{
"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": "2026-05-25T20:26:51.923787",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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% — 1,367/1,375 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: premium clean KI-image cuts; avoid labels/arrows/boxes/target markers unless approved; Wan only after approval.\n\nConversation started: Monday, May 25, 2026 08:26 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": 169,
"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 ---"
},
{
"role": "assistant",
"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: “[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 rout\n...[truncated]...\n\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.”\n\n## Goal\nStrategischer Reset der Video-Produktion: Der bisherige abstrakte KI-Pilot wird pausiert/archiviert. Ab jetzt sollen zuerst reale Viewer-Probleme mit sofort sichtbarem Schmerz, sichtbarem Fail und direktem Nutzen validiert werden. Neuer erster Pilot soll ein 25–35s Proof Short zu „Digital Red Flags — Fake Invoice Payment Route“ werden, nicht ein Premium-KI-Film.\n\n## Constraints & Preferences\n- Sprache/Arbeitskontext: Deutsch; Script-Hook und Short-Text können Englisch sein, wie vom User vorgegeben.\n- Bisheriger Pilot `ai-output-autopsy-decision-summary-38s` darf nicht weiter optimiert, gerendert oder gepostet werden.\n- Neuer Sprint: `Sprint 3.0 — Real Viewer Problem Reset / Proof Short 1`.\n- Fokus für neue Inhalte:\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- Proof Shorts sind Default für die ersten 10–20 Videos.\n- Premium Episodes erst später, wenn Thema/Format durch echte Zuschauerreaktionen validiert ist.\n- Neuer Content-Modus `ProofShortMode`:\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- Sicherheits-/Ethik-Constraints für Fake-Invoice-Thema:\n - Keine echten Firmen.\n - Keine echten Scam-Vorlagen, die missbraucht werden können.\n - Keine echten Zugangsdaten, API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings speichern; falls vorhanden immer als `[REDACTED]` behandeln.\n- Nicht ausführen:\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- Telegram Delivery für Preview-MP4 gewünscht; wenn nicht möglich, klar melden: „lokal erzeugt, Telegram-Versand nicht erfolgt.“\n- ReviewPackageV2 muss enthalten:\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\n## Completed Actions\n1. KONTEXT ÜBERNOMMEN vorheriger Stand — vorherige Kompaktierung war nicht verfügbar; 21 Nachrichten wurden entfernt und konnten nicht zusammengefasst werden. Es ist nur bekannt, dass es früheren Arbeitsstand in der Session gab. [tool: context]\n2. EMPFANGEN Assistant-Platzhalter zu Sprint 3.0 — Assistant hatte zuvor nur leere Platzhalter geliefert: „Preview MP4:“, „Contact Sheet / QA Frames:“, „ReviewPackageV2 JSON:“ ohne konkrete Pfade, Dateien oder Werte. [tool: chat]\n3. ERFASST strategischer Reset — User hat explizit festgelegt, dass der bisherige Pilot `ai-output-autopsy-decision-summary-38s` pausiert und als Lernfall archiviert werden soll; noch nicht umgesetzt. [tool: chat]\n4. ERFASST neue Quality-Gate-Anforderung — `pretty_empty_visual_blocker` soll als Gate eingeführt werden; noch nicht umgesetzt. [tool: chat]\n5. ERFASST neuer Produktionsmodus — `ProofShortMode` soll angelegt/dokumentiert werden; noch nicht umgesetzt. [tool: chat]\n6. ERFASST neuer Hauptpilot — `digital-red-flags-fake-invoice-payment-route-30s` / „Digital Red Flags — Fake Invoice Payment Route“ soll erstellt werden; noch nicht umgesetzt. [tool: chat]\n7. ERFASST Review-/Delivery-Anforderungen — ReviewPackageV2 und Telegram-Delivery-Verifikation für Preview-MP4 gewünscht; noch nicht umgesetzt. [tool: chat]\n\n## Active State\n- Working directory: unbekannt; keine Shell-/Dateisystem-Inspektion im neuen Kontext durchgeführt.\n- Branch: unbekannt.\n- Modified/created files: keine bestätigten Änderungen seit der letzten Kompaktierung.\n- Teststatus: unbekannt; keine Tests ausgeführt.\n- Running processes/servers: unbekannt.\n- Environment details: unbekannt.\n- Aktueller Projektstatus:\n - Alter Pilot `ai-output-autopsy-decision-summary-38s` ist nach User-Entscheidung fachlich pausiert, aber Archivierung/Postmortem noch nicht bestätigt.\n - Neuer Sprint 3.0 ist definiert, aber Artefakte sind noch nicht erstellt.\n - Neuer Proof-Short-Kandidat `digital-red-flags-fake-invoice-payment-route-30s` ist ausgewählt, aber ScriptDraft, Visual Plan, Preview MP4, SHA256 und Telegram-Status fehlen noch.\n\n## In Progress\n- Keine technische Arbeit wurde seit dem neuen User-Request begonnen.\n- Nächster logischer Arbeitsstrang: Projekt-/Repository-Struktur prüfen, passende Speicherorte finden, alten Kandidaten archivieren, Quality Gate und ProofShortMode dokumentieren/implementieren, neuen Proof Short inklusive Script/Visual Plan/Preview/ReviewPackageV2 erstellen.\n\n## Blocked\n- Vorherige 21 Nachrichten sind nicht verfügbar; frühere Dateien, Pfade, Tools, Skripte, Delivery-Mechanik und vorhandene Projektstruktur sind aus dem Kontext nicht bekannt.\n- Kein konkreter Dateipfad für Archiv, Kandidaten-Metadaten, Quality-Gates oder ReviewPackageV2 bekannt.\n- Kein Preview-MP4 vorhanden oder bestätigt.\n- Telegram-Versand kann erst nach Erzeugung einer Preview-MP4 und Vorhandensein funktionierender Telegram-Konfiguration erfolgen. Keine Credentials speichern; falls Credentials sichtbar werden, als `[REDACTED]` behandeln.\n- Die User-Nachricht war im Quellmaterial mittig trunciert (`...[truncated]...`), daher fehlen möglicherweise Details zu Punkten 6–9 und vollständigem Script/Visual-Anforderungsteil.\n\n## Key Decisions\n- Alter Pilot `ai-output-autopsy-decision-summary-38s` wird nicht weiter optimiert/rendered/gepostet, weil er „pretty but semantically weak“ ist: zu abstrakt, keine sichtbare reale Viewer-Pain, geringe Retention-Erwartung.\n- Neue Primärfrage: Nicht „Wie machen wir ein schönes KI-Video?“, sondern „Welches echte Problem erkennt der Zuschauer in Sekunde 1 — und warum hilft ihm dieses Video sofort?“\n- `Pretty Empty Visual Blocker` wird als neue Qualitätsregel benötigt, um hochwertige, aber semantisch leere Mood-/B-Roll-Visuals zu blockieren.\n- `ProofShortMode` wird Default für die ersten 10–20 Videos, um Format und Thema schnell zu validieren, bevor Premium-Produktion eingesetzt wird.\n- Neuer Hauptpilot ist „Digital Red Flags — Fake Invoice Payment Route“, weil das Thema reales Problem, sofort sichtbar, hoher Nutzwert, hoher Share-Wert, klarer Schmerz und leicht visuell darstellbar ist.\n- Kein Wan/I2V/Full-Final: Für Proof Short soll keine teure KI-Film-Pipeline genutzt werden; stattdessen schnelle, klare, problemgeführte Preview.\n- Keine echten Firmen oder missbrauchbaren echten Scam-Vorlagen: Thema soll sicher und generisch demonstriert werden.\n\n## Resolved Questions\n- Warum wird der bisherige Pilot pausiert? Antwort: „Pretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.“\n- Was funktionierte beim alten Pilot? Antwort laut User:\n - Approval-Gates\n - Captions ohne Box\n - Delivery\n - Styleframe-Workflow-Lernen\n - I2V-Sanity-Test\n- Was funktionierte nicht beim alten Pilot? Antwort laut User:\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- Was ist die neue Regel? Antwort: `Pretty Empty Visual Blocker`.\n- Was soll der neue Pilot sein? Antwort: `digital-red-flags-fake-invoice-payment-route-30s`, Thema „Digital Red Flags — Fake Invoice Payment Route“.\n- Was ist der Hook-Vorschlag? Antwort: “This invoice looks normal. The trap is not the logo — it’s the payment route.”\n- Welche Kommandos sind später möglich?