{
"session_id": "20260526_071201_bb1762f5",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-26T17:40:17.524730",
"last_updated": "2026-05-26T18:40:53.676050",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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) [98% — 1,349/1,375 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. New visual standard for proof shorts: detailed AI-generated real UI/screen images with readable concrete text (e.g. Outlook-style invite/login/calendar), no extra focus lines/arrows/boxes; active-word subtitles; export MP4 with explicit 9:16 SAR/DAR.\n\nConversation started: Tuesday, May 26, 2026 07:12 AM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "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": "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": "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": 221,
"messages": [
{
"role": "user",
"content": "[M G] Aspect ratio stimmt nicht, bzw oben und unten sind schwarze balken und das sichtbare bild ist in der höhe zusammengestaucht."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_i7hB781HGxx48tSAxxiqot00",
"call_id": "call_i7hB781HGxx48tSAxxiqot00",
"response_item_id": "fc_05d87c710653a55b016a152ba619cc819186f38d1a34ce02b5",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\"}"
}
}
],
"finish_reason": "tool_calls",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFSumiwG1dnXNnrTMs52Rj-aFHoqVu8LAhiQwDsZMuOisuLw62h2vTf0kD1OzeKnSSbNoX-3IuQQ0xPMFDQoRibEF9RZ_E1isG5zKNFn8XlW5F4TewPeBkEQmjrAAQg8bTT-n-Jl_V_4BDMIW90VRLyk25Bd3rLa1HreXi1MSUBD4H0lKO5SI7ohhmDikxasT9L0PdhZpeX2QlXxiVHA6JZgC_rYnc9_kLOKKHQRUX4zrBLT4t_SAAvHi_BVyZAQ8e_etJ8eoY4vjUwIp4_e8XuJb-2RE-FYwRpDb-j9RVIsuRXAaa_pp1AxMl1uIgwRsxTjvqUxVVkXWzGCveQEGw3IWSKsJPQu2LFD60qT-13T8oFMVk9_xSkumS6OpUNHiestF0MlwFHOwtDPm_KBp4fkplIUsN88a5VEIgSd_y2ezp1m7efSTCjS6Ki2O3WnIqs2_C7JWEUsTYx2xMG6H6cWBjOChcVIdafbgicJSarEWT27W9LLSOvfts8c8hsQomArqWVJ-LVp97C1yqMfk4dwhkNzJDUHCxf-GNqeshlz_8opG6wTJaADBkJ0C8O4PSFb0nQxvkzfQ4T6n-GAf3tle2P03X0CuipI2pGjS9kXhiayugoY34Ar3nb6XgEV6CwegrEwYFQk-9SBwusi0aCApftq8XcfEJUepgzwaNIswkBDNQU7Xb3WBt7K1ThYTpJsSt7Q9QeTG1PD8bW3sv2FrHsbnG870hRscoSynMoN7blCkjKYLNzcmc42Y83sZbhkrcg4xoUphzX387U6ESe27lIzqL-_WFf5aUmiHoWTmCetkpyX8jVi7mGfueX5EE63Do8A0NFLwhQ3WpQjAagLXRnZj0rNCAixQgYMtM5cJ4Gqg-MYweBr29Zi0ythSLhafQxUDvQlQILXLLoKPpZLxZ4QRBF9tNAl-7y-1L2nhcUSwSyHy31P_r1mcawJ1exfQc3dMaINqzqMAsbDMdNgyBz0mntML7C9689z9zrze0V5H_08KKNRBDrItYhcnUmipQb0D_eTntJqRk5LhzvxIqx3ZsjRmhz3vQ4ghZt0DzTJLtrhj9z7g8QQIGQxwEDCbp6OD9sDI9gbz8PSuF-v2nxHveDA6rJrsmkA27qU1ExdPVyPNS0HyuJ0DLcujyU1ajJYdIBLT0dGvYOzpzbF2Ewb4TgFdlvXjQpfg4xeitMx8H0Dj_AWjNepjT8AfEyAW2fsX_HAbK34U8Mj8SR6J-AFzzRRNcKr0NovVsd9IhbXo0oYxU2h2XtmDYDZlBvoyD2oby8FT6ZTesYOxVYDDFTQvVYAH5SmuZmcWTNgUDEivB5B6ftT2y2tz6cjDnXymOy7xW07e6ghrufASS_OQzZcWITtlXg8XLTRsNYAgn6ssVtDvmZ7AYgor9M-w445LzBTY39Wrpm76ym15fkpCpgQihRLjiUMN4Z6Ie98wEzZPajtOesNADJZP2PrVFe69cKNbgpxzTPaTA6jRDmdJYXA3L0m39ukvoiBMYxi68iSTO4lrF7vF91D3cobAZjZ0A3W--DWq5SbnfZ1Etx5_8EVKV04yuUz8Gff7Wqcwlso=",
"id": "rs_05d87c710653a55b016a152ba260a4819196e0429495bf001f",
"summary": []
}
]
},
{
"role": "tool",
"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nReference note: see `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected caption style and controlled final pilot render pattern: static 3–7 word phrase segments with only the active spoken word highlighted, hash-bound final render gates, one Wan text-free background layer, deterministic overlays, and draft-only manual posting packages. If a final render is rejected as preview-overlay-dominated or not visual-first, follow `references/visual-first-final-render-repair.md`: mark the render rejected/not-postable, fix Wan output parsing, enforce final overlay limits, produce only a short visual proof under `APPROVED_FOR_VISUAL_PROOF`, and do not run another full final until the visual direction is accepted. If even the visual proof is rejected as abstract/generic/dark or “blind T2V”, switch to `references/keyframe-first-final-visuals.md`: plan and approve start/end/style keyframes first, then run only a short I2V/first-last-frame proof with minimal renderer-owned overlays. If the user says the production method itself is wrong, keyframes are not I2V-ready, or an animatic was incorrectly substituted for a failed Wan proof, follow `references/visual-production-reset-i2v-first.md`: reject/audit the proof, stop content rendering, run technical I2V sanity first, then generate at most three styleframes for direction review. If deterministic layout proofs are useful as composition blueprints but rejected as too wireframe/PowerPoint for final style, switch to `references/premium-ai-styleframe-direction.md`: use the layouts only as mechanism blueprints, generate at most 3 tightly briefed premium text-free AI styleframes, deliver a contact sheet with verified media metadata, and keep Wan/I2V/endframes/full-final blocked until style direction approval. If those premium styleframes are better quality but still too abstract, switch to `references/real-life-premium-styleframes.md`: anchor the mechanism in a realistic work/meeting/desk scene, keep all readable overlays renderer-owned, generate at most 3 new real-life styleframes plus contact sheet, and still block Wan/I2V/endframes/full-final until explicit approval. If the user approves one or more real-life styleframe directions but wants to compare A/B/C before Wan, use `references/prerelease-animatic-preview.md`: build only a small AI-keyframe + deterministic-motion animatic per approved direction, reuse existing voiceover/captions, deliver MP4/contact/QA sheets with metadata, and keep Wan/I2V/final/posting blocked. If the user then rejects the renderer-added circles/arrows/labels/boxes/decision-target markers as destroying the premium look, switch to `references/clean-image-cut-no-overlay-pilot.md`: render only AI images + voiceover + clean subtitles, with a hard subtitles-only NoOverlayPolicy and no Wan/I2V/full-final/posting. If the user performs a strategic reset because the pilot is pretty but semantically weak, stop optimizing that candidate and follow `references/proof-short-real-problem-reset.md`: archive the old pilot as a learning case, enforce the Pretty Empty Visual Blocker, and build a screen/mockup-led Proof Short around an immediately visible real viewer problem before any premium/Wan production. 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. 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`. For the follow-up A/B test where the user asks whether pure KI images can match or beat hybrid mockups, see `references/clean-ai-cut-vs-hybrid-mechanism.md`: compare clean image-only premium feel against mechanism clarity, keep word-aligned subtitles, and consider `90% clean AI images + 10% minimal deterministic mechanism` as the likely next style.\\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 provider word-boundary events before sending another final-near clip. For EdgeTTS previews, request `boundary='WordBoundary'` and render active-word highlights from those offsets; do not merely distribute words evenly over sentence SRT durations. 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 the user says the ending is confusing, rushed, or does not offer a real solution, treat this as a content-blocking issue, not a minor copy tweak. Rewrite the final third so non-technical viewers are explicitly walked through the concrete action: what to close/avoid, what to open/check instead, how they know it worked, and the memorable rule/fazit. Avoid vague imperatives like “open the service yourself” unless immediately unpacked with a concrete example such as “close the invite; open the calendar/work app from the normal icon or bookmark; if the meeting is real, it will still be there.”\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style.\\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`.\\nIf the user rejects the 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`. If the user then asks whether pure KI-Bilder can achieve the same or better effect, run an A/B proof against the hybrid version and assess premium look vs mechanism clarity; the likely final style may be `90% clean AI images + 10% minimal renderer-owned mechanism`. See `references/clean-ai-cut-vs-hybrid-mechanism.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/mobile-vertical-aspect-qa.md` for the mobile 9:16 export QA/repair pattern: probe final MP4 SAR/DAR, extract a frame, and re-export full-bleed 1080x1920 with `setsar=1,setdar=9/16` when Telegram/Shorts previews show black bars or squeezed height. 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. See `references/word-boundary-caption-sync-and-solution-gate.md` for the stronger EdgeTTS `WordBoundary` timing pattern and the explicit solution/fazit gate for Proof Shorts.\\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 deliver final-near Shorts/Reels/TikTok MP4s based only on nominal render dimensions. Probe `width,height,sample_aspect_ratio,display_aspect_ratio`, extract QA frames, and repair black bars or squeezed-height playback with a full-bleed 1080x1920 square-pixel export (`setsar=1,setdar=9/16`) before resending.\\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. 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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/mobile-vertical-aspect-qa.md\", \"content\": \"# Mobile vertical aspect QA and repair\\n\\nUse this when a short/reel/Telegram preview appears with black bars at top/bottom, wrong aspect ratio, or the visible image looks vertically compressed even though the source clip was intended as 9:16.\\n\\n## Durable lesson\\n\\nDo not rely on nominal source dimensions alone. A clip can be near-vertical, but still display badly on mobile platforms if the export lacks explicit square-pixel and display-aspect metadata, or if an assembly step has letterboxed instead of producing a true vertical canvas.\\n\\n## Required checks\\n\\nProbe every final-near MP4 before delivery:\\n\\n```bash\\nffprobe -v error -select_streams v:0 \\\\\\n -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio \\\\\\n -show_entries format=duration \\\\\\n -of json path/to/video.mp4\\n```\\n\\nExpected mobile-safe target for Shorts/Reels/TikTok review exports:\\n\\n- `width=1080`\\n- `height=1920`\\n- `sample_aspect_ratio=1:1`\\n- `display_aspect_ratio=9:16`\\n- no visible black bars in extracted QA frames\\n\\nExtract a frame for visual QA:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i path/to/video.mp4 \\\\\\n -vf \\\"select=eq(n\\\\,10)\\\" -frames:v 1 /tmp/frame.png\\n```\\n\\nInspect the frame for top/bottom black bars and obvious squeezing before sending to the user.\\n\\n## Repair command\\n\\nFor source clips that should fill a vertical mobile canvas, create a post-render normalized export:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i input.mp4 \\\\\\n -vf \\\"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -preset veryfast -crf 18 -movflags +faststart -an \\\\\\n output_1080x1920_sar1.mp4\\n```\\n\\nUse `increase,crop` for full-bleed visual proofs where black bars are unacceptable. Use `decrease,pad` only when preserving every pixel is more important than full-screen mobile presentation, and label it as letterboxed.\\n\\n## Workflow rule\\n\\nIf the user reports black bars or squeezed height, treat it as a blocking export QA issue:\\n\\n1. Probe the delivered MP4 metadata.\\n2. Extract at least one representative frame.\\n3. Re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n4. Probe the repaired file and report the verified SAR/DAR.\\n5. Deliver the repaired media, not more explanations.\\n\\n## Pitfall\\n\\nDo not say “the source is 9:16 enough” based on dimensions like `416x736` alone. Platform players can still expose metadata or canvas mistakes. Verify the final delivered artifact.\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"query\": \"Aspect ratio schwarze Balken zusammengedrückt AutoShorts OR TrueTraceShorts OR 1080x1920 OR SAR DAR\", \"results\": [{\"session_id\": \"20260526_070136_e248717a\", \"when\": \"May 26, 2026 at 07:01 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The user reported that a generated AutoShorts/TrueTraceShorts video had the wrong aspect ratio: black bars at the top and bottom, and the visible image looked vertically compressed (“in der Höhe zusammengestaucht”).\\n\\nActions taken and outcomes:\\n\\n- The assistant loaded the `social-media-shorts-automation` skill because the issue related to the AutoShorts/TrueTraceShorts video-rendering pipeline.\\n- Recent and keyword session searches were run for terms related to aspect ratio, black bars, subtitles, final videos, and generated clips.\\n- The assistant searched the AutoShortsBot runtime for relevant MP4 assets and found generated Wan/I2V clips, especially under:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/\\n ```\\n\\n- Several generated clips were inspected with video metadata probing. The important results were:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/.../scene_01/seed_101.mp4\\n 416x736 SAR=None DAR=None duration=5.08s\\n\\n /home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/.../scene_02/seed_101.mp4\\n 416x736 SAR=None DAR=None duration=5.08s\\n ```\\n\\n Other Wan/ComfyUI outputs were also checked and were mostly `416x736`, `480x832`, or similar vertical-ish dimensions, but without explicit SAR/DAR metadata.\\n\\n- The assistant inspected relevant code paths:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/scripts/run_i2v_clip_jobs.py\\n /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/i2v_clip_jobs.py\\n /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/comfyui_client.py\\n ```\\n\\n- In `autoshorts/i2v_clip_jobs.py`, the native Wan2.2 I2V workflow used ComfyUI resolution values from the prompt plan and generated clips through nodes such as:\\n\\n ```python\\n ImageScale\\n Wan22ImageToVideoLatent\\n CreateVideo\\n SaveVideo\\n ```\\n\\n The relevant workflow used the scene’s configured `width` and `height`, e.g. `416x736`.\\n\\n- The keyframes were inspected and were already vertical, with dimensions such as:\\n\\n ```text\\n scene_01.png (416, 736)\\n scene_02.png (416, 736)\\n variant_1.png (768, 1344)\\n variant_2.png (768, 1344)\\n variant_3.png (768, 1344)\\n ```\\n\\n- A frame/screenshot was visually analyzed to check whether the content itself had black bars or visible squeezing. The conclusion was that the issue was likely an export/playback/aspect metadata problem rather than the keyframes themselves being obviously letterboxed.\\n\\nKey solution/conclusion:\\n\\n- The assistant concluded that the proof clips should be normalized for mobile/social playback to a strict vertical format:\\n\\n ```text\\n 1080x1920\\n SAR=1:1\\n DAR=9:16\\n ```\\n\\n- A normalization/fix step was applied using an FFmpeg-style filter chain conceptually described as:\\n\\n ```text\\n scale → crop → setsar=1 → setdar=9/16\\n ```\\n\\n- The assistant generated corrected proof clips under:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/\\n ```\\n\\n- The two corrected files were:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01_seed_101_1080x1920_sar1.mp4\\n\\n /home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02_seed_101_1080x1920_sar1.mp4\\n ```\\n\\n- Their probed metadata confirmed the fix:\\n\\n ```text\\n scene_01_seed_101_1080x1920_sar1.mp4\\n 1080x1920 SAR=1:1 DAR=9:16 sha=73b1b6b50d78\\n\\n scene_02_seed_101_1080x1920_sar1.mp4\\n 1080x1920 SAR=1:1 DAR=9:16 sha=031d84d9ee44\\n ```\\n\\n- A test output also confirmed:\\n\\n ```json\\n {\\n \\\"output_path\\\": \\\"/tmp/autoshorts_aspect_check/scene_01_seed_101_fixed_by_script.mp4\\\",\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"duration\\\": 5.083333\\n }\\n ```\\n\\n- The assistant created a reusable fix script for future exports:\\n\\n ```text\\n /home/agent/jarvis_runtime/AutoShortsBot/scripts/fix_vertical_aspect.py\\n ```\\n\\n- The assistant reported back to the user that the problem was an export/playback issue, not just a visual detail, and that Telegram/mobile playback should now display the clips correctly as high-quality vertical video without the black-bar/squeezed-image problem.\\n\\nNotable unresolved points:\\n\\n- The immediate proof clips were fixed and delivered as corrected `MEDIA:` paths.\\n- It was not yet shown in the transcript whether the user confirmed that Telegram displayed the corrected clips properly.\\n- The deeper renderer/export pipeline likely still needed the new `fix_vertical_aspect.py` or equivalent normalization step integrated permanently so all future final/proof exports were emitted as `1080x1920`, `SAR=1:1`, `DAR=9:16`.\"}, {\"session_id\": \"20260525_081301_675d674d\", \"when\": \"May 25, 2026 at 08:13 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The conversation was a recap/search session about an AutoShorts/TrueTraceShorts video rendering issue, specifically around vertical `1080x1920` output, aspect ratio, possible black bars (`schwarze Balken`), squeezed video (`zusammengedrückt`), and SAR/DAR concerns.\\n\\nKey points:\\n\\n1. **User goal / search topic**\\n - The user wanted the prior conversation summarized with focus on the topic:\\n - Aspect ratio problems\\n - Black bars\\n - Squished/compressed rendering\\n - AutoShorts / TrueTraceShorts\\n - `1080x1920`\\n - SAR/DAR metadata\\n\\n2. **Actions taken**\\n - The assistant loaded the `social-media-shorts-automation` skill, which described the AutoShorts/TrueTraceShorts workflow and its rendering principles.\\n - The assistant also opened an unrelated `google-workspace` reference and a `codebase-inspection` skill, but those did not materially affect the aspect-ratio topic.\\n - Several searches were run:\\n - `search_files` initially returned no matches.\\n - `session_search` found one related Telegram session from **May 24, 2026 at 09:12 AM**, involving AutoShortsBot changes, but the result was only a raw/truncated preview.\\n - A broader `search_files` found many files under:\\n\\n ```text\\n /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/\\n ```\\n\\n - Important discovered output artifact:\\n\\n ```text\\n /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-1080.mp4\\n ```\\n\\n - Related generated files were also found in the same folder:\\n - `final-motion-audio-caption-1080.ass`\\n - `voice.mp3`\\n - `motion-preview.mp4`\\n - `frames.txt`\\n - Many rendered PNG frames under `frames/frame_XXXXX.png`\\n\\n3. **Repository inspected**\\n - The assistant listed the AutoShortsBot repo structure.\\n - Important modules/files included:\\n - `autoshorts/rendering/final_video_plan.py`\\n - `autoshorts/rendering/motion_renderer.py`\\n - `autoshorts/rendering/ffmpeg_plan.py`\\n - `autoshorts/rendering/ffmpeg_renderer.py`\\n - `autoshorts/audio/final_preview.py`\\n - `autoshorts/social/final_approval.py`\\n - `tests/test_final_video_plan.py`\\n - `tests/test_motion_renderer.py`\\n - `tests/test_audio_caption_preview.py`\\n - `docs/final_motion_prototype.md`\\n\\n4. **Relevant technical details discovered**\\n - The README stated that the render manifest used vertical settings:\\n\\n ```text\\n 1080x1920\\n 30 fps\\n static_card_limit=0\\n no_generated_text_inside_assets=True\\n ```\\n\\n - Local preview settings were reduced vertical previews:\\n\\n ```text\\n 540x960\\n 24 fps\\n ```\\n\\n - The FFmpeg preview renderer exported MP4 using:\\n - `yuv420p`\\n - `+faststart`\\n - Tests were said to smoke-probe:\\n - width\\n - height\\n - frame rate\\n - approximate duration\\n\\n - The documented local preview command was:\\n\\n ```bash\\n python -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo-local-preview\\n ```\\n\\n - The general test command was:\\n\\n ```bash\\n python -m pytest tests -q\\n ```\\n\\n5. **Important architecture/context**\\n - AutoShortsBot was described as an approval-first short-form content engine for YouTube Shorts, TikTok, and Instagram Reels.\\n - TrueTraceShorts was the active brand/channel direction.\\n - The project emphasized deterministic renderer-owned overlays and final social-media vertical video output.\\n - The final-near motion prototype used a hybrid model:\\n - AI/Codex/ChatGPT images may provide text-free background texture.\\n - The deterministic renderer owns inbox cards, arrows, labels, captions, subtitles, and FFmpeg assembly.\\n\\n6. **Aspect-ratio-specific conclusion**\\n - The transcript showed that the assistant identified the relevant final export and rendering modules but did **not** show a completed diagnosis of the black-bar/squished-video problem.\\n - No explicit `ffprobe` output, SAR/DAR values, FFmpeg command line, or fix was present in the visible transcript.\\n - The most likely next diagnostic step would have been to inspect the actual final MP4 metadata with something like:\\n\\n ```bash\\n ffprobe -v error \\\\\\n -select_streams v:0 \\\\\\n -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,pix_fmt,r_frame_rate,avg_frame_rate \\\\\\n -of default=noprint_wrappers=1 \\\\\\n data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-1080.mp4\\n ```\\n\\n - If SAR/DAR were wrong, the probable FFmpeg-side fix would involve forcing square pixels / correct DAR for vertical Shorts, e.g. ensuring output had:\\n\\n ```text\\n width=1080\\n height=1920\\n SAR=1:1\\n DAR=9:16\\n ```\\n\\n with an FFmpeg filter such as:\\n\\n ```bash\\n -vf \\\"scale=1080:1920,setsar=1,setdar=9/16\\\"\\n ```\\n\\n or equivalent inside the renderer.\\n\\n7. **Unresolved / notable**\\n - 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"reasoning_content": "**Evaluating scaling and cropping**\n\nI’m considering using `scale=1080:-2, crop=1080:1920`, which would scale an image from 416x736 to 1085x1920 without any vertical squeeze, which is good. I think I should also make a no-bars version and include some visual quality assurance. But wait, should I duplicate the output or just have one version? It’s a bit confusing, but I want to make sure I’m getting this right!",
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{
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"text": "**Evaluating scaling and cropping**\n\nI’m considering using `scale=1080:-2, crop=1080:1920`, which would scale an image from 416x736 to 1085x1920 without any vertical squeeze, which is good. I think I should also make a no-bars version and include some visual quality assurance. But wait, should I duplicate the output or just have one version? It’s a bit confusing, but I want to make sure I’m getting this right!"
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"content": "Stimmt — das war ein echtes Export-/Playback-Problem, kein “Geschmacksthema”. Ich habe die Clips jetzt nochmals **Telegram-safe full-bleed** neu exportiert:\n\n- **1080×1920**\n- **SAR 1:1**\n- **DAR 9:16**\n- **30 fps**\n- **yuv420p**\n- ohne Audio\n- ohne Rotation-Metadata\n- QA-Frame geprüft: keine schwarzen Balken im Bildbereich\n\nNeue korrigierte Dateien:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4\n\nVerifikation:\n\n```text\nscene_01:\n1080x1920, SAR=1:1, DAR=9:16, 30fps\nsha256: 7ae0a7cd18e1…\n\nscene_02:\n1080x1920, SAR=1:1, DAR=9:16, 30fps\nsha256: 4610da44c262…\n```\n\nWenn Telegram *diese* noch mit Balken zeigt, liegt es sehr wahrscheinlich an Telegrams Preview/Player-Cropping — die Videodateien selbst sind jetzt korrekt vertikal. Aber immerhin: diesmal nicht “fast 9:16, wird schon passen” — das war offenbar die charmante britische Version von “nein”.",
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"text": "Stimmt — das war ein echtes Export-/Playback-Problem, kein “Geschmacksthema”. Ich habe die Clips jetzt nochmals **Telegram-safe full-bleed** neu exportiert:\n\n- **1080×1920**\n- **SAR 1:1**\n- **DAR 9:16**\n- **30 fps**\n- **yuv420p**\n- ohne Audio\n- ohne Rotation-Metadata\n- QA-Frame geprüft: keine schwarzen Balken im Bildbereich\n\nNeue korrigierte Dateien:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4\n\nVerifikation:\n\n```text\nscene_01:\n1080x1920, SAR=1:1, DAR=9:16, 30fps\nsha256: 7ae0a7cd18e1…\n\nscene_02:\n1080x1920, SAR=1:1, DAR=9:16, 30fps\nsha256: 4610da44c262…\n```\n\nWenn Telegram *diese* noch mit Balken zeigt, liegt es sehr wahrscheinlich an Telegrams Preview/Player-Cropping — die Videodateien selbst sind jetzt korrekt vertikal. Aber immerhin: diesmal nicht “fast 9:16, wird schon passen” — das war offenbar die charmante britische Version von “nein”."
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"content": "[M G] Sende mir nochmals das aktuelle social media video mit korrigiertem aspectratio. Die autobilder brauche ich nicht."
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/mobile-vertical-aspect-qa.md\", \"content\": \"# Mobile vertical aspect QA and repair\\n\\nUse this when a short/reel/Telegram preview appears with black bars at top/bottom, wrong aspect ratio, or the visible image looks vertically compressed even though the source clip was intended as 9:16.\\n\\n## Durable lesson\\n\\nDo not rely on nominal source dimensions alone. A clip can be near-vertical, but still display badly on mobile platforms if the export lacks explicit square-pixel and display-aspect metadata, or if an assembly step has letterboxed instead of producing a true vertical canvas.\\n\\n## Required checks\\n\\nProbe every final-near MP4 before delivery:\\n\\n```bash\\nffprobe -v error -select_streams v:0 \\\\\\n -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio \\\\\\n -show_entries format=duration \\\\\\n -of json path/to/video.mp4\\n```\\n\\nExpected mobile-safe target for Shorts/Reels/TikTok review exports:\\n\\n- `width=1080`\\n- `height=1920`\\n- `sample_aspect_ratio=1:1`\\n- `display_aspect_ratio=9:16`\\n- no visible black bars in extracted QA frames\\n\\nExtract a frame for visual QA:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i path/to/video.mp4 \\\\\\n -vf \\\"select=eq(n\\\\,10)\\\" -frames:v 1 /tmp/frame.png\\n```\\n\\nInspect the frame for top/bottom black bars and obvious squeezing before sending to the user.\\n\\n## Repair command\\n\\nFor source clips that should fill a vertical mobile canvas, create a post-render normalized export:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i input.mp4 \\\\\\n -vf \\\"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -preset veryfast -crf 18 -movflags +faststart -an \\\\\\n output_1080x1920_sar1.mp4\\n```\\n\\nUse `increase,crop` for full-bleed visual proofs where black bars are unacceptable. Use `decrease,pad` only when preserving every pixel is more important than full-screen mobile presentation, and label it as letterboxed.\\n\\n## Workflow rule\\n\\nIf the user reports black bars or squeezed height, treat it as a blocking export QA issue:\\n\\n1. Probe the delivered MP4 metadata.\\n2. Extract at least one representative frame.\\n3. Re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n4. Probe the repaired file and report the verified SAR/DAR.\\n5. Deliver the repaired media, not more explanations.\\n\\n## Pitfall\\n\\nDo not say “the source is 9:16 enough” based on dimensions like `416x736` alone. Platform players can still expose metadata or canvas mistakes. Verify the final delivered artifact.\", \"file_type\": \".md\"}",
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"reasoning_content": "**Finalizing file export**\n\nI need to check the final file because it lacks the SAR/DAR fields. I’ll re-export to set the SAR/DAR while preserving the audio. First, I should confirm if the audio is present using ffprobe streams. I might also need to use normalize and extract a frame for QA. If the audio is there, I can use ffmpeg to re-encode the video while copying the audio. I’ll ensure the output path is aspect_fixed and check the mapping carefully!",
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"content": "Hier ist das **aktuelle Social-Media-Video** nochmals mit korrigiertem Aspect Ratio — **nicht** die Auto-/Car-Clips.\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n30 fps\nAudio vorhanden\nDauer: 36.864s\n```\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/aspect_fixed/final-motion-audio-caption-1080_SAR1_DAR9x16.mp4",
"reasoning": "**Clarifying media requirements**\n\nI need to finalize the media guidelines. It's important to specify that this is a social media video and not related to car assets. Also, I should mention that it has to be verified to ensure authenticity. I might want to double-check what other details need to be included to avoid any confusion. I want to deliver clear instructions that meet expectations without any misunderstandings.",
"reasoning_content": "**Clarifying media requirements**\n\nI need to finalize the media guidelines. It's important to specify that this is a social media video and not related to car assets. Also, I should mention that it has to be verified to ensure authenticity. I might want to double-check what other details need to be included to avoid any confusion. I want to deliver clear instructions that meet expectations without any misunderstandings.",
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"text": "**Clarifying media requirements**\n\nI need to finalize the media guidelines. It's important to specify that this is a social media video and not related to car assets. Also, I should mention that it has to be verified to ensure authenticity. I might want to double-check what other details need to be included to avoid any confusion. I want to deliver clear instructions that meet expectations without any misunderstandings."
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"text": "Hier ist das **aktuelle Social-Media-Video** nochmals mit korrigiertem Aspect Ratio — **nicht** die Auto-/Car-Clips.\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n30 fps\nAudio vorhanden\nDauer: 36.864s\n```\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/aspect_fixed/final-motion-audio-caption-1080_SAR1_DAR9x16.mp4"
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{
"role": "user",
"content": "[M G] Nein. Ich meinte unser neues video mit den hochwertigen , detailierten KI-bildern, bei welchem ich dir nachher gesagt habe dass dies der neue standard ist."
},
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"reasoning": "**Searching for new video content**\n\nI need to find some newer video content. I’m considering whether the skills are relevant to what I’ve loaded so far. It seems like I should ensure that whatever I find really aligns with the current needs. I'll utilize session_search to help with this. I want to make sure it's a good match for what's being sought after, so I'm eager to see what new options are out there!",
"reasoning_content": "**Searching for new video content**\n\nI need to find some newer video content. I’m considering whether the skills are relevant to what I’ve loaded so far. It seems like I should ensure that whatever I find really aligns with the current needs. I'll utilize session_search to help with this. I want to make sure it's a good match for what's being sought after, so I'm eager to see what new options are out there!",
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"text": "**Searching for new video content**\n\nI need to find some newer video content. I’m considering whether the skills are relevant to what I’ve loaded so far. It seems like I should ensure that whatever I find really aligns with the current needs. I'll utilize session_search to help with this. I want to make sure it's a good match for what's being sought after, so I'm eager to see what new options are out there!"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/proof-short-ai-background-rendering-qa.md\", \"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.\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"query\": \"neuer Standard KI Bilder AutoShorts OR TrueTraceShorts OR detaillierte OR hochwertige OR Outlook OR calendar OR login\", \"results\": [{\"session_id\": \"20260525_081301_675d674d\", \"when\": \"May 25, 2026 at 08:13 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nr events, delete Drive files, share files, or modify Docs/Sheets without confirming with the user first.** Show what will be done (recipients, file IDs, content, share role) and ask for approval. For `drive delete`, prefer the default trash (reversible) over `--permanent`.\\\\n2. **Check auth before first use** — run `setup.py --check`. If it fails, guide the user through setup.\\\\n3. **Use the Gmail search syntax reference** for complex queries — load it with \\n…[truncated]\"}, {\"session_id\": \"20260507_152045_39cf70a4\", \"when\": \"May 07, 2026 at 03:20 PM\", \"source\": \"telegram\", \"model\": \"qwen-3.6-agent\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nZ Diverse Pendenzen gem. Liste\\\\n\\\", \\\"tool_calls_made\\\": 1, \\\"duration_seconds\\\": 1.21}\\n\\n[ASSISTANT]: Hier Ihre Termine für **morgen, Freitag, 8. Mai 2026** (Lokalzeit Zürich):\\n\\n---\\n\\n**📋 Morgen — 8. Mai 2026:**\\n\\n**Google Calendar:** Keine Termine\\n\\n**Outlook Kalender (importiert):**\\n\\n1. **Lena S. Kurs Einführung NPK Hochbau**\\n - Ganztägig (All-Day)\\n\\n2. **📍 Furkastrasse — PL Jour fixe**\\n - 10:00 – 11:00 Uhr\\n\\n3. **Jour fixe Olaf**\\n - 11:00 – 11:30 Uhr\\n\\n4. *\\n…[truncated]\"}], \"count\": 2, \"sessions_searched\": 2}",
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"reasoning": "**Searching for files**\n\nI need to look for files related to the \"new standard\" in the user's profile which mentions AI-generated UI images, specifically in an Outlook style. I think I should search for recent MP4 files across all directories, including any hidden ones. Let’s see if I can locate videos that might provide the needed references or examples. I’ll make sure to cover all possible paths to ensure I find everything relevant!",
"reasoning_content": "**Searching for files**\n\nI need to look for files related to the \"new standard\" in the user's profile which mentions AI-generated UI images, specifically in an Outlook style. I think I should search for recent MP4 files across all directories, including any hidden ones. Let’s see if I can locate videos that might provide the needed references or examples. I’ll make sure to cover all possible paths to ensure I find everything relevant!",
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"text": "**Searching for files**\n\nI need to look for files related to the \"new standard\" in the user's profile which mentions AI-generated UI images, specifically in an Outlook style. I think I should search for recent MP4 files across all directories, including any hidden ones. Let’s see if I can locate videos that might provide the needed references or examples. I’ll make sure to cover all possible paths to ensure I find everything relevant!"
}
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"content": "{\"total_count\": 59, \"files\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/aspect_fixed/final-motion-audio-caption-1080_SAR1_DAR9x16.mp4\", \"/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_02_seed_101_FULLBLEED_1080x1920_30fps.mp4\", \"/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/scene_01_seed_101_FULLBLEED_1080x1920_30fps.mp4\", \"/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02_seed_101_1080x1920_sar1.mp4\", \"/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01_seed_101_1080x1920_sar1.mp4\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-1080.mp4\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/final_exports/truetrace-final-motion-v7-1080/truetrace-ai-automation-quiet-rot-38s/motion-preview.mp4\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v7/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v7/truetrace-ai-automation-quiet-rot-38s/motion-preview.mp4\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v6/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", 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Set plugins.allow to explicit trusted ids.\\\\nplugin runtime config.loadConfig() is deprecated (runtime-config-load-write); use config.current().\\\\nErledigt, Sir. Erstellt/aktualisiert und verifiziert:\\\\n\\\\n- `/home/agent/jarvis_memory/README.md`\\\\n- `/home/agent/jarvis_memory/00_core_profile.md`\\\\n- `/home/ag\\n…[truncated]\"}, {\"session_id\": \"20260509_094024_6f941a82\", \"when\": \"May 09, 2026 at 09:40 AM\", \"source\": \"telegram\", \"model\": \"qwen-3.6-agent\", \"summary\": \"[Raw preview — summarization unavailable]\\n[USER]: Aufgabe: Plane die optimale Ordnerstruktur und Dateinamenskonvention für meinen neuen Obsidian-Vault. Die Struktur muss so flexibel sein, dass ich sie via iPhone/Git synchronisieren kann.\\nStruktur-Vorgabe (nach PARA-Prinzip):\\n1. 01_Eingang (Capture): - Hier landen alle schnellen Notizen von meinem iPhone. Du verschiebst diese später in die richtigen Kategorien.\\n2. 02_Gesundheit (Zentrale):\\n• Tagebuch: Tägliche Logs für Ernährung (Pescetarier, histaminarm), Symptome (Aphthen-Check) und Vi\\n…[truncated]\"}, {\"session_id\": \"20260523_083854_4d3b77f3\", \"when\": \"May 23, 2026 at 08:38 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The user asked to continue building the “hochwertige Content-Maschine” for the AutoShortsBot project, i.e. a semi-automated high-quality short-form content pipeline.\\n\\nActions taken and outcomes:\\n\\n- The assistant loaded relevant short-form/social-media growth skills and searched prior sessions for the AutoShortsBot/content-machine context.\\n- Prior project memory was reviewed:\\n - Project: **AutoShortsBot**\\n - GitHub repo: `https://github.com/Gamexgit/AutoShortsBot`\\n - Local project path: `/home/agent/jarvis_runtime/AutoShortsBot`\\n - Telegram topic/thread: AutoShortsBot in `JARVIS Command Center`, thread id `4850`\\n - Content direction had shifted away from FaithTok for now; current focus was Life Optimization, AI Life Systems, and Attention Design.\\n- Existing files and implementation state were inspected:\\n - `README.md`\\n - `pyproject.toml`\\n - `autoshorts/preview_renderer.py`\\n - `autoshorts/render_manifest.py`\\n - `autoshorts/voiceover_finalizer.py`\\n - `scripts/render_preview.py`\\n - `scripts/generate_render_manifests.py`\\n - tests for preview rendering, render manifests, voiceover finalization, etc.\\n- The existing preview system was confirmed to render simple vertical MP4 preview videos from structured render manifests using Pillow frames and `ffmpeg`.\\n- The pipeline already had top-3 preview candidates:\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4`\\n\\nKey technical work completed:\\n\\n- A new **Telegram Review Layer** was built for high-quality/manual approval workflow.\\n- New/updated files included:\\n - `autoshorts/review_package.py`\\n - `scripts/generate_telegram_review_packages.py`\\n - `tests/test_review_package.py`\\n- The review package generator produced Telegram-ready Markdown cards for the top 3 rendered previews:\\n - `data/telegram_reviews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.md`\\n - `data/telegram_reviews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.md`\\n - `data/telegram_reviews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.md`\\n- The review cards included:\\n - `MEDIA:` preview path\\n - Title\\n - Pillar\\n - Target platforms\\n - Duration\\n - Quality Score\\n - Hook\\n - Caption\\n - CTA\\n - Render check, including visual scene count, caption beat count, and scene types\\n - Risk notes\\n - Explicit approval commands\\n\\nImportant commands/results:\\n\\n- Tests were run successfully:\\n\\n```text\\n28 passed in 0.94s\\n```\\n\\nThen after patching:\\n\\n```text\\n28 passed in 0.99s\\nGenerated 3 Telegram review packages in data/telegram_reviews/testbatch_002_top3\\ndata/telegram_reviews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.md\\ndata/telegram_reviews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.md\\ndata/telegram_reviews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.md\\n```\\n\\n- A bug/quality issue was fixed: review packages initially used relative `MEDIA:` paths. `autoshorts/review_package.py` was patched so `preview_path` used `preview_path.resolve()`, producing absolute paths such as:\\n\\n```text\\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\\n```\\n\\nImportant details relevant to the search topic:\\n\\n- The conversation emphasized raising the standard of the content machine from simple generation toward a higher-quality approval pipeline.\\n- The renderer was still a preview renderer, not the final “high-quality AI image/video” layer. It used deterministic cards/placeholders instead of AI-generated visuals.\\n- Existing visual rules explicitly warned against “AI image slop,” fake UI text, gibberish signs, distorted elements, or low-trust generated images.\\n- The render manifest system included asset types such as:\\n - `kinetic_text`\\n - `screen_recording`\\n - `card_animation`\\n - `stock_broll`\\n - `generated_background`\\n- The render manifest included captions/subtitle timing via `caption_beats` and caption timeline items.\\n- The review package’s “Render-Check” summarized scene count and caption beats, e.g. for `ai-004`:\\n - `5 visual scenes`\\n - `13 caption beats`\\n - types: `card animation`, `kinetic text`, `screen recording`\\n\\nKey decisions/conclusions:\\n\\n- The system would remain **semi-automatic** with human approval before any public posting.\\n- Valid approval commands remained:\\n - `Freigabe alle`\\n - `Freigabe YouTube`\\n - `Freigabe TikTok`\\n - `Freigabe Instagram`\\n - `Ablehnen`\\n - `Ändern: `\\n- No posting would happen without explicit approval.\\n- The next quality step was implicitly to continue improving the production standard beyond placeholder previews, likely toward better visuals/screens/subtitles/rendering.\\n\\nNotable unresolved items:\\n\\n- The Telegram review packages were generated, but there was no actual posting/publication.\\n- The three preview videos still required user review/approval.\\n- The pipeline had not yet moved from placeholder-style preview rendering to final high-quality production visuals, detailed screens, polished subtitles, or AI-image/video assets.\\n- The final “new standard” for high-quality images/screens/subtitles was not fully defined yet in this session; only the review/approval layer was completed.\"}], \"count\": 5, \"sessions_searched\": 5}",
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"content": "{\"success\": true, \"query\": \"Subtitle-Sync OR Video-Fazit OR final third OR Fazit OR voiceover OR active-word OR hochwertige Content-Maschine\", \"results\": [{\"session_id\": \"20260526_070136_e248717a\", \"when\": \"May 26, 2026 at 07:01 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The user reported in Telegram that the video aspect ratio was wrong: there were black bars at the top and bottom, and the visible image looked vertically compressed (“Aspect ratio stimmt nicht, bzw oben und unten sind schwarze balken und das sichtbare bild ist in der höhe zusammengestaucht.”).\\n\\nActions taken and outcomes:\\n\\n- The `social-media-shorts-automation` skill was loaded because the issue related to AutoShorts/social video rendering.\\n- A recent-session search found a relevant previous Telegram session titled **“Subtitle-Sync und Video-Fazit #7”** (`session_id: 20260525_225614_efb6a8`) whose preview began with the user complaining that subtitles were not exactly synchronized with the voiceover. That session was noted as context but not deeply opened in this transcript.\\n- Additional searches were run for aspect/video-related terms such as:\\n - `aspect ratio OR schwarze Balken OR final_near OR subtitles OR video`\\n - `MEDIA OR generated_clips OR seed_101 OR seed_202 OR seed_303 OR hidden-money-systems`\\n- The assistant inspected existing generated video assets under the AutoShortsBot project, especially the “hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann” pilot.\\n\\nImportant files and paths inspected:\\n\\n- Generated Wan/I2V clips:\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_202.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_303.mp4`\\n - same structure for `scene_02`\\n- Keyframes:\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png`\\n- Related code:\\n - `/home/agent/jarvis_runtime/AutoShortsBot/scripts/run_i2v_clip_jobs.py`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/i2v_clip_jobs.py`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/comfyui_client.py`\\n\\nTechnical findings:\\n\\n- The generated clips were mostly **416×736** with about **5.08s** duration:\\n - `416x736 SAR=None DAR=None duration=5.08s`\\n- Some ComfyUI/Wan test outputs were also inspected:\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4`\\n - `/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan22_scene01_bright_416x736_00001_.mp4`\\n- The assistant checked keyframe dimensions:\\n - `scene_01.png (416, 736)`\\n - `scene_02.png (416, 736)`\\n - variants were `768×1344`\\n- A crop/bounding-box check showed the scene keyframes used the full image area:\\n - `scene_01.png (416, 736) (0, 0, 416, 736)`\\n - `scene_02.png (416, 736) (0, 0, 416, 736)`\\n- This suggested the black bars/compression were likely caused by export/playback/aspect metadata or non-final vertical normalization rather than by black bars baked into the keyframes.\\n\\nSolution implemented:\\n\\n- The assistant created a new fix script:\\n\\n```text\\n/home/agent/jarvis_runtime/AutoShortsBot/scripts/fix_vertical_aspect.py\\n```\\n\\n- The script normalized vertical clips to mobile-safe output using an FFmpeg-style filter chain described as:\\n\\n```text\\nscale → crop → setsar=1 → setdar=9/16\\n```\\n\\n- The goal was to force:\\n - resolution: **1080×1920**\\n - sample aspect ratio: **1:1**\\n - display aspect ratio: **9:16**\\n - no letterbox/pillarbox metadata problems\\n\\n- A test run produced:\\n\\n```json\\n{\\n \\\"output_path\\\": \\\"/tmp/autoshorts_aspect_check/scene_01_seed_101_fixed_by_script.mp4\\\",\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"duration\\\": 5.083333\\n}\\n```\\n\\n- A visual/crop check on the fixed output confirmed:\\n - `(1080, 1920) (0, 0, 1080, 1920)`\\n\\nFinal corrected proof clips delivered to the user:\\n\\n```text\\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01_seed_101_1080x1920_sar1.mp4\\n```\\n\\n```text\\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02_seed_101_1080x1920_sar1.mp4\\n```\\n\\nConclusion reached:\\n\\n- The issue was treated as an **export/playback aspect-ratio problem**, not merely a cosmetic issue.\\n- The corrected outputs were regenerated as true **1080×1920 vertical 9:16** clips with **SAR 1:1**, intended to display properly in Telegram/mobile players.\\n- No further work on subtitle sync, active-word captions, voiceover timing, or the final-third/Fazit content happened in this specific transcript, though prior session context indicated those had been active topics.\"}, {\"session_id\": \"20260523_083854_4d3b77f3\", \"when\": \"May 23, 2026 at 08:38 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n[USER]: [M G] OK, nächster schritt. Bleib dran unsere Hochwertige content maschine zu bauen.\\n\\n[ASSISTANT]: [Called: skill_view, skill_view, session_search]\\n\\n[TOOL]: {\\\"success\\\": true, \\\"name\\\": \\\"social-media-shorts-growth-engine\\\", \\\"description\\\": \\\"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\\", \\\"tags\\\": [\\\"shorts\\\", \\\"tiktok\\\", \\\"youtube-shorts\\\", \\\"reels\\\", \\\"content-strategy\\\", \\\"growth\\\", \\\"mo\\n…[truncated]\"}, {\"session_id\": \"20260525_081301_675d674d\", \"when\": \"May 25, 2026 at 08:13 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nr events, delete Drive files, share files, or modify Docs/Sheets without confirming with the user first.** Show what will be done (recipients, file IDs, content, share role) and ask for approval. For `drive delete`, prefer the default trash (reversible) over `--permanent`.\\\\n2. **Check auth before first use** — run `setup.py --check`. If it fails, guide the user through setup.\\\\n3. **Use the Gmail search syntax reference** for complex queries — load it with \\n…[truncated]\"}, {\"session_id\": \"20260522_230108_c351cf3a\", \"when\": \"May 22, 2026 at 11:01 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nay/Telegram deaktiviert; nur explizit/manuell nutzen.\\\", \\\"Health backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\\\", \\\"API-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\\\", \\\"Google Workspace (gog): **Credentials-Datei:** `~/.\\n…[truncated]\"}, {\"session_id\": \"20260523_074449_3d9cee59\", \"when\": \"May 23, 2026 at 07:44 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nnds\\\": [], \\\"setup_needed\\\": false, \\\"setup_skipped\\\": false, \\\"readiness_status\\\": \\\"available\\\", \\\"metadata\\\": {\\\"hermes\\\": {\\\"tags\\\": [\\\"testing\\\", \\\"tdd\\\", \\\"development\\\", \\\"quality\\\", \\\"red-green-refactor\\\"], \\\"related_skills\\\": [\\\"systematic-debugging\\\", \\\"writing-plans\\\", \\\"subagent-driven-development\\\"]}}}\\n\\n[TOOL]: {\\\"success\\\": true, \\\"name\\\": \\\"social-media-shorts-automation\\\", \\\"description\\\": \\\"Design, generate, review, publish, and optimize automated short-form social videos across Y\\n…[truncated]\"}], \"count\": 5, \"sessions_searched\": 5}",
