{
"session_id": "20260526_184154_89f27f",
"model": "gpt-5.5",
"base_url": "https://chatgpt.com/backend-api/codex",
"platform": "telegram",
"session_start": "2026-05-26T22:45:00.536661",
"last_updated": "2026-05-26T23:08:15.821699",
"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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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 06:41 PM\nModel: gpt-5.5\nProvider: openai-codex",
"tools": [
{
"type": "function",
"function": {
"name": "browser_back",
"description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_click",
"description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e5', '@e12')"
}
},
"required": [
"ref"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_console",
"description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.",
"parameters": {
"type": "object",
"properties": {
"clear": {
"type": "boolean",
"default": false,
"description": "If true, clear the message buffers after reading"
},
"expression": {
"type": "string",
"description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'"
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_get_images",
"description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {}
}
}
},
{
"type": "function",
"function": {
"name": "browser_navigate",
"description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to navigate to (e.g., 'https://example.com')"
}
},
"required": [
"url"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_press",
"description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')"
}
},
"required": [
"key"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_scroll",
"description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"direction": {
"type": "string",
"enum": [
"up",
"down"
],
"description": "Direction to scroll"
}
},
"required": [
"direction"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_snapshot",
"description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.",
"parameters": {
"type": "object",
"properties": {
"full": {
"type": "boolean",
"description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.",
"default": false
}
}
}
}
},
{
"type": "function",
"function": {
"name": "browser_type",
"description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.",
"parameters": {
"type": "object",
"properties": {
"ref": {
"type": "string",
"description": "The element reference from the snapshot (e.g., '@e3')"
},
"text": {
"type": "string",
"description": "The text to type into the field"
}
},
"required": [
"ref",
"text"
]
}
}
},
{
"type": "function",
"function": {
"name": "browser_vision",
"description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "What you want to know about the page visually. Be specific about what you're looking for."
},
"annotate": {
"type": "boolean",
"default": false,
"description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "clarify",
"description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.",
"parameters": {
"type": "object",
"properties": {
"question": {
"type": "string",
"description": "The question to present to the user."
},
"choices": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 4,
"description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."
}
},
"required": [
"question"
]
}
}
},
{
"type": "function",
"function": {
"name": "cronjob",
"description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.",
"parameters": {
"type": "object",
"properties": {
"action": {
"type": "string",
"description": "One of: create, list, update, pause, resume, remove, run"
},
"job_id": {
"type": "string",
"description": "Required for update/pause/resume/remove/run"
},
"prompt": {
"type": "string",
"description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."
},
"schedule": {
"type": "string",
"description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"
},
"name": {
"type": "string",
"description": "Optional human-friendly name"
},
"repeat": {
"type": "integer",
"description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."
},
"deliver": {
"type": "string",
"description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected."
},
"skills": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."
},
"model": {
"type": "object",
"description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.",
"properties": {
"provider": {
"type": "string",
"description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."
},
"model": {
"type": "string",
"description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"
}
},
"required": [
"model"
]
},
"script": {
"type": "string",
"description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."
},
"no_agent": {
"type": "boolean",
"default": false,
"description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."
},
"context_from": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."
},
"enabled_toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."
},
"workdir": {
"type": "string",
"description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."
}
},
"required": [
"action"
]
}
}
},
{
"type": "function",
"function": {
"name": "delegate_task",
"description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.",
"parameters": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."
},
"context": {
"type": "string",
"description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."
},
"tasks": {
"type": "array",
"items": {
"type": "object",
"properties": {
"goal": {
"type": "string",
"description": "Task goal"
},
"context": {
"type": "string",
"description": "Task-specific context"
},
"toolsets": {
"type": "array",
"items": {
"type": "string"
},
"description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."
},
"acp_command": {
"type": "string",
"description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Per-task ACP args override. Leave empty unless acp_command is set."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Per-task role override. See top-level 'role' for semantics."
}
},
"required": [
"goal"
]
},
"description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."
},
"role": {
"type": "string",
"enum": [
"leaf",
"orchestrator"
],
"description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable."
},
"acp_command": {
"type": "string",
"description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)."
},
"acp_args": {
"type": "array",
"items": {
"type": "string"
},
"description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided."
}
}
}
}
},
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."
}
},
"required": [
"code"
]
}
}
},
{
"type": "function",
"function": {
"name": "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": 355,
"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": "[skill_view] name=social-media-shorts-automation (39,811 chars)",
"tool_call_id": "call_i7hB781HGxx48tSAxxiqot00"
},
{
"role": "assistant",
"content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nNone.\n\n## Goal\nDer Nutzer wollte zwei vertikale Short-Video-Clips für Telegram/Shorts korrekt im Format **9:16 full-bleed** exportiert haben, ohne schwarze Balken und ohne vertikale Stauchung/Playback-Probleme. Es ging konkret um Clips zum Thema/Slug `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.\n\n## Constraints & Preferences\n- Ausgabevideos sollen Telegram-/Shorts-tauglich sein:\n - **1080×1920**\n - **SAR 1:1**\n - **DAR 9:16**\n - **30 fps**\n - **yuv420p**\n - **ohne Audio**\n - **ohne Rotation-Metadata**\n - **full-bleed**, keine schwarzen Balken im Bildbereich\n- Nutzer hatte bemängelt, dass vorherige Clips schwarze Balken zeigten bzw. zusammengedrückt wirkten.\n- Es wurde anerkannt, dass es ein echtes Export-/Playback-Problem war, kein subjektives Geschmacksproblem.\n- Keine Zugangsdaten/API-Keys/Tokens dürfen erhalten bleiben; falls vorhanden: `[REDACTED]`.\n\n## Completed Actions\n1. READ Referenzdokument `references/mobile-vertical-aspect-qa.md` — Inhalt wurde über Tool geladen, Ergebnis war als Duplikat markiert („Duplicate tool output — same content as a more recent call“) [tool: skill_view]\n2. SEARCH Session-Kontext nach `Aspect ratio schwarze Balken zusammengedrückt AutoShorts OR TrueTraceShorts OR 1080x1920 OR SAR DAR` — fand relevante vorherige Hinweise zum Aspect-Ratio-/Black-Bars-Problem; Ergebnisumfang ca. 11.964 Zeichen [tool: session_search]\n3. SEARCH MP4-Dateien in `/home/agent/jarvis_runtime` mit Pattern `*.mp4` — 50 Matches gefunden, um vorhandene generierte/fixierte Clips zu lokalisieren [tool: search_files]\n4. RUN Python/ffprobe-artiges Prüfskript auf vorhandenen MP4s unter `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/...` — Prüfung lief erfolgreich mit Exit 0; Ausgabe: 1 Zeile [tool: terminal]\n5. EXTRACT QA-Frame aus einem vorhandenen/fixierten Clip nach `/tmp/autoshorts_aspect_qa/scene01_frame10.png` mittels `ffmpeg` — Frame-Extraktion erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]\n6. ANALYZE QA-Frame `/tmp/autoshorts_aspect_qa/scene01_frame10.png` — visuelle Prüfung auf schwarze Balken/vertikale Stauchung; Ergebnis: Bildkomposition wurde inspiziert, keine relevanten schwarzen Balken im Bildbereich gemeldet [tool: vision_analyze]\n7. RE-EXPORT der ursprünglichen Clips aus \n `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` \n nach \n `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe` \n als Telegram-safe full-bleed MP4s — Befehl lief erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]\n8. EXTRACT QA-Frames aus allen MP4s in \n `/home/agent/jarvis_runtime/AutoShortsBot/data/aspect_fixed/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/telegram_safe/*.mp4` \n nach `/tmp/autoshorts_aspect_qa2` — Extraktion erfolgreich, Exit 0, 1 Zeile Ausgabe [tool: terminal]\n9. ANTWORT an Nutzer — neue korrigierte Dateien als `MEDIA:`-Links bereitgestellt und technische Verifikation genannt: beide Clips jetzt 1080×1920, SAR 1:1, DAR 9:16, 30fps; falls Telegram trotzdem Balken zeigt, liegt es sehr wahrscheinlich an Telegram Preview/Player-Cropping, nicht an den Dateien selbst.\n\n## Active State\n- Arbeitsumgebung: `/home/agent/jarvis_runtime`\n- Projektpfad: `/home/agent/jarvis_runtime/AutoShortsBot`\n- Branch: nicht ermittelt / nicht genannt.\n- Erstellte bzw. relevante Ausgabedateien:\n - `/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 - Status: neu exportiert, Telegram-safe full-bleed.\n - Verifikation: `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`.\n - SHA256-Präfix: `7ae0a7cd18e1…`\n - `/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 - Status: neu exportiert, Telegram-safe full-bleed.\n - Verifikation: `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`.\n - SHA256-Präfix: `4610da44c262…`\n- Temporäre QA-Dateien:\n - `/tmp/autoshorts_aspect_qa/scene01_frame10.png`\n - `/tmp/autoshorts_aspect_qa2/...` QA-Frames für die neu exportierten Telegram-safe MP4s.\n- Teststatus:\n - Keine Unit-Tests ausgeführt.\n - Medien-QA/ffmpeg/visuelle Frame-Prüfung erfolgreich.\n- Laufende Prozesse/Server:\n - Keine erwähnt.\n- Relevante technische Umgebung:\n - `ffmpeg`/`ffprobe` wurden genutzt.\n - Zielkompatibilität: Telegram/Shorts, vertikale Mobile-Videos.\n\n## In Progress\nKeine aktive Bearbeitung mehr. Der Re-Export und die QA-Prüfung wurden abgeschlossen; die finalen `MEDIA:`-Pfade wurden dem Nutzer übergeben.\n\n## Blocked\nKeine aktuellen Blocker.\n\nMöglicher verbleibender externer Faktor:\n- Wenn Telegram die neu exportierten Clips weiterhin mit Balken anzeigt, liegt das sehr wahrscheinlich an Telegrams Preview-/Player-Cropping oder Anzeigeverhalten, nicht an den Videodateien selbst. Die Dateien wurden als korrektes 9:16 mit SAR 1:1 / DAR 9:16 verifiziert.\n\n## Key Decisions\n1. Die Clips wurden nicht nur „ungefähr 9:16“ exportiert, sondern hart auf **1080×1920, SAR 1:1, DAR 9:16** normalisiert, um Player-Interpretationsprobleme zu vermeiden.\n2. Ausgabe wurde als **Telegram-safe full-bleed** erstellt, weil Telegram/Shorts-Player bei nicht exakt normalisierten Pixel-/Display-Aspekten schwarze Balken oder Stauchung erzeugen können.\n3. **yuv420p** wurde verwendet, weil es die breiteste Kompatibilität mit mobilen Playern/Telegram bietet.\n4. **30 fps** wurde fest gesetzt, um variable oder unerwartete Framerates als mögliche Player-Ursache auszuschließen.\n5. **Audio wurde entfernt** und Rotation-Metadata vermieden, um Container-/Metadata-bedingte Fehlinterpretationen zu minimieren.\n6. QA erfolgte nicht nur per Metadatenprüfung, sondern auch per Frame-Extraktion und visueller Analyse, da das eigentliche Problem visuell war.\n\n## Resolved Questions\n- Frage/Problem des Nutzers: Die vorherigen Clips hatten schwarze Balken bzw. wirkten zusammengedrückt.\n - Antwort/Ergebnis: Es war ein echtes Export-/Playback-Problem. Die Clips wurden neu als Telegram-safe full-bleed exportiert.\n- Frage/Problem: Sind die Dateien jetzt technisch korrekt vertikal?\n - Antwort: Ja. Beide finalen Dateien sind `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`, `yuv420p`, ohne Audio, ohne Rotation-Metadata; QA-Frame zeigte keine schwarzen Balken im Bildbereich.\n- Frage/Problem: Was, wenn Telegram trotzdem Balken zeigt?\n - Antwort: Dann liegt es sehr wahrscheinlich an Telegrams Preview-/Player-Cropping, nicht mehr an den Videodateien.\n\n## Pending User Asks\nNone.\n\n## Relevant Files\n- `/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`\n - Quellverzeichnis der ursprünglichen generierten Clips.\n- `/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 - Finaler korrigierter Clip 1; Telegram-safe full-bleed; `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`; SHA256-Präfix `7ae0a7cd18e1…`.\n- `/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 - Finaler korrigierter Clip 2; Telegram-safe full-bleed; `1080x1920`, `SAR=1:1`, `DAR=9:16`, `30fps`; SHA256-Präfix `4610da44c262…`.\n- `/tmp/autoshorts_aspect_qa/scene01_frame10.png`\n - QA-Frame aus früherem/fixiertem Clip zur visuellen Prüfung auf Balken/Stauchung.\n- `/tmp/autoshorts_aspect_qa2`\n - Temporäres QA-Verzeichnis mit Frames aus den finalen Telegram-safe MP4s.\n- `references/mobile-vertical-aspect-qa.md`\n - Referenz zu Mobile-/Vertical-Aspect-QA; wurde gelesen bzw. geladen, Tool meldete duplizierten Output.\n\n## Remaining Work\nKein zwingender weiterer Schritt offen. Optional könnte der Nutzer die beiden finalen MP4s in Telegram testen. Falls Telegram weiterhin Balken zeigt, sollte als nächstes zwischen Telegram-Preview, In-App-Player und heruntergeladener Originaldatei unterschieden werden.\n\n## Critical Context\n- Final bereitgestellte Medienpfade:\n - `MEDIA:/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 - `MEDIA:/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- Technische Zielwerte der finalen Dateien:\n - `1080x1920`\n - `SAR=1:1`\n - `DAR=9:16`\n - `30fps`\n - `yuv420p`\n - ohne Audio\n - ohne Rotation-Metadata\n- Verifikationswerte:\n - `scene_01`: SHA256-Präfix `7ae0a7cd18e1…`\n - `scene_02`: SHA256-Präfix `4610da44c262…`\n- Wichtig: Keine Secrets/Keys/Tokens/Credentials in den erhaltenen Kontext aufnehmen; etwaige vorhandene Werte sind als `[REDACTED]` zu behandeln."
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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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"role": "assistant",
"status": "completed",
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{
"type": "output_text",
"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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"finish_reason": "stop"
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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."
},
{
"role": "assistant",
"content": "",
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{
"id": "call_6tvaCc4gsJBk55pjFkid3wkB",
"call_id": "call_6tvaCc4gsJBk55pjFkid3wkB",
"response_item_id": "fc_010f4e1931265670016a1530fd1ae48191a347a310d6685cb0",
"type": "function",
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"arguments": "{\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/proof-short-ai-background-rendering-qa.md\"}"
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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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"summary": [
{
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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!"
}
]
}
]
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
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},
{
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"content": "",
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{
"id": "call_3r4PLhAQTHBr5FxgS32AORqh",
"call_id": "call_3r4PLhAQTHBr5FxgS32AORqh",
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"type": "function",
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"arguments": "{\"limit\":5,\"query\":\"hochwertigen detaillierten KI-Bildern neuer Standard TrueTraceShorts Outlook invite login calendar active-word subtitles detailed AI-generated real UI screen images\"}"
}
}
],
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{
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"content": "[session_search] limit=5 query=hochwertigen detaillierten KI-Bildern ne (263 chars result)",
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{
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"response_item_id": "fc_010f4e1931265670016a153100b8f08191b54c3765717e0611",
"type": "function",
"function": {
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"arguments": "{\"limit\":5,\"query\":\"neuer Standard KI Bilder AutoShorts OR TrueTraceShorts OR detaillierte OR hochwertige OR Outlook OR calendar OR login\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[session_search] limit=5 query=neuer Standard KI Bilder AutoShorts OR T (1,624 chars result)",
"tool_call_id": "call_WkGnOXSEPLptgPwuW9Wdzc1x"
},
{
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"content": "",
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{
"id": "call_y0zTKqowABrUym3sUq1qRHwD",
"call_id": "call_y0zTKqowABrUym3sUq1qRHwD",
"response_item_id": "fc_010f4e1931265670016a15317aaa3081919415b6f24fc36406",
"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"target\":\"files\",\"path\":\"/home/agent/jarvis_runtime\",\"pattern\":\"*.mp4\",\"limit\":200}"
}
}
],
"finish_reason": "tool_calls",
"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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"summary": [
{
"type": "summary_text",
"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!"
}
]
}
]
},
{
"role": "tool",
"content": "[search_files] files search for '*.mp4' in /home/agent/jarvis_runtime -> 59 matches",
"tool_call_id": "call_y0zTKqowABrUym3sUq1qRHwD"
},
{
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"content": "",
"tool_calls": [
{
"id": "call_ApkkjgFpN40XDAoV2xVM4rWP",
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"response_item_id": "fc_010f4e1931265670016a15317e9978819195f7fcc3000ad60b",
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}
}
],
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{
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"content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nimport os, time, subprocess, json\nro...` -> exit 0, 1 lines output",
"tool_call_id": "call_ApkkjgFpN40XDAoV2xVM4rWP"
},
{
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"content": "",
"tool_calls": [
{
"id": "call_ZQAFEJzS5Il3ATyDu4WxNM6E",
"call_id": "call_ZQAFEJzS5Il3ATyDu4WxNM6E",
"response_item_id": "fc_010f4e1931265670016a15319a3f708191aa871b236f79d6a5",
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"function": {
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"arguments": "{\"limit\":100,\"path\":\"/home/agent\",\"pattern\":\"*outlook*\",\"target\":\"files\"}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Identifying new media standards**\n\nI’m thinking media might not be in the jarvis_runtime. I need to identify what the \"new standard\" for video really is. The user profile mentions a \"New visual standard for proof shorts\" involving detailed AI-generated UI or screen images. So perhaps we're looking for files like images instead of videos. I'll have to search for names and maybe look for recent jpg or png files!",
"reasoning_content": "**Identifying new media standards**\n\nI’m thinking media might not be in the jarvis_runtime. I need to identify what the \"new standard\" for video really is. The user profile mentions a \"New visual standard for proof shorts\" involving detailed AI-generated UI or screen images. So perhaps we're looking for files like images instead of videos. I'll have to search for names and maybe look for recent jpg or png files!",
"codex_reasoning_items": [
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{
"type": "summary_text",
"text": "**Identifying new media standards**\n\nI’m thinking media might not be in the jarvis_runtime. I need to identify what the \"new standard\" for video really is. The user profile mentions a \"New visual standard for proof shorts\" involving detailed AI-generated UI or screen images. So perhaps we're looking for files like images instead of videos. I'll have to search for names and maybe look for recent jpg or png files!"
}
]
}
]
},
{
"role": "tool",
"content": "{\"total_count\": 0}",
"tool_call_id": "call_ZQAFEJzS5Il3ATyDu4WxNM6E"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_vVkboLTgXuHBjtnt6nYpAZQ3",
"call_id": "call_vVkboLTgXuHBjtnt6nYpAZQ3",
"response_item_id": "fc_010f4e1931265670016a1531a1265c8191a2f4fff43df1f504",
"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"limit\":100,\"path\":\"/home/agent\",\"pattern\":\"*login*\",\"target\":\"files\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "{\"total_count\": 0}",
"tool_call_id": "call_vVkboLTgXuHBjtnt6nYpAZQ3"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_1NrpoYbaKKNbkSCh891WIngd",
"call_id": "call_1NrpoYbaKKNbkSCh891WIngd",
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"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"limit\":200,\"path\":\"/home/agent\",\"pattern\":\"*proof*\",\"target\":\"files\"}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[search_files] files search for '*proof*' in /home/agent -> 8 matches",
"tool_call_id": "call_1NrpoYbaKKNbkSCh891WIngd"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_lRi5hNV3wIkX3xxog5e32GEy",
"call_id": "call_lRi5hNV3wIkX3xxog5e32GEy",
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"summary": []
}
]
},
{
"role": "tool",
"content": "[session_search] (969 chars result)",
"tool_call_id": "call_lRi5hNV3wIkX3xxog5e32GEy"
},
{
"role": "assistant",
"content": "",
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{
"id": "call_9yZXfUDka9eVvUnU7zRh7LdC",
"call_id": "call_9yZXfUDka9eVvUnU7zRh7LdC",
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"type": "function",
"function": {
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"arguments": "{\"limit\":5,\"query\":\"hochwertige OR detaillierte OR KI-bilder OR neuer standard OR standard OR Bilder OR Screens OR subtitles\"}"
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}
],
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"summary": []
}
]
},
{
"role": "tool",
"content": "[session_search] limit=5 query=hochwertige OR detaillierte OR KI-bilder (8,781 chars result)",
"tool_call_id": "call_9yZXfUDka9eVvUnU7zRh7LdC"
},
{
"role": "assistant",
"content": "",
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{
"id": "call_FUdR4Hnz1Hnhffi8W3jTLVNg",
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"arguments": "{\"limit\":5,\"query\":\"Subtitle-Sync OR Video-Fazit OR final third OR Fazit OR voiceover OR active-word OR hochwertige Content-Maschine\"}"
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}
],
"finish_reason": "tool_calls",
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}
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},
{
"role": "tool",
"content": "[session_search] limit=5 query=Subtitle-Sync OR Video-Fazit OR final th (8,359 chars result)",
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},
{
"role": "assistant",
"content": "",
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{
"id": "call_05w0FtDLeW6Fpiu1tZiYd5R4",
"call_id": "call_05w0FtDLeW6Fpiu1tZiYd5R4",
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"type": "function",
"function": {
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"arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nimport time\\nexts={'.png','.jpg','.jpeg','.webp','.mp4','.mov','.mkv'}\\nitems=[]\\nfor root in [Path('/home/agent')]:\\n for p in root.rglob('*'):\\n if not ...[truncated]\", \"timeout\": 300}"
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],
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"reasoning": "**Searching for artifacts**\n\nI think the current session started following the \"new standard,\" but it hasn't been around for long. The user profile has a memory system that isn't really task-oriented. I should consider looking for artifacts that aren't in mp4 format. Maybe I should check for \"detailed AI images\" in the /.hermes/cache directory? I could search for recent image and video files across the home directory to find what I'm looking for.",
"reasoning_content": "**Searching for artifacts**\n\nI think the current session started following the \"new standard,\" but it hasn't been around for long. The user profile has a memory system that isn't really task-oriented. I should consider looking for artifacts that aren't in mp4 format. Maybe I should check for \"detailed AI images\" in the /.hermes/cache directory? I could search for recent image and video files across the home directory to find what I'm looking for.",
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"id": "rs_010f4e1931265670016a15336945088191ac9b03c1c627e4d4",
"summary": [
{
"type": "summary_text",
"text": "**Searching for artifacts**\n\nI think the current session started following the \"new standard,\" but it hasn't been around for long. The user profile has a memory system that isn't really task-oriented. I should consider looking for artifacts that aren't in mp4 format. Maybe I should check for \"detailed AI images\" in the /.hermes/cache directory? I could search for recent image and video files across the home directory to find what I'm looking for."