\n - `APPROVED_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s`\n - `REVISE_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Änderungswunsch\"`\n - `REJECT_PROOF_SHORT digital-red-flags-fake-invoice-payment-route-30s \"Grund\"`\n - `APPROVED_FOR_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s`\n\n## Pending User Asks\n- Alten Kandidaten `ai-output-autopsy-decision-summary-38s` archivieren mit Status `paused_learning_case` und Reason: “Pretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.”\n- Kurzes Postmortem zum alten Pilot speichern:\n - Was funktionierte: Approval-Gates, Captions ohne Box, Delivery, Styleframe-Workflow-Lernen, I2V-Sanity-Test.\n - Was nicht funktionierte: Thema zu abstrakt, kein sichtbarer Fail, zu viel Produktionsaufwand vor validiertem Format, zu viele visuelle Metaphern, Wan/Bild-KI zu früh als Story-Träger.\n - Neue Regel: Pretty Empty Visual Blocker.\n- `pretty_empty_visual_blocker` als Quality Gate ergänzen.\n- `ProofShortMode` anlegen oder dokumentieren.\n- Neuen Hauptpilot `digital-red-flags-fake-invoice-payment-route-30s` erstellen.\n- ScriptDraft für ca. 28–35 Sekunden erstellen.\n- Visual Plan erstellen, der konkreten Fail und Nutzen sichtbar macht.\n- Preview-MP4 erzeugen, aber kein Full-Final/Wan/I2V.\n- ReviewPackageV2 liefern mit allen geforderten Feldern.\n- Telegram Preview-MP4 senden und verifizieren, inkl.:\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- Falls Telegram nicht klappt, klar melden: „lokal erzeugt, Telegram-Versand nicht erfolgt.“\n\n## Relevant Files\n- Keine konkreten Dateien aus dem aktuellen Arbeitsverlauf bekannt.\n- Erwartete/zu findende Artefakte:\n - Archiv-/Metadaten-Datei für Kandidat `ai-output-autopsy-decision-summary-38s`.\n - Dokumentation oder Config für Quality Gates, wo `pretty_empty_visual_blocker` ergänzt werden soll.\n - Dokumentation oder Config für Produktionsmodi, wo `ProofShortMode` ergänzt werden soll.\n - Neue Kandidaten-/Script-/ReviewPackage-Dateien für `digital-red-flags-fake-invoice-payment-route-30s`.\n - Preview-MP4-Datei für neuen Proof Short.\n - ReviewPackageV2 JSON für neuen Proof Short.\n- Vorher vom Assistant genannt, aber ohne konkrete Werte:\n - „Preview MP4:“ leer.\n - „Contact Sheet / QA Frames:“ leer.\n - „ReviewPackageV2 JSON:“ leer.\n\n## Remaining Work\n- Repository/Arbeitsverzeichnis inspizieren, um bestehende Struktur, Kandidatenverwaltung, Quality-Gate-Mechanik, Render-/Preview-Skripte und Telegram-Delivery-Tooling zu finden.\n- Alten Pilot korrekt archivieren und Postmortem an passender Stelle speichern.\n- `pretty_empty_visual_blocker` als dokumentiertes oder implementiertes Gate hinzufügen.\n- `ProofShortMode` als neuer Produktionsmodus dokumentieren/implementieren.\n- Neuen Proof-Short-Kandidaten `digital-red-flags-fake-invoice-payment-route-30s` anlegen.\n- ScriptDraft 28–35s ausarbeiten basierend auf vorgegebenem Hook und Struktur:\n - 0–2s: sichtbare Rechnung/E-Mail/Zahlungsaufforderung + Hook.\n - 2–7s: “Same layout. Same professional tone. Same urgency.”\n - 7–14s: “But one thing changed: where the money goes.”\n - 14–24s: “Before you pay, check three things: sender domain, urgency language, and payment rout...” Quelle war trunciert; vollständige Formulierung rekonstruieren.\n- Sicheren Visual Plan bauen:\n - generische/fiktive Mockups\n - keine echten Firmen\n - keine missbrauchbaren echten Scam-Vorlagen\n - sichtbarer Payment-Route-Fail\n- Preview-MP4 lokal erzeugen, Dauer/Resolution/SHA256 ermitteln.\n- Telegram-Versand versuchen und Status verifizieren oder klar als nicht erfolgt melden.\n- ReviewPackageV2 mit allen Pflichtfeldern ausgeben.\n\n## Critical Context\n- Alter Kandidat: `ai-output-autopsy-decision-summary-38s`.\n- Neuer Kandidat / Arbeitsname: `digital-red-flags-fake-invoice-payment-route-30s`.\n- Neuer Sprint: `Sprint 3.0 — Real Viewer Problem Reset / Proof Short 1`.\n- Alter Pilot Status soll werden: `paused_learning_case`.\n- Alter Pilot Reason exakt: “Pretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.”\n- Neue Quality Gate ID: `pretty_empty_visual_blocker`.\n- Neuer Produktionsmodus: `ProofShortMode`.\n- Hook exakt: “This invoice looks normal. The trap is not the logo — it’s the payment route.”\n- Pretty Empty Visual Blocker blockiert, wenn:\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- Guter Visual Plan muss beantworten:\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- ProofShortMode Eigenschaften:\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- ReviewPackageV2 Pflichtfelder:\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- Telegram-Verifikationsfelder:\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- Wenn Telegram nicht klappt, exakt/inhaltlich klar sagen: „lokal erzeugt, Telegram-Versand nicht erfolgt.“\n- Secrets/Credentials dürfen niemals gespeichert werden; falls im Arbeitskontext sichtbar, durch `[REDACTED]` ersetzen.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---\n\nSir, **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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}
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{
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{
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{
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{
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"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.",
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"id": "rs_0890c728d0b832c0016a148671daec81918f6ab270c1fe6b5d",
"summary": [
{
"type": "summary_text",
"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": "",
"tool_calls": [
{
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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-...[truncated]\"}"
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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!",
"codex_reasoning_items": [
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"reasoning_content": "**Clarifying file status**\n\nI’m noticing there are six files mentioned, but it says “not included”? Is that what static, docs, tests, config, and policy add up to? I’m wondering if something is missing. I should check if my git status is clean to ensure everything is properly tracked. That’ll help me understand what’s really going on here. Let’s see if I can figure it out!",
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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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"summary": []
}
]
},
{
"role": "tool",
"content": "[execute_code] `from hermes_tools import terminal cmd = r''' set -euo pipefa...` (1 lines output)",
"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!",
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}
]
}
]
},
{
"role": "tool",
"content": "[vision_analyze] 'Final QA contact sheet for a 9:16 manual upload vi' (332 chars)",
"tool_call_id": "call_rIuPwihAe1nGqrTcYrvZcjot"
},
{
"role": "assistant",
"content": "",
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{
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"call_id": "call_lOy1d8WcR21Q5wCMTOyhdN59",
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"type": "function",
"function": {
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"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.",
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"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": "[patch] replace in /tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py (858 chars result)",
"tool_call_id": "call_lOy1d8WcR21Q5wCMTOyhdN59"
},
{
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"type": "summary_text",
"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."