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"content": "{\"content\": \" 1|{\\\"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. 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Search.\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 3|{\\\"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. 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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. 193 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. 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[truncated]\\n 9|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_223248_ad88505c.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cinematic keyframe for a cybersecurity short. Shot 1 visible problem: a laptop on a modern desk clearly shows a large calendar invitation card layout on screen: blank title block, blank time row, attendee dots, large blank JOIN button shape. It must be instantly recognizable as a calendar invite, but completely text-free. No readable words, no letters, no numbers, no logos, no brand icons, no QR/barcodes, no people, no hands, no faces, no watermarks. 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Subtle warning amber/red glow, premium social video look.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_Rw7I9D9rI0fhsLrlxN1x5wen\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 11|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_223442_75635a77.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 keyframe. Shot 3 suspicious moment: laptop screen displays a large blank calendar invite card, the join button area glows subtly red/amber, and a blank link strip underneath suggests a risky path without any readable content. Keep all UI shapes abstract and text-free. No words, no letters, no numbers, no logos/icons, no QR/barcodes, no people/hands/faces, no watermarks. Cinematic dark office desk, negative space for subtitles.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_3t3PhWU1WWvCvNEmCcnjTLT5\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 12|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_z1CWrLOHnJXrebSOYtYUxlIp\\\", \\\"call_id\\\": \\\"call_z1CWrLOHnJXrebSOYtYUxlIp\\\", \\\"response_item_id\\\": \\\"fc_051a8a0b2fb58918016a14b26c429481918c7f0ac9789490b3\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"image_generate\\\", \\\"arguments\\\": \\\"{\\\\\\\"aspect_ratio\\\\\\\":\\\\\\\"portrait\\\\\\\",\\\\\\\"prompt\\\\\\\":\\\\\\\"Premium vertical 9:16 cinematic keyframe. 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--enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\\\\\\\n libavutil 58. 29.100 / 58. 29.100\\\\\\\\n libavcodec 60. 31.102 / 60. 31.102\\\\\\\\n libavformat 60. 16.100 / 60. 16.100\\\\\\\\n libavdevice 60. 3.100 / 60. 3.100\\\\\\\\n libavfilter 9. 12.100 / 9. 12.100\\\\\\\\n libswscale 7. 5.100 / 7. 5.100\\\\\\\\n libswresample 4. 12.100 / 4. 12.100\\\\\\\\n libpostproc 57. 3.100 / 57. 3.100\\\\\\\\nInput #0, rawvideo, from 'fd:':\\\\\\\\n Duration: N/A, start... [truncated]\\n 26|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Planning QA for Outputs**\\\\n\\\\nI'm thinking I need to ensure quality assurance (QA) with a focus on b1, b2, and also using ffprobe’s output, if that's ready. It seems like I could employ a parallel approach to streamline the process. This might help me manage the tasks more efficiently and gather all necessary information in one go. I should clarify the specifics and see how best to implement this. Let’s keep it organized!\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Planning QA for Outputs**\\\\n\\\\nI'm thinking I need to ensure quality assurance (QA) with a focus on b1, b2, and also using ffprobe’s output, if that's ready. It seems like I could employ a parallel approach to streamline the process. This might help me manage the tasks more efficiently and gather all necessary information in one go. I should clarify the specifics and see how best to implement this. Let’s keep it organized!\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...f9lG... [truncated]\\n 27|{\\\"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 B1 contact sheet: AI-visible invite only + subtitles. Are captions readable and safe? Is calendar invite/login situation visible enough? Any problematic text/logos/QR/people/hands? Does it carry the story without focus overlays?\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 28|{\\\"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 B2 contact sheet: visible invite + minimal focus outlines + subtitles. Are captions readable? Are focus outlines too PowerPoint-like or acceptable? Is mechanism clearer than B1? Any problematic text/logos/QR/people/hands?\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 29|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"Argument '/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b2/calendar-invite-login-trap-30s_b2.mp4' provided as input filename, but '/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b1/calendar-invite-login-trap-30s_b1.mp4' was already specified.\\\\\\\\n04d26c5eff645ec852755f072695693c3f862232ad9e49b891656054933ab16e /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b1/calendar-invite-login-trap-30s_b1.mp4\\\\\\\\n63d8cdd8347f27b86ed4e03046160a1d19e10491ad747b0c1df72fa0897480b7 /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b2/calendar-invite-login-trap-30s_b2.mp4\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_GTICBQYhFBefoQqML0tNMyJ3\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 30|{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"[Your active task list was preserved across context compression]\\\\n- [>] render-b1. Variant B1 rendern: AI-visible invite only + Voiceover + Subtitles (in_progress)\\\\n- [ ] render-b2. Variant B2 rendern: AI-visible invite + minimaler Fokus-Layer + Subtitles (pending)\\\\n- [ ] qa-deliver. A/B QA, Manifest, SHA256 und Lieferung mit Empfehlung (pending)\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 31|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Finalizing media tasks**\\\\n\\\\nI need to continue with the project. It looks like I have to finalize the media delivery, but first, I should verify ffprobe separately. I also need to update my to-do list. There's a thought about creating relevant social shorts, which could be useful. I should load the necessary elements because this task is about automating shorts. But wait, I realize I didn't do that yet! So, I must load it now.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Finalizing media tasks**\\\\n\\\\nI need to continue with the project. It looks like I have to finalize the media delivery, but first, I should verify ffprobe separately. I also need to update my to-do list. There's a thought about creating relevant social shorts, which could be useful. I should load the necessary elements because this task is about automating shorts. But wait, I realize I didn't do that yet! So, I must load it now.\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...Qpts... [truncated]\\n 32|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"skill_view\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"name\\\\\\\": \\\\\\\"social-media-shorts-automation\\\\\\\", \\\\\\\"description\\\\\\\": \\\\\\\"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\\\\\\", \\\\\\\"tags\\\\\\\": [\\\\\\\"shorts\\\\\\\", \\\\\\\"tiktok\\\\\\\", \\\\\\\"youtube-shorts\\\\\\\", \\\\\\\"reels\\\\\\\", \\\\\\\"creator-monetization\\\\\\\", \\\\\\\"content-automation\\\\\\\", \\\\\\\"social-media\\\\\\\"], \\\\\\\"related_skills\\\\\\\": [], \\\\\\\"content\\\\\\\": \\\\\\\"---\\\\\\\\nname: social-media-shorts-automation\\\\\\\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\\\\\\\nversion: 1.0.0\\\\\\\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\\\\\\\n---\\\\\\\\n\\\\\\\\n# Social-Media Shorts Automation\\\\\\\\n\\\\\\\\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`, a... [truncated]\\n 33|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_c30AC9rxnEEK48ISPlCWBSXS\\\", \\\"call_id\\\": \\\"call_c30AC9rxnEEK48ISPlCWBSXS\\\", \\\"response_item_id\\\": \\\"fc_030089ba91e8432c016a14b776d18c819186c840575cbb985c\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"skill_view\\\", \\\"arguments\\\": \\\"{\\\\\\\"name\\\\\\\":\\\\\\\"social-media-shorts-automation\\\\\\\",\\\\\\\"file_path\\\\\\\":\\\\\\\"references/clean-ai-cut-vs-hybrid-mechanism.md\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 34|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"skill_view\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"name\\\\\\\": \\\\\\\"social-media-shorts-automation\\\\\\\", \\\\\\\"file\\\\\\\": \\\\\\\"references/clean-ai-cut-vs-hybrid-mechanism.md\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"# Clean AI Cut vs Hybrid Mechanism — session learning\\\\\\\\n\\\\\\\\nUse this when comparing a pure premium AI-image sequence against a proof short with deterministic mechanism/UI overlays.\\\\\\\\n\\\\\\\\n## Session-derived pattern\\\\\\\\n\\\\\\\\nFor a scam/cybersecurity proof short, a clean image sequence can look significantly more premium and native to TikTok/Reels, but it often weakens immediate mechanism clarity.\\\\\\\\n\\\\\\\\nObserved comparison:\\\\\\\\n\\\\\\\\n```text\\\\\\\\nClean AI Cut = premium mood, calmer, less explainer-board\\\\\\\\nHybrid = clearer mechanism, more immediately useful\\\\\\\\nBest likely final direction = mostly clean images + one minimal deterministic mechanism moment\\\\\\\\n```\\\\\\\\n\\\\\\\\nRecommended framing:\\\\\\\\n\\\\\\\\n```text\\\\\\\\n90% Clean AI Images\\\\\\\\n10% precise renderer-owned mechanism\\\\\\\\n```\\\\\\\\n\\\\\\\\nExamples of acceptable minimal mechanism moments:\\\\\\\\n\\\\\\\\n- one brief `suspicious link/domain` reveal\\\\\\\\n- one brief `close invite -> open trusted app/bookmark` action moment\\\\\\\\n- one simple before/after state, not a full checklist board\\\\\\\\n\\\\\\\\nAvoid returning to:\\\\\\\\n\\\\\\\\n- checklist-heavy UI cards\\\\\\\\n- boxed explainer overlays\\\\\\\\n- arrows/circles/diagram markers\\\\\\\\n- repeated labels and decision-target graphics\\\\\\\\n\\\\\\\\n## Viewer explanation rule\\\\\\\\n\\\\\\\\nWhen the viewer is not technical, do not use shorthand such as `open the service yourself` without explanation. Spell out the exact action:\\\\\\\\n\\\\\\\\n```text\\\\\\\\nClose the invite.\\\\\\\\nOpen your calendar or work app from the normal icon/bookmark.\\\\\\\\nIf the meeting is real, it will still be there.\\\\\\\\nTrust the path you chose, not the button that chose you.\\\\\\\\n```\\\\\\\\n\\\\\\\\nThis converts a security maxim into a concrete next action.\\\\\\\\n\\\\\\\\n## Caption synchronization rule\\\\\\\\n\\\\\\\\nFor final-near shorts where the user complains that subtitles do not match voiceover exactly, use actual word-boundary/forced-alignment timing rathe... [truncated]\\n 35|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_hmbB2edC0KVFpTXjG23UvQl5\\\", \\\"call_id\\\": \\\"call_hmbB2edC0KVFpTXjG23UvQl5\\\", \\\"response_item_id\\\": \\\"fc_0d23da4b61c18128016a14b77b8fac81919323dd989aba97b9\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"skill_view\\\", \\\"arguments\\\": \\\"{\\\\\\\"file_path\\\\\\\":\\\\\\\"references/word-boundary-caption-sync-and-solution-gate.md\\\\\\\",\\\\\\\"name\\\\\\\":\\\\\\\"social-media-shorts-automation\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 36|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"skill_view\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"name\\\\\\\": \\\\\\\"social-media-shorts-automation\\\\\\\", \\\\\\\"file\\\\\\\": \\\\\\\"references/word-boundary-caption-sync-and-solution-gate.md\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"# Word-boundary caption sync + explicit solution gate\\\\\\\\n\\\\\\\\nSession learning from a TrueTraceShorts Proof Short review where the user found captions irritating because they were not exactly simultaneous with the voiceover, and also noted that the video lacked a real solution/fazit.\\\\\\\\n\\\\\\\\n## Trigger\\\\\\\\n\\\\\\\\nUse this pattern when producing final-near Shorts with voiceover + subtitles, especially Proof Shorts / Digital Red Flags / mechanism reveal videos.\\\\\\\\n\\\\\\\\nUser-visible failure signals:\\\\\\\\n\\\\\\\\n- subtitles lag or lead the voiceover;\\\\\\\\n- captions are approximated by sentence duration and feel “off” while listening/reading;\\\\\\\\n- the video explains a mechanism but the viewer does not leave with a concrete action;\\\\\\\\n- the ending feels like an observation rather than a solution.\\\\\\\\n\\\\\\\\n## Caption timing rule\\\\\\\\n\\\\\\\\nDo not rely on evenly distributing words across sentence SRT durations when a better timing source is available. For EdgeTTS, request word-boundary metadata directly:\\\\\\\\n\\\\\\\\n```python\\\\\\\\ncommunicate = edge_tts.Communicate(\\\\\\\\n script,\\\\\\\\n voice,\\\\\\\\n rate='+0%',\\\\\\\\n boundary='WordBoundary',\\\\\\\\n)\\\\\\\\n\\\\\\\\nasync for chunk in communicate.stream():\\\\\\\\n if chunk['type'] == 'audio':\\\\\\\\n audio_bytes.extend(chunk['data'])\\\\\\\\n elif chunk['type'] == 'WordBoundary':\\\\\\\\n start = chunk['offset'] / 10_000_000\\\\\\\\n duration = chunk['duration'] / 10_000_000\\\\\\\\n word = chunk['text']\\\\\\\\n```\\\\\\\\n\\\\\\\\nBuild `WordCue(start, end, word, sentence)` from those offsets, then feed the existing static active-word caption renderer:\\\\\\\\n\\\\\\\\n```text\\\\\\\\nEdgeTTS WordBoundary cues\\\\\\\\n→ 3–7 word static phrase segments\\\\\\\\n→ active spoken word highlighted/pulsed\\\\\\\\n→ phrase changes only at phrase/sentence boundary\\\\\\\\n```\\\\\\\\n\\\\\\\\nThis gives the Hormozi/TikTok-style feel the user prefers: several wo... [truncated]\\n 37|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...Gz13... [truncated]\\n 38|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"--- calendar-invite-login-trap-30s_b1.mp4 ---\\\\\\\\n{\\\\\\\\n \\\\\\\\\\\\\\\"programs\\\\\\\\\\\\\\\": [\\\\\\\\n\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"streams\\\\\\\\\\\\\\\": [\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"video\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"width\\\\\\\\\\\\\\\": 1080,\\\\\\\\n \\\\\\\\\\\\\\\"height\\\\\\\\\\\\\\\": 1920,\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"24/1\\\\\\\\\\\\\\\"\\\\\\\\n },\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"audio\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0/0\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"format\\\\\\\\\\\\\\\": {\\\\\\\\n \\\\\\\\\\\\\\\"duration\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"41.875000\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"size\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"12551970\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n}\\\\\\\\n--- calendar-invite-login-trap-30s_b2.mp4 ---\\\\\\\\n{\\\\\\\\n \\\\\\\\\\\\\\\"programs\\\\\\\\\\\\\\\": [\\\\\\\\n\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"streams\\\\\\\\\\\\\\\": [\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"video\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"width\\\\\\\\\\\\\\\": 1080,\\\\\\\\n \\\\\\\\\\\\\\\"height\\\\\\\\\\\\\\\": 1920,\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"24/1\\\\\\\\\\\\\\\"\\\\\\\\n },\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"audio\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0/0\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"format\\\\\\\\\\\\\\\": {\\\\\\\\n \\\\\\\\\\\\\\\"duration\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"41.875000\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"size\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"12650916\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n}\\\\\\\\n\\\\\\\", \\\\\\\"tool_calls_made\\\\\\\": 2, \\\\\\\"duration_seconds\\\\\\\": 0.86}\\\", \\\"tool_call_id\\\": \\\"call_g9iQJLVvEyDlntwyaZTJfUSK\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 39|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_Ud7HXgSIYXot9OpJbVZNb1Zb\\\", \\\"call_id\\\": \\\"call_Ud7HXgSIYXot9OpJbVZNb1Zb\\\", \\\"response_item_id\\\": \\\"fc_069d8218d149e21f016a14b78a39408191b3efc24cb57486ed\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Variant B1 rendern: AI-visible invite only + Voiceover + Subtitles\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"render-b1\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Variant B2 rendern: AI-visible invite + minimaler Fokus-Layer + Subtitles\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"render-b2\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"A/B QA, Manifest, SHA256 und Lieferung mit Empfehlung\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"qa-deliver\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 40|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"gen\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"6 konkrete Visible-Invite-Cinematic KI-Keyframes erzeugen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"qaimg\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Keyframes prüfen: sichtbare Einladung, kein lesbarer Text/Logo/QR/People\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"render-b1\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Variant B1 rendern: AI-visible invite only + Voiceover + Subtitles\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"render-b2\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Variant B2 rendern: AI-visible invite + minimaler Fokus-Layer + Subtitles\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"qa-deliver\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"A/B QA, Manifest, SHA256 und Lieferung mit Empfehlung\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 5, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 0, \\\\\\\"completed\\\\\\\": 5, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_Ud7HXgSIYXot9OpJbVZNb1Zb\\\", \\\"timestamp\\\": \\\"2026-05-25T22:56:56.849504\\\"}\\n 41|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"Erledigt, Sir. Zwei sauber gelabelte A/B-Previews — **nicht gepostet**, nur Review.\\\\n\\\\n## A/B Preview\\\\n\\\\n**B1 — Clean AI Visible Invite**\\\\n- Nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\n- Premium/Cinematic wirkt ruhiger\\\\n- Mechanismus ist sichtbar, aber etwas stärker abhängig von Voiceover/Subtitles\\\\n\\\\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b1/calendar-invite-login-trap-30s_b1.mp4\\\\n\\\\n**B2 — Visible Invite + minimaler Fokus-Layer**\\\\n- Gleiche KI-Bilder + sehr dezente Fokus-Outlines\\\\n- Mechanismus ist schneller verständlich\\\\n- Minimal mehr „explainer“, aber noch nicht PowerPoint-Hölle. Ein kleiner Sieg der Zivilisation.\\\\n\\\\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b2/calendar-invite-login-trap-30s_b2.mp4\\\\n\\\\n## QA / Fakten\\\\n\\\\n- Format: 1080×1920, 24 fps\\\\n- Länge: 41.875 s\\\\n- Audio: enthalten\\\\n- Caption-Sync: Word-boundary/aktive Wort-Hervorhebung verwendet\\\\n- Ende/Fazit: konkret ergänzt:\\\\n - Close the invite\\\\n - Open calendar/work app via normal icon/bookmark\\\\n - If real, it will still be there\\\\n - Rule: trust the path you chose, not the button that chose you\\\\n- Kein Wan/I2V\\\\n- Kein Platform-Posting\\\\n- Keine Menschen/Hände/Gesichter/QRs/Logos in den geprüften Keyframes\\\\n\\\\n## Hashes\\\\n\\\\n- B1 SHA256: `04d26c5eff645ec852755f072695693c3f862232ad9e49b891656054933ab16e`\\\\n- B2 SHA256: `63d8cdd8347f27b86ed4e03046160a1d19e10491ad747b0c1df72fa0897480b7`\\\\n\\\\n## Meine Empfehlung\\\\n\\\\n**B2 gewinnt für diesen Proof Short.** \\\\nB1 sieht cleaner aus, aber B2 erklärt den Scam schneller — und genau das war der Kernkritikpunkt: Der Zuschauer muss nach dem Video denken: *„Ah, ich weiss jetzt konkret, was ich tun soll.“*\\\\n\\\\nWenn du freigeben willst:\\\\n\\\\n`APPROVED_PROOF_SHORT calendar-invite-login-trap-30s v1.3.0-b2-visible-invite-minimal-focus`\\\\n\\\\nOder Revision:\\\\n\\\\n`REVISE_PROOF_SHORT calendar-invite-login-trap-30s \\\\\\\"...\\\\\\\"`\\\"... [truncated]\\n 42|{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"[M G] B2 bringt keinen mehrwert mit den linien im bild… versuche die KI bilder mit mehr details zu füllen (sichtbare outlookeinladung mit text usw). Das Image-modell ist hier ziemlich zuverlässig; du musst nur den prompt sehr detailiert formulieren.\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 43|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...vNhx... [truncated]\\n 44|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"b3-gen\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Neue B3-KI-Keyframes mit detaillierter sichtbarer Outlook-artiger Einladung/Text erzeugen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qaimg\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Keyframes auf Lesbarkeit, Details und Artefakte prüfen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-render\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 ohne Linien/Fokus-Overlay rendern, nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qa-deliver\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 technisch prüfen, Hash/Manifest erstellen und liefern\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 3, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 0, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_hbGGgR5cZ1eqBq8rnzc6DJjt\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 45|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_SlmGghi25e8HolypQnaijNC0\\\", \\\"call_id\\\": \\\"call_SlmGghi25e8HolypQnaijNC0\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14ba672bf48191a33f460bea619941\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"image_generate\\\", \\\"arguments\\\": \\\"{\\\\\\\"aspect_ratio\\\\\\\":\\\\\\\"portrait\\\\\\\",\\\\\\\"prompt\\\\\\\":\\\\\\\"Premium vertical 9:16 cinematic social-video keyframe, realistic modern desk with laptop screen in sharp focus. The laptop shows a detailed Outlook-style calendar invitation email UI, but no Microsoft logo and no brand logo. Use clean readable English text on the invite only. Exact visible UI text: Title line: “Quarterly Review Meeting”; sender: “Alex Morgan”; time row: “Today, 10:00 AM”; location row: “Online meeting”; large blue button text: “Join meeting”; smaller link line below: “Sign in to continue”. Add attendee avatars, calendar icon, email header bars, sidebar folders, and realistic UI details. The visual problem must be instantly clear: a normal-looking meeting invite asks for login. No people, no hands, no faces, no QR codes, no watermarks. Premium navy/amber lighting, shallow depth of field, clean lower safe space for subtitles.\\\\\\\"}\\\"}}, {\\\"id\\\": \\\"call_12nNiCGlgalW4Bnhxtaupc8H\\\", \\\"call_id\\\": \\\"call_12nNiCGlgalW4Bnhxtaupc8H\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14ba672c248191b8bfa8efbe29e41b\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"image_generate\\\", \\\"arguments\\\": \\\"{\\\\\\\"aspect_ratio\\\\\\\":\\\\\\\"portrait\\\\\\\",\\\\\\\"prompt\\\\\\\":\\\\\\\"Premium vertical 9:16 cinematic close-up of a smartphone on a desk, screen sharp and readable. The phone displays a detailed Outlook-style calendar invite notification/email, no logo or brand mark. Exact visible text: “Quarterly Review Meeting”; “Today 10:00 AM”; “Organizer: Alex Morgan”; “Join meeting”; “Sign in to continue”; “This meeting starts soon”. Include realistic message rows, attendee circles, a blue join button, small calendar header, and subtle warning red/amber glo... [truncated]\\n 46|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_230948_055dd350.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cinematic social-video keyframe, realistic modern desk with laptop screen in sharp focus. The laptop shows a detailed Outlook-style calendar invitation email UI, but no Microsoft logo and no brand logo. Use clean readable English text on the invite only. Exact visible UI text: Title line: \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d; sender: \\\\\\\\u201cAlex Morgan\\\\\\\\u201d; time row: \\\\\\\\u201cToday, 10:00 AM\\\\\\\\u201d; location row: \\\\\\\\u201cOnline meeting\\\\\\\\u201d; large blue button text: \\\\\\\\u201cJoin meeting\\\\\\\\u201d; smaller link line below: \\\\\\\\u201cSign in to continue\\\\\\\\u201d. Add attendee avatars, calendar icon, email header bars, sidebar folders, and realistic UI details. The visual problem must be instantly clear: a normal-looking meeting invite asks for login. No people, no hands, no faces, no QR codes, no watermarks. Premium navy/amber lighting, shallow depth of field, clean lower safe space for subtitles.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_SlmGghi25e8HolypQnaijNC0\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 47|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231101_a6d41601.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cinematic close-up of a smartphone on a desk, screen sharp and readable. The phone displays a detailed Outlook-style calendar invite notification/email, no logo or brand mark. Exact visible text: \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d; \\\\\\\\u201cToday 10:00 AM\\\\\\\\u201d; \\\\\\\\u201cOrganizer: Alex Morgan\\\\\\\\u201d; \\\\\\\\u201cJoin meeting\\\\\\\\u201d; \\\\\\\\u201cSign in to continue\\\\\\\\u201d; \\\\\\\\u201cThis meeting starts soon\\\\\\\\u201d. Include realistic message rows, attendee circles, a blue join button, small calendar header, and subtle warning red/amber glow around the sign-in area. No people, no hands, no faces, no QR codes, no watermark. High-end social video style, clean bottom safe zone for subtitles.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_12nNiCGlgalW4Bnhxtaupc8H\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 48|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231303_bdbc188f.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cybersecurity keyframe, laptop screen close-up showing an Outlook-style meeting invitation with many believable UI details, no logo. The invite should be readable and concrete. Exact text on screen: \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d; \\\\\\\\u201cToday, 10:00 AM\\\\\\\\u201d; \\\\\\\\u201cJoin meeting\\\\\\\\u201d; \\\\\\\\u201cSign in to continue\\\\\\\\u201d; \\\\\\\\u201cmeeting-login.example-security.com\\\\\\\\u201d. Show a suspicious login prompt panel opening after the invite button, with fields labeled \\\\\\\\u201cEmail\\\\\\\\u201d and \\\\\\\\u201cPassword\\\\\\\\u201d and button \\\\\\\\u201cSign in\\\\\\\\u201d. The login panel should look slightly suspicious but still professional. No people, no hands, no faces, no QR/barcodes, no watermarks. Cinematic dark desk lighting, premium, subtitles-safe lower area.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_mK1TuNSCGEOWqa84p7x69dcL\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 49|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_cuotyaiqzG28GoTJ0qYBeVZy\\\", \\\"call_id\\\": \\\"call_cuotyaiqzG28GoTJ0qYBeVZy\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bb6ace188191a5e0e2dfaa5257e3\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"image_generate\\\", \\\"arguments\\\": \\\"{\\\\\\\"aspect_ratio\\\\\\\":\\\\\\\"portrait\\\\\\\",\\\\\\\"prompt\\\\\\\":\\\\\\\"Premium vertical 9:16 cinematic keyframe, realistic laptop on desk, screen sharp. Show a detailed email/calendar invite UI like Outlook, no brand logo. This is the safe action beat: the suspicious email invite is minimized in the background and a clean calendar app window is open in front. Exact readable text in the calendar window: “Calendar”; “Quarterly Review Meeting”; “Today 10:00 AM”; “Online meeting”; “Open from calendar”. The suspicious invite in background can show “Sign in to continue” dimmed. Add realistic panes, event list, calendar grid, attendee dots. No people, no hands, no faces, no QR codes, no watermarks. Premium blue secure lighting, lower safe space for subtitles.\\\\\\\"}\\\"}}, {\\\"id\\\": \\\"call_DpkRakvk9kOd1jsHr37gS48I\\\", \\\"call_id\\\": \\\"call_DpkRakvk9kOd1jsHr37gS48I\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bb6ace2c8191b52f136cf1f6dd6a\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"image_generate\\\", \\\"arguments\\\": \\\"{\\\\\\\"aspect_ratio\\\\\\\":\\\\\\\"portrait\\\\\\\",\\\\\\\"prompt\\\\\\\":\\\\\\\"Premium vertical 9:16 cybersecurity social-video keyframe. Detailed close-up of laptop and phone together on a modern desk. Laptop shows a browser/email invite UI, phone shows calendar app. Both screens have readable text, no logos. Exact visible text on laptop: “Quarterly Review Meeting”, “Join meeting”, “Sign in to continue”. Exact visible text on phone: “Calendar”, “Quarterly Review Meeting”, “Today 10:00 AM”. The contrast should explain the safe rule: do not trust the button; open your own calendar. Make it cinematic and realistic, with many small UI details but no extra gibberish text. No people, no hands, no faces, no QR/barcodes... [truncated]\\n 50|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231408_c83f61fc.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cinematic keyframe, realistic laptop on desk, screen sharp. Show a detailed email/calendar invite UI like Outlook, no brand logo. This is the safe action beat: the suspicious email invite is minimized in the background and a clean calendar app window is open in front. Exact readable text in the calendar window: \\\\\\\\u201cCalendar\\\\\\\\u201d; \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d; \\\\\\\\u201cToday 10:00 AM\\\\\\\\u201d; \\\\\\\\u201cOnline meeting\\\\\\\\u201d; \\\\\\\\u201cOpen from calendar\\\\\\\\u201d. The suspicious invite in background can show \\\\\\\\u201cSign in to continue\\\\\\\\u201d dimmed. Add realistic panes, event list, calendar grid, attendee dots. No people, no hands, no faces, no QR codes, no watermarks. Premium blue secure lighting, lower safe space for subtitles.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_cuotyaiqzG28GoTJ0qYBeVZy\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 51|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231615_47b2fd03.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 cybersecurity social-video keyframe. Detailed close-up of laptop and phone together on a modern desk. Laptop shows a browser/email invite UI, phone shows calendar app. Both screens have readable text, no logos. Exact visible text on laptop: \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d, \\\\\\\\u201cJoin meeting\\\\\\\\u201d, \\\\\\\\u201cSign in to continue\\\\\\\\u201d. Exact visible text on phone: \\\\\\\\u201cCalendar\\\\\\\\u201d, \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d, \\\\\\\\u201cToday 10:00 AM\\\\\\\\u201d. The contrast should explain the safe rule: do not trust the button; open your own calendar. Make it cinematic and realistic, with many small UI details but no extra gibberish text. No people, no hands, no faces, no QR/barcodes, no watermarks. Clean lower subtitle-safe area.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_DpkRakvk9kOd1jsHr37gS48I\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 52|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"image_generate\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"image\\\\\\\": \\\\\\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231708_8f122fd9.png\\\\\\\", \\\\\\\"model\\\\\\\": \\\\\\\"gpt-image-2-high\\\\\\\", \\\\\\\"prompt\\\\\\\": \\\\\\\"Premium vertical 9:16 final resolution keyframe for a cybersecurity short. A calm modern desk with laptop screen showing a detailed clean calendar event page, no logo. Exact readable text: \\\\\\\\u201cCalendar\\\\\\\\u201d; \\\\\\\\u201cQuarterly Review Meeting\\\\\\\\u201d; \\\\\\\\u201cToday, 10:00 AM\\\\\\\\u201d; \\\\\\\\u201cMeeting verified\\\\\\\\u201d; \\\\\\\\u201cOpen meeting from calendar\\\\\\\\u201d. In the corner/background, a dim email invite card says \\\\\\\\u201cSign in to continue\\\\\\\\u201d but it is visually ignored. Add realistic UI details: sidebar, calendar grid, attendee circles, blue meeting card, subtle secure glow. No people, no hands, no faces, no QR codes, no watermarks. Premium cinematic look, bottom safe area for subtitles.\\\\\\\", \\\\\\\"aspect_ratio\\\\\\\": \\\\\\\"portrait\\\\\\\", \\\\\\\"provider\\\\\\\": \\\\\\\"openai-codex\\\\\\\", \\\\\\\"size\\\\\\\": \\\\\\\"1024x1536\\\\\\\", \\\\\\\"quality\\\\\\\": \\\\\\\"high\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_AnhTg3kOT3AmNMVqfmfPNb65\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 53|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_CIoaxe7HM1PGpmVVXEBIK6ZK\\\", \\\"call_id\\\": \\\"call_CIoaxe7HM1PGpmVVXEBIK6ZK\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bc59b12481918d3fd7d23068e66b\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"execute_code\\\", \\\"arguments\\\": \\\"{\\\\\\\"code\\\\\\\":\\\\\\\"from pathlib import Path\\\\\\\\nimport shutil, json, hashlib\\\\\\\\nfrom PIL import Image, ImageDraw, ImageFont\\\\\\\\nsrcs=['/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_230948_055dd350.png','/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231101_a6d41601.png','/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231303_bdbc188f.png','/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231408_c83f61fc.png','/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231615_47b2fd03.png','/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260525_231708_8f122fd9.png']\\\\\\\\nbase=Path('/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1')\\\\\\\\nai=base/'ai_keyframes_b3_text_detail'; ai.mkdir(parents=True, exist_ok=True)\\\\\\\\nitems=[]\\\\\\\\nfor i,s in enumerate(srcs,1):\\\\\\\\n dst=ai/f'b3_text_detail_keyframe_{i:02d}_openai_codex_gpt-image-2-high.png'\\\\\\\\n shutil.copy2(s,dst)\\\\\\\\n im=Image.open(dst)\\\\\\\\n items.append({'index':i,'path':str(dst),'sha256':hashlib.sha256(dst.read_bytes()).hexdigest(),'resolution':f'{im.width}x{im.height}'})\\\\\\\\nsheet=Image.new('RGB',(1080,1120),(12,16,24)); d=ImageDraw.Draw(sheet)\\\\\\\\ntry: f=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',28)\\\\\\\\nexcept: f=None\\\\\\\\nfor idx,item in enumerate(items):\\\\\\\\n im=Image.open(item['path']).convert('RGB'); im.thumbnail((330,495))\\\\\\\\n cell=Image.new('RGB',(360,560),(12,16,24)); cd=ImageDraw.Draw(cell)\\\\\\\\n cell.paste(im,((360-im.width)//2,42)); cd.text((14,14),f\\\\\\\\\\\\\\\"B3 KF {item['index']:02d}\\\\\\\\\\\\\\\",font=f,fill=... [truncated]\\n 54|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3_text_detail_keyframes_contact_sheet_v1.jpg\\\\\\\\n\\\\\\\", \\\\\\\"tool_calls_made\\\\\\\": 0, \\\\\\\"duration_seconds\\\\\\\": 0.44}\\\", \\\"tool_call_id\\\": \\\"call_CIoaxe7HM1PGpmVVXEBIK6ZK\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 55|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_RaHqfybf4FFNqKJK17JBpZ39\\\", \\\"call_id\\\": \\\"call_RaHqfybf4FFNqKJK17JBpZ39\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bc6e4b948191beaf051ebbef35da\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"vision_analyze\\\", \\\"arguments\\\": \\\"{\\\\\\\"image_url\\\\\\\":\\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3_text_detail_keyframes_contact_sheet_v1.jpg\\\\\\\",\\\\\\\"question\\\\\\\":\\\\\\\"QA the six B3 keyframes. Need detailed visible Outlook-style calendar invite/login/safe-calendar screens with readable text. Identify which frames are usable, weak, or problematic. Check for gibberish text, logos, QR, people/hands/faces, and whether the core scam mechanism is clearer without added focus lines.\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 56|{\\\"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 six B3 keyframes. Need detailed visible Outlook-style calendar invite/login/safe-calendar screens with readable text. Identify which frames are usable, weak, or problematic. Check for gibberish text, logos, QR, people/hands/faces, and whether the core scam mechanism is clearer without added focus lines.\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 57|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Considering script modifications**\\\\n\\\\nI need to focus on modifying the script for b3. Currently, the script renders both b1 and b2 when set to main, and I’m wondering if we could create the b3 script by programmatically changing KEYFRAMES and the render functionality. The b3 variant should choose between modes based on whether it's b1 or not. It looks like I'd need to manage output paths carefully so they don't overwrite each other. I might script these changes to streamline the process.\\\\n**Modifying output paths**\\\\n\\\\nI'm considering creating a new version from the previous file and adjusting the output path to 'visible_invite_cinematic_ab_v1/b3_text_detail_v1'. However, the function seems to expect a specific OUT/ai_keyframes setup, and I need to set KEYFRAMES as absolute. I might use render_variant('b1', 'v1.4.0-b3-text-detail-ai-only') initially, but ideally, I want to call render_variant('b3') directly. There shouldn’t be any focus because it will only draw when variant == 'b2'. It's a bit messy, but it’s manageable.\\\\n**Fixing variant issues**\\\\n\\\\nIt looks like the current variant isn't matching b1, which is incorrect. I think it's important to patch the ternary function to handle b3 properly. I'll write out the necessary Python transformations and run them. I also wonder if lowering subtitles might help obscure the screen better since there's already a safe black veil at the bottom. After rendering, I definitely need to consider doing a contact sheet quality assurance check. I'll utilize the available tools for this.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Considering script modifications**\\\\n\\\\nI need to focus on modifying the script for b3. Currently, the script renders both b1 and b2 when set to main, and I’m wondering if we could create the b3 script by programmatically changing KEYFRAMES and the render functionality. The b3 variant should choose between modes based on whether it's b1 or not... [truncated]\\n 58|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"/tmp/autoshorts_proof_short_sprint31/render_b3_text_detail_v1.py\\\\\\\\n\\\\\\\", \\\\\\\"tool_calls_made\\\\\\\": 0, \\\\\\\"duration_seconds\\\\\\\": 0.24}\\\", \\\"tool_call_id\\\": \\\"call_upI8A67Oa7tsfEFAD5rE40nE\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 59|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_Sdbk719HxcxuqRqzYOCBLBEu\\\", \\\"call_id\\\": \\\"call_Sdbk719HxcxuqRqzYOCBLBEu\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bc8f8c7881919f83e7d3021610d7\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"Neue B3-KI-Keyframes mit detaillierter sichtbarer Outlook-artiger Einladung/Text erzeugen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"b3-gen\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Keyframes auf Lesbarkeit, Details und Artefakte prüfen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"b3-qaimg\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"B3 ohne Linien/Fokus-Overlay rendern, nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"b3-render\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"in_progress\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 60|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"b3-gen\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Neue B3-KI-Keyframes mit detaillierter sichtbarer Outlook-artiger Einladung/Text erzeugen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qaimg\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Keyframes auf Lesbarkeit, Details und Artefakte prüfen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-render\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 ohne Linien/Fokus-Overlay rendern, nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qa-deliver\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 technisch prüfen, Hash/Manifest erstellen und liefern\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 1, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 2, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_Sdbk719HxcxuqRqzYOCBLBEu\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 61|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_vsaqUPVd327o0PH1Zidgs25W\\\", \\\"call_id\\\": \\\"call_vsaqUPVd327o0PH1Zidgs25W\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bc93b66c8191b6b3d87e442019c4\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"terminal\\\", \\\"arguments\\\": \\\"{\\\\\\\"command\\\\\\\":\\\\\\\"python /tmp/autoshorts_proof_short_sprint31/render_b3_text_detail_v1.py\\\\\\\",\\\\\\\"timeout\\\\\\\":600}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 62|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\\\\\\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\\\\\\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\\\\\\\n libavutil 58. 29.100 / 58. 29.100\\\\\\\\n libavcodec 60. 31.102 / 60. 31.102\\\\\\\\n libavformat 60. 16.100 / 60. 16.100\\\\\\\\n libavdevice 60. 3.100 / 60. 3.100\\\\\\\\n libavfilter 9. 12.100 / 9. 12.100\\\\\\\\n libswscale 7. 5.100 / 7. 5.100\\\\\\\\n libswresample 4. 12.100 / 4. 12.100\\\\\\\\n libpostproc 57. 3.100 / 57. 3.100\\\\\\\\nInput #0, rawvideo, from 'fd:':\\\\\\\\n Duration: N/A, start... [truncated]\\n 63|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...Ow==\\\", \\\"id\\\": \\\"rs_0d4a723b463df28d016a14bd073a8c81918310ccdf95ed546f\\\", \\\"summary\\\": []}], \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_xe4FlyBB7fX3Pn20Zl3vs9FF\\\", \\\"call_id\\\": \\\"call_xe4FlyBB7fX3Pn20Zl3vs9FF\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bd0c68388191a3721e6373915a91\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"vision_analyze\\\", \\\"arguments\\\": \\\"{\\\\\\\"image_url\\\\\\\":\\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/b3_contact_sheet_v1.jpg\\\\\\\",\\\\\\\"question\\\\\\\":\\\\\\\"QA B3 rendered contact sheet. It should have no focus lines/boxes/arrows except subtitles, detailed AI-generated Outlook-style invite/login/calendar screens, readable captions, no people/hands/faces/QR/watermark. Assess if it is clearer than B2 and if any generated screen text looks harmful/gibberish.\\\\\\\"}\\\"}}, {\\\"id\\\": \\\"call_xBrG8SPBhm2l2zS2hgkMaWCk\\\", \\\"call... [truncated]\\n 64|{\\\"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 B3 rendered contact sheet. It should have no focus lines/boxes/arrows except subtitles, detailed AI-generated Outlook-style invite/login/calendar screens, readable captions, no people/hands/faces/QR/watermark. Assess if it is clearer than B2 and if any generated screen text looks harmful/gibberish.\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 65|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"{\\\\\\\\n \\\\\\\\\\\\\\\"programs\\\\\\\\\\\\\\\": [\\\\\\\\n\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"streams\\\\\\\\\\\\\\\": [\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"video\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"width\\\\\\\\\\\\\\\": 1080,\\\\\\\\n \\\\\\\\\\\\\\\"height\\\\\\\\\\\\\\\": 1920,\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"24/1\\\\\\\\\\\\\\\"\\\\\\\\n },\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"audio\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0/0\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n ],\\\\\\\\n \\\\\\\\\\\\\\\"format\\\\\\\\\\\\\\\": {\\\\\\\\n \\\\\\\\\\\\\\\"duration\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"41.875000\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"size\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"16404399\\\\\\\\\\\\\\\"\\\\\\\\n }\\\\\\\\n}\\\\\\\\n4fb33b4201e9253ff39072fb175b40415d96d40db71e8051cd741c0193a1a9b8 /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3.mp4\\\\\\\\n\\\\\\\", \\\\\\\"tool_calls_made\\\\\\\": 2, \\\\\\\"duration_seconds\\\\\\\": 0.84}\\\", \\\"tool_call_id\\\": \\\"call_xBrG8SPBhm2l2zS2hgkMaWCk\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 66|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_k6pXr37l7plvhTaGOmsxKjv3\\\", \\\"call_id\\\": \\\"call_k6pXr37l7plvhTaGOmsxKjv3\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bd10ab64819180885295f74ba632\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"B3 ohne Linien/Fokus-Overlay rendern, nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"b3-render\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"B3 technisch prüfen, Hash/Manifest erstellen und liefern\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"b3-qa-deliver\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 67|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"b3-gen\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Neue B3-KI-Keyframes mit detaillierter sichtbarer Outlook-artiger Einladung/Text erzeugen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qaimg\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Keyframes auf Lesbarkeit, Details und Artefakte prüfen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-render\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 ohne Linien/Fokus-Overlay rendern, nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"b3-qa-deliver\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 technisch prüfen, Hash/Manifest erstellen und liefern\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 4, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 0, \\\\\\\"completed\\\\\\\": 4, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_k6pXr37l7plvhTaGOmsxKjv3\\\", \\\"timestamp\\\": \\\"2026-05-25T23:20:29.663939\\\"}\\n 68|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"Verstanden — du hattest recht. Die Linien in B2 bringen kaum Mehrwert und beschädigen eher den Premium-Look.\\\\n\\\\nIch habe deshalb **B3** gebaut:\\\\n\\\\n## B3 — Detailed AI Invite Cut\\\\n\\\\n- Keine Linien\\\\n- Keine Pfeile\\\\n- Keine Boxen\\\\n- Keine Fokus-Markierungen\\\\n- Nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\n- Dafür deutlich detailliertere, sichtbare Outlook-artige Einladung:\\\\n - `Quarterly Review Meeting`\\\\n - `Join meeting`\\\\n - `Sign in to continue`\\\\n - Login-Panel\\\\n - Kalender-App als sichere Alternative\\\\n - Meeting verified / Open meeting from calendar\\\\n\\\\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3.mp4\\\\n\\\\n## QA\\\\n\\\\n- Länge: 41.875 s\\\\n- Format: 1080×1920, 24 fps\\\\n- Audio: enthalten\\\\n- Caption-Sync: aktive Wort-Hervorhebung bleibt erhalten\\\\n- NoOverlayPolicy: pass — nur Subtitles\\\\n- Keine Menschen/Hände/Gesichter/QR/Watermark sichtbar\\\\n- Die Bild-Story ist jetzt klarer als B1/B2, weil der Scam visuell wirklich im Screen stattfindet — nicht mehr nur durch Voiceover erklärt wird.\\\\n\\\\n## Hash\\\\n\\\\n`4fb33b4201e9253ff39072fb175b40415d96d40db71e8051cd741c0193a1a9b8`\\\\n\\\\nMeine Einschätzung: **B3 ist klar die bessere Richtung.** \\\\nDas wirkt mehr wie ein echter Social-Short über einen konkreten Scam und weniger wie ein hübsches Moodboard mit Sicherheits-Predigt.\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"stop\\\", \\\"codex_message_items\\\": [{\\\"type\\\": \\\"message\\\", \\\"role\\\": \\\"assistant\\\", \\\"status\\\": \\\"completed\\\", \\\"content\\\": [{\\\"type\\\": \\\"output_text\\\", \\\"text\\\": \\\"Verstanden — du hattest recht. Die Linien in B2 bringen kaum Mehrwert und beschädigen eher den Premium-Look.\\\\n\\\\nIch habe deshalb **B3** gebaut:\\\\n\\\\n## B3 — Detailed AI Invite Cut\\\\n\\\\n- Keine Linien\\\\n- Keine Pfeile\\\\n- Keine Boxen\\\\n- Keine Fokus-Markierungen\\\\n- Nur KI-Bilder + Voiceover + aktive Wort-Subtitles\\\\n- Dafür deutlich detailliertere, sichtbare Outlook-artige Einladung:\\\\n - `Quarterly Review Meeting`\\\\n ... [truncated]\\n 69|{\\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"[M G] Sehr gut. Das ist nun unser neuer standard! Nur eine korrektur: das videoformat stimmt nicht. Es ist quadratisch wenn ich es abspiele (in der höhe zusammengestaucht). Bitte noch korrigieren und dann weiterbauen.\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 70|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Addressing memory and format updates**\\\\n\\\\nI need to respond and possibly save a memory or skill update since there's a new standard for durability. I also have to fix the format and inspect the video for any display issues, possibly due to SAR on Telegram. I think I should use ffprobe for that. The skill is already loaded, but I need to scan it. It might be relevant for social media shorts automation, so I need to check the files too.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Addressing memory and format updates**\\\\n\\\\nI need to respond and possibly save a memory or skill update since there's a new standard for durability. I also have to fix the format and inspect the video for any display issues, possibly due to SAR on Telegram. I think I should use ffprobe for that. The skill is already loaded, but I need to scan it. It might be relevant for social media shorts automation, so I need to check the files too.\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...kx8F... [truncated]\\n 71|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"fmt-inspect\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 MP4-Format/Metadata prüfen: Pixelgröße, SAR/DAR, Rotation, Stream-Tags\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"in_progress\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"fmt-fix\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 als echtes 9:16 Telegram-kompatibles MP4 neu muxen/encoden\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"fmt-verify\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Korrigierte Datei mit ffprobe + Screenshot/Frame-QA verifizieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"standard-save\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Neuen visuellen Standard als Skill/Memory verankern\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"continue-build\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Nach Formatfix nächsten Build-Schritt vorbereiten/ausführen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"pending\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 5, \\\\\\\"pending\\\\\\\": 4, \\\\\\\"in_progress\\\\\\\": 1, \\\\\\\"completed\\\\\\\": 0, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_5sRFstFJfVxhEo5JOJh0mtp3\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 72|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_drhj5wnJKO23tDJu83QbuzHY\\\", \\\"call_id\\\": \\\"call_drhj5wnJKO23tDJu83QbuzHY\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bf18fd5c8191980038263a892e70\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"skill_view\\\", \\\"arguments\\\": \\\"{\\\\\\\"name\\\\\\\":\\\\\\\"social-media-shorts-automation\\\\\\\",\\\\\\\"file_path\\\\\\\":\\\\\\\"references/clean-ai-cut-vs-hybrid-mechanism.md\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 73|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"skill_view\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"name\\\\\\\": \\\\\\\"social-media-shorts-automation\\\\\\\", \\\\\\\"file\\\\\\\": \\\\\\\"references/clean-ai-cut-vs-hybrid-mechanism.md\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"# Clean AI Cut vs Hybrid Mechanism — session learning\\\\\\\\n\\\\\\\\nUse this when comparing a pure premium AI-image sequence against a proof short with deterministic mechanism/UI overlays.\\\\\\\\n\\\\\\\\n## Session-derived pattern\\\\\\\\n\\\\\\\\nFor a scam/cybersecurity proof short, a clean image sequence can look significantly more premium and native to TikTok/Reels, but it often weakens immediate mechanism clarity.\\\\\\\\n\\\\\\\\nObserved comparison:\\\\\\\\n\\\\\\\\n```text\\\\\\\\nClean AI Cut = premium mood, calmer, less explainer-board\\\\\\\\nHybrid = clearer mechanism, more immediately useful\\\\\\\\nBest likely final direction = mostly clean images + one minimal deterministic mechanism moment\\\\\\\\n```\\\\\\\\n\\\\\\\\nRecommended framing:\\\\\\\\n\\\\\\\\n```text\\\\\\\\n90% Clean AI Images\\\\\\\\n10% precise renderer-owned mechanism\\\\\\\\n```\\\\\\\\n\\\\\\\\nExamples of acceptable minimal mechanism moments:\\\\\\\\n\\\\\\\\n- one brief `suspicious link/domain` reveal\\\\\\\\n- one brief `close invite -> open trusted app/bookmark` action moment\\\\\\\\n- one simple before/after state, not a full checklist board\\\\\\\\n\\\\\\\\nAvoid returning to:\\\\\\\\n\\\\\\\\n- checklist-heavy UI cards\\\\\\\\n- boxed explainer overlays\\\\\\\\n- arrows/circles/diagram markers\\\\\\\\n- repeated labels and decision-target graphics\\\\\\\\n\\\\\\\\n## Viewer explanation rule\\\\\\\\n\\\\\\\\nWhen the viewer is not technical, do not use shorthand such as `open the service yourself` without explanation. Spell out the exact action:\\\\\\\\n\\\\\\\\n```text\\\\\\\\nClose the invite.\\\\\\\\nOpen your calendar or work app from the normal icon/bookmark.\\\\\\\\nIf the meeting is real, it will still be there.\\\\\\\\nTrust the path you chose, not the button that chose you.\\\\\\\\n```\\\\\\\\n\\\\\\\\nThis converts a security maxim into a concrete next action.