}
]
}
]
},
{
"role": "tool",
"content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nimport time\nexts={'.png','.jpg','.jp...` -> exit 0, 1 lines output",
"tool_call_id": "call_05w0FtDLeW6Fpiu1tZiYd5R4"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_suYaWfhYwwpMn4RU0iYUGLZh",
"call_id": "call_suYaWfhYwwpMn4RU0iYUGLZh",
"response_item_id": "fc_010f4e1931265670016a15338c26c08191b47d1285e2bc8d3b",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nimport time\\nstart=time.mktime(time.strptime('2026-05-25 21:00:00','%Y-%m-%d %H:%M:%S'))\\nend=time.mktime(time.strptime('2026-05-26 00:30:00','%Y-%m-%d %H:%M:%S'...[truncated]\", \"timeout\": 300}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Searching for recent images**\n\nI’m looking into images that might have been modified around 23:17; it seems like there’s a new standard. Maybe I need a video, even if it’s not rendered? I should search for files modified at that time, which could include data folders. Inspecting the metadata or prompts might also help, in case it’s just image cache. I’ll search through all files around that specific time to find what I need.",
"reasoning_content": "**Searching for recent images**\n\nI’m looking into images that might have been modified around 23:17; it seems like there’s a new standard. Maybe I need a video, even if it’s not rendered? I should search for files modified at that time, which could include data folders. Inspecting the metadata or prompts might also help, in case it’s just image cache. I’ll search through all files around that specific time to find what I need.",
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"id": "rs_010f4e1931265670016a1533872c1081918562fbb30a83b421",
"summary": [
{
"type": "summary_text",
"text": "**Searching for recent images**\n\nI’m looking into images that might have been modified around 23:17; it seems like there’s a new standard. Maybe I need a video, even if it’s not rendered? I should search for files modified at that time, which could include data folders. Inspecting the metadata or prompts might also help, in case it’s just image cache. I’ll search through all files around that specific time to find what I need."
}
]
}
]
},
{
"role": "tool",
"content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nimport time\nstart=time.mktime(time.s...` -> exit 0, 1 lines output",
"tool_call_id": "call_suYaWfhYwwpMn4RU0iYUGLZh"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_7pkg8vEeBgxFlznRWpSXjmUG",
"call_id": "call_7pkg8vEeBgxFlznRWpSXjmUG",
"response_item_id": "fc_010f4e1931265670016a1533a7c1088191bf151f4ff967a40f",
"type": "function",
"function": {
"name": "search_files",
"arguments": "{\"file_glob\":\"*.jsonl\",\"limit\":50,\"output_mode\":\"content\",\"path\":\"/home/agent/.hermes/sessions\",\"pattern\":\"neue?r? standard|hochwert|detaill|KI-bilder|KI-Bilder|neuer Standard|new standard|Outlook|calendar|readable|screen images|UI/screen\",\"target\":\"content\",\"context\":2}"
}
}
],
"finish_reason": "tool_calls",
"reasoning": "**Inspecting session standards**\n\nI'm thinking about how I need to inspect the session regarding the new standards. It seems like a good idea to use the read_file function on a jsonl file to search through the relevant data. I'll make sure to evaluate the session thoroughly, since understanding new standards is really important. I want to ensure I’m accessing everything efficiently and accurately. Let’s see what I can find!",
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"content": "[read_file] read /home/agent/.hermes/sessions/20260525_225614_efb6a8.jsonl from line 20 (92,087 chars)",
"tool_call_id": "call_t2Ybb237za8b9mM52WVeqAyO"
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{
"role": "assistant",
"content": "",
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{
"id": "call_gJItpm8aPYeQxt7UrANxo00E",
"call_id": "call_gJItpm8aPYeQxt7UrANxo00E",
"response_item_id": "fc_010f4e1931265670016a1533bb9fbc81919f1d8173a3005ce8",
"type": "function",
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"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'; test -f \\\"$p\\\" && ffprobe -v error -select_streams v:0 -show_entries stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate,pix_fmt -show_entries format=duration -of json \\\"$p\\\" && sha256sum \\\"$p\\\"\",\"timeout\":120}"
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{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_gJItpm8aPYeQxt7UrANxo00E"
},
{
"role": "assistant",
"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```",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"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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],
"finish_reason": "stop"
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{
"role": "user",
"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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"call_id": "call_nRcgJHLEEYocyq8HWAYSATBl",
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"type": "function",
"function": {
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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": "**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.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_010f4e1931265670016a15358c9ee48191818f73313ba2a35e",
"summary": [
{
"type": "summary_text",
"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."
}
]
}
]
},
{
"role": "tool",
"content": "[Duplicate tool output — same content as a more recent call]",
"tool_call_id": "call_nRcgJHLEEYocyq8HWAYSATBl"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_Ke6rQqlRSeUiyTapW0B5YCsn",
"call_id": "call_Ke6rQqlRSeUiyTapW0B5YCsn",
"response_item_id": "fc_010f4e1931265670016a15359525d48191be5e42eb2553ea1b",
"type": "function",
"function": {
"name": "terminal",
"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_...[truncated]\", \"timeout\": 120}"
}
}
],
"finish_reason": "tool_calls"
},
{
"role": "tool",
"content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nfrom PIL import Image\nimport subproc...` -> exit 0, 1 lines output",
"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": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFTWoJe7xEzNp77c2aHtMw03lhsxmmbY0NlLjdOCGxI6dOsx6Gv09C3O30reRBtSb4-o-0rO9V_Znk-tA7c1jZcXvlTjpIGIH5cBIsOCNCEN6ZJAsvSNxykP-uJRw7M1Feti-3sTlmaLZ_-liMFQ5LeBu-NtFw4SotJ1LEfUHAot-2x5rE4ZuqTNVAF_l0gla54gnWb1UocRJCe4lyoAgZkTLmwa5zRWQQo-udDa7Nljm8gXc17o5U8LZlh3sTuW7Cc38x5UY1tWie-TpLCAQsAT-PlWmY8eslJFv75gN8PWYOEK756IpAiKxkH6k5ErsfFiKE-9NXZWM_TnhMxZTfNHCg5aRY_CulX_f5JWAjQV-Ts-3s1HA9SWgdJOM7RUGvGZQt1B4DWdGMWn3tvKpY8R9SIYRNIupY3vOB5LmpIwThltNFzMxofnBIvxcqM4qYsK9Cg1IObvA9F2N5GzGn2la8RQb3v1Kdb22mTHJD2VV4cHrjRm_M2u9jLXcgv0iQ9uER-zJVg8BfKjEE6XmaZ8ZlcLPeCu_P5wLIKM9BAlCaMcxOBBskLP0e9UkYDVnx3HVG5df5oyvmsTx9txpESZGhArMuUH5jY1NTt6BSvWj2IV3ZSCDmgi0n86STbhcFwJ-vaqMnjY7QKBfVKyffo6mRDRE8iurKmLHwn0uw9E-3F5Hx4dsHiBYZNbUiXp2oycxBLMRXaD_admXvlFxZq5c0WHgbFytmmjRFo_U51AiqIwKp1nUU-tGyzId0ZAC7HjpXlmLxQV_OnI9xVxnuK7o_pzbb_rKCHKxeDnKG-C6XZrSMRTnx0Rp1d4Q8Lmrc26ah2yso9JzYi5xxPUNJsbVkLpEWcJ7Kk1Rlil5vItllVqZkPdHJxZ_yGgA1TjecFuvx5bUxkkjduJ7UtUlF_8H41YUE_vDlsyvW0u6TlhSS4lNDOFQDT4NfotAEymx88Y0Fw-O26bW6REaR7zs0pqvWdbQdZK0zVx1Ijkd1JTq8ZGKQBLKGJx-H-t6080Cx04uhQlb-jj6rFC37ZnHfaAlG5qAWWV5_InkubZPLbriy59D6MU-ddQsq0tBpd3HEwHMIhFVW1vQmkWhzDOLb4F7UXpI8XIsx9mc8ZAs9J24T9L6e9uxkqR9wRzaEoKvA0sP0Xelz70BGs8HweiVgDlhgpzwQLXntugo6n64ORD_OP0phqLJ9w3IDAICIqvHhtgNaxoZnaaWxw-VbVviX6OhGR5XztxKdrPLeSeIDWJgJJtk4uOSHk374O93Jzk2yV7zyroQoX5WBTDQp6I1KiYK2GxEkFhsZj1arDC4X_EoA_PUiS3j0RZBQmAsiZ-6t8GYCT-SpqWR9wNE_o2J96ZaqNxLcHZ8Q4PRzR-EMn-ricFdXSt-8lPD6pnd8DGKpJU7oqN2UN31nfuhUeCbfpyiv_gthfKrLnkHIJlOnAGyPJ9sdCLSe_K5s9V_qZ6zskE1o9jRUN3rxGpKaHFAQNMeoSbnV70fOrZmXoPyj-zQX9B6Xhfl1wwgtiYqdxHDgA9upMNyTtz49peQ_a0qF8DOmWlQnxvgnxdDMbf6r0q5lE1yFaLsM32UlhH_xZBtHDHp6bJLUaHTAlJT2wQnAiGxD6o8sDawIqGbu0D4AlKDC6p2o92t4rwH11lPKAV1oRFMoGkVmpfyVDYBY9uHic2kUtyGDMChMxzQspxpKfhr535ru5ZNN9_GjeI1fu8wYVc7witVWfPlPTOparBs4obPm5c8eo9-MyzhEfV0EMTPMHRKZGHchulqOtEnf-SSLEc_DpBM6XpaDCaKFgeFUvPXs3PTTbEb3FoTuUdux6hgnlmeuM4Pah0np1Pwhn0jsUcm",
"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": "[vision_analyze] 'Inspect this frame: are there visible black bars t' (303 chars)",
"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_spri...[truncated]\", \"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!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFTW8ALWg_NRe94fa6D3L3rA5KAHUhcxKbSrVNN8x-FVQXuhE7x7UQN5x_D3CF65nQXhkEL1Kl7EmFIGeZvwxTySgS-M4Yl8XuwjgPUDneG9ThoszueSaC69cQC_4shiOd11YPW8lLpFsW0cE8C8FFNcUn9iGWQsKJ5pj6A7FZ_-bjXoRsjExG6rNi02LqHyolxShUf-c6MoOFmLSyreZKmyLriLFLgfzLtY-vyAeRVCLkXjJzlQ2NeBZytuiBtO13tir3xtnDR4M_UuTf5r29UDX19RdsdGzwufJlsYmK96BZ4FzW4Y9RWk_HE3cGQS31br7ZZqSjW5YmmTEPF2LcIMNdzzRJPIOT_6DtfMdOYiVjGIV3zCPypMFlCTLYWb0J-m3bKgyLtiamvyh_xjhVYgxxV8ROKBKueKLIcsXK3iHGqRh7p2yFeK8BHYhk0C_lhxmR6c4jtGwZn7Q9BnhFApmrvRds4V-HcuZRXxGiyIEB45ecukaIj5v724cdRkgXptcaQ6xRUpKkMimZoHtDJ0rxBijixdnhfgQwqbOZpGaEv4uY7jZSFOZoiOLLO3MKs9sj0pJ85vAnOswTMFlCnMok9b4W_bgTLK8KhEZmOZTewBO8LqM5LE2VN4sNDaQNFZ1Fs3md3GJJZs7W-seRZnzQkhvbX4oBuwtPIOC1P8X768J_O9EvX1xqt4XU57f6UYbHNdDivmkuwaQ_7JZGJFN305jxxjX2pa9C_6D902LLdy4Yqb564UeG41dl5pc6lI4WE8T0h9_oLrMB5EKugDT2ueD8cXRWYPl-Rg2AgiQGamVVVJ9LbXlSCw6nQs0m8da43t3GmloT-OwUSbU01FjBlhpljIm12MBFzso_7DZ-pSErk8or0TdAuYnA9WfB7pFedMMx1RLoa99qRLEPx8KZX64314O5V9PWVRL9wRSU61rMyeHZUh4lh_c84F2HMKFT_aKe-q4lK0CGQ7tCAytcy3wf9eIGDk4_QBkSRszZKawaowx3Ol70Q13QlJfq4A9-GGTr1qDs4d3cwoyBd9vnGJFiSar-W2UfksmXR7PCVtsT392TX2uhAtwv5P5Vj87Gdf-n04Va_wDpIgOKOABx3a3CcBA6X79UGs_qOsA_G-2NY93Ab8gTsK_kWjogsVvXIJv18qmzHp0GzSutd2Ldbg7Tit88Wfva8FuCYmSrvuo9lWN1rFWhpSLFD0xrvy102MdButwLFA1KWe8DtRYPMMA2aqxkt05uKXOVxvAsadS-miL3udv_4SWQ403iM5cV8Os0U2o4XmdbeAOY0dWh0l1QTNrgkih6S65-LiaNGRJldWVFQaSSS437soU6txv3g_WwjM84UI0YTVePHfSZ7Y74fWwK-u_OdvgGekt71QtS68qbRFN7RHaHYskSbdTG8jVdWZ-r2tkkVheTAO12pWnL6qCj1rkJs9M6bkRaYua4XT1UF3END2MEgjby7beUn267VgNlqLb_np-SXZQzJhtFq-H2LI7wPwLtPUbrzfzxZ7F8_kpoJTQeTYVOJ9OWdYSLHB4Q-0k7-jZ5478_ULHg70JZ7VVrEk0dQF9fzChQA7ss5wxt-XGsjxyWDLKKRqZOSiviD0m20RWaUqE4jcW8Z6aahEREk0j2Dot4i8az6OELud2WcY0FEL-BTupyybLUI17PcB2egQMzvibgc94J7vry6YKlkn0XxmKRupMlnkHHfmH9tnxI-9gcT5X7iPDK5smpn3YNRp0YPFUnLWqmxxN_2mwQN_9wqx9qSNaDDclAxFYWs_0xQkTkuxsDJymbXIprzORzbAM6sq0Ub5ju2JIIE6H_i1DT5T7UwrK6eJvsV1dme9N7zciRBh2l39Mkq3Tdilcm6yER5XvCCNvP1n1dI-odTplYego3aQxNAeOm3J7X5PgYm0kZrt1kNpEnTsxcZ_StQlOku9zbuJt5r0h1TVSzo28D5QMVB9M0wubcrIqZiRmlGKg7iOQwbdPStDZbH16wbRALMIYZTsZwU97ct4GtDPIsRdsCdlIatbUbjmOlISnP8IveSJLR4Dq9gENx0oMRn1fRLZDYDds3RgNFT-hFgY-klIiYEHfn6f7ebZbr6JEx71IxrrJ9jYxujH1qRU7toy5cnrqef0fn0fLEyk3fajkY6TX5us-1NoSth4ii02gUMOn7nIYf8Y1zIvh3Nd6rw-gRXJVmQ4JjgCIvOo24yO2qtNUfnN9EBrn0dfEkI04OmGguImLZQeXpPM5E3qAe2z0Ky6Z_VE8R0VrhPP_zgQ3Edn2lVtuo--TME5DIXzVGt3thcCtiSS0DElk-WcPJBmojufkC2x0x2NPdzSqU-vuhgnKk91HzezRW74xykU0UEUKw-UdpGCOuzzD0gETDgTU95-QGYxuNJujUbHJF5E1zhUXlMDVKeI1viRd5m_A-upHhsjGlVamF3mZ7KPfxQIFujLQLR-ScztSE7b9QaxYZEn2RPOfNZWtaIuZwvjTuwzm6oiA7T3aLt_Xry_UBEA0trHrNYKR4yacWy-OpOnzvCrgIt5DZKB1PqojgL6ajqj7E3RioOQgKnKR3xQrArnpdteaqX5ukIIb8MNwjvUg7qItyTa3StSQtkeKkScUNbcL8dpaw7xLALC7NKNujCUg-nAIoBZhaaS9OLy2_3PtE1olPXd7PCmUwRWLg7b4ePKi2JDzPU33AkAOd5mv1SRwuBH9f1GtyzmvG-CN4SqcYqdtt67qHWgjMq14NJHYaLJU-hA4OI_McMG9-kc-m5HSV0EkHNXA_bguwrVNiPXe58TZHnIP8hJaxweI5--u4dbtxzk6Y4FZLmDiFPxsRcBqTJg8TKNxhxlVE2_fSxEa6jTMEJDlLeaXoEVABU62lYzE3KVrC7sU_8wgzrMQFWMbRafPEsE7KWf4TT_LU9OmUuvBjzCuE0bOwFc16OC-5Uorcmht8GgX7dFlmQdyEjt7znrBuDaXj8HUnCIuEWq4K-BuXgUf5ofTU_M0MItZgCC4v-XVXWMCnPDUguE7-fmD8KUeB4oj9BoV7Iy1_Tw63mtVXBABXoQxvlO7cQHQyCrbequAgpUq2NZqNMHj1w1TH7NlVb54VDCk4-WS0RsjyQUkShXWVltmveOgKUm2Q68niqjDDPkHjGFG1szzONx30UBYaWcl8_25L83nn3Jgtv9xkKHg0LDsNAhdxmeS2QettHvLVmwOQLPZf17Dhf1Kwj4ZaGvBzslhEfY9UoGJ7HVlM0rKnt2D2-tyi9mf4CzmOoKiPnRJvpsofeRfdeB7ElS-uaOPHpPOv8EDHsI1QaidedQekKO6pRxqY2iBBmGbLlGdTRcHCFNKDLDEgKggx-XRIa_FFbpB-d9ma_7PKAST6vfjXjk_u-gZGdxpQkQQZQ1rGsGRWKp6XNIiZmMzp6DZClVL5GWGu4s4Vfj2GOMGLnDJ6w8SVIAO5rxQJRTZS-o4TDnd-J1vM1sJRALG2CyZp2ul5KpSVj-dmlJPep2t6wms8vH23Wt81hEBwfDtN9HELmCPkkuox-vLXMOZhL7xUC63M3qfr5qKv45jxbpUDikplYd8qDfGG3uV8klCoMvu8cIGmTI5HFjUiPJWUPJQYIffPjohbDLWvjJB_dxVIRY=",
"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": "[terminal] ran `set -e\nIN='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30...` -> exit 0, 1 lines output",
"tool_call_id": "call_20WHzwU6R0fB4isxQfkqnrkZ"
},
{
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"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!",
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"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!"
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"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!"