}
]
}
]
},
{
"role": "tool",
"content": "[todo] updated task list",
"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": "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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"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. 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.\\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 - 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...”.\\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 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.\\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/proof-short-ai-visual-upgrade.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. 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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/proof-short-ai-visual-upgrade.md\", \"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.\", \"file_type\": \".md\"}",
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"content": "{\"status\": \"success\", \"output\": \"[\\n {\\n \\\"id\\\": \\\"ai_background_01\\\",\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_01_openai_codex_gpt-image-2-high_20260525_201404_e90dd6b8.png\\\",\\n \\\"file_size\\\": 1689119,\\n \\\"sha256\\\": \\\"7bdbc27f3da5ca7b2dff1812d5a777a834dadac8234de25632acd64b4d689d12\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"provider\\\": \\\"openai-codex\\\",\\n \\\"model\\\": \\\"gpt-image-2-high\\\",\\n \\\"quality\\\": \\\"high\\\",\\n \\\"intended_use\\\": \\\"text-free atmosphere background only; deterministic renderer owns all UI/text/facts\\\"\\n },\\n {\\n \\\"id\\\": \\\"ai_background_02\\\",\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_02_openai_codex_gpt-image-2-high_20260525_201453_36f56d2f.png\\\",\\n \\\"file_size\\\": 1798497,\\n \\\"sha256\\\": \\\"546e94e40171e68a4b5686ebb2ca690da694b1231d0e319df4aac766310951c0\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"provider\\\": \\\"openai-codex\\\",\\n \\\"model\\\": \\\"gpt-image-2-high\\\",\\n \\\"quality\\\": \\\"high\\\",\\n \\\"intended_use\\\": \\\"text-free atmosphere background only; deterministic renderer owns all UI/text/facts\\\"\\n },\\n {\\n \\\"id\\\": \\\"ai_background_03\\\",\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_03_openai_codex_gpt-image-2-high_20260525_201556_875b1851.png\\\",\\n \\\"file_size\\\": 1738065,\\n \\\"sha256\\\": \\\"afd420c7863f01e265028c28ffdc5e670644d8a10422f3f6d84450841b285ac6\\\",\\n \\\"resolution\\\": \\\"1024x1536\\\",\\n \\\"png_chunks\\\": [\\n \\\"IHDR\\\",\\n \\\"caBX\\\",\\n \\\"IDAT\\\",\\n \\\"IEND\\\"\\n ],\\n \\\"provider\\\": \\\"openai-codex\\\",\\n \\\"model\\\": \\\"gpt-image-2-high\\\",\\n \\\"quality\\\": \\\"high\\\",\\n \\\"intended_use\\\": \\\"text-free atmosphere background only; deterministic renderer owns all UI/text/facts\\\"\\n },\\n {\\n \\\"id\\\": \\\"ai_keyframes_contact_sheet\\\",\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes_contact_sheet_v1.jpg\\\",\\n \\\"file_size\\\": 65845,\\n \\\"sha256\\\": \\\"502197702dce0bf260bfd73232faeb86c84ded865502812255e27fdf4ca39814\\\",\\n \\\"resolution\\\": \\\"810x460\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\"\\n }\\n]\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.64}",
"tool_call_id": "call_3T8An34EBYFXiAE5K8bIkAI7"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_gPJVlWmNpwkF14KjFaVTlpWm",
"call_id": "call_gPJVlWmNpwkF14KjFaVTlpWm",
"response_item_id": "fc_051a8a0b2fb58918016a1491fdabe88191a3f1c49315ed28e7",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes_contact_sheet_v1.jpg\",\"question\":\"QA these three AI-generated background keyframes. Are they text-free enough? Any visible logos, brands, QR codes, bank data, real people/hands, or pseudo-readable text that would block use as atmosphere backgrounds?\"}"
}
}
]
},
{
"role": "tool",
"name": "vision_analyze",
"content": [
{
"type": "text",
"text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA these three AI-generated background keyframes. Are they text-free enough? Any visible logos, brands, QR codes, bank data, real people/hands, or pseudo-readable text that would block use as atmosphere backgrounds?"
},
{
"type": "image_url",
"image_url": {
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"
}
}
],
"tool_call_id": "call_gPJVlWmNpwkF14KjFaVTlpWm"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Evaluating background options**\n\nIt seems like BG2 has some pseudo lines, but they’re not readable, which might be okay. However, for high trust, it might be better to stick with BG1 and BG3. Since the user asked for 2-3 backgrounds, maybe we could just discard BG2? I’m thinking I could use BG1 for the hook and BG3 for verification. I’ll need to plan a new render script that imports from the previous one and programmatically creates the background.\n**Planning visual upgrades**\n\nI’m thinking about writing a shorter script for the visuals, possibly patching the previous one. It’ll be important to render a final visual upgrade, using images as backgrounds with a slow zoom and slight blur. I want to draw a mockup that’s semi-translucent so the background shows around the edges, as the current card covers too much. I’m considering reducing opacity and adjusting dimensions to achieve a better look and making modifications to the draw_frame to paste the background effectively.\n**Creating a new script**\n\nI need to write a new script by modifying the existing file in memory. The plan is to include background image paths and adjust the draw_frame process, possibly removing the existing bg call. I'll use Python to generate a new script by replacing certain elements, adding BG_PATHS, and preparing the background function. But since the draw_frame creates an image and then calls bg(draw), I’ll have to find a way to adjust this. Maybe I can use draw.bitmap for better integration?",
"finish_reason": "tool_calls",
"reasoning_content": "**Evaluating background options**\n\nIt seems like BG2 has some pseudo lines, but they’re not readable, which might be okay. However, for high trust, it might be better to stick with BG1 and BG3. Since the user asked for 2-3 backgrounds, maybe we could just discard BG2? I’m thinking I could use BG1 for the hook and BG3 for verification. I’ll need to plan a new render script that imports from the previous one and programmatically creates the background.\n**Planning visual upgrades**\n\nI’m thinking about writing a shorter script for the visuals, possibly patching the previous one. It’ll be important to render a final visual upgrade, using images as backgrounds with a slow zoom and slight blur. I want to draw a mockup that’s semi-translucent so the background shows around the edges, as the current card covers too much. I’m considering reducing opacity and adjusting dimensions to achieve a better look and making modifications to the draw_frame to paste the background effectively.\n**Creating a new script**\n\nI need to write a new script by modifying the existing file in memory. The plan is to include background image paths and adjust the draw_frame process, possibly removing the existing bg call. I'll use Python to generate a new script by replacing certain elements, adding BG_PATHS, and preparing the background function. But since the draw_frame creates an image and then calls bg(draw), I’ll have to find a way to adjust this. Maybe I can use draw.bitmap for better integration?",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": 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"id": "rs_051a8a0b2fb58918016a1492097e5881918a6f56b2d91e9e51",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating background options**\n\nIt seems like BG2 has some pseudo lines, but they’re not readable, which might be okay. However, for high trust, it might be better to stick with BG1 and BG3. Since the user asked for 2-3 backgrounds, maybe we could just discard BG2? I’m thinking I could use BG1 for the hook and BG3 for verification. I’ll need to plan a new render script that imports from the previous one and programmatically creates the background."
},
{
"type": "summary_text",
"text": "**Planning visual upgrades**\n\nI’m thinking about writing a shorter script for the visuals, possibly patching the previous one. It’ll be important to render a final visual upgrade, using images as backgrounds with a slow zoom and slight blur. I want to draw a mockup that’s semi-translucent so the background shows around the edges, as the current card covers too much. I’m considering reducing opacity and adjusting dimensions to achieve a better look and making modifications to the draw_frame to paste the background effectively."
},
{
"type": "summary_text",
"text": "**Creating a new script**\n\nI need to write a new script by modifying the existing file in memory. The plan is to include background image paths and adjust the draw_frame process, possibly removing the existing bg call. I'll use Python to generate a new script by replacing certain elements, adding BG_PATHS, and preparing the background function. But since the draw_frame creates an image and then calls bg(draw), I’ll have to find a way to adjust this. Maybe I can use draw.bitmap for better integration?"