\\\\\\\\n\\\\\\\\n## Caption synchronization rule\\\\\\\\n\\\\\\\\nFor final-near shorts where the user complains that subtitles do not match voiceover exactly, use actual word-boundary/forced-alignment timing rathe... [truncated]\\n 74|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_LBz3w9D1U4NCjVkyohu08gsL\\\", \\\"call_id\\\": \\\"call_LBz3w9D1U4NCjVkyohu08gsL\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14bf1cf11c8191a869a1546ba3e3e0\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"execute_code\\\", \\\"arguments\\\": \\\"{\\\\\\\"code\\\\\\\":\\\\\\\"from hermes_tools import terminal\\\\\\\\np='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3.mp4'\\\\\\\\nfor cmd in [\\\\\\\\n f\\\\\\\\\\\\\\\"ffprobe -v error -show_streams -show_format -of json {p!r}\\\\\\\\\\\\\\\",\\\\\\\\n f\\\\\\\\\\\\\\\"mediainfo {p!r} || true\\\\\\\\\\\\\\\"\\\\\\\\n]:\\\\\\\\n print('CMD',cmd)\\\\\\\\n print(terminal(cmd, timeout=60)['output'][:12000])\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 75|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"CMD ffprobe -v error -show_streams -show_format -of json '/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3.mp4'\\\\\\\\n{\\\\\\\\n \\\\\\\\\\\\\\\"streams\\\\\\\\\\\\\\\": [\\\\\\\\n {\\\\\\\\n \\\\\\\\\\\\\\\"index\\\\\\\\\\\\\\\": 0,\\\\\\\\n \\\\\\\\\\\\\\\"codec_name\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"h264\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"codec_long_name\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"profile\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"High\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"codec_type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"video\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"codec_tag_string\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"avc1\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"codec_tag\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0x31637661\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"width\\\\\\\\\\\\\\\": 1080,\\\\\\\\n \\\\\\\\\\\\\\\"height\\\\\\\\\\\\\\\": 1920,\\\\\\\\n \\\\\\\\\\\\\\\"coded_width\\\\\\\\\\\\\\\": 1080,\\\\\\\\n \\\\\\\\\\\\\\\"coded_height\\\\\\\\\\\\\\\": 1920,\\\\\\\\n \\\\\\\\\\\\\\\"closed_captions\\\\\\\\\\\\\\\": 0,\\\\\\\\n \\\\\\\\\\\\\\\"film_grain\\\\\\\\\\\\\\\": 0,\\\\\\\\n \\\\\\\\\\\\\\\"has_b_frames\\\\\\\\\\\\\\\": 2,\\\\\\\\n \\\\\\\\\\\\\\\"pix_fmt\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"yuv420p\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"level\\\\\\\\\\\\\\\": 40,\\\\\\\\n \\\\\\\\\\\\\\\"chroma_location\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"left\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"field_order\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"progressive\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"refs\\\\\\\\\\\\\\\": 1,\\\\\\\\n \\\\\\\\\\\\\\\"is_avc\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"true\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"nal_length_size\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"4\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0x1\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"r_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"24/1\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"avg_frame_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"24/1\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"time_base\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"1/12288\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"start_pts\\\\\\\\\\\\\\\": 0,\\\\\\\\n \\\\\\\\\\\\\\\"start_time\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"0.000000\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"duration_ts\\\\\\\\\\\\\\\": 514560,\\\\\\\\n \\\\\\\\\\\\\\\"duration\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"41.875000\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"bit_rate\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"3066439\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"bits_per_raw_sample\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"8\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"nb_frames\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"1005\\\\\\\\\\\\\\\",\\\\\\\\n \\\\\\\\\\\\\\\"extradata_size\\\\\\\\\\\\\\\": 47,\\\\\\\\n \\\\\\\\\\\\\\\"disposition\\\\\\\\\\\\\\\": {\\\\\\\\n \\\\\\\\\\\\\\\"default\\\\\\\\\\\\\\\": 1,\\\\\\\\n \\\\\\\\\\\\\\\"dub\\\\\\\\\\\\\\\": 0,\\\\\\\\n \\\\\\\\\\\\\\\"original\\\\\\\\\\\\\\\":... [truncated]\\n 76|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Considering video format compatibility**\\\\n\\\\nI’m thinking about creating videos in both 720x1280 with an explicit display aspect ratio and a fixed 1080 version for compatibility. The error \\\\\\\"videoformat stimmt nicht\\\\\\\" likely indicates an issue with display metadata, which I need to correct using ffmpeg and verify with ffprobe. Once that's done, I should consider developing a standard template for future videos, especially since the user mentioned a “new standard.” I'll need to update my memory with this new information as well.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Considering video format compatibility**\\\\n\\\\nI’m thinking about creating videos in both 720x1280 with an explicit display aspect ratio and a fixed 1080 version for compatibility. The error \\\\\\\"videoformat stimmt nicht\\\\\\\" likely indicates an issue with display metadata, which I need to correct using ffmpeg and verify with ffprobe. Once that's done, I should consider developing a standard template for future videos, especially since the user mentioned a “new standard.” I'll need to update my memory with this new information as well.\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...4io8... [truncated]\\n 77|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"ffmpeg version 6.1.1-3ubuntu5 Copyright (c) 2000-2023 the FFmpeg developers\\\\\\\\n built with gcc 13 (Ubuntu 13.2.0-23ubuntu3)\\\\\\\\n configuration: --prefix=/usr --extra-version=3ubuntu5 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --disable-omx --enable-gnutls --enable-libaom --enable-libass --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libglslang --enable-libgme --enable-libgsm --enable-libharfbuzz --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-openal --enable-opencl --enable-opengl --disable-sndio --enable-libvpl --disable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-ladspa --enable-libbluray --enable-libjack --enable-libpulse --enable-librabbitmq --enable-librist --enable-libsrt --enable-libssh --enable-libsvtav1 --enable-libx264 --enable-libzmq --enable-libzvbi --enable-lv2 --enable-sdl2 --enable-libplacebo --enable-librav1e --enable-pocketsphinx --enable-librsvg --enable-libjxl --enable-shared\\\\\\\\n libavutil 58. 29.100 / 58. 29.100\\\\\\\\n libavcodec 60. 31.102 / 60. 31.102\\\\\\\\n libavformat 60. 16.100 / 60. 16.100\\\\\\\\n libavdevice 60. 3.100 / 60. 3.100\\\\\\\\n libavfilter 9. 12.100 / 9. 12.100\\\\\\\\n libswscale 7. 5.100 / 7. 5.100\\\\\\\\n libswresample 4. 12.100 / 4. 12.100\\\\\\\\n libpostproc 57. 3.100 / 57. 3.100\\\\\\\\nInput #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/autoshorts... [truncated]\\n 78|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Considering video formats**\\\\n\\\\nI'm analyzing the audio and video lengths—41.37 seconds for audio versus 41.875 for video. Maybe I don't need to shorten it, just trim the 0.5 seconds of silence at the end. Verifying the visuals is important, so I'll likely extract frames to check them. I’m thinking about creating a 720p version since Telegram uses a square format, but I should stick to delivering the fixed 1080p one. Still, it might be worth producing a 720 version, considering user experience and video formats.\\\\n**Updating user memory**\\\\n\\\\nI'm considering how to use memory for the user effectively, especially regarding content strategy. The user's profile already has AutoShorts preferences, which means I might need to replace the existing line since memory is almost full. I want to update the AutoShorts/TrueTraceShorts description to include new details about B3 without overloading it. It seems best to replace the old text about manual approval and posting with a more compact version. After that, I’ll patch the skill!\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Considering video formats**\\\\n\\\\nI'm analyzing the audio and video lengths—41.37 seconds for audio versus 41.875 for video. Maybe I don't need to shorten it, just trim the 0.5 seconds of silence at the end. Verifying the visuals is important, so I'll likely extract frames to check them. I’m thinking about creating a 720p version since Telegram uses a square format, but I should stick to delivering the fixed 1080p one. Still, it might be worth producing a 720 version, considering user experience and video formats.\\\\n**Updating user memory**\\\\n\\\\nI'm considering how to use memory for the user effectively, especially regarding content strategy. The user's profile already has AutoShorts preferences, which means I might need to replace the existing line since memory is almost full. I want to update the AutoShorts/TrueTraceShorts description to include ne... [truncated]\\n 79|{\\\"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: Re-check the rendered B3 contact sheet for shape/format: it should be vertical 9:16, not square or horizontally squeezed, with readable subtitles and detailed screen visuals. Note any obvious issue.\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 80|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Clarifying frame extraction**\\\\n\\\\nI'm thinking about how the vision shows a contact sheet in a grid format, which seems to be square. It's not a fixed video, so I need to verify the actual dimensions of the first frame. I might use ffmpeg to extract this frame and verify its details. The contact sheet being square doesn't help, and I should focus on extracting a potentially taller image. I should use the terminal to execute the frame extraction.\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Clarifying frame extraction**\\\\n\\\\nI'm thinking about how the vision shows a contact sheet in a grid format, which seems to be square. It's not a fixed video, so I need to verify the actual dimensions of the first frame. I might use ffmpeg to extract this frame and verify its details. The contact sheet being square doesn't help, and I should focus on extracting a potentially taller image. I should use the terminal to execute the frame extraction.\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...9fRG... [truncated]\\n 81|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"terminal\\\", \\\"content\\\": \\\"{\\\\\\\"output\\\\\\\": \\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/b3_9x16_fixed_frame_5s.jpg JPEG 1080x1920 1080x1920+0+0 8-bit sRGB 81870B 0.000u 0:00.000\\\\\\\\n\\\\\\\\n/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/b3_9x16_fixed_frame_5s.jpg\\\\\\\", \\\\\\\"exit_code\\\\\\\": 0, \\\\\\\"error\\\\\\\": null}\\\", \\\"tool_call_id\\\": \\\"call_I3I5JZZyyLkza806A9LE1pJX\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 82|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_sMSCKZ2443dwdcUhmfqdsMt2\\\", \\\"call_id\\\": \\\"call_sMSCKZ2443dwdcUhmfqdsMt2\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14c0d46d508191a72f42725376bd4c\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"vision_analyze\\\", \\\"arguments\\\": \\\"{\\\\\\\"image_url\\\\\\\":\\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/b3_9x16_fixed_frame_5s.jpg\\\\\\\",\\\\\\\"question\\\\\\\":\\\\\\\"Verify this extracted frame is vertical 9:16 and not squeezed or square. Also check the subtitle is readable.\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 83|{\\\"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: Verify this extracted frame is vertical 9:16 and not squeezed or square. Also check the subtitle is readable.\\\"}, {\\\"type\\\": \\\"image_url\\\", \\\"image_url\\\": {\\\"url\\\": \\\"data:image/jpeg;base64,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... [truncated]\\n 84|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": \\\"**Considering patch updates**\\\\n\\\\nI need to implement a patch for my skills. Maybe I should create a 720 fallback? Right now, the extracted image is 1080x1920, which our interface shows as 1080x1920 too. That seems fine. For the patch skill text, I will replace the previous B1/B2 outcome section with the new one. I'll just need to use the skill_manage patch. It sounds straightforward!\\\", \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"reasoning_content\\\": \\\"**Considering patch updates**\\\\n\\\\nI need to implement a patch for my skills. Maybe I should create a 720 fallback? Right now, the extracted image is 1080x1920, which our interface shows as 1080x1920 too. That seems fine. For the patch skill text, I will replace the previous B1/B2 outcome section with the new one. I'll just need to use the skill_manage patch. It sounds straightforward!\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...Fn5e... [truncated]\\n 85|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"skill_manage\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"message\\\\\\\": \\\\\\\"Patched references/clean-ai-cut-vs-hybrid-mechanism.md in skill 'social-media-shorts-automation' (1 replacement).\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_3nCRKscWwoEfFlSZg67uot39\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 86|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_rxLH4lUjEpYeagXQpuZ2AO9p\\\", \\\"call_id\\\": \\\"call_rxLH4lUjEpYeagXQpuZ2AO9p\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14c0e7b53881918c69902475003c92\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"memory\\\", \\\"arguments\\\": \\\"{\\\\\\\"action\\\\\\\":\\\\\\\"replace\\\\\\\",\\\\\\\"target\\\\\\\":\\\\\\\"user\\\\\\\",\\\\\\\"old_text\\\\\\\":\\\\\\\"AutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN.\\\\\\\",\\\\\\\"content\\\\\\\":\\\\\\\"AutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN. New visual standard for proof shorts: detailed AI-generated real UI/screen images with readable concrete text (e.g. Outlook-style invite/login/calendar), no extra focus lines/arrows/boxes; active-word subtitles; export MP4 with explicit 9:16 SAR/DAR.\\\\\\\"}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 87|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"memory\\\", \\\"content\\\": \\\"{\\\\\\\"success\\\\\\\": true, \\\\\\\"target\\\\\\\": \\\\\\\"user\\\\\\\", \\\\\\\"entries\\\\\\\": [\\\\\\\"**Notes:** Mein Herr. Loyaler Partner.\\\\\\\", \\\\\\\"**Name:** Sir\\\\\\\", \\\\\\\"**Voice preference:** de-DE-KillianNeural (German male voice, natural/clear) — default for all TTS output\\\\\\\", \\\\\\\"**Language:** Deutsch mit englischen Fachbegriffen\\\\\\\", \\\\\\\"Kommunikationspräferenzen: **Sprachnachricht → Antwort per Sprachnachricht**\\\\\\\", \\\\\\\"Kommunikationspräferenzen: **Textnachricht → Antwort per Text**\\\\\\\", \\\\\\\"Health 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.\\\\\\\", \\\\\\\"Telegram 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.\\\\\\\", \\\\\\\"Finance/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.\\\\\\\", \\\\\\\"AutoShorts/TrueTraceShorts: manual approval/posting; previews DE, finals EN. New visual standard for proof shorts: detailed AI-generated real UI/screen images with readable concrete text (e.g. Outlook-style invite/login/calendar), no extra focus lines/arrows/boxes; active-word subtitles; export MP4 with explicit 9:16 SAR/DAR.\\\\\\\"], \\\\\\\"usage\\\\\\\": \\\\\\\"98% — 1,349/1,375 chars\\\\\\\", \\\\\\\"entry_count\\\\\\\": 10, \\\\\\\"message\\\\\\\": \\\\\\\"Entry replaced.\\\\\\\"}\\\", \\\"tool_call_id\\\": \\\"call_rxLH4lUjEpYeagXQpuZ2AO9p\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 88|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"codex_reasoning_items\\\": [{\\\"type\\\": \\\"reasoning\\\", \\\"encrypted_content\\\": \\\"gAAAAA...N2Np... [truncated]\\n 89|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"execute_code\\\", \\\"content\\\": \\\"{\\\\\\\"status\\\\\\\": \\\\\\\"success\\\\\\\", \\\\\\\"output\\\\\\\": \\\\\\\"/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/manifest_b3_9x16_fixed_v1.json\\\\\\\\ndc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f\\\\\\\\n\\\\\\\", \\\\\\\"tool_calls_made\\\\\\\": 0, \\\\\\\"duration_seconds\\\\\\\": 0.26}\\\", \\\"tool_call_id\\\": \\\"call_kQ0iPeqP08emM2wM9Ol3flRn\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 90|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"tool_calls\\\", \\\"tool_calls\\\": [{\\\"id\\\": \\\"call_qEIhz8Pci1gsfAR5txmN0Aoj\\\", \\\"call_id\\\": \\\"call_qEIhz8Pci1gsfAR5txmN0Aoj\\\", \\\"response_item_id\\\": \\\"fc_0d4a723b463df28d016a14c0fa8c84819190eed59aec66c37d\\\", \\\"type\\\": \\\"function\\\", \\\"function\\\": {\\\"name\\\": \\\"todo\\\", \\\"arguments\\\": \\\"{\\\\\\\"merge\\\\\\\":true,\\\\\\\"todos\\\\\\\":[{\\\\\\\"content\\\\\\\":\\\\\\\"B3 MP4-Format/Metadata prüfen: Pixelgröße, SAR/DAR, Rotation, Stream-Tags\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"fmt-inspect\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"B3 als echtes 9:16 Telegram-kompatibles MP4 neu muxen/encoden\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"fmt-fix\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Korrigierte Datei mit ffprobe + Screenshot/Frame-QA verifizieren\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"fmt-verify\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Neuen visuellen Standard als Skill/Memory verankern\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"standard-save\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"},{\\\\\\\"content\\\\\\\":\\\\\\\"Nach Formatfix nächsten Build-Schritt vorbereiten/ausführen\\\\\\\",\\\\\\\"id\\\\\\\":\\\\\\\"continue-build\\\\\\\",\\\\\\\"status\\\\\\\":\\\\\\\"completed\\\\\\\"}]}\\\"}}], \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 91|{\\\"role\\\": \\\"tool\\\", \\\"name\\\": \\\"todo\\\", \\\"content\\\": \\\"{\\\\\\\"todos\\\\\\\": [{\\\\\\\"id\\\\\\\": \\\\\\\"fmt-inspect\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 MP4-Format/Metadata prüfen: Pixelgröße, SAR/DAR, Rotation, Stream-Tags\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"fmt-fix\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"B3 als echtes 9:16 Telegram-kompatibles MP4 neu muxen/encoden\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"fmt-verify\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Korrigierte Datei mit ffprobe + Screenshot/Frame-QA verifizieren\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"standard-save\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Neuen visuellen Standard als Skill/Memory verankern\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}, {\\\\\\\"id\\\\\\\": \\\\\\\"continue-build\\\\\\\", \\\\\\\"content\\\\\\\": \\\\\\\"Nach Formatfix nächsten Build-Schritt vorbereiten/ausführen\\\\\\\", \\\\\\\"status\\\\\\\": \\\\\\\"completed\\\\\\\"}], \\\\\\\"summary\\\\\\\": {\\\\\\\"total\\\\\\\": 5, \\\\\\\"pending\\\\\\\": 0, \\\\\\\"in_progress\\\\\\\": 0, \\\\\\\"completed\\\\\\\": 5, \\\\\\\"cancelled\\\\\\\": 0}}\\\", \\\"tool_call_id\\\": \\\"call_qEIhz8Pci1gsfAR5txmN0Aoj\\\", \\\"timestamp\\\": \\\"2026-05-25T23:37:10.296638\\\"}\\n 92|{\\\"role\\\": \\\"assistant\\\", \\\"content\\\": \\\"Korrigiert, Sir.\\\\n\\\\nDer Fehler war nicht der Inhalt, sondern die **Playback-/Aspect-Metadata**. Ich habe die Datei neu encodiert mit explizitem:\\\\n\\\\n- `1080x1920`\\\\n- `SAR 1:1`\\\\n- `DAR 9:16`\\\\n- `yuv420p`\\\\n- H.264 High Level 4.2\\\\n- `+faststart` für Mobile/Telegram-Playback\\\\n\\\\n## Korrigierte Version\\\\n\\\\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\\\\n\\\\n## Verifiziert\\\\n\\\\n- Auflösung: `1080x1920`\\\\n- Display Aspect Ratio: `9:16`\\\\n- Sample Aspect Ratio: `1:1`\\\\n- Länge: `41.386 s`\\\\n- SHA256: `dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f`\\\\n\\\\n## Neuer Standard gespeichert\\\\n\\\\nIch habe den neuen Standard dauerhaft verankert:\\\\n\\\\n- **Detaillierte KI-Screenbilder mit lesbarem konkretem UI-Text**\\\\n- **Keine Linien/Pfeile/Boxen/Fokus-Markierungen**\\\\n- **Subtitles mit aktivem Wort-Highlight**\\\\n- **Explizite 9:16 Export-Metadata vor Lieferung prüfen**\\\\n\\\\nDas ist jetzt unser Default für diese Proof-Short-Richtung.\\\", \\\"reasoning\\\": null, \\\"finish_reason\\\": \\\"stop\\\", \\\"codex_message_items\\\": [{\\\"type\\\": \\\"message\\\", \\\"role\\\": \\\"assistant\\\", \\\"status\\\": \\\"completed\\\", \\\"content\\\": [{\\\"type\\\": \\\"output_text\\\", \\\"text\\\": \\\"Korrigiert, Sir.\\\\n\\\\nDer Fehler war nicht der Inhalt, sondern die **Playback-/Aspect-Metadata**. Ich habe die Datei neu encodiert mit explizitem:\\\\n\\\\n- `1080x1920`\\\\n- `SAR 1:1`\\\\n- `DAR 9:16`\\\\n- `yuv420p`\\\\n- H.264 High Level 4.2\\\\n- `+faststart` für Mobile/Telegram-Playback\\\\n\\\\n## Korrigierte Version\\\\n\\\\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\\\\n\\\\n## Verifiziert\\\\n\\\\n- Auflösung: `1080x1920`\\\\n- Display Aspect Ratio: `9:16`\\\\n- Sample Aspect Ratio: `1:1`\\\\n- Länge: `41.386 s`\\\\n- SHA256: `dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f`\\\\n\\\\n## Neuer Standard gespeichert\\\\n\\\\nIch habe den neuen Standard dauerhaft verankert:\\\\n\\\\... [truncated]\\n 93|\", \"total_lines\": 92, \"file_size\": 2181462, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"output\": \"{\\n \\\"programs\\\": [\\n\\n ],\\n \\\"streams\\\": [\\n {\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"pix_fmt\\\": \\\"yuv420p\\\",\\n \\\"r_frame_rate\\\": \\\"24/1\\\"\\n }\\n ],\\n \\\"format\\\": {\\n \\\"duration\\\": \\\"41.386009\\\"\\n }\\n}\\ndc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\", \"exit_code\": 0, \"error\": null}",
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"content": "Verstanden — genau dieses **B3 “Detailed AI Invite Cut”** mit den hochwertigen, detaillierten KI-Screenbildern.\n\nHier nochmals die **korrigierte 9:16-Version**:\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.386 s\nSHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f\n```",
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"text": "Verstanden — genau dieses **B3 “Detailed AI Invite Cut”** mit den hochwertigen, detaillierten KI-Screenbildern.\n\nHier nochmals die **korrigierte 9:16-Version**:\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.386 s\nSHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f\n```"
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"content": "[M G] Ja das ist das richtige video, jedoch sind unten und oben schwarze balken. Diese mössen entfernt werden und das bild auf die höhe des bildschirms gestreckt werden."
},
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"reasoning": "**Addressing black bars in images**\n\nI need to act on fixing the content with black bars, likely because the AI images (1024x1536, 2:3) are letterboxed within a 9:16 frame. The user wants to stretch it to height, which means I could crop or scale non-uniformly. I'll check the black bars by inspecting the frame. Using ffmpeg cropdetect will help identify them, and I’ll think about how to preserve subtitles while making adjustments, especially if they are affected by the cropping.",
"reasoning_content": "**Addressing black bars in images**\n\nI need to act on fixing the content with black bars, likely because the AI images (1024x1536, 2:3) are letterboxed within a 9:16 frame. The user wants to stretch it to height, which means I could crop or scale non-uniformly. I'll check the black bars by inspecting the frame. Using ffmpeg cropdetect will help identify them, and I’ll think about how to preserve subtitles while making adjustments, especially if they are affected by the cropping.",
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"text": "**Addressing black bars in images**\n\nI need to act on fixing the content with black bars, likely because the AI images (1024x1536, 2:3) are letterboxed within a 9:16 frame. The user wants to stretch it to height, which means I could crop or scale non-uniformly. I'll check the black bars by inspecting the frame. Using ffmpeg cropdetect will help identify them, and I’ll think about how to preserve subtitles while making adjustments, especially if they are affected by the cropping."
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/mobile-vertical-aspect-qa.md\", \"content\": \"# Mobile vertical aspect QA and repair\\n\\nUse this when a short/reel/Telegram preview appears with black bars at top/bottom, wrong aspect ratio, or the visible image looks vertically compressed even though the source clip was intended as 9:16.\\n\\n## Durable lesson\\n\\nDo not rely on nominal source dimensions alone. A clip can be near-vertical, but still display badly on mobile platforms if the export lacks explicit square-pixel and display-aspect metadata, or if an assembly step has letterboxed instead of producing a true vertical canvas.\\n\\n## Required checks\\n\\nProbe every final-near MP4 before delivery:\\n\\n```bash\\nffprobe -v error -select_streams v:0 \\\\\\n -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio \\\\\\n -show_entries format=duration \\\\\\n -of json path/to/video.mp4\\n```\\n\\nExpected mobile-safe target for Shorts/Reels/TikTok review exports:\\n\\n- `width=1080`\\n- `height=1920`\\n- `sample_aspect_ratio=1:1`\\n- `display_aspect_ratio=9:16`\\n- no visible black bars in extracted QA frames\\n\\nExtract a frame for visual QA:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i path/to/video.mp4 \\\\\\n -vf \\\"select=eq(n\\\\,10)\\\" -frames:v 1 /tmp/frame.png\\n```\\n\\nInspect the frame for top/bottom black bars and obvious squeezing before sending to the user.\\n\\n## Repair command\\n\\nFor source clips that should fill a vertical mobile canvas, create a post-render normalized export:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i input.mp4 \\\\\\n -vf \\\"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -preset veryfast -crf 18 -movflags +faststart -an \\\\\\n output_1080x1920_sar1.mp4\\n```\\n\\nUse `increase,crop` for full-bleed visual proofs where black bars are unacceptable. Use `decrease,pad` only when preserving every pixel is more important than full-screen mobile presentation, and label it as letterboxed.\\n\\n## Workflow rule\\n\\nIf the user reports black bars or squeezed height, treat it as a blocking export QA issue:\\n\\n1. Probe the delivered MP4 metadata.\\n2. Extract at least one representative frame.\\n3. Re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n4. Probe the repaired file and report the verified SAR/DAR.\\n5. Deliver the repaired media, not more explanations.\\n\\n## Pitfall\\n\\nDo not say “the source is 9:16 enough” based on dimensions like `416x736` alone. Platform players can still expose metadata or canvas mistakes. Verify the final delivered artifact.\", \"file_type\": \".md\"}",
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"arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom PIL import Image\\nimport subprocess, json, numpy as np, os\\np='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'\\noutdir='/tmp/b3_bar_inspect'; Path(outdir).mkdir(exist_ok=True)\\n# extract frames at times\\nfor t in [0.5,5,12,22,35,40]:\\n out=f'{outdir}/frame_{str(t).replace(\\\".\\\",\\\"_\\\")}.jpg'\\n subprocess.run(['ffmpeg','-y','-hide_banner','-loglevel','error','-ss',str(t),'-i',p,'-frames:v','1',out],check=True)\\n im=Image.open(out).convert('RGB'); arr=np.array(im); lum=arr.mean(axis=2)\\n # first/last rows where mean > 12 across width\\n row=lum.mean(axis=1)\\n non=np.where(row>12)[0]\\n bbox=(int(non[0]), int(non[-1])) if len(non) else None\\n print(out, im.size, 'top/bottom nonblack rows', bbox, 'top120',round(float(row[:120].mean()),1),'bottom120',round(float(row[-120:].mean()),1),'minrow first20',row[:20].round(1).tolist())\\nPY\",\"timeout\":120}"
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},
{
"role": "tool",
"content": "{\"output\": \"/tmp/b3_bar_inspect/frame_0_5.jpg (1080, 1920) top/bottom nonblack rows (105, 1919) top120 10.4 bottom120 15.6 minrow first20 [9.3, 9.3, 9.3, 9.3, 9.3, 9.3, 9.3, 9.3, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4]\\n/tmp/b3_bar_inspect/frame_5.jpg (1080, 1920) top/bottom nonblack rows (88, 1919) top120 11.3 bottom120 17.9 minrow first20 [9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.5, 9.7, 9.7, 9.8, 9.8]\\n/tmp/b3_bar_inspect/frame_12.jpg (1080, 1920) top/bottom nonblack rows (0, 1919) top120 58.9 bottom120 13.2 minrow first20 [51.6, 51.7, 51.8, 51.9, 52.1, 52.3, 52.4, 52.4, 53.4, 53.5, 53.6, 53.8, 54.0, 54.1, 54.3, 54.3, 55.3, 55.3, 55.4, 55.5]\\n/tmp/b3_bar_inspect/frame_22.jpg (1080, 1920) top/bottom nonblack rows (0, 1919) top120 23.9 bottom120 22.6 minrow first20 [23.4, 23.3, 23.3, 23.2, 23.0, 22.6, 22.2, 21.8, 21.5, 21.1, 20.5, 20.1, 19.7, 19.4, 19.2, 19.1, 19.5, 19.5, 19.5, 19.4]\\n/tmp/b3_bar_inspect/frame_35.jpg (1080, 1920) top/bottom nonblack rows (0, 1919) top120 48.7 bottom120 32.2 minrow first20 [47.0, 47.0, 46.9, 46.9, 46.9, 46.8, 46.8, 46.8, 47.0, 47.0, 47.0, 46.9, 46.9, 46.9, 46.9, 46.9, 47.1, 47.0, 47.0, 47.0]\\n/tmp/b3_bar_inspect/frame_40.jpg (1080, 1920) top/bottom nonblack rows (0, 1919) top120 47.8 bottom120 28.2 minrow first20 [42.5, 42.5, 42.5, 42.6, 42.7, 42.8, 42.8, 42.9, 43.3, 43.3, 43.3, 43.4, 43.5, 43.6, 43.6, 43.6, 43.9, 43.9, 43.9, 43.9]\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_Ke6rQqlRSeUiyTapW0B5YCsn"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_YFOm4P8N0GAMC1iADOu2rzJY",
"call_id": "call_YFOm4P8N0GAMC1iADOu2rzJY",
"response_item_id": "fc_010f4e1931265670016a1535a88ad88191bfee5a046afd020a",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/b3_bar_inspect/frame_5.jpg\",\"question\":\"Inspect this frame: are there visible black bars top/bottom? Where are subtitles relative to the bars? What crop/stretch would remove bars without losing important content?\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Examining video frame adjustments**\n\nI think I need to start by focusing on the top black bar in the video. The user sees these bars, so I have to consider how to create a full-bleed effect by cropping them out. The aspect ratio is 2:3 for images and 9:16 for videos, which is narrower and taller. This gives a height of 1620 pixels, but I want to ensure that subtitles at the bottom are placed correctly after cropping. I should inspect the actual frame for clarity.",
"reasoning_content": "**Examining video frame adjustments**\n\nI think I need to start by focusing on the top black bar in the video. The user sees these bars, so I have to consider how to create a full-bleed effect by cropping them out. The aspect ratio is 2:3 for images and 9:16 for videos, which is narrower and taller. This gives a height of 1620 pixels, but I want to ensure that subtitles at the bottom are placed correctly after cropping. I should inspect the actual frame for clarity.",
"codex_reasoning_items": [
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"id": "rs_010f4e1931265670016a1535a03d4081919d7b9a44ff434f8b",
"summary": [
{
"type": "summary_text",
"text": "**Examining video frame adjustments**\n\nI think I need to start by focusing on the top black bar in the video. The user sees these bars, so I have to consider how to create a full-bleed effect by cropping them out. The aspect ratio is 2:3 for images and 9:16 for videos, which is narrower and taller. This gives a height of 1620 pixels, but I want to ensure that subtitles at the bottom are placed correctly after cropping. I should inspect the actual frame for clarity."
}
]
}
]
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Inspect this frame: are there visible black bars top/bottom? Where are subtitles relative to the bars? What crop/stretch would remove bars without losing important content?\n[screenshot]",
"tool_call_id": "call_YFOm4P8N0GAMC1iADOu2rzJY"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_20WHzwU6R0fB4isxQfkqnrkZ",
"call_id": "call_20WHzwU6R0fB4isxQfkqnrkZ",
"response_item_id": "fc_010f4e1931265670016a1535bce93c8191b14cfab0d3de834f",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -e\\nIN='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'\\nOUT='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_FULLHEIGHT_no_bars.mp4'\\nffmpeg -y -hide_banner -loglevel error -i \\\"$IN\\\" \\\\\\n -map 0:v:0 -map 0:a:0? \\\\\\n -vf \\\"crop=1080:1620:0:150,scale=1080:1920:flags=lanczos,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -profile:v high -level 4.2 -preset slow -crf 16 \\\\\\n -c:a aac -b:a 192k -movflags +faststart -metadata:s:v:0 rotate=0 \\\"$OUT\\\"\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json \\\"$OUT\\\"\\nsha256sum \\\"$OUT\\\"\\nmkdir -p /tmp/b3_fullheight_qa\\nfor t in 0.5 5 12 22 35 40; do ffmpeg -y -hide_banner -loglevel error -ss \\\"$t\\\" -i \\\"$OUT\\\" -frames:v 1 \\\"/tmp/b3_fullheight_qa/frame_${t}.jpg\\\"; done\\npython - <<'PY'\\nfrom pathlib import Path\\nfrom PIL import Image\\nimport numpy as np\\nfor p in sorted(Path('/tmp/b3_fullheight_qa').glob('*.jpg')):\\n im=Image.open(p).convert('RGB'); arr=np.array(im); lum=arr.mean(axis=2); row=lum.mean(axis=1)\\n print(p.name, im.size, 'top80', round(float(row[:80].mean()),1), 'bottom80', round(float(row[-80:].mean()),1), 'min top row', round(float(row[:10].mean()),1), 'min bottom row', round(float(row[-10:].mean()),1))\\nPY\",\"timeout\":600}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Calculating stretch height**\n\nI'm considering how this stretch translates to height. Given that this stretches vertically by 18.5%, I need to think about how to convey that information clearly to the user. I want to ensure I provide the correct measurements and context, especially since the user is asking specifically about the height stretch. It’s important for me to present this accurately for their understanding. So, let’s lay out the details!",
"reasoning_content": "**Calculating stretch height**\n\nI'm considering how this stretch translates to height. Given that this stretches vertically by 18.5%, I need to think about how to convey that information clearly to the user. I want to ensure I provide the correct measurements and context, especially since the user is asking specifically about the height stretch. It’s important for me to present this accurately for their understanding. So, let’s lay out the details!",
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"id": "rs_010f4e1931265670016a1535ab9aec819189ee6080cf431ee4",
"summary": [
{
"type": "summary_text",
"text": "**Calculating stretch height**\n\nI'm considering how this stretch translates to height. Given that this stretches vertically by 18.5%, I need to think about how to convey that information clearly to the user. I want to ensure I provide the correct measurements and context, especially since the user is asking specifically about the height stretch. It’s important for me to present this accurately for their understanding. So, let’s lay out the details!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"output\": \"{\\n \\\"programs\\\": [\\n\\n ],\\n \\\"streams\\\": [\\n {\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"pix_fmt\\\": \\\"yuv420p\\\",\\n \\\"r_frame_rate\\\": \\\"24/1\\\"\\n }\\n ],\\n \\\"format\\\": {\\n \\\"duration\\\": \\\"41.400998\\\"\\n }\\n}\\n68f92384e4659ee5128b6cbde09bf57de23c3e6e24527284187133a8f8bd04e6 /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_FULLHEIGHT_no_bars.mp4\\nframe_0.5.jpg (1080, 1920) top80 13.3 bottom80 21.5 min top row 14.4 min bottom row 20.2\\nframe_12.jpg (1080, 1920) top80 113.2 bottom80 11.4 min top row 103.3 min bottom row 12.0\\nframe_22.jpg (1080, 1920) top80 29.8 bottom80 25.0 min top row 30.5 min bottom row 24.6\\nframe_35.jpg (1080, 1920) top80 70.3 bottom80 35.9 min top row 71.4 min bottom row 34.9\\nframe_40.jpg (1080, 1920) top80 71.9 bottom80 32.4 min top row 61.7 min bottom row 31.3\\nframe_5.jpg (1080, 1920) top80 12.7 bottom80 24.1 min top row 14.0 min bottom row 22.9\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_20WHzwU6R0fB4isxQfkqnrkZ"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_F05CTkRupPSTaQ8QgoFSmpyX",
"call_id": "call_F05CTkRupPSTaQ8QgoFSmpyX",
"response_item_id": "fc_010f4e1931265670016a1535f2a8108191a2f7a075b8926200",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/b3_fullheight_qa/frame_5.jpg\",\"question\":\"QA this corrected full-height frame. Are the top/bottom black bars removed compared with the prior frame? Is the image now filling vertical height, and are subtitles still readable?\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA this corrected full-height frame. Are the top/bottom black bars removed compared with the prior frame? Is the image now filling vertical height, and are subtitles still readable?\n[screenshot]",
"tool_call_id": "call_F05CTkRupPSTaQ8QgoFSmpyX"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ZDMCAPS0SwMPNx1kNr3DTX6O",
"call_id": "call_ZDMCAPS0SwMPNx1kNr3DTX6O",
"response_item_id": "fc_010f4e1931265670016a1536058dec8191b89aeb90e93eba40",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python - <<'PY'\\nfrom PIL import Image\\nimport numpy as np\\np='/tmp/b3_bar_inspect/frame_5.jpg'\\nim=Image.open(p).convert('RGB'); arr=np.array(im)\\n# text bright white/yellow thresholds\\nmask=((arr[:,:,0]>180)&(arr[:,:,1]>170)&(arr[:,:,2]>120)) | ((arr[:,:,0]>230)&(arr[:,:,1]>230)&(arr[:,:,2]>230))\\nys,xs=np.where(mask)\\nprint('orig subtitle bright bbox', (xs.min(), ys.min(), xs.max(), ys.max()), 'height', ys.max()-ys.min()+1)\\nfor crop_y,crop_h in [(100,1720),(120,1680),(150,1620),(200,1520),(250,1420)]:\\n # transformed y positions for bbox after crop scale to 1920\\n y1=(ys.min()-crop_y)*1920/crop_h; y2=(ys.max()-crop_y)*1920/crop_h\\n print(crop_y,crop_h,'transformed subtitle y',round(y1),round(y2),'cut?',ys.min()crop_y+crop_h)\\nPY\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Considering video cropping**\n\nI'm noticing that the video has top and bottom dark areas, possibly because of black bars from the laptop. Maybe the player itself shows these bars too? I see the need to stretch the screen layer to remove the black at the top and bottom. If I crop too much from the bottom, subtitles might get cut off. If I crop the top and bottom before scaling, I need to ensure subtitles remain visible to prevent losing important content.\n**Adjusting video frame**\n\nIt looks like the current frame has a black band at around y=300, probably from the laptop bezel, and there's a dark area below the subtitles. The user wants the image stretched to fit the screen, but I'm wondering if we need to adjust the rendering source first. \n\nAlternatively, we could crop the visual region and overlay the subtitles, but since they're baked in, that's tricky. Using smart vertical stretching seems like a good option to keep the subtitles. However, if I crop the bottom too much, the subtitles might get cut off. I need to find the right balance!\n**Exploring cover effect options**\n\nI'm considering creating a 9:16 \"cover\" effect based on generated images, since the current full frame is already that size. The black bars appear because the image doesn't cover the whole area. The only way forward seems to be cropping the total frame. I'll use a crop value of 1080:1400:0:260, then scale for a greater stretch. However, it looks like this would cut the bottom at around 1660, likely missing the subtitles, so I need to assess the original vertical placement of the subtitles carefully!",
"reasoning_content": "**Considering video cropping**\n\nI'm noticing that the video has top and bottom dark areas, possibly because of black bars from the laptop. Maybe the player itself shows these bars too? I see the need to stretch the screen layer to remove the black at the top and bottom. If I crop too much from the bottom, subtitles might get cut off. If I crop the top and bottom before scaling, I need to ensure subtitles remain visible to prevent losing important content.\n**Adjusting video frame**\n\nIt looks like the current frame has a black band at around y=300, probably from the laptop bezel, and there's a dark area below the subtitles. The user wants the image stretched to fit the screen, but I'm wondering if we need to adjust the rendering source first. \n\nAlternatively, we could crop the visual region and overlay the subtitles, but since they're baked in, that's tricky. Using smart vertical stretching seems like a good option to keep the subtitles. However, if I crop the bottom too much, the subtitles might get cut off. I need to find the right balance!\n**Exploring cover effect options**\n\nI'm considering creating a 9:16 \"cover\" effect based on generated images, since the current full frame is already that size. The black bars appear because the image doesn't cover the whole area. The only way forward seems to be cropping the total frame. I'll use a crop value of 1080:1400:0:260, then scale for a greater stretch. However, it looks like this would cut the bottom at around 1660, likely missing the subtitles, so I need to assess the original vertical placement of the subtitles carefully!",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_010f4e1931265670016a1535f64b7c81919ac114f61d30c3d0",
"summary": [
{
"type": "summary_text",
"text": "**Considering video cropping**\n\nI'm noticing that the video has top and bottom dark areas, possibly because of black bars from the laptop. Maybe the player itself shows these bars too? I see the need to stretch the screen layer to remove the black at the top and bottom. If I crop too much from the bottom, subtitles might get cut off. If I crop the top and bottom before scaling, I need to ensure subtitles remain visible to prevent losing important content."
},
{
"type": "summary_text",
"text": "**Adjusting video frame**\n\nIt looks like the current frame has a black band at around y=300, probably from the laptop bezel, and there's a dark area below the subtitles. The user wants the image stretched to fit the screen, but I'm wondering if we need to adjust the rendering source first. \n\nAlternatively, we could crop the visual region and overlay the subtitles, but since they're baked in, that's tricky. Using smart vertical stretching seems like a good option to keep the subtitles. However, if I crop the bottom too much, the subtitles might get cut off. I need to find the right balance!"
},
{
"type": "summary_text",
"text": "**Exploring cover effect options**\n\nI'm considering creating a 9:16 \"cover\" effect based on generated images, since the current full frame is already that size. The black bars appear because the image doesn't cover the whole area. The only way forward seems to be cropping the total frame. I'll use a crop value of 1080:1400:0:260, then scale for a greater stretch. However, it looks like this would cut the bottom at around 1660, likely missing the subtitles, so I need to assess the original vertical placement of the subtitles carefully!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"output\": \"orig subtitle bright bbox (np.int64(0), np.int64(320), np.int64(1079), np.int64(1606)) height 1287\\n100 1720 transformed subtitle y 246 1681 cut? False\\n120 1680 transformed subtitle y 229 1698 cut? False\\n150 1620 transformed subtitle y 201 1726 cut? False\\n200 1520 transformed subtitle y 152 1776 cut? False\\n250 1420 transformed subtitle y 95 1833 cut? False\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_ZDMCAPS0SwMPNx1kNr3DTX6O"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_pOORgZFW6d09gF5sT3Y0tQSC",
"call_id": "call_pOORgZFW6d09gF5sT3Y0tQSC",
"response_item_id": "fc_010f4e1931265670016a15360d7b64819184788856af11e186",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"set -e\\nIN='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'\\nOUT='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_SCREENFILL_no_top_bottom_bars.mp4'\\nffmpeg -y -hide_banner -loglevel error -i \\\"$IN\\\" \\\\\\n -map 0:v:0 -map 0:a:0? \\\\\\n -vf \\\"crop=1080:1420:0:250,scale=1080:1920:flags=lanczos,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -profile:v high -level 4.2 -preset slow -crf 16 \\\\\\n -c:a aac -b:a 192k -movflags +faststart -metadata:s:v:0 rotate=0 \\\"$OUT\\\"\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json \\\"$OUT\\\"\\nsha256sum \\\"$OUT\\\"\\nmkdir -p /tmp/b3_screenfill_qa\\nfor t in 0.5 5 12 22 35 40; do ffmpeg -y -hide_banner -loglevel error -ss \\\"$t\\\" -i \\\"$OUT\\\" -frames:v 1 \\\"/tmp/b3_screenfill_qa/frame_${t}.jpg\\\"; done\",\"timeout\":600}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Adjusting video crop**\n\nI’m considering how to crop the video. I want to crop 250 pixels from the top and bottom, which would help remove those dark bands. I need to keep in mind the frames that include text, as cutting too much could affect the readability. The user wants a stronger \"screen fill,\" so I'm thinking of using a crop of 1080:1420:0:250 and scaling to 1080x1920. I’ll aim for a better outcome!",
"reasoning_content": "**Adjusting video crop**\n\nI’m considering how to crop the video. I want to crop 250 pixels from the top and bottom, which would help remove those dark bands. I need to keep in mind the frames that include text, as cutting too much could affect the readability. The user wants a stronger \"screen fill,\" so I'm thinking of using a crop of 1080:1420:0:250 and scaling to 1080x1920. I’ll aim for a better outcome!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFTYNxtiWM-k2X2kPGRqfId96hr2uX9F6S7Sb7U_rXoKHccVxY-J8lSW5ex1usAtg0j0yTLA9BcMSLLQzSrTx3RydH7csK3HW9U7L18yW7rY-ImINYMse3zTex-D8IUhJP3W7mszua_sM_1BuroTl7jPpDHw-2ITS4-xOZL0OQ8h0oEZfapayN4hhxMHFx1T9K3ioClIfkSDMQ8mQnx8GhJp9QaZJ6mZF9sE1LHPY4nzYJ49zXnQZIbaOFNht1UIzbeahvTASkSmoLkquG3wfx6g0xtWTn7HAdHc3ZNfuYxoMcDcrtCjh872tN18w63HrEKWL4Hpdk4_Q2W9oG3pIQn9eAPTBdjGqpCGQbND9wVsKEvWA27eOAQE6XJfnv8aCyxOdVO9FirU2CxMI7-JyOm0thhc8uRFsUfI2OrVCSHHx-jC_Ml_693zf6q2U6_5kExx0lCMWS7_Ky0z55bf6SdPC-eMTaS_wJHChkhtGy0dQ3Xho8fPN7WigzpbqwC_KClCMBoNM6YNygWk436-UxABhJSJPkFQuzmsKnJA40H2IRyYNKrfVafSdTEirI_1MkLmGdlheXkhWW-8KxZggLf1AOhIhRix0M0pSXInqinjs3HfwOFORrWur4HGNOSoCFdepY9-c9Ybtp0SLqKM9-Z2szX_r9KLWM_hg5GkDqojOjZ9KTJknD92JG_3f6UFWDUwuCtvRITEC54y3LlPg6wfEMv1lYvMGGi996qirncWTIsOtGiS1ao8OwkGyg_ClUxfPX-W7YirAuGLFdZ7rC4QFTMXj2REZxrX99lSr-gHx0or7vBHVUVtziaVwdnKBE3FeU6A5ad6-j003gJOKgwdFVrKKi0Lrn8E09HxgoS3yUTQLwoMLoV7LBlWvMRc_m1tJCE_H3vbO-_om-VlVKXhb3JZ2zs5CGMPOMlZceT7tHE9e-XBArSKhZFSzejrHDRCFmrnvdTD9hK3jxu7iuCJyMfmf1uXQtVzCsQGMiWORYdosP8rbDSpToF8ockmuBTmEepAy-GYX1jm6_9dk0BAx7oyRi8zTnZm6rr9iYA5WmNwluwCUM56-e1l6a1fK5Nh86Jwmbq9LHumnzosJqD7snC7N6UR51_3aC2iFfouHheM8fROLZ-XyTIzoC5K52sbszNzqSctnVu1v2RE55XKe08EhOykiAeAE59_xO5yt17hJTArl8Lu4UkClyFEnbe-3EhzJOZW-VlP1SwBfyx3UmM7yqEENEha1qh9-vIC0-S2bjbCyM83aiwoaGqsiCICGM1_meb0L2q30pYi5lw6pN2WieCZISsoENNv71XTZLUpvC_LOuAoZ97q0DhBNgOHP-iZgBUyExHBpXscJn_cE6EcIMo04OO3lKBGv-AV_xiai1GWwe7ZBL3H7w7LoAEP5L5HkkRn6bH-WHkI2I5H3Jg==",
"id": "rs_010f4e1931265670016a1536094e2c8191a3e4589f10c454fd",
"summary": [
{
"type": "summary_text",
"text": "**Adjusting video crop**\n\nI’m considering how to crop the video. I want to crop 250 pixels from the top and bottom, which would help remove those dark bands. I need to keep in mind the frames that include text, as cutting too much could affect the readability. The user wants a stronger \"screen fill,\" so I'm thinking of using a crop of 1080:1420:0:250 and scaling to 1080x1920. I’ll aim for a better outcome!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"output\": \"{\\n \\\"programs\\\": [\\n\\n ],\\n \\\"streams\\\": [\\n {\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"pix_fmt\\\": \\\"yuv420p\\\",\\n \\\"r_frame_rate\\\": \\\"24/1\\\"\\n }\\n ],\\n \\\"format\\\": {\\n \\\"duration\\\": \\\"41.400998\\\"\\n }\\n}\\n073b2f2cc371914494384eb8aea8ba28c84457cd37c26c32355e555b1ec1d89c /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_SCREENFILL_no_top_bottom_bars.mp4\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_pOORgZFW6d09gF5sT3Y0tQSC"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_v70Pd7R5Jkke7QYeAtKCzNZp",
"call_id": "call_v70Pd7R5Jkke7QYeAtKCzNZp",
"response_item_id": "fc_010f4e1931265670016a15364627b48191a6b6937eef86e5fe",
"type": "function",
"function": {
"name": "vision_analyze",
"arguments": "{\"image_url\":\"/tmp/b3_screenfill_qa/frame_5.jpg\",\"question\":\"QA this stronger screen-fill version. Have the top/bottom black bars been removed by filling the vertical frame? Are important UI and subtitles still visible/readable? Any obvious over-crop?\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: QA this stronger screen-fill version. Have the top/bottom black bars been removed by filling the vertical frame? Are important UI and subtitles still visible/readable? Any obvious over-crop?\n[screenshot]",
"tool_call_id": "call_v70Pd7R5Jkke7QYeAtKCzNZp"
},
{
"role": "assistant",
"content": "Korrigiert — ich habe jetzt nicht nur SAR/DAR gesetzt, sondern die **eingebackenen Top-/Bottom-Balken entfernt**:\n\n- vertikal gecroppt\n- Bild auf volle Höhe gestreckt\n- wieder als echtes `1080x1920`\n- `SAR 1:1`\n- `DAR 9:16`\n- Audio erhalten\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_SCREENFILL_no_top_bottom_bars.mp4\n\nVerifikation:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.401 s\nSHA256: 073b2f2cc371914494384eb8aea8ba28c84457cd37c26c32355e555b1ec1d89c\n```",
"reasoning": "**Evaluating video adjustments**\n\nI'm checking if the adjustments made to the video meet the user's request for a stretch without those black bars at the top. They've explicitly asked for it to be stretched, so I need to ensure the final version reflects that. I should mention that I made more aggressive crops and that some darkness may still be present due to the source content. If there's still an issue, I might suggest regenerating in native 9:16 format. I’ll verify that there are no black borders at the canvas edges.",
"reasoning_content": "**Evaluating video adjustments**\n\nI'm checking if the adjustments made to the video meet the user's request for a stretch without those black bars at the top. They've explicitly asked for it to be stretched, so I need to ensure the final version reflects that. I should mention that I made more aggressive crops and that some darkness may still be present due to the source content. If there's still an issue, I might suggest regenerating in native 9:16 format. I’ll verify that there are no black borders at the canvas edges.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_010f4e1931265670016a153649683081918c56b63d5069fc5a",
"summary": [
{
"type": "summary_text",
"text": "**Evaluating video adjustments**\n\nI'm checking if the adjustments made to the video meet the user's request for a stretch without those black bars at the top. They've explicitly asked for it to be stretched, so I need to ensure the final version reflects that. I should mention that I made more aggressive crops and that some darkness may still be present due to the source content. If there's still an issue, I might suggest regenerating in native 9:16 format. I’ll verify that there are no black borders at the canvas edges."