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"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!",
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"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?\"}"
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"content": "[vision_analyze] 'QA this stronger screen-fill version. Have the top' (321 chars)",
"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.",
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"encrypted_content": "gAAAAABqFTZRKpv6SCF7GJqI5QRsPKbJQAhVAo9zW2-hRDG_4kDLcfDFL8jMgBmEqow5A1Z4AnzwoNbOFv7WDSuxxE6hGbQKA2kc5He9eoJB8T9mQb4_62OWjRP0tpxOSOhPfwDhkWpoZO80V5tvTU9OgGfaN8f_TsjrLmNW6HuaaJPK8lyIJddSA1JXKN-DuvucxeYUORHQmB3e7KPkqd9-2qAnUNiiAM9YdmYancZqGDR2t52yNDNKJFeGjOx86DGJ48Do9C99LVRtDLib5lukxIe-muCRMNkFPCKQcgT42tbMNMXqp1AC-XZT_sc7KTkJF7IqCt1bJ_-AtoXeMdKLmKpJIElgfKKGuMazUBqTYz2IoLXfpneb86kXcxLhIWPbydoBA-ROkCN4v99e8BHojBwr5eBHqSFTKqpl5Jd8fFWnAie_-XqL16Osou95h97BSPTxG9dXRiAmFUxQEERr5G4xK2oyoMbAx_9DPCw5n3KH6losKaxepB5asXhzHEIP2mV-pfDODxLn9SU1aTpMdXJEPCczlVpsAUfJ5bl-50k3D-W9aUhnb-VcFqIgtW1r-OqvU3oQ8-KZFb_aFKiVRuvf-4mB8cm7JjKAil3K4MruaFZ0Iqp0KY4Xv0ErjD9rwf8j0_zgRCYb88WcN-hBsUqxvLoVGtl0UKoRDjhY-jssMliDAHVhDYqsSrOMox3vG14GPTiOF_pbl7o11viK9JjEVg2dV9r70dX_V0_uv4l0iVPJ3pjf8ELkljlKqagK93nHBboXeVN20vChBk0G1ApYkmnpJr0z3k-vrZFBBbRywMpLHAoABmRgx8FwLYvDouqdPUgu50W6wCP2I3v9EL7pWzrVZdWmmEtXExby0g83TKOVpnkz9P9uPkqfhIISMF21W5QB7YF178W91_We_2kstaRmbGyCietF35LlaVVSX5H36WWflA476OhxNCatE-lztevWLuzCpdVQIbSgGxNF7-Oj273mnTpePowOo95-BVXtFFMmMVw859rtKz2bnhqtjxqOJXbmTzo3rn-0uQuJyIa_S0uO0z-wjPMB-wOe0_rx7_NzxEPgu0iGw_lLRVuq1O6_ppSET_u4oBZXts04-YaJb0RyhH64PFm8aYt41Jg4Hw6VDjn36HdDhf-bB_Gei6NT6Y4xs8NYWTSNq4Os6ds7e7oyrjq9id2pam4JfTajEGzrwe0FVImvRs8B4ticHs9KCit8H4VZYuZK05IikeviCfNk8BtnTupyqVeJKDOziMA-KysntwoUBoMsnrstAI6JTpSKidmuGGQDMha4OKNMWb02b5JZky9fW0HVgYwq6DU0XVmE-wKj9FKGTLK5i00BWU9sXhjwIkAemxbHRI2bCj7G3bJsAWrtJeJQsjVD3MyJV93Xl_CgEIGvl6tkvIRDQMVWgUxx-xqXj0fEW2xymee7jshLjBXuV9teLAJHIP0-cevaGF5PI-YrUOEkOvH_ZQmUsxLXCqtssKwnWaJTC3A4Zi5GMS9yBvYyfNsRW_udl-rqPelyZ3BEoIgUpfdO4tVSeFNZUCBIZwiAqhpSbV1YPLjNcGk6S_ZDdg9b4TdIqkmc9nN8PbsZSYN1IAXrVLuYgtGUn-jwBHSu32wWBjGaBDVa8LpytCUy5Fad64AjEsS5DcvaPpXLkOSl41eoF55rTVN0bNm8kav80aAQnN1J1G6zNQKztib-sJiSl82xadBGh-dcUz2kMBEU8q0Zlg8F6SspGT0BXON_0NPApTCg4SXHrq41rvD33_aKiXunwIizCK2dr_81xusmQhjWfPVIqvaSJN0KLfvZX-Ht2Na3uIHhoTjxghrajYoEAQ7RhrCMT0uomYpsOv_koRcMH1rX9WTa_VuI6gLT7CLFMI4MDZVxPLGZbt2IlfObWblREGPM5MD5DzvRiB85UDiUbGbzjjbDHxPgc7J-OMk3-vg_xOZcoN0yJNIwVjZIBDZZ1DmeOcHizYLr-sSzahrj_ZgR3difnnFhQpn2qV42oCZs2B0Vgvzr8NnEudZ1-OTc8BibMwYrmQquDKyMIPPDtyPCQms_OUUm9EmoRVKUjofkGAzGmXTkxrICX0t3QWNRzdVi3Xi6cHofd1oFx_ne5lMzw-pg4G5uKOrr84T9SQ==",
"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."
}
]
}
],
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"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```"
}
],
"id": "msg_010f4e1931265670016a15365187848191a3274da60c45a774",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Sorry, mein fehler, mach die anpassung rückgängig. Es lag an meinem telegram-player"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_ujqfnGYgV9vjUzYOPejWD0BC",
"call_id": "call_ujqfnGYgV9vjUzYOPejWD0BC",
"response_item_id": "fc_010f4e1931265670016a1536f3cad481918d48d8146411fd71",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"file_path\":\"references/mobile-vertical-aspect-qa.md\",\"name\":\"social-media-shorts-automation\"}"
}
}
],
"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!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFTbz_i43JMF1tN3jk8TC0MYr0rJJuYmmjj3fSEuf3uUWNRT6lLXSV3xKaR1bFzln9kqNwlWZkZF9axGm_bYU3stuqTqOQclqVHUjOxQfI9Lhqv-bZBV1sM06FBICYqUIW198RiWEw22svG6IWSxvdvYRZI1MFTs5yKEwGCb9-A7TrK5vIDu07zjsfedFuAIiJMQnTIaIXbg3vGQGuMVqQQRrGlm9HPGFR1gondBXV_rRl-DuO66EMcy_wI5_73qR4e66ouqq1wMdna1K_eyFnsGaaLGqTWp2yydr1DrDtaqiTx5HX6I2AfgtLY1qGLQ8k5tX82ejxv_UfPnM8a1zzsqt9VvBBelQVeYgvLli5bAHjPFXtIzOg0gP5bxbkPzwOfsxjPibxcYxZPb-kd8e_JsUxl9MDwp_7-T7QHNADQJod2wAOpfWhOFq_K8OlLZV4e66KJu81hKT1ByU1pqpvyyNvFRYZiprVAs4tQbNTfsnR8DSHwdNBq2Y5UkZA_lx_clpTBh0sxADUPcIIwkavWQq3QMZjBUYgHTKmlsOFcr_OjkuvLLyWrSZqBSCqT3_jrWo4tEHb7kPtNYWrVhwPgPzBEJxYJJnAwiwRlhOkfmJWDazdNJXSWbGaq3yRGiWRzZ-BReFWpqPXIBkDVC_2e4o8u82ZB6HULvp89jfZjx-fHS4zZaCqnd9y_dj1B2Gjjhzyyi31nT4zs7TSVB-tLpvTOL65YmfIEOb6MTTEe9KxK8MEnyQv5CkqIgwvfz5lGkKDhGZ3s7biyC2GDbB7c0WJW8KklEvxnpgPJ1CMYIDcinBfQdQjMY9GqBtBwoomKWG4-dwGt0fu_cWhWju35DR0xCNBEivkczsn7GRGgxu1WBoOF4MJp4iglUAYsV06ND29tswPsDi54TqILi3SQSmfEibpLOhBOQmm67Mi6qonwNkmNkD1pWOT_7UQbnDQ-noiCU-zoMCphe41z7JiSpfgVFEfZAGzLDtzsq3dx2sDukYRJ4xlCPFGjDYR6NmhaC8upKD7TWq4xf7wgzrlCC4yyHu_549PTplw9X3WQpyS7p5zfu2dK5_8JzVQkNb_gR3AF7KlBOcY1jl6G-CC26qi9reVjHo5zYsYvsbpWavjWCSchLwMLZPUmdy12OmSzuk3S16Ua1YG538Nr-CiQNUP_XAYffHVceWS_u9ZJuScNBM85HGDGY9CzAzLDxk8fQDctVII5wJ19vpPUfA33WMChKwSlSpvxGsWPqs19XK3vtvKBFM3lVq5lGE6D9mvTPG",
"id": "rs_010f4e1931265670016a1536efb42481919cf94a793bc5829f",
"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!"
}
]
}
]
},
{
"role": "tool",
"content": "[skill_view] name=social-media-shorts-automation (2,659 chars)",
"tool_call_id": "call_ujqfnGYgV9vjUzYOPejWD0BC"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_RXRbAiE4HVwozVYnxtAWQ6JH",
"call_id": "call_RXRbAiE4HVwozVYnxtAWQ6JH",
"response_item_id": "fc_010f4e1931265670016a1536f5ee98819189c6b076a6aa39f2",
"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": "[terminal] ran `p='/tmp/autoshorts_proof_short_sprint31/calendar-invite-login-trap-30s/visibl...` -> exit 0, 1 lines output",
"tool_call_id": "call_RXRbAiE4HVwozVYnxtAWQ6JH"
},
{
"role": "assistant",
"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."
}
],
"id": "msg_010f4e1931265670016a1536fa6b6481918da180efff167898",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Ok, erarbeite nun den nächsten Postpreview zur freigabe"
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_8HgNJg4cZxOCOOdvJlNKb2Zo",
"call_id": "call_8HgNJg4cZxOCOOdvJlNKb2Zo",
"response_item_id": "fc_010f4e1931265670016a156d327cf88191b47c28439c96ff3a",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/clean-ai-cut-vs-hybrid-mechanism.md\"}"
}
},
{
"id": "call_k2B2I3vPjeugsLJ5cKE10jL5",
"call_id": "call_k2B2I3vPjeugsLJ5cKE10jL5",
"response_item_id": "fc_010f4e1931265670016a156d327d10819196522d3d5f56d559",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/social-draft-approval-package.md\"}"
}
},
{
"id": "call_4IsQHVz9tZf3ddftkdQUxKyl",
"call_id": "call_4IsQHVz9tZf3ddftkdQUxKyl",
"response_item_id": "fc_010f4e1931265670016a156d327d18819191b68ca2dec9c636",
"type": "function",
"function": {
"name": "skill_view",
"arguments": "{\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/proof-short-ai-background-rendering-qa.md\"}"
}
}
],
"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!",
"codex_reasoning_items": [
{
"type": "reasoning",
"encrypted_content": "gAAAAABqFW0yTrhMKx9n55AHs5IFQk49t0n98n6Ltm-GX6iEMuHJV6qfF4B6HFBHb1ujcMJIY69765XXQFs_lMJH9nafrHuwwew3zp_-DMDhbawj--Y5YPBLV6B_MwKVXTBVdC0yl-zmJJqS0UNj1QHw843QAMQEPVFx_jaKQQ6ZAA7MUUk8armAwiTrhanHXCTfi1lLjHvpNIiP2vs3zJ68r4bC6Ba86Egh9Kd26Kt19gTmbxnBPsujhtCEb93cWnCrfclwvv-MFI7OqccfYub04T0tFTuplPh8E-6CCSb8YbwJuDsAYvEDDUB0RTZuijyEklsivT1k68dwLkkcloLknjK35RVbFJfQadEk3jeln9IMDOVlQaDSKfeLTu22uLr-sQqYjO62Ke1P-Yu_e76Bl1uR_hYA5VGJKN3TODCc86vrl8e045SJLplwcJIdGPTIlhEVVfr_SKMY7o-VA8BnteRAVUaX7LLD1xJOkaYSEm0NTh9H9xuPX9Asf7bYuQ6IGVR691GqE4OkU_qomUafAdwCyRglkGWrYeRgLU-4rJPAE5z_ImG8ER5x9c8ZG4TguQ0DDYFnfpjqjWtJND1hVAnlp7zkqfiEFh87PJWLLqbr6jG_wkPmwHAZkTqBBNFfbcVQyXkJ-qKXlZ_wP8cTE-BHAqxEXb5aVcWjHx_NUqIbN97i7aPC-zoxChINN3oKfWH2_NRpArGNBrRQqRWcsRANIWHPGaVfqjhKNOd2vTD_fao1Le9JlTu61iEv9V35VCTjLLWfjUfpJj9tka22JBBhts9I1UaYgAI69dWSHQD_gVPJIu8AtjWRnbhR4LK286qv1cVkIPQH-kk6jm9RxibhieIDeds2iSxqJUQa8biiatdcoNDksgRl5ZtsiQHEWOMNq8Jzfvi-wOVPBb_t2zyLyriLp8hnKcxP_3xxRAPPDohzx_PNZltU_jvH4ePPOROHWW1fEdAhgpijrM_zsblhB7jGfSEXtkLlWD6sldFg4O_SZ1Ka-VLDiaBnIMl6zIEqB6FBrcXzXTHMJKdgNigd6oEFgXKQ7Cv-tK-nWIJIuB-PmZelbGZYI8FhJ-rcNI8Q4RqAjGFWs9NVZmba6N5uTDyfiO9_MsE0ANtYnPEB9PAalrgXC4qLlsYFur3DJeCTv0b37BoZmzaAmllJZEFMIROD9C0PMMkZPxYulhEAOqAboJSHIUrCYjCLjNvCrTeNSaN9Cn_xRmx3tRy-68En0ybeyROp5xTfNQH-pda4SxEvCRHXVRlwZCAW0gQ7tTcwWtcfObDKAIEzSz5jXNCXZEKf3sVpsSsjivkeCNN2vSXRG5ex8DI8bidSYULc2XQk695HaBo86oxKubF8nBn2lBRBhg2PF3VhWxmkCbEISeQ43DX7nr7dmLYABKV1IlW_mkxXEkCRrCpdRfDbQK2-9d80Zp7HLnG4TbvK6gLetEjSuNQSgNsOP0sL8FvjByzj79EU5qE7SzBner_vwSyZTDNImckpBNWdzbsiyXJ0Bp6dgxM3yguv-K-5wOyWI99RzwCpAyp0eeNLWiBKs9Mc_4IfqrnHFzXmSlCBqQ5Q_0P2mNycDphvkhRC6QD58-YtbXstJOQ5TwNFVG7xEfXUHL6xpyxrm0kA48885oNBR5qz-4McANHsICJoeGcmNd-v1WcQanSSZ8CU_nXQylCXd7A-pkH1e2FOLUMaXg0BE5W8EO6S9Ncg9M0tc_mYyBKyW7HMS0JnJMx-IHicMUbcY5m4EwV2NzYRkCJUo6jLobhzOsUVnD-22t2FOkUkL8_dpTMNXvH-VMhNpWuJaGeu6sasJ1twTQ21OGSpM8lt2NEbcb2KxTmat68ddVSl7qfjHyuv0PGSs2slgZwNoh8JvZqM8xZ2whPlLpgq67pXixHv8w4e3v0dF0ZpsbhXe9_csxLZTE4hCireGD3_Fu-h30gCdGTTc58ZA6tcXhfjjv0F0Rk3U4cuxls4pMlFERUTgV1-Oc2oQllVMYoW_BbNul5KKrLxb97PM0JgUABlYqKMQEgDHiz64599-WPZ-dekGqIITHlfmjS4AJah9U0t9zTcY7x52zj675JMc585i_6JKaR8f5xXbI4OM_Ck17bbGHCCPIOBnIsWwxeBshdL4n4o1VdXi7gJ35oYbcfjpRlaJReNT_Nd51wkxHPX_nFsazsVGDSnfTNpzTz2cO3g7pnF_5K4e_NOVKwwWb8gl0TleiYj9qEjeYwYdcLDhV4cdjdyWURYrtQx6WhBSirWEQWmEiky9oSJBwgiAv_lq-9QRgxh4R__Mf06IH5xzscS7rx7wlQCriHCs4OxCOo_sAsWPQa6sreFHDKvHiAyXf3SmbmD0QIdk6dNOcxjbmNkiXwf8ilYZZerE_V4Nh_yqBzsP4h0RWpDhGWOWk9WswOPp6A=",
"id": "rs_010f4e1931265670016a156d2913ac81918723a75ebb7fa13c",
"summary": [
{
"type": "summary_text",
"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."
},
{
"type": "summary_text",
"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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}
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"role": "tool",
"content": "[terminal] ran `p='/tmp/autoshorts_proof_short_sprint32/shared-document-login-trap-38s/postpr...` -> exit 0, 1 lines output",
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"content": "[todo] updated task list",
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{
"role": "assistant",
"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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"role": "user",
"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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"reasoning_content": "**Considering content creation**\n\nI need to take action and create a next suggestion that might not be a full video. The idea of a \"Vorschlag\" or concept proposal for approval sounds good. It might be beneficial to ask the user about using a dramatic first image. \n\nI should produce a proposal package, possibly without rendering just yet. The user is asking for the next suggestion and raises a conceptual question. I think a strong hook and a dramatic first frame would work well. I need to decide on the right skills for this.",
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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...[truncated]\"}"
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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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"phase": "final_answer"
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"finish_reason": "stop"
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"role": "user",
"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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"reasoning": "**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.",
"reasoning_content": "**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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{
"type": "summary_text",
"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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"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 me...[truncated]\"}"
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"reasoning": "**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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"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. Close the page and check through the official app.\n\nSave this for the next suspicious message.\nGepinnter Kommentar:\n\nQuick check:\n\nDid they rush you?\nIs there a payment or login link?\nCan you open the app yourself?\nVisuelle Regeln\n\nDefault-Visualstil:\n\nscreen/mockup-led\n\nNicht:\n\nabstrakte Glaswolken\nschöne, aber leere AI-Bilder\ngenerische Laptop-B-Roll\nabstrakte Funnel/Ring-Metaphern\nPowerPoint\ntechnische Erklärtafeln\nunnötige Wan-Videos\n\nSondern:\n\nHandy-Screen\nSMS\nE-Mail\nRechnung\nChat\nPop-up\nLogin\nQR-Code-Situation\nMarketplace-Chat\neinfache fiktive Mockups\n\nMockups bleiben renderer-owned/deterministisch, wenn möglich.\n\nWarum:\nSo behalten wir Kontrolle über:\n\nkeine echten Marken\nkeine echten Bankdaten\nkeine echten Domains\nkeine echten Telefonnummern\nkeine echten QR-Codes\nkeine gefährlichen Vorlagen\nkeine Pseudo-Schrift\nklare mobile Lesbarkeit\n\nBild-KI-Regel\n\nOpenAI/Codex Image Generation ist jetzt auf gpt-image-2-high gesetzt und darf für Premium-Styleframes genutzt werden.\n\nAber:\nFür Scam-Screen-Mockups ist deterministisches Rendering meist besser.\n\nAI-Bilder nur, wenn:\n\nrealistische Umgebung gebraucht wird\nkein Text/keine Daten im Bild wichtig sind\nes nur Atmosphäre liefert\nes nicht den eigentlichen Scam-Screen erzeugt\n\nNicht:\nAI soll Fake-Rechnung, Fake-Bankseite oder Fake-SMS komplett frei erfinden.\n\nDiese Screens müssen kontrolliert sein.\n\nUntertitel-Regeln\n\nSubtitles bleiben:\n\nstatic phrase segments\nactive-word highlight\nwhite inactive words\nyellow active word\nkeine Box\nkeine Vollbreiten-Bar\nkeine dunkle Fläche\nsubtiler Schatten/Outline\nmax. 2 Zeilen\nnie über den Rand\nsafe-zone unten einhalten\neinfache Sprache\nZeilenumbrüche bewusst setzen\n\nWenn ein Satz zu lang ist:\nCaption-Segment splitten.\n\nNicht:\nText kleiner quetschen, bis niemand ihn lesen kann.\n\nSafety-Regeln bei Scam-Content\n\nImmer beachten:\n\nKeine echten:\n\nMarken\nLogos\nTelefonnummern\nBankdaten\nIBANs\nQR-Codes\nDomains\nPersonen\nFirmen\nechten Betrugsbeispiele\n\nKeine operationalisierbaren Scam-Anleitungen.\n\nNicht sagen:\n\n“So erkennst du jeden Scam”\n“Diese drei Checks schützen immer”\n“Garantiert sicher”\n“Alle Betrüger machen X”\n\nBesser:\n\n“A common red flag is…”\n“Before you pay, check…”\n“Verify through another channel.”\n“This does not catch everything, but it gives you a safer next step.”\n\nNeue Scoring-Kriterien\n\nBitte ergänze oder priorisiere im Scoring:\n\nviewer pain in first second\nscreen clarity without audio\nred flag clarity\nsafe move clarity\nfamily/share potential\nscreenshot checklist value\nbeginner language score\nmockup safety score\nno-fearmongering score\npretty-empty-visual blocker\n\nEin Kandidat darf nicht durchkommen, wenn:\n\ner schön aussieht, aber kein Problem zeigt\ndie Sprache zu hochgestochen ist\nder Red Flag unklar ist\nkein sicherer nächster Schritt existiert\ndie Szene auch für 50 andere Videos passen würde\n\nNächste Content-Reihenfolge\n\nNach Fake Invoice als erster Proof Short soll die nächste Kandidatenliste so aussehen:\n\nDelivery SMS Trap\n Candidate ID:\n delivery-sms-trap-35s\n\nHook:\n“This delivery text is designed to make you panic.”\n\nTakeaway:\n“If it makes you hurry, slow down.”\n\nFake Support Pop-up\n Candidate ID:\n fake-support-popup-call-now-30s\n\nHook:\n“If a website tells you to call support, stop.”\n\nTakeaway:\n“Real support does not hijack your screen.”\n\nMarketplace Buyer Scam\n Candidate ID:\n marketplace-buyer-off-platform-30s\n\nHook:\n“The buyer is not buying. They are moving you off-platform.”\n\nTakeaway:\n“Keep the chat and payment inside the platform.”\n\nFake QR Login\n Candidate ID:\n fake-qr-bank-login-page-35s\n\nHook:\n“The QR code was not the problem. The page after it was.”\n\nTakeaway:\n“If money or login is involved, open the app yourself.”\n\nPassword Reuse Domino\n Candidate ID:\n password-reuse-domino-30s\n\nHook:\n“One leaked password does not stay on one website.”\n\nTakeaway:\n“One account, one password.”\n\nSofortiger nächster Schritt\n\nBitte nicht gleich 30 Videos bauen.\n\nNächster Schritt:\n\nA. Fake-Invoice Proof Short finalisieren, falls noch nicht gemacht:\n\nManual Upload Pack\nPosting-Texte\nAnalytics-Template\n\nB. Danach genau 3 neue Kandidaten als ReviewPackages erzeugen:\n\nDelivery SMS Trap\nFake Support Pop-up\nMarketplace Buyer Scam\n\nNur ReviewPackages + optional sehr leichte preview plans.\nKeine Renderings, bis freigegeben.\n\nDocumentation Update\n\nBitte aktualisiere:\n\nBusinessplan / Content-Konzept\nStrategy docs\nproof short docs\nscoring docs\ncontent pillar registry\nseries registry\nimage generation guidelines\nmockup safety guidelines\ncaption guidelines\n\nNeue Überschrift:\n\nTrueTraceShorts — Digital Red Flags\nScam Self-Defense for Normal People\n\nKernsatz:\n\nOne screen. One red flag. 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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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"text": "**Considering updates and modules**\n\nI need to look for updates and possibly check the documentation. I wonder if I should create a new module for the strategy or if it might be simpler to patch the existing one. I'm thinking about generating review packages as well and maybe writing a script for that. Also, I need to search in the /tmp directory for a fake invoice pack. There's a lot to consider here!"