}
]
}
],
"tool_calls": [
{
"id": "call_V4dEKNBFNAGPHkQx9clJJVv3",
"call_id": "call_V4dEKNBFNAGPHkQx9clJJVv3",
"response_item_id": "fc_051a8a0b2fb58918016a149215855481919650b222fb28ab44",
"type": "function",
"function": {
"name": "execute_code",
"arguments": "{\"code\":\"from hermes_tools import read_file, write_file, terminal\\nsrc_path='/tmp/autoshorts_proof_short_sprint30/render_fake_invoice_preview_v2.py'\\nsrc=read_file(src_path, limit=2000)['content']\\n# strip line nums\\nlines=[]\\nfor line in src.splitlines():\\n if '|' in line[:8]: lines.append(line.split('|',1)[1])\\n else: lines.append(line)\\ns='\\\\n'.join(lines)\\ns=s.replace(\\\"OUT = BASE / 'preview_v2_caption_safe'\\\", \\\"OUT = BASE / 'visual_upgrade_v1'\\\")\\ns=s.replace(\\\"f'{CANDIDATE}_preview_v2_caption_safe_silent.mp4'\\\", \\\"f'{CANDIDATE}_visual_upgrade_v1_silent.mp4'\\\")\\ns=s.replace(\\\"f'{CANDIDATE}_preview_v2_caption_safe.mp4'\\\", \\\"f'{CANDIDATE}_visual_upgrade_v1.mp4'\\\")\\ns=s.replace(\\\"contact=OUT/'digital-red-flags_caption_safe_contact_sheet_v2.jpg'\\\", \\\"contact=OUT/'digital-red-flags_visual_upgrade_contact_sheet_v1.jpg'\\\")\\ns=s.replace(\\\"sp=OUT/'digital-red-flags_subtitle_style_preview_v2.jpg'\\\", \\\"sp=OUT/'digital-red-flags_visual_upgrade_subtitle_preview_v1.jpg'\\\")\\ns=s.replace(\\\" 'version':'preview_v2_caption_safe',\\\", \\\" 'version':'visual_upgrade_v1',\\\")\\ns=s.replace(\\\"'changes':['caption safe-zone wrap/shrink/split only','same voiceover','deterministic renderer-owned mockups'],\\\", \\\"'changes':['ai text-free background layer added','same voiceover','same safe captions','deterministic renderer-owned mockups remain source of truth'],\\\")\\ns=s.replace(\\\"'new_ai_images':False\\\", \\\"'new_ai_images':True\\\")\\ns=s.replace(\\\"'caption_rules':\\\", \\\"'visual_upgrade_mode':'ai_background_plus_deterministic_mockups',\\\\n 'ai_backgrounds':[\\\\n '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_01_openai_codex_gpt-image-2-high_20260525_201404_e90dd6b8.png',\\\\n '/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_03_openai_codex_gpt-image-2-high_20260525_201556_875b1851.png'\\\\n ],\\\\n 'discarded_ai_backgrounds':['ai_background_02: contains grey pseudo-document lines on laptop, kept out of final render'],\\\\n 'caption_rules':\\\")\\ninsert = \\\"\\\"\\\"\\nBG_PATHS = [\\n Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_01_openai_codex_gpt-image-2-high_20260525_201404_e90dd6b8.png'),\\n Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_03_openai_codex_gpt-image-2-high_20260525_201556_875b1851.png'),\\n]\\n_BG_CACHE = [Image.open(p).convert('RGB') for p in BG_PATHS]\\n\\ndef background_for(t):\\n idx = 0 if t < 20.9 else 1\\n base = _BG_CACHE[idx]\\n # cover-crop portrait 1024x1536 into 1080x1920, with a tiny deterministic push-in\\n scale = max(W / base.width, H / base.height) * (1.02 + 0.018 * ((t % 8) / 8))\\n nw, nh = int(base.width * scale), int(base.height * scale)\\n im = base.resize((nw, nh), Image.Resampling.LANCZOS)\\n x = max(0, (nw - W) // 2)\\n y = max(0, (nh - H) // 2)\\n im = im.crop((x, y, x + W, y + H))\\n # keep it premium but subordinate to deterministic UI/caption truth layer\\n im = im.filter(ImageFilter.GaussianBlur(radius=1.1))\\n overlay = Image.new('RGBA', (W, H), (8, 12, 22, 112))\\n im = Image.alpha_composite(im.convert('RGBA'), overlay).convert('RGB')\\n return im\\n\\\"\\\"\\\"\\ns=s.replace(\\\"FONT_REG='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'\\\\n\\\", \\\"FONT_REG='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'\\\\n\\\"+insert+\\\"\\\\n\\\")\\ns=s.replace(\\\"def draw_frame(t):\\\\n img=Image.new('RGB',(W,H),(14,18,29)); draw=ImageDraw.Draw(img,'RGBA')\\\\n bg(draw)\\\\n\\\", \\\"def draw_frame(t):\\\\n img=background_for(t)\\\\n draw=ImageDraw.Draw(img,'RGBA')\\\\n\\\")\\n# make outer card more translucent in visual upgrade by changing first card fill and invoice fill? Use RGBA tuples accepted\\ns=s.replace(\\\"rounded(draw, (82,170,998,1330), 44, (235,239,244), (78,92,122), 3)\\\", \\\"rounded(draw, (82,170,998,1330), 44, (235,239,244,228), (78,92,122,210), 3)\\\")\\ns=s.replace(\\\"rounded(draw, (118,220,962,420), 26, (255,255,255), (218,224,234), 2)\\\", \\\"rounded(draw, (118,220,962,420), 26, (255,255,255,232), (218,224,234,220), 2)\\\")\\ns=s.replace(\\\"rounded(draw, (150,470,930,1198), 24, (250,251,253), (199,207,222), 2)\\\", \\\"rounded(draw, (150,470,930,1198), 24, (250,251,253,236), (199,207,222,220), 2)\\\")\\ns=s.replace(\\\"(OUT/'manifest_preview_v2_caption_safe.json')\\\", \\\"(OUT/'manifest_visual_upgrade_v1.json')\\\")\\nwrite_file('/tmp/autoshorts_proof_short_sprint30/render_visual_upgrade_v1.py', s)\\nprint(terminal('python /tmp/autoshorts_proof_short_sprint30/render_visual_upgrade_v1.py', timeout=600, workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'][-12000:])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \": 6932\\n[libx264 @ 0x59915b22a3c0] frame B:558 Avg QP:18.06 size: 835\\n[libx264 @ 0x59915b22a3c0] consecutive B-frames: 1.1% 0.0% 0.0% 98.9%\\n[libx264 @ 0x59915b22a3c0] mb I I16..4: 31.9% 48.2% 19.9%\\n[libx264 @ 0x59915b22a3c0] mb P I16..4: 0.3% 0.5% 0.3% P16..4: 15.9% 2.9% 2.8% 0.0% 0.0% skip:77.2%\\n[libx264 @ 0x59915b22a3c0] mb B I16..4: 0.0% 0.0% 0.0% B16..8: 10.7% 0.0% 0.0% direct: 0.0% skip:89.2% L0:46.2% L1:53.7% BI: 0.1%\\n[libx264 @ 0x59915b22a3c0] 8x8 transform intra:47.5% inter:96.0%\\n[libx264 @ 0x59915b22a3c0] coded y,uvDC,uvAC intra: 37.4% 44.0% 32.0% inter: 1.8% 1.2% 0.1%\\n[libx264 @ 0x59915b22a3c0] i16 v,h,dc,p: 62% 30% 2% 7%\\n[libx264 @ 0x59915b22a3c0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 39% 19% 30% 2% 2% 2% 2% 2% 2%\\n[libx264 @ 0x59915b22a3c0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 46% 21% 12% 3% 4% 4% 4% 3% 3%\\n[libx264 @ 0x59915b22a3c0] i8c dc,h,v,p: 58% 21% 18% 3%\\n[libx264 @ 0x59915b22a3c0] Weighted P-Frames: Y:0.0% UV:0.0%\\n[libx264 @ 0x59915b22a3c0] ref P L0: 76.7% 0.9% 13.8% 8.6%\\n[libx264 @ 0x59915b22a3c0] ref B L0: 89.9% 9.2% 1.0%\\n[libx264 @ 0x59915b22a3c0] ref B L1: 96.8% 3.2%\\n[libx264 @ 0x59915b22a3c0] kb/s:538.10\\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/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1_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: 540 kb/s\\n Stream #0:0[0x1](und): Video: h264 (High) (avc1 / 0x31637661), yuv420p(progressive), 1080x1920, 538 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 @ 0x654083d2fa80] 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/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.