neutral capability test image, not a batch. Save provider/model/quality/size/response metadata, file path, SHA256, and resolution.\\n7. Inspect PNG chunks for provenance. Codex/OpenAI PNG outputs may contain a `caBX` JUMBF/C2PA chunk (`jumb`, `jumdc2pa`, `urn:c2pa:`). PIL may show no normal `info` keys; raw chunk inspection is more reliable.\\n8. Note that C2PA/provenance in the original PNG cache is not reliably preserved after converting into JPEG contact sheets or MP4 renders.\\n\\n## AutoShorts image policy\\n\\nUse image generation only for approved premium styleframes/keyframes. Keep renderer-owned/deterministic assets for:\\n\\n- fake invoices\\n- fake emails\\n- payment-route cards\\n- UI mockups\\n- documents with labels/numbers/text\\n\\nRecommended policy values:\\n\\n```text\\nallowed_image_provider = openai-codex\\npreferred_image_model = best_available_gpt_image_model\\npreferred_quality = highest_available_reasonable\\nmax_styleframes_per_scene_without_approval = 3\\nmax_retries_per_styleframe = 1\\nno_random_batches = true\\nno_ai_images_for_mockups_when_deterministic_renderer_is_better = true\\n```\\n\\n## Caption safe-zone implementation pattern\\n\\nHard rules for mobile subtitles:\\n\\n- No caption text may exceed the left/right safe area.\\n- Use max width around 86–90% of video width.\\n- At 1080px width, keep at least 60px horizontal margin; 70px is safer.\\n- Keep bottom margin platform-safe; for 1080x1920, around 260–310px works better than very low captions.\\n- Maximum two lines per segment.\\n- Prefer 4–7 words per static phrase segment.\\n- If a phrase would exceed two lines, split the phrase earlier.\\n- Dynamically shrink font size only down to a minimum; after that, split.\\n- Active-word highlight geometry must also fit inside safe bounds.\\n- Treat long domains like `example-company.com` as special width stress tests.\\n\\n## Tests to add\\n\\nAdd tests for:\\n\\n- long phrase wraps into two lines\\n- caption never exceeds safe width\\n- active word highlight never exceeds bounds\\n- no word clipped at left/right edge\\n- max two lines enforced by earlier phrase splitting\\n- bottom safe-zone respected\\n- long domain strings wrap or shrink safely\\n\\n## Preview QA\\n\\nAfter rendering, produce:\\n\\n- MP4 preview\\n- contact sheet at known problem timestamps\\n- single subtitle-style preview frame\\n- manifest with file path, SHA256, size, duration/resolution, caption validation result, and delivery metadata\\n\\nUse visual QA on the contact sheet. If any renderer-owned text card is clipped, fix it too; otherwise the preview still feels broken even if the formal subtitles pass.\", \"file_type\": \".md\"}",
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"content": "Gute Frage, Sir — und ja: **wir sollten KI-Bilder verwenden**, aber **nicht für alles**.\n\nDer Grund, warum ich sie bei diesem Proof Short bewusst *nicht* eingesetzt habe: \nBei Fake-Invoice / Email / Payment-Route ist der kritische Inhalt **Text, UI, Dokumentstruktur und Sicherheits-Kontext**. Genau dort sind KI-Bilder riskant:\n\n- KI erzeugt gerne Fake-Text, Pseudo-Logos, Fantasie-Bankdaten.\n- Sie kann echte Marken/Lookalikes halluzinieren.\n- Sie macht UI oft hübsch, aber unlesbar oder inkonsistent.\n- Bei Scam-/Invoice-Themen kann ein KI-Bild schnell wie eine echte Vorlage wirken.\n- Der Proof Short sollte zuerst validieren: **funktioniert das Problem + Regel + Retention?**\n\nDeshalb war die Entscheidung:\n\n```text\nDeterministische Mockups für Invoice / Email / Payment Route\nKI-Bilder erst für Atmosphäre / Premium-Look / Hintergründe / Keyframes\n```\n\nAlso: **nicht “keine KI-Bilder”**, sondern: \n**Keine KI-Bilder als Träger der faktischen UI-/Invoice-Information.