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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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"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.",
"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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"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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"arguments": "{\"content\":\"# Caption Guidelines — Digital Red Flags\\n\\n## Default Caption Style\\n\\n- static phrase segments\\n- active spoken word highlighted in yellow\\n- inactive words white\\n- no subtitle box\\n- no full-width dark bar\\n- subtle shadow/outline only\\n- max two lines\\n- mobile-safe lower safe zone\\n- never over the edge\\n\\n## Language\\n\\nUse beginner-friendly English:\\n\\n- short sentences\\n- simple words\\n- no security jargon\\n- B1/B2 readability\\n\\nExamples:\\n\\nGood:\\n\\n```text\\nDon’t tap the link.\\nOpen the app yourself.\\nIf it makes you hurry, slow down.\\n```\\n\\nBad:\\n\\n```text\\nValidate the communication channel before initiating the transaction flow.\\n```\\n\\n## Segment Rule\\n\\nIf a sentence is too long, split the caption segment.\\n\\nDo not shrink text until it becomes unreadable.\\n\\nPreferred segment length:\\n\\n- 3–7 words\\n- max two lines\\n- one clear spoken thought\\n\\n## End Checklist\\n\\nScreenshot checklist cards may use larger static text, separate from subtitles:\\n\\n- max three bullets\\n- 1.5–3s visible\\n- large font\\n- mobile-safe\\n- no paragraph blocks\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/caption_guidelines_digital_red_flags.md\"}"
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"arguments": "{\"content\":\"# Image Generation Guidelines for AutoShortsBot\\n\\nCurrent strategy: **TrueTraceShorts — Digital Red Flags / Scam Self-Defense for Normal People**.\\n\\nCore rule:\\n\\n> One screen. One red flag. One safer move.\\n\\n## Default Boundary\\n\\nFor scam-screen content, deterministic renderer-owned mockups are usually better than free-form AI images.\\n\\nDeterministic renderer should own:\\n\\n- SMS / delivery text screens\\n- invoices and payment routes\\n- login pages\\n- marketplace chats\\n- popup warnings\\n- screenshot checklist cards\\n- any factual text, URLs, amounts, warning labels, or safe-move instructions\\n\\nUse AI image generation for:\\n\\n- premium atmosphere\\n- realistic phone/laptop/desk environment\\n- text-free background or styleframe texture\\n- cinematic first-frame mood when the critical scam screen is still controlled\\n\\n## Prompt Template for AI Atmosphere/Styleframes\\n\\n```text\\nAutoShortsBot premium keyframe, vertical 9:16 social-video frame.\\nSeries: Digital Red Flags / Scam Self-Defense for normal people.\\nScene role: .\\nVisible real-world problem: .\\nSubject and environment: .\\nComposition: .\\nLighting/style: realistic premium social video, not abstract AI art.\\nRenderer-owned screen: leave area for deterministic mockup if text/data matters.\\nForbidden: logos, brands, QR codes, real bank data, phone numbers, real domains, people/faces/hands unless explicitly approved, pseudo text, watermarks.\\nOutput: 9:16, high fidelity, immediately understandable without voiceover.\\n```\\n\\n## Quality Checklist\\n\\n- The first frame shows a concrete viewer problem without voiceover.\\n- The red flag is visible or can be rendered deterministically on-screen.\\n- The scene is not generic laptop B-roll or abstract glassmorphism.\\n- The visual would still make sense to a non-technical viewer.\\n- It is safe to send to parents/partner/colleagues.\\n- It leaves safe lower space for active-word subtitles.\\n- It contains no real brands/logos/QR/bank data/phone numbers/domains.\\n\\n## Rejection Criteria\\n\\nReject if:\\n\\n- Pretty Empty Visual Blocker: premium image, no visible problem.\\n- It needs a voiceover to explain what is at stake.\\n- It contains fake-real text, pseudo-writing, QR codes, brand marks, or financial/legal details.\\n- It looks like an operational scam template.\\n- It cannot support a simple “one safer move”.\\n- It is not mobile-legible at 9:16.\\n\\n## Provider Policy\\n\\n- `allowed_image_provider = openai-codex`\\n- `preferred_image_model = gpt-image-2-high`\\n- `preferred_quality = high`\\n- max 3 styleframes per scene without explicit approval\\n- max 1 retry per styleframe\\n- no random batches\\n\\n## Text Rule\\n\\nFor this strategy, screen text is usually deterministic. If the user explicitly approves AI-generated readable screen text, it must pass visual QA and must not contain real brands, real phone numbers, real domains, QR codes, bank data, or copied scam examples.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/image_generation_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`. The brief carries positioning, tone, visual rules, no-go topics, target platforms, and target duration before any script or renderer exists.\\n 120|\\n 121|\", \"total_lines\": 419, \"file_size\": 27601, \"truncated\": true, \"hint\": \"Use offset=121 to continue reading (showing 1-120 of 419 lines)\", \"is_binary\": false, \"is_image\": false}",
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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. 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"content": "{\"output\": \"F......F..........F. [100%]\\n=================================== FAILURES ===================================\\n_________ test_default_registry_contains_digital_red_flags_positioning _________\\n\\n def 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\\nE AssertionError: assert 'one normal screen' in ('screen/mockup-led: phone SMS, email, invoice, chat, popup, login, QR, marketplace chat', 'first frame shows a visibl...er text/data matters', 'large mobile-safe text; no abstract glass clouds, generic laptop B-roll, or PowerPoint boards')\\nE + where ('screen/mockup-led: phone SMS, email, invoice, chat, popup, login, QR, marketplace chat', 'first frame shows a visibl...er text/data matters', 'large mobile-safe text; no abstract glass clouds, generic laptop B-roll, or PowerPoint boards') = ContentPillar(pillar_id='text_delivery_scams', audience='normal smartphone users, families, parents, teens, workers, s...ok', 'instagram'), monetization_angle='trust-building channel, newsletter/checklists, family/team safety guides later').visual_rules\\n\\ntests/test_pillars.py:49: AssertionError\\n_ test_default_series_registry_contains_public_safe_scam_self_defense_series_only _\\n\\n def 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\\nE AssertionError: assert False is True\\nE + where False = ValidationResult(is_valid=False, errors=('series one_screen_one_red_flag uses forbidden public topic: user', 'series before_you_click uses forbidden public topic: user')).is_valid\\n\\ntests/test_content_series.py:36: AssertionError\\n_ test_build_candidate_strategy_review_allows_specific_scam_self_defense_candidate _\\n\\n def 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\\\"\\nE AssertionError: assert 'One Screen. One Red Flag.' == 'Before You Click'\\nE \\nE - Before You Click\\nE + One Screen. One Red Flag.\\n\\ntests/test_content_strategy_score.py:136: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_pillars.py::test_default_registry_contains_digital_red_flags_positioning\\nFAILED tests/test_content_series.py::test_default_series_registry_contains_public_safe_scam_self_defense_series_only\\nFAILED tests/test_content_strategy_score.py::test_build_candidate_strategy_review_allows_specific_scam_self_defense_candidate\\n3 failed, 17 passed in 0.14s\", \"exit_code\": 1, \"error\": null}",
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"arguments": "{\"mode\":\"replace\",\"new_string\":\" 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 for series_name in SERIES_NAMES:\\n if series_name.casefold() in text:\\n return series_name\\n return \\\"One Screen. One Red Flag.\\\"\\n\",\"old_string\":\" 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\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\"}"
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"content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\\n@@ -161,9 +161,6 @@\\n \\n def _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@@ -174,6 +171,9 @@\\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+ for series_name in SERIES_NAMES:\\n+ if series_name.casefold() in text:\\n+ return series_name\\n return \\\"One Screen. One Red Flag.\\\"\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/strategy/score.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}",
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"content": "{\"content\": \" 1|\\\"\\\"\\\"Approval-first social media draft artifacts.\\\"\\\"\\\"\\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 SUPPORTED_PLATFORMS, ValidationResult, VideoCandidate, validate_candidate\\n 8|from autoshorts.rendering.preview import RenderPreview, validate_render_preview\\n 9|\\n 10|_PLATFORM_LABELS = {\\n 11| \\\"youtube\\\": \\\"YouTube Shorts\\\",\\n 12| \\\"tiktok\\\": \\\"TikTok\\\",\\n 13| \\\"instagram\\\": \\\"Instagram Reels\\\",\\n 14|}\\n 15|\\n 16|\\n 17|@dataclass(frozen=True)\\n 18|class SocialMediaDraft:\\n 19| candidate_id: str\\n 20| platforms: tuple[str, ...]\\n 21| platform_labels: tuple[str, ...]\\n 22| title_variants: tuple[str, ...]\\n 23| caption: str\\n 24| hashtags: tuple[str, ...]\\n 25| cta: str\\n 26| hook: str\\n 27| planned_duration_seconds: int\\n 28| quality_risk_summary: str\\n 29| preview_reference: str\\n 30| preview_type: str\\n 31| requires_human_approval: bool = True\\n 32| side_effects: tuple[str, ...] = ()\\n 33|\\n 34|\\n 35|def _is_blank(value: str) -> bool:\\n 36| return not (value or \\\"\\\").strip()\\n 37|\\n 38|\\n 39|def _quality_risk_summary(candidate: VideoCandidate) -> str:\\n 40| score = candidate.quality_score\\n 41| return (\\n 42| f\\\"Quality average: {score.average_quality:.1f}; \\\"\\n 43| f\\\"AI-slop risk: {score.ai_slop_risk}; \\\"\\n 44| f\\\"Policy risk: {score.policy_risk}\\\"\\n 45| )\\n 46|\\n 47|\\n 48|def _source_brief_id(candidate: VideoCandidate) -> str | None:\\n 49| prefix = \\\"Source brief: \\\"\\n 50| for note in candidate.policy_notes:\\n 51| if note.startswith(prefix):\\n 52| return note.removeprefix(prefix).strip()\\n 53| return None\\n 54|\\n 55|\\n 56|def build_social_media_draft(candidate: VideoCandidate, preview: RenderPreview) -> SocialMediaDraft:\\n 57| \\\"\\\"\\\"Build a side-effect-free platform draft from a candidate and preview.\\\"\\\"\\\"\\n 58|\\n 59| candidate_validation = validate_candidate(candidate)\\n 60| if not candidate_validation.is_valid:\\n 61| raise ValueError(\\\"; \\\".join(candidate_validation.errors))\\n 62|\\n 63| preview_validation = validate_render_preview(preview)\\n 64| if not preview_validation.is_valid:\\n 65| raise ValueError(\\\"; \\\".join(preview_validation.errors))\\n 66|\\n 67| source_brief_id = _source_brief_id(candidate)\\n 68| if source_brief_id is not None and preview.script_id != source_brief_id:\\n 69| raise ValueError(\\\"preview script_id must match candidate source brief\\\")\\n 70|\\n 71| draft = SocialMediaDraft(\\n 72| candidate_id=candidate.candidate_id,\\n 73| platforms=candidate.platforms,\\n 74| platform_labels=tuple(_PLATFORM_LABELS[platform] for platform in candidate.platforms),\\n 75| title_variants=(candidate.title, candidate.hook),\\n 76| caption=candidate.caption,\\n 77| hashtags=candidate.hashtags,\\n 78| cta=candidate.cta,\\n 79| hook=candidate.hook,\\n 80| planned_duration_seconds=candidate.duration_seconds,\\n 81| quality_risk_summary=_quality_risk_summary(candidate),\\n 82| preview_reference=\\\"HTML timeline preview available\\\",\\n 83| preview_type=preview.preview_type,\\n 84| requires_human_approval=True,\\n 85| side_effects=(),\\n 86| )\\n 87| validation = validate_social_media_draft(draft)\\n 88| if not validation.is_valid:\\n 89| raise ValueError(\\\"; \\\".join(validation.errors))\\n 90| return draft\\n 91|\\n 92|\\n 93|def validate_social_media_draft(draft: SocialMediaDraft) -> ValidationResult:\\n 94| errors: list[str] = []\\n 95| for field_name, value in {\\n 96| \\\"candidate_id\\\": draft.candidate_id,\\n 97| \\\"caption\\\": draft.caption,\\n 98| \\\"cta\\\": draft.cta,\\n 99| \\\"hook\\\": draft.hook,\\n 100| \\\"quality_risk_summary\\\": draft.quality_risk_summary,\\n 101| \\\"preview_reference\\\": draft.preview_reference,\\n 102| \\\"preview_type\\\": draft.preview_type,\\n 103| }.items():\\n 104| if _is_blank(value):\\n 105| errors.append(f\\\"{field_name} is required\\\")\\n 106|\\n 107| if not draft.platforms:\\n 108| errors.append(\\\"at least one platform is required\\\")\\n 109| for platform in draft.platforms:\\n 110| if platform not in SUPPORTED_PLATFORMS:\\n 111| errors.append(f\\\"unsupported platform: {platform}\\\")\\n 112|\\n 113| if len(draft.platform_labels) != len(draft.platforms):\\n 114| errors.append(\\\"platform_labels must match platforms\\\")\\n 115| if not draft.title_variants:\\n 116| errors.append(\\\"at least one title variant is required\\\")\\n 117| elif any(_is_blank(title) for title in draft.title_variants):\\n 118| errors.append(\\\"title variants must not be blank\\\")\\n 119| if not draft.hashtags:\\n 120| errors.append(\\\"at least one hashtag is required\\\")\\n 121| elif any(_is_blank(hashtag) for hashtag in draft.hashtags):\\n 122| errors.append(\\\"hashtags must not be blank\\\")\\n 123| if not 10 <= draft.planned_duration_seconds <= 180:\\n 124| errors.append(\\\"planned_duration_seconds must be between 10 and 180\\\")\\n 125| if not draft.requires_human_approval:\\n 126| errors.append(\\\"human approval is required\\\")\\n 127| if draft.side_effects:\\n 128| errors.append(\\\"social media drafts must not have side effects\\\")\\n 129|\\n 130| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 131|\", \"total_lines\": 130, \"file_size\": 4730, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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First test the new positioning with 10-30 high-quality Shorts. If analytics later show strong traction, then consider a refined visual identity, banner, and maybe a secondary brand line.\\n 58|\\n 59|## New brand meaning\\n 60|\\n 61|`True Trace Shorts` should mean:\\n 62|\\n 63|> Short videos that trace the real mechanism behind AI systems, workflow claims, and productivity hype.\\n 64|\\n 65|Public-safe positioning:\\n 66|\\n 67|> We trace what actually works in AI workflows: systems over prompts, mechanisms over hype, and practical rules over vague advice.\\n 68|\\n 69|Internal operating note:\\n 70|\\n 71|- JARVIS operates the pipeline.\\n 72|- The public brand must not mention JARVIS/Hermes, the user, or their collaboration.\\n 73|\\n 74|## Proposed YouTube channel description\\n 75|\\n 76|```text\\n 77|True Trace Shorts traces the real mechanisms behind AI workflows, productivity systems, and tech hype.\\n 78|\\n 79|No prompt worship. No generic AI-tool lists. No fake guru theatre.\\n 80|\\n 81|Expect short, practical breakdowns on:\\n 82|- AI systems that are more reliable than one-off prompts\\n 83|- workflow mistakes and how to fix them\\n 84|- hype claims tested against reality\\n 85|- simple rules for using AI without losing control\\n 86|\\n 87|Built for people who want useful systems, not another pile of hacks.\\n 88|\\n 89|New Shorts regularly.\\n 90|```\\n 91|\\n 92|## Shorter TikTok bio options\\n 93|\\n 94|Option A:\\n 95|\\n 96|```text\\n 97|Tracing what actually works in AI workflows.\\n 98|Systems > prompts. Mechanisms > hype.\\n 99|```\\n 100|\\n 101|Option B:\\n 102|\\n 103|```text\\n 104|AI systems, workflow teardowns & hype checks.\\n 105|No prompt worship. Just mechanisms.\\n 106|```\\n 107|\\n 108|Option C:\\n 109|\\n 110|```text\\n 111|Short AI workflow breakdowns.\\n 112|Real mechanisms. 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"arguments": "{\"content\":\"# Proof Short Playbook — Digital Red Flags\\n\\n## Purpose\\n\\nEvery proof short must help a normal person spot one concrete danger and take one safer next step.\\n\\n> One screen. One red flag. One safer move.\\n\\n## Default Structure\\n\\n1. **0–2s Visible Trap / Hook**\\n - Show a normal screen with visible danger.\\n - Hook is short and plain.\\n2. **2–7s Why It Feels Normal**\\n - Tiny fee, familiar message, official-looking screen, helpful buyer, urgent warning.\\n3. **7–15s Red Flag Reveal**\\n - One specific detail.\\n4. **15–24s Safer Move**\\n - One action the viewer can do now.\\n5. **24–30s Screenshot Checklist**\\n - Max three points.\\n6. **30–34s Rule + CTA**\\n - Memorable rule and soft follow/save/share reason.\\n\\n## Render Gate Questions\\n\\nBefore rendering, answer:\\n\\n- Normal screen?\\n- Visible red flag?\\n- Problem clear in second 1 without sound?\\n- Viewer consequence?\\n- One safer action?\\n- Shareable to family/team?\\n- Save/screenshot reason?\\n- Beginner language?\\n- Useful without tech knowledge?\\n\\nIf any answer is weak: do not render.\\n\\n## First Frame Rule\\n\\nThe first frame should be dramatic only through concrete risk, not fake hacker theatre.\\n\\nGood first frames:\\n\\n- delivery SMS with tiny fee and short deadline\\n- fake support popup telling user to call now\\n- marketplace buyer moving payment off-platform\\n- invoice with changed payment route\\n- login page after QR scan\\n\\nBad first frames:\\n\\n- generic dark hacker hood\\n- abstract glowing lock\\n- random laptop B-roll\\n- text-only title card\\n\\n## End Checklist Rule\\n\\nUse a mobile-safe card:\\n\\n```text\\nSAVE THIS\\n\\nSmall fee?\\nShort deadline?\\nSuspicious link?\\n\\nOpen the app yourself.\\n```\\n\\nor:\\n\\n```text\\nQuick check\\n\\nDid they rush you?\\nIs money involved?\\nCan you verify another way?\\n```\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/proof_short_digital_red_flags.md\"}"
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"arguments": "{\"content\":\"# Scoring Guide — Digital Red Flags\\n\\nCandidates are scored on usefulness to normal viewers, not internal cleverness.\\n\\n## Priority Criteria\\n\\n- viewer pain in first second\\n- screen clarity without audio\\n- red flag clarity\\n- safe move clarity\\n- family/share potential\\n- screenshot checklist value\\n- beginner language score\\n- mockup safety score\\n- no-fearmongering score\\n- pretty-empty-visual blocker\\n\\n## Hard Blockers\\n\\nDo not render if:\\n\\n- it looks good but shows no problem\\n- first frame is only generic text or mood\\n- language is too technical\\n- red flag is unclear\\n- no safe next step exists\\n- scene could fit 50 other videos\\n- real brands, numbers, QR codes, bank data, or live domains appear\\n- it teaches operational scam mechanics\\n\\n## Preferred Score Shape\\n\\nA candidate should earn high marks because:\\n\\n- the hook is understandable immediately\\n- the screen is familiar\\n- the red flag is specific\\n- the safer move is practical\\n- it has a reason to save/share\\n- it fits an official series\\n\\n## Language Penalty\\n\\nReplace:\\n\\n- “credential harvesting” → “stealing your login”\\n- “validate communication channel” → “check it another way”\\n- “MFA prompt fatigue” → “a login prompt you did not start”\\n\\n## Advancement Rule\\n\\nOnly render after the candidate answers:\\n\\n```text\\nWhat screen?\\nWhat red flag?\\nWhat safer move?\\nWhy save/share?\\n```\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/scoring_guide_digital_red_flags.md\"}"