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, 538 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 @ 0x654083c9efc0] video:2059kB audio:242kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.835162%\\nsize= 2320kB time=00:00:31.31 bitrate= 606.8kbits/s speed=77.1x \\n[aac @ 0x654083da68c0] Qavg: 18380.342\\n{\\n \\\"candidate_id\\\": \\\"digital-red-flags-fake-invoice-payment-route-30s\\\",\\n \\\"version\\\": \\\"visual_upgrade_v1\\\",\\n \\\"changes\\\": [\\n \\\"ai text-free background layer added\\\",\\n \\\"same voiceover\\\",\\n \\\"same safe captions\\\",\\n \\\"deterministic renderer-owned mockups remain source of truth\\\"\\n ],\\n \\\"wan_used\\\": false,\\n \\\"i2v_used\\\": false,\\n \\\"t2v_used\\\": false,\\n \\\"new_ai_images\\\": true,\\n \\\"platform_api\\\": false,\\n \\\"posting\\\": false,\\n \\\"visual_upgrade_mode\\\": \\\"ai_background_plus_deterministic_mockups\\\",\\n \\\"ai_backgrounds\\\": [\\n \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_01_openai_codex_gpt-image-2-high_20260525_201404_e90dd6b8.png\\\",\\n \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes/ai_background_03_openai_codex_gpt-image-2-high_20260525_201556_875b1851.png\\\"\\n ],\\n \\\"discarded_ai_backgrounds\\\": [\\n \\\"ai_background_02: contains grey pseudo-document lines on laptop, kept out of final render\\\"\\n ],\\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\\\": 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\\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1_silent.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 2118139,\\n \\\"sha256\\\": \\\"3f449e3b8956231dc3a76ae3d56eb478a48e4843d3b9f9187b511b6e0446edb1\\\",\\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/visual_upgrade_v1/digital-red-flags_visual_upgrade_contact_sheet_v1.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 248984,\\n \\\"sha256\\\": \\\"3b5e9eef0572ab62e9e0cc50b1db720c07aa66e8dd1d90759b0abc86e6b061c4\\\",\\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/visual_upgrade_v1/digital-red-flags_visual_upgrade_subtitle_preview_v1.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 243429,\\n \\\"sha256\\\": \\\"07e019f6b25beef0713614fddab7c47d87cab9632e75998be685b9309c05c51b\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"1080x1920\\\"\\n }\\n }\\n}\\n\", \"tool_calls_made\": 3, \"duration_seconds\": 129.22}",
"tool_call_id": "call_V4dEKNBFNAGPHkQx9clJJVv3"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Creating a visual contact sheet**\n\nI realize I need to put together a QA visual contact sheet. It's important to gather all the necessary images and ensure they are presented clearly and effectively. This should help the team to visually coordinate and organize our assets properly. I want to make sure that all relevant information is included so nothing gets overlooked. Let’s get started on assembling everything in a user-friendly format!",
"finish_reason": "tool_calls",
"reasoning_content": "**Creating a visual contact sheet**\n\nI realize I need to put together a QA visual contact sheet. It's important to gather all the necessary images and ensure they are presented clearly and effectively. This should help the team to visually coordinate and organize our assets properly. I want to make sure that all relevant information is included so nothing gets overlooked. Let’s get started on assembling everything in a user-friendly format!",
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"id": "rs_051a8a0b2fb58918016a1492c265408191832699a06fa0a50a",
"summary": [
{
"type": "summary_text",
"text": "**Creating a visual contact sheet**\n\nI realize I need to put together a QA visual contact sheet. It's important to gather all the necessary images and ensure they are presented clearly and effectively. This should help the team to visually coordinate and organize our assets properly. I want to make sure that all relevant information is included so nothing gets overlooked. Let’s get started on assembling everything in a user-friendly format!"
}
]
}
],
"tool_calls": [
{
"id": "call_bAERVkg9TyMmhINL8BDuXlaF",
"call_id": "call_bAERVkg9TyMmhINL8BDuXlaF",
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"arguments": "{\"image_url\":\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags_visual_upgrade_contact_sheet_v1.jpg\",\"question\":\"QA the visual_upgrade_v1 contact sheet. Check: captions clipped/off safe area? deterministic fake invoice/email visible? any real logos, real bank data, QR codes, people/hands, problematic AI pseudo-text? Is AI background subordinate and premium enough?\"}"
}
}
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},
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"role": "tool",
"name": "vision_analyze",
"content": [
{
"type": "text",
"text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA the visual_upgrade_v1 contact sheet. Check: captions clipped/off safe area? deterministic fake invoice/email visible? any real logos, real bank data, QR codes, people/hands, problematic AI pseudo-text? Is AI background subordinate and premium enough?"
},
{
"type": "image_url",
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}
}
],
"tool_call_id": "call_bAERVkg9TyMmhINL8BDuXlaF"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
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"id": "rs_051a8a0b2fb58918016a1492cb6ca8819190e837576be5d675",
"summary": []
}
],
"tool_calls": [
{
"id": "call_fIXvwHHPaIqOW5qYepnw3R87",
"call_id": "call_fIXvwHHPaIqOW5qYepnw3R87",
"response_item_id": "fc_051a8a0b2fb58918016a1492d29c648191b0fa76afac550ac2",
"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, subprocess\\nBASE=Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s')\\nOUT=BASE/'visual_upgrade_v1'\\nmanifest_path=OUT/'manifest_visual_upgrade_v1.json'\\nmanifest=json.loads(manifest_path.read_text())\\n# load keyframe manifest\\nkeyframes=json.loads((OUT/'ai_keyframes_manifest_v1.json').read_text())\\n\\ndef sha(p): return hashlib.sha256(Path(p).read_bytes()).hexdigest()\\ndef probe(p):\\n return json.loads(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration:stream=width,height,r_frame_rate,codec_name','-of','json',str(p)], text=True))\\ndef meta(path, media_type):\\n p=Path(path)\\n 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 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']),3),'resolution':f\\\"{vs.get('width')}x{vs.get('height')}\\\",'fps':vs.get('r_frame_rate'),'codec':vs.get('codec_name')})\\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\\n# ensure fresh metadata for key artifacts\\nmanifest['artifacts']['ai_keyframes_contact_sheet']=meta(OUT/'ai_keyframes_contact_sheet_v1.jpg','image/jpeg')\\nmanifest['artifacts']['ai_keyframes_manifest']=meta(OUT/'ai_keyframes_manifest_v1.json','application/json')\\nmanifest['ai_keyframes']=keyframes\\nmanifest['qa_result']='pass'\\nmanifest['visual_qa']={\\n 'captions_safe_zone':'pass',\\n 'deterministic_invoice_email_payment_route_visible':'pass',\\n 'ai_background_subordinate':'pass',\\n 'no_real_logos':'pass',\\n 'no_real_bank_data':'pass',\\n 'no_qr_codes':'pass',\\n 'no_people_or_hands':'pass',\\n 'discarded_background':'ai_background_02 excluded because grey pseudo-document lines could be read as AI pseudo text',\\n 'notes':['AI backgrounds provide atmosphere only. All readable UI/document/payment/caption layers are deterministic renderer-owned.']\\n}\\nmanifest['claim_gate']={'claim_type':'general safety advice / low-to-medium risk','result':'pass','not_claimed':['three checks prevent all scams','legal or financial advice','all fake invoices use this route-change pattern']}\\nmanifest['ai_disclosure']={'ai_images_used':True,'usage':'text-free atmosphere backgrounds only','ui_invoice_payment_route_deterministic':True,'realistic_people_or_real_events':False,'platform_disclosure_likely_required':False,'conservative_note':'If platform asks about AI-assisted media, disclose conservatively.'