**\n\n## Wo KI-Bilder sinnvoll wären\n\nFür die nächste Version können wir KI sehr gut einsetzen, um das Short aufzuwerten:\n\n### 1. Cinematic Background Layer\n\nZum Beispiel:\n\n- moderner Arbeitsplatz\n- Laptop/Desk-Szene\n- unscharfer Office-Hintergrund\n- Rechnungsprüfung bei Abendlicht\n- Security-/Finance-Team-Atmosphäre\n\nAber:\n\n```text\nKeine lesbaren Texte\nKeine Logos\nKeine echten Marken\nKeine QR-Codes\nKeine Bankdaten\n```\n\nDer Renderer legt danach die Fake-Invoice / Email / Payment Route sauber darüber.\n\n### 2. Premium Keyframes\n\nWir könnten 2–3 hochwertige textfreie KI-Keyframes erzeugen:\n\n- **Frame A:** Close-up auf Laptop/Invoice-Review Situation\n- **Frame B:** Hand/Mouse near payment approval, tense moment\n- **Frame C:** Clean desk / second-channel verification moment\n\nDann deterministisch darüber:\n\n- Captions\n- fake UI\n- red-flag highlights\n- sender / urgency / payment route checks\n\n### 3. Thumbnail / Cover\n\nKI-Bilder sind sehr gut für einen starken Cover-Look:\n\n```text\n“CHECK THE ROUTE”\n```\n\noder textfrei als Hintergrund, mit Renderer-Text darüber.\n\n## Wo KI-Bilder weiterhin falsch wären\n\nNicht verwenden für:\n\n- die Rechnung selbst\n- Email-Screen mit lesbaren Details\n- Zahlungsroute\n- Bankkarte / IBAN / Account-Felder\n- Warnhinweise\n- QR-Codes\n- Logos\n- UI mit faktischen Labels\n\nDas muss renderer-owned bleiben. Sonst bekommen wir hübschen Unsinn mit Sicherheitsrisiko. Charmant, aber dumm — also exakt das, was wir nicht bauen wollen.\n\n## Mein Vorschlag\n\nWir machen eine **Visual Upgrade v1**, ohne das Konzept zu ändern:\n\n```text\nUPGRADE_PROOF_SHORT_VISUALS\ndigital-red-flags-fake-invoice-payment-route-30s\nmode: ai_background_plus_deterministic_mockups\n```\n\nUmfang:\n\n- 2–3 GPT-Image-2-high textfreie Background-Keyframes\n- keine echten Marken / keine Texte / keine Personen nötig\n- bestehende Voiceover + Captions behalten\n- deterministische Fake-Invoice/UI bleibt\n- neuer Preview: `visual_upgrade_v1`\n- kein Wan/I2V/Posting\n\nDas wäre der richtige Mittelweg: **professioneller Look**, aber ohne die faktische Kontrolle über das Safety-Thema zu verlieren.",
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"text": "Gute Frage, Sir — und ja: **wir sollten KI-Bilder verwenden**, aber **nicht für alles**.\n\nDer Grund, warum ich sie bei diesem Proof Short bewusst *nicht* eingesetzt habe: \nBei Fake-Invoice / Email / Payment-Route ist der kritische Inhalt **Text, UI, Dokumentstruktur und Sicherheits-Kontext**. Genau dort sind KI-Bilder riskant:\n\n- KI erzeugt gerne Fake-Text, Pseudo-Logos, Fantasie-Bankdaten.\n- Sie kann echte Marken/Lookalikes halluzinieren.\n- Sie macht UI oft hübsch, aber unlesbar oder inkonsistent.\n- Bei Scam-/Invoice-Themen kann ein KI-Bild schnell wie eine echte Vorlage wirken.\n- Der Proof Short sollte zuerst validieren: **funktioniert das Problem + Regel + Retention?**\n\nDeshalb war die Entscheidung:\n\n```text\nDeterministische Mockups für Invoice / Email / Payment Route\nKI-Bilder erst für Atmosphäre / Premium-Look / Hintergründe / Keyframes\n```\n\nAlso: **nicht “keine KI-Bilder”**, sondern: \n**Keine KI-Bilder als Träger der faktischen UI-/Invoice-Information.**\n\n## Wo KI-Bilder sinnvoll wären\n\nFür die nächste Version können wir KI sehr gut einsetzen, um das Short aufzuwerten:\n\n### 1. Cinematic Background Layer\n\nZum Beispiel:\n\n- moderner Arbeitsplatz\n- Laptop/Desk-Szene\n- unscharfer Office-Hintergrund\n- Rechnungsprüfung bei Abendlicht\n- Security-/Finance-Team-Atmosphäre\n\nAber:\n\n```text\nKeine lesbaren Texte\nKeine Logos\nKeine echten Marken\nKeine QR-Codes\nKeine Bankdaten\n```\n\nDer Renderer legt danach die Fake-Invoice / Email / Payment Route sauber darüber.