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"arguments": "{\"path\":\"/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/build_review_and_posting_packs.py\",\"content\":\"from __future__ import annotations\\n\\nfrom pathlib import Path\\nimport hashlib\\n\\nfrom autoshorts.ideas.candidate import VideoCandidate\\nfrom autoshorts.review.package import render_telegram_review_package\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\nfrom autoshorts.social.draft import SocialMediaDraft\\nfrom autoshorts.social.manual_posting import build_manual_posting_pack, render_manual_posting_instructions\\nfrom autoshorts.strategy.score import build_candidate_strategy_review\\n\\nOUT = Path('/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner')\\nOUT.mkdir(parents=True, exist_ok=True)\\n\\nVIDEO = Path('/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1_9x16_sar1.mp4')\\n\\n\\ndef sha(path: Path) -> str:\\n h = hashlib.sha256()\\n with path.open('rb') as f:\\n for chunk in iter(lambda: f.read(1024 * 1024), b''):\\n h.update(chunk)\\n return h.hexdigest()\\n\\n\\ndef score(hook=9, novelty=8, clarity=9, retention=8, slop=2):\\n return ScriptQualityScore(hook, novelty, clarity, retention, slop, 'low')\\n\\n\\n# A) Fake-Invoice Manual Posting Pack\\nfake_invoice_draft = SocialMediaDraft(\\n candidate_id='digital-red-flags-fake-invoice-payment-route-30s',\\n platforms=('youtube', 'tiktok', 'instagram'),\\n platform_labels=('YouTube Shorts', 'TikTok', 'Instagram Reels'),\\n title_variants=(\\n 'This invoice looks normal. Check this line first.',\\n 'If the payment route changed, verify it another way.',\\n 'One invoice red flag before you pay.',\\n ),\\n caption=(\\n 'A common red flag is a changed payment route. Before you pay, check it another way. '\\n 'This does not catch everything, but it gives you a safer next step.'\\n ),\\n hashtags=('#ScamAwareness', '#CyberSafety', '#DigitalRedFlags', '#SmallBusiness', '#TrueTraceShorts'),\\n cta='Follow for one red flag before you click, pay, scan, or log in.',\\n hook='This invoice looks normal. The red flag is the new route.',\\n planned_duration_seconds=31,\\n quality_risk_summary='Manual upload pack only; no platform action. Scam mockup is fictional and renderer-owned.',\\n preview_reference=str(VIDEO),\\n preview_type='final_manual_upload_mp4',\\n)\\nmanual_pack = build_manual_posting_pack(fake_invoice_draft, video_path=str(VIDEO))\\nmanual_text = render_manual_posting_instructions(manual_pack)\\nmanual_text += '\\\\n\\\\n## Pinned Comment Suggestion\\\\nQuick check:\\\\n\\\\nDid the payment route change?\\\\nIs money involved?\\\\nCan you verify another way?\\\\n'\\nmanual_text += '\\\\n## Analytics Template\\\\n- platform:\\\\n- post_time:\\\\n- video_id/url:\\\\n- series_id: money_move_red_flags\\\\n- candidate_id: digital-red-flags-fake-invoice-payment-route-30s\\\\n- hook_type: invoice_red_flag\\\\n- first_frame_type: invoice_screen\\\\n- duration: 31.4s\\\\n- at_1h: views / likes / comments / shares / saves / follows\\\\n- at_24h: views / likes / comments / shares / saves / follows\\\\n- at_72h: views / likes / comments / shares / saves / follows\\\\n- at_7d: views / likes / comments / shares / saves / follows\\\\n- diagnosis notes:\\\\n'\\nif VIDEO.exists():\\n manual_text += f'\\\\n## Media Verification\\\\n- file: `{VIDEO}`\\\\n- sha256: `{sha(VIDEO)}`\\\\n'\\nelse:\\n manual_text += f'\\\\n## Media Verification\\\\n- missing file: `{VIDEO}`\\\\n'\\n(OUT / 'fake_invoice_manual_posting_pack_v1.md').write_text(manual_text, encoding='utf-8')\\n\\n\\n# B) Three next ReviewPackages — no rendering.\\ndef candidate(**kw):\\n base = dict(\\n platforms=('youtube', 'tiktok', 'instagram'),\\n duration_seconds=35,\\n quality_score=score(),\\n )\\n base.update(kw)\\n return VideoCandidate(**base)\\n\\ncandidates = [\\n candidate(\\n candidate_id='delivery-sms-trap-35s',\\n pillar='text_delivery_scams',\\n title='Delivery SMS Trap',\\n hook='This delivery text is designed to make you panic.',\\n script_outline=(\\n '0-2s: phone SMS says package on hold and tiny fee. 2-7s: tiny fee, short deadline, official-looking link. '\\n '7-15s: red flag is pressure. 15-24s: do not tap the link; open the delivery app or official site yourself. '\\n '24-30s: SAVE THIS checklist: small fee, short deadline, suspicious link. 30-34s: If it makes you hurry, slow down.'\\n ),\\n visual_concept='Renderer-owned phone SMS mockup; fictional sender; no real courier/logo/domain/phone/QR; dramatic first frame from urgency and fee, not hacker imagery.',\\n caption='A common red flag is pressure: a tiny fee, a short deadline, and a link. Open the app yourself instead.',\\n cta='Follow for one red flag before you click.',\\n hashtags=('#ScamAwareness', '#CyberSafety', '#DigitalRedFlags', '#DeliveryScam', '#TrueTraceShorts'),\\n policy_notes=('series:before_you_click', 'mockup:deterministic', 'no_rendering_done'),\\n hypothesis='Delivery SMS scams are familiar enough to create immediate recognition, saves, and family shares.',\\n ),\\n candidate(\\n candidate_id='fake-support-popup-call-now-30s',\\n pillar='fake_support_popup_scams',\\n title='Fake Support Pop-up',\\n duration_seconds=30,\\n hook='If a website tells you to call support, stop.',\\n script_outline=(\\n '0-2s: browser popup says device infected and call support now. 2-7s: it looks serious because it fills the screen and uses a timer. '\\n '7-15s: red flag is the phone call demand. 15-24s: do not call; close the tab/browser and find official support yourself if needed. '\\n '24-30s: Quick check: Did it scare you? Did it give a number? Can you close it? Rule: Real support does not hijack your screen.'\\n ),\\n visual_concept='Deterministic browser popup mockup; fictional text; no real support numbers, brands, logos, remote-access instructions, or scary exploit detail.',\\n caption='A scary popup wants you to call before you think. Close it. Find support yourself if you need it.',\\n cta='Follow for one red flag before you click.',\\n hashtags=('#ScamAwareness', '#CyberSafety', '#DigitalRedFlags', '#OnlineSafety', '#TrueTraceShorts'),\\n policy_notes=('series:send_this_to_your_parents', 'mockup:deterministic', 'no_rendering_done'),\\n hypothesis='Fake support popups have strong family-share potential and a very clear safer move.',\\n ),\\n candidate(\\n candidate_id='marketplace-buyer-off-platform-30s',\\n pillar='marketplace_buyer_scams',\\n title='Marketplace Buyer Scam',\\n duration_seconds=30,\\n hook='The buyer is not buying. They are moving you off-platform.',\\n script_outline=(\\n '0-2s: marketplace chat where buyer asks to continue by email/phone and sends shipping payment link. '\\n '2-7s: it feels normal because they sound polite and ready to pay. 7-15s: red flag is leaving the platform. '\\n '15-24s: keep chat and payment inside the platform. 24-30s: SAVE THIS: off-platform chat, shipping link, overpayment. Rule: If payment leaves the platform, slow down.'\\n ),\\n visual_concept='Renderer-owned marketplace chat mockup; fictional platform UI; no real brand/logo/payment URL/phone number; first frame shows the off-platform request.',\\n caption='A friendly buyer can still be a trap. Keep the chat and payment inside the platform.',\\n cta='Follow for one red flag before you click, pay, scan, or log in.',\\n hashtags=('#ScamAwareness', '#MarketplaceScam', '#CyberSafety', '#DigitalRedFlags', '#TrueTraceShorts'),\\n policy_notes=('series:money_move_red_flags', 'mockup:deterministic', 'no_rendering_done'),\\n hypothesis='Marketplace scams are relatable and comment-friendly because many viewers have seen similar buyer messages.',\\n ),\\n]\\n\\nreview_texts = []\\nfor c in candidates:\\n strategy_review = build_candidate_strategy_review(c)\\n pkg = c.to_review_package(preview_path=None)\\n text = render_telegram_review_package(pkg)\\n text += '\\\\n\\\\n## Strategy Gate\\\\n'\\n text += f'- Render allowed by strategy review: `{strategy_review.render_allowed}`\\\\n'\\n text += f'- Format family: `{strategy_review.strategy_score.format_family}`\\\\n'\\n text += f'- Viewer identity: `{strategy_review.strategy_score.viewer_identity}`\\\\n'\\n text += f'- Future promise: `{strategy_review.strategy_score.future_value_promise}`\\\\n'\\n if strategy_review.reject_reasons:\\n text += '- Reject reasons: ' + '; '.join(strategy_review.reject_reasons) + '\\\\n'\\n if strategy_review.improvement_suggestions:\\n text += '- Suggestions: ' + '; '.join(strategy_review.improvement_suggestions) + '\\\\n'\\n text += '\\\\n## No Render Status\\\\nNo MP4, styleframe batch, platform API, or Wan/I2V/T2V action was performed.\\\\n'\\n path = OUT / f'{c.candidate_id}_review_package_v1.md'\\n path.write_text(text, encoding='utf-8')\\n review_texts.append(f'- `{c.candidate_id}` → `{path}`')\\n\\n(OUT / 'next_three_review_packages_index_v1.md').write_text(\\n '# Next Three ReviewPackages — No Rendering\\\\n\\\\n' + '\\\\n'.join(review_texts) + '\\\\n',\\n encoding='utf-8',\\n)\\n\\nprint(OUT)\\n\"}"
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[100%]\\n=================================== FAILURES ===================================\\n___________ test_create_content_brief_from_default_pillar_and_topic ____________\\n\\n def test_create_content_brief_from_default_pillar_and_topic():\\n> brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n\\ntests/test_content_brief.py:7: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n________ test_validate_content_brief_rejects_bad_duration_and_platforms ________\\n\\n def test_validate_content_brief_rejects_bad_duration_and_platforms():\\n> brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"AI tools create noise.\\\",\\n payoff=\\\"one weekly control checkpoint\\\",\\n platforms=(\\\"linkedin\\\",),\\n target_duration_seconds=9,\\n )\\n\\ntests/test_content_brief.py:61: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n__ test_build_ai_life_systems_demo_delivery_runs_full_review_preparation_path __\\n\\n def test_build_ai_life_systems_demo_delivery_runs_full_review_preparation_path():\\n> result = build_ai_life_systems_demo_delivery()\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_demo_pipeline.py:9: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\nautoshorts/content/demo_pipeline.py:30: in build_ai_life_systems_demo_delivery\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n___ test_build_ai_life_systems_demo_delivery_has_no_send_or_publish_language ___\\n\\n def test_build_ai_life_systems_demo_delivery_has_no_send_or_publish_language():\\n> result = build_ai_life_systems_demo_delivery()\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_demo_pipeline.py:31: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\nautoshorts/content/demo_pipeline.py:30: in build_ai_life_systems_demo_delivery\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_ test_build_content_preview_approval_package_combines_video_and_social_review _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_build_content_preview_app0')\\n\\n def test_build_content_preview_approval_package_combines_video_and_social_review(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:37: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_preview_package.py:22: in _draft_and_review\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_____ test_validate_content_preview_approval_package_accepts_valid_package _____\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_validate_content_preview_0')\\n\\n def test_validate_content_preview_approval_package_accepts_valid_package(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:67: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_preview_package.py:22: in _draft_and_review\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______ test_build_content_preview_approval_package_rejects_missing_video _______\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_build_content_preview_app1')\\n\\n def test_build_content_preview_approval_package_rejects_missing_video(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:84: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_preview_package.py:22: in _draft_and_review\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_ test_validate_content_preview_approval_package_rejects_side_effects_and_action_language _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_validate_content_preview_1')\\n\\n def test_validate_content_preview_approval_package_rejects_side_effects_and_action_language(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:95: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_preview_package.py:22: in _draft_and_review\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_ test_render_content_preview_approval_text_is_telegram_safe_and_references_video _\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_render_content_preview_ap0')\\n\\n def test_render_content_preview_approval_text_is_telegram_safe_and_references_video(tmp_path):\\n> draft, review_text = _draft_and_review()\\n ^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_content_preview_package.py:125: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_preview_package.py:22: in _draft_and_review\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n___ test_convert_script_draft_to_candidate_draft_preserves_content_strategy ____\\n\\n def test_convert_script_draft_to_candidate_draft_preserves_content_strategy():\\n> brief = _brief()\\n ^^^^^^^^\\n\\ntests/test_content_to_candidate.py:23: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_to_candidate.py:14: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n____ test_convert_script_draft_to_candidate_uses_existing_candidate_factory ____\\n\\n def test_convert_script_draft_to_candidate_uses_existing_candidate_factory():\\n> brief = _brief()\\n ^^^^^^^^\\n\\ntests/test_content_to_candidate.py:48: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_to_candidate.py:14: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n________ test_convert_script_draft_to_candidate_rejects_invalid_script _________\\n\\n def test_convert_script_draft_to_candidate_rejects_invalid_script():\\n> brief = _brief()\\n ^^^^^^^^\\n\\ntests/test_content_to_candidate.py:62: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_to_candidate.py:14: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_______ test_convert_script_draft_to_candidate_rejects_mismatched_brief ________\\n\\n def test_convert_script_draft_to_candidate_rejects_mismatched_brief():\\n> brief = _brief()\\n ^^^^^^^^\\n\\ntests/test_content_to_candidate.py:70: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_content_to_candidate.py:14: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______ test_demo_local_preview_video_cli_renders_mp4_and_prints_metadata _______\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-150/test_demo_local_preview_video_0')\\ncapsys = <_pytest.capture.CaptureFixture object at 0x725d1f0fce90>\\n\\n def test_demo_local_preview_video_cli_renders_mp4_and_prints_metadata(tmp_path, capsys):\\n> exit_code = main([\\\"--out-dir\\\", str(tmp_path)])\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_demo_local_preview_video_cli.py:12: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\nautoshorts/cli/demo_local_preview_video.py:64: in main\\n preview_plan, frame_result, render_result = _build_preview_video(\\nautoshorts/cli/demo_local_preview_video.py:38: in _build_preview_video\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n________ test_demo_render_preview_cli_prints_metadata_and_html_preview _________\\n\\ncapsys = <_pytest.capture.CaptureFixture object at 0x725d1f0e6a50>\\n\\n def test_demo_render_preview_cli_prints_metadata_and_html_preview(capsys):\\n> exit_code = main([])\\n ^^^^^^^^\\n\\ntests/test_demo_render_preview_cli.py:5: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\nautoshorts/cli/demo_render_preview.py:34: in main\\n preview = _build_demo_preview()\\n ^^^^^^^^^^^^^^^^^^^^^\\nautoshorts/cli/demo_render_preview.py:17: in _build_demo_preview\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_____ test_demo_social_draft_cli_prints_delivery_metadata_and_review_text ______\\n\\ncapsys = <_pytest.capture.CaptureFixture object at 0x725d1f090fd0>\\n\\n def test_demo_social_draft_cli_prints_delivery_metadata_and_review_text(capsys):\\n> exit_code = main([])\\n ^^^^^^^^\\n\\ntests/test_demo_social_draft_cli.py:5: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\nautoshorts/cli/demo_social_draft.py:40: in main\\n delivery = _build_demo_delivery()\\n ^^^^^^^^^^^^^^^^^^^^^^\\nautoshorts/cli/demo_social_draft.py:21: in _build_demo_delivery\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_ test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note _\\n\\n def test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note():\\n> candidate = create_candidate(_draft(), registry=DEFAULT_PILLARS)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_factory.py:67: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\ndraft = CandidateDraft(pillar='ai_life_systems', title='AI ist ein schlechter Gott, aber ein guter Diener', platforms=('youtub...('No fake earnings claims',), hypothesis='Values-first AI positioning gets more saves than hype content.', hashtags=())\\n\\n def create_candidate(\\n draft: CandidateDra\\n\\n... [OUTPUT TRUNCATED - 27419 chars omitted out of 77419 total] ...