}\\nmanifest_path.write_text(json.dumps(manifest,indent=2),encoding='utf-8')\\n# compact review package\\nreview={\\n 'candidate_id':'digital-red-flags-fake-invoice-payment-route-30s',\\n 'version':'visual_upgrade_v1',\\n 'visual_upgrade_mode':'ai_background_plus_deterministic_mockups',\\n 'status':'preview_for_review_not_posted',\\n 'hook':'This invoice looks normal. The trap is not the logo — it’s the payment route.',\\n 'provider':'openai-codex','model':'gpt-image-2-high','generated_keyframes':3,'used_keyframes':2,'discarded_keyframes':1,\\n 'proof_short_mode':True,\\n 'wan_used':False,'i2v_used':False,'t2v_used':False,'platform_api_used':False,'posting':False,\\n 'pretty_empty_visual_blocker':'pass — visible invoice/payment-route fail remains in deterministic foreground',\\n 'claim_gate':manifest['claim_gate'],\\n 'ai_disclosure':manifest['ai_disclosure'],\\n 'artifacts':manifest['artifacts'],\\n 'visual_qa':manifest['visual_qa']\\n}\\n(OUT/'review_package_visual_upgrade_v1.json').write_text(json.dumps(review,indent=2),encoding='utf-8')\\nmanifest['artifacts']['review_package_visual_upgrade']=meta(OUT/'review_package_visual_upgrade_v1.json','application/json')\\nmanifest_path.write_text(json.dumps(manifest,indent=2),encoding='utf-8')\\nprint(json.dumps({'manifest':str(manifest_path),'review':str(OUT/'review_package_visual_upgrade_v1.json'),'preview':manifest['artifacts']['preview_mp4'],'contact':manifest['artifacts']['qa_frame_sheet']},indent=2))\\nPY'''\\nprint(terminal(cmd, timeout=60)['output'])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"manifest\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/manifest_visual_upgrade_v1.json\\\",\\n \\\"review\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/review_package_visual_upgrade_v1.json\\\",\\n \\\"preview\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 2375265,\\n \\\"sha256\\\": \\\"2c0c49cda01ab8b84b636e34184a3d825ef25b095f8056336ccdcb715502d4eb\\\",\\n \\\"media_type\\\": \\\"video/mp4\\\",\\n \\\"duration\\\": 31.32,\\n \\\"resolution\\\": \\\"1080x1920\\\",\\n \\\"fps\\\": \\\"24/1\\\"\\n },\\n \\\"contact\\\": {\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags_visual_upgrade_contact_sheet_v1.jpg\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 248984,\\n \\\"sha256\\\": \\\"3b5e9eef0572ab62e9e0cc50b1db720c07aa66e8dd1d90759b0abc86e6b061c4\\\",\\n \\\"media_type\\\": \\\"image/jpeg\\\",\\n \\\"duration\\\": \\\"n/a\\\",\\n \\\"resolution\\\": \\\"810x1440\\\"\\n }\\n}\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.45}",
"tool_call_id": "call_fIXvwHHPaIqOW5qYepnw3R87"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_M1L13OucRsdxNInb1JtzRmV9",
"call_id": "call_M1L13OucRsdxNInb1JtzRmV9",
"response_item_id": "fc_051a8a0b2fb58918016a1493aac1e08191b6c405a7b6831fa7",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Visual-upgrade scope festlegen: AI background only, deterministic mockups bleiben\",\"id\":\"plan\",\"status\":\"completed\"},{\"content\":\"2–3 textfreie GPT-Image-2-high Background-Keyframes erzeugen und prüfen\",\"id\":\"generate\",\"status\":\"completed\"},{\"content\":\"visual_upgrade_v1 MP4 mit bestehenden Voiceover/Captions rendern\",\"id\":\"render\",\"status\":\"completed\"},{\"content\":\"QA/Manifest/ReviewPackage mit Safety, AI Disclosure, SHA256 erzeugen\",\"id\":\"qa\",\"status\":\"completed\"},{\"content\":\"Preview, Contact Sheet, Keyframes und Manifest per Telegram senden/verifizieren\",\"id\":\"deliver\",\"status\":\"in_progress\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"plan\", \"content\": \"Visual-upgrade scope festlegen: AI background only, deterministic mockups bleiben\", \"status\": \"completed\"}, {\"id\": \"generate\", \"content\": \"2–3 textfreie GPT-Image-2-high Background-Keyframes erzeugen und prüfen\", \"status\": \"completed\"}, {\"id\": \"render\", \"content\": \"visual_upgrade_v1 MP4 mit bestehenden Voiceover/Captions rendern\", \"status\": \"completed\"}, {\"id\": \"qa\", \"content\": \"QA/Manifest/ReviewPackage mit Safety, AI Disclosure, SHA256 erzeugen\", \"status\": \"completed\"}, {\"id\": \"deliver\", \"content\": \"Preview, Contact Sheet, Keyframes und Manifest per Telegram senden/verifizieren\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}",
"tool_call_id": "call_M1L13OucRsdxNInb1JtzRmV9"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_ZxgBBqSShCwswF0LkW3pOgjE",
"call_id": "call_ZxgBBqSShCwswF0LkW3pOgjE",
"response_item_id": "fc_051a8a0b2fb58918016a1493b2d8ac819185c0295cf448740b",
"type": "function",
"function": {
"name": "send_message",
"arguments": "{\"action\":\"send\",\"target\":\"telegram:JARVIS Command Center / topic 4850\",\"message\":\"Sprint 3.3 — AI Visual Upgrade v1\\n\\nVisual Upgrade Preview MP4:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.mp4\\n\\nQA Contact Sheet:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags_visual_upgrade_contact_sheet_v1.jpg\\n\\nAI Keyframes Contact Sheet:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/ai_keyframes_contact_sheet_v1.jpg\\n\\nSubtitle Style Preview:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags_visual_upgrade_subtitle_preview_v1.jpg\\n\\nReview Package JSON:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/review_package_visual_upgrade_v1.json\\n\\nManifest JSON:\\nMEDIA:/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/manifest_visual_upgrade_v1.json\"}"
}
}
]
},
{
"role": "tool",
"name": "send_message",
"content": "{\"success\": true, \"platform\": \"telegram\", \"chat_id\": \"-1003907117629\", \"message_id\": \"6002\", \"mirrored\": true}",
"tool_call_id": "call_ZxgBBqSShCwswF0LkW3pOgjE"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_FQf0sQDZ5SFoBy54B1HwOVdB",
"call_id": "call_FQf0sQDZ5SFoBy54B1HwOVdB",
"response_item_id": "fc_051a8a0b2fb58918016a1493c438788191bd8f2f7471074aeb",
"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/visual_upgrade_v1')\\nfor fn in ['manifest_visual_upgrade_v1.json','review_package_visual_upgrade_v1.json']:\\n p=OUT/fn\\n data=json.loads(p.read_text())\\n artifacts=data.get('artifacts',{})\\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']='6002'\\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':'6002','chat_id':'-1003907117629','target':'telegram:JARVIS Command Center / topic 4850'}\\n p.write_text(json.dumps(data,indent=2),encoding='utf-8')\\nprint(json.dumps(json.loads((OUT/'manifest_visual_upgrade_v1.json').read_text())['artifacts']['preview_mp4'], indent=2))\\nPY\\n\\ngit status --short\\n'''\\nprint(terminal(cmd, timeout=60, workdir='/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')['output'])\"}"
}
}
]
},
{
"role": "tool",
"name": "execute_code",
"content": "{\"status\": \"success\", \"output\": \"{\\n \\\"file_path\\\": \\\"/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.mp4\\\",\\n \\\"file_exists\\\": true,\\n \\\"file_size\\\": 2375265,\\n \\\"sha256\\\": \\\"2c0c49cda01ab8b84b636e34184a3d825ef25b095f8056336ccdcb715502d4eb\\\",\\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\\\": \\\"6002\\\",\\n \\\"telegram_target\\\": \\\"telegram:JARVIS Command Center / topic 4850\\\"\\n}\\n\", \"tool_calls_made\": 1, \"duration_seconds\": 0.44}",
"tool_call_id": "call_FQf0sQDZ5SFoBy54B1HwOVdB"
},
{
"role": "assistant",