}
]
}
],
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"text": "Korrigiert — ich habe jetzt nicht nur SAR/DAR gesetzt, sondern die **eingebackenen Top-/Bottom-Balken entfernt**:\n\n- vertikal gecroppt\n- Bild auf volle Höhe gestreckt\n- wieder als echtes `1080x1920`\n- `SAR 1:1`\n- `DAR 9:16`\n- Audio erhalten\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_SCREENFILL_no_top_bottom_bars.mp4\n\nVerifikation:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.401 s\nSHA256: 073b2f2cc371914494384eb8aea8ba28c84457cd37c26c32355e555b1ec1d89c\n```"
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"id": "msg_010f4e1931265670016a15365187848191a3274da60c45a774",
"phase": "final_answer"
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"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Sorry, mein fehler, mach die anpassung rückgängig. Es lag an meinem telegram-player"
},
{
"role": "assistant",
"content": "",
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"arguments": "{\"file_path\":\"references/mobile-vertical-aspect-qa.md\",\"name\":\"social-media-shorts-automation\"}"
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"finish_reason": "tool_calls",
"reasoning": "**Verifying previous files**\n\nI need to provide the user with the original fixed file they requested. It's good to check whether that file exists first. If it’s relevant, I'll ensure to load it before replying to the user. I might need to use some tools for that action. I just want to ensure I get everything right and that the user has the file they need. Let's make sure I follow through with this correctly!",
"reasoning_content": "**Verifying previous files**\n\nI need to provide the user with the original fixed file they requested. It's good to check whether that file exists first. If it’s relevant, I'll ensure to load it before replying to the user. I might need to use some tools for that action. I just want to ensure I get everything right and that the user has the file they need. Let's make sure I follow through with this correctly!",
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"summary": [
{
"type": "summary_text",
"text": "**Verifying previous files**\n\nI need to provide the user with the original fixed file they requested. It's good to check whether that file exists first. If it’s relevant, I'll ensure to load it before replying to the user. I might need to use some tools for that action. I just want to ensure I get everything right and that the user has the file they need. Let's make sure I follow through with this correctly!"
}
]
}
]
},
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"role": "tool",
"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/mobile-vertical-aspect-qa.md\", \"content\": \"# Mobile vertical aspect QA and repair\\n\\nUse this when a short/reel/Telegram preview appears with black bars at top/bottom, wrong aspect ratio, or the visible image looks vertically compressed even though the source clip was intended as 9:16.\\n\\n## Durable lesson\\n\\nDo not rely on nominal source dimensions alone. A clip can be near-vertical, but still display badly on mobile platforms if the export lacks explicit square-pixel and display-aspect metadata, or if an assembly step has letterboxed instead of producing a true vertical canvas.\\n\\n## Required checks\\n\\nProbe every final-near MP4 before delivery:\\n\\n```bash\\nffprobe -v error -select_streams v:0 \\\\\\n -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio \\\\\\n -show_entries format=duration \\\\\\n -of json path/to/video.mp4\\n```\\n\\nExpected mobile-safe target for Shorts/Reels/TikTok review exports:\\n\\n- `width=1080`\\n- `height=1920`\\n- `sample_aspect_ratio=1:1`\\n- `display_aspect_ratio=9:16`\\n- no visible black bars in extracted QA frames\\n\\nExtract a frame for visual QA:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i path/to/video.mp4 \\\\\\n -vf \\\"select=eq(n\\\\,10)\\\" -frames:v 1 /tmp/frame.png\\n```\\n\\nInspect the frame for top/bottom black bars and obvious squeezing before sending to the user.\\n\\n## Repair command\\n\\nFor source clips that should fill a vertical mobile canvas, create a post-render normalized export:\\n\\n```bash\\nffmpeg -y -hide_banner -loglevel error -i input.mp4 \\\\\\n -vf \\\"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920,setsar=1,setdar=9/16,format=yuv420p\\\" \\\\\\n -c:v libx264 -preset veryfast -crf 18 -movflags +faststart -an \\\\\\n output_1080x1920_sar1.mp4\\n```\\n\\nUse `increase,crop` for full-bleed visual proofs where black bars are unacceptable. Use `decrease,pad` only when preserving every pixel is more important than full-screen mobile presentation, and label it as letterboxed.\\n\\n## Workflow rule\\n\\nIf the user reports black bars or squeezed height, treat it as a blocking export QA issue:\\n\\n1. Probe the delivered MP4 metadata.\\n2. Extract at least one representative frame.\\n3. Re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n4. Probe the repaired file and report the verified SAR/DAR.\\n5. Deliver the repaired media, not more explanations.\\n\\n## Pitfall\\n\\nDo not say “the source is 9:16 enough” based on dimensions like `416x736` alone. Platform players can still expose metadata or canvas mistakes. Verify the final delivered artifact.\", \"file_type\": \".md\"}",
"tool_call_id": "call_ujqfnGYgV9vjUzYOPejWD0BC"
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{
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"id": "call_RXRbAiE4HVwozVYnxtAWQ6JH",
"call_id": "call_RXRbAiE4HVwozVYnxtAWQ6JH",
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"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4'; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json \\\"$p\\\" && sha256sum \\\"$p\\\"\",\"timeout\":120}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"output\": \"{\\n \\\"programs\\\": [\\n\\n ],\\n \\\"streams\\\": [\\n {\\n \\\"width\\\": 1080,\\n \\\"height\\\": 1920,\\n \\\"sample_aspect_ratio\\\": \\\"1:1\\\",\\n \\\"display_aspect_ratio\\\": \\\"9:16\\\",\\n \\\"pix_fmt\\\": \\\"yuv420p\\\",\\n \\\"r_frame_rate\\\": \\\"24/1\\\"\\n }\\n ],\\n \\\"format\\\": {\\n \\\"duration\\\": \\\"41.386009\\\"\\n }\\n}\\ndc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f /tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_RXRbAiE4HVwozVYnxtAWQ6JH"
},
{
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"content": "Alles gut — passiert. Dann nehmen wir wieder die **korrekte unverzerrte Version** zurück. Die “Screenfill”-Variante mit Stretch/Crop ignorieren wir.\n\nHier nochmals die richtige Datei:\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.386 s\nSHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f\n```\n\nDie gestreckte Datei ist damit verworfen. Telegram macht Telegram-Dinge — traditionsgemäss unnötig selbstbewusst.",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Alles gut — passiert. Dann nehmen wir wieder die **korrekte unverzerrte Version** zurück. Die “Screenfill”-Variante mit Stretch/Crop ignorieren wir.\n\nHier nochmals die richtige Datei:\n\nMEDIA:/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visible_invite_cinematic_ab_v1/b3/calendar-invite-login-trap-30s_b3_9x16_fixed.mp4\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 41.386 s\nSHA256: dc764bcb70d5c32783caa49b25e1bf44cafd14b25dc265bc44e2b8661149f66f\n```\n\nDie gestreckte Datei ist damit verworfen. Telegram macht Telegram-Dinge — traditionsgemäss unnötig selbstbewusst."
}
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"phase": "final_answer"
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],
"finish_reason": "stop"
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{
"role": "user",
"content": "[M G] Ok, erarbeite nun den nächsten Postpreview zur freigabe"
},
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"finish_reason": "tool_calls",
"reasoning": "**Planning AutoShorts Content**\n\nI need to act on continuing with AutoShorts and load relevant skills. I should create a preview for the next post and understand the workflow, maybe developing a proof short from that. Inspecting past scripts could help generate new concepts. I think it’s wise to utilize the partially loaded social skills. There’s a need to prepare a new preview for approval, focusing on detailed AI screen images. I’ll plan the next steps and think about what the topic might be for the next video.\n**Preparing for the Next Video**\n\nI think it’s time to generate the next video, and I know I need to be efficient about it. I should load the relevant skill references, particularly around social draft approvals and the proof-short concepts. Maybe I’ll need to follow any clean AI standards while doing this. It feels important to keep the process streamlined to ensure everything goes smoothly as I work on this next step!",
"reasoning_content": "**Planning AutoShorts Content**\n\nI need to act on continuing with AutoShorts and load relevant skills. I should create a preview for the next post and understand the workflow, maybe developing a proof short from that. Inspecting past scripts could help generate new concepts. I think it’s wise to utilize the partially loaded social skills. There’s a need to prepare a new preview for approval, focusing on detailed AI screen images. I’ll plan the next steps and think about what the topic might be for the next video.\n**Preparing for the Next Video**\n\nI think it’s time to generate the next video, and I know I need to be efficient about it. I should load the relevant skill references, particularly around social draft approvals and the proof-short concepts. Maybe I’ll need to follow any clean AI standards while doing this. It feels important to keep the process streamlined to ensure everything goes smoothly as I work on this next step!",
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"text": "**Planning AutoShorts Content**\n\nI need to act on continuing with AutoShorts and load relevant skills. I should create a preview for the next post and understand the workflow, maybe developing a proof short from that. Inspecting past scripts could help generate new concepts. I think it’s wise to utilize the partially loaded social skills. There’s a need to prepare a new preview for approval, focusing on detailed AI screen images. I’ll plan the next steps and think about what the topic might be for the next video."
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"text": "**Preparing for the Next Video**\n\nI think it’s time to generate the next video, and I know I need to be efficient about it. I should load the relevant skill references, particularly around social draft approvals and the proof-short concepts. Maybe I’ll need to follow any clean AI standards while doing this. It feels important to keep the process streamlined to ensure everything goes smoothly as I work on this next step!"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/clean-ai-cut-vs-hybrid-mechanism.md\", \"content\": \"# Clean AI Cut vs Hybrid Mechanism — session learning\\n\\nUse this when comparing a pure premium AI-image sequence against a proof short with deterministic mechanism/UI overlays.\\n\\n## Session-derived pattern\\n\\nFor a scam/cybersecurity proof short, a clean image sequence can look significantly more premium and native to TikTok/Reels, but it often weakens immediate mechanism clarity.\\n\\nObserved comparison:\\n\\n```text\\nClean AI Cut = premium mood, calmer, less explainer-board\\nHybrid = clearer mechanism, more immediately useful\\nBest likely final direction = mostly clean images + one minimal deterministic mechanism moment\\n```\\n\\nRecommended framing:\\n\\n```text\\n90% Clean AI Images\\n10% precise renderer-owned mechanism\\n```\\n\\nExamples of acceptable minimal mechanism moments:\\n\\n- one brief `suspicious link/domain` reveal\\n- one brief `close invite -> open trusted app/bookmark` action moment\\n- one simple before/after state, not a full checklist board\\n\\nAvoid returning to:\\n\\n- checklist-heavy UI cards\\n- boxed explainer overlays\\n- arrows/circles/diagram markers\\n- repeated labels and decision-target graphics\\n\\n## Viewer explanation rule\\n\\nWhen the viewer is not technical, do not use shorthand such as `open the service yourself` without explanation. Spell out the exact action:\\n\\n```text\\nClose the invite.\\nOpen your calendar or work app from the normal icon/bookmark.\\nIf the meeting is real, it will still be there.\\nTrust the path you chose, not the button that chose you.\\n```\\n\\nThis converts a security maxim into a concrete next action.\\n\\n## Caption synchronization rule\\n\\nFor final-near shorts where the user complains that subtitles do not match voiceover exactly, use actual word-boundary/forced-alignment timing rather than distributing words evenly across sentence SRT blocks. For EdgeTTS previews, request `boundary='WordBoundary'`, store word cues, then render static phrase segments where only the active spoken word highlights.\\n\\nKeep the established caption policy:\\n\\n- static phrase segment, usually 3–7 words\\n- active spoken word highlighted in yellow\\n- inactive words white\\n- no subtitle box/bar\\n- bottom mobile-safe position\\n\\n## A/B decision criteria\\n\\nAfter rendering both versions, explicitly answer:\\n\\n1. Which version looks more premium?\\n2. Which version explains the mechanism faster without audio?\\n3. Does the clean cut rely too much on VO/subtitles?\\n4. Would one minimal deterministic mechanism moment recover clarity without killing the premium look?\\n5. Is Wan/I2V worth testing, or is image-sequence motion enough for this stage?\\n\\nDefault recommendation: do not jump to full Wan/I2V after this test. If clean images work visually, test exactly one short motion micro-proof only after approval.\\n\\n## Visible-problem keyframe A/B pattern\\n\\nWhen the user asks whether pure KI images can carry a Proof Short, do not generate generic premium atmosphere. The first image batch must make the real viewer problem literally visible without text:\\n\\n- show the concrete object/situation (e.g. a calendar invite card on laptop/phone, a join/login button shape, attendee dots, blank time rows);\\n- keep all generated UI text-free: no letters, pseudo-letters, numbers, logos, QR/barcodes, watermarks, people, hands, or faces;\\n- use contact-sheet QA before rendering and discard frames that only look like generic laptop/desk B-roll;\\n- expect later safe/resolution frames to be less semantically explicit, but the first half must visually anchor the trap.\\n\\nFor the calendar-invite login-trap proof, the updated session result was:\\n\\n```text\\nB1 = clean visible-invite AI images only + voiceover + active-word subtitles; premium but still a bit VO-dependent.\\nB2 = same images + subtle focus outlines; rejected because the lines added little value and reduced the premium look.\\nB3 = detailed AI-generated Outlook-style invite/login/calendar screens with readable UI text + voiceover + active-word subtitles; accepted as the new standard.\\nOutcome = B3 recommended/default: no renderer-added lines/arrows/boxes, but prompt the image model with very detailed, concrete, readable screen/UI content so the problem and safe action are visible in the image itself.\\n```\\n\\nUse this as the default next-step bias: if clean images are beautiful but semantically weak, first make the AI keyframes themselves more specific and content-rich (visible app/email/invite/login/calendar screens, exact readable text, realistic UI details) rather than adding focus lines, arrows, circles, labels, or checklist cards. Deterministic renderer text remains appropriate for captions; visible screen text may be AI-generated when the user explicitly wants detailed realistic UI images and the keyframes pass visual QA.\\n\\nExport QA pitfall: Telegram/mobile playback may misinterpret vertical files if aspect metadata is weak. For final/review MP4s, explicitly encode with `setsar=1,setdar=9/16`, `yuv420p`, `-profile:v high -level:v 4.2`, `-movflags +faststart`, and verify `ffprobe` reports `width=1080 height=1920 sample_aspect_ratio=1:1 display_aspect_ratio=9:16` before delivery.\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/social-draft-approval-package.md\", \"content\": \"# Social Draft Approval Package Pattern\\n\\nUse this when moving from render planning toward the first user-facing social-media post draft for approval.\\n\\n## Goal\\n\\nProduce a complete approval draft before TTS, video rendering, or platform APIs:\\n\\n```text\\nVideoCandidate + RenderManifest/Preview\\n→ SocialMediaDraft\\n→ SocialMediaDraft review text\\n→ side-effect-free delivery plan\\n→ demo CLI output\\n```\\n\\nThis gives the user a concrete post candidate to approve or revise while still avoiding accidental publishing, external calls, and wasted GPU/TTS work.\\n\\n## Recommended typed artifacts\\n\\n1. `SocialMediaDraft`\\n - candidate id\\n - target platforms: YouTube Shorts, TikTok, Instagram Reels\\n - title variants\\n - caption\\n - hashtags\\n - CTA\\n - hook\\n - planned duration\\n - quality/risk summary\\n - preview reference, e.g. HTML timeline available or MP4 pending\\n - `requires_human_approval=True`\\n - `side_effects=()`\\n\\n2. Social review renderer\\n - Telegram-safe text block\\n - includes candidate, platforms, hook, titles, caption, hashtags, preview note, quality/risk summary\\n - includes approval commands: `Freigabe alle`, platform-specific approvals, `Ablehnen`, `Ändern: <...>`\\n - must not imply automatic upload or publishing\\n\\n3. Draft delivery preparation\\n - validates message length and safety language\\n - returns typed delivery plan with `requires_human_approval=True` and `side_effects=()`\\n - no Telegram API call yet\\n\\n4. Demo CLI\\n - e.g. `python -m autoshorts.cli.demo_social_draft`\\n - prints delivery metadata first, then the review text\\n - no file writes, platform calls, renderer calls, or credential access\\n\\n## Sequence before TTS\\n\\nDo not start with TTS. First get approval on the content package:\\n\\n```text\\nContentBrief\\n→ ScriptDraft\\n→ VideoCandidate\\n→ ShotPlan\\n→ RenderManifest\\n→ Preview\\n→ SocialMediaDraft\\n→ Review text\\n→ Approval delivery preview\\n```\\n\\nA robust implementation can be split into small TDD slices:\\n\\n1. `social/draft.py` — `build_social_media_draft(candidate, preview)` and `validate_social_media_draft(draft)`.\\n2. `social/review.py` — `render_social_media_draft_review(draft)` for Telegram-safe approval text.\\n3. `social/delivery.py` — `prepare_social_draft_delivery(text, topic=\\\"review\\\")` returning a side-effect-free delivery object.\\n4. `cli/demo_social_draft.py` — local demo command that prints metadata first, then the review text.\\n\\nRecommended invariant tests for every slice:\\n\\n- `requires_human_approval is True`\\n- `side_effects == ()`\\n- no platform API calls, Telegram calls, renderer calls, file writes, or credential access\\n- review/delivery text stays under Telegram's 4096-character limit\\n- review/delivery text does not contain `publish` or `post`\\n- platform labels are human-readable (`YouTube Shorts`, `TikTok`, `Instagram Reels`) while stored platform ids remain normalized (`youtube`, `tiktok`, `instagram`)\\n- preview script/reference matches the candidate source brief before building the social draft\\n\\nOnly after the draft is structurally approved should TTS design start:\\n\\n- voice selection\\n- final voiceover script\\n- words-per-second / timing\\n- caption sync\\n- audio format\\n- provider choice\\n- separate voice approval if needed\\n\\n## Guardrails\\n\\n- No credentials in draft output.\\n- No platform API calls.\\n- No publish/post semantics except inside explicit approval-command documentation if the codebase already uses that language carefully.\\n- Preview references are allowed; generated media paths should be placeholders unless a later renderer created real artifacts.\\n- Keep captions, numbers, labels, arrows, UI text, claims, and platform metadata renderer-owned / system-owned, never baked into generated AI assets.\\n\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/proof-short-ai-background-rendering-qa.md\", \"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.\", \"file_type\": \".md\"}",
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"content": "{\"content\": \" 1|from __future__ import annotations\\n 2|\\n 3|import hashlib, json, math, subprocess, sys\\n 4|from pathlib import Path\\n 5|from PIL import Image, ImageDraw, ImageFont, ImageEnhance\\n 6|\\n 7|sys.path.insert(0, '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot')\\n 8|from autoshorts.rendering.static_captions import CaptionLayoutConfig, WordCue, active_word_index_at, build_safe_caption_segments, layout_caption_segment, segment_for_active_word\\n 9|\\n 10|BASE = Path('/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s')\\n 11|OUT = BASE / 'visible_invite_cinematic_ab_v1'\\n 12|AUDIO = BASE / 'audio/voiceover_word_boundary.mp3'\\n 13|WORD_CUES_JSON = BASE / 'audio/word_cues.json'\\n 14|CANDIDATE = 'calendar-invite-login-trap-30s'\\n 15|W,H,FPS = 1080,1920,24\\n 16|FONT_BOLD='/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf'\\n 17|FONT_REG='/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf'\\n 18|SAFE = CaptionLayoutConfig(video_width=W, video_height=H, horizontal_margin_px=70, bottom_margin_px=300, max_width_ratio=0.86, font_size_px=66, min_font_size_px=48, word_gap_px=18)\\n 19|SCRIPT = \\\"This calendar invite looks normal. The trap is the login button. Same meeting title. Same urgent tone. But the link points somewhere else. Before you sign in, slow down. Check who sent it. Check the link domain. Notice unusual login requests. If the domain feels off, do not sign in from the invite. That means: close the invite. Open your calendar or work app from the icon or bookmark you normally use. If the meeting is real, it will still be there. The rule: trust the path you chose, not the button that chose you.\\\"\\n 20|KEYFRAMES = [OUT/'ai_keyframes_b3_text_detail'/f'b3_text_detail_keyframe_{i:02d}_openai_codex_gpt-image-2-high.png' for i in range(1,7)]\\n 21|SCENES = [(0,6.2,0),(6.2,13.4,1),(13.4,20.8,2),(20.8,28.8,3),(28.8,35.6,4),(35.6,42.0,5)]\\n 22|FADE=0.75\\n 23|\\n 24|\\n 25|def sha256(path: Path)->str:\\n 26| h=hashlib.sha256()\\n 27| with path.open('rb') as f:\\n 28| for c in iter(lambda:f.read(1024*1024), b''): h.update(c)\\n 29| return h.hexdigest()\\n 30|\\n 31|def font(size,bold=True): return ImageFont.truetype(FONT_BOLD if bold else FONT_REG, size)\\n 32|\\n 33|def ffprobe_duration(path: Path)->float:\\n 34| return float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nokey=1:noprint_wrappers=1',str(path)], text=True).strip())\\n 35|\\n 36|def load_cues():\\n 37| return tuple(WordCue(start=float(x['start']),end=float(x['end']),word=x['word'],sentence=x.get('sentence','')) for x in json.loads(WORD_CUES_JSON.read_text()))\\n 38|WORD_CUES=load_cues(); SEGMENTS=build_safe_caption_segments(WORD_CUES, layout=SAFE, min_words=3, target_words=5, max_words=7)\\n 39|AUDIO_DURATION=ffprobe_duration(AUDIO); DURATION=max(AUDIO_DURATION+0.5, WORD_CUES[-1].end+0.6)\\n 40|SCENES[-1]=(SCENES[-1][0],DURATION,SCENES[-1][2])\\n 41|\\n 42|def cover_resize(im):\\n 43| im=im.convert('RGB'); target=W/H; ratio=im.width/im.height\\n 44| if ratio>target:\\n 45| nw=int(im.height*target); left=(im.width-nw)//2; im=im.crop((left,0,left+nw,im.height))\\n 46| else:\\n 47| nh=int(im.width/target); top=(im.height-nh)//2; im=im.crop((0,top,im.width,top+nh))\\n 48| return im.resize((W,H), Image.Resampling.LANCZOS)\\n 49|IMAGES=[]\\n 50|for p in KEYFRAMES:\\n 51| im=cover_resize(Image.open(p)); im=ImageEnhance.Contrast(im).enhance(1.03); im=ImageEnhance.Color(im).enhance(0.96); IMAGES.append(im)\\n 52|\\n 53|def scene_at(t):\\n 54| for i,(s,e,idx) in enumerate(SCENES):\\n 55| if s<=t str:\\n h = hashlib.sha256()\\n with path.open('rb') as f:\\n for c in iter(lambda: f.read(1024 * 1024), b''):\\n h.update(c)\\n return h.hexdigest()\\n\\n\\ndef font(size: int, bold: bool = True):\\n return ImageFont.truetype(FONT_BOLD if bold else FONT_REG, size)\\n\\n\\nasync def make_audio_and_cues() -> tuple[Path, Path]:\\n import edge_tts\\n AUDIO_DIR.mkdir(parents=True, exist_ok=True)\\n audio = AUDIO_DIR / 'voiceover_word_boundary.mp3'\\n cues_path = AUDIO_DIR / 'word_cues.json'\\n communicate = edge_tts.Communicate(SCRIPT, VOICE, rate='+0%', boundary='WordBoundary')\\n audio_bytes = bytearray()\\n cues = []\\n async for chunk in communicate.stream():\\n if chunk['type'] == 'audio':\\n audio_bytes.extend(chunk['data'])\\n elif chunk['type'] == 'WordBoundary':\\n start = chunk['offset'] / 10_000_000\\n dur = max(0.08, chunk.get('duration', 0) / 10_000_000)\\n word = chunk.get('text', '').strip()\\n if word:\\n cues.append({'start': start, 'end': start + dur, 'word': word, 'sentence': SCRIPT})\\n audio.write_bytes(bytes(audio_bytes))\\n # Edge sometimes returns very short durations; extend each cue to next cue for cleaner active-word highlighting.\\n for i in range(len(cues) - 1):\\n cues[i]['end'] = max(cues[i]['end'], min(cues[i + 1]['start'], cues[i]['start'] + 0.55))\\n if cues:\\n cues[-1]['end'] = max(cues[-1]['end'], cues[-1]['start'] + 0.35)\\n cues_path.write_text(json.dumps(cues, indent=2), encoding='utf-8')\\n return audio, cues_path\\n\\n\\ndef ffprobe_duration(path: Path) -> float:\\n return float(subprocess.check_output(['ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=nokey=1:noprint_wrappers=1', str(path)], text=True).strip())\\n\\n\\ndef cover_resize(im: Image.Image) -> Image.Image:\\n im = im.convert('RGB')\\n target = W / H\\n ratio = im.width / im.height\\n if ratio > target:\\n nw = int(im.height * target)\\n left = (im.width - nw) // 2\\n im = im.crop((left, 0, left + nw, im.height))\\n else:\\n nh = int(im.width / target)\\n top = (im.height - nh) // 2\\n im = im.crop((0, top, im.width, top + nh))\\n return im.resize((W, H), Image.Resampling.LANCZOS)\\n\\n\\ndef load_assets():\\n for p in KEYFRAMES:\\n if not p.exists():\\n raise FileNotFoundError(p)\\n images = []\\n for p in KEYFRAMES:\\n im = cover_resize(Image.open(p))\\n im = ImageEnhance.Contrast(im).enhance(1.04)\\n im = ImageEnhance.Color(im).enhance(0.98)\\n images.append(im)\\n return images\\n\\n\\ndef load_cues(cues_path: Path):\\n raw = json.loads(cues_path.read_text())\\n return tuple(WordCue(start=float(x['start']), end=float(x['end']), word=x['word'], sentence=x.get('sentence', '')) for x in raw)\\n\\n\\ndef make_renderer(images, word_cues, duration):\\n scenes = list(SCENES)\\n scenes[-1] = (scenes[-1][0], duration, scenes[-1][2])\\n segments = build_safe_caption_segments(word_cues, layout=SAFE, min_words=3, target_words=5, max_words=7)\\n\\n def scene_at(t: float):\\n for i, (s, e, idx) in enumerate(scenes):\\n if s <= t < e:\\n return i, s, e, idx\\n return len(scenes) - 1, *scenes[-1]\\n\\n def motion_image(idx: int, local: float, dur: float):\\n base = images[idx]\\n p = max(0, min(1, local / max(0.01, dur)))\\n scale = (1.035 + 0.055 * p) if idx % 2 == 0 else (1.08 - 0.035 * p)\\n sw, sh = int(W * scale), int(H * scale)\\n im = base.resize((sw, sh), Image.Resampling.LANCZOS)\\n px = int((14 if idx % 2 == 0 else -12) * math.sin(p * math.pi))\\n py = int(9 * math.cos(p * math.pi))\\n left = max(0, min(sw - W, (sw - W) // 2 + px))\\n top = max(0, min(sh - H, (sh - H) // 2 + py))\\n return im.crop((left, top, left + W, top + H))\\n\\n def background_at(t: float):\\n si, s, e, idx = scene_at(t)\\n im = motion_image(idx, t - s, e - s)\\n if si < len(scenes) - 1 and e - t < FADE:\\n ns, ne, nidx = scenes[si + 1]\\n nim = motion_image(nidx, max(0, t - ns), ne - ns)\\n im = Image.blend(im, nim, max(0, min(1, 1 - (e - t) / FADE)))\\n veil = Image.new('RGBA', (W, H), (0, 0, 0, 0))\\n d = ImageDraw.Draw(veil, 'RGBA')\\n # translucent only, no caption box/bar\\n d.rectangle((0, 1240, W, H), fill=(3, 6, 12, 70))\\n return Image.alpha_composite(im.convert('RGBA'), veil).convert('RGB')\\n\\n def draw_caption(draw: ImageDraw.ImageDraw, t: float):\\n active = active_word_index_at(word_cues, t)\\n seg = segment_for_active_word(segments, active)\\n active_word = word_cues[active].word\\n layout = layout_caption_segment(seg.words, active_word=active_word, config=SAFE)\\n f = font(layout.font_size_px, True)\\n flat = []\\n for line in layout.lines:\\n flat.extend(line)\\n for word, box in zip(flat, layout.word_boxes):\\n x, y, _, _ = box\\n fill = (247, 202, 76) if word == active_word else (255, 255, 255)\\n for dx, dy in [(-2, 2), (2, 2), (0, 3)]:\\n draw.text((x + dx, y + dy), word, font=f, fill=(0, 0, 0, 170), stroke_width=2, stroke_fill=(0, 0, 0, 150))\\n draw.text((x, y), word, font=f, fill=fill, stroke_width=2, stroke_fill=(6, 10, 18))\\n\\n def draw_frame(t: float):\\n img = background_at(t)\\n draw = ImageDraw.Draw(img, 'RGBA')\\n draw_caption(draw, t)\\n return img\\n\\n return draw_frame\\n\\n\\ndef render():\\n OUT.mkdir(parents=True, exist_ok=True)\\n audio, cues_path = asyncio.run(make_audio_and_cues())\\n images = load_assets()\\n word_cues = load_cues(cues_path)\\n audio_duration = ffprobe_duration(audio)\\n duration = max(audio_duration + 0.25, word_cues[-1].end + 0.55)\\n draw_frame = make_renderer(images, word_cues, duration)\\n\\n silent = OUT / f'{CANDIDATE}_silent.mp4'\\n final_raw = OUT / f'{CANDIDATE}_raw.mp4'\\n final = OUT / f'{CANDIDATE}_postpreview_v1.mp4'\\n proc = subprocess.Popen(['ffmpeg', '-y', '-hide_banner', '-loglevel', 'error', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-pix_fmt', 'rgb24', '-s', f'{W}x{H}', '-r', str(FPS), '-i', '-', '-an', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-crf', '18', str(silent)], stdin=subprocess.PIPE)\\n for i in range(int(math.ceil(duration * FPS))):\\n proc.stdin.write(draw_frame(i / FPS).tobytes())\\n proc.stdin.close(); proc.wait()\\n if proc.returncode:\\n raise SystemExit(proc.returncode)\\n subprocess.run(['ffmpeg', '-y', '-hide_banner', '-loglevel', 'error', '-i', str(silent), '-i', str(audio), '-map', '0:v:0', '-map', '1:a:0', '-c:v', 'copy', '-c:a', 'aac', '-b:a', '192k', str(final_raw)], check=True)\\n # final mobile-safe export with explicit SAR/DAR metadata\\n subprocess.run(['ffmpeg', '-y', '-hide_banner', '-loglevel', 'error', '-i', str(final_raw), '-map', '0:v:0', '-map', '0:a:0?', '-vf', 'scale=1080:1920,setsar=1,setdar=9/16,format=yuv420p', '-c:v', 'libx264', '-profile:v', 'high', '-level:v', '4.2', '-preset', 'slow', '-crf', '16', '-c:a', 'aac', '-b:a', '192k', '-movflags', '+faststart', '-metadata:s:v:0', 'rotate=0', str(final)], check=True)\\n\\n qadir = OUT / 'qa_frames'; qadir.mkdir(exist_ok=True)\\n sample = [1, 5, 9, 15, 22, 29, 36, min(duration - 0.5, 40.5)]\\n thumbs = []\\n for ts in sample:\\n im = draw_frame(ts)\\n p = qadir / f'frame_{str(round(ts,1)).replace(\\\".\\\", \\\"_\\\")}s.jpg'\\n im.save(p, quality=92)\\n thumbs.append((ts, im.resize((270, 480))))\\n sheet = Image.new('RGB', (1080, 960), (12, 16, 24))\\n sd = ImageDraw.Draw(sheet)\\n for j, (ts, thumb) in enumerate(thumbs):\\n x = (j % 4) * 270\\n y = (j // 4) * 480\\n sheet.paste(thumb, (x, y))\\n sd.rectangle((x, y, x + 269, y + 479), outline=(80, 90, 115), width=2)\\n sd.text((x + 12, y + 12), f'{ts:.1f}s', font=font(24), fill=(247, 202, 76))\\n contact = OUT / 'render_contact_sheet_v1.jpg'\\n sheet.save(contact, quality=92)\\n\\n probe = json.loads(subprocess.check_output(['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt', '-show_entries', 'format=duration', '-of', 'json', str(final)], text=True))\\n manifest = {\\n 'candidate_id': CANDIDATE,\\n 'version': VERSION,\\n 'status': 'postpreview_review_only_not_posted',\\n 'visual_standard': 'detailed_ai_generated_screen_images_readable_ui_text_no_focus_lines_active_word_subtitles',\\n 'topic': 'fake shared document access request / login trap',\\n 'script': SCRIPT,\\n 'voice': VOICE,\\n 'preview_mp4_path': str(final),\\n 'preview_sha256': sha256(final),\\n 'duration_seconds': duration,\\n 'ffprobe': probe,\\n 'keyframes': [str(p) for p in KEYFRAMES],\\n 'keyframes_contact_sheet': str(BASE / 'shared_doc_keyframes_contact_sheet_v1.jpg'),\\n 'render_contact_sheet': str(contact),\\n 'blocked': {'posting': False, 'platform_api_used': False, 'wan_used': False, 'i2v_used': False, 't2v_used': False},\\n 'approval_commands': [\\n f'APPROVED_POSTPREVIEW {CANDIDATE} {VERSION}',\\n f'REVISE_POSTPREVIEW {CANDIDATE} \\\"...\\\"',\\n f'REJECT_POSTPREVIEW {CANDIDATE}'\\n ],\\n 'social_draft': {\\n 'platforms': ['YouTube Shorts', 'TikTok', 'Instagram Reels'],\\n 'title_variants': ['The shared file trap', 'Never open shared docs this way', 'This fake document steals logins'],\\n 'caption': 'A shared file can be the cleanest-looking login trap. Close the email. Open your work drive yourself. If it is real, it will still be there.',\\n 'hashtags': ['#cybersecurity', '#phishing', '#productivity', '#workflows', '#TrueTrace'],\\n 'cta': 'Follow for hidden digital traps you can actually fix.'\\n }\\n }\\n (OUT / 'review_package_v1.json').write_text(json.dumps(manifest, indent=2), encoding='utf-8')\\n print(json.dumps({'preview': str(final), 'sha256': manifest['preview_sha256'], 'duration': duration, 'contact': str(contact), 'probe': probe}, indent=2))\\n\\n\\nif __name__ == '__main__':\\n render()\\n\"}"
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Static active-word caption segment logic.\\n 2|\\n 3|Caption rule for TrueTraceShorts previews/finals:\\n 4|- A subtitle phrase segment stays fixed on screen.\\n 5|- At least three words are visible per default segment whenever possible.\\n 6|- Only the active spoken word changes highlight.\\n 7|- The highlight moves left-to-right until the next phrase segment replaces it.\\n 8|- Caption layout must remain inside the mobile safe zone.\\n 9|\\\"\\\"\\\"\\n 10|\\n 11|from __future__ import annotations\\n 12|\\n 13|from dataclasses import dataclass\\n 14|from typing import Sequence\\n 15|\\n 16|\\n 17|@dataclass(frozen=True)\\n 18|class WordCue:\\n 19| start: float\\n 20| end: float\\n 21| word: str\\n 22| sentence: str = \\\"\\\"\\n 23|\\n 24|\\n 25|@dataclass(frozen=True)\\n 26|class StaticCaptionSegment:\\n 27| start: float\\n 28| end: float\\n 29| word_indices: tuple[int, ...]\\n 30| words: tuple[str, ...]\\n 31| sentence: str = \\\"\\\"\\n 32|\\n 33| @property\\n 34| def text(self) -> str:\\n 35| return \\\" \\\".join(self.words)\\n 36|\\n 37|\\n 38|@dataclass(frozen=True)\\n 39|class CaptionLayoutConfig:\\n 40| video_width: int = 1080\\n 41| video_height: int = 1920\\n 42| horizontal_margin_px: int = 60\\n 43| bottom_margin_px: int = 260\\n 44| max_width_ratio: float = 0.88\\n 45| max_lines: int = 2\\n 46| font_size_px: int = 68\\n 47| min_font_size_px: int = 46\\n 48| line_gap_px: int = 14\\n 49| word_gap_px: int = 18\\n 50| highlight_pad_x_px: int = 10\\n 51| highlight_pad_y_px: int = 5\\n 52|\\n 53| @property\\n 54| def safe_width_px(self) -> int:\\n 55| ratio_width = int(self.video_width * self.max_width_ratio)\\n 56| margin_width = self.video_width - 2 * self.horizontal_margin_px\\n 57| return min(ratio_width, margin_width)\\n 58|\\n 59|\\n 60|@dataclass(frozen=True)\\n 61|class CaptionLayout:\\n 62| lines: tuple[tuple[str, ...], ...]\\n 63| font_size_px: int\\n 64| left_px: int\\n 65| right_px: int\\n 66| top_px: int\\n 67| bottom_px: int\\n 68| width_px: int\\n 69| height_px: int\\n 70| word_boxes: tuple[tuple[int, int, int, int], ...]\\n 71| active_word_box: tuple[int, int, int, int] | None\\n 72|\\n 73|\\n 74|def build_static_caption_segments(\\n 75| word_cues: Sequence[WordCue],\\n 76| *,\\n 77| min_words: int = 3,\\n 78| target_words: int = 6,\\n 79| max_words: int = 7,\\n 80|) -> tuple[StaticCaptionSegment, ...]:\\n 81| if min_words < 2:\\n 82| raise ValueError(\\\"min_words must be >= 2\\\")\\n 83| if target_words < min_words:\\n 84| raise ValueError(\\\"target_words must be >= min_words\\\")\\n 85| if max_words < target_words:\\n 86| raise ValueError(\\\"max_words must be >= target_words\\\")\\n 87|\\n 88| grouped: list[list[tuple[int, WordCue]]] = []\\n 89| current_sentence: str | None = None\\n 90| current: list[tuple[int, WordCue]] = []\\n 91| for index, cue in enumerate(word_cues):\\n 92| sentence_key = cue.sentence or \\\"__no_sentence__\\\"\\n 93| if current and sentence_key != current_sentence:\\n 94| grouped.append(current)\\n 95| current = []\\n 96| current_sentence = sentence_key\\n 97| current.append((index, cue))\\n 98| if current:\\n 99| grouped.append(current)\\n 100|\\n 101| segments: list[StaticCaptionSegment] = []\\n 102| for group in grouped:\\n 103| chunks: list[list[tuple[int, WordCue]]] = []\\n 104| i = 0\\n 105| while i < len(group):\\n 106| remaining = len(group) - i\\n 107| size = min(target_words, remaining)\\n 108| if remaining < min_words and chunks:\\n 109| chunks[-1].extend(group[i:])\\n 110| break\\n 111| if size < min_words and len(group) >= min_words:\\n 112| start = max(0, len(group) - min_words)\\n 113| chunks.append(group[start:])\\n 114| break\\n 115| chunks.append(group[i : i + size])\\n 116| i += size\\n 117| for chunk in chunks:\\n 118| if not chunk:\\n 119| continue\\n 120| if len(chunk) > max_words:\\n 121| # Split oversized merged chunks conservatively while preserving min_words.\\n 122| while len(chunk) > max_words:\\n 123| head = chunk[:max_words]\\n 124| segments.append(_segment_from_chunk(head))\\n 125| chunk = chunk[max_words:]\\n 126| if 0 < len(chunk) < min_words and segments:\\n 127| previous = segments.pop()\\n 128| prev_pairs = [(idx, word_cues[idx]) for idx in previous.word_indices]\\n 129| chunk = prev_pairs + chunk\\n 130| segments.append(_segment_from_chunk(chunk))\\n 131| return tuple(segments)\\n 132|\\n 133|\\n 134|def build_safe_caption_segments(\\n 135| word_cues: Sequence[WordCue],\\n 136| *,\\n 137| layout: CaptionLayoutConfig | None = None,\\n 138| min_words: int = 3,\\n 139| target_words: int = 6,\\n 140| max_words: int = 7,\\n 141|) -> tuple[StaticCaptionSegment, ...]:\\n 142| \\\"\\\"\\\"Build phrase segments that are pre-split to fit the caption safe zone.\\n 143|\\n 144| The function keeps the established 3–7 word phrase behavior, but refuses to\\n 145| create a segment that would need more than two lines or exceed the safe\\n 146| width at the minimum allowed font size. Very long tokens are handled by\\n 147| dynamic shrink in layout, and if a phrase is still too wide the segment is\\n 148| split earlier.\\n 149| \\\"\\\"\\\"\\n 150| cfg = layout or CaptionLayoutConfig()\\n 151| base_segments = build_static_caption_segments(\\n 152| word_cues,\\n 153| min_words=min_words,\\n 154| target_words=target_words,\\n 155| max_words=max_words,\\n 156| )\\n 157| safe_segments: list[StaticCaptionSegment] = []\\n 158| for segment in base_segments:\\n 159| pairs = [(idx, word_cues[idx]) for idx in segment.word_indices]\\n 160| current: list[tuple[int, WordCue]] = []\\n 161| for pair in pairs:\\n 162| candidate = current + [pair]\\n 163| candidate_words = tuple(cue.word for _, cue in candidate)\\n 164| if len(candidate_words) <= max_words and _fits_caption_layout(candidate_words, cfg):\\n 165| current = candidate\\n 166| continue\\n 167| if current:\\n 168| safe_segments.append(_segment_from_chunk(current))\\n 169| current = [pair]\\n 170| else:\\n 171| current = [pair]\\n 172| if current:\\n 173| # Try to merge a too-small tail back only when still safe.\\n 174| if (\\n 175| len(current) < min_words\\n 176| and safe_segments\\n 177| and len(safe_segments[-1].words) + len(current) <= max_words\\n 178| ):\\n 179| prev = safe_segments[-1]\\n 180| merged_pairs = [(idx, word_cues[idx]) for idx in prev.word_indices] + current\\n 181| merged_words = tuple(cue.word for _, cue in merged_pairs)\\n 182| if _fits_caption_layout(merged_words, cfg):\\n 183| safe_segments[-1] = _segment_from_chunk(merged_pairs)\\n 184| else:\\n 185| safe_segments.append(_segment_from_chunk(current))\\n 186| else:\\n 187| safe_segments.append(_segment_from_chunk(current))\\n 188| return tuple(safe_segments)\\n 189|\\n 190|\\n 191|def _segment_from_chunk(chunk: Sequence[tuple[int, WordCue]]) -> StaticCaptionSegment:\\n 192| return StaticCaptionSegment(\\n 193| start=chunk[0][1].start,\\n 194| end=chunk[-1][1].end,\\n 195| word_indices=tuple(index for index, _ in chunk),\\n 196| words=tuple(cue.word for _, cue in chunk),\\n 197| sentence=chunk[0][1].sentence,\\n 198| )\\n 199|\\n 200|\\n 201|def active_word_index_at(word_cues: Sequence[WordCue], timestamp: float) -> int:\\n 202| for index, cue in enumerate(word_cues):\\n 203| if cue.start <= timestamp < cue.end:\\n 204| return index\\n 205| previous = [index for index, cue in enumerate(word_cues) if cue.start <= timestamp]\\n 206| return previous[-1] if previous else 0\\n 207|\\n 208|\\n 209|def segment_for_active_word(segments: Sequence[StaticCaptionSegment], active_word_index: int) -> StaticCaptionSegment:\\n 210| for segment in segments:\\n 211| if active_word_index in segment.word_indices:\\n 212| return segment\\n 213| raise ValueError(f\\\"active word index {active_word_index} is not present in any segment\\\")\\n 214|\\n 215|\\n 216|def caption_state_at(word_cues: Sequence[WordCue], segments: Sequence[StaticCaptionSegment], timestamp: float) -> dict[str, object]:\\n 217| active_index = active_word_index_at(word_cues, timestamp)\\n 218| segment = segment_for_active_word(segments, active_index)\\n 219| return {\\n 220| \\\"segment_text\\\": segment.text,\\n 221| \\\"segment_words\\\": segment.words,\\n 222| \\\"word_indices\\\": segment.word_indices,\\n 223| \\\"active_word_index\\\": active_index,\\n 224| \\\"active_word\\\": word_cues[active_index].word,\\n 225| }\\n 226|\\n 227|\\n 228|def layout_caption_segment(\\n 229| words: Sequence[str],\\n 230| *,\\n 231| active_word: str | None = None,\\n 232| config: CaptionLayoutConfig | None = None,\\n 233|) -> CaptionLayout:\\n 234| cfg = config or CaptionLayoutConfig()\\n 235| if not words:\\n 236| raise ValueError(\\\"words must not be empty\\\")\\n 237|\\n 238| for size in range(cfg.font_size_px, cfg.min_font_size_px - 1, -2):\\n 239| lines = _wrap_words(words, cfg, size)\\n 240| metrics = _measure_lines(lines, cfg, size, active_word=active_word)\\n 241|\", \"total_lines\": 360, \"file_size\": 12920, \"truncated\": true, \"hint\": \"Use offset=241 to continue reading (showing 1-240 of 360 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"todos\": [{\"id\": \"concept\", \"content\": \"Nächstes Proof-Short-Konzept festgelegt: fake shared-document access request / login trap\", \"status\": \"completed\"}, {\"id\": \"images\", \"content\": \"Detaillierte KI-Screen-Keyframes im neuen Standard erzeugen und QA prüfen\", \"status\": \"completed\"}, {\"id\": \"render\", \"content\": \"Preview-MP4 mit Voiceover + active-word subtitles + 9:16 Export bauen\", \"status\": \"completed\"}, {\"id\": \"qa\", \"content\": \"ffprobe, Frame-QA, Hash und Freigabe-Paket erstellen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}",
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"content": "Erledigt, Sir — nächster **Postpreview zur Freigabe** ist gebaut.\n\n## Postpreview #2 — Shared Document Login Trap\n\n**Thema:** Fake “shared document” request / Login-Trap \n**Format:** Neuer Standard — detaillierte KI-Screenbilder mit lesbarem UI-Text, keine Linien/Pfeile/Boxen, active-word subtitles.\n\nMEDIA:/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpreview_v1/shared-document-login-trap-38s_postpreview_v1.mp4\n\n## QA / Technik\n\n- Auflösung: `1080x1920`\n- SAR: `1:1`\n- DAR: `9:16`\n- FPS: `24`\n- Länge: `40.208 s`\n- Audio: enthalten\n- Subtitles: active-word highlight\n- Keine Fokus-Linien / Pfeile / Boxen\n- Keine Menschen / Hände / QR / Watermark sichtbar\n- Kein Wan / I2V / T2V\n- Kein Posting / keine Platform-API\n\nSHA256:\n\n```text\n83b97208711af68112b722f09e6451989ccca8a6ce8780efb444ba4fcc60cd63\n```\n\n## Social Draft\n\n**Titelvarianten:**\n- The shared file trap\n- Never open shared docs this way\n- This fake document steals logins\n\n**Caption:**\nA shared file can be the cleanest-looking login trap. Close the email. Open your work drive yourself. If it is real, it will still be there.\n\n**Hashtags:**\n`#cybersecurity #phishing #productivity #workflows #TrueTrace`\n\n**CTA:**\nFollow for hidden digital traps you can actually fix.\n\n## Freigabe-Kommandos\n\nWenn passend:\n\n`APPROVED_POSTPREVIEW shared-document-login-trap-38s v1.0.0-detailed-ai-screens`\n\nWenn ändern:\n\n`REVISE_POSTPREVIEW shared-document-login-trap-38s \"...\"`\n\nWenn verwerfen:\n\n`REJECT_POSTPREVIEW shared-document-login-trap-38s`",
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"text": "Erledigt, Sir — nächster **Postpreview zur Freigabe** ist gebaut.\n\n## Postpreview #2 — Shared Document Login Trap\n\n**Thema:** Fake “shared document” request / Login-Trap \n**Format:** Neuer Standard — detaillierte KI-Screenbilder mit lesbarem UI-Text, keine Linien/Pfeile/Boxen, active-word subtitles.\n\nMEDIA:/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpreview_v1/shared-document-login-trap-38s_postpreview_v1.mp4\n\n## QA / Technik\n\n- Auflösung: `1080x1920`\n- SAR: `1:1`\n- DAR: `9:16`\n- FPS: `24`\n- Länge: `40.208 s`\n- Audio: enthalten\n- Subtitles: active-word highlight\n- Keine Fokus-Linien / Pfeile / Boxen\n- Keine Menschen / Hände / QR / Watermark sichtbar\n- Kein Wan / I2V / T2V\n- Kein Posting / keine Platform-API\n\nSHA256:\n\n```text\n83b97208711af68112b722f09e6451989ccca8a6ce8780efb444ba4fcc60cd63\n```\n\n## Social Draft\n\n**Titelvarianten:**\n- The shared file trap\n- Never open shared docs this way\n- This fake document steals logins\n\n**Caption:**\nA shared file can be the cleanest-looking login trap. Close the email. Open your work drive yourself. If it is real, it will still be there.\n\n**Hashtags:**\n`#cybersecurity #phishing #productivity #workflows #TrueTrace`\n\n**CTA:**\nFollow for hidden digital traps you can actually fix.\n\n## Freigabe-Kommandos\n\nWenn passend:\n\n`APPROVED_POSTPREVIEW shared-document-login-trap-38s v1.0.0-detailed-ai-screens`\n\nWenn ändern:\n\n`REVISE_POSTPREVIEW shared-document-login-trap-38s \"...\"`\n\nWenn verwerfen:\n\n`REJECT_POSTPREVIEW shared-document-login-trap-38s`"
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"content": "[M G] Ok, jetzt den nächsten vorschlag. Schau das du etwas abwechslung rein bringst und der Hook gut ist. Macht es sinn das erste bim (bzw das was man beim durchscroolen sieht) etwas dramatischer zu machen? So dass mab auch ohne lesen gleich versteht worum es geht?"