\\n\\nnce_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_________ test_validate_render_manifest_rejects_unsafe_render_settings _________\\n\\n def test_validate_render_manifest_rejects_unsafe_render_settings():\\n> manifest = build_render_manifest(_shot_plan())\\n ^^^^^^^^^^^^\\n\\ntests/test_render_manifest.py:84: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_manifest.py:15: in _shot_plan\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_ test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers _\\n\\n def test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers():\\n> manifest = build_render_manifest(_shot_plan())\\n ^^^^^^^^^^^^\\n\\ntests/test_render_manifest.py:110: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_manifest.py:15: in _shot_plan\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______ test_build_html_preview_creates_side_effect_free_timeline_preview _______\\n\\n def test_build_html_preview_creates_side_effect_free_timeline_preview():\\n> manifest = _manifest()\\n ^^^^^^^^^^^\\n\\ntests/test_render_preview.py:22: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_preview.py:11: in _manifest\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_____________ test_validate_render_preview_accepts_default_preview _____________\\n\\n def test_validate_render_preview_accepts_default_preview():\\n> result = validate_render_preview(build_html_preview(_manifest()))\\n ^^^^^^^^^^^\\n\\ntests/test_render_preview.py:42: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_preview.py:11: in _manifest\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_______________ test_build_html_preview_rejects_invalid_manifest _______________\\n\\n def test_build_html_preview_rejects_invalid_manifest():\\n> manifest = _manifest()\\n ^^^^^^^^^^^\\n\\ntests/test_render_preview.py:49: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_preview.py:11: in _manifest\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_____ test_validate_render_preview_rejects_side_effects_and_external_calls _____\\n\\n def test_validate_render_preview_rejects_side_effects_and_external_calls():\\n> preview = build_html_preview(_manifest())\\n ^^^^^^^^^^^\\n\\ntests/test_render_preview.py:62: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_preview.py:11: in _manifest\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n________________ test_build_html_preview_escapes_timeline_text _________________\\n\\n def test_build_html_preview_escapes_timeline_text():\\n> manifest = _manifest()\\n ^^^^^^^^^^^\\n\\ntests/test_render_preview.py:80: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_render_preview.py:11: in _manifest\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_________________ test_create_script_draft_from_content_brief __________________\\n\\n def test_create_script_draft_from_content_brief():\\n> draft = create_script_draft(_brief())\\n ^^^^^^^^\\n\\ntests/test_script_draft.py:21: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_script_draft.py:12: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n____________ test_validate_script_draft_accepts_retention_led_draft ____________\\n\\n def test_validate_script_draft_accepts_retention_led_draft():\\n> result = validate_script_draft(create_script_draft(_brief()))\\n ^^^^^^^^\\n\\ntests/test_script_draft.py:38: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_script_draft.py:12: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_______ test_validate_script_draft_enforces_retention_beats_and_duration _______\\n\\n def test_validate_script_draft_enforces_retention_beats_and_duration():\\n> draft = create_script_draft(_brief(), retention_beats=(\\\"only\\\", \\\"two\\\"), estimated_duration_seconds=181)\\n ^^^^^^^^\\n\\ntests/test_script_draft.py:69: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_script_draft.py:12: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_________ test_validate_script_draft_rejects_generic_ai_slop_language __________\\n\\n def test_validate_script_draft_rejects_generic_ai_slop_language():\\n draft = create_script_draft(\\n> _brief(),\\n ^^^^^^^^\\n script_outline=\\\"In this video, we explore productivity tips and unlock your full potential.\\\",\\n )\\n\\ntests/test_script_draft.py:80: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_script_draft.py:12: in _brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_____ test_create_shot_plan_builds_five_retention_scenes_from_script_draft _____\\n\\n def test_create_shot_plan_builds_five_retention_scenes_from_script_draft():\\n> script = _script()\\n ^^^^^^^^^\\n\\ntests/test_shot_plan.py:24: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_shot_plan.py:14: in _script\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_________________ test_validate_shot_plan_accepts_default_plan _________________\\n\\n def test_validate_shot_plan_accepts_default_plan():\\n> result = validate_shot_plan(create_shot_plan(_script()))\\n ^^^^^^^^^\\n\\ntests/test_shot_plan.py:58: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_shot_plan.py:14: in _script\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______________ test_create_shot_plan_rejects_invalid_script_draft ______________\\n\\n def test_create_shot_plan_rejects_invalid_script_draft():\\n> script = create_script_draft(_script_to_brief(), hook=\\\"\\\")\\n ^^^^^^^^^^^^^^^^^^\\n\\ntests/test_shot_plan.py:117: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_shot_plan.py:124: in _script_to_brief\\n return create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_______ test_build_social_media_draft_creates_approval_first_post_draft ________\\n\\n def test_build_social_media_draft_creates_approval_first_post_draft():\\n> candidate, preview = _candidate_and_preview()\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_social_draft.py:30: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_draft.py:17: in _candidate_and_preview\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n____________ test_validate_social_media_draft_accepts_default_draft ____________\\n\\n def test_validate_social_media_draft_accepts_default_draft():\\n> candidate, preview = _candidate_and_preview()\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_social_draft.py:57: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_draft.py:17: in _candidate_and_preview\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______ test_build_social_media_draft_rejects_preview_for_different_script ______\\n\\n def test_build_social_media_draft_rejects_preview_for_different_script():\\n> candidate, preview = _candidate_and_preview()\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_social_draft.py:66: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_draft.py:17: in _candidate_and_preview\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n__ test_validate_social_media_draft_rejects_missing_approval_and_side_effects __\\n\\n def test_validate_social_media_draft_rejects_missing_approval_and_side_effects():\\n> candidate, preview = _candidate_and_preview()\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n\\ntests/test_social_draft.py:81: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_draft.py:17: in _candidate_and_preview\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n__ test_render_social_media_draft_review_creates_telegram_safe_approval_text ___\\n\\n def test_render_social_media_draft_review_creates_telegram_safe_approval_text():\\n> review_text = render_social_media_draft_review(_draft())\\n ^^^^^^^^\\n\\ntests/test_social_review.py:27: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_review.py:14: in _draft\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n_________ test_render_social_media_draft_review_rejects_invalid_draft __________\\n\\n def test_render_social_media_draft_review_rejects_invalid_draft():\\n> draft = _draft()\\n ^^^^^^^^\\n\\ntests/test_social_review.py:55: \\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\ntests/test_social_review.py:14: in _draft\\n brief = create_content_brief(\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\n\\n def create_content_brief(\\n *,\\n pillar_id: str,\\n topic: str,\\n audience_problem: str,\\n payoff: str,\\n platforms: tuple[str, ...] | None = None,\\n target_duration_seconds: int = 35,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n ) -> ContentBrief:\\n pillar = find_pillar(pillar_id, registry)\\n if pillar is None:\\n> raise ValueError(f\\\"unknown pillar: {pillar_id}\\\")\\nE ValueError: unknown pillar: ai_life_systems\\n\\nautoshorts/content/brief.py:51: ValueError\\n______________ test_truetrace_concepts_pass_brand_and_series_fit _______________\\n\\n def test_truetrace_concepts_pass_brand_and_series_fit():\\n expected_series = {\\n \\\"truetrace-ai-automation-quiet-rot-38s\\\": \\\"ai_system_autopsy\\\",\\n \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\": \\\"prompt_vs_system\\\",\\n \\\"truetrace-ai-notes-equal-importance-34s\\\": \\\"one_rule_one_example\\\",\\n \\\"truetrace-ai-agents-tasks-not-teams-40s\\\": \\\"ai_hype_court\\\",\\n \\\"truetrace-review-before-automation-32s\\\": \\\"one_rule_one_example\\\",\\n }\\n \\n for candidate in build_truetrace_concept_batch().candidates:\\n brand_result = validate_candidate_brand_fit(candidate, TRUETRACE_SHORTS_BRAND)\\n series_result = validate_candidate_series_fit(candidate, expected_series[candidate.candidate_id])\\n \\n assert brand_result.is_valid is True, brand_result.errors\\n> assert series_result.is_valid is True, series_result.errors\\nE AssertionError: ('unknown series: ai_system_autopsy',)\\nE assert False is True\\nE + where False = ValidationResult(is_valid=False, errors=('unknown series: ai_system_autopsy',)).is_valid\\n\\ntests/test_truetrace_concept_batch.py:39: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_content_brief.py::test_create_content_brief_from_default_pillar_and_topic\\nFAILED tests/test_content_brief.py::test_validate_content_brief_rejects_bad_duration_and_platforms\\nFAILED tests/test_content_demo_pipeline.py::test_build_ai_life_systems_demo_delivery_runs_full_review_preparation_path\\nFAILED tests/test_content_demo_pipeline.py::test_build_ai_life_systems_demo_delivery_has_no_send_or_publish_language\\nFAILED tests/test_content_preview_package.py::test_build_content_preview_approval_package_combines_video_and_social_review\\nFAILED tests/test_content_preview_package.py::test_validate_content_preview_approval_package_accepts_valid_package\\nFAILED tests/test_content_preview_package.py::test_build_content_preview_approval_package_rejects_missing_video\\nFAILED tests/test_content_preview_package.py::test_validate_content_preview_approval_package_rejects_side_effects_and_action_language\\nFAILED tests/test_content_preview_package.py::test_render_content_preview_approval_text_is_telegram_safe_and_references_video\\nFAILED tests/test_content_to_candidate.py::test_convert_script_draft_to_candidate_draft_preserves_content_strategy\\nFAILED tests/test_content_to_candidate.py::test_convert_script_draft_to_candidate_uses_existing_candidate_factory\\nFAILED tests/test_content_to_candidate.py::test_convert_script_draft_to_candidate_rejects_invalid_script\\nFAILED tests/test_content_to_candidate.py::test_convert_script_draft_to_candidate_rejects_mismatched_brief\\nFAILED tests/test_demo_local_preview_video_cli.py::test_demo_local_preview_video_cli_renders_mp4_and_prints_metadata\\nFAILED tests/test_demo_render_preview_cli.py::test_demo_render_preview_cli_prints_metadata_and_html_preview\\nFAILED tests/test_demo_social_draft_cli.py::test_demo_social_draft_cli_prints_delivery_metadata_and_review_text\\nFAILED tests/test_factory.py::test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note\\nFAILED tests/test_ffmpeg_plan.py::test_build_ffmpeg_concat_plan_creates_side_effect_free_concat_plan\\nFAILED tests/test_ffmpeg_plan.py::test_validate_ffmpeg_concat_plan_accepts_default_plan\\nFAILED tests/test_ffmpeg_plan.py::test_build_ffmpeg_concat_plan_rejects_mismatched_frame_count\\nFAILED tests/test_ffmpeg_plan.py::test_build_ffmpeg_concat_plan_rejects_non_png_frame_paths\\nFAILED tests/test_ffmpeg_plan.py::test_validate_ffmpeg_concat_plan_rejects_bad_output_and_external_calls\\nFAILED tests/test_ffmpeg_renderer.py::test_render_ffmpeg_preview_video_writes_concat_file_and_mp4\\nFAILED tests/test_ffmpeg_renderer.py::test_render_ffmpeg_preview_video_smoke_probes_dimensions_and_duration\\nFAILED tests/test_ffmpeg_renderer.py::test_render_ffmpeg_preview_video_rejects_invalid_plan\\nFAILED tests/test_local_frame_writer.py::test_write_local_preview_frames_writes_valid_png_scene_cards\\nFAILED tests/test_local_frame_writer.py::test_write_local_preview_frames_preserves_plan_order_and_paths\\nFAILED tests/test_local_frame_writer.py::test_write_local_preview_frames_rejects_invalid_plan\\nFAILED tests/test_local_frame_writer.py::test_write_local_preview_frames_rejects_output_dir_outside_runtime_area\\nFAILED tests/test_local_frame_writer.py::test_localize_frame_copy_defaults_to_german_approval_preview_text\\nFAILED tests/test_local_preview.py::test_build_local_preview_plan_creates_side_effect_free_frame_plan\\nFAILED tests/test_local_preview.py::test_validate_local_preview_plan_accepts_default_plan\\nFAILED tests/test_local_preview.py::test_build_local_preview_plan_rejects_invalid_manifest\\nFAILED tests/test_local_preview.py::test_validate_local_preview_plan_rejects_side_effects_external_calls_and_bad_size\\nFAILED tests/test_local_preview.py::test_validate_local_preview_plan_rejects_bad_frame_timing_and_missing_fields\\nFAILED tests/test_manual_posting_pack.py::test_build_manual_posting_pack_creates_youtube_and_tiktok_copy_without_upload_side_effects\\nFAILED tests/test_manual_posting_pack.py::test_validate_manual_posting_pack_rejects_platform_actions_and_missing_manual_upload\\nFAILED tests/test_manual_posting_pack.py::test_render_manual_posting_instructions_contains_video_path_and_platform_copy\\nFAILED tests/test_render_manifest.py::test_build_render_manifest_turns_shot_plan_into_vertical_timeline\\nFAILED tests/test_render_manifest.py::test_validate_render_manifest_accepts_default_manifest\\nFAILED tests/test_render_manifest.py::test_validate_render_manifest_rejects_unsafe_render_settings\\nFAILED tests/test_render_manifest.py::test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers\\nFAILED tests/test_render_preview.py::test_build_html_preview_creates_side_effect_free_timeline_preview\\nFAILED tests/test_render_preview.py::test_validate_render_preview_accepts_default_preview\\nFAILED tests/test_render_preview.py::test_build_html_preview_rejects_invalid_manifest\\nFAILED tests/test_render_preview.py::test_validate_render_preview_rejects_side_effects_and_external_calls\\nFAILED tests/test_render_preview.py::test_build_html_preview_escapes_timeline_text\\nFAILED tests/test_script_draft.py::test_create_script_draft_from_content_brief\\nFAILED tests/test_script_draft.py::test_validate_script_draft_accepts_retention_led_draft\\nFAILED tests/test_script_draft.py::test_validate_script_draft_enforces_retention_beats_and_duration\\nFAILED 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"content": "{\"output\": \"...................F.......................F............................ [ 30%]\\n........................................................................ [ 60%]\\n........................................................................ [ 91%]\\n..................... [100%]\\n=================================== FAILURES ===================================\\n___________ test_create_content_brief_from_default_pillar_and_topic ____________\\n\\n def test_create_content_brief_from_default_pillar_and_topic():\\n brief = create_content_brief(\\n pillar_id=\\\"ai_life_systems\\\",\\n topic=\\\"AI weekly review routine\\\",\\n audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n )\\n \\n assert isinstance(brief, ContentBrief)\\n assert brief.brief_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n assert brief.pillar_id == \\\"ai_life_systems\\\"\\n assert brief.topic == \\\"AI weekly review routine\\\"\\n assert brief.audience_problem.startswith(\\\"I use AI tools\\\")\\n assert \\\"10-minute Sunday review\\\" in brief.payoff\\n assert \\\"AI as a servant\\\" in brief.positioning\\n assert \\\"human remains in control\\\" in brief.visual_rules\\n> assert \\\"fake earnings\\\" in brief.no_go\\nE AssertionError: assert 'fake earnings' in ('generic AI tips', 'prompt worship', 'private JARVIS/user collaboration')\\nE + where ('generic AI tips', 'prompt worship', 'private JARVIS/user collaboration') = ContentBrief(brief_id='ai_life_systems-ai-weekly-review-routine', pillar_id='ai_life_systems', topic='AI weekly review...rship', 'private JARVIS/user collaboration'), platforms=('youtube', 'tiktok', 'instagram'), target_duration_seconds=35).no_go\\n\\ntests/test_content_brief.py:22: AssertionError\\n___ test_convert_script_draft_to_candidate_draft_preserves_content_strategy ____\\n\\n def test_convert_script_draft_to_candidate_draft_preserves_content_strategy():\\n brief = _brief()\\n script = create_script_draft(brief)\\n \\n candidate_draft = convert_script_draft_to_candidate_draft(brief, script)\\n \\n assert isinstance(candidate_draft, CandidateDraft)\\n assert candidate_draft.pillar == \\\"ai_life_systems\\\"\\n assert candidate_draft.title == \\\"AI weekly review routine\\\"\\n assert candidate_draft.platforms == (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n assert candidate_draft.duration_seconds == 35\\n assert candidate_draft.hook == script.hook\\n assert candidate_draft.script_outline == script.script_outline\\n assert candidate_draft.visual_concept == script.visual_concept\\n assert candidate_draft.caption == script.caption\\n assert candidate_draft.cta == script.cta\\n assert candidate_draft.hypothesis == \\\"A human-approval AI routine creates saves because it gives viewers control without hype.\\\"\\n assert candidate_draft.quality_score.average_quality == 8.0\\n assert candidate_draft.quality_score.ai_slop_risk == 3\\n assert candidate_draft.quality_score.policy_risk == \\\"low\\\"\\n assert \\\"Source brief: ai_life_systems-ai-weekly-review-routine\\\" in candidate_draft.policy_notes\\n> assert \\\"No-go inherited: fake earnings\\\" in candidate_draft.policy_notes\\nE AssertionError: assert 'No-go inherited: fake earnings' in ('Source brief: ai_life_systems-ai-weekly-review-routine', 'Human approval remains explicit', 'No-go inherited: generic AI tips', 'No-go inherited: prompt worship', 'No-go inherited: private JARVIS/user collaboration')\\nE + where ('Source brief: ai_life_systems-ai-weekly-review-routine', 'Human approval remains explicit', 'No-go inherited: generic AI tips', 'No-go inherited: prompt worship', 'No-go inherited: private JARVIS/user collaboration') = CandidateDraft(pillar='ai_life_systems', title='AI weekly review routine', platforms=('youtube', 'tiktok', 'instagram'...'), hypothesis='A human-approval AI routine creates saves because it gives viewers control without hype.', hashtags=()).policy_notes\\n\\ntests/test_content_to_candidate.py:43: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_content_brief.py::test_create_content_brief_from_default_pillar_and_topic\\nFAILED tests/test_content_to_candidate.py::test_convert_script_draft_to_candidate_draft_preserves_content_strategy\\n2 failed, 235 passed in 32.27s\", \"exit_code\": 1, \"error\": null}\n\n[Tool loop warning: repeated_exact_failure_warning; count=2; terminal has failed 2 times with identical arguments. This looks like a loop; inspect the error and change strategy instead of retrying it unchanged.]",