"content": "",
"reasoning": null,
"finish_reason": "tool_calls",
"tool_calls": [
{
"id": "call_qX38I4JxGuW1gff4W7tLKbiH",
"call_id": "call_qX38I4JxGuW1gff4W7tLKbiH",
"response_item_id": "fc_051a8a0b2fb58918016a1493ce40e481919712e6aa37cd16f7",
"type": "function",
"function": {
"name": "todo",
"arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Preview, Contact Sheet, Keyframes und Manifest per Telegram senden/verifizieren\",\"id\":\"deliver\",\"status\":\"completed\"}]}"
}
}
]
},
{
"role": "tool",
"name": "todo",
"content": "{\"todos\": [{\"id\": \"plan\", \"content\": \"Visual-upgrade scope festlegen: AI background only, deterministic mockups bleiben\", \"status\": \"completed\"}, {\"id\": \"generate\", \"content\": \"2–3 textfreie GPT-Image-2-high Background-Keyframes erzeugen und prüfen\", \"status\": \"completed\"}, {\"id\": \"render\", \"content\": \"visual_upgrade_v1 MP4 mit bestehenden Voiceover/Captions rendern\", \"status\": \"completed\"}, {\"id\": \"qa\", \"content\": \"QA/Manifest/ReviewPackage mit Safety, AI Disclosure, SHA256 erzeugen\", \"status\": \"completed\"}, {\"id\": \"deliver\", \"content\": \"Preview, Contact Sheet, Keyframes und Manifest per Telegram senden/verifizieren\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}",
"tool_call_id": "call_qX38I4JxGuW1gff4W7tLKbiH"
},
{
"role": "assistant",
"content": "Erledigt, Sir. **Sprint 3.3 — AI Visual Upgrade v1** ist gebaut und per Telegram geliefert.\n\nIch habe den Proof Short **nicht in einen KI-Film umgebaut**, sondern genau den sauberen Mittelweg umgesetzt:\n\n```text\nAI = textfreie Premium-Atmosphäre / Background\nRenderer = Invoice, Email, Payment Route, Labels, Captions, Claims\n```\n\n## Ergebnis\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nVersion:\n\n```text\nvisual_upgrade_v1\n```\n\nModus:\n\n```text\nai_background_plus_deterministic_mockups\n```\n\n## Was geändert wurde\n\n- 3 GPT-Image-2-high Background-Keyframes erzeugt.\n- 2 davon im Video verwendet.\n- 1 Keyframe verworfen, weil auf dem Laptop graue pseudo-dokumentartige Linien sichtbar waren.\n- Bestehendes Voiceover beibehalten.\n- Bestehende safe captions beibehalten.\n- Deterministische Fake-Invoice / Email / Payment Route bleiben Source of Truth.\n- AI-Bilder dienen nur als dezente, textfreie Atmosphäre im Hintergrund.\n\n## Nicht ausgeführt\n\n```text\nwan_used: false\ni2v_used: false\nt2v_used: false\nplatform_api_used: false\nposting: false\nauto_upload: false\n```\n\n## Gelieferte Artefakte\n\nTelegram:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 6002\ntarget: telegram:JARVIS Command Center / topic 4850\n```\n\nGesendet:\n\n- Visual Upgrade Preview MP4\n- QA Contact Sheet\n- AI Keyframes Contact Sheet\n- Subtitle Style Preview\n- Review Package JSON\n- Manifest JSON\n\n## Preview MP4\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.mp4\n```\n\nMetadata:\n\n```text\nfile_size: 2375265 bytes\nsha256: 2c0c49cda01ab8b84b636e34184a3d825ef25b095f8056336ccdcb715502d4eb\nduration: 31.32s\nresolution: 1080x1920\nfps: 24/1\n```\n\n## QA\n\nVisual QA:\n\n```text\nqa_result: pass\n```\n\nChecks:\n\n```text\ncaptions_safe_zone: pass\ndeterministic_invoice_email_payment_route_visible: pass\nai_background_subordinate: pass\nno_real_logos: pass\nno_real_bank_data: pass\nno_qr_codes: pass\nno_people_or_hands: pass\n```\n\nDiscarded AI Background:\n\n```text\nai_background_02 excluded because grey pseudo-document lines could be read as AI pseudo text\n```\n\n## AI Disclosure\n\n```text\nai_images_used: true\nusage: text-free atmosphere backgrounds only\nui_invoice_payment_route_deterministic: true\nrealistic_people_or_real_events: false\nplatform_disclosure_likely_required: false\n```\n\nKonservative Notiz:\n\n```text\nIf platform asks about AI-assisted media, disclose conservatively.\n```\n\n## Einschätzung\n\nDas Upgrade wirkt **sichtbar hochwertiger**, ohne die Faktenkontrolle zu verlieren. \nDie Rechnung / Email / Route bleiben klar und lesbar, aber das Ganze fühlt sich weniger nach reiner Demo-UI und mehr nach Social-Short mit Atmosphäre an.\n\nKurz: **ein bisschen mehr Premium, ohne die KI wieder ans Steuer zu lassen.** Sehr vernünftig. Fast verdächtig.",
"reasoning": null,
"finish_reason": "stop",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt, Sir. **Sprint 3.3 — AI Visual Upgrade v1** ist gebaut und per Telegram geliefert.\n\nIch habe den Proof Short **nicht in einen KI-Film umgebaut**, sondern genau den sauberen Mittelweg umgesetzt:\n\n```text\nAI = textfreie Premium-Atmosphäre / Background\nRenderer = Invoice, Email, Payment Route, Labels, Captions, Claims\n```\n\n## Ergebnis\n\nCandidate:\n\n```text\ndigital-red-flags-fake-invoice-payment-route-30s\n```\n\nVersion:\n\n```text\nvisual_upgrade_v1\n```\n\nModus:\n\n```text\nai_background_plus_deterministic_mockups\n```\n\n## Was geändert wurde\n\n- 3 GPT-Image-2-high Background-Keyframes erzeugt.\n- 2 davon im Video verwendet.\n- 1 Keyframe verworfen, weil auf dem Laptop graue pseudo-dokumentartige Linien sichtbar waren.\n- Bestehendes Voiceover beibehalten.\n- Bestehende safe captions beibehalten.\n- Deterministische Fake-Invoice / Email / Payment Route bleiben Source of Truth.\n- AI-Bilder dienen nur als dezente, textfreie Atmosphäre im Hintergrund.\n\n## Nicht ausgeführt\n\n```text\nwan_used: false\ni2v_used: false\nt2v_used: false\nplatform_api_used: false\nposting: false\nauto_upload: false\n```\n\n## Gelieferte Artefakte\n\nTelegram:\n\n```text\ntelegram_delivery_attempted: true\ntelegram_delivery_verified: true\ntelegram_message_id: 6002\ntarget: telegram:JARVIS Command Center / topic 4850\n```\n\nGesendet:\n\n- Visual Upgrade Preview MP4\n- QA Contact Sheet\n- AI Keyframes Contact Sheet\n- Subtitle Style Preview\n- Review Package JSON\n- Manifest JSON\n\n## Preview MP4\n\n```text\n/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/visual_upgrade_v1/digital-red-flags-fake-invoice-payment-route-30s_visual_upgrade_v1.mp4\n```\n\nMetadata:\n\n```text\nfile_size: 2375265 bytes\nsha256: 2c0c49cda01ab8b84b636e34184a3d825ef25b095f8056336ccdcb715502d4eb\nduration: 31.32s\nresolution: 1080x1920\nfps: 24/1\n```\n\n## QA\n\nVisual QA:\n\n```text\nqa_result: pass\n```\n\nChecks:\n\n```text\ncaptions_safe_zone: pass\ndeterministic_invoice_email_payment_route_visible: pass\nai_background_subordinate: pass\nno_real_logos: pass\nno_real_bank_data: pass\nno_qr_codes: pass\nno_people_or_hands: pass\n```\n\nDiscarded AI Background:\n\n```text\nai_background_02 excluded because grey pseudo-document lines could be read as AI pseudo text\n```\n\n## AI Disclosure\n\n```text\nai_images_used: true\nusage: text-free atmosphere backgrounds only\nui_invoice_payment_route_deterministic: true\nrealistic_people_or_real_events: false\nplatform_disclosure_likely_required: false\n```\n\nKonservative Notiz:\n\n```text\nIf platform asks about AI-assisted media, disclose conservatively.\n```\n\n## Einschätzung\n\nDas Upgrade wirkt **sichtbar hochwertiger**, ohne die Faktenkontrolle zu verlieren. \nDie Rechnung / Email / Route bleiben klar und lesbar, aber das Ganze fühlt sich weniger nach reiner Demo-UI und mehr nach Social-Short mit Atmosphäre an.\n\nKurz: **ein bisschen mehr Premium, ohne die KI wieder ans Steuer zu lassen.** Sehr vernünftig. Fast verdächtig."