\n\n### 2. Premium Keyframes\n\nWir könnten 2–3 hochwertige textfreie KI-Keyframes erzeugen:\n\n- **Frame A:** Close-up auf Laptop/Invoice-Review Situation\n- **Frame B:** Hand/Mouse near payment approval, tense moment\n- **Frame C:** Clean desk / second-channel verification moment\n\nDann deterministisch darüber:\n\n- Captions\n- fake UI\n- red-flag highlights\n- sender / urgency / payment route checks\n\n### 3. Thumbnail / Cover\n\nKI-Bilder sind sehr gut für einen starken Cover-Look:\n\n```text\n“CHECK THE ROUTE”\n```\n\noder textfrei als Hintergrund, mit Renderer-Text darüber.\n\n## Wo KI-Bilder weiterhin falsch wären\n\nNicht verwenden für:\n\n- die Rechnung selbst\n- Email-Screen mit lesbaren Details\n- Zahlungsroute\n- Bankkarte / IBAN / Account-Felder\n- Warnhinweise\n- QR-Codes\n- Logos\n- UI mit faktischen Labels\n\nDas muss renderer-owned bleiben. Sonst bekommen wir hübschen Unsinn mit Sicherheitsrisiko. Charmant, aber dumm — also exakt das, was wir nicht bauen wollen.\n\n## Mein Vorschlag\n\nWir machen eine **Visual Upgrade v1**, ohne das Konzept zu ändern:\n\n```text\nUPGRADE_PROOF_SHORT_VISUALS\ndigital-red-flags-fake-invoice-payment-route-30s\nmode: ai_background_plus_deterministic_mockups\n```\n\nUmfang:\n\n- 2–3 GPT-Image-2-high textfreie Background-Keyframes\n- keine echten Marken / keine Texte / keine Personen nötig\n- bestehende Voiceover + Captions behalten\n- deterministische Fake-Invoice/UI bleibt\n- neuer Preview: `visual_upgrade_v1`\n- kein Wan/I2V/Posting\n\nDas wäre der richtige Mittelweg: **professioneller Look**, aber ohne die faktische Kontrolle über das Safety-Thema zu verlieren."
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"id": "msg_051a8a0b2fb58918016a1490e4f11c8191838b604d4ae90bde",
"phase": "final_answer"
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},
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"content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"reasoning": "**Updating social media skill**\n\nI need to update the social media skill after loading the automation and TDD. I have to implement the user's correction about using AI images — making sure they enhance, but aren’t factual. It seems I should add something like \"AI polish layer after Proof Short approval\" into the skill, and possibly reference that file as well. I'll use skill_manage to make these updates, including patching potential pitfalls, without needing any other tools.",
"finish_reason": "tool_calls",
"reasoning_content": "**Updating social media skill**\n\nI need to update the social media skill after loading the automation and TDD. I have to implement the user's correction about using AI images — making sure they enhance, but aren’t factual. It seems I should add something like \"AI polish layer after Proof Short approval\" into the skill, and possibly reference that file as well. I'll use skill_manage to make these updates, including patching potential pitfalls, without needing any other tools.",
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"id": "rs_051a8a0b2fb58918016a1490f7a234819191f2f1306e59de9f",
"summary": [
{
"type": "summary_text",
"text": "**Updating social media skill**\n\nI need to update the social media skill after loading the automation and TDD. I have to implement the user's correction about using AI images — making sure they enhance, but aren’t factual. It seems I should add something like \"AI polish layer after Proof Short approval\" into the skill, and possibly reference that file as well. I'll use skill_manage to make these updates, including patching potential pitfalls, without needing any other tools."