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"content": "{\"content\": \" 1|## Manual Posting Pack\\n 2|Candidate: digital-red-flags-fake-invoice-payment-route-30s\\n 3|Video file: /tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1_9x16_sar1.mp4\\n 4|No platform upload was performed.\\n 5|\\n 6|## YouTube Shorts\\n 7|Manual upload: copy this description into YouTube Shorts.\\n 8|Title: This invoice looks normal. Check this line first.\\n 9|Description:\\n 10|A common red flag is a changed payment route. Before you pay, check it another way. This does not catch everything, but it gives you a safer next step.\\n 11|\\n 12|Follow for one red flag before you click, pay, scan, or log in.\\n 13|\\n 14|#ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 15|Hashtags: #ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 16|Tags: ScamAwareness, CyberSafety, DigitalRedFlags, SmallBusiness, TrueTraceShorts\\n 17|\\n 18|## TikTok\\n 19|Manual upload: copy this caption into TikTok.\\n 20|Title: This invoice looks normal. Check this line first.\\n 21|Description:\\n 22|A common red flag is a changed payment route. Before you pay, check it another way. This does not catch everything, but it gives you a safer next step. #ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 23|Hashtags: #ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 24|Tags: n/a\\n 25|\\n 26|## Instagram Reels\\n 27|Manual upload: copy this caption into Instagram Reels.\\n 28|Title: This invoice looks normal. Check this line first.\\n 29|Description:\\n 30|A common red flag is a changed payment route. Before you pay, check it another way. This does not catch everything, but it gives you a safer next step. #ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 31|Hashtags: #ScamAwareness #CyberSafety #DigitalRedFlags #SmallBusiness #TrueTraceShorts\\n 32|Tags: n/a\\n 33|\\n 34|## Pinned Comment Suggestion\\n 35|Quick check:\\n 36|\\n 37|Did the payment route change?\\n 38|Is money involved?\\n 39|Can you verify another way?\\n 40|\\n 41|## Analytics Template\\n 42|- platform:\\n 43|- post_time:\\n 44|- video_id/url:\\n 45|- series_id: money_move_red_flags\\n 46|- candidate_id: digital-red-flags-fake-invoice-payment-route-30s\\n 47|- hook_type: invoice_red_flag\\n 48|- first_frame_type: invoice_screen\\n 49|- duration: 31.4s\\n 50|- at_1h: views / likes / comments / shares / saves / follows\\n 51|- at_24h: views / likes / comments / shares / saves / follows\\n 52|- at_72h: views / likes / comments / shares / saves / follows\\n 53|- at_7d: views / likes / comments / shares / saves / follows\\n 54|- diagnosis notes:\\n 55|\\n 56|## Media Verification\\n 57|- file: `/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1_9x16_sar1.mp4`\\n 58|- sha256: `82e4dba233ffe69315570988faff48bfd1633e02e59377e456ca871febba842c`\\n 59|\", \"total_lines\": 58, \"file_size\": 2772, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "{\"content\": \" 1|## Review: delivery-sms-trap-35s\\n 2|Title: Delivery SMS Trap\\n 3|Pillar: text_delivery_scams\\n 4|Platforms: YouTube, TikTok, Instagram\\n 5|Duration: 35s\\n 6|\\n 7|Hook: This delivery text is designed to make you panic.\\n 8|Script: 0-2s: phone SMS says package on hold and tiny fee. 2-7s: tiny fee, short deadline, official-looking link. 7-15s: red flag is pressure. 15-24s: do not tap the link; open the delivery app or official site yourself. 24-30s: SAVE THIS checklist: small fee, short deadline, suspicious link. 30-34s: If it makes you hurry, slow down.\\n 9|Visual: Renderer-owned phone SMS mockup; fictional sender; no real courier/logo/domain/phone/QR; dramatic first frame from urgency and fee, not hacker imagery.\\n 10|Caption: A common red flag is pressure: a tiny fee, a short deadline, and a link. Open the app yourself instead.\\n 11|Hashtags: #ScamAwareness #CyberSafety #DigitalRedFlags #DeliveryScam #TrueTraceShorts\\n 12|\\n 13|Quality: 8.5/10\\n 14|AI-slop risk: 2/10\\n 15|Policy: series:before_you_click; mockup:deterministic; no_rendering_done\\n 16|Render gate: READY\\n 17|Preview: -\\n 18|\\n 19|Approval commands:\\n 20|- `Freigabe alle`\\n 21|- `Freigabe YouTube`\\n 22|- `Freigabe TikTok`\\n 23|- `Freigabe Instagram`\\n 24|- `Ablehnen`\\n 25|- `Ändern: `\\n 26|\\n 27|## Strategy Gate\\n 28|- Render allowed by strategy review: `True`\\n 29|- Format family: `Before You Click`\\n 30|- Viewer identity: `normal smartphone/internet users`\\n 31|- Future promise: `one red flag before you click, pay, scan, or log in`\\n 32|\\n 33|## No Render Status\\n 34|No MP4, styleframe batch, platform API, or Wan/I2V/T2V action was performed.\\n 35|\", \"total_lines\": 34, \"file_size\": 1497, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
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"content": "Erledigt, Sir. Strategische Neuorientierung ist umgesetzt.\n\n## Neue offizielle Richtung\n\n**TrueTraceShorts — Digital Red Flags** \n**Scam Self-Defense for Normal People**\n\nKernsatz:\n\n> **One screen. One red flag. One safer move.**\n\nKanalversprechen:\n\n> **I show you the one red flag before you click, pay, scan, or log in.**\n\nAI-/Workflow-/Prompt-Themen sind nicht gelöscht, aber klar **zurückgestuft** als Legacy-/Spezialformate. Hauptstrategie ist jetzt Scam Self-Defense für normale Menschen.\n\n## Aktualisierte Strategie-/Dokumentationsdateien\n\nNeu oder aktualisiert:\n\n- `README.md`\n- `docs/truetrace_digital_red_flags_strategy.md`\n- `docs/branding_truetrace_strategy.md`\n- `docs/proof_short_digital_red_flags.md`\n- `docs/scoring_guide_digital_red_flags.md`\n- `docs/image_generation_guidelines.md`\n- `docs/mockup_safety_guidelines.md`\n- `docs/caption_guidelines_digital_red_flags.md`\n\n## Angepasste Code-/Config-Dateien\n\nAktualisiert:\n\n- `autoshorts/config/pillars.py`\n - neue Hauptpfeiler:\n - Text & Delivery Scams\n - Login & Account Traps\n - Payment & Invoice Red Flags\n - Marketplace & Buyer Scams\n - Family & Emotion Scams\n - Fake Support / Popup Scams\n - Password / Account Safety Basics\n - Job / Side-Hustle Scams\n - `ai_life_systems` bleibt als **Legacy Secondary Pillar** erhalten, damit bestehende Pipeline/Tests nicht unnötig sterben. Altlasten entsorgt man kontrolliert, nicht mit der Abrissbirne. Meistens.\n\n- `autoshorts/strategy/series.py`\n - neue Hauptserien:\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 - alte AI-Serien bleiben als Legacy Secondary Series erhalten:\n - `prompt_vs_system`\n - `ai_system_autopsy`\n - `one_rule_one_example`\n - `ai_hype_court`\n\n- `autoshorts/strategy/score.py`\n - Scoring auf Digital-Red-Flags-Logik umgestellt:\n - first-second viewer pain\n - screen clarity without audio\n - red flag clarity\n - safe move clarity\n - family/share potential\n - screenshot checklist value\n - beginner language\n - pretty-empty-visual blocker\n - technical jargon blocker\n\n- `autoshorts/config/image_generation_policy.json`\n - `gpt-image-2-high` als preferred model gesetzt\n - klare Grenze: Scam-Screen-Mockups deterministisch, AI-Bilder nur für Atmosphäre/Styleframes\n\n- Tests aktualisiert:\n - `tests/test_pillars.py`\n - `tests/test_content_series.py`\n - `tests/test_content_strategy_score.py`\n\n## Fake-Invoice Manual Posting Pack\n\nErzeugt für:\n\n`digital-red-flags-fake-invoice-payment-route-30s`\n\nDatei:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/fake_invoice_manual_posting_pack_v1.md`\n\nFinale Video-Datei, Telegram-/Shorts-safe normalisiert:\n\n`/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1_9x16_sar1.mp4`\n\nVerifikation:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 31.400 s\nSHA256: 82e4dba233ffe69315570988faff48bfd1633e02e59377e456ca871febba842c\n```\n\nDas Pack enthält:\n\n- YouTube Shorts Copy\n- TikTok Caption\n- Instagram Reels Caption\n- Hashtags\n- Tags\n- Pinned Comment Suggestion\n- Analytics Template\n- explizit: **No platform upload was performed**\n\n## Nächste 3 ReviewPackages vorbereitet\n\nOhne Rendering. Keine Platform-API. Keine Wan-/I2V-/T2V-Aktion. Keine Styleframe-Batches.\n\nIndex:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/next_three_review_packages_index_v1.md`\n\nEinzeldateien:\n\n1. **Delivery SMS Trap**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/delivery-sms-trap-35s_review_package_v1.md`\n\nHook:\n\n> This delivery text is designed to make you panic.\n\nTakeaway:\n\n> If it makes you hurry, slow down.\n\n2. **Fake Support Pop-up**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/fake-support-popup-call-now-30s_review_package_v1.md`\n\nHook:\n\n> If a website tells you to call support, stop.\n\nTakeaway:\n\n> Real support does not hijack your screen.\n\n3. **Marketplace Buyer Scam**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/marketplace-buyer-off-platform-30s_review_package_v1.md`\n\nHook:\n\n> The buyer is not buying. They are moving you off-platform.\n\nTakeaway:\n\n> Keep the chat and payment inside the platform.\n\n## Tests\n\nVollständige Test-Suite ausgeführt:\n\n```text\n237 passed in 32.40s\n```\n\n## Git-Status / geänderte Dateien\n\nGeändert:\n\n- `README.md`\n- `autoshorts/config/image_generation_policy.json`\n- `autoshorts/config/pillars.py`\n- `autoshorts/strategy/score.py`\n- `autoshorts/strategy/series.py`\n- `docs/branding_truetrace_strategy.md`\n- `docs/image_generation_guidelines.md`\n- `tests/test_content_series.py`\n- `tests/test_content_strategy_score.py`\n- `tests/test_pillars.py`\n\nNeu:\n\n- `docs/caption_guidelines_digital_red_flags.md`\n- `docs/mockup_safety_guidelines.md`\n- `docs/proof_short_digital_red_flags.md`\n- `docs/scoring_guide_digital_red_flags.md`\n- `docs/truetrace_digital_red_flags_strategy.md`\n\n## Nicht ausgeführt\n\nWie gewünscht:\n\n- kein Auto-Posting\n- keine Plattform-API\n- keine Render-Serie\n- keine Wan-Videos\n- keine AI-Styleframe-Batches\n\n## Mögliche nächste Kommandos\n\nFür Strategie/Review:\n\n`REVIEW_PACKAGE delivery-sms-trap-35s`\n\n`REVIEW_PACKAGE fake-support-popup-call-now-30s`\n\n`REVIEW_PACKAGE marketplace-buyer-off-platform-30s`\n\nFür Rendering nach Freigabe:\n\n`APPROVED_CONCEPT delivery-sms-trap-35s`\n\noder:\n\n`APPROVED_CONCEPT fake-support-popup-call-now-30s`\n\noder:\n\n`APPROVED_CONCEPT marketplace-buyer-off-platform-30s`\n\nFür Fake-Invoice Posting:\n\n`SHOW_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s`\n\nDie neue Leitplanke steht jetzt sauber im Projekt:\n\n> Wir bauen keinen schönen AI-Film. \n> Wir bauen nützliche Kurzvideos, die normale Menschen vor echten digitalen Fallen schützen.",
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"text": "Erledigt, Sir. Strategische Neuorientierung ist umgesetzt.\n\n## Neue offizielle Richtung\n\n**TrueTraceShorts — Digital Red Flags** \n**Scam Self-Defense for Normal People**\n\nKernsatz:\n\n> **One screen. One red flag. One safer move.**\n\nKanalversprechen:\n\n> **I show you the one red flag before you click, pay, scan, or log in.**\n\nAI-/Workflow-/Prompt-Themen sind nicht gelöscht, aber klar **zurückgestuft** als Legacy-/Spezialformate. Hauptstrategie ist jetzt Scam Self-Defense für normale Menschen.\n\n## Aktualisierte Strategie-/Dokumentationsdateien\n\nNeu oder aktualisiert:\n\n- `README.md`\n- `docs/truetrace_digital_red_flags_strategy.md`\n- `docs/branding_truetrace_strategy.md`\n- `docs/proof_short_digital_red_flags.md`\n- `docs/scoring_guide_digital_red_flags.md`\n- `docs/image_generation_guidelines.md`\n- `docs/mockup_safety_guidelines.md`\n- `docs/caption_guidelines_digital_red_flags.md`\n\n## Angepasste Code-/Config-Dateien\n\nAktualisiert:\n\n- `autoshorts/config/pillars.py`\n - neue Hauptpfeiler:\n - Text & Delivery Scams\n - Login & Account Traps\n - Payment & Invoice Red Flags\n - Marketplace & Buyer Scams\n - Family & Emotion Scams\n - Fake Support / Popup Scams\n - Password / Account Safety Basics\n - Job / Side-Hustle Scams\n - `ai_life_systems` bleibt als **Legacy Secondary Pillar** erhalten, damit bestehende Pipeline/Tests nicht unnötig sterben. Altlasten entsorgt man kontrolliert, nicht mit der Abrissbirne. Meistens.\n\n- `autoshorts/strategy/series.py`\n - neue Hauptserien:\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 - alte AI-Serien bleiben als Legacy Secondary Series erhalten:\n - `prompt_vs_system`\n - `ai_system_autopsy`\n - `one_rule_one_example`\n - `ai_hype_court`\n\n- `autoshorts/strategy/score.py`\n - Scoring auf Digital-Red-Flags-Logik umgestellt:\n - first-second viewer pain\n - screen clarity without audio\n - red flag clarity\n - safe move clarity\n - family/share potential\n - screenshot checklist value\n - beginner language\n - pretty-empty-visual blocker\n - technical jargon blocker\n\n- `autoshorts/config/image_generation_policy.json`\n - `gpt-image-2-high` als preferred model gesetzt\n - klare Grenze: Scam-Screen-Mockups deterministisch, AI-Bilder nur für Atmosphäre/Styleframes\n\n- Tests aktualisiert:\n - `tests/test_pillars.py`\n - `tests/test_content_series.py`\n - `tests/test_content_strategy_score.py`\n\n## Fake-Invoice Manual Posting Pack\n\nErzeugt für:\n\n`digital-red-flags-fake-invoice-payment-route-30s`\n\nDatei:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/fake_invoice_manual_posting_pack_v1.md`\n\nFinale Video-Datei, Telegram-/Shorts-safe normalisiert:\n\n`/tmp/autoshorts_proof_short_sprint30/digital-red-flags-fake-invoice-payment-route-30s/manual_upload_v1/digital-red-flags-fake-invoice-payment-route-30s_manual_upload_v1_9x16_sar1.mp4`\n\nVerifikation:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 31.400 s\nSHA256: 82e4dba233ffe69315570988faff48bfd1633e02e59377e456ca871febba842c\n```\n\nDas Pack enthält:\n\n- YouTube Shorts Copy\n- TikTok Caption\n- Instagram Reels Caption\n- Hashtags\n- Tags\n- Pinned Comment Suggestion\n- Analytics Template\n- explizit: **No platform upload was performed**\n\n## Nächste 3 ReviewPackages vorbereitet\n\nOhne Rendering. Keine Platform-API. Keine Wan-/I2V-/T2V-Aktion. Keine Styleframe-Batches.\n\nIndex:\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/next_three_review_packages_index_v1.md`\n\nEinzeldateien:\n\n1. **Delivery SMS Trap**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/delivery-sms-trap-35s_review_package_v1.md`\n\nHook:\n\n> This delivery text is designed to make you panic.\n\nTakeaway:\n\n> If it makes you hurry, slow down.\n\n2. **Fake Support Pop-up**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/fake-support-popup-call-now-30s_review_package_v1.md`\n\nHook:\n\n> If a website tells you to call support, stop.\n\nTakeaway:\n\n> Real support does not hijack your screen.\n\n3. **Marketplace Buyer Scam**\n\n`/tmp/autoshorts_strategy_repositioning_cybersecurity_beginner/marketplace-buyer-off-platform-30s_review_package_v1.md`\n\nHook:\n\n> The buyer is not buying. They are moving you off-platform.\n\nTakeaway:\n\n> Keep the chat and payment inside the platform.\n\n## Tests\n\nVollständige Test-Suite ausgeführt:\n\n```text\n237 passed in 32.40s\n```\n\n## Git-Status / geänderte Dateien\n\nGeändert:\n\n- `README.md`\n- `autoshorts/config/image_generation_policy.json`\n- `autoshorts/config/pillars.py`\n- `autoshorts/strategy/score.py`\n- `autoshorts/strategy/series.py`\n- `docs/branding_truetrace_strategy.md`\n- `docs/image_generation_guidelines.md`\n- `tests/test_content_series.py`\n- `tests/test_content_strategy_score.py`\n- `tests/test_pillars.py`\n\nNeu:\n\n- `docs/caption_guidelines_digital_red_flags.md`\n- `docs/mockup_safety_guidelines.md`\n- `docs/proof_short_digital_red_flags.md`\n- `docs/scoring_guide_digital_red_flags.md`\n- `docs/truetrace_digital_red_flags_strategy.md`\n\n## Nicht ausgeführt\n\nWie gewünscht:\n\n- kein Auto-Posting\n- keine Plattform-API\n- keine Render-Serie\n- keine Wan-Videos\n- keine AI-Styleframe-Batches\n\n## Mögliche nächste Kommandos\n\nFür Strategie/Review:\n\n`REVIEW_PACKAGE delivery-sms-trap-35s`\n\n`REVIEW_PACKAGE fake-support-popup-call-now-30s`\n\n`REVIEW_PACKAGE marketplace-buyer-off-platform-30s`\n\nFür Rendering nach Freigabe:\n\n`APPROVED_CONCEPT delivery-sms-trap-35s`\n\noder:\n\n`APPROVED_CONCEPT fake-support-popup-call-now-30s`\n\noder:\n\n`APPROVED_CONCEPT marketplace-buyer-off-platform-30s`\n\nFür Fake-Invoice Posting:\n\n`SHOW_MANUAL_POSTING_PACK digital-red-flags-fake-invoice-payment-route-30s`\n\nDie neue Leitplanke steht jetzt sauber im Projekt:\n\n> Wir bauen keinen schönen AI-Film. \n> Wir bauen nützliche Kurzvideos, die normale Menschen vor echten digitalen Fallen schützen."
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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/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. Inspect the extracted frame itself before changing the image geometry.\\n4. If metadata is wrong, re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n5. If the extracted frame contains baked-in top/bottom bars, only then use a full-bleed crop/scale repair and label it as geometry-changing.\\n6. If metadata is correct and extracted frames are vertical/full-frame, suspect the Telegram/mobile player UI first. Re-deliver the verified original instead of stretching/cropping.\\n7. Probe the final file and report the verified SAR/DAR.\\n8. Deliver the corrected or verified media, not a theory lecture.\\n\\n## Telegram/player false-positive pattern\\n\\nA Telegram player can show apparent top/bottom bars or a square-looking preview even when the underlying MP4 is valid 9:16. Before destructive repair, compare:\\n\\n- `ffprobe` metadata: `width=1080 height=1920 SAR=1:1 DAR=9:16`\\n- extracted frame dimensions: `1080x1920`\\n- visual QA of the extracted frame, not just Telegram's inline player\\n\\nIf all three pass, do **not** crop/stretch. Tell the user the player is the likely cause and keep the unwarped file as canonical. Crop/stretch variants are review experiments only and should be discarded if the player diagnosis is confirmed.\\n\\n## Pitfalls\\n\\n- Do 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.\\n- Do not overcorrect by stretching a valid 9:16 video just because Telegram preview looks wrong. Destructive geometry repair can make the real asset worse.\", \"file_type\": \".md\"}",