}
],
"id": "msg_051a8a0b2fb58918016a1493d2e6a8819196fec7c6392b7fdc",
"phase": "final_answer"
}
]
},
{
"role": "user",
"content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them."
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"arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/proof-short-ai-background-rendering-qa.md\",\"file_content\":\"# Proof Short AI Background Rendering + QA\\n\\nUse this after a Proof Short has validated the real viewer problem and the user wants the video to look more premium without losing factual/control safety.\\n\\n## Pattern\\n\\n```text\\nAI image layer = text-free atmosphere/background/keyframes\\nDeterministic renderer = invoice, email, UI, payment route, labels, facts, captions, claims\\n```\\n\\nThis is especially useful for scam-awareness, finance, invoice, email, cybersecurity, health, or other high-trust topics where generated text or UI could become misleading.\\n\\n## Generation scope\\n\\nGenerate only a small set of text-free keyframes, usually 2–3:\\n\\n- provider/model: highest available reasonable GPT Image tier, e.g. `openai-codex` + `gpt-image-2-high`\\n- aspect: vertical 9:16 / portrait\\n- purpose: premium atmosphere only\\n- no readable text, logos, brands, QR/barcodes, people/hands, company names, bank data, account numbers, invoice IDs, addresses, watermarks, pseudo-writing\\n- ask for clean negative space for renderer-owned captions/UI overlays\\n\\nDo not generate the invoice/email/payment-route itself. Those remain renderer-owned.\\n\\n## QA and selection\\n\\nDo not blindly use every generated keyframe. Create a quick contact sheet and inspect before render.\\n\\nReject/discard a keyframe if it contains:\\n\\n- pseudo-readable document lines or fake text that might be interpreted as content\\n- brand-like marks/logos\\n- QR/barcodes\\n- people/hands if not explicitly desired\\n- bank/payment/account-like details\\n- overly busy composition that competes with captions or deterministic UI\\n\\nIt is acceptable, and often correct, to generate 3 and use only 2. Record discarded images and why.\\n\\n## Rendering treatment\\n\\nKeep AI backgrounds subordinate to the truth layer:\\n\\n- cover-crop to 9:16\\n- add subtle deterministic push-in if needed\\n- blur very slightly if background details compete with foreground\\n- darken/desaturate or add translucent navy overlay so UI/captions remain readable\\n- place deterministic invoice/email/payment-route overlays above the background\\n- keep active-word captions and safe-zone bounds unchanged from the approved proof short\\n\\nThe upgrade should look more premium, but the first-second visible fail must remain obvious.\\n\\n## Review package fields\\n\\nInclude:\\n\\n- `visual_upgrade_mode: ai_background_plus_deterministic_mockups`\\n- AI provider/model/quality\\n- generated keyframe count\\n- used keyframe count\\n- discarded keyframe count + discard reasons\\n- per-image path/SHA256/resolution\\n- statement that all UI/invoice/payment-route/factual/caption layers are deterministic renderer-owned\\n- `wan_used: false`, `i2v_used: false`, `t2v_used: false` unless separately approved\\n- visual QA result\\n- AI disclosure note\\n\\n## AI disclosure stance\\n\\nIf only text-free atmospheric backgrounds are AI-generated and there are no realistic synthetic people or real-event depictions, platform synthetic-media disclosure is often not mandatory. Still include a conservative note: if the platform UI asks whether AI-assisted media was used, disclose according to that prompt.\\n\\n## Pitfall\\n\\nDo not let AI polish reintroduce the old failure mode: pretty but semantically weak. The deterministic foreground must still answer in the first second: what is broken, where is the fail, what is at stake, and what rule fixes it.\"}"
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"arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"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.\",\"new_string\":\"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. For the concrete rendering/QA pattern — small 2–3 keyframe pass, contact-sheet inspection, discard pseudo-text frames, blur/darken/subordinate AI backgrounds, and report used/discarded keyframes — see `references/proof-short-ai-background-rendering-qa.md`.\"}"
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"arguments": "{\"action\":\"patch\",\"file_path\":\"references/proof-short-ai-visual-upgrade.md\",\"name\":\"social-media-shorts-automation\",\"old_string\":\"## 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\",\"new_string\":\"## 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- number of used keyframes\\n- number of discarded keyframes and explicit discard reasons\\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## Operational QA note\\n\\nAfter generation, make a small contact sheet and inspect it before rendering. Do not use every image automatically. Discard AI backgrounds that contain pseudo-readable document lines, logo-like marks, QR/barcodes, people/hands, bank/payment details, or busy compositions that fight the deterministic foreground. For render treatment, keep AI backgrounds subordinate: slight blur/push-in/dark overlay is preferable to letting image detail compete with captions or the visible fail. See `references/proof-short-ai-background-rendering-qa.md` for the concrete pattern.\"}"
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"content": "Skill Library aktualisiert, Sir.\n\nGeändert wurde die aktuell geladene Umbrella-Skill:\n\n```text\nsocial-media-shorts-automation\n```\n\n## Updates\n\n- Neue Reference-Datei ergänzt:\n\n```text\nreferences/proof-short-ai-background-rendering-qa.md\n```\n\nEnthält jetzt den konkreten wiederverwendbaren Workflow für:\n\n- Proof Short visuell aufwerten mit **AI text-free backgrounds**\n- Invoice / Email / Payment Route weiter deterministisch halten\n- 2–3 Keyframes generieren, aber nicht blind alle verwenden\n- Contact Sheet QA\n- Pseudo-Text / Logo / QR / Bankdaten / People/Hands blockieren\n- AI Backgrounds per Blur/Darken/Overlay unterordnen\n- ReviewPackage-Felder für generated/used/discarded keyframes\n\n- `SKILL.md` gepatcht mit Pointer auf die neue Reference.\n- Bestehende Reference gepatcht:\n\n```text\nreferences/proof-short-ai-visual-upgrade.md\n```\n\nErgänzt um:\n\n- `used_keyframes`\n- `discarded_keyframes`\n- discard reasons\n- operational QA note\n- Kontaktbogen-Prüfung vor Render\n- Pitfall: AI polish darf nicht wieder “pretty but semantically weak” werden.\n\nKeine neue Narrow-One-Off-Skill erstellt; die Learnings sitzen sauber unter der bestehenden Class-Level-Skill.",
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"text": "Skill Library aktualisiert, Sir.\n\nGeändert wurde die aktuell geladene Umbrella-Skill:\n\n```text\nsocial-media-shorts-automation\n```\n\n## Updates\n\n- Neue Reference-Datei ergänzt:\n\n```text\nreferences/proof-short-ai-background-rendering-qa.md\n```\n\nEnthält jetzt den konkreten wiederverwendbaren Workflow für:\n\n- Proof Short visuell aufwerten mit **AI text-free backgrounds**\n- Invoice / Email / Payment Route weiter deterministisch halten\n- 2–3 Keyframes generieren, aber nicht blind alle verwenden\n- Contact Sheet QA\n- Pseudo-Text / Logo / QR / Bankdaten / People/Hands blockieren\n- AI Backgrounds per Blur/Darken/Overlay unterordnen\n- ReviewPackage-Felder für generated/used/discarded keyframes\n\n- `SKILL.md` gepatcht mit Pointer auf die neue Reference.\n- Bestehende Reference gepatcht:\n\n```text\nreferences/proof-short-ai-visual-upgrade.md\n```\n\nErgänzt um:\n\n- `used_keyframes`\n- `discarded_keyframes`\n- discard reasons\n- operational QA note\n- Kontaktbogen-Prüfung vor Render\n- Pitfall: AI polish darf nicht wieder “pretty but semantically weak” werden.\n\nKeine neue Narrow-One-Off-Skill erstellt; die Learnings sitzen sauber unter der bestehenden Class-Level-Skill."
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