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/retention-led-shorts.md\", \"content\": \"# Retention-Led Shorts Development Notes\\n\\nUse these notes when turning a short-form video bot from a renderer into a growth engine.\\n\\n## What tends to stop the scroll\\n\\nPrioritize ideas and scripts with these traits:\\n\\n1. **Immediate self-relevance** — the first line names a concrete situation the viewer recognizes: “If your brain has 47 tabs open...”\\n2. **Open loop** — the hook promises a specific resolution: “do this before...”, “why...”, “stop...”, “not...”, “instead...”.\\n3. **Specificity** — numbers, named platforms/tools, exact timeframes, visible objects, or concrete scenarios beat generic advice.\\n4. **Tension / contrarian angle** — challenge a common belief without cheap outrage: “You do not need more discipline. You need a smaller first step.”\\n5. **Concrete payoff** — a prompt, template, before/after, checklist, exact micro-action, or decision rule.\\n6. **Visual pattern interrupt** — a motion/change in the first 1–2 seconds; avoid static AI-art slideshows.\\n7. **Conversation trigger** — a specific comment keyword or question tied to the payoff.\\n8. **Niche authority** — recognizable viewpoint and repeated format, not broad generic life advice.\\n\\n## Practical scoring signals\\n\\nAdd or reward:\\n\\n- direct address (`you`, `your`, `du`, `dein`)\\n- open-loop words (`do this`, `before`, `why`, `stop`, `not`, `instead`)\\n- specificity markers (numbers, named tools, `minute`, `tabs`, `AI/KI`, platform names)\\n- tension or myth-busting (`not`, `without`, `stop`, `instead`)\\n- short hook (roughly <= 14 words when possible)\\n- visual energy (before/after, screen recording, timer, cards, close-up, fast cut)\\n- concrete payoff (template, prompt, calendar block, two-minute action)\\n- comment CTA (`Comment RESET`, `Comment CRITIC`, “Which app...?”)\\n\\nPenalize or reject:\\n\\n- slow intros: “In this video...”, “Today I will talk about...”\\n- generic value: “productivity tips”, “habits”, “mindset” without a concrete mechanism\\n- static slideshow / abstract background with no visual transformation\\n- unproven videos over ~90s\\n- AI-generated fake UI/text, illegible charts, or obvious “AI slop” artifacts\\n\\n## Testbatch pattern\\n\\nFor early channels, structure tests as:\\n\\n- 2–3 non-conflicting content pillars.\\n- 3 ideas per pillar.\\n- 6 growth candidates around 25–45s.\\n- 3 monetization candidates around 61–75s if TikTok Creator Rewards is relevant.\\n- Generate review drafts first, then finalize voiceover for top 3, then build shot plans, then render.\\n\\n## Example safer pillars for AI/productivity channels\\n\\nIf faith/religion creates brand or policy ambiguity, pause it and test:\\n\\n- `life_optimization` — systems over motivation, calendars, task triage, small first steps.\\n- `ai_life_systems` — AI as critic, organizer, assistant, approval-gated automation.\\n- `attention_design` — doomscrolling, defaults, phone/app environment design, algorithm incentives.\\n\\n## Voiceover finalization rules\\n\\n- Keep the hook as the exact first spoken sentence.\\n- Make 25–45s growth scripts roughly 70–110 words.\\n- Make 61–75s TikTok reward candidates roughly 140–180 words.\\n- Use plain spoken language; no academic transitions.\\n- For AI content, keep the human decision/approval moment explicit.\\n- For attention design, avoid moral panic; frame changes as defaults and incentives.\\n- Generate caption beats and production notes before rendering.\\n\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/proof-short-real-problem-reset.md\", \"content\": \"# Proof Short Real-Problem Reset\\n\\nUse this when an early short/pilot looks increasingly polished but still fails because viewers cannot see a real problem in the first second.\\n\\n## Trigger\\n\\nSwitch to this mode when the user says a pilot is:\\n\\n- pretty but semantically empty\\n- too abstract or generic\\n- missing a visible fail / real viewer pain\\n- overproduced before the format is validated\\n- relying on AI images, Wan/I2V, glassmorphism, or B-roll as the story carrier\\n\\n## Archive the old candidate first\\n\\nMark the old candidate as a paused learning case, not a render target.\\n\\nRecommended archive fields:\\n\\n```json\\n{\\n \\\"status\\\": \\\"paused_learning_case\\\",\\n \\\"reason\\\": \\\"Pretty but semantically weak. Too abstract for first pilot. Visuals do not create immediate viewer pain or retention.\\\",\\n \\\"do_not_continue\\\": {\\n \\\"further_optimization\\\": false,\\n \\\"rendering\\\": false,\\n \\\"posting\\\": false,\\n \\\"wan\\\": false,\\n \\\"i2v\\\": false,\\n \\\"platform_api\\\": false\\n }\\n}\\n```\\n\\nPostmortem structure:\\n\\n- Worked: approval gates, clean captions, delivery discipline, workflow/styleframe learning, technical I2V sanity where applicable.\\n- Failed: topic too abstract, no visible fail, too much production before validation, too many visual metaphors, AI video/image generation used too early as story carrier.\\n- New rule: `Pretty Empty Visual Blocker`.\\n\\n## Pretty Empty Visual Blocker\\n\\nBlock a candidate or visual plan if:\\n\\n- the image is high-quality but does not show a clear problem without voiceover\\n- no visible fail exists\\n- the visual is only mood/B-roll\\n- the viewer cannot tell within 1 second what is at stake\\n- usefulness only emerges through abstract explanation\\n- the scene is interchangeable with dozens of other AI/productivity videos\\n\\nA visual plan must answer:\\n\\n1. What is concretely broken?\\n2. Where can the viewer see the error?\\n3. What is at stake for the viewer?\\n4. Which simple rule repairs it?\\n5. Why would someone save or share this?\\n\\n## ProofShortMode defaults\\n\\nProof Shorts are the default for the first 10–20 unvalidated videos.\\n\\n- 25–35 seconds\\n- screen/mockup-led or real-problem-led\\n- no Wan final\\n- no I2V/T2V full render\\n- no premium styleframe pass\\n- no abstract metaphors\\n- no AI B-roll as main visual\\n- fast useful preview to validate topic/format\\n- manual approval before any posting pack\\n\\nPremium episodes come only after the theme/format is proven by viewer response.\\n\\n## Good first Proof Short shape\\n\\nFor digital safety/workflow/AI-mechanism content:\\n\\n```text\\nvisible fail → recognition → mechanism → simple check/rule → safe next action → memorable takeaway\\n```\\n\\nExample topic class: fake invoice/payment-route red flag.\\n\\n- Use controlled fictional mockups.\\n- Show invoice/email/payment route in the first frame.\\n- Avoid real brands, addresses, IBANs, QR codes, logos, and real people.\\n- Use renderer-owned UI text/highlights; do not use real scam templates.\\n- Keep claims as general safety advice, not legal/financial advice.\\n\\n## Caption style\\n\\nKeep the user's corrected style:\\n\\n- static phrase segments\\n- active-word highlight\\n- white inactive words\\n- yellow active word\\n- no frame/box/full-width bar/dark subtitle block\\n- subtle shadow or outline only\\n- mobile-safe bottom position\\n\\n## ReviewPackageV2 additions\\n\\nFor Proof Shorts include:\\n\\n- `ProofShortMode = true`\\n- selected hook plus hook variants\\n- script\\n- visual plan\\n- Pretty Empty Visual Blocker result\\n- claim gate\\n- AI disclosure\\n- save/share/follow reasons\\n- platform fit\\n- preview MP4 path, SHA256, duration, resolution\\n- Telegram delivery status\\n\\n## Hard blocks until approval\\n\\nDo not run:\\n\\n- Wan/I2V/T2V full render\\n- full-final render\\n- platform API\\n- auto-posting\\n- scheduling\\n- render series\\n\\nAllowed next commands should be explicit, e.g.:\\n\\n```text\\nAPPROVED_PROOF_SHORT \\nREVISE_PROOF_SHORT \\\"Änderungswunsch\\\"\\nREJECT_PROOF_SHORT \\\"Grund\\\"\\nAPPROVED_FOR_MANUAL_POSTING_PACK \\n```\\n\", \"file_type\": \".md\"}",
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/clean-ai-cut-vs-hybrid-mechanism.md\", \"content\": \"# Clean AI Cut vs Hybrid Mechanism — session learning\\n\\nUse this when comparing a pure premium AI-image sequence against a proof short with deterministic mechanism/UI overlays.\\n\\n## Session-derived pattern\\n\\nFor a scam/cybersecurity proof short, a clean image sequence can look significantly more premium and native to TikTok/Reels, but it often weakens immediate mechanism clarity.\\n\\nObserved comparison:\\n\\n```text\\nClean AI Cut = premium mood, calmer, less explainer-board\\nHybrid = clearer mechanism, more immediately useful\\nBest likely final direction = mostly clean images + one minimal deterministic mechanism moment\\n```\\n\\nRecommended framing:\\n\\n```text\\n90% Clean AI Images\\n10% precise renderer-owned mechanism\\n```\\n\\nExamples of acceptable minimal mechanism moments:\\n\\n- one brief `suspicious link/domain` reveal\\n- one brief `close invite -> open trusted app/bookmark` action moment\\n- one simple before/after state, not a full checklist board\\n\\nAvoid returning to:\\n\\n- checklist-heavy UI cards\\n- boxed explainer overlays\\n- arrows/circles/diagram markers\\n- repeated labels and decision-target graphics\\n\\n## Viewer explanation rule\\n\\nWhen the viewer is not technical, do not use shorthand such as `open the service yourself` without explanation. Spell out the exact action:\\n\\n```text\\nClose the invite.\\nOpen your calendar or work app from the normal icon/bookmark.\\nIf the meeting is real, it will still be there.\\nTrust the path you chose, not the button that chose you.\\n```\\n\\nThis converts a security maxim into a concrete next action.\\n\\n## Caption synchronization rule\\n\\nFor final-near shorts where the user complains that subtitles do not match voiceover exactly, use actual word-boundary/forced-alignment timing rather than distributing words evenly across sentence SRT blocks. For EdgeTTS previews, request `boundary='WordBoundary'`, store word cues, then render static phrase segments where only the active spoken word highlights.\\n\\nKeep the established caption policy:\\n\\n- static phrase segment, usually 3–7 words\\n- active spoken word highlighted in yellow\\n- inactive words white\\n- no subtitle box/bar\\n- bottom mobile-safe position\\n\\n## A/B decision criteria\\n\\nAfter rendering both versions, explicitly answer:\\n\\n1. Which version looks more premium?\\n2. Which version explains the mechanism faster without audio?\\n3. Does the clean cut rely too much on VO/subtitles?\\n4. Would one minimal deterministic mechanism moment recover clarity without killing the premium look?\\n5. Is Wan/I2V worth testing, or is image-sequence motion enough for this stage?\\n\\nDefault recommendation: do not jump to full Wan/I2V after this test. If clean images work visually, test exactly one short motion micro-proof only after approval.\\n\\n## Visible-problem keyframe A/B pattern\\n\\nWhen the user asks whether pure KI images can carry a Proof Short, do not generate generic premium atmosphere. The first image batch must make the real viewer problem literally visible without text:\\n\\n- show the concrete object/situation (e.g. a calendar invite card on laptop/phone, a join/login button shape, attendee dots, blank time rows);\\n- keep all generated UI text-free: no letters, pseudo-letters, numbers, logos, QR/barcodes, watermarks, people, hands, or faces;\\n- use contact-sheet QA before rendering and discard frames that only look like generic laptop/desk B-roll;\\n- expect later safe/resolution frames to be less semantically explicit, but the first half must visually anchor the trap.\\n\\nFor the calendar-invite login-trap proof, the updated session result was:\\n\\n```text\\nB1 = clean visible-invite AI images only + voiceover + active-word subtitles; premium but still a bit VO-dependent.\\nB2 = same images + subtle focus outlines; rejected because the lines added little value and reduced the premium look.\\nB3 = detailed AI-generated Outlook-style invite/login/calendar screens with readable UI text + voiceover + active-word subtitles; accepted as the new standard.\\nOutcome = B3 recommended/default: no renderer-added lines/arrows/boxes, but prompt the image model with very detailed, concrete, readable screen/UI content so the problem and safe action are visible in the image itself.\\n```\\n\\nUse this as the default next-step bias: if clean images are beautiful but semantically weak, first make the AI keyframes themselves more specific and content-rich (visible app/email/invite/login/calendar screens, exact readable text, realistic UI details) rather than adding focus lines, arrows, circles, labels, or checklist cards. Deterministic renderer text remains appropriate for captions; visible screen text may be AI-generated when the user explicitly wants detailed realistic UI images and the keyframes pass visual QA.\\n\\nExport QA pitfall: Telegram/mobile playback may misinterpret vertical files if aspect metadata is weak. For final/review MP4s, explicitly encode with `setsar=1,setdar=9/16`, `yuv420p`, `-profile:v high -level:v 4.2`, `-movflags +faststart`, and verify `ffprobe` reports `width=1080 height=1920 sample_aspect_ratio=1:1 display_aspect_ratio=9:16` before delivery.\", \"file_type\": \".md\"}",
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"arguments": "{\"path\":\"/tmp/autoshorts_proof_short_sprint33/ai-email-send-gate/proposal_v1.md\",\"content\":\"# Postpreview Proposal #3 — AI Email Send Gate\\n\\n## Candidate ID\\n`ai-email-send-gate-35s`\\n\\n## Why this adds variety\\nAfter two security/login-trap posts, this shifts to an AI/workflow failure: the risky moment is not a scam link, but an AI-assisted email being sent without a human approval gate.\\n\\n## Core idea\\nAn AI draft looks polished, but it is addressed to the wrong audience and contains a sensitive attachment. The fix is a simple rule: never let AI cross the final send boundary without a human gate.\\n\\n## Hook variants\\n1. `Your AI draft is not the danger. The Send button is.`\\n2. `This is how AI turns one email into a problem.`\\n3. `Before AI sends anything, check this one screen.`\\n4. `The mistake happens after the AI writes it.`\\n\\nSelected hook: **Your AI draft is not the danger. The Send button is.**\\n\\n## First-frame direction\\nYes: the first image should be more dramatic, but still concrete and believable — not clickbait fire/explosions. The viewer should understand the risk even without reading subtitles.\\n\\nVisual: close-up laptop screen with a polished AI-written email draft, large red/amber `Send` button, recipient field showing `Entire Team`, attachment chip `Salary_Changes.xlsx`, AI assistant side panel saying `Draft ready`. The visual tension is obvious: a dangerous email is one click from being sent.\\n\\n## Script draft EN\\nYour AI draft is not the danger. The Send button is. The message can sound perfect and still go to the wrong people. One copied recipient. One sensitive attachment. One confident AI summary. That is enough. Before anything leaves your inbox, add a final gate. Check the recipient. Check the attachment. Check the claim. Then decide yourself. AI can write the draft. It should not own the boundary. The rule: let AI prepare the message, but make the human approve the send.\\n\\nApprox duration: 34–38s.\\n\\n## Visual plan\\n1. Dramatic visible fail: AI email draft ready to send to `Entire Team` with `Salary_Changes.xlsx` attached.\\n2. Close-up: AI assistant panel says `Draft ready`; email body looks polished.\\n3. Risk detail: recipient field / attachment chip / Send button visible.\\n4. Pause moment: review checklist built into the email client UI: `Recipient`, `Attachment`, `Claim`.\\n5. Safe action: button state changes to `Needs approval`; user-controlled review screen.\\n6. Takeaway: `AI drafts. Humans approve send.` shown as part of the app UI, not a renderer box.\\n\\n## New-standard prompt notes\\n- detailed AI-generated real UI/screen images with readable text\\n- no extra focus lines, arrows, circles, boxes\\n- no real company logos or brands\\n- no people/hands/faces\\n- active-word subtitles only\\n- first frame must work as a scroll-stopper thumbnail\\n\\n## Approval commands\\n`APPROVED_CONCEPT ai-email-send-gate-35s`\\n`REVISE_CONCEPT ai-email-send-gate-35s \\\"...\\\"`\\n`REJECT_CONCEPT ai-email-send-gate-35s`\\n\"}"
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"reasoning": "**Planning proposal package**\n\nI'm looking to create a detailed screen image that differs from phishing concepts. Maybe I can generate a \"proposal package\" that includes a concept, script, visual plan, and a prompt for the first frame. I might use a writing tool to draft this. \n\nIt seems logical to produce a suggestion for the first image prompt since the user asked about it, but I want to keep in mind that rendering the postpreview takes time. Since they used \"nächsten Vorschlag,\" a text proposal should suffice.",
"reasoning_content": "**Planning proposal package**\n\nI'm looking to create a detailed screen image that differs from phishing concepts. Maybe I can generate a \"proposal package\" that includes a concept, script, visual plan, and a prompt for the first frame. I might use a writing tool to draft this. \n\nIt seems logical to produce a suggestion for the first image prompt since the user asked about it, but I want to keep in mind that rendering the postpreview takes time. Since they used \"nächsten Vorschlag,\" a text proposal should suffice.",
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"text": "**Planning proposal package**\n\nI'm looking to create a detailed screen image that differs from phishing concepts. Maybe I can generate a \"proposal package\" that includes a concept, script, visual plan, and a prompt for the first frame. I might use a writing tool to draft this. \n\nIt seems logical to produce a suggestion for the first image prompt since the user asked about it, but I want to keep in mind that rendering the postpreview takes time. Since they used \"nächsten Vorschlag,\" a text proposal should suffice."
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"content": "Ja — absolut. Das **erste Bild muss dramatischer werden**, aber nicht im Sinne von “Hollywood-Alarm”, sondern als **sofort sichtbarer Fehlerzustand**. Beim Durchscrollen muss man ohne Ton und ohne Untertitel in <1 Sekunde denken:\n\n> “Oh, das könnte schiefgehen.”\n\nFür den nächsten Vorschlag würde ich bewusst **weg von Login-/Phishing-Traps** gehen, damit mehr Abwechslung entsteht:\n\n## Vorschlag #3 — AI Email Send Gate\n\n**Candidate ID:** `ai-email-send-gate-35s`\n\n## Thema\n\nNicht der AI-Draft ist gefährlich — sondern der Moment, in dem er ungeprüft gesendet wird.\n\n**Variety gegenüber den letzten Posts:**\n- Kein Fake-Login\n- Kein Shared Document\n- Kein Scam-Link\n- Stattdessen: AI + Workflows + menschliche Approval-Grenze\n\n## Hook\n\n**Selected Hook:**\n\n> Your AI draft is not the danger. The Send button is.\n\nAlternativen:\n- This is how AI turns one email into a problem.\n- Before AI sends anything, check this one screen.\n- The mistake happens after the AI writes it.\n\n## Dramatisches erstes Bild\n\nJa, das erste Bild sollte stärker sein.\n\n**Visual-Idee:**\nEin Laptop-Screen in Nahaufnahme:\n\n- AI-written email draft\n- großer `Send` Button\n- Empfänger: `Entire Team`\n- Attachment: `Salary_Changes.xlsx`\n- AI panel: `Draft ready`\n- warmes/rotes Warning-Licht\n- keine Pfeile, keine Linien, keine Boxen — die UI selbst erzählt die Gefahr\n\nDas versteht man auch ohne Lesen der Untertitel: \n**Eine sensible Mail ist einen Klick vom falschen Versand entfernt.**\n\n## Script Draft EN\n\n```text\nYour AI draft is not the danger. The Send button is.\n\nThe message can sound perfect and still go to the wrong people.\n\nOne copied recipient.\nOne sensitive attachment.\nOne confident AI summary.\n\nThat is enough.\n\nBefore anything leaves your inbox, add a final gate.\n\nCheck the recipient.\nCheck the attachment.\nCheck the claim.\n\nThen decide yourself.\n\nAI can write the draft.\nIt should not own the boundary.\n\nThe rule: let AI prepare the message, but make the human approve the send.\n```\n\n## Visual Plan\n\n1. **Visible Fail:** AI email draft ready to send to `Entire Team` with `Salary_Changes.xlsx`.\n2. **AI Confidence:** side panel says `Draft ready`.\n3. **Risk Detail:** recipient, attachment, send button visible.\n4. **Pause Moment:** email UI shows review state: `Recipient`, `Attachment`, `Claim`.\n5. **Safe Action:** send button becomes `Needs approval`.\n6. **Takeaway:** `AI drafts. Humans approve send.` as part of the app UI.\n\n## Meine Empfehlung\n\nDas ist ein guter nächster Kandidat, weil er:\n- mehr Abwechslung bringt,\n- stark zur TrueTrace-Richtung passt,\n- AI relevant hält,\n- sofort visuell verständlich ist,\n- einen klaren praktischen Fix liefert.\n\nIch habe den Vorschlag hier abgelegt:\n\n`/tmp/autoshorts_proof_short_sprint33/ai-email-send-gate/proposal_v1.md`\n\nWenn du ihn freigibst, rendere ich daraus als nächsten Schritt den Postpreview im neuen Standard:\n\n`APPROVED_CONCEPT ai-email-send-gate-35s`",
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"text": "Ja — absolut. Das **erste Bild muss dramatischer werden**, aber nicht im Sinne von “Hollywood-Alarm”, sondern als **sofort sichtbarer Fehlerzustand**. Beim Durchscrollen muss man ohne Ton und ohne Untertitel in <1 Sekunde denken:\n\n> “Oh, das könnte schiefgehen.”\n\nFür den nächsten Vorschlag würde ich bewusst **weg von Login-/Phishing-Traps** gehen, damit mehr Abwechslung entsteht:\n\n## Vorschlag #3 — AI Email Send Gate\n\n**Candidate ID:** `ai-email-send-gate-35s`\n\n## Thema\n\nNicht der AI-Draft ist gefährlich — sondern der Moment, in dem er ungeprüft gesendet wird.\n\n**Variety gegenüber den letzten Posts:**\n- Kein Fake-Login\n- Kein Shared Document\n- Kein Scam-Link\n- Stattdessen: AI + Workflows + menschliche Approval-Grenze\n\n## Hook\n\n**Selected Hook:**\n\n> Your AI draft is not the danger. The Send button is.\n\nAlternativen:\n- This is how AI turns one email into a problem.\n- Before AI sends anything, check this one screen.\n- The mistake happens after the AI writes it.\n\n## Dramatisches erstes Bild\n\nJa, das erste Bild sollte stärker sein.\n\n**Visual-Idee:**\nEin Laptop-Screen in Nahaufnahme:\n\n- AI-written email draft\n- großer `Send` Button\n- Empfänger: `Entire Team`\n- Attachment: `Salary_Changes.xlsx`\n- AI panel: `Draft ready`\n- warmes/rotes Warning-Licht\n- keine Pfeile, keine Linien, keine Boxen — die UI selbst erzählt die Gefahr\n\nDas versteht man auch ohne Lesen der Untertitel: \n**Eine sensible Mail ist einen Klick vom falschen Versand entfernt.**\n\n## Script Draft EN\n\n```text\nYour AI draft is not the danger. The Send button is.\n\nThe message can sound perfect and still go to the wrong people.\n\nOne copied recipient.\nOne sensitive attachment.\nOne confident AI summary.\n\nThat is enough.\n\nBefore anything leaves your inbox, add a final gate.\n\nCheck the recipient.\nCheck the attachment.\nCheck the claim.\n\nThen decide yourself.\n\nAI can write the draft.\nIt should not own the boundary.\n\nThe rule: let AI prepare the message, but make the human approve the send.\n```\n\n## Visual Plan\n\n1. **Visible Fail:** AI email draft ready to send to `Entire Team` with `Salary_Changes.xlsx`.\n2. **AI Confidence:** side panel says `Draft ready`.\n3. **Risk Detail:** recipient, attachment, send button visible.\n4. **Pause Moment:** email UI shows review state: `Recipient`, `Attachment`, `Claim`.\n5. **Safe Action:** send button becomes `Needs approval`.\n6. **Takeaway:** `AI drafts. Humans approve send.` as part of the app UI.\n\n## Meine Empfehlung\n\nDas ist ein guter nächster Kandidat, weil er:\n- mehr Abwechslung bringt,\n- stark zur TrueTrace-Richtung passt,\n- AI relevant hält,\n- sofort visuell verständlich ist,\n- einen klaren praktischen Fix liefert.\n\nIch habe den Vorschlag hier abgelegt:\n\n`/tmp/autoshorts_proof_short_sprint33/ai-email-send-gate/proposal_v1.md`\n\nWenn du ihn freigibst, rendere ich daraus als nächsten Schritt den Postpreview im neuen Standard:\n\n`APPROVED_CONCEPT ai-email-send-gate-35s`"
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"content": "[M G] Ich glaube nicht dass ,AI bersendet Emails‘ gerade ein thema ist dass die meisten viewer beschäftigt. Cybersexurity für anfänger jedoch schon… wir müssen uns neu positionieren. Mach mir vernünftige vorschläge"
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"text": "**Considering proposal strategies**\n\nI think I should respond with some strategic proposals. I might not need any tools since I already have relevant skills loaded. The user asked for strategy proposals, so I could focus on creating a plan or document. The phrase \"Mach mir vernünftige Vorschläge\" suggests a request for reasonable suggestions. However, I need to make sure I’m using tools as needed, especially if I need to create a proposal file or utilize a specific skill."
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"content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/truetrace-strategic-neuausrichtung.md\", \"content\": \"# TrueTraceShorts strategic refactor notes\\n\\nUse this reference when developing or revising the user's AutoShortsBot / TrueTraceShorts strategy, briefs, scoring, review packages, or implementation roadmap.\\n\\n## Core strategic shift\\n\\nTrueTraceShorts should not be positioned as another AI/prompt-tips channel. The stronger positioning is:\\n\\n> Trace the hidden system. Fix the outcome.\\n\\nGerman explanation:\\n\\n> Wir zeigen, warum digitale Dinge im Alltag schiefgehen — und welche kleine Systemregel sie repariert.\\n\\nKeep `Systems over prompts.` as a usable tagline, but interpret it broadly: not just better prompts, but better digital thinking, work, attention, decision, safety, and workflow systems.\\n\\nThe bot is a learning editorial system, not a content automaton:\\n\\n```text\\nResearch signal → hypothesis → ContentBrief → ScriptDraft → VideoCandidate\\n→ score/gate → review/preview → human approval → manual post pack\\n→ analytics feedback → next experiment\\n```\\n\\n## Content mix target\\n\\nFor the next strategic test batch, avoid an all-AI feed. Suggested mix for ~60 videos:\\n\\n- 35% — AI That Actually Helps / AI Output Autopsy\\n- 20% — Digital Self-Defense: scams, phishing, fake reviews, privacy/data traps, hallucination risks\\n- 20% — Attention & Decision Systems: focus, to-dos, meetings, decisions, overload\\n- 15% — Workflow Teardowns: email, calendar, notes, automations, broken work systems\\n- 10% — Algorithm & Social Search Literacy: feeds, search, recommendations, content consumption\\n\\nAI remains important, but should usually be 30–40% of the content universe, not 100%.\\n\\n## Recommended series\\n\\n- `AI Output Autopsy` — bad AI answer → why it sounds right but fails → rule.\\n- `Digital Red Flags` — normal-looking digital situation → one warning signal exposes the trap.\\n- `One Broken Workflow` / `Workflow Teardown` — mini-workflow fails → missing boundary/decision/exception path.\\n- `Decision Design` — people ask tools for answers when they need decision criteria.\\n- `Attention Traps` — normal digital behavior explained as an attention mechanism; avoid medical claims.\\n- `Search Smarter` — TikTok/YouTube/Google/AI search done badly vs. better.\\n- `Worklife Systems` — meetings, email, notes, calendar, to-dos.\\n\\n## Standard video structure\\n\\nUse `visible fail → identification → mechanism → fix → before/after → memorable takeaway`:\\n\\n1. 0–1.5s: visible conflict in the first frame, not just text.\\n2. 1.5–5s: viewer recognition / identity.\\n3. 5–14s: mechanism reveal — why it happens.\\n4. 14–28s: practical fix — rule, phrase, template, checklist, or decision frame.\\n5. 28–38s: before/after.\\n6. final 2–4s: memorable sentence with save/share/follow reason.\\n\\nEvery video must answer:\\n\\n- What concretely goes wrong?\\n- Why does it happen?\\n- What is the simple correction?\\n- What sentence should stick?\\n- Why would the viewer save or share it?\\n- Why would they follow this channel/series?\\n\\n## ContentBrief fields to add or require\\n\\nAdd these fields before scripting so JARVIS cannot generate generic AI-topic sludge:\\n\\n- `audience_need`: boredom | urgency | fear | competence | clarity | status | safety | curiosity\\n- `job_to_be_done`: “When I …, I want to …, so I can …”\\n- `viewer_identity`: beginner | creator | solo_founder | office_worker | student | parent | small_team | freelancer\\n- `viewer_emotion_before`: confused | annoyed | overwhelmed | skeptical | curious | embarrassed | anxious\\n- `viewer_emotion_after`: relieved | smarter | in_control | warned | motivated | equipped\\n- `share_reason`: “I would send this to someone because …”\\n- `save_reason`: “I would save this because …”\\n- `follow_reason`: “I would follow this channel because …”\\n- `search_intent_phrase`: platform-native problem/search phrase\\n- `series_id`: ai_output_autopsy | digital_red_flags | decision_design | workflow_teardown | attention_traps | search_smarter | worklife_systems\\n- `saturation_risk`: low | medium | high\\n- `human_texture`: real example | mini case | tested prompt | screen recording | personal observation | source-based claim\\n- `claim_risk`: low | medium | high\\n- `ai_disclosure_required`: yes | no | unclear\\n\\n## 100-point scoring model\\n\\nScore candidates on viewer behavior, not internal cleverness:\\n\\n- Problem Urgency — 15\\n- First-Frame Clarity — 12\\n- Hook Curiosity — 12\\n- Mechanism Value — 12\\n- Practical Payoff — 15\\n- Retention Path — 10\\n- Share/Save Potential — 10\\n- Trust/Human Texture — 8\\n- Series Fit — 4\\n- Saturation Defense — 2\\n\\nHard-block if:\\n\\n- no concrete problem\\n- no clear solution\\n- first frame is only text / no visible conflict\\n- hook sounds like a generic AI tip\\n- claim is not sourceable\\n- too similar to the last ~10 videos\\n- no save/share/follow reason\\n- realistic AI-generated content has unclear disclosure status\\n\\n## Research, claims, and compliance layers\\n\\nInsert a research layer before scripting:\\n\\n```text\\nSignal → hypothesis → brief → script → candidate → score → render\\n```\\n\\nUseful planned modules:\\n\\n- `autoshorts.research.trend_signals`\\n- `autoshorts.research.social_search`\\n- `autoshorts.research.topic_saturation`\\n- `autoshorts.claims.fact_gate`\\n- `autoshorts.strategy.experiment`\\n- `autoshorts.compliance.ai_disclosure`\\n\\nEvery factual claim should carry source/risk. Any `unclear` disclosure should block or require manual review before publishing.\\n\\n## Visual grammar\\n\\nMotion must explain meaning, not just interrupt attention. Add/maintain a visual grammar keyed by mechanism type:\\n\\n- missing boundary → open container / leaking information\\n- decision gap → three paths, one lights up\\n- false confidence → foggy cards becoming clear with constraints\\n- urgency trap → timer + red urgency line\\n- overload → stack of cards sorted by one rule\\n- before/after → split-screen with marked difference\\n- exception path → workflow line breaks, detour appears\\n- attention capture → cursor/thought pulled by app tiles\\n- hidden cost → free/cheap path reveals later cost\\n\\nPotential module: `autoshorts.rendering.visual_grammar`.\\n\\n## Captions, voice, and human texture\\n\\n- Forced alignment is a top priority. If captions drift, simplify visuals before tolerating bad sync.\\n- Support three caption modes:\\n - `word_by_word` — fast hooks / high-energy TikTok style\\n - `phrase_by_phrase` — clearer for complex explanations\\n - `keyword_pulse` — calmer premium look for key ideas\\n- Avoid the vibe: “an AI voice explaining an AI tip about AI.”\\n- Require human texture: real/credible mini-situation, tested prompt, screen recording, personal observation, or source-based claim.\\n\\n## Review package v2\\n\\nFuture Telegram/content review packages should include strategy and risk, not only visuals:\\n\\n- Candidate ID, version, media hash, script hash\\n- Series, audience need, viewer emotion before/after\\n- Hook, first-frame description, open loop, mechanism, takeaway\\n- Save/share/follow reason\\n- Platform fit per YouTube/TikTok/Instagram\\n- Claims: source + risk\\n- AI disclosure: yes/no/unclear per platform\\n- Policy/brand/saturation/repetition risks\\n- Experiment hypothesis and expected winning metric\\n\\nApproval should bind to exact version/hash.\\n\\n## Analytics early\\n\\nDo not wait for auto-publishing to add analytics. In phase 1, manually record:\\n\\n- platform, video_id, post_time, series_id, hook_type, topic_cluster, duration\\n- first_frame_type, caption_style, voice_style, visual_template, CTA type\\n- at 1h/24h/72h/7d: views, likes, comments, shares, saves, follows\\n- YouTube: shown in feed, viewed vs swiped away/chose to view, engaged views, average view duration, average percentage viewed\\n\\nDiagnosis rules:\\n\\n- High view-vs-swipe, low completion → hook strong, body weak; get to mechanism faster.\\n- Low view-vs-swipe, high completion → content good, first frame weak; retest hook/visual.\\n- High completion, low follows → useful but weak series promise; strengthen CTA/series branding.\\n- Many saves, few shares → practical but not social; add “send this to …” context.\\n- Many shares, few saves → relatable but not useful enough; add concrete takeaway.\\n- Many views, no follows → too generic or not brand-defining.\\n\\n## 30-day test plan\\n\\nPrefer 30 controlled experiments over volume:\\n\\n- 5 series × 6 videos\\n- test 2 hook types per series\\n- test 2 length bands: 25–35s and 40–55s\\n- generate 3 candidates/day but publish only 1/day manually\\n- compare follow/view, saves/view, shares/view, completion, real problem-resonance comments, and repeatability\\n\\nWinning format = repeatable reach + trust + follow reason, not just most views.\\n\", \"file_type\": \".md\"}",
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"arguments": "{\"path\":\"/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/proposals_v1.md\",\"content\":\"# TrueTraceShorts Repositioning — Cybersecurity for Beginners\\n\\n## Strategic reset\\nPosition as beginner-friendly digital self-defense: concrete everyday cyber risks, visible first-frame failure, one memorable rule.\\n\\nTagline options:\\n- Digital self-defense for normal people.\\n- Spot the trap before you click.\\n- One screen. One red flag. One safer move.\\n\\n## Series pillars\\n1. Digital Red Flags — phishing, fake login, malicious attachments, urgent prompts.\\n2. Account Safety Basics — password reuse, MFA prompts, recovery codes, session hijack, QR traps.\\n3. Everyday Scam Screens — delivery SMS, banking-like page, fake support, marketplace buyer/seller scams.\\n4. Safe Workflow Rules — open app yourself, verify sender, do not trust urgency, check domains.\\n5. AI-era Safety — deepfake voice, fake screenshots, AI-written scam messages, hallucinated links.\\n\\n## Proposal candidates\\n\\n### 1. Delivery SMS Trap\\nHook: \\\"This delivery text is designed to make you panic.\\\"\\nFirst frame: phone lock screen with urgent package SMS, fee due, fake short link, countdown feel.\\nFix: open the delivery app/site yourself; never pay from SMS link.\\nWhy strong: universal, beginner-relevant, instantly visual.\\n\\n### 2. Fake Bank Login After QR Code\\nHook: \\\"The QR code was not the problem. The page after it was.\\\"\\nFirst frame: phone camera scanning QR on a poster/letter, fake bank-like login page with URL mismatch.\\nFix: if money/login is involved, close it and open bank app manually.\\nWhy strong: QR scams are common and visually obvious.\\n\\n### 3. Password Reuse Domino\\nHook: \\\"One leaked password does not stay on one website.\\\"\\nFirst frame: dramatic split-screen of one password unlocking email, shopping, social icons as red chain reaction.\\nFix: unique passwords + password manager + MFA.\\nWhy strong: beginner evergreen, high save value.\\n\\n### 4. Fake Support Pop-up\\nHook: \\\"If a website tells you to call support, stop.\\\"\\nFirst frame: laptop with full-screen fake virus/support warning, phone number, red warning UI.\\nFix: close tab/browser, never call popup number, use official support path.\\nWhy strong: very beginner-relevant, especially family-shareable.\\n\\n### 5. MFA Push Fatigue\\nHook: \\\"If you get a login prompt you did not start, it is not a notification.\\\"\\nFirst frame: phone showing repeated approve/deny prompts while laptop is idle.\\nFix: deny, change password, review sessions.\\nWhy strong: modern, practical, visually dramatic.\\n\\n### 6. Marketplace Buyer Scam\\nHook: \\\"The buyer is not buying. They are moving you off-platform.\\\"\\nFirst frame: chat showing buyer asking for email/phone/shipping-payment link.\\nFix: keep payment and messages inside platform.\\nWhy strong: relatable for normal viewers, good comments.\\n\\n## Recommended next preview\\nCandidate: Delivery SMS Trap.\\nReason: highest beginner relevance, first-frame clarity, and emotional hook without needing niche cyber knowledge.\\n\\n## Visual standard for next preview\\n- First frame: dramatic phone lock screen / SMS with urgent package problem.\\n- Readable concrete UI text, fictional provider names only.\\n- No real logos, no real URLs, no QR codes if not needed.\\n- No arrows/boxes/labels; UI itself carries the story.\\n- Active-word subtitles only.\\n- 1080x1920 SAR=1:1 DAR=9:16 verified.\\n\"}"
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"text": "**Considering proposal options**\n\nI need to create reasonable proposals based on the user's request. They mentioned repositioning, so I’ll consider how to adjust the current proposals accordingly. It might also be a good idea to save the proposal file for future reference. I can use the write_file function to manage that. I want to ensure the proposals meet the user's expectations while being clear and effective. Let's proceed with that in mind!"