}
]
}
],
"tool_calls": [
{
"id": "call_INIRn1J3GIdXDAbFFu1gYzsx",
"call_id": "call_INIRn1J3GIdXDAbFFu1gYzsx",
"response_item_id": "fc_051a8a0b2fb58918016a1491008bf88191ae65a897bd815016",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/proof-short-ai-visual-upgrade.md\",\"file_content\":\"# Proof Short AI Visual Upgrade Pattern\\n\\nUse this when a Proof Short concept is approved as useful/clear, but the user asks why it does not use AI images to make the video look more premium.\\n\\n## Core decision\\n\\nDo not treat `ProofShortMode` as `no AI visuals forever`. Treat it as:\\n\\n```text\\nvalidate real viewer problem first → then optionally add AI polish without surrendering factual/UI control\\n```\\n\\nFor security, finance, scam-awareness, invoice, email, payment-route, medical, or other high-trust topics, distinguish between:\\n\\n- **factual/semantic assets**: UI, invoice, email, payment route, numbers, labels, warnings, claims, routing/account fields. These stay deterministic and renderer-owned.\\n- **atmospheric/premium assets**: desk, laptop, office, screen glow, neutral background, product-film texture, cover/thumbnail background, text-free keyframes. These may use AI image generation after the concept is validated.\\n\\n## Recommended upgrade mode\\n\\n```text\\nmode: ai_background_plus_deterministic_mockups\\nprovider: openai-codex\\nmodel: highest available reasonable GPT Image tier, usually gpt-image-2-high\\noutputs: 2–3 text-free background/keyframes\\nrenderer remains source of truth for all readable text/facts\\n```\\n\\nUse this to make a Proof Short look less flat without turning it into an uncontrolled AI-image story.\\n\\n## Safe AI image uses\\n\\nGood uses:\\n\\n- cinematic desk/laptop/office background\\n- neutral invoice-review atmosphere with blank papers/screens\\n- second-channel verification mood/background\\n- thumbnail/cover background\\n- premium lighting and product-film texture\\n- subtle abstract security atmosphere if it still supports the visible real problem\\n\\nBad uses:\\n\\n- generating the invoice itself\\n- generating the email UI itself\\n- generating bank/payment details, account fields, IBANs, QR codes, barcodes, addresses, logos, fake company marks, legal text, or claims\\n- using AI image text as part of the message\\n- letting AI-generated visuals replace the visible fail/fix logic\\n\\n## Prompt constraints\\n\\nEvery AI image prompt should say:\\n\\n- text-free image\\n- no readable text\\n- no logos or brands\\n- no QR codes/barcodes\\n- no real companies, banks, people, addresses, account numbers, invoice numbers, or payment details\\n- leave clean safe space for renderer-owned captions and UI overlays\\n- realistic/premium social-video keyframe, 9:16\\n- show atmosphere/problem context, not factual content\\n\\n## Review package additions\\n\\nWhen using this upgrade, include:\\n\\n- `visual_upgrade_mode: ai_background_plus_deterministic_mockups`\\n- AI image provider/model/quality\\n- number of generated keyframes\\n- per-image SHA256/path/resolution\\n- statement that UI/invoice/payment route remained deterministic\\n- no Wan/I2V/full-final unless separately approved\\n- whether AI disclosure is required or only conservative optional disclosure\\n\\n## Pitfall\\n\\nDo not answer the user's “why no AI images?” as if AI images are forbidden. The correct answer is: AI images are useful for premium atmosphere after the proof concept is validated, but not for the factual invoice/email/payment-route layer. Offer a small controlled visual-upgrade pass instead of defending a flat deterministic preview.\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"File 'references/proof-short-ai-visual-upgrade.md' written to skill 'social-media-shorts-automation'.\", \"path\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation/references/proof-short-ai-visual-upgrade.md\"}",
"tool_call_id": "call_INIRn1J3GIdXDAbFFu1gYzsx"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_27bLiUijm6ErzlJwHiIJQp24",
"call_id": "call_27bLiUijm6ErzlJwHiIJQp24",
"response_item_id": "fc_051a8a0b2fb58918016a14910a1db0819182c9edb14131e6ab",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"If the next review focuses on image provider quality/provenance or subtitles running off-screen, follow `references/image-provider-audit-and-caption-safezone.md`: audit the configured image provider/model/tier from config + provider registry + cache metadata, run at most one neutral capability test, keep UI/invoice mockups deterministic, and add tested caption layout safe-zone bounds before re-rendering.\\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.\",\"new_string\":\"If the next review focuses on image provider quality/provenance or subtitles running off-screen, follow `references/image-provider-audit-and-caption-safezone.md`: audit the configured image provider/model/tier from config + provider registry + cache metadata, run at most one neutral capability test, keep UI/invoice mockups deterministic, and add tested caption layout safe-zone bounds before re-rendering. If an approved Proof Short feels too visually flat and the user asks why no AI images were used, follow `references/proof-short-ai-visual-upgrade.md`: use AI images as a controlled text-free atmosphere/background/keyframe layer while keeping invoices, emails, payment routes, labels, facts, and captions deterministic renderer-owned.\\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.\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_27bLiUijm6ErzlJwHiIJQp24"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_D3NvzW0Mr44eQ2RlloUWpQoZ",