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"content": "Ja — du hast recht. **“AI versendet Emails”** ist zu sehr Creator-/Workflow-Nische. Das beschäftigt vielleicht uns, aber nicht genug normale Viewer beim Scrollen. Wenn wir **Cybersecurity für Anfänger** machen, müssen die Themen näher an Alltagsangst, Geld, Accounts, Familie und “das könnte mir passieren” liegen.\n\n## Neue Positionierung\n\n**TrueTraceShorts = Cybersecurity für normale Menschen.**\n\nNicht:\n> “AI Workflows und digitale Systeme für Fortgeschrittene”\n\nSondern:\n> “Du siehst einen normalen Screen. Ich zeige dir den einen Red Flag, bevor du klickst.”\n\nMögliche Taglines:\n- **Spot the trap before you click.**\n- **Digital self-defense for normal people.**\n- **One screen. One red flag. One safer move.**\n- **Cybersecurity ohne Nerd-Gelaber.**\n\nDas ist klarer, massentauglicher und besser als aktuelle Positionierung.\n\n## Was der erste Frame leisten muss\n\nJa: **dramatischer, aber konkret.**\n\nNicht abstrakt “dark hacker vibes”. Sondern ein sichtbarer Alltagsmoment mit Risiko:\n\n- Handy zeigt Fake-SMS\n- Laptop zeigt Fake-Support-Popup\n- Bank/Login-Seite wirkt “fast echt”\n- MFA-Prompt erscheint unerwartet\n- Passwort-Domino wird sichtbar\n- Marketplace-Chat versucht, dich off-platform zu ziehen\n\nDer Viewer muss ohne Audio und ohne Untertitel sofort denken:\n\n> “Oh shit, das kenne ich.”\n\n## Vernünftige Vorschläge\n\n### 1. Delivery SMS Trap\n\n**Hook:**\n> This delivery text is designed to make you panic.\n\n**First frame:**\nHandy-Lockscreen mit Paket-SMS:\n\n- `Your package is on hold`\n- `Pay CHF 1.95 customs fee`\n- Fake short link\n- rotes/amber Licht\n- Uhrzeit spät abends / urgency vibe\n\n**Fix:**\nNicht auf SMS-Link klicken. Liefer-App oder Website selbst öffnen.\n\n**Warum stark:**\nJeder kennt Paket-SMS. Sehr massentauglich, sofort verständlich, guter Beginner-Topic.\n\n**Meine Empfehlung für nächsten Preview.**\n\n---\n\n### 2. Fake Bank Login After QR Code\n\n**Hook:**\n> The QR code was not the problem. The page after it was.\n\n**First frame:**\nPhone scannt QR-Code auf Brief/Flyer. Danach fake bankähnliche Login-Seite mit minimal falscher URL.\n\n**Fix:**\nWenn Login/Geld involviert ist: QR schliessen, Bank-App manuell öffnen.\n\n**Warum stark:**\nQR-Scams sind visuell gut erklärbar und aktuell genug.\n\n---\n\n### 3. Password Reuse Domino\n\n**Hook:**\n> One leaked password does not stay on one website.\n\n**First frame:**\nEin Passwortfeld, danach rote Kettenreaktion über Email, Shopping, Social, Cloud.\n\n**Fix:**\nUnique passwords + Passwortmanager + MFA.\n\n**Warum stark:**\nEvergreen, Anfänger-tauglich, hoher Save-Wert. Etwas weniger “akuter Scam”, aber sehr nützlich.\n\n---\n\n### 4. Fake Support Pop-up\n\n**Hook:**\n> If a website tells you to call support, stop.\n\n**First frame:**\nLaptop mit Vollbild-Warnung:\n\n- `Your device is infected`\n- `Call support now`\n- Telefonnummer\n- fake progress bar\n- rotes Warnfenster\n\n**Fix:**\nNicht anrufen. Tab/Browser schliessen. Offiziellen Support nur manuell suchen.\n\n**Warum stark:**\nSehr verständlich, besonders teilbar an Eltern/Familie. “Send this to your parents”-Potential.\n\n---\n\n### 5. MFA Push Fatigue\n\n**Hook:**\n> If you get a login prompt you did not start, it is not a notification.\n\n**First frame:**\nPhone mit mehreren `Approve login?` Prompts, Laptop daneben unbenutzt.\n\n**Fix:**\nDeny. Passwort ändern. Sessions prüfen.\n\n**Warum stark:**\nModerner als klassische Phishing-Themen, aber immer noch simpel.\n\n---\n\n### 6. Marketplace Buyer Scam\n\n**Hook:**\n> The buyer is not buying. They are moving you off-platform.\n\n**First frame:**\nChat-Screen:\n\n- Buyer asks for email/phone\n- “I will send shipping payment link”\n- suspicious payment link\n\n**Fix:**\nPayment und Chat in der Plattform behalten.\n\n**Warum stark:**\nRelatable, comment-friendly, gutes “das ist mir passiert”-Potential.\n\n## Meine klare Empfehlung\n\nAls nächstes sollten wir **Delivery SMS Trap** machen.\n\nWarum:\n- extrem beginner-friendly\n- sofort visuell verständlich\n- hoher Alltagsbezug\n- guter Hook\n- kein AI-/Workflow-Nischenthema\n- passt perfekt zu “Cybersecurity für Anfänger”\n- kann dramatisch starten, ohne unseriös zu wirken\n\n## Nächster Preview-Kandidat\n\n**Candidate ID:** `delivery-sms-trap-35s`\n\n**Hook:**\n> This delivery text is designed to make you panic.\n\n**Erstes Bild:**\nEin Handy in Nahaufnahme mit Fake-Paket-SMS:\n\n```text\nYour package is on hold\nPay CHF 1.95 customs fee\nDelivery expires today\ntrack-secure.example\n```\n\nVisuell:\n- rote/amber Notification-Beleuchtung\n- Paket liegt unscharf im Hintergrund\n- SMS ist riesig und klar lesbar\n- kein Logo, keine echte Marke\n- keine Pfeile/Boxen\n- nur active-word subtitles\n\n**Takeaway:**\n> If a message makes you hurry, open the app yourself.\n\nIch habe die neue Vorschlagsliste hier abgelegt:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/proposals_v1.md`\n\nWenn du einverstanden bist, baue ich als nächsten Schritt den Preview für:\n\n`APPROVED_CONCEPT delivery-sms-trap-35s`",
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"text": "Ja — du hast recht. **“AI versendet Emails”** ist zu sehr Creator-/Workflow-Nische. Das beschäftigt vielleicht uns, aber nicht genug normale Viewer beim Scrollen. Wenn wir **Cybersecurity für Anfänger** machen, müssen die Themen näher an Alltagsangst, Geld, Accounts, Familie und “das könnte mir passieren” liegen.\n\n## Neue Positionierung\n\n**TrueTraceShorts = Cybersecurity für normale Menschen.**\n\nNicht:\n> “AI Workflows und digitale Systeme für Fortgeschrittene”\n\nSondern:\n> “Du siehst einen normalen Screen. Ich zeige dir den einen Red Flag, bevor du klickst.”\n\nMögliche Taglines:\n- **Spot the trap before you click.**\n- **Digital self-defense for normal people.**\n- **One screen. One red flag. One safer move.**\n- **Cybersecurity ohne Nerd-Gelaber.**\n\nDas ist klarer, massentauglicher und besser als aktuelle Positionierung.\n\n## Was der erste Frame leisten muss\n\nJa: **dramatischer, aber konkret.**\n\nNicht abstrakt “dark hacker vibes”. Sondern ein sichtbarer Alltagsmoment mit Risiko:\n\n- Handy zeigt Fake-SMS\n- Laptop zeigt Fake-Support-Popup\n- Bank/Login-Seite wirkt “fast echt”\n- MFA-Prompt erscheint unerwartet\n- Passwort-Domino wird sichtbar\n- Marketplace-Chat versucht, dich off-platform zu ziehen\n\nDer Viewer muss ohne Audio und ohne Untertitel sofort denken:\n\n> “Oh shit, das kenne ich.”\n\n## Vernünftige Vorschläge\n\n### 1. Delivery SMS Trap\n\n**Hook:**\n> This delivery text is designed to make you panic.\n\n**First frame:**\nHandy-Lockscreen mit Paket-SMS:\n\n- `Your package is on hold`\n- `Pay CHF 1.95 customs fee`\n- Fake short link\n- rotes/amber Licht\n- Uhrzeit spät abends / urgency vibe\n\n**Fix:**\nNicht auf SMS-Link klicken. Liefer-App oder Website selbst öffnen.\n\n**Warum stark:**\nJeder kennt Paket-SMS. Sehr massentauglich, sofort verständlich, guter Beginner-Topic.\n\n**Meine Empfehlung für nächsten Preview.**\n\n---\n\n### 2. Fake Bank Login After QR Code\n\n**Hook:**\n> The QR code was not the problem. The page after it was.\n\n**First frame:**\nPhone scannt QR-Code auf Brief/Flyer. Danach fake bankähnliche Login-Seite mit minimal falscher URL.\n\n**Fix:**\nWenn Login/Geld involviert ist: QR schliessen, Bank-App manuell öffnen.\n\n**Warum stark:**\nQR-Scams sind visuell gut erklärbar und aktuell genug.\n\n---\n\n### 3. Password Reuse Domino\n\n**Hook:**\n> One leaked password does not stay on one website.\n\n**First frame:**\nEin Passwortfeld, danach rote Kettenreaktion über Email, Shopping, Social, Cloud.\n\n**Fix:**\nUnique passwords + Passwortmanager + MFA.\n\n**Warum stark:**\nEvergreen, Anfänger-tauglich, hoher Save-Wert. Etwas weniger “akuter Scam”, aber sehr nützlich.\n\n---\n\n### 4. Fake Support Pop-up\n\n**Hook:**\n> If a website tells you to call support, stop.\n\n**First frame:**\nLaptop mit Vollbild-Warnung:\n\n- `Your device is infected`\n- `Call support now`\n- Telefonnummer\n- fake progress bar\n- rotes Warnfenster\n\n**Fix:**\nNicht anrufen. Tab/Browser schliessen. Offiziellen Support nur manuell suchen.\n\n**Warum stark:**\nSehr verständlich, besonders teilbar an Eltern/Familie. “Send this to your parents”-Potential.\n\n---\n\n### 5. MFA Push Fatigue\n\n**Hook:**\n> If you get a login prompt you did not start, it is not a notification.\n\n**First frame:**\nPhone mit mehreren `Approve login?` Prompts, Laptop daneben unbenutzt.\n\n**Fix:**\nDeny. Passwort ändern. Sessions prüfen.\n\n**Warum stark:**\nModerner als klassische Phishing-Themen, aber immer noch simpel.\n\n---\n\n### 6. Marketplace Buyer Scam\n\n**Hook:**\n> The buyer is not buying. They are moving you off-platform.\n\n**First frame:**\nChat-Screen:\n\n- Buyer asks for email/phone\n- “I will send shipping payment link”\n- suspicious payment link\n\n**Fix:**\nPayment und Chat in der Plattform behalten.\n\n**Warum stark:**\nRelatable, comment-friendly, gutes “das ist mir passiert”-Potential.\n\n## Meine klare Empfehlung\n\nAls nächstes sollten wir **Delivery SMS Trap** machen.\n\nWarum:\n- extrem beginner-friendly\n- sofort visuell verständlich\n- hoher Alltagsbezug\n- guter Hook\n- kein AI-/Workflow-Nischenthema\n- passt perfekt zu “Cybersecurity für Anfänger”\n- kann dramatisch starten, ohne unseriös zu wirken\n\n## Nächster Preview-Kandidat\n\n**Candidate ID:** `delivery-sms-trap-35s`\n\n**Hook:**\n> This delivery text is designed to make you panic.\n\n**Erstes Bild:**\nEin Handy in Nahaufnahme mit Fake-Paket-SMS:\n\n```text\nYour package is on hold\nPay CHF 1.95 customs fee\nDelivery expires today\ntrack-secure.example\n```\n\nVisuell:\n- rote/amber Notification-Beleuchtung\n- Paket liegt unscharf im Hintergrund\n- SMS ist riesig und klar lesbar\n- kein Logo, keine echte Marke\n- keine Pfeile/Boxen\n- nur active-word subtitles\n\n**Takeaway:**\n> If a message makes you hurry, open the app yourself.\n\nIch habe die neue Vorschlagsliste hier abgelegt:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/proposals_v1.md`\n\nWenn du einverstanden bist, baue ich als nächsten Schritt den Preview für:\n\n`APPROVED_CONCEPT delivery-sms-trap-35s`"
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"content": "[M G] JARVIS, wir machen jetzt eine klare strategische Neuorientierung für AutoShortsBot / TrueTraceShorts.\n\nDer aktuelle Proof Short “digital-red-flags-fake-invoice-payment-route-30s” ist als Richtung deutlich besser als der alte AI-Summary-Pilot. Jetzt soll diese neue Richtung zur offiziellen Content-Strategie werden.\n\nWichtigste Entscheidung:\n\nTrueTraceShorts soll nicht mehr primär ein AI-/Workflow-/Automation-Kanal sein.\n\nNeue Hauptausrichtung:\n\nScam Self-Defense für normale Menschen.\n\nKurz gesagt:\n\nOne screen. One red flag. One safer move.\n\nOder auf Deutsch:\n\nEin normaler Screen. Ein gefährlicher Hinweis. Eine sichere Handlung.\n\nNeue Positionierung\n\nBitte aktualisiere die Projektstrategie entsprechend.\n\nNicht mehr als Hauptpositionierung:\n\nAI Workflow Tips\nPrompt Engineering\ndigitale Systeme für Fortgeschrittene\nabstrakte Productivity-Mechanismen\nschöne AI-Explainer ohne sichtbaren Schmerz\n\nNeue Hauptpositionierung:\n\nTrueTraceShorts = Digital Red Flags / Scam Self-Defense for normal people.\n\nKanalversprechen:\n\n“I show you the one red flag before you click, pay, scan, or log in.”\n\nMögliche Tagline:\n\n“One screen. One red flag. One safer move.”\n\nDeutsch erklärt:\n\nWir zeigen normalen Menschen auf einem normalen Screen den einen gefährlichen Hinweis, bevor sie klicken, zahlen, scannen oder Login-Daten eingeben.\n\nZielgruppe\n\nBitte nicht als “Boomer-Kanal” positionieren.\n\nIntern dürfen die Inhalte “boomer-safe” sein:\n\neinfache Sprache\ngroße Lesbarkeit\nkeine Fachbegriffe\nkonkrete Beispiele\nklare Handlung\n\nExtern aber nicht:\n“Cybersecurity für Boomer”\n\nSondern:\n“Digital self-defense for normal people.”\n\nZielgruppe:\n\nnormale Smartphone-/Internetnutzer\nEltern\nJugendliche\nBerufstätige\nKleinunternehmer\nFamilien\nTeams\nMenschen, die ihre Eltern/Partner/Kollegen schützen wollen\n\nPsychologischer Hebel:\nDer Zuschauer soll denken:\n\n“Das hätte mir oder meiner Familie passieren können.”\n\nSprache / Tonalität\n\nSehr wichtig:\nDie Sprache darf nicht hochgestochen sein.\n\nKeine Nerd-Sprache.\nKeine Corporate-Security-Sprache.\nKeine Fachvorträge.\nKeine komplizierten Begriffe ohne Erklärung.\n\nVerwende einfache Alltagssprache.\n\nFinale Social Videos aktuell weiterhin Englisch, aber sehr einfaches Englisch.\n\nSprachlevel:\n\neinfach\nklar\nkurze Sätze\nAlltagssprache\nungefähr B1/B2 verständlich\nkeine verschachtelten Sätze\nkeine Buzzwords\n\nBeispiele guter Sprache:\n\nGut:\n“This message wants you to hurry.”\n“Don’t tap the link.”\n“Open the app yourself.”\n“If the payment route changed, verify it another way.”\n“If it makes you hurry, slow down.”\n\nSchlecht:\n“This phishing attempt leverages urgency-based social engineering to exploit cognitive load.”\n\nSchlecht:\n“Validate the authenticity of the communication channel before initiating a transactional flow.”\n\nStatt “validate communication channel”:\n“Check it another way.”\n\nStatt “credential harvesting”:\n“stealing your login.”\n\nStatt “multi-factor authentication prompt fatigue”:\n“a login prompt you did not start.”\n\nContent-Kernformel\n\nJedes Video folgt ab jetzt dieser Formel:\n\nOne screen.\nOne red flag.\nOne safer move.\n\nStandardstruktur:\n\n0–2s:\nVisible trap + Hook\n\n2–7s:\nWhy it feels normal\n\n7–15s:\nThe one red flag\n\n15–24s:\nThe safer move\n\n24–30s:\nScreenshot checklist\n\n30–34s:\nMemorable rule + soft CTA\n\nNicht jedes Video muss exakt gleich lang sein, aber diese Struktur ist der Default.\n\nWas jedes Video beantworten muss\n\nVor jedem Render muss der Kandidat diese Fragen bestehen:\n\nWelchen normalen Screen sieht der Zuschauer?\nWelcher konkrete Fehler / Scam / Red Flag ist sichtbar?\nErkennt man das Problem in Sekunde 1 auch ohne Ton?\nWas könnte dem Zuschauer passieren?\nWas ist die eine sichere Handlung?\nKann man das Video an Eltern, Partner, Kollegen oder Team schicken?\nHat es einen Save- oder Screenshot-Grund?\nIst die Sprache einfach genug für Durchschnittsnutzer?\nIst es nützlich, auch wenn man kein Tech-Wissen hat?\n\nWenn diese Fragen nicht klar beantwortet werden:\nnicht rendern.\n\nNeue Content-Pillars\n\nBitte alte AI-/Workflow-Pillars zurückstufen und neue Hauptpfeiler setzen.\n\nNeue Hauptpfeiler:\n\nA. Text & Delivery Scams\nBeispiele:\n\nPaket-SMS\nZollgebühr\nMautgebühr\n“Your delivery is on hold”\n“Pay CHF 1.95”\nShort links\nFake tracking\n\nB. Login & Account Traps\nBeispiele:\n\nFake Bank Login\nFake Microsoft/Google/Apple Login\n“Your account will be closed”\nFake password reset\nLogin prompt you did not start\n\nC. Payment & Invoice Red Flags\nBeispiele:\n\nFake invoice\nchanged payment route\nvendor bank change\nCEO payment request\nQR payment trap\n\nD. Marketplace & Buyer Scams\nBeispiele:\n\nbuyer moves you off-platform\nfake shipping link\nfake escrow\noverpayment\n“courier will pick it up”\n\nE. Family & Emotion Scams\nBeispiele:\n\n“Hi mom, new number”\nemergency money request\ngrandparent scam\nwrong-number scam\nAI voice warning, aber nur einfach erklärt\n\nF. Fake Support / Popup Scams\nBeispiele:\n\n“Your device is infected”\ncall support now\nfake antivirus\nbrowser lock\nremote access request\n\nG. Password / Account Safety Basics\nBeispiele:\n\npassword reuse domino\none leaked password spreads\npassword manager basics\nMFA basics\nrecovery email risk\n\nH. Job / Side-Hustle Scams\nBeispiele:\n\nfake recruiter\ntask scam\n“pay to get paid”\nremote job deposit\ncrypto onboarding trap\n\nThemen, die vorerst NICHT Hauptfokus sind\n\nNicht als Hauptformat:\n\nAI Workflow Automation\nPrompt Engineering\nTool Reviews\n“Top 5 AI tools”\nabstrakte Produktivitätsmechanismen\nallgemeine Cybersecurity-News\nHacker-Taktiken\nOSINT\nVPN-Vergleiche\nDark-Web-Erklärungen\ntechnische Angriffsanleitungen\n\nDiese Themen können später Spezialformate sein, aber nicht als Kern.\n\nNächste beste Serien\n\nBitte diese Serien als offizielle Serienlogik anlegen:\n\nSerie 1:\n“One Screen. One Red Flag.”\n\nFormat:\nEin normaler Screen, ein gefährliches Detail, eine sichere Handlung.\n\nSerie 2:\n“Send This To Your Parents”\n\nFormat:\nSehr einfache Videos für Familie/Eltern, aber nicht herablassend.\n\nSerie 3:\n“Before You Click”\n\nFormat:\nLink, SMS, QR, Login, Popup — was man vor dem Klick prüft.\n\nSerie 4:\n“Money Move Red Flags”\n\nFormat:\nZahlung, Rechnung, Bankroute, QR-Code, Marketplace.\n\nSerie 5:\n“Account Trap”\n\nFormat:\nLogin, Passwort, MFA, Fake-Reset, Konto-Schließung.\n\nStandardformat pro Video\n\nJeder Clip soll möglichst so aufgebaut sein:\n\nScene 1 — Visible Trap / Hook\nDauer: 0–2s\nBild:\nnormaler Screen, z. B. Handy-SMS, Pop-up, E-Mail, Rechnung, Chat.\n\nVoiceover:\nkurzer Hook.\n\nBeispiel:\n“This delivery text is designed to make you panic.”\n\nScene 2 — Why it feels normal\nDauer: 2–7s\nZeige:\nwarum man es glauben könnte.\n\nBeispiel:\n“Tiny fee. Short deadline. Official-looking link.”\n\nScene 3 — Red Flag Reveal\nDauer: 7–15s\nZeige:\nein konkretes Detail.\n\nBeispiel:\n“The red flag is the pressure.”\n\nScene 4 — Safer Move\nDauer: 15–24s\nZeige:\nwas man stattdessen tun soll.\n\nBeispiel:\n“Don’t tap the link. Open the app yourself.”\n\nScene 5 — Screenshot Checklist\nDauer: 24–30s\nMaximal 3 Punkte.\n\nBeispiel:\n“SAVE THIS:\n\nWho sent it?\nWhy the rush?\nCan I open it myself?”\n\nScene 6 — Memorable Rule / CTA\nDauer: 30–34s\n\nBeispiel:\n“If it makes you hurry, slow down.”\n\nCTA:\n“Follow for one red flag before you click.”\n\nScreenshot-Checkliste\n\nJa, jedes Video soll am Ende möglichst eine kurze Screenshot-Karte haben.\n\nRegeln:\n\nmaximal 3 Punkte\nsehr kurze Wörter\ngroße Schrift\n1.5 bis 3 Sekunden sichtbar\nmobile-safe\nkein überladener Text\nkein langer Absatz\nkeine zehn Bulletpoints\nklarer Titel wie “SAVE THIS” oder “Quick check”\n\nGute Beispiele:\n\nSAVE THIS\n\nDelivery text?\nCheck:\n\nSmall fee?\nShort deadline?\nSuspicious link?\n\nOpen the app yourself.\n\nOder:\n\nQuick check:\n\nDid they rush you?\nIs money involved?\nCan you verify another way?\nBeschreibung / Caption / Pinned Comment\n\nDie genauere Anleitung gehört NICHT komplett ins Video.\nSie gehört in:\n\nBeschreibung\nCaption\ngepinnter Kommentar\n\nVideo = schnell und klar.\nBeschreibung = etwas mehr Hilfe.\n\nTemplate Beschreibung:\n\nA common red flag is pressure: a small fee, a short deadline, and a link.\n\nDon’t tap the link from the message.\nOpen the app or official website yourself.\n\nIf you already clicked: don’t enter payment or login details. 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"content": "{\"content\": \" 1|\\\"\\\"\\\"Public-safe content series registry and fit validation.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n 8|\\n 9|FORBIDDEN_PUBLIC_TOPIC_PATTERNS = {\\n 10| \\\"jarvis\\\": (\\\"jarvis\\\",),\\n 11| \\\"hermes\\\": (\\\"hermes\\\",),\\n 12| \\\"user\\\": (\\\"user\\\", \\\"my user\\\"),\\n 13| \\\"collaboration\\\": (\\\"collaboration\\\", \\\"our collaboration\\\"),\\n 14| \\\"private\\\": (\\\"private\\\", \\\"behind the scenes\\\"),\\n 15| \\\"politics\\\": (\\\"politics\\\", \\\"political\\\", \\\"election\\\", \\\"voter\\\", \\\"campaign\\\"),\\n 16| \\\"legal loophole\\\": (\\\"legal loophole\\\", \\\"loophole\\\"),\\n 17| \\\"bypass\\\": (\\\"bypass\\\",),\\n 18| \\\"illegal\\\": (\\\"illegal\\\", \\\"break the law\\\"),\\n 19| \\\"private data\\\": (\\\"private data\\\", \\\"scrape private data\\\"),\\n 20| \\\"morally questionable\\\": (\\\"morally questionable\\\",),\\n 21| \\\"exploit\\\": (\\\"exploit\\\",),\\n 22|}\\n 23|FORBIDDEN_PUBLIC_TOPICS = tuple(FORBIDDEN_PUBLIC_TOPIC_PATTERNS)\\n 24|\\n 25|\\n 26|@dataclass(frozen=True)\\n 27|class ContentSeries:\\n 28| series_id: str\\n 29| name: str\\n 30| promise: str\\n 31| audience: str\\n 32| allowed_hook_types: tuple[str, ...]\\n 33| format_rules: tuple[str, ...]\\n 34| visual_grammar: tuple[str, ...]\\n 35| no_go_patterns: tuple[str, ...]\\n 36| follow_reason: str\\n 37|\\n 38|\\n 39|DEFAULT_CONTENT_SERIES = (\\n 40| ContentSeries(\\n 41| series_id=\\\"prompt_vs_system\\\",\\n 42| name=\\\"Prompt vs System\\\",\\n 43| promise=\\\"Contrast fragile prompt hacks with repeatable AI workflow systems.\\\",\\n 44| audience=\\\"serious AI workflow builders\\\",\\n 45| allowed_hook_types=(\\\"contradiction\\\", \\\"myth_bust\\\", \\\"direct_challenge\\\"),\\n 46| format_rules=(\\n 47| \\\"show the fragile prompt or tactic\\\",\\n 48| \\\"show the system boundary that makes it repeatable\\\",\\n 49| \\\"end with one practical workflow principle\\\",\\n 50| ),\\n 51| visual_grammar=(\\\"prompt card\\\", \\\"workflow diagram\\\", \\\"mechanism reveal\\\"),\\n 52| no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n 53| follow_reason=\\\"More repeatable AI systems instead of disposable prompt tricks.\\\",\\n 54| ),\\n 55| ContentSeries(\\n 56| series_id=\\\"ai_system_autopsy\\\",\\n 57| name=\\\"AI System Autopsy\\\",\\n 58| promise=\\\"Break down automation failures into cause, system fix, and safer operating rule.\\\",\\n 59| audience=\\\"builders who want reliable automation\\\",\\n 60| allowed_hook_types=(\\\"failure\\\", \\\"cause_reveal\\\", \\\"risk_warning\\\"),\\n 61| format_rules=(\\n 62| \\\"state the automation failure\\\",\\n 63| \\\"identify the root cause\\\",\\n 64| \\\"show the safer system rule\\\",\\n 65| ),\\n 66| visual_grammar=(\\\"failure card\\\", \\\"cause map\\\", \\\"fixed workflow\\\"),\\n 67| no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n 68| follow_reason=\\\"Useful teardown patterns for safer AI workflows.\\\",\\n 69| ),\\n 70| ContentSeries(\\n 71| series_id=\\\"one_rule_one_example\\\",\\n 72| name=\\\"One Rule, One Example\\\",\\n 73| promise=\\\"Teach one AI-systems principle through one concrete safe example.\\\",\\n 74| audience=\\\"people who prefer practical operating rules\\\",\\n 75| allowed_hook_types=(\\\"rule\\\", \\\"example\\\", \\\"before_after\\\"),\\n 76| format_rules=(\\n 77| \\\"state one rule\\\",\\n 78| \\\"show one example\\\",\\n 79| \\\"end with one reusable decision check\\\",\\n 80| ),\\n 81| visual_grammar=(\\\"rule card\\\", \\\"example diagram\\\", \\\"decision checklist\\\"),\\n 82| no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n 83| follow_reason=\\\"A library of clear rules for useful AI systems.\\\",\\n 84| ),\\n 85| ContentSeries(\\n 86| series_id=\\\"ai_hype_court\\\",\\n 87| name=\\\"AI Hype Court\\\",\\n 88| promise=\\\"Test AI claims with evidence, verdict, and a safer alternative action.\\\",\\n 89| audience=\\\"skeptical AI adopters\\\",\\n 90| allowed_hook_types=(\\\"claim\\\", \\\"verdict\\\", \\\"myth_bust\\\"),\\n 91| format_rules=(\\n 92| \\\"state the public claim without endorsing it\\\",\\n 93| \\\"weigh evidence and constraints\\\",\\n 94| \\\"give a practical verdict and safer alternative\\\",\\n 95| ),\\n 96| visual_grammar=(\\\"claim card\\\", \\\"evidence scale\\\", \\\"verdict stamp\\\"),\\n 97| no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n 98| follow_reason=\\\"Clear verdicts on AI hype without falling for it.\\\",\\n 99| ),\\n 100|)\\n 101|\\n 102|\\n 103|def _is_blank(value: str) -> bool:\\n 104| return not (value or \\\"\\\").strip()\\n 105|\\n 106|\\n 107|def _series_text(series: ContentSeries) -> str:\\n 108| return \\\" \\\".join(\\n 109| (\\n 110| series.series_id,\\n 111| series.name,\\n 112| series.promise,\\n 113| series.audience,\\n 114| \\\" \\\".join(series.allowed_hook_types),\\n 115| \\\" \\\".join(series.format_rules),\\n 116| \\\" \\\".join(series.visual_grammar),\\n 117| series.follow_reason,\\n 118| )\\n 119| ).casefold()\\n 120|\\n 121|\\n 122|def _candidate_public_text(candidate: VideoCandidate) -> str:\\n 123| return \\\" \\\".join(\\n 124| (\\n 125| candidate.title,\\n 126| candidate.hook,\\n 127| candidate.script_outline,\\n 128| candidate.visual_concept,\\n 129| candidate.caption,\\n 130| candidate.cta,\\n 131| )\\n 132| ).casefold()\\n 133|\\n 134|\\n 135|def find_series(\\n 136| series_id: str,\\n 137| registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n 138|) -> ContentSeries | None:\\n 139| for series in registry:\\n 140| if series.series_id == series_id:\\n 141| return series\\n 142| return None\\n 143|\\n 144|\\n 145|def _forbidden_matches(text: str) -> tuple[str, ...]:\\n 146| matches: list[str] = []\\n 147| for canonical_topic, patterns in FORBIDDEN_PUBLIC_TOPIC_PATTERNS.items():\\n 148| if any(pattern in text for pattern in patterns):\\n 149| matches.append(canonical_topic)\\n 150| return tuple(matches)\\n 151|\\n 152|\\n 153|def _candidate_matches_series(candidate: VideoCandidate, series: ContentSeries) -> bool:\\n 154| public_text = _candidate_public_text(candidate)\\n 155| series_keywords = {\\n 156| \\\"prompt_vs_system\\\": (\\\"prompt\\\", \\\"system\\\", \\\"workflow\\\", \\\"boundary\\\"),\\n 157| \\\"ai_system_autopsy\\\": (\\\"failure\\\", \\\"broken\\\", \\\"cause\\\", \\\"fix\\\", \\\"risk\\\"),\\n 158| \\\"one_rule_one_example\\\": (\\\"rule\\\", \\\"example\\\", \\\"checklist\\\", \\\"principle\\\"),\\n 159| \\\"ai_hype_court\\\": (\\\"claim\\\", \\\"evidence\\\", \\\"verdict\\\", \\\"hype\\\", \\\"myth\\\"),\\n 160| }\\n 161| keywords = series_keywords.get(series.series_id, ())\\n 162| return any(keyword in public_text for keyword in keywords)\\n 163|\\n 164|\\n 165|def validate_content_series_registry(\\n 166| registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n 167|) -> ValidationResult:\\n 168| errors: list[str] = []\\n 169| seen: set[str] = set()\\n 170|\\n 171| if not registry:\\n 172| errors.append(\\\"at least one content series is required\\\")\\n 173|\\n 174| for series in registry:\\n 175| if _is_blank(series.series_id):\\n 176| errors.append(\\\"series_id is required\\\")\\n 177| elif series.series_id in seen:\\n 178| errors.append(f\\\"duplicate series_id: {series.series_id}\\\")\\n 179| seen.add(series.series_id)\\n 180|\\n 181| for field_name, value in {\\n 182| \\\"name\\\": series.name,\\n 183| \\\"promise\\\": series.promise,\\n 184| \\\"audience\\\": series.audience,\\n 185| \\\"follow_reason\\\": series.follow_reason,\\n 186| }.items():\\n 187| if _is_blank(value):\\n 188| errors.append(f\\\"series {series.series_id} {field_name} is required\\\")\\n 189|\\n 190| for field_name, values in {\\n 191| \\\"allowed_hook_types\\\": series.allowed_hook_types,\\n 192| \\\"format_rules\\\": series.format_rules,\\n 193| \\\"visual_grammar\\\": series.visual_grammar,\\n 194| \\\"no_go_patterns\\\": series.no_go_patterns,\\n 195| }.items():\\n 196| if not values:\\n 197| errors.append(f\\\"series {series.series_id} {field_name} is required\\\")\\n 198|\\n 199| series_text = _series_text(series)\\n 200| for match in _forbidden_matches(series_text):\\n 201| errors.append(f\\\"series {series.series_id} uses forbidden public topic: {match}\\\")\\n 202|\\n 203| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 204|\\n 205|\\n 206|def validate_candidate_series_fit(\\n 207| candidate: VideoCandidate,\\n 208| series_id: str,\\n 209| *,\\n 210| registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n 211|) -> ValidationResult:\\n 212| errors: list[str] = []\\n 213| series = find_series(series_id, registry)\\n 214| if series is None:\\n 215| return ValidationResult(False, (f\\\"unknown series: {series_id}\\\",))\\n 216|\\n 217| public_text = _candidate_public_text(candidate)\\n 218| for match in _forbidden_matches(public_text):\\n 219| errors.append(f\\\"candidate uses forbidden public topic: {match}\\\")\\n 220|\\n 221|\", \"total_lines\": 224, \"file_size\": 8097, \"truncated\": true, \"hint\": \"Use offset=221 to continue reading (showing 1-220 of 224 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Strategy scoring for follower-converting short-form candidates.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n 8|from autoshorts.strategy.series import FORBIDDEN_PUBLIC_TOPIC_PATTERNS\\n 9|\\n 10|MIN_SIGNAL_SCORE = 7\\n 11|MIN_AVERAGE_SCORE = 7.5\\n 12|GENERIC_AI_SLOP_PHRASES = (\\n 13| \\\"in this video\\\",\\n 14| \\\"unlock your potential\\\",\\n 15| \\\"unlock your full potential\\\",\\n 16| \\\"best ai tools\\\",\\n 17| \\\"ai tools you need\\\",\\n 18| \\\"follow for more ai tips\\\",\\n 19| \\\"everyone should use\\\",\\n 20|)\\n 21|DISTINCTIVE_TERMS = (\\n 22| \\\"system\\\",\\n 23| \\\"systems\\\",\\n 24| \\\"workflow\\\",\\n 25| \\\"boundary\\\",\\n 26| \\\"operating system\\\",\\n 27| \\\"quality gate\\\",\\n 28| \\\"mechanism\\\",\\n 29|)\\n 30|SERIES_NAMES = (\\n 31| \\\"Prompt vs System\\\",\\n 32| \\\"AI System Autopsy\\\",\\n 33| \\\"One Rule, One Example\\\",\\n 34| \\\"AI Hype Court\\\",\\n 35|)\\n 36|\\n 37|\\n 38|@dataclass(frozen=True)\\n 39|class StrategySignal:\\n 40| score: int\\n 41| rationale: str\\n 42|\\n 43|\\n 44|@dataclass(frozen=True)\\n 45|class ContentStrategyScore:\\n 46| hook: StrategySignal\\n 47| curiosity: StrategySignal\\n 48| identity: StrategySignal\\n 49| emotion: StrategySignal\\n 50| trust: StrategySignal\\n 51| follow_reason: StrategySignal\\n 52| originality: StrategySignal\\n 53| variety: StrategySignal\\n 54| target_emotion: str\\n 55| viewer_identity: str\\n 56| future_value_promise: str\\n 57| format_family: str\\n 58| variation_axis: str\\n 59|\\n 60| @property\\n 61| def average_score(self) -> float:\\n 62| signals = (\\n 63| self.hook,\\n 64| self.curiosity,\\n 65| self.identity,\\n 66| self.emotion,\\n 67| self.trust,\\n 68| self.follow_reason,\\n 69| self.originality,\\n 70| self.variety,\\n 71| )\\n 72| return sum(signal.score for signal in signals) / len(signals)\\n 73|\\n 74|\\n 75|@dataclass(frozen=True)\\n 76|class CandidateStrategyReview:\\n 77| candidate_id: str\\n 78| strategy_score: ContentStrategyScore\\n 79| render_allowed: bool\\n 80| reject_reasons: tuple[str, ...]\\n 81| improvement_suggestions: tuple[str, ...]\\n 82|\\n 83|\\n 84|def _is_blank(value: str) -> bool:\\n 85| return not (value or \\\"\\\").strip()\\n 86|\\n 87|\\n 88|def _visible_candidate_text(candidate: VideoCandidate) -> str:\\n 89| return \\\" \\\".join(\\n 90| (\\n 91| candidate.title,\\n 92| candidate.hook,\\n 93| candidate.script_outline,\\n 94| candidate.visual_concept,\\n 95| candidate.caption,\\n 96| candidate.cta,\\n 97| )\\n 98| ).casefold()\\n 99|\\n 100|\\n 101|def _combined_candidate_text(candidate: VideoCandidate) -> str:\\n 102| return \\\" \\\".join(\\n 103| (\\n 104| _visible_candidate_text(candidate),\\n 105| candidate.hypothesis,\\n 106| \\\" \\\".join(candidate.policy_notes),\\n 107| )\\n 108| ).casefold()\\n 109|\\n 110|\\n 111|def _has_distinctive_terms(text: str) -> bool:\\n 112| return any(term in text for term in DISTINCTIVE_TERMS)\\n 113|\\n 114|\\n 115|def _has_generic_ai_slop(text: str) -> bool:\\n 116| return any(phrase in text for phrase in GENERIC_AI_SLOP_PHRASES)\\n 117|\\n 118|\\n 119|def _infer_format_family(candidate: VideoCandidate) -> str:\\n 120| text = _combined_candidate_text(candidate)\\n 121| for series_name in SERIES_NAMES:\\n 122| if series_name.casefold() in text:\\n 123| return series_name\\n 124| if \\\"ai tools\\\" in text:\\n 125| return \\\"AI Tools List\\\"\\n 126| if \\\"prompt\\\" in text and \\\"system\\\" in text:\\n 127| return \\\"Prompt vs System\\\"\\n 128| if \\\"claim\\\" in text and \\\"verdict\\\" in text:\\n 129| return \\\"AI Hype Court\\\"\\n 130| if \\\"rule\\\" in text and \\\"example\\\" in text:\\n 131| return \\\"One Rule, One Example\\\"\\n 132| if \\\"failure\\\" in text or \\\"broken\\\" in text:\\n 133| return \\\"AI System Autopsy\\\"\\n 134| return \\\"Unclassified\\\"\\n 135|\\n 136|\\n 137|def _score_signal(score: int, rationale: str) -> StrategySignal:\\n 138| return StrategySignal(score=max(0, min(10, score)), rationale=rationale)\\n 139|\\n 140|\\n 141|def _forbidden_public_topic_matches(text: str) -> tuple[str, ...]:\\n 142| matches: list[str] = []\\n 143| for canonical_topic, patterns in FORBIDDEN_PUBLIC_TOPIC_PATTERNS.items():\\n 144| if any(pattern in text for pattern in patterns):\\n 145| matches.append(canonical_topic)\\n 146| return tuple(matches)\\n 147|\\n 148|\\n 149|def validate_content_strategy_score(score: ContentStrategyScore) -> ValidationResult:\\n 150| errors: list[str] = []\\n 151| signals = {\\n 152| \\\"hook\\\": score.hook,\\n 153| \\\"curiosity\\\": score.curiosity,\\n 154| \\\"identity\\\": score.identity,\\n 155| \\\"emotion\\\": score.emotion,\\n 156| \\\"trust\\\": score.trust,\\n 157| \\\"follow_reason\\\": score.follow_reason,\\n 158| \\\"originality\\\": score.originality,\\n 159| \\\"variety\\\": score.variety,\\n 160| }\\n 161| for name, signal in signals.items():\\n 162| if not 0 <= signal.score <= 10:\\n 163| errors.append(f\\\"{name} score must be between 0 and 10\\\")\\n 164| if _is_blank(signal.rationale):\\n 165| errors.append(f\\\"{name} rationale is required\\\")\\n 166|\\n 167| for field_name, value in {\\n 168| \\\"target_emotion\\\": score.target_emotion,\\n 169| \\\"viewer_identity\\\": score.viewer_identity,\\n 170| \\\"future_value_promise\\\": score.future_value_promise,\\n 171| \\\"format_family\\\": score.format_family,\\n 172| \\\"variation_axis\\\": score.variation_axis,\\n 173| }.items():\\n 174| if _is_blank(value):\\n 175| errors.append(f\\\"{field_name} is required\\\")\\n 176|\\n 177| if score.hook.score < MIN_SIGNAL_SCORE:\\n 178| errors.append(\\\"hook must be at least 7\\\")\\n 179| if score.curiosity.score < MIN_SIGNAL_SCORE:\\n 180| errors.append(\\\"curiosity must be at least 7\\\")\\n 181| if score.identity.score < MIN_SIGNAL_SCORE:\\n 182| errors.append(\\\"identity must be at least 7\\\")\\n 183| if score.follow_reason.score < MIN_SIGNAL_SCORE:\\n 184| errors.append(\\\"follow_reason must be at least 7\\\")\\n 185| if score.originality.score < MIN_SIGNAL_SCORE:\\n 186| errors.append(\\\"originality must be at least 7\\\")\\n 187| if score.average_score < MIN_AVERAGE_SCORE:\\n 188| errors.append(\\\"average strategy score must be at least 7.5\\\")\\n 189|\\n 190| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 191|\\n 192|\\n 193|def should_advance_strategy(score: ContentStrategyScore) -> bool:\\n 194| return validate_content_strategy_score(score).is_valid\\n 195|\\n 196|\\n 197|def build_candidate_strategy_review(\\n 198| candidate: VideoCandidate,\\n 199| *,\\n 200| recent_format_families: tuple[str, ...] = (),\\n 201|) -> CandidateStrategyReview:\\n 202| text = _combined_candidate_text(candidate)\\n 203| visible_text = _visible_candidate_text(candidate)\\n 204| format_family = _infer_format_family(candidate)\\n 205| generic_slop = _has_generic_ai_slop(visible_text)\\n 206| distinctive = _has_distinctive_terms(visible_text)\\n 207| forbidden_public_topics = _forbidden_public_topic_matches(visible_text)\\n 208| repeated_recently = bool(recent_format_families) and recent_format_families.count(format_family) >= 2\\n 209|\\n 210| strategy_score = ContentStrategyScore(\\n 211| hook=_score_signal(\\n 212| 8 if not generic_slop else 5,\\n 213| \\\"Hook creates a concrete systems-vs-hacks tension.\\\"\\n 214| if not generic_slop\\n 215| else \\\"Hook uses generic AI advice language.\\\",\\n 216| ),\\n 217| curiosity=_score_signal(\\n 218| 8 if \\\"?\\\" in candidate.hook or \\\"broken\\\" in text or \\\"because\\\" in text or \\\"if\\\" in text else 6,\\n 219| \\\"Viewer has a specific open loop about the mechanism or fix.\\\",\\n 220| ),\\n 221| identity=_score_signal(\\n 222| 9 if distinctive else 5,\\n 223| \\\"Targets serious AI workflow builders.\\\"\\n 224| if distinctive\\n 225| else \\\"Audience identity is too broad.\\\",\\n 226| ),\\n 227| emotion=_score_signal(\\n 228| 8 if not generic_slop else 6,\\n 229| \\\"Targets relief/frustration with shallow AI advice.\\\",\\n 230| ),\\n 231| trust=_score_signal(\\n 232| 8 if distinctive else 6,\\n 233| \\\"Trust comes from concrete workflow/system language.\\\"\\n 234| if distinctive\\n 235| else \\\"Needs more proof, constraints, or a concrete artifact.\\\",\\n 236| ),\\n 237| follow_reason=_score_signal(\\n 238| 9 if distinctive else 4,\\n 239| \\\"Future value promise is repeatable AI systems.\\\"\\n 240| if distinctive\\n 241| else \\\"Future value promise is not distinctive enough.\\\",\\n 242| ),\\n 243| originality=_score_signal(\\n 244| 8 if not generic_slop and distinctive else 4,\\n 245| \\\"Mechanism-first angle avoids generic AI tips.\\\"\\n 246| if not generic_slop and distinctive\\n 247| else \\\"Generic AI-slop language detected.\\\",\\n 248| ),\\n 249| variety=_score_signal(\\n 250| 5 if repeated_recently else 8,\\n 251| \\\"Recent format repetition risk.\\\" if repeated_recently else \\\"Format is fresh enough against recent context.\\\",\\n 252| ),\\n 253| target_emotion=\\\"relief\\\" if not generic_slop else \\\"mild interest\\\",\\n 254| viewer_identity=\\\"serious AI workflow builders\\\" if distinctive else \\\"generic AI users\\\",\\n 255| future_value_promise=\\\"repeatable AI systems, not prompt tricks\\\" if distinctive else \\\"more AI tips\\\",\\n 256| format_family=format_family,\\n 257| variation_axis=\\\"contrarian hook + mechanism reveal\\\" if distinctive else \\\"generic listicle\\\",\\n 258| )\\n 259|\\n 260| validation = validate_content_strategy_score(strategy_score)\\n 261|\", \"total_lines\": 288, \"file_size\": 10136, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 288 lines)\", \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|from autoshorts.config.pillars import (\\n 2| ContentPillar,\\n 3| DEFAULT_PILLARS,\\n 4| find_pillar,\\n 5| validate_candidate_against_pillars,\\n 6| validate_pillar_registry,\\n 7|)\\n 8|from autoshorts.ideas.candidate import VideoCandidate\\n 9|from autoshorts.scripting.quality import ScriptQualityScore\\n 10|\\n 11|\\n 12|def _score() -> ScriptQualityScore:\\n 13| return ScriptQualityScore(\\n 14| hook_strength=9,\\n 15| novelty=8,\\n 16| clarity=9,\\n 17| retention=8,\\n 18| ai_slop_risk=2,\\n 19| policy_risk=\\\"low\\\",\\n 20| )\\n 21|\\n 22|\\n 23|def _candidate(**overrides) -> VideoCandidate:\\n 24| data = {\\n 25| \\\"candidate_id\\\": \\\"vid-001\\\",\\n 26| \\\"pillar\\\": \\\"ai_life_systems\\\",\\n 27| \\\"title\\\": \\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n 28| \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n 29| \\\"duration_seconds\\\": 34,\\n 30| \\\"hook\\\": \\\"AI sollte dir dienen, nicht dich führen.\\\",\\n 31| \\\"script_outline\\\": \\\"Show AI chaos, then a controlled human-approved workflow.\\\",\\n 32| \\\"visual_concept\\\": \\\"Calm desk setup, explicit approval checkpoint, no fake dashboards.\\\",\\n 33| \\\"caption\\\": \\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n 34| \\\"cta\\\": \\\"Follow for practical AI life systems.\\\",\\n 35| \\\"hashtags\\\": (\\\"#AI\\\", \\\"#Productivity\\\"),\\n 36| \\\"quality_score\\\": _score(),\\n 37| \\\"policy_notes\\\": (\\\"No fake earnings claims\\\",),\\n 38| \\\"hypothesis\\\": \\\"Values-first AI positioning gets more saves than hype content.\\\",\\n 39| }\\n 40| data.update(overrides)\\n 41| return VideoCandidate(**data)\\n 42|\\n 43|\\n 44|def test_default_registry_contains_ai_life_systems_positioning():\\n 45| pillar = find_pillar(\\\"ai_life_systems\\\", DEFAULT_PILLARS)\\n 46|\\n 47| assert pillar is not None\\n 48| assert \\\"servant\\\" in pillar.positioning\\n 49| assert \\\"human remains in control\\\" in pillar.visual_rules\\n 50| assert \\\"fake earnings\\\" in pillar.no_go\\n 51| assert \\\"youtube\\\" in pillar.platform_fit\\n 52|\\n 53|\\n 54|def test_pillar_registry_validation_rejects_duplicate_ids():\\n 55| first = ContentPillar(\\n 56| pillar_id=\\\"dup\\\",\\n 57| audience=\\\"builders\\\",\\n 58| positioning=\\\"first\\\",\\n 59| tone=(\\\"clear\\\",),\\n 60| hook_patterns=(\\\"hook\\\",),\\n 61| visual_rules=(\\\"rule\\\",),\\n 62| no_go=(\\\"no\\\",),\\n 63| platform_fit=(\\\"youtube\\\",),\\n 64| monetization_angle=\\\"newsletter\\\",\\n 65| )\\n 66| second = ContentPillar(\\n 67| pillar_id=\\\"dup\\\",\\n 68| audience=\\\"builders\\\",\\n 69| positioning=\\\"second\\\",\\n 70| tone=(\\\"clear\\\",),\\n 71| hook_patterns=(\\\"hook\\\",),\\n 72| visual_rules=(\\\"rule\\\",),\\n 73| no_go=(\\\"no\\\",),\\n 74| platform_fit=(\\\"youtube\\\",),\\n 75| monetization_angle=\\\"newsletter\\\",\\n 76| )\\n 77|\\n 78| result = validate_pillar_registry((first, second))\\n 79|\\n 80| assert result.is_valid is False\\n 81| assert \\\"pillar ids must be unique\\\" in result.errors\\n 82|\\n 83|\\n 84|def test_candidate_passes_known_pillar_and_platform_fit():\\n 85| result = validate_candidate_against_pillars(_candidate(), DEFAULT_PILLARS)\\n 86|\\n 87| assert result.is_valid is True\\n 88| assert result.errors == ()\\n 89|\\n 90|\\n 91|def test_candidate_rejects_unknown_pillar():\\n 92| result = validate_candidate_against_pillars(\\n 93| _candidate(pillar=\\\"unknown_pillar\\\"), DEFAULT_PILLARS\\n 94| )\\n 95|\\n 96| assert result.is_valid is False\\n 97| assert \\\"unknown pillar: unknown_pillar\\\" in result.errors\\n 98|\\n 99|\\n 100|def test_candidate_rejects_platform_not_fit_for_pillar():\\n 101| registry = (\\n 102| ContentPillar(\\n 103| pillar_id=\\\"narrow\\\",\\n 104| audience=\\\"specific audience\\\",\\n 105| positioning=\\\"narrow test pillar\\\",\\n 106| tone=(\\\"clear\\\",),\\n 107| hook_patterns=(\\\"hook\\\",),\\n 108| visual_rules=(\\\"rule\\\",),\\n 109| no_go=(\\\"no\\\",),\\n 110| platform_fit=(\\\"youtube\\\",),\\n 111| monetization_angle=\\\"newsletter\\\",\\n 112| ),\\n 113| )\\n 114|\\n 115| result = validate_candidate_against_pillars(\\n 116| _candidate(pillar=\\\"narrow\\\", platforms=(\\\"youtube\\\", \\\"tiktok\\\")), registry\\n 117| )\\n 118|\\n 119| assert result.is_valid is False\\n 120| assert \\\"platform tiktok is not configured for pillar narrow\\\" in result.errors\\n 121|\\n 122|\\n 123|def test_registry_validation_rejects_empty_required_pillar_fields():\\n 124| pillar = ContentPillar(\\n 125| pillar_id=\\\"\\\",\\n 126| audience=\\\"\\\",\\n 127| positioning=\\\"\\\",\\n 128| tone=(),\\n 129| hook_patterns=(),\\n 130| visual_rules=(),\\n 131| no_go=(),\\n 132| platform_fit=(),\\n 133| monetization_angle=\\\"\\\",\\n 134| )\\n 135|\\n 136| result = validate_pillar_registry((pillar,))\\n 137|\\n 138| assert result.is_valid is False\\n 139| assert \\\"pillar_id is required\\\" in result.errors\\n 140| assert \\\"audience is required\\\" in result.errors\\n 141| assert \\\"at least one hook pattern is required\\\" in result.errors\\n 142| assert \\\"at least one platform fit is required\\\" in result.errors\\n 143|\", \"total_lines\": 142, \"file_size\": 4413, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|from autoshorts.ideas.candidate import VideoCandidate\\n 2|from autoshorts.scripting.quality import ScriptQualityScore\\n 3|from autoshorts.strategy.series import (\\n 4| DEFAULT_CONTENT_SERIES,\\n 5| ContentSeries,\\n 6| find_series,\\n 7| validate_candidate_series_fit,\\n 8| validate_content_series_registry,\\n 9|)\\n 10|\\n 11|\\n 12|def _candidate(**overrides):\\n 13| values = {\\n 14| \\\"candidate_id\\\": \\\"prompt-vs-system-35s\\\",\\n 15| \\\"pillar\\\": \\\"ai_life_systems\\\",\\n 16| \\\"title\\\": \\\"Prompt hacks fail without a workflow boundary\\\",\\n 17| \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n 18| \\\"duration_seconds\\\": 35,\\n 19| \\\"hook\\\": \\\"Your best AI prompt still fails if the workflow is broken.\\\",\\n 20| \\\"script_outline\\\": \\\"Contrast a fragile prompt with a repeatable system boundary.\\\",\\n 21| \\\"visual_concept\\\": \\\"Mechanism reveal: prompt card becomes workflow diagram.\\\",\\n 22| \\\"caption\\\": \\\"Prompts are not systems. Build the boundary first.\\\",\\n 23| \\\"cta\\\": \\\"Follow for practical AI workflow systems.\\\",\\n 24| \\\"hashtags\\\": (\\\"#AISystems\\\", \\\"#Workflow\\\", \\\"#Automation\\\"),\\n 25| \\\"quality_score\\\": ScriptQualityScore(9, 8, 9, 8, 2, \\\"low\\\"),\\n 26| \\\"policy_notes\\\": (\\\"series:prompt_vs_system\\\",),\\n 27| \\\"hypothesis\\\": \\\"Systems-over-prompts positioning attracts serious builders.\\\",\\n 28| }\\n 29| values.update(overrides)\\n 30| return VideoCandidate(**values)\\n 31|\\n 32|\\n 33|def test_default_series_registry_contains_public_safe_series_only():\\n 34| result = validate_content_series_registry(DEFAULT_CONTENT_SERIES)\\n 35|\\n 36| assert result.is_valid is True\\n 37| series_ids = {series.series_id for series in DEFAULT_CONTENT_SERIES}\\n 38| assert series_ids == {\\n 39| \\\"prompt_vs_system\\\",\\n 40| \\\"ai_system_autopsy\\\",\\n 41| \\\"one_rule_one_example\\\",\\n 42| \\\"ai_hype_court\\\",\\n 43| }\\n 44| assert \\\"jarvis_build_log\\\" not in series_ids\\n 45|\\n 46|\\n 47|def test_series_registry_rejects_duplicate_ids_and_forbidden_public_topics():\\n 48| unsafe = ContentSeries(\\n 49| series_id=\\\"jarvis_build_log\\\",\\n 50| name=\\\"The JARVIS Build Log\\\",\\n 51| promise=\\\"Publicly discuss JARVIS and the user's collaboration.\\\",\\n 52| audience=\\\"AI builders\\\",\\n 53| allowed_hook_types=(\\\"build_log\\\",),\\n 54| format_rules=(\\\"Mention JARVIS and the user's workflow.\\\",),\\n 55| visual_grammar=(\\\"private build log\\\",),\\n 56| no_go_patterns=(\\\"politics\\\",),\\n 57| follow_reason=\\\"Watch the private collaboration evolve.\\\",\\n 58| )\\n 59|\\n 60| result = validate_content_series_registry((unsafe, unsafe))\\n 61|\\n 62| assert result.is_valid is False\\n 63| assert \\\"duplicate series_id: jarvis_build_log\\\" in result.errors\\n 64| assert \\\"series jarvis_build_log uses forbidden public topic: jarvis\\\" in result.errors\\n 65| assert \\\"series jarvis_build_log uses forbidden public topic: collaboration\\\" in result.errors\\n 66|\\n 67|\\n 68|def test_candidate_series_fit_accepts_matching_public_safe_candidate():\\n 69| candidate = _candidate()\\n 70|\\n 71| result = validate_candidate_series_fit(candidate, \\\"prompt_vs_system\\\")\\n 72|\\n 73| assert result.is_valid is True\\n 74|\\n 75|\\n 76|def test_candidate_series_fit_rejects_public_content_about_jarvis_or_user_collaboration():\\n 77| candidate = _candidate(\\n 78| title=\\\"How JARVIS and my user built a content machine\\\",\\n 79| hook=\\\"This is how our collaboration works behind the scenes.\\\",\\n 80| script_outline=\\\"Explain JARVIS, the user, and private approval workflow details.\\\",\\n 81| visual_concept=\\\"Private chat screenshots and build log.\\\",\\n 82| caption=\\\"Our JARVIS collaboration is the secret.\\\",\\n 83| cta=\\\"Follow to watch us build it live.\\\",\\n 84| )\\n 85|\\n 86| result = validate_candidate_series_fit(candidate, \\\"prompt_vs_system\\\")\\n 87|\\n 88| assert result.is_valid is False\\n 89| assert \\\"candidate uses forbidden public topic: jarvis\\\" in result.errors\\n 90| assert \\\"candidate uses forbidden public topic: collaboration\\\" in result.errors\\n 91| assert \\\"candidate uses forbidden public topic: private\\\" in result.errors\\n 92|\\n 93|\\n 94|def test_candidate_series_fit_rejects_politics_and_legal_or_moral_risk():\\n 95| candidate = _candidate(\\n 96| title=\\\"Political AI persuasion system\\\",\\n 97| hook=\\\"This legal loophole lets you scrape private data for campaigns.\\\",\\n 98| script_outline=\\\"Explain how to bypass platform rules and influence voters.\\\",\\n 99| visual_concept=\\\"Campaign dashboard and private data map.\\\",\\n 100| caption=\\\"Use this loophole before it is banned.\\\",\\n 101| cta=\\\"Try this campaign hack.\\\",\\n 102| )\\n 103|\\n 104| result = validate_candidate_series_fit(candidate, \\\"ai_hype_court\\\")\\n 105|\\n 106| assert result.is_valid is False\\n 107| assert \\\"candidate uses forbidden public topic: politics\\\" in result.errors\\n 108| assert \\\"candidate uses forbidden public topic: legal loophole\\\" in result.errors\\n 109| assert \\\"candidate uses forbidden public topic: private data\\\" in result.errors\\n 110| assert \\\"candidate uses forbidden public topic: bypass\\\" in result.errors\\n 111|\\n 112|\\n 113|def test_candidate_series_fit_rejects_unknown_series():\\n 114| result = validate_candidate_series_fit(_candidate(), \\\"missing_series\\\")\\n 115|\\n 116| assert result.is_valid is False\\n 117| assert \\\"unknown series: missing_series\\\" in result.errors\\n 118|\", \"total_lines\": 117, \"file_size\": 4899, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|import pytest\\n 2|\\n 3|from autoshorts.ideas.candidate import VideoCandidate\\n 4|from autoshorts.scripting.quality import ScriptQualityScore\\n 5|from autoshorts.strategy.score import (\\n 6| ContentStrategyScore,\\n 7| StrategySignal,\\n 8| build_candidate_strategy_review,\\n 9| should_advance_strategy,\\n 10| validate_content_strategy_score,\\n 11|)\\n 12|\\n 13|\\n 14|def _candidate(**overrides):\\n 15| values = {\\n 16| \\\"candidate_id\\\": \\\"ai-life-systems-prompt-vs-system-35s\\\",\\n 17| \\\"pillar\\\": \\\"ai_life_systems\\\",\\n 18| \\\"title\\\": \\\"Prompt hacks fail because they have no operating system\\\",\\n 19| \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n 20| \\\"duration_seconds\\\": 35,\\n 21| \\\"hook\\\": \\\"Your best AI prompt is useless if the workflow around it is broken.\\\",\\n 22| \\\"script_outline\\\": \\\"Show fragile prompt hack, then reveal the repeatable system boundary.\\\",\\n 23| \\\"visual_concept\\\": \\\"Mechanism reveal with prompt card collapsing into a workflow diagram.\\\",\\n 24| \\\"caption\\\": \\\"Prompts are not systems. Build the boundary first.\\\",\\n 25| \\\"cta\\\": \\\"Follow for AI systems that survive real life.\\\",\\n 26| \\\"hashtags\\\": (\\\"#AISystems\\\", \\\"#Automation\\\", \\\"#Workflow\\\"),\\n 27| \\\"quality_score\\\": ScriptQualityScore(\\n 28| hook_strength=9,\\n 29| novelty=9,\\n 30| clarity=8,\\n 31| retention=8,\\n 32| ai_slop_risk=2,\\n 33| policy_risk=\\\"low\\\",\\n 34| ),\\n 35| \\\"policy_notes\\\": (\\\"duration_class:growth_short\\\", \\\"series:Prompt vs System\\\"),\\n 36| \\\"hypothesis\\\": \\\"A systems-over-prompts contrast converts generic AI viewers into followers.\\\",\\n 37| }\\n 38| values.update(overrides)\\n 39| return VideoCandidate(**values)\\n 40|\\n 41|\\n 42|def test_content_strategy_score_validates_complete_high_intent_score():\\n 43| score = ContentStrategyScore(\\n 44| hook=StrategySignal(9, \\\"Contradicts prompt-hack advice in first sentence.\\\"),\\n 45| curiosity=StrategySignal(8, \\\"Viewer wants to know what system replaces the prompt.\\\"),\\n 46| identity=StrategySignal(9, \\\"For builders who prefer systems over hacks.\\\"),\\n 47| emotion=StrategySignal(8, \\\"Relief and mild frustration at shallow AI advice.\\\"),\\n 48| trust=StrategySignal(8, \\\"Uses a concrete workflow boundary instead of claims.\\\"),\\n 49| follow_reason=StrategySignal(9, \\\"Promises future AI operating-system breakdowns.\\\"),\\n 50| originality=StrategySignal(8, \\\"Mechanism-first angle avoids generic tips.\\\"),\\n 51| variety=StrategySignal(7, \\\"Uses Prompt vs System format without repeating the last hook.\\\"),\\n 52| target_emotion=\\\"relief\\\",\\n 53| viewer_identity=\\\"serious AI workflow builders\\\",\\n 54| future_value_promise=\\\"repeatable AI systems, not prompt tricks\\\",\\n 55| format_family=\\\"Prompt vs System\\\",\\n 56| variation_axis=\\\"contrarian hook + mechanism reveal\\\",\\n 57| )\\n 58|\\n 59| result = validate_content_strategy_score(score)\\n 60|\\n 61| assert result.is_valid is True\\n 62| assert score.average_score == pytest.approx(8.25)\\n 63| assert should_advance_strategy(score) is True\\n 64|\\n 65|\\n 66|def test_content_strategy_score_rejects_low_follow_reason_even_with_good_hook():\\n 67| score = ContentStrategyScore(\\n 68| hook=StrategySignal(9, \\\"Strong contradiction.\\\"),\\n 69| curiosity=StrategySignal(8, \\\"Specific open loop.\\\"),\\n 70| identity=StrategySignal(7, \\\"Somewhat targeted.\\\"),\\n 71| emotion=StrategySignal(8, \\\"Relief.\\\"),\\n 72| trust=StrategySignal(8, \\\"Specific example.\\\"),\\n 73| follow_reason=StrategySignal(4, \\\"Could belong to any AI tips account.\\\"),\\n 74| originality=StrategySignal(8, \\\"Specific.\\\"),\\n 75| variety=StrategySignal(8, \\\"Fresh enough.\\\"),\\n 76| target_emotion=\\\"relief\\\",\\n 77| viewer_identity=\\\"AI users\\\",\\n 78| future_value_promise=\\\"more tips\\\",\\n 79| format_family=\\\"One Rule, One Example\\\",\\n 80| variation_axis=\\\"generic listicle\\\",\\n 81| )\\n 82|\\n 83| result = validate_content_strategy_score(score)\\n 84|\\n 85| assert result.is_valid is False\\n 86| assert \\\"follow_reason must be at least 7\\\" in result.errors\\n 87| assert should_advance_strategy(score) is False\\n 88|\\n 89|\\n 90|def test_build_candidate_strategy_review_blocks_generic_ai_slop_language():\\n 91| candidate = _candidate(\\n 92| title=\\\"5 AI tools you need right now\\\",\\n 93| hook=\\\"In this video I will show you the best AI tools to unlock your potential.\\\",\\n 94| script_outline=\\\"Here are 5 amazing AI tools everyone should use.\\\",\\n 95| visual_concept=\\\"Generic AI stock footage and floating robot icons.\\\",\\n 96| caption=\\\"Unlock your full potential with these AI tools.\\\",\\n 97| cta=\\\"Follow for more AI tips.\\\",\\n 98| policy_notes=(\\\"duration_class:growth_short\\\",),\\n 99| )\\n 100|\\n 101| review = build_candidate_strategy_review(\\n 102| candidate,\\n 103| recent_format_families=(\\\"AI Tools List\\\", \\\"AI Tools List\\\"),\\n 104| )\\n 105|\\n 106| assert review.render_allowed is False\\n 107| assert \\\"generic AI-slop language detected\\\" in review.reject_reasons\\n 108| assert \\\"future value promise is not distinctive enough\\\" in review.reject_reasons\\n 109| assert \\\"recent format repetition risk\\\" in review.reject_reasons\\n 110|\\n 111|\\n 112|def test_build_candidate_strategy_review_blocks_forbidden_public_topics():\\n 113| candidate = _candidate(\\n 114| title=\\\"How JARVIS and the user built this approval workflow\\\",\\n 115| hook=\\\"This is the private collaboration behind the content machine.\\\",\\n 116| script_outline=\\\"Show our JARVIS build log and personal workflow.\\\",\\n 117| visual_concept=\\\"Private screenshots and internal chat timeline.\\\",\\n 118| caption=\\\"Our collaboration is the moat.\\\",\\n 119| cta=\\\"Follow for the next JARVIS build log.\\\",\\n 120| policy_notes=(\\\"series:jarvis_build_log\\\",),\\n 121| )\\n 122|\\n 123| review = 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"content": "{\"content\": \" 1|# Image Generation Guidelines for AutoShortsBot\\n 2|\\n 3|These rules apply to premium styleframes/keyframes only. Renderer-owned mockups (fake invoices, emails, payment cards, UI screens) should remain deterministic unless explicitly approved otherwise.\\n 4|\\n 5|## Prompt Template\\n 6|\\n 7|```text\\n 8|AutoShortsBot premium keyframe, vertical 9:16 social-video frame.\\n 9|Scene role: .\\n 10|Visible real-world problem: .\\n 11|Subject and environment: .\\n 12|Composition: .\\n 13|Lighting and style: .\\n 14|Motion-readiness: startframe suitable for subtle push-in or I2V; stable subject; no chaotic elements.\\n 15|Renderer-owned overlays: leave clean safe space for captions/labels; no baked text.\\n 16|Forbidden elements: no logos, brands, QR codes, real bank data, readable personal data, fake legal/banking details, watermark, pseudo text, random abstract glass clouds.\\n 17|Output: text-free image, 9:16, high fidelity, realistic, immediately understandable without voiceover.\\n 18|```\\n 19|\\n 20|## Quality Checklist\\n 21|\\n 22|- The frame shows a real viewer problem without relying on the voiceover.\\n 23|- The focal point is obvious within one second.\\n 24|- The scene is not interchangeable with 50 other AI/productivity videos.\\n 25|- The image is text-free or uses only intentionally blank/generic surfaces.\\n 26|- There is safe negative space for deterministic captions/labels.\\n 27|- The scene can connect to the previous and next scene through consistent objects, color, camera logic, and problem state.\\n 28|- No brand, logo, QR code, real company, real person, IBAN, routing number, account number, invoice number, address, or pseudo-legal document detail appears.\\n 29|- No AI artifacts that distract on mobile: warped hands, broken UI, nonsense writing, impossible reflections, random symbols.\\n 30|\\n 31|## Rejection Criteria\\n 32|\\n 33|Reject the styleframe if any of these are true:\\n 34|\\n 35|- Pretty Empty Visual Blocker: it looks premium but does not show a concrete visible fail.\\n 36|- It needs the voiceover to explain what problem exists.\\n 37|- It is generic laptop B-roll, abstract SaaS glassmorphism, mood-only B-roll, or a random futuristic metaphor.\\n 38|- It contains baked text, pseudo-writing, fake logos, QR codes, or fake-real financial/legal details.\\n 39|- It is not mobile-legible at 9:16.\\n 40|- It cannot be used as a stable startframe/endframe for scene continuity.\\n 41|- It violates the deterministic-mockup rule for UI/invoice/email/payment assets.\\n 42|\\n 43|## Styleframe Budget Policy\\n 44|\\n 45|- `allowed_image_provider = openai-codex`\\n 46|- `preferred_image_model = best_available_gpt_image_model`\\n 47|- `preferred_quality = highest_available_reasonable`\\n 48|- Use `gpt-image-2-high` for final/high-fidelity keyframes when available.\\n 49|- Use `gpt-image-2-medium` only for balanced exploration when high is not needed or unavailable.\\n 50|- Maximum styleframes per scene without explicit approval: `3`.\\n 51|- Maximum retries per styleframe: `1`.\\n 52|- No random batches. Every generated image must have a scene role, rejection criteria, and storage path.\\n 53|- Stop when one image passes the frame's job; do not keep generating just because the next one might be prettier. We are not mining JPEGs like a Victorian coal baron.\\n 54|\\n 55|## Scene Consistency Rules\\n 56|\\n 57|- Keep recurring objects stable: same desk, same generic document shape, same device orientation, same color family.\\n 58|- Use consistent camera language: e.g. close-up → route-change insert → verification action → takeaway.\\n 59|- Keep factual text, arrows, labels, UI words, numbers, warnings, and captions renderer-owned.\\n 60|- Use AI images for atmosphere, realism, and premium texture only.\\n 61|- For startframe/endframe planning, specify the changed state explicitly: e.g. `start: normal invoice route visible as blank field`, `end: route field visually highlighted by renderer overlay later`.\\n 62|\\n 63|## Text/Logo/Pseudo-Writing Blocker\\n 64|\\n 65|AI-generated images must not contain:\\n 66|\\n 67|- readable text intended as a claim or instruction\\n 68|- fake company names, fake bank details, fake invoice IDs, QR codes, barcodes\\n 69|- real logos, product marks, app icons, bank names, government marks\\n 70|- pseudo-writing that looks like real legal/financial information\\n 71|- watermarks or provenance labels baked visibly into the image\\n 72|\\n 73|If a scene needs text, render it deterministically after generation. For Fake-Invoice Proof Shorts, use deterministic renderer-owned mockups only; do not use AI image generation for the invoice, email, payment route, or checks.\\n 74|\", \"total_lines\": 73, \"file_size\": 4572, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Manual platform copy packs for phase-1 human uploads.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult\\n 8|from autoshorts.social.draft import SocialMediaDraft, validate_social_media_draft\\n 9|\\n 10|\\n 11|@dataclass(frozen=True)\\n 12|class ManualPlatformPost:\\n 13| platform: str\\n 14| title: str\\n 15| description: str\\n 16| hashtags: tuple[str, ...]\\n 17| tags: tuple[str, ...]\\n 18| instructions: str\\n 19|\\n 20|\\n 21|@dataclass(frozen=True)\\n 22|class ManualPostingPack:\\n 23| candidate_id: str\\n 24| phase: str\\n 25| video_path: str\\n 26| platform_posts: tuple[ManualPlatformPost, ...]\\n 27| requires_human_manual_upload: bool = True\\n 28| side_effects: tuple[str, ...] = ()\\n 29| external_calls: tuple[str, ...] = ()\\n 30|\\n 31|\\n 32|def _is_blank(value: str) -> bool:\\n 33| return not (value or \\\"\\\").strip()\\n 34|\\n 35|\\n 36|def _normalize_hashtag_to_tag(hashtag: str) -> str:\\n 37| return hashtag.lstrip(\\\"#\\\").strip()\\n 38|\\n 39|\\n 40|def _hashtag_block(hashtags: tuple[str, ...]) -> str:\\n 41| return \\\" \\\".join(hashtags)\\n 42|\\n 43|\\n 44|def _youtube_description(draft: SocialMediaDraft) -> str:\\n 45| return \\\"\\\\n\\\\n\\\".join(\\n 46| part\\n 47| for part in (\\n 48| draft.caption.strip(),\\n 49| draft.cta.strip(),\\n 50| _hashtag_block(draft.hashtags),\\n 51| )\\n 52| if part\\n 53| )\\n 54|\\n 55|\\n 56|def _tiktok_caption(draft: SocialMediaDraft) -> str:\\n 57| caption = f\\\"{draft.caption.strip()} {_hashtag_block(draft.hashtags)}\\\".strip()\\n 58| return caption[:2200]\\n 59|\\n 60|\\n 61|def _post_for_platform(draft: SocialMediaDraft, platform: str) -> ManualPlatformPost:\\n 62| title = draft.title_variants[0]\\n 63| if platform == \\\"youtube\\\":\\n 64| return ManualPlatformPost(\\n 65| platform=\\\"youtube\\\",\\n 66| title=title,\\n 67| description=_youtube_description(draft),\\n 68| hashtags=draft.hashtags,\\n 69| tags=tuple(_normalize_hashtag_to_tag(hashtag) for hashtag in draft.hashtags),\\n 70| instructions=\\\"Manual upload: copy this description into YouTube Shorts.\\\",\\n 71| )\\n 72| if platform == \\\"tiktok\\\":\\n 73| return ManualPlatformPost(\\n 74| platform=\\\"tiktok\\\",\\n 75| title=title,\\n 76| description=_tiktok_caption(draft),\\n 77| hashtags=draft.hashtags,\\n 78| tags=(),\\n 79| instructions=\\\"Manual upload: copy this caption into TikTok.\\\",\\n 80| )\\n 81| if platform == \\\"instagram\\\":\\n 82| return ManualPlatformPost(\\n 83| platform=\\\"instagram\\\",\\n 84| title=title,\\n 85| description=_tiktok_caption(draft),\\n 86| hashtags=draft.hashtags,\\n 87| tags=(),\\n 88| instructions=\\\"Manual upload: copy this caption into Instagram Reels.\\\",\\n 89| )\\n 90| raise ValueError(f\\\"unsupported platform: {platform}\\\")\\n 91|\\n 92|\\n 93|def build_manual_posting_pack(draft: SocialMediaDraft, *, video_path: str) -> ManualPostingPack:\\n 94| draft_validation = validate_social_media_draft(draft)\\n 95| if not draft_validation.is_valid:\\n 96| raise ValueError(\\\"; \\\".join(draft_validation.errors))\\n 97| pack = ManualPostingPack(\\n 98| candidate_id=draft.candidate_id,\\n 99| phase=\\\"manual_posting\\\",\\n 100| video_path=video_path,\\n 101| platform_posts=tuple(_post_for_platform(draft, platform) for platform in draft.platforms),\\n 102| requires_human_manual_upload=True,\\n 103| side_effects=(),\\n 104| external_calls=(),\\n 105| )\\n 106| validation = validate_manual_posting_pack(pack)\\n 107| if not validation.is_valid:\\n 108| raise ValueError(\\\"; \\\".join(validation.errors))\\n 109| return pack\\n 110|\\n 111|\\n 112|def validate_manual_posting_pack(pack: ManualPostingPack) -> ValidationResult:\\n 113| errors: list[str] = []\\n 114| if _is_blank(pack.candidate_id):\\n 115| errors.append(\\\"candidate_id is required\\\")\\n 116| if pack.phase != \\\"manual_posting\\\":\\n 117| errors.append(\\\"phase must be manual_posting\\\")\\n 118| if _is_blank(pack.video_path):\\n 119| errors.append(\\\"video_path is required\\\")\\n 120| if not pack.platform_posts:\\n 121| errors.append(\\\"at least one platform post is required\\\")\\n 122| for post in pack.platform_posts:\\n 123| if _is_blank(post.platform):\\n 124| errors.append(\\\"platform is required\\\")\\n 125| if _is_blank(post.title):\\n 126| errors.append(f\\\"{post.platform} title is required\\\")\\n 127| if _is_blank(post.description):\\n 128| errors.append(f\\\"{post.platform} description is required\\\")\\n 129| if not post.hashtags:\\n 130| errors.append(f\\\"{post.platform} hashtags are required\\\")\\n 131| if _is_blank(post.instructions):\\n 132| errors.append(f\\\"{post.platform} instructions are required\\\")\\n 133| if not pack.requires_human_manual_upload:\\n 134| errors.append(\\\"human manual upload is required in phase 1\\\")\\n 135| if pack.side_effects:\\n 136| errors.append(\\\"manual posting packs must not have side effects\\\")\\n 137| if pack.external_calls:\\n 138| errors.append(\\\"manual posting packs must not call external services\\\")\\n 139| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 140|\\n 141|\\n 142|def render_manual_posting_instructions(pack: ManualPostingPack) -> str:\\n 143| validation = validate_manual_posting_pack(pack)\\n 144| if not validation.is_valid:\\n 145| raise ValueError(\\\"; \\\".join(validation.errors))\\n 146|\\n 147| lines = [\\n 148| \\\"## Manual Posting Pack\\\",\\n 149| f\\\"Candidate: {pack.candidate_id}\\\",\\n 150| f\\\"Video file: {pack.video_path}\\\",\\n 151| \\\"No platform upload was performed.\\\",\\n 152| \\\"\\\",\\n 153| ]\\n 154| labels = {\\n 155| \\\"youtube\\\": \\\"YouTube Shorts\\\",\\n 156| \\\"tiktok\\\": \\\"TikTok\\\",\\n 157| \\\"instagram\\\": \\\"Instagram Reels\\\",\\n 158| }\\n 159| for post in pack.platform_posts:\\n 160| lines.extend(\\n 161| [\\n 162| f\\\"## {labels.get(post.platform, post.platform)}\\\",\\n 163| post.instructions,\\n 164| f\\\"Title: {post.title}\\\",\\n 165| \\\"Description:\\\",\\n 166| post.description,\\n 167| f\\\"Hashtags: {' '.join(post.hashtags)}\\\",\\n 168| f\\\"Tags: {', '.join(post.tags) if post.tags else 'n/a'}\\\",\\n 169| \\\"\\\",\\n 170| ]\\n 171| )\\n 172| return \\\"\\\\n\\\".join(lines).strip()\\n 173|\", \"total_lines\": 172, \"file_size\": 5801, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"arguments": "{\"command\":\"for p in /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/*/*.mp4; do echo '---' $p; ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of default=nw=1 \\\"$p\\\" | head -20; done\",\"timeout\":120}"
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"reasoning": "**Normalizing metadata for upload**\n\nI’m thinking about not rendering just yet, but rather normalizing the metadata for a manual package. The user is okay with no renderings for now, meaning I can focus on finalizing the fake invoice. I need to consider whether to use ffmpeg to re-encode or copy with setdar. But it looks like setdar requires a filter for re-encoding, so maybe I should proceed with the manual upload as planned.",
"finish_reason": "tool_calls",
"reasoning_content": "**Normalizing metadata for upload**\n\nI’m thinking about not rendering just yet, but rather normalizing the metadata for a manual package. The user is okay with no renderings for now, meaning I can focus on finalizing the fake invoice. I need to consider whether to use ffmpeg to re-encode or copy with setdar. But it looks like setdar requires a filter for re-encoding, so maybe I should proceed with the manual upload as planned.",
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"reasoning": "**Updating code for file writing**\n\nI need to modify the code to use the `write_file` function for five different files. It feels like a straightforward task, but I want to make sure I get it right. I'll double-check the details to ensure everything goes smoothly. Maybe I should verify the file paths and the data being written, just to avoid any potential issues! Okay, let's tackle this step by step to make sure it works perfectly.",
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"reasoning_content": "**Updating code for file writing**\n\nI need to modify the code to use the `write_file` function for five different files. It feels like a straightforward task, but I want to make sure I get it right. I'll double-check the details to ensure everything goes smoothly. Maybe I should verify the file paths and the data being written, just to avoid any potential issues! Okay, let's tackle this step by step to make sure it works perfectly.",
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"arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/config/pillars.py\",\"content\":\"\\\"\\\"\\\"Content pillar registry and validation for TrueTraceShorts.\\n\\nThe current channel strategy is **Digital Red Flags / Scam Self-Defense for\\nnormal people**. AI/workflow content is no longer the primary positioning; it\\nmay exist later as a specialist format, but default candidate generation should\\nprefer concrete scam/self-defense screens.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n\\nSUPPORTED_PILLAR_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n\\n\\n@dataclass(frozen=True)\\nclass ContentPillar:\\n pillar_id: str\\n audience: str\\n positioning: str\\n tone: tuple[str, ...]\\n hook_patterns: tuple[str, ...]\\n visual_rules: tuple[str, ...]\\n no_go: tuple[str, ...]\\n platform_fit: tuple[str, ...]\\n monetization_angle: str\\n\\n\\nDIGITAL_RED_FLAGS_VISUAL_RULES = (\\n \\\"screen/mockup-led: phone SMS, email, invoice, chat, popup, login, QR, marketplace chat\\\",\\n \\\"first frame shows a visible trap within one second without audio\\\",\\n \\\"one normal screen, one concrete red flag, one safer move\\\",\\n \\\"renderer-owned deterministic scam screens whenever text/data matters\\\",\\n \\\"large mobile-safe text; no abstract glass clouds, generic laptop B-roll, or PowerPoint boards\\\",\\n)\\n\\nDIGITAL_RED_FLAGS_NO_GO = (\\n \\\"real brands/logos/phone numbers/bank data/IBANs/QR codes/domains/people/firms\\\",\\n \\\"operational scam instructions or reusable fraud templates\\\",\\n \\\"guarantees such as 'always safe' or 'catches every scam'\\\",\\n \\\"technical jargon without plain-language explanation\\\",\\n \\\"beautiful AI visuals with no visible viewer pain\\\",\\n)\\n\\nDEFAULT_PILLARS: tuple[ContentPillar, ...] = (\\n ContentPillar(\\n pillar_id=\\\"text_delivery_scams\\\",\\n audience=\\\"normal smartphone users, families, parents, teens, workers, small business owners\\\",\\n positioning=\\\"Spot fake delivery texts, customs fees, toll fees, short links, and urgent payment messages before tapping.\\\",\\n tone=(\\\"plain\\\", \\\"calm\\\", \\\"useful\\\", \\\"non-technical\\\", \\\"family-shareable\\\"),\\n hook_patterns=(\\n \\\"This delivery text is designed to make you panic.\\\",\\n \\\"If a tiny fee comes with a short deadline, slow down.\\\",\\n \\\"Before you tap the tracking link, check this.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"trust-building channel, newsletter/checklists, family/team safety guides later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"login_account_traps\\\",\\n audience=\\\"normal internet users who want to protect accounts without learning security jargon\\\",\\n positioning=\\\"Show fake logins, password resets, account-closure threats, and login prompts people did not start.\\\",\\n tone=(\\\"simple\\\", \\\"direct\\\", \\\"reassuring\\\", \\\"beginner-safe\\\"),\\n hook_patterns=(\\n \\\"This login page is stealing your login.\\\",\\n \\\"If you did not start the login, do not approve it.\\\",\\n \\\"The page after the QR code is the real test.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"account-safety checklists and beginner courses later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"payment_invoice_red_flags\\\",\\n audience=\\\"families, workers, freelancers, small teams, and small business owners handling payments\\\",\\n positioning=\\\"Reveal fake invoices, changed payment routes, CEO payment requests, vendor-bank changes, and QR payment traps.\\\",\\n tone=(\\\"clear\\\", \\\"practical\\\", \\\"non-alarmist\\\", \\\"money-safe\\\"),\\n hook_patterns=(\\n \\\"If the payment route changed, verify it another way.\\\",\\n \\\"This invoice looks normal. The red flag is the new route.\\\",\\n \\\"Before you pay, check this one line.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"payment-safety checklists/templates for families and small teams later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"marketplace_buyer_scams\\\",\\n audience=\\\"normal people buying or selling on marketplaces\\\",\\n positioning=\\\"Show buyer/seller scams, off-platform moves, fake shipping links, fake escrow, and overpayment traps.\\\",\\n tone=(\\\"relatable\\\", \\\"plain\\\", \\\"specific\\\", \\\"shareable\\\"),\\n hook_patterns=(\\n \\\"The buyer is not buying. They are moving you off-platform.\\\",\\n \\\"If the payment leaves the platform, slow down.\\\",\\n \\\"This friendly buyer message has one red flag.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"consumer-safety guides and platform-specific checklists later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"family_emotion_scams\\\",\\n audience=\\\"families, parents, partners, grandparents, and people who protect relatives\\\",\\n positioning=\\\"Explain urgent family/emotion scams in kind, non-patronizing language.\\\",\\n tone=(\\\"respectful\\\", \\\"gentle\\\", \\\"plain\\\", \\\"family-shareable\\\"),\\n hook_patterns=(\\n \\\"If a new number asks for money, pause.\\\",\\n \\\"Emergency messages are designed to skip your thinking.\\\",\\n \\\"Before you send money to family, check another way.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"family safety resources later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"fake_support_popup_scams\\\",\\n audience=\\\"normal web users and families dealing with scary browser popups\\\",\\n positioning=\\\"Show fake support, fake antivirus, browser-lock screens, and remote-access requests.\\\",\\n tone=(\\\"calm\\\", \\\"plain\\\", \\\"reassuring\\\", \\\"actionable\\\"),\\n hook_patterns=(\\n \\\"If a website tells you to call support, stop.\\\",\\n \\\"Real support does not hijack your screen.\\\",\\n \\\"This warning is trying to make you call the attacker.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"family/team browser safety checklist later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"password_account_safety_basics\\\",\\n audience=\\\"beginners who need simple account-safety rules\\\",\\n positioning=\\\"Teach password reuse, password managers, MFA basics, recovery-email risk, and session checks through one visible screen.\\\",\\n tone=(\\\"simple\\\", \\\"encouraging\\\", \\\"practical\\\", \\\"non-judgmental\\\"),\\n hook_patterns=(\\n \\\"One leaked password does not stay on one website.\\\",\\n \\\"One account, one password.\\\",\\n \\\"A login prompt you did not start is not just a notification.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"beginner safety course/checklist later\\\",\\n ),\\n ContentPillar(\\n pillar_id=\\\"job_side_hustle_scams\\\",\\n audience=\\\"job seekers, students, freelancers, and people exploring side hustles\\\",\\n positioning=\\\"Expose fake recruiter, task scam, pay-to-get-paid, remote deposit, and crypto-onboarding traps.\\\",\\n tone=(\\\"plain\\\", \\\"protective\\\", \\\"specific\\\", \\\"non-fearmongering\\\"),\\n hook_patterns=(\\n \\\"If you must pay to get paid, stop.\\\",\\n \\\"This job offer is moving you into a trap.\\\",\\n \\\"A real recruiter does not need your deposit.\\\",\\n ),\\n visual_rules=DIGITAL_RED_FLAGS_VISUAL_RULES,\\n no_go=DIGITAL_RED_FLAGS_NO_GO,\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"job-scam checklist and safe-search resources later\\\",\\n ),\\n)\\n\\n\\nLEGACY_SECONDARY_PILLARS: tuple[str, ...] = (\\n \\\"ai_life_systems\\\",\\n \\\"ai_workflow_automation\\\",\\n \\\"prompt_engineering\\\",\\n \\\"abstract_productivity_mechanisms\\\",\\n)\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef find_pillar(pillar_id: str, registry: tuple[ContentPillar, ...]) -> ContentPillar | None:\\n for pillar in registry:\\n if pillar.pillar_id == pillar_id:\\n return pillar\\n return None\\n\\n\\ndef validate_pillar_registry(registry: tuple[ContentPillar, ...]) -> ValidationResult:\\n errors: list[str] = []\\n ids = [pillar.pillar_id for pillar in registry]\\n\\n if len(ids) != len(set(ids)):\\n errors.append(\\\"pillar ids must be unique\\\")\\n\\n for pillar in registry:\\n if _is_blank(pillar.pillar_id):\\n errors.append(\\\"pillar_id is required\\\")\\n if _is_blank(pillar.audience):\\n errors.append(\\\"audience is required\\\")\\n if _is_blank(pillar.positioning):\\n errors.append(\\\"positioning is required\\\")\\n if _is_blank(pillar.monetization_angle):\\n errors.append(\\\"monetization_angle is required\\\")\\n if not pillar.tone:\\n errors.append(\\\"at least one tone is required\\\")\\n if not pillar.hook_patterns:\\n errors.append(\\\"at least one hook pattern is required\\\")\\n if not pillar.visual_rules:\\n errors.append(\\\"at least one visual rule is required\\\")\\n if not pillar.no_go:\\n errors.append(\\\"at least one no-go is required\\\")\\n if not pillar.platform_fit:\\n errors.append(\\\"at least one platform fit is required\\\")\\n for platform in pillar.platform_fit:\\n if platform not in SUPPORTED_PILLAR_PLATFORMS:\\n errors.append(f\\\"unsupported platform in pillar {pillar.pillar_id}: {platform}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef validate_candidate_against_pillars(\\n candidate: VideoCandidate,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n) -> ValidationResult:\\n errors: list[str] = []\\n pillar = find_pillar(candidate.pillar, registry)\\n\\n if pillar is None:\\n errors.append(f\\\"unknown pillar: {candidate.pillar}\\\")\\n return ValidationResult(is_valid=False, errors=tuple(errors))\\n\\n for platform in candidate.platforms:\\n if platform not in pillar.platform_fit:\\n errors.append(f\\\"platform {platform} is not configured for pillar {pillar.pillar_id}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\"}"
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"arguments": "{\"content\":\"\\\"\\\"\\\"Public-safe content series registry and fit validation.\\n\\nTrueTraceShorts now defaults to Digital Red Flags / Scam Self-Defense for\\nnormal people: one screen, one red flag, one safer move.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n\\nFORBIDDEN_PUBLIC_TOPIC_PATTERNS = {\\n \\\"jarvis\\\": (\\\"jarvis\\\",),\\n \\\"hermes\\\": (\\\"hermes\\\",),\\n \\\"user\\\": (\\\"user\\\", \\\"my user\\\"),\\n \\\"collaboration\\\": (\\\"collaboration\\\", \\\"our collaboration\\\"),\\n \\\"private\\\": (\\\"private\\\", \\\"behind the scenes\\\"),\\n \\\"politics\\\": (\\\"politics\\\", \\\"political\\\", \\\"election\\\", \\\"voter\\\", \\\"campaign\\\"),\\n \\\"legal loophole\\\": (\\\"legal loophole\\\", \\\"loophole\\\"),\\n \\\"bypass\\\": (\\\"bypass\\\",),\\n \\\"illegal\\\": (\\\"illegal\\\", \\\"break the law\\\"),\\n \\\"private data\\\": (\\\"private data\\\", \\\"scrape private data\\\"),\\n \\\"morally questionable\\\": (\\\"morally questionable\\\",),\\n \\\"exploit\\\": (\\\"exploit\\\",),\\n \\\"hacking instructions\\\": (\\\"how to hack\\\", \\\"steal password\\\", \\\"build a scam\\\", \\\"phishing kit\\\"),\\n}\\nFORBIDDEN_PUBLIC_TOPICS = tuple(FORBIDDEN_PUBLIC_TOPIC_PATTERNS)\\n\\n\\n@dataclass(frozen=True)\\nclass ContentSeries:\\n series_id: str\\n name: str\\n promise: str\\n audience: str\\n allowed_hook_types: tuple[str, ...]\\n format_rules: tuple[str, ...]\\n visual_grammar: tuple[str, ...]\\n no_go_patterns: tuple[str, ...]\\n follow_reason: str\\n\\n\\nDEFAULT_CONTENT_SERIES = (\\n ContentSeries(\\n series_id=\\\"one_screen_one_red_flag\\\",\\n name=\\\"One Screen. One Red Flag.\\\",\\n promise=\\\"Show one normal screen, the one dangerous detail, and one safer move.\\\",\\n audience=\\\"normal smartphone and internet users\\\",\\n allowed_hook_types=(\\\"visible_trap\\\", \\\"red_flag_reveal\\\", \\\"before_you_click\\\"),\\n format_rules=(\\n \\\"0-2s visible trap and short hook\\\",\\n \\\"2-7s why it feels normal\\\",\\n \\\"7-15s reveal one concrete red flag\\\",\\n \\\"15-24s show one safer move\\\",\\n \\\"24-30s screenshot checklist with max three points\\\",\\n \\\"30-34s memorable rule and soft CTA\\\",\\n ),\\n visual_grammar=(\\\"phone screen\\\", \\\"email\\\", \\\"invoice\\\", \\\"chat\\\", \\\"popup\\\", \\\"login\\\", \\\"QR situation\\\"),\\n no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n follow_reason=\\\"A repeatable library of simple red flags before people click, pay, scan, or log in.\\\",\\n ),\\n ContentSeries(\\n series_id=\\\"send_this_to_your_parents\\\",\\n name=\\\"Send This To Your Parents\\\",\\n promise=\\\"Simple non-patronizing scam-defense videos people can share with family.\\\",\\n audience=\\\"families and people protecting parents, partners, teens, or colleagues\\\",\\n allowed_hook_types=(\\\"family_share\\\", \\\"urgent_message\\\", \\\"simple_rule\\\"),\\n format_rules=(\\n \\\"avoid Boomer framing externally\\\",\\n \\\"use large readable text and everyday language\\\",\\n \\\"show a share-worthy family-safety situation\\\",\\n \\\"end with a screenshot checklist or rule\\\",\\n ),\\n visual_grammar=(\\\"SMS\\\", \\\"family chat\\\", \\\"support popup\\\", \\\"payment request\\\", \\\"new number message\\\"),\\n no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n follow_reason=\\\"Videos worth sending to someone you want to protect.\\\",\\n ),\\n ContentSeries(\\n series_id=\\\"before_you_click\\\",\\n name=\\\"Before You Click\\\",\\n promise=\\\"Quick checks before links, QR codes, logins, payments, or popups.\\\",\\n audience=\\\"normal users who want a simple pause rule\\\",\\n allowed_hook_types=(\\\"pause\\\", \\\"link_warning\\\", \\\"qr_warning\\\", \\\"login_warning\\\"),\\n format_rules=(\\n \\\"show the click/scan/login moment\\\",\\n \\\"name why it feels safe\\\",\\n \\\"reveal what to check before acting\\\",\\n \\\"give a safer path the viewer can do immediately\\\",\\n ),\\n visual_grammar=(\\\"link\\\", \\\"QR\\\", \\\"login page\\\", \\\"payment page\\\", \\\"popup warning\\\"),\\n no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n follow_reason=\\\"One safer habit before the next risky click.\\\",\\n ),\\n ContentSeries(\\n series_id=\\\"money_move_red_flags\\\",\\n name=\\\"Money Move Red Flags\\\",\\n promise=\\\"Spot risky payment, invoice, QR, marketplace, and bank-route changes before money moves.\\\",\\n audience=\\\"families, workers, freelancers, small teams, and small business owners\\\",\\n allowed_hook_types=(\\\"payment_warning\\\", \\\"invoice_red_flag\\\", \\\"route_change\\\", \\\"marketplace_warning\\\"),\\n format_rules=(\\n \\\"show the payment/invoice/chat screen\\\",\\n \\\"show the one money-related red flag\\\",\\n \\\"give one verification move outside the suspicious message\\\",\\n \\\"avoid financial guarantees or advice claims\\\",\\n ),\\n visual_grammar=(\\\"invoice\\\", \\\"payment route\\\", \\\"bank-change email\\\", \\\"marketplace chat\\\", \\\"QR payment\\\"),\\n no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n follow_reason=\\\"Small checks before money leaves the account.\\\",\\n ),\\n ContentSeries(\\n series_id=\\\"account_trap\\\",\\n name=\\\"Account Trap\\\",\\n promise=\\\"Explain fake logins, passwords, MFA prompts, and account-closure threats in plain language.\\\",\\n audience=\\\"beginners who want to protect accounts without security jargon\\\",\\n allowed_hook_types=(\\\"fake_login\\\", \\\"password_warning\\\", \\\"login_prompt\\\", \\\"account_closure\\\"),\\n format_rules=(\\n \\\"show the login/account screen\\\",\\n \\\"replace jargon with plain words like 'stealing your login'\\\",\\n \\\"give one safer account action\\\",\\n \\\"avoid technical attack walkthroughs\\\",\\n ),\\n visual_grammar=(\\\"login page\\\", \\\"password field\\\", \\\"MFA prompt\\\", \\\"account warning\\\", \\\"session screen\\\"),\\n no_go_patterns=FORBIDDEN_PUBLIC_TOPICS,\\n follow_reason=\\\"Simple account safety rules that normal people remember.\\\",\\n ),\\n)\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef _series_text(series: ContentSeries) -> str:\\n return \\\" \\\".join(\\n (\\n series.series_id,\\n series.name,\\n series.promise,\\n series.audience,\\n \\\" \\\".join(series.allowed_hook_types),\\n \\\" \\\".join(series.format_rules),\\n \\\" \\\".join(series.visual_grammar),\\n series.follow_reason,\\n )\\n ).casefold()\\n\\n\\ndef _candidate_public_text(candidate: VideoCandidate) -> str:\\n return \\\" \\\".join(\\n (\\n candidate.title,\\n candidate.hook,\\n candidate.script_outline,\\n candidate.visual_concept,\\n candidate.caption,\\n candidate.cta,\\n )\\n ).casefold()\\n\\n\\ndef find_series(\\n series_id: str,\\n registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n) -> ContentSeries | None:\\n for series in registry:\\n if series.series_id == series_id:\\n return series\\n return None\\n\\n\\ndef _forbidden_matches(text: str) -> tuple[str, ...]:\\n matches: list[str] = []\\n for canonical_topic, patterns in FORBIDDEN_PUBLIC_TOPIC_PATTERNS.items():\\n if any(pattern in text for pattern in patterns):\\n matches.append(canonical_topic)\\n return tuple(matches)\\n\\n\\ndef _candidate_matches_series(candidate: VideoCandidate, series: ContentSeries) -> bool:\\n public_text = _candidate_public_text(candidate)\\n series_keywords = {\\n \\\"one_screen_one_red_flag\\\": (\\\"red flag\\\", \\\"screen\\\", \\\"safer\\\", \\\"trap\\\", \\\"scam\\\"),\\n \\\"send_this_to_your_parents\\\": (\\\"family\\\", \\\"parents\\\", \\\"mom\\\", \\\"dad\\\", \\\"message\\\", \\\"send this\\\"),\\n \\\"before_you_click\\\": (\\\"click\\\", \\\"tap\\\", \\\"link\\\", \\\"qr\\\", \\\"scan\\\", \\\"login\\\"),\\n \\\"money_move_red_flags\\\": (\\\"invoice\\\", \\\"payment\\\", \\\"pay\\\", \\\"bank\\\", \\\"money\\\", \\\"marketplace\\\"),\\n \\\"account_trap\\\": (\\\"login\\\", \\\"password\\\", \\\"account\\\", \\\"mfa\\\", \\\"reset\\\", \\\"sign in\\\"),\\n }\\n keywords = series_keywords.get(series.series_id, ())\\n return any(keyword in public_text for keyword in keywords)\\n\\n\\ndef validate_content_series_registry(\\n registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n) -> ValidationResult:\\n errors: list[str] = []\\n seen: set[str] = set()\\n\\n if not registry:\\n errors.append(\\\"at least one content series is required\\\")\\n\\n for series in registry:\\n if _is_blank(series.series_id):\\n errors.append(\\\"series_id is required\\\")\\n elif series.series_id in seen:\\n errors.append(f\\\"duplicate series_id: {series.series_id}\\\")\\n seen.add(series.series_id)\\n\\n for field_name, value in {\\n \\\"name\\\": series.name,\\n \\\"promise\\\": series.promise,\\n \\\"audience\\\": series.audience,\\n \\\"follow_reason\\\": series.follow_reason,\\n }.items():\\n if _is_blank(value):\\n errors.append(f\\\"series {series.series_id} {field_name} is required\\\")\\n\\n for field_name, values in {\\n \\\"allowed_hook_types\\\": series.allowed_hook_types,\\n \\\"format_rules\\\": series.format_rules,\\n \\\"visual_grammar\\\": series.visual_grammar,\\n \\\"no_go_patterns\\\": series.no_go_patterns,\\n }.items():\\n if not values:\\n errors.append(f\\\"series {series.series_id} {field_name} is required\\\")\\n\\n series_text = _series_text(series)\\n for match in _forbidden_matches(series_text):\\n errors.append(f\\\"series {series.series_id} uses forbidden public topic: {match}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef validate_candidate_series_fit(\\n candidate: VideoCandidate,\\n series_id: str,\\n *,\\n registry: tuple[ContentSeries, ...] = DEFAULT_CONTENT_SERIES,\\n) -> ValidationResult:\\n errors: list[str] = []\\n series = find_series(series_id, registry)\\n if series is None:\\n return ValidationResult(False, (f\\\"unknown series: {series_id}\\\",))\\n\\n public_text = _candidate_public_text(candidate)\\n for match in _forbidden_matches(public_text):\\n errors.append(f\\\"candidate uses forbidden public topic: {match}\\\")\\n\\n if not _candidate_matches_series(candidate, series):\\n errors.append(f\\\"candidate does not clearly match series: {series_id}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/series.py\"}"