"call_id": "call_D3NvzW0Mr44eQ2RlloUWpQoZ",
"response_item_id": "fc_051a8a0b2fb58918016a14911531908191859ca88d73f25aee",
"type": "function",
"function": {
"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\" - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - After a Proof Short concept is approved for usefulness/clarity, do not interpret `ProofShortMode` as a permanent ban on AI images. If the user wants the video “aufgepeppt”, add a controlled AI visual-upgrade layer: 2–3 text-free premium backgrounds/keyframes, while all invoice/email/UI/payment-route text, labels, facts, captions, claims, and red-flag overlays remain deterministic renderer-owned. See `references/proof-short-ai-visual-upgrade.md`.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\",\"old_string\":\" - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\",\"replace_all\":false}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_D3NvzW0Mr44eQ2RlloUWpQoZ"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_5IUt5Vn5PXFEMOz6vSiW3EWk",
"call_id": "call_5IUt5Vn5PXFEMOz6vSiW3EWk",
"response_item_id": "fc_051a8a0b2fb58918016a14911ed39c81919c679f243064e2ac",
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"name": "skill_manage",
"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"new_string\":\"- Do not keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not overcorrect from “validate with Proof Shorts” into “never use AI images.” Once the real problem/rule is approved, AI images can legitimately improve perceived quality as text-free atmosphere, backgrounds, keyframes, or thumbnails. Keep factual UI/document/payment layers deterministic; use AI for polish, not truth.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, visual checks for clipped/overcrowded text, and hard mobile safe-zone tests for captions/highlights before delivery.\",\"old_string\":\"- Do not keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, visual checks for clipped/overcrowded text, and hard mobile safe-zone tests for captions/highlights before delivery.\"}"
}
}
]
},
{
"role": "tool",
"name": "skill_manage",
"content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-automation' (1 replacement).\"}",
"tool_call_id": "call_5IUt5Vn5PXFEMOz6vSiW3EWk"
},
{
"role": "assistant",
"content": "Skill library updated.\n\nUpdated existing loaded umbrella skill:\n\n- **`social-media-shorts-automation`**\n\nChanges made:\n\n- Added new reference file:\n - `references/proof-short-ai-visual-upgrade.md`\n- Patched `SKILL.md` to point to that reference.\n- Added workflow rule:\n - ProofShortMode is **not** a permanent ban on AI images.\n - After a Proof Short concept is approved, AI images may be used for **text-free atmosphere/background/keyframes/thumbnail polish**.\n - Factual layers remain deterministic: invoice, email UI, payment route, labels, numbers, captions, claims.\n- Added pitfall:\n - Do not overcorrect from “validate real problem first” into “never use AI images.”\n - Use AI for polish, not truth.\n\nThis captures the lesson from your correction: **Proof Shorts validate usefulness first; once approved, controlled AI visual upgrade is appropriate.**",
"reasoning": null,
"finish_reason": "stop",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Skill library updated.\n\nUpdated existing loaded umbrella skill:\n\n- **`social-media-shorts-automation`**\n\nChanges made:\n\n- Added new reference file:\n - `references/proof-short-ai-visual-upgrade.md`\n- Patched `SKILL.md` to point to that reference.\n- Added workflow rule:\n - ProofShortMode is **not** a permanent ban on AI images.\n - After a Proof Short concept is approved, AI images may be used for **text-free atmosphere/background/keyframes/thumbnail polish**.\n - Factual layers remain deterministic: invoice, email UI, payment route, labels, numbers, captions, claims.\n- Added pitfall:\n - Do not overcorrect from “validate real problem first” into “never use AI images.”\n - Use AI for polish, not truth.\n\nThis captures the lesson from your correction: **Proof Shorts validate usefulness first; once approved, controlled AI visual upgrade is appropriate.**"
}
],
"id": "msg_051a8a0b2fb58918016a1491296d3081918a7613a0bc4aea8f",
"phase": "final_answer"
}
]
}
]
}