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"arguments": "{\"content\":\"\\\"\\\"\\\"Strategy scoring for beginner-friendly Scam Self-Defense candidates.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\nfrom autoshorts.strategy.series import FORBIDDEN_PUBLIC_TOPIC_PATTERNS\\n\\nMIN_SIGNAL_SCORE = 7\\nMIN_AVERAGE_SCORE = 7.5\\nGENERIC_AI_SLOP_PHRASES = (\\n \\\"in this video\\\",\\n \\\"unlock your potential\\\",\\n \\\"unlock your full potential\\\",\\n \\\"best ai tools\\\",\\n \\\"ai tools you need\\\",\\n \\\"follow for more ai tips\\\",\\n \\\"everyone should use\\\",\\n \\\"prompt engineering\\\",\\n)\\nJARGON_PHRASES = (\\n \\\"credential harvesting\\\",\\n \\\"social engineering\\\",\\n \\\"validate the authenticity\\\",\\n \\\"transactional flow\\\",\\n \\\"attack vector\\\",\\n \\\"threat actor\\\",\\n \\\"cognitive load\\\",\\n)\\nVISIBLE_SCREEN_TERMS = (\\n \\\"screen\\\",\\n \\\"sms\\\",\\n \\\"text\\\",\\n \\\"email\\\",\\n \\\"invoice\\\",\\n \\\"chat\\\",\\n \\\"popup\\\",\\n \\\"login\\\",\\n \\\"qr\\\",\\n \\\"app\\\",\\n \\\"message\\\",\\n \\\"page\\\",\\n)\\nRED_FLAG_TERMS = (\\n \\\"red flag\\\",\\n \\\"trap\\\",\\n \\\"scam\\\",\\n \\\"fake\\\",\\n \\\"suspicious\\\",\\n \\\"pressure\\\",\\n \\\"rush\\\",\\n \\\"urgent\\\",\\n \\\"changed\\\",\\n \\\"link\\\",\\n)\\nSAFE_MOVE_TERMS = (\\n \\\"open the app yourself\\\",\\n \\\"check it another way\\\",\\n \\\"verify\\\",\\n \\\"close\\\",\\n \\\"do not tap\\\",\\n \\\"don't tap\\\",\\n \\\"slow down\\\",\\n \\\"deny\\\",\\n \\\"inside the platform\\\",\\n)\\nSERIES_NAMES = (\\n \\\"One Screen. One Red Flag.\\\",\\n \\\"Send This To Your Parents\\\",\\n \\\"Before You Click\\\",\\n \\\"Money Move Red Flags\\\",\\n \\\"Account Trap\\\",\\n)\\n\\n\\n@dataclass(frozen=True)\\nclass StrategySignal:\\n score: int\\n rationale: str\\n\\n\\n@dataclass(frozen=True)\\nclass ContentStrategyScore:\\n hook: StrategySignal\\n curiosity: StrategySignal\\n identity: StrategySignal\\n emotion: StrategySignal\\n trust: StrategySignal\\n follow_reason: StrategySignal\\n originality: StrategySignal\\n variety: StrategySignal\\n target_emotion: str\\n viewer_identity: str\\n future_value_promise: str\\n format_family: str\\n variation_axis: str\\n\\n @property\\n def average_score(self) -> float:\\n signals = (\\n self.hook,\\n self.curiosity,\\n self.identity,\\n self.emotion,\\n self.trust,\\n self.follow_reason,\\n self.originality,\\n self.variety,\\n )\\n return sum(signal.score for signal in signals) / len(signals)\\n\\n\\n@dataclass(frozen=True)\\nclass CandidateStrategyReview:\\n candidate_id: str\\n strategy_score: ContentStrategyScore\\n render_allowed: bool\\n reject_reasons: tuple[str, ...]\\n improvement_suggestions: tuple[str, ...]\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef _visible_candidate_text(candidate: VideoCandidate) -> str:\\n return \\\" \\\".join(\\n (\\n candidate.title,\\n candidate.hook,\\n candidate.script_outline,\\n candidate.visual_concept,\\n candidate.caption,\\n candidate.cta,\\n )\\n ).casefold()\\n\\n\\ndef _combined_candidate_text(candidate: VideoCandidate) -> str:\\n return \\\" \\\".join(\\n (\\n _visible_candidate_text(candidate),\\n candidate.hypothesis,\\n \\\" \\\".join(candidate.policy_notes),\\n )\\n ).casefold()\\n\\n\\ndef _has_any(text: str, terms: tuple[str, ...]) -> bool:\\n return any(term in text for term in terms)\\n\\n\\ndef _has_generic_ai_slop(text: str) -> bool:\\n return any(phrase in text for phrase in GENERIC_AI_SLOP_PHRASES)\\n\\n\\ndef _has_jargon(text: str) -> bool:\\n return any(phrase in text for phrase in JARGON_PHRASES)\\n\\n\\ndef _infer_format_family(candidate: VideoCandidate) -> str:\\n text = _combined_candidate_text(candidate)\\n for series_name in SERIES_NAMES:\\n if series_name.casefold() in text:\\n return series_name\\n if \\\"marketplace\\\" in text:\\n return \\\"Money Move Red Flags\\\"\\n if \\\"invoice\\\" in text or \\\"payment\\\" in text or \\\"bank\\\" in text:\\n return \\\"Money Move Red Flags\\\"\\n if \\\"login\\\" in text or \\\"password\\\" in text or \\\"account\\\" in text or \\\"mfa\\\" in text:\\n return \\\"Account Trap\\\"\\n if \\\"sms\\\" in text or \\\"delivery\\\" in text or \\\"text\\\" in text or \\\"link\\\" in text or \\\"qr\\\" in text:\\n return \\\"Before You Click\\\"\\n if \\\"parents\\\" in text or \\\"mom\\\" in text or \\\"family\\\" in text:\\n return \\\"Send This To Your Parents\\\"\\n return \\\"One Screen. One Red Flag.\\\"\\n\\n\\ndef _score_signal(score: int, rationale: str) -> StrategySignal:\\n return StrategySignal(score=max(0, min(10, score)), rationale=rationale)\\n\\n\\ndef _forbidden_public_topic_matches(text: str) -> tuple[str, ...]:\\n matches: list[str] = []\\n for canonical_topic, patterns in FORBIDDEN_PUBLIC_TOPIC_PATTERNS.items():\\n if any(pattern in text for pattern in patterns):\\n matches.append(canonical_topic)\\n return tuple(matches)\\n\\n\\ndef validate_content_strategy_score(score: ContentStrategyScore) -> ValidationResult:\\n errors: list[str] = []\\n signals = {\\n \\\"hook\\\": score.hook,\\n \\\"curiosity\\\": score.curiosity,\\n \\\"identity\\\": score.identity,\\n \\\"emotion\\\": score.emotion,\\n \\\"trust\\\": score.trust,\\n \\\"follow_reason\\\": score.follow_reason,\\n \\\"originality\\\": score.originality,\\n \\\"variety\\\": score.variety,\\n }\\n for name, signal in signals.items():\\n if not 0 <= signal.score <= 10:\\n errors.append(f\\\"{name} score must be between 0 and 10\\\")\\n if _is_blank(signal.rationale):\\n errors.append(f\\\"{name} rationale is required\\\")\\n\\n for field_name, value in {\\n \\\"target_emotion\\\": score.target_emotion,\\n \\\"viewer_identity\\\": score.viewer_identity,\\n \\\"future_value_promise\\\": score.future_value_promise,\\n \\\"format_family\\\": score.format_family,\\n \\\"variation_axis\\\": score.variation_axis,\\n }.items():\\n if _is_blank(value):\\n errors.append(f\\\"{field_name} is required\\\")\\n\\n if score.hook.score < MIN_SIGNAL_SCORE:\\n errors.append(\\\"hook must be at least 7\\\")\\n if score.curiosity.score < MIN_SIGNAL_SCORE:\\n errors.append(\\\"curiosity must be at least 7\\\")\\n if score.identity.score < MIN_SIGNAL_SCORE:\\n errors.append(\\\"identity must be at least 7\\\")\\n if score.follow_reason.score < MIN_SIGNAL_SCORE:\\n errors.append(\\\"follow_reason must be at least 7\\\")\\n if score.originality.score < MIN_SIGNAL_SCORE:\\n errors.append(\\\"originality must be at least 7\\\")\\n if score.average_score < MIN_AVERAGE_SCORE:\\n errors.append(\\\"average strategy score must be at least 7.5\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef should_advance_strategy(score: ContentStrategyScore) -> bool:\\n return validate_content_strategy_score(score).is_valid\\n\\n\\ndef build_candidate_strategy_review(\\n candidate: VideoCandidate,\\n *,\\n recent_format_families: tuple[str, ...] = (),\\n) -> CandidateStrategyReview:\\n text = _combined_candidate_text(candidate)\\n visible_text = _visible_candidate_text(candidate)\\n format_family = _infer_format_family(candidate)\\n generic_slop = _has_generic_ai_slop(visible_text)\\n jargon = _has_jargon(visible_text)\\n visible_screen = _has_any(visible_text, VISIBLE_SCREEN_TERMS)\\n red_flag = _has_any(visible_text, RED_FLAG_TERMS)\\n safe_move = _has_any(visible_text, SAFE_MOVE_TERMS)\\n screenshot_value = \\\"save\\\" in text or \\\"screenshot\\\" in text or \\\"quick check\\\" in text or \\\"checklist\\\" in text\\n family_share = any(term in text for term in (\\\"family\\\", \\\"parents\\\", \\\"partner\\\", \\\"colleague\\\", \\\"team\\\", \\\"send this\\\"))\\n forbidden_public_topics = _forbidden_public_topic_matches(visible_text)\\n repeated_recently = bool(recent_format_families) and recent_format_families.count(format_family) >= 2\\n pretty_empty = not visible_screen or not red_flag\\n\\n strategy_score = ContentStrategyScore(\\n hook=_score_signal(\\n 9 if red_flag and not generic_slop and not jargon else 5,\\n \\\"Hook names a visible everyday scam/red flag in plain language.\\\"\\n if red_flag and not generic_slop and not jargon\\n else \\\"Hook is too generic, too AI-ish, or too technical.\\\",\\n ),\\n curiosity=_score_signal(\\n 9 if red_flag and (\\\"before\\\" in text or \\\"if\\\" in text or \\\"designed\\\" in text or \\\"stop\\\" in text) else 6,\\n \\\"Viewer gets a concrete open loop before clicking/paying/logging in.\\\",\\n ),\\n identity=_score_signal(\\n 9 if visible_screen else 5,\\n \\\"Targets normal users through a familiar screen.\\\"\\n if visible_screen\\n else \\\"Audience/problem is too abstract.\\\",\\n ),\\n emotion=_score_signal(\\n 8 if red_flag else 5,\\n \\\"Creates useful concern without fearmongering.\\\",\\n ),\\n trust=_score_signal(\\n 9 if safe_move else 6,\\n \\\"Trust comes from one practical safer move.\\\"\\n if safe_move\\n else \\\"Needs a clearer safe next action.\\\",\\n ),\\n follow_reason=_score_signal(\\n 9 if family_share or screenshot_value else 7,\\n \\\"Viewer can save/share this as a simple digital self-defense rule.\\\"\\n if family_share or screenshot_value\\n else \\\"Follow reason exists but could be more share/save oriented.\\\",\\n ),\\n originality=_score_signal(\\n 8 if not generic_slop and not pretty_empty else 4,\\n \\\"Specific screen + red flag avoids pretty-empty AI content.\\\"\\n if not generic_slop and not pretty_empty\\n else \\\"Pretty-empty visual or generic topic risk.\\\",\\n ),\\n variety=_score_signal(\\n 5 if repeated_recently else 8,\\n \\\"Recent format repetition risk.\\\" if repeated_recently else \\\"Format is fresh enough against recent context.\\\",\\n ),\\n target_emotion=\\\"warned but in control\\\" if red_flag else \\\"mild interest\\\",\\n viewer_identity=\\\"normal smartphone/internet users\\\" if visible_screen else \\\"generic viewers\\\",\\n future_value_promise=\\\"one red flag before you click, pay, scan, or log in\\\" if safe_move else \\\"general tips\\\",\\n format_family=format_family,\\n variation_axis=\\\"screen + red flag + safer move\\\" if not pretty_empty else \\\"pretty-empty visual risk\\\",\\n )\\n\\n validation = validate_content_strategy_score(strategy_score)\\n reject_reasons = list(validation.errors)\\n if generic_slop:\\n reject_reasons.append(\\\"generic AI-slop language detected\\\")\\n if jargon:\\n reject_reasons.append(\\\"language is too technical for normal users\\\")\\n if pretty_empty:\\n reject_reasons.append(\\\"pretty-empty visual blocker: first screen/red flag is not clear\\\")\\n if not safe_move:\\n reject_reasons.append(\\\"safe move clarity is missing\\\")\\n if forbidden_public_topics:\\n reject_reasons.extend(f\\\"candidate uses forbidden public topic: {topic}\\\" for topic in forbidden_public_topics)\\n if repeated_recently:\\n reject_reasons.append(\\\"recent format repetition risk\\\")\\n\\n suggestions: list[str] = []\\n if not visible_screen:\\n suggestions.append(\\\"Start with a familiar phone/email/invoice/chat/login/popup screen.\\\")\\n if not red_flag:\\n suggestions.append(\\\"Name one concrete red flag visible in the screen.\\\")\\n if not safe_move:\\n suggestions.append(\\\"Add one plain safer move: open the app yourself, check another way, or keep it inside the platform.\\\")\\n if jargon:\\n suggestions.append(\\\"Replace security jargon with everyday words.\\\")\\n if not screenshot_value:\\n suggestions.append(\\\"Add a max-three-point SAVE THIS or Quick check card.\\\")\\n\\n return CandidateStrategyReview(\\n candidate_id=candidate.candidate_id,\\n strategy_score=strategy_score,\\n render_allowed=not reject_reasons,\\n reject_reasons=tuple(dict.fromkeys(reject_reasons)),\\n improvement_suggestions=tuple(suggestions),\\n )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\"}"
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"arguments": "{\"content\":\"from autoshorts.config.pillars import (\\n ContentPillar,\\n DEFAULT_PILLARS,\\n find_pillar,\\n validate_candidate_against_pillars,\\n validate_pillar_registry,\\n)\\nfrom autoshorts.ideas.candidate import VideoCandidate\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\n\\ndef _score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef _candidate(**overrides) -> VideoCandidate:\\n data = {\\n \\\"candidate_id\\\": \\\"delivery-sms-trap-35s\\\",\\n \\\"pillar\\\": \\\"text_delivery_scams\\\",\\n \\\"title\\\": \\\"Delivery SMS Trap\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n \\\"duration_seconds\\\": 35,\\n \\\"hook\\\": \\\"This delivery text is designed to make you panic.\\\",\\n \\\"script_outline\\\": \\\"Show a fake delivery SMS, reveal pressure as the red flag, then open the app yourself.\\\",\\n \\\"visual_concept\\\": \\\"Phone SMS mockup with urgent fee, suspicious link, and screenshot checklist.\\\",\\n \\\"caption\\\": \\\"If it makes you hurry, slow down.\\\",\\n \\\"cta\\\": \\\"Follow for one red flag before you click.\\\",\\n \\\"hashtags\\\": (\\\"#ScamAwareness\\\", \\\"#CyberSafety\\\"),\\n \\\"quality_score\\\": _score(),\\n \\\"policy_notes\\\": (\\\"No real brands, URLs, QR codes, or phone numbers\\\",),\\n \\\"hypothesis\\\": \\\"Beginner delivery-scam screens create higher saves and family shares.\\\",\\n }\\n data.update(overrides)\\n return VideoCandidate(**data)\\n\\n\\ndef test_default_registry_contains_digital_red_flags_positioning():\\n pillar = find_pillar(\\\"text_delivery_scams\\\", DEFAULT_PILLARS)\\n\\n assert pillar is not None\\n assert \\\"delivery\\\" in pillar.positioning.casefold()\\n assert \\\"one normal screen\\\" in pillar.visual_rules\\n assert \\\"beautiful AI visuals with no visible viewer pain\\\" in pillar.no_go\\n assert \\\"youtube\\\" in pillar.platform_fit\\n\\n\\ndef test_default_registry_prioritizes_scam_self_defense_pillars_not_ai_workflow():\\n pillar_ids = {pillar.pillar_id for pillar in DEFAULT_PILLARS}\\n\\n assert \\\"text_delivery_scams\\\" in pillar_ids\\n assert \\\"login_account_traps\\\" in pillar_ids\\n assert \\\"payment_invoice_red_flags\\\" in pillar_ids\\n assert \\\"marketplace_buyer_scams\\\" in pillar_ids\\n assert \\\"fake_support_popup_scams\\\" in pillar_ids\\n assert \\\"ai_life_systems\\\" not in pillar_ids\\n\\n\\ndef test_pillar_registry_validation_rejects_duplicate_ids():\\n first = ContentPillar(\\n pillar_id=\\\"dup\\\",\\n audience=\\\"normal users\\\",\\n positioning=\\\"first\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n )\\n second = ContentPillar(\\n pillar_id=\\\"dup\\\",\\n audience=\\\"normal users\\\",\\n positioning=\\\"second\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n )\\n\\n result = validate_pillar_registry((first, second))\\n\\n assert result.is_valid is False\\n assert \\\"pillar ids must be unique\\\" in result.errors\\n\\n\\ndef test_candidate_passes_known_pillar_and_platform_fit():\\n result = validate_candidate_against_pillars(_candidate(), DEFAULT_PILLARS)\\n\\n assert result.is_valid is True\\n assert result.errors == ()\\n\\n\\ndef test_candidate_rejects_unknown_pillar():\\n result = validate_candidate_against_pillars(\\n _candidate(pillar=\\\"unknown_pillar\\\"), DEFAULT_PILLARS\\n )\\n\\n assert result.is_valid is False\\n assert \\\"unknown pillar: unknown_pillar\\\" in result.errors\\n\\n\\ndef test_candidate_rejects_platform_not_fit_for_pillar():\\n registry = (\\n ContentPillar(\\n pillar_id=\\\"narrow\\\",\\n audience=\\\"specific audience\\\",\\n positioning=\\\"narrow test pillar\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n ),\\n )\\n\\n result = validate_candidate_against_pillars(\\n _candidate(pillar=\\\"narrow\\\", platforms=(\\\"youtube\\\", \\\"tiktok\\\")), registry\\n )\\n\\n assert result.is_valid is False\\n assert \\\"platform tiktok is not configured for pillar narrow\\\" in result.errors\\n\\n\\ndef test_registry_validation_rejects_empty_required_pillar_fields():\\n pillar = ContentPillar(\\n pillar_id=\\\"\\\",\\n audience=\\\"\\\",\\n positioning=\\\"\\\",\\n tone=(),\\n hook_patterns=(),\\n visual_rules=(),\\n no_go=(),\\n platform_fit=(),\\n monetization_angle=\\\"\\\",\\n )\\n\\n result = validate_pillar_registry((pillar,))\\n\\n assert result.is_valid is False\\n assert \\\"pillar_id is required\\\" in result.errors\\n assert \\\"audience is required\\\" in result.errors\\n assert \\\"at least one hook pattern is required\\\" in result.errors\\n assert \\\"at least one platform fit is required\\\" in result.errors\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_pillars.py\"}"
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"arguments": "{\"content\":\"from autoshorts.ideas.candidate import VideoCandidate\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\nfrom autoshorts.strategy.series import (\\n DEFAULT_CONTENT_SERIES,\\n ContentSeries,\\n find_series,\\n validate_candidate_series_fit,\\n validate_content_series_registry,\\n)\\n\\n\\ndef _candidate(**overrides):\\n values = {\\n \\\"candidate_id\\\": \\\"delivery-sms-trap-35s\\\",\\n \\\"pillar\\\": \\\"text_delivery_scams\\\",\\n \\\"title\\\": \\\"Delivery SMS Trap\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n \\\"duration_seconds\\\": 35,\\n \\\"hook\\\": \\\"This delivery text is designed to make you panic.\\\",\\n \\\"script_outline\\\": \\\"Show a fake delivery text, reveal the pressure, and tell viewers to open the app themselves.\\\",\\n \\\"visual_concept\\\": \\\"Phone SMS screen with tiny fee, short deadline, suspicious link, and SAVE THIS checklist.\\\",\\n \\\"caption\\\": \\\"If it makes you hurry, slow down.\\\",\\n \\\"cta\\\": \\\"Follow for one red flag before you click.\\\",\\n \\\"hashtags\\\": (\\\"#ScamAwareness\\\", \\\"#CyberSafety\\\", \\\"#DigitalRedFlags\\\"),\\n \\\"quality_score\\\": ScriptQualityScore(9, 8, 9, 8, 2, \\\"low\\\"),\\n \\\"policy_notes\\\": (\\\"series:one_screen_one_red_flag\\\",),\\n \\\"hypothesis\\\": \\\"One visible red flag and one safer move can earn saves and family shares.\\\",\\n }\\n values.update(overrides)\\n return VideoCandidate(**values)\\n\\n\\ndef test_default_series_registry_contains_public_safe_scam_self_defense_series_only():\\n result = validate_content_series_registry(DEFAULT_CONTENT_SERIES)\\n\\n assert result.is_valid is True\\n series_ids = {series.series_id for series in DEFAULT_CONTENT_SERIES}\\n assert series_ids == {\\n \\\"one_screen_one_red_flag\\\",\\n \\\"send_this_to_your_parents\\\",\\n \\\"before_you_click\\\",\\n \\\"money_move_red_flags\\\",\\n \\\"account_trap\\\",\\n }\\n assert \\\"prompt_vs_system\\\" not in series_ids\\n assert \\\"jarvis_build_log\\\" not in series_ids\\n\\n\\ndef test_series_registry_rejects_duplicate_ids_and_forbidden_public_topics():\\n unsafe = ContentSeries(\\n series_id=\\\"jarvis_build_log\\\",\\n name=\\\"The JARVIS Build Log\\\",\\n promise=\\\"Publicly discuss JARVIS and the user's collaboration.\\\",\\n audience=\\\"AI builders\\\",\\n allowed_hook_types=(\\\"build_log\\\",),\\n format_rules=(\\\"Mention JARVIS and the user's workflow.\\\",),\\n visual_grammar=(\\\"private build log\\\",),\\n no_go_patterns=(\\\"politics\\\",),\\n follow_reason=\\\"Watch the private collaboration evolve.\\\",\\n )\\n\\n result = validate_content_series_registry((unsafe, unsafe))\\n\\n assert result.is_valid is False\\n assert \\\"duplicate series_id: jarvis_build_log\\\" in result.errors\\n assert \\\"series jarvis_build_log uses forbidden public topic: jarvis\\\" in result.errors\\n assert \\\"series jarvis_build_log uses forbidden public topic: collaboration\\\" in result.errors\\n\\n\\ndef test_candidate_series_fit_accepts_matching_public_safe_candidate():\\n candidate = _candidate()\\n\\n result = validate_candidate_series_fit(candidate, \\\"one_screen_one_red_flag\\\")\\n\\n assert result.is_valid is True\\n\\n\\ndef test_candidate_series_fit_accepts_before_you_click_candidate():\\n candidate = _candidate(\\n hook=\\\"Before you tap the tracking link, check this.\\\",\\n script_outline=\\\"Show the SMS link and safer move: open the app yourself.\\\",\\n visual_concept=\\\"Phone text screen with link and app icon path.\\\",\\n )\\n\\n result = validate_candidate_series_fit(candidate, \\\"before_you_click\\\")\\n\\n assert result.is_valid is True\\n\\n\\ndef test_candidate_series_fit_rejects_public_content_about_jarvis_or_user_collaboration():\\n candidate = _candidate(\\n title=\\\"How JARVIS and my user built a content machine\\\",\\n hook=\\\"This is how our collaboration works behind the scenes.\\\",\\n script_outline=\\\"Explain JARVIS, the user, and private approval workflow details.\\\",\\n visual_concept=\\\"Private chat screenshots and build log.\\\",\\n caption=\\\"Our JARVIS collaboration is the secret.\\\",\\n cta=\\\"Follow to watch us build it live.\\\",\\n )\\n\\n result = validate_candidate_series_fit(candidate, \\\"one_screen_one_red_flag\\\")\\n\\n assert result.is_valid is False\\n assert \\\"candidate uses forbidden public topic: jarvis\\\" in result.errors\\n assert \\\"candidate uses forbidden public topic: collaboration\\\" in result.errors\\n assert \\\"candidate uses forbidden public topic: private\\\" in result.errors\\n\\n\\ndef test_candidate_series_fit_rejects_politics_and_legal_or_moral_risk():\\n candidate = _candidate(\\n title=\\\"Political scam system\\\",\\n hook=\\\"This legal loophole lets you scrape private data for campaigns.\\\",\\n script_outline=\\\"Explain how to bypass platform rules and influence voters.\\\",\\n visual_concept=\\\"Campaign dashboard and private data map.\\\",\\n caption=\\\"Use this loophole before it is banned.\\\",\\n cta=\\\"Try this campaign hack.\\\",\\n )\\n\\n result = validate_candidate_series_fit(candidate, \\\"one_screen_one_red_flag\\\")\\n\\n assert result.is_valid is False\\n assert \\\"candidate uses forbidden public topic: politics\\\" in result.errors\\n assert \\\"candidate uses forbidden public topic: legal loophole\\\" in result.errors\\n assert \\\"candidate uses forbidden public topic: private data\\\" in result.errors\\n assert \\\"candidate uses forbidden public topic: bypass\\\" in result.errors\\n\\n\\ndef test_candidate_series_fit_rejects_unknown_series():\\n result = validate_candidate_series_fit(_candidate(), \\\"missing_series\\\")\\n\\n assert result.is_valid is False\\n assert \\\"unknown series: missing_series\\\" in result.errors\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_series.py\"}"
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"arguments": "{\"content\":\"import pytest\\n\\nfrom autoshorts.ideas.candidate import VideoCandidate\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\nfrom autoshorts.strategy.score import (\\n ContentStrategyScore,\\n StrategySignal,\\n build_candidate_strategy_review,\\n should_advance_strategy,\\n validate_content_strategy_score,\\n)\\n\\n\\ndef _candidate(**overrides):\\n values = {\\n \\\"candidate_id\\\": \\\"delivery-sms-trap-35s\\\",\\n \\\"pillar\\\": \\\"text_delivery_scams\\\",\\n \\\"title\\\": \\\"Delivery SMS Trap\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n \\\"duration_seconds\\\": 35,\\n \\\"hook\\\": \\\"This delivery text is designed to make you panic.\\\",\\n \\\"script_outline\\\": \\\"Show a fake delivery SMS, reveal the pressure, then show the safer move: do not tap the link; open the app yourself.\\\",\\n \\\"visual_concept\\\": \\\"Phone SMS screen with tiny fee, short deadline, suspicious link, and SAVE THIS checklist.\\\",\\n \\\"caption\\\": \\\"If it makes you hurry, slow down.\\\",\\n \\\"cta\\\": \\\"Follow for one red flag before you click. Send this to family.\\\",\\n \\\"hashtags\\\": (\\\"#ScamAwareness\\\", \\\"#CyberSafety\\\", \\\"#DigitalRedFlags\\\"),\\n \\\"quality_score\\\": ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n ),\\n \\\"policy_notes\\\": (\\\"duration_class:growth_short\\\", \\\"series:One Screen. One Red Flag.\\\"),\\n \\\"hypothesis\\\": \\\"A familiar delivery SMS red flag earns saves and family shares.\\\",\\n }\\n values.update(overrides)\\n return VideoCandidate(**values)\\n\\n\\ndef test_content_strategy_score_validates_complete_beginner_safety_score():\\n score = ContentStrategyScore(\\n hook=StrategySignal(9, \\\"Names a visible scam screen immediately.\\\"),\\n curiosity=StrategySignal(8, \\\"Viewer wants to know the one red flag.\\\"),\\n identity=StrategySignal(9, \\\"For normal smartphone users.\\\"),\\n emotion=StrategySignal(8, \\\"Warned but in control.\\\"),\\n trust=StrategySignal(9, \\\"Gives one safer move.\\\"),\\n follow_reason=StrategySignal(9, \\\"Repeatable one-screen red flag promise.\\\"),\\n originality=StrategySignal(8, \\\"Specific screen, not generic advice.\\\"),\\n variety=StrategySignal(8, \\\"Fresh enough.\\\"),\\n target_emotion=\\\"warned but in control\\\",\\n viewer_identity=\\\"normal smartphone/internet users\\\",\\n future_value_promise=\\\"one red flag before you click, pay, scan, or log in\\\",\\n format_family=\\\"One Screen. One Red Flag.\\\",\\n variation_axis=\\\"screen + red flag + safer move\\\",\\n )\\n\\n result = validate_content_strategy_score(score)\\n\\n assert result.is_valid is True\\n assert score.average_score == pytest.approx(8.5)\\n assert should_advance_strategy(score) is True\\n\\n\\ndef test_content_strategy_score_rejects_low_follow_reason_even_with_good_hook():\\n score = ContentStrategyScore(\\n hook=StrategySignal(9, \\\"Strong visible problem.\\\"),\\n curiosity=StrategySignal(8, \\\"Specific open loop.\\\"),\\n identity=StrategySignal(7, \\\"Somewhat targeted.\\\"),\\n emotion=StrategySignal(8, \\\"Useful concern.\\\"),\\n trust=StrategySignal(8, \\\"Specific example.\\\"),\\n follow_reason=StrategySignal(4, \\\"Could belong to any tips account.\\\"),\\n originality=StrategySignal(8, \\\"Specific.\\\"),\\n variety=StrategySignal(8, \\\"Fresh enough.\\\"),\\n target_emotion=\\\"warned\\\",\\n viewer_identity=\\\"normal users\\\",\\n future_value_promise=\\\"more tips\\\",\\n format_family=\\\"One Screen. One Red Flag.\\\",\\n variation_axis=\\\"generic tip\\\",\\n )\\n\\n result = validate_content_strategy_score(score)\\n\\n assert result.is_valid is False\\n assert \\\"follow_reason must be at least 7\\\" in result.errors\\n assert should_advance_strategy(score) is False\\n\\n\\ndef test_build_candidate_strategy_review_blocks_generic_ai_slop_language():\\n candidate = _candidate(\\n title=\\\"5 AI tools you need right now\\\",\\n hook=\\\"In this video I will show you the best AI tools to unlock your potential.\\\",\\n script_outline=\\\"Here are 5 amazing AI tools everyone should use.\\\",\\n visual_concept=\\\"Generic AI stock footage and floating robot icons.\\\",\\n caption=\\\"Unlock your full potential with these AI tools.\\\",\\n cta=\\\"Follow for more AI tips.\\\",\\n policy_notes=(\\\"duration_class:growth_short\\\",),\\n )\\n\\n review = build_candidate_strategy_review(candidate)\\n\\n assert review.render_allowed is False\\n assert \\\"generic AI-slop language detected\\\" in review.reject_reasons\\n assert \\\"pretty-empty visual blocker: first screen/red flag is not clear\\\" in review.reject_reasons\\n\\n\\ndef test_build_candidate_strategy_review_blocks_forbidden_public_topics():\\n candidate = _candidate(\\n title=\\\"How JARVIS and the user built this approval workflow\\\",\\n hook=\\\"This is the private collaboration behind the content machine.\\\",\\n script_outline=\\\"Show our JARVIS build log and personal workflow.\\\",\\n visual_concept=\\\"Private screenshots and internal chat timeline.\\\",\\n caption=\\\"Our collaboration is the moat.\\\",\\n cta=\\\"Follow for the next JARVIS build log.\\\",\\n policy_notes=(\\\"series:jarvis_build_log\\\",),\\n )\\n\\n review = build_candidate_strategy_review(candidate)\\n\\n assert review.render_allowed is False\\n assert \\\"candidate uses forbidden public topic: jarvis\\\" in review.reject_reasons\\n assert \\\"candidate uses forbidden public topic: collaboration\\\" in review.reject_reasons\\n assert \\\"candidate uses forbidden public topic: private\\\" in review.reject_reasons\\n\\n\\ndef test_build_candidate_strategy_review_allows_specific_scam_self_defense_candidate():\\n candidate = _candidate()\\n\\n review = build_candidate_strategy_review(\\n candidate,\\n recent_format_families=(\\\"Account Trap\\\", \\\"Money Move Red Flags\\\"),\\n )\\n\\n assert review.render_allowed is True\\n assert review.strategy_score.format_family == \\\"Before You Click\\\"\\n assert review.strategy_score.viewer_identity == \\\"normal smartphone/internet users\\\"\\n assert review.reject_reasons == ()\\n assert review.improvement_suggestions == ()\\n\\n\\ndef test_build_candidate_strategy_review_blocks_jargon_and_missing_safe_move():\\n candidate = _candidate(\\n hook=\\\"This phishing attempt leverages urgency-based social engineering.\\\",\\n script_outline=\\\"Explain the attack vector and credential harvesting risk.\\\",\\n visual_concept=\\\"Abstract hacker background without a phone, message, or app screen.\\\",\\n caption=\\\"Validate the authenticity of the communication channel.\\\",\\n cta=\\\"Stay secure.\\\",\\n )\\n\\n review = build_candidate_strategy_review(candidate)\\n\\n assert review.render_allowed is False\\n assert \\\"language is too technical for normal users\\\" in review.reject_reasons\\n assert \\\"safe move clarity is missing\\\" in review.reject_reasons\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_content_strategy_score.py\"}"
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"arguments": "{\"content\":\"# TrueTraceShorts — Digital Red Flags\\n\\n## Core Positioning\\n\\n**Scam Self-Defense for Normal People.**\\n\\nKernsatz:\\n\\n> One screen. One red flag. One safer move.\\n\\nDeutsch:\\n\\n> Ein normaler Screen. Ein gefährlicher Hinweis. Eine sichere Handlung.\\n\\nChannel promise:\\n\\n> I show you the one red flag before you click, pay, scan, or log in.\\n\\nTrueTraceShorts is no longer primarily an AI/workflow/automation channel. Those topics can appear later as specialist side formats, but the default editorial strategy is now beginner-friendly digital self-defense.\\n\\n## Audience\\n\\nNot “Cybersecurity for Boomers” externally. Internally, content must be boomer-safe: simple language, large readability, concrete examples, clear action.\\n\\nAudience:\\n\\n- normal smartphone and internet users\\n- parents, teenagers, partners, families\\n- workers and teams\\n- small business owners\\n- people who want to protect parents, partners, colleagues, or clients\\n\\nPsychological lever:\\n\\n> “This could have happened to me or my family.”\\n\\n## Language\\n\\nFinal social videos remain English for now, but with B1/B2-level plain English.\\n\\nUse:\\n\\n- short sentences\\n- everyday words\\n- no nested clauses\\n- no corporate/security jargon\\n- no nerd explanations unless immediately translated\\n\\nGood:\\n\\n- “This message wants you to hurry.”\\n- “Don’t tap the link.”\\n- “Open the app yourself.”\\n- “Check it another way.”\\n- “If it makes you hurry, slow down.”\\n\\nAvoid:\\n\\n- “credential harvesting” → say “stealing your login”\\n- “validate the communication channel” → say “check it another way”\\n- “MFA prompt fatigue” → say “a login prompt you did not start”\\n\\n## Content Formula\\n\\nEvery video defaults to:\\n\\n```text\\nOne screen.\\nOne red flag.\\nOne safer move.\\n```\\n\\nStandard structure:\\n\\n1. **0–2s — Visible trap + hook**\\n - normal screen: SMS, email, invoice, chat, popup, login, QR, marketplace chat\\n - the problem is visible without audio\\n2. **2–7s — Why it feels normal**\\n - tiny fee, familiar sender, official-looking page, short deadline, helpful buyer\\n3. **7–15s — The one red flag**\\n - pressure, changed route, off-platform move, login prompt, new number, fake support call\\n4. **15–24s — The safer move**\\n - open app yourself, verify another way, keep payment inside platform, deny prompt\\n5. **24–30s — Screenshot checklist**\\n - max three points, large text, mobile-safe\\n6. **30–34s — Memorable rule + soft CTA**\\n - simple save/share sentence\\n\\n## Candidate Pre-Render Gate\\n\\nDo not render unless these are clear:\\n\\n- What normal screen does the viewer see?\\n- What concrete red flag is visible?\\n- Can the problem be recognized in second 1 without audio?\\n- What could happen to the viewer?\\n- What is the one safer action?\\n- Can someone send it to parents, partner, colleagues, or team?\\n- Does it have save/screenshot value?\\n- Is the language simple enough for average users?\\n- Is it useful without tech knowledge?\\n\\nIf not: revise the candidate, do not render.\\n\\n## Main Pillars\\n\\nA. Text & Delivery Scams — package SMS, customs fee, toll fee, short links.\\n\\nB. Login & Account Traps — fake bank/login, fake reset, account closure, login prompt not started.\\n\\nC. Payment & Invoice Red Flags — fake invoice, changed payment route, CEO payment request, QR payment trap.\\n\\nD. Marketplace & Buyer Scams — off-platform move, fake shipping link, fake escrow, overpayment.\\n\\nE. Family & Emotion Scams — “Hi mom new number”, emergency money, grandparent scam, wrong-number scam, AI voice warning in simple terms.\\n\\nF. Fake Support / Popup Scams — infected-device popup, call support, fake antivirus, remote access request.\\n\\nG. Password / Account Safety Basics — password reuse, password manager basics, MFA basics, recovery-email risk.\\n\\nH. Job / Side-Hustle Scams — fake recruiter, task scam, pay-to-get-paid, remote job deposit.\\n\\n## Deprioritized Topics\\n\\nNot main format for now:\\n\\n- AI Workflow Automation\\n- Prompt Engineering\\n- Tool Reviews / Top 5 AI tools\\n- abstract productivity mechanisms\\n- general cybersecurity news\\n- hacker tactics / OSINT / VPN comparisons\\n- dark-web explainers\\n- technical attack walkthroughs\\n\\n## Official Series\\n\\n1. **One Screen. One Red Flag.**\\n - one normal screen, one dangerous detail, one safer move\\n2. **Send This To Your Parents**\\n - simple, family-shareable, never patronizing\\n3. **Before You Click**\\n - links, SMS, QR, login, popup checks\\n4. **Money Move Red Flags**\\n - payments, invoices, bank routes, QR payments, marketplaces\\n5. **Account Trap**\\n - login, password, MFA, fake reset, account closure\\n\\n## Screenshot Checklist Rules\\n\\nEnd card where possible:\\n\\n- max three points\\n- 1.5–3 seconds visible\\n- large font\\n- mobile-safe\\n- no long paragraphs\\n\\nExamples:\\n\\n```text\\nSAVE THIS\\n\\nSmall fee?\\nShort deadline?\\nSuspicious link?\\n\\nOpen the app yourself.\\n```\\n\\n```text\\nQuick check\\n\\nDid they rush you?\\nIs money involved?\\nCan you verify another way?\\n```\\n\\n## Visual Direction\\n\\nDefault: **screen/mockup-led**.\\n\\nUse:\\n\\n- phone screen\\n- SMS\\n- email\\n- invoice\\n- chat\\n- popup\\n- login\\n- QR situation\\n- marketplace chat\\n- deterministic fictional mockups\\n\\nAvoid:\\n\\n- abstract glass clouds\\n- empty AI B-roll\\n- generic laptop shots\\n- funnel/ring metaphors\\n- PowerPoint explainers\\n- unnecessary Wan/I2V/T2V\\n\\n## Safety Rules\\n\\nNo real:\\n\\n- brands/logos\\n- phone numbers\\n- IBANs/bank details\\n- QR codes\\n- domains\\n- people/firms\\n- real scam examples copied as-is\\n\\nAvoid guarantees. Prefer:\\n\\n- “A common red flag is…”\\n- “This does not catch everything, but it gives you a safer next step.”\\n- “Before you pay, check another way.”\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/truetrace_digital_red_flags_strategy.md\"}"
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"arguments": "{\"content\":\"# Mockup Safety Guidelines — Digital Red Flags\\n\\nTrueTraceShorts scam screens must be controlled, fictional, and safe.\\n\\n## Default\\n\\nFor scam-screen content, prefer deterministic renderer-owned mockups over free-form AI images.\\n\\nUse deterministic mockups for:\\n\\n- SMS screens\\n- email screens\\n- invoices\\n- login pages\\n- payment routes\\n- QR/payment situations\\n- marketplace chats\\n- popup warnings\\n- screenshot checklist cards\\n\\n## Forbidden in Mockups\\n\\nNo real:\\n\\n- brand names or logos\\n- bank names or bank details\\n- IBANs, account numbers, routing numbers\\n- phone numbers\\n- QR codes or barcodes\\n- real domains\\n- real addresses\\n- real people, firms, or copied scam examples\\n\\nUse reserved fictional domains only when needed, e.g.:\\n\\n- `example.com`\\n- `example-login.com`\\n- `example-delivery.com`\\n\\nDo not create realistic malicious paths or reusable phishing templates.\\n\\n## Safe Copy Rules\\n\\nUse plain, fictional text that shows the red flag without teaching the scam.\\n\\nGood:\\n\\n```text\\nYour package is on hold\\nPay CHF 1.95\\nDelivery expires today\\ntrack.example-delivery.com\\n```\\n\\nBad:\\n\\n- real courier names\\n- real bank names\\n- live-looking QR codes\\n- real phone numbers\\n- complete step-by-step fraud flow\\n\\n## Claim Safety\\n\\nDo not say:\\n\\n- “This catches every scam.”\\n- “You are safe if you do this.”\\n- “All scammers do X.”\\n\\nSay:\\n\\n- “A common red flag is…”\\n- “Before you pay, check another way.”\\n- “This does not catch everything, but it gives you a safer next step.”\\n\\n## AI Image Boundary\\n\\nAI images may provide atmosphere: desk, phone in hand-free setup, room, lighting, premium texture.\\n\\nAI images should not invent the actual scam screen when exact text/data/legibility matters. The scam screen should be deterministic whenever possible.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/mockup_safety_guidelines.md\"}"
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`autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n 16|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 17|- `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n 18|- `autoshorts.content.demo_pipeline` — deterministic content-to-Telegram-review demo pipeline.\\n 19|- `autoshorts.content.script_draft` — structured script drafts with retention beats, visual concept, caption, CTA, and AI-slop validation.\\n 20|- `autoshorts.content.to_candidate` — converts content briefs and script drafts into candidate drafts and validated video candidates.\\n 21|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n 22|- `autoshorts.demo.truetrace_concepts` — five concrete TrueTraceShorts concept candidates with metadata for strategy review.\\n 23|- `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n 24|- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n 25|- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning: EdgeTTS phase-1 baseline, Magpie A/B candidate, Whisper alignment-only.\\n 26|- `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.\\n 27|- `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP4 rendering boundary.\\n 28|- `autoshorts.audio.probe` — FFprobe duration helper used to align subtitle tracks to generated audio.\\n 29|- `autoshorts.audio.voiceover` — beginner-friendly final English TrueTrace voiceover scripts and a single-source script-draft bridge for aligned visuals/captions.\\n 30|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 31|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 32|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 33|- `autoshorts.preview.package` — combines MP4 timing preview plus social draft review into an approval package.\\n 34|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 35|- `autoshorts.strategy.brand` — TrueTraceShorts brand profile, default descriptions/bios/tags, and brand-fit validation.\\n 36|- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n 37|- `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.\\n 38|- `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.\\n 39|- `autoshorts.rendering.scene_backgrounds` — scene-specific ChatGPT Image/Codex background prompt plans that explicitly forbid readable text/logos and require renderer-owned overlays.\\n 40|- `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.\\n 41|- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n 42|- `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n 43|- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n 44|- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n 45|- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n 46|- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n 47|- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n 48|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 49|- `autoshorts.social.delivery` — side-effect-free delivery preparation for social draft review messages.\\n 50|- `autoshorts.social.final_approval` — final-video approval packages with media path, checksum, platform metadata, and Telegram-safe review copy.\\n 51|- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n 52|- `autoshorts.social.manual_posting` — side-effect-free manual upload copy pack for phase-1 YouTube/TikTok metadata.\\n 53|- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n 54|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 55|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 56|- `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n 57|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 58|- Tests for content briefs, content demo pipeline, content-to-candidate conversion, content pillars, candidate factory, idea candidates, script drafts, shot plans, testbatches, sample review CLI, sample Telegram delivery CLI, sample review demos, review workflow, review-batch rendering, approval parsing, review formatting, Telegram delivery preparation, and script quality gating.\\n 59|\\n 60|## Brand profile\\n 61|\\n 62|`autoshorts.strategy.brand.TRUETRACE_SHORTS_BRAND` captures the phase-1 public brand contract for `True Trace Shorts`:\\n 63|\\n 64|- channel name and handles;\\n 65|- tagline: `Systems over prompts.`;\\n 66|- public YouTube description and TikTok bio;\\n 67|- default hashtags and YouTube tags;\\n 68|- tone and visual rules;\\n 69|- forbidden public topics;\\n 70|- generic AI-tips patterns;\\n 71|- mechanism keywords for brand fit.\\n 72|\\n 73|`validate_candidate_brand_fit()` rejects candidates that violate no-go topics, generic AI-tips positioning, or the mechanisms-over-hype promise. `apply_brand_metadata()` appends default hashtags and brand policy notes without changing the candidate content or performing side effects.\\n 74|\\n 75|`autoshorts.demo.truetrace_concepts.build_truetrace_concept_batch()` provides the current five-candidate TrueTrace concept testbatch for strategy review:\\n 76|\\n 77|- `Why Most AI Automations Break After Three Days`\\n 78|- `This Prompt Looks Smart — But the Workflow Still Breaks`\\n 79|- `The Hidden Reason Your AI Notes Become Useless`\\n 80|- `Claim: AI Agents Will Replace Teams`\\n 81|- `One Rule Before You Automate Anything With AI`\\n 82|\\n 83|`render_truetrace_concept_summary()` renders those candidates with hooks, outlines, visuals, hypotheses, YouTube descriptions, TikTok captions, and tags. It performs no platform action and avoids action language.\\n 84|\\n 85|`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate. Each preview artifact also now carries a deterministic English subtitle cue track and the phase-1 EdgeTTS provider plan, so audio/caption decisions travel with the candidate before any real TTS call happens.\\n 86|\\n 87|Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; final-motion videos use compact phrase-boundary subtitle cues; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later only after script/retention quality is proven.\\n 88|\\n 89|The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n 90|\\n 91|The first final-near motion prototype uses a hybrid model: ChatGPT Image/Codex can provide text-free background texture, while `autoshorts.rendering.motion_renderer` owns the actual inbox cards, arrows, labels, captions, and FFmpeg assembly. This keeps generated imagery useful without trusting it to spell or explain anything.\\n 92|\\n 93|## Strategy scoring\\n 94|\\n 95|`autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n 96|\\n 97|- hook;\\n 98|- curiosity;\\n 99|- identity;\\n 100|- emotion;\\n 101|- trust;\\n 102|- follow reason;\\n 103|- originality;\\n 104|- variety.\\n 105|\\n 106|`build_candidate_strategy_review()` returns a `CandidateStrategyReview` with `render_allowed`, reject reasons, and improvement suggestions. It blocks generic AI-slop language, weak future-value promises, forbidden public topics, and recent format repetition before render capacity is wasted.\\n 107|\\n 108|`autoshorts.strategy.series.DEFAULT_CONTENT_SERIES` currently exposes only public-safe series:\\n 109|\\n 110|- `Prompt vs System`\\n 111|- `AI System Autopsy`\\n 112|- `One Rule, One Example`\\n 113|- `AI Hype Court`\\n 114|\\n 115|`validate_candidate_series_fit()` rejects public content about JARVIS/Hermes, the user, their collaboration, private/behind-the-scenes material, politics, legal loopholes, private-data misuse, rule-bypassing, exploitation, or anything that could enable lawbreaking.\\n 116|\\n 117|## Content development\\n 118|\\n 119|`autoshorts.content.brief.create_content_brief()` turns a pillar, topic, audience problem, and payoff into a deterministic `ContentBrief`. 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