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"arguments": "{\"path\":\"/tmp/autoshorts_delivery_sms_trap_render.py\",\"content\":\"from __future__ import annotations\\n\\nimport asyncio\\nimport json\\nimport math\\nimport re\\nimport subprocess\\nfrom pathlib import Path\\n\\nimport edge_tts\\nimport numpy as np\\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter\\n\\nOUT = Path('/tmp/autoshorts_delivery_sms_trap_35s/postpreview_v1')\\nOUT.mkdir(parents=True, exist_ok=True)\\nFRAMES = OUT / 'frames'\\nFRAMES.mkdir(exist_ok=True)\\nAUDIO = OUT / 'delivery_sms_trap_voice.mp3'\\nASS = OUT / 'captions.ass'\\nRAW_VIDEO = OUT / 'delivery-sms-trap-35s_raw.mp4'\\nFINAL_VIDEO = OUT / 'delivery-sms-trap-35s_postpreview_v1.mp4'\\nCONTACT = OUT / 'render_contact_sheet_v1.jpg'\\nMETA = OUT / 'metadata.json'\\nW, H = 1080, 1920\\nFPS = 24\\nVOICE = 'en-US-GuyNeural'\\n\\nSCRIPT = [\\n ('This delivery text is designed to make you panic.', 0.0),\\n ('It looks normal.', 3.2),\\n ('A package.', 4.6),\\n ('A tiny fee.', 5.6),\\n ('A deadline.', 6.7),\\n ('The red flag is the pressure.', 8.4),\\n ('Scammers want you to tap before you think.', 11.7),\\n (\\\"Don't tap the link.\\\", 16.0),\\n ('Open the delivery app yourself.', 18.2),\\n ('If it is real, it will still be there.', 21.2),\\n ('Save this.', 25.2),\\n ('Small fee.', 26.6),\\n ('Short deadline.', 27.7),\\n ('Suspicious link.', 28.8),\\n ('If it makes you hurry, slow down.', 31.0),\\n]\\nFULL_TEXT = ' '.join(s for s, _ in SCRIPT)\\n\\n\\ndef font(size: int, bold: bool = False):\\n paths = [\\n '/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf' if bold else '/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',\\n '/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf' if bold else '/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf',\\n ]\\n for p in paths:\\n if Path(p).exists():\\n return ImageFont.truetype(p, size)\\n return ImageFont.load_default()\\n\\nF_TITLE = font(66, True)\\nF_BIG = font(58, True)\\nF_MED = font(42, True)\\nF_BODY = font(36)\\nF_BODY_B = font(36, True)\\nF_SMALL = font(28)\\nF_CAP = font(64, True)\\nF_CAP_SMALL = font(52, True)\\n\\n\\ndef rr(draw, xy, r, fill, outline=None, width=1):\\n draw.rounded_rectangle(xy, radius=r, fill=fill, outline=outline, width=width)\\n\\n\\ndef text_center(draw, xy, text, fnt, fill):\\n x1, y1, x2, y2 = xy\\n b = draw.textbbox((0, 0), text, font=fnt)\\n draw.text(((x1+x2-(b[2]-b[0]))/2, (y1+y2-(b[3]-b[1]))/2), text, font=fnt, fill=fill)\\n\\n\\ndef wrap_text(draw, text, fnt, max_w):\\n words = text.split()\\n lines=[]; cur=''\\n for w in words:\\n cand=(cur+' '+w).strip()\\n if draw.textbbox((0,0), cand, font=fnt)[2] <= max_w or not cur:\\n cur=cand\\n else:\\n lines.append(cur); cur=w\\n if cur: lines.append(cur)\\n return lines\\n\\n\\ndef draw_bg(draw, t):\\n # dark blue/black gradient with subtle red urgency glow in hook scenes\\n for y in range(0, H, 6):\\n v = int(10 + 22*y/H)\\n draw.rectangle((0,y,W,y+6), fill=(5, 9+v//2, 18+v))\\n # danger glow pulse\\n pulse = 0.5 + 0.5*math.sin(t*2.2)\\n if t < 15:\\n overlay = Image.new('RGBA', (W,H), (0,0,0,0))\\n od = ImageDraw.Draw(overlay)\\n for r in range(420, 980, 80):\\n alpha = int(42*pulse*(1-r/1100))\\n od.ellipse((W//2-r, 170-r, W//2+r, 170+r), fill=(255, 72, 50, max(0, alpha)))\\n return overlay\\n return None\\n\\n\\ndef draw_phone(draw, x, y, w, h, t, mode):\\n rr(draw, (x, y, x+w, y+h), 70, (20, 24, 33), (95, 104, 122), 4)\\n rr(draw, (x+24, y+24, x+w-24, y+h-24), 52, (235, 239, 245), None)\\n # notch\\n rr(draw, (x+w//2-95, y+36, x+w//2+95, y+66), 18, (18, 20, 27))\\n sx, sy, sw, sh = x+48, y+96, w-96, h-150\\n if mode in {'sms','detail','redflag'}:\\n draw.text((sx, sy), 'Messages', font=F_MED, fill=(28,38,55))\\n draw.text((sx, sy+56), 'Today 18:42', font=F_SMALL, fill=(115,123,135))\\n rr(draw, (sx, sy+110, sx+sw, sy+520), 32, (255,255,255), (214,220,230), 2)\\n draw.text((sx+28, sy+138), 'Parcel Notice', font=F_BODY_B, fill=(31,39,52))\\n lines = [\\n 'Your package is on hold.',\\n 'Pay CHF 1.95 customs fee.',\\n 'Delivery expires today.',\\n 'delivery.example.com/hold'\\n ]\\n yy = sy+205\\n for i,line in enumerate(lines):\\n col=(35,45,62)\\n if i==1: col=(164,54,38)\\n if i==2: col=(190,72,35)\\n if i==3: col=(32,92,186)\\n draw.text((sx+28, yy), line, font=F_BODY, fill=col)\\n yy += 62\\n rr(draw, (sx+28, sy+455, sx+sw-28, sy+505), 22, (230,60,48))\\n text_center(draw, (sx+28, sy+455, sx+sw-28, sy+505), 'PAY NOW', F_BODY_B, (255,255,255))\\n if mode == 'redflag':\\n # deterministic highlight around pressure details, not arrows/extra focus lines in final standard? red translucent UI state is okay\\n rr(draw, (sx+18, sy+250, sx+sw-18, sy+378), 26, (255,226,217), (220,68,48), 4)\\n draw.text((sx+42, sy+272), 'Tiny fee + deadline', font=F_BODY_B, fill=(135,38,30))\\n draw.text((sx+42, sy+326), 'The red flag is pressure.', font=F_BODY, fill=(135,38,30))\\n elif mode == 'safe':\\n draw.text((sx, sy), 'Home Screen', font=F_MED, fill=(28,38,55))\\n apps=[('Delivery', (38,132,255)),('Bank', (53,180,120)),('Mail',(110,124,255)),('Browser',(255,152,45))]\\n for idx,(name,col) in enumerate(apps):\\n ax=sx+48+(idx%2)*210; ay=sy+150+(idx//2)*190\\n rr(draw,(ax,ay,ax+112,ay+112),28,col)\\n text_center(draw,(ax-35,ay+124,ax+147,ay+160),name,F_SMALL,(38,44,55))\\n rr(draw,(sx+36,sy+560,sx+sw-36,sy+710),30,(230,248,239),(72,172,110),3)\\n draw.text((sx+70, sy+590), 'Safer move', font=F_BODY_B, fill=(36,106,66))\\n draw.text((sx+70, sy+642), 'Open the app yourself.', font=F_BODY, fill=(36,86,58))\\n\\n\\ndef draw_scene(t):\\n img = Image.new('RGB',(W,H),(8,14,28))\\n draw=ImageDraw.Draw(img)\\n overlay = draw_bg(draw, t)\\n if overlay:\\n img = Image.alpha_composite(img.convert('RGBA'), overlay).convert('RGB')\\n draw=ImageDraw.Draw(img)\\n # top brand pill\\n rr(draw,(54,54,404,112),29,(20,30,48),(80,96,128),2)\\n draw.text((82,69),'TRUE TRACE SHORTS',font=F_SMALL,fill=(226,235,248))\\n if t < 2.4:\\n draw.text((74,150),'DELIVERY TEXT',font=F_TITLE,fill=(255,255,255))\\n draw.text((74,222),'designed to make you panic',font=F_MED,fill=(255,210,90))\\n draw_phone(draw, 250, 420, 580, 1020, t, 'sms')\\n elif t < 7.5:\\n draw.text((74,150),'It feels normal.',font=F_TITLE,fill=(255,255,255))\\n draw.text((74,226),'Package. Tiny fee. Deadline.',font=F_MED,fill=(230,236,248))\\n draw_phone(draw, 250, 395, 580, 1040, t, 'detail')\\n elif t < 15.5:\\n draw.text((74,150),'The red flag:',font=F_TITLE,fill=(255,255,255))\\n draw.text((74,226),'pressure',font=font(76,True),fill=(255,210,90))\\n draw_phone(draw, 250, 395, 580, 1040, t, 'redflag')\\n elif t < 24.5:\\n draw.text((74,150),\\\"Don't tap the link.\\\",font=F_TITLE,fill=(255,255,255))\\n draw.text((74,226),'Open the app yourself.',font=F_MED,fill=(135,245,176))\\n draw_phone(draw, 250, 395, 580, 1040, t, 'safe')\\n elif t < 30.5:\\n draw.text((74,150),'SAVE THIS',font=font(82,True),fill=(255,255,255))\\n rr(draw,(104,340,976,1220),46,(238,242,249),(144,158,185),3)\\n draw.text((164,420),'Delivery text?',font=font(62,True),fill=(22,34,54))\\n draw.text((164,535),'Check:',font=F_BIG,fill=(64,76,98))\\n items=['Small fee?','Short deadline?','Suspicious link?']\\n yy=670\\n for item in items:\\n rr(draw,(164,yy,228,yy+64),18,(255,210,90))\\n text_center(draw,(164,yy,228,yy+64),'✓',F_MED,(35,42,55))\\n draw.text((260,yy+6),item,font=F_BIG,fill=(25,35,55))\\n yy+=118\\n draw.text((164,1085),'Open the app yourself.',font=F_MED,fill=(38,118,72))\\n else:\\n draw.text((74,310),'If it makes you hurry,',font=font(72,True),fill=(255,255,255))\\n draw.text((74,410),'slow down.',font=font(96,True),fill=(255,210,90))\\n rr(draw,(104,650,976,900),44,(236,245,255),(118,142,188),3)\\n draw.text((150,705),'Follow for one red flag',font=F_BIG,fill=(22,34,54))\\n draw.text((150,790),'before you click.',font=F_BIG,fill=(22,34,54))\\n # subtle bottom safe area; captions added by ASS later, keep dark underlay gradient only\\n return img\\n\\nasync def tts():\\n communicate = edge_tts.Communicate(FULL_TEXT, VOICE, rate='-3%', volume='+0%')\\n words=[]\\n with AUDIO.open('wb') as f:\\n async for chunk in communicate.stream():\\n if chunk['type'] == 'audio':\\n f.write(chunk['data'])\\n elif chunk['type'] == 'WordBoundary':\\n words.append({\\n 'word': chunk['text'],\\n 'start': chunk['offset']/10_000_000,\\n 'duration': chunk['duration']/10_000_000,\\n })\\n return words\\n\\n\\ndef probe_duration(path):\\n out=subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nw=1:nk=1',str(path)], text=True)\\n return float(out.strip())\\n\\n\\ndef words_to_phrases(words):\\n # Use punctuation-informed target phrases from script; assign contiguous words by count.\\n phrases=[]; idx=0\\n for sentence,_ in SCRIPT:\\n toks=re.findall(r\\\"[A-Za-z']+\\\", sentence)\\n n=len(toks)\\n sent_words=words[idx:idx+n]\\n idx+=n\\n # split long sentences manually into chunks <= 6 words\\n chunks=[]\\n cur=[]\\n for w in sent_words:\\n cur.append(w)\\n if len(cur)>=6:\\n chunks.append(cur); cur=[]\\n if cur: chunks.append(cur)\\n phrases.extend(chunks)\\n return phrases\\n\\n\\ndef ass_escape(s):\\n return s.replace('{','').replace('}','')\\n\\n\\ndef make_ass(words, duration):\\n phrases=words_to_phrases(words)\\n lines=[\\n '[Script Info]',\\n 'ScriptType: v4.00+',\\n 'PlayResX: 1080',\\n 'PlayResY: 1920',\\n 'WrapStyle: 2',\\n '',\\n '[V4+ Styles]',\\n 'Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding',\\n 'Style: Default,DejaVu Sans,60,&H00FFFFFF,&H0000D7FF,&HCC07101E,&H00000000,-1,0,0,0,100,100,0,0,1,4,2,2,70,70,190,1',\\n '',\\n '[Events]',\\n 'Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text',\\n ]\\n def ts(sec):\\n sec=max(0,sec); h=int(sec//3600); m=int((sec%3600)//60); s=sec%60\\n return f'{h}:{m:02d}:{s:05.2f}'\\n for phrase in phrases:\\n if not phrase: continue\\n pstart=phrase[0]['start']; pend=phrase[-1]['start']+phrase[-1]['duration']+0.12\\n for i,w in enumerate(phrase):\\n start=w['start']; end=(phrase[i+1]['start'] if i+1=6:\\n phrases.append(cur); cur=[]\\n if cur: phrases.append(cur)\\n\\ndef ts(sec):\\n sec=max(0,sec); h=int(sec//3600); m=int((sec%3600)//60); s=sec%60\\n return f'{h}:{m:02d}:{s:05.2f}'\\nlines=['[Script Info]','ScriptType: v4.00+','PlayResX: 1080','PlayResY: 1920','WrapStyle: 2','','[V4+ Styles]','Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding','Style: Default,DejaVu Sans,60,&H00FFFFFF,&H0000D7FF,&HCC07101E,&H00000000,-1,0,0,0,100,100,0,0,1,4,2,2,70,70,190,1','','[Events]','Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text']\\nfor phrase in phrases:\\n pend=phrase[-1]['start']+phrase[-1]['duration']+0.08\\n for i,w in enumerate(phrase):\\n start=w['start']; end=(phrase[i+1]['start'] if i+1/tmp/delivery_render.log && python - <<'PY'\\n# regenerate manual ASS because edge word boundaries are unavailable in this environment\\nfrom pathlib import Path\\nimport re, subprocess, json\\nOUT=Path('/tmp/autoshorts_delivery_sms_trap_35s/postpreview_v1')\\nASS=OUT/'captions.ass'; AUDIO=OUT/'delivery_sms_trap_voice.mp3'; FRAMES=OUT/'frames'; FINAL=OUT/'delivery-sms-trap-35s_postpreview_v1.mp4'\\nSCRIPT=[('This delivery text is designed to make you panic.',0.0),('It looks normal.',3.2),('A package.',4.6),('A tiny fee.',5.6),('A deadline.',6.7),('The red flag is the pressure.',8.4),('Scammers want you to tap before you think.',11.7),(\\\"Don\\\\'t tap the link.\\\",16.0),('Open the delivery app yourself.',18.2),('If it is real, it will still be there.',21.2),('Save this.',25.2),('Small fee.',26.6),('Short deadline.',27.7),('Suspicious link.',28.8),('If it makes you hurry, slow down.',31.0)]\\nD=float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nw=1:nk=1',str(AUDIO)], text=True).strip())+0.45\\nwords=[]\\nfor idx,(sent,start) in enumerate(SCRIPT):\\n end=SCRIPT[idx+1][1]-0.15 if idx+1=6: phrases.append(cur); cur=[]\\n if cur: phrases.append(cur)\\ndef ts(sec):\\n h=int(sec//3600); m=int((sec%3600)//60); s=sec%60; return f'{h}:{m:02d}:{s:05.2f}'\\nlines=['[Script Info]','ScriptType: v4.00+','PlayResX: 1080','PlayResY: 1920','WrapStyle: 2','','[V4+ Styles]','Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding','Style: Default,DejaVu Sans,60,&H00FFFFFF,&H0000D7FF,&HCC07101E,&H00000000,-1,0,0,0,100,100,0,0,1,4,2,2,70,70,190,1','','[Events]','Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text']\\nfor phrase in phrases:\\n pend=phrase[-1]['start']+phrase[-1]['duration']+0.08\\n for i,w in enumerate(phrase):\\n start=w['start']; end=phrase[i+1]['start'] if i+1 This delivery text is designed to make you panic.\n\n**Takeaway:** \n> If it makes you hurry, slow down.\n\nMEDIA:/tmp/autoshorts_delivery_sms_trap_35s/postpreview_v1/delivery-sms-trap-35s_postpreview_v1.mp4\n\n## QA\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 35.424 s\nAudio: vorhanden\nActive-word subtitles: vorhanden\nSHA256: 39de6f363e1779a6068c5153f5fc17df832bfd240463e3b739fd3491cb49f648\n```\n\n## Inhalt / Safety\n\n- Deterministisches Mockup, keine AI-Styleframe-Batches.\n- Keine echten Marken.\n- Keine echten Telefonnummern.\n- Kein QR-Code.\n- Keine realen Bank-/Personendaten.\n- Fake-Link nutzt `example.com`.\n- Erstes Bild zeigt direkt: Paket-SMS + kleine Gebühr + Zeitdruck.\n- Sprache bewusst einfach gehalten.\n\n## Social Draft\n\n**Caption:**\n\nA common red flag is pressure: a tiny fee, a short deadline, and a link. \nDon’t tap the link. Open the app yourself.\n\n**Hashtags:**\n\n`#ScamAwareness #CyberSafety #DigitalRedFlags #DeliveryScam #TrueTraceShorts`\n\n## Freigabe\n\nWenn passend:\n\n`APPROVED_POSTPREVIEW delivery-sms-trap-35s v1`\n\nWenn ändern:\n\n`REVISE_POSTPREVIEW delivery-sms-trap-35s \"...\"`",
"codex_reasoning_items": [
{
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"id": "rs_010f4e1931265670016a1607a263648191899fa812ec9b47d7",
"summary": []
}
],
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Erledigt — nächstes Video ist gebaut: **Delivery SMS Trap**.\n\n## Postpreview zur Freigabe\n\n**Candidate:** `delivery-sms-trap-35s` \n**Serie:** `Before You Click` / `One Screen. One Red Flag.` \n**Hook:** \n> This delivery text is designed to make you panic.\n\n**Takeaway:** \n> If it makes you hurry, slow down.\n\nMEDIA:/tmp/autoshorts_delivery_sms_trap_35s/postpreview_v1/delivery-sms-trap-35s_postpreview_v1.mp4\n\n## QA\n\nVerifiziert:\n\n```text\n1080x1920\nSAR: 1:1\nDAR: 9:16\n24 fps\nDauer: 35.424 s\nAudio: vorhanden\nActive-word subtitles: vorhanden\nSHA256: 39de6f363e1779a6068c5153f5fc17df832bfd240463e3b739fd3491cb49f648\n```\n\n## Inhalt / Safety\n\n- Deterministisches Mockup, keine AI-Styleframe-Batches.\n- Keine echten Marken.\n- Keine echten Telefonnummern.\n- Kein QR-Code.\n- Keine realen Bank-/Personendaten.\n- Fake-Link nutzt `example.com`.\n- Erstes Bild zeigt direkt: Paket-SMS + kleine Gebühr + Zeitdruck.\n- Sprache bewusst einfach gehalten.\n\n## Social Draft\n\n**Caption:**\n\nA common red flag is pressure: a tiny fee, a short deadline, and a link. \nDon’t tap the link. Open the app yourself.\n\n**Hashtags:**\n\n`#ScamAwareness #CyberSafety #DigitalRedFlags #DeliveryScam #TrueTraceShorts`\n\n## Freigabe\n\nWenn passend:\n\n`APPROVED_POSTPREVIEW delivery-sms-trap-35s v1`\n\nWenn ändern:\n\n`REVISE_POSTPREVIEW delivery-sms-trap-35s \"...\"`"
}
],
"id": "msg_010f4e1931265670016a1607ac58e88191b8d0e6e553619aee",
"phase": "final_answer"
}
],
"finish_reason": "stop"
},
{
"role": "user",
"content": "[M G] Ok, konzept ok. Nun mit hochwertigen derailierten KI Bildern gemäss definiertem Standard."
},
{
"role": "assistant",
"content": "",
"reasoning": "**Planning video creation**\n\nI need to act and focus on using the relevant skills I've already accessed. The goal is to create an upgraded video featuring high-quality AI images that meet specific standards, specifically with AI images. I’ll leverage tools like image_generate for premium backgrounds and combine those with deterministic phone screens over the AI environments. \n\nI plan to create three to four dramatic AI portrait backgrounds, ensuring there's no readable text, and I’ll work on rendering the phone mockups onto these backgrounds. I'll also modify the existing script to load the generated images effectively.",
"finish_reason": "tool_calls",
"reasoning_content": "**Planning video creation**\n\nI need to act and focus on using the relevant skills I've already accessed. The goal is to create an upgraded video featuring high-quality AI images that meet specific standards, specifically with AI images. I’ll leverage tools like image_generate for premium backgrounds and combine those with deterministic phone screens over the AI environments. \n\nI plan to create three to four dramatic AI portrait backgrounds, ensuring there's no readable text, and I’ll work on rendering the phone mockups onto these backgrounds. I'll also modify the existing script to load the generated images effectively.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_010f4e1931265670016a16089a10dc819199baf5dfff5a8952",
"summary": [
{
"type": "summary_text",
"text": "**Planning video creation**\n\nI need to act and focus on using the relevant skills I've already accessed. The goal is to create an upgraded video featuring high-quality AI images that meet specific standards, specifically with AI images. I’ll leverage tools like image_generate for premium backgrounds and combine those with deterministic phone screens over the AI environments. \n\nI plan to create three to four dramatic AI portrait backgrounds, ensuring there's no readable text, and I’ll work on rendering the phone mockups onto these backgrounds. I'll also modify the existing script to load the generated images effectively."
}
]
}
],
"tool_calls": [
{
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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, \"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. Inspect the extracted frame itself before changing the image geometry.\\n4. If metadata is wrong, re-export with explicit 1080x1920, `setsar=1`, `setdar=9/16`.\\n5. If the extracted frame contains baked-in top/bottom bars, only then use a full-bleed crop/scale repair and label it as geometry-changing.\\n6. If metadata is correct and extracted frames are vertical/full-frame, suspect the Telegram/mobile player UI first. Re-deliver the verified original instead of stretching/cropping.\\n7. Probe the final file and report the verified SAR/DAR.\\n8. Deliver the corrected or verified media, not a theory lecture.\\n\\n## Telegram/player false-positive pattern\\n\\nA Telegram player can show apparent top/bottom bars or a square-looking preview even when the underlying MP4 is valid 9:16. Before destructive repair, compare:\\n\\n- `ffprobe` metadata: `width=1080 height=1920 SAR=1:1 DAR=9:16`\\n- extracted frame dimensions: `1080x1920`\\n- visual QA of the extracted frame, not just Telegram's inline player\\n\\nIf all three pass, do **not** crop/stretch. Tell the user the player is the likely cause and keep the unwarped file as canonical. Crop/stretch variants are review experiments only and should be discarded if the player diagnosis is confirmed.\\n\\n## Pitfalls\\n\\n- Do 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.\\n- Do not overcorrect by stretching a valid 9:16 video just because Telegram preview looks wrong. Destructive geometry repair can make the real asset worse.\", \"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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"
}
}
],
"tool_call_id": "call_koF380znbgEPpFyXhiTEenGC"
},
{
"role": "assistant",
"content": "",
"reasoning": "**Improving render quality**\n\nI'm looking at the backgrounds and deciding which ones to keep. The second background with a barcode label is a discard, but the third one with no text seems okay. I want to render the upgraded version, using a script that adapts AI backgrounds to fill a 1080x1920 space with a blurred and darkened effect. Since the AI background already has a phone/box, I can draw the phone over it, but keeping high-quality detailed images is essential. I'm considering creating a compositing script to manage these layers effectively.\n**Creating a rendering script**\n\nI'm planning to create a new script, `render_ai_upgrade.py`, based on the original one, possibly simplifying functions by importing from a temporary location. I might import the render script as a module and use monkey patching for functions like `draw_bg` and `draw_scene`. Instead of executing a risky code patch directly, I think it’s safer to modify paths and functions in the original script. I need to ensure that the frame count remains the same while managing background compositing effectively. Using a copy of the original script for these changes seems like a good idea.",
"finish_reason": "tool_calls",
"reasoning_content": "**Improving render quality**\n\nI'm looking at the backgrounds and deciding which ones to keep. The second background with a barcode label is a discard, but the third one with no text seems okay. I want to render the upgraded version, using a script that adapts AI backgrounds to fill a 1080x1920 space with a blurred and darkened effect. Since the AI background already has a phone/box, I can draw the phone over it, but keeping high-quality detailed images is essential. I'm considering creating a compositing script to manage these layers effectively.\n**Creating a rendering script**\n\nI'm planning to create a new script, `render_ai_upgrade.py`, based on the original one, possibly simplifying functions by importing from a temporary location. I might import the render script as a module and use monkey patching for functions like `draw_bg` and `draw_scene`. Instead of executing a risky code patch directly, I think it’s safer to modify paths and functions in the original script. I need to ensure that the frame count remains the same while managing background compositing effectively. Using a copy of the original script for these changes seems like a good idea.",
"codex_reasoning_items": [
{
"type": "reasoning",
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"id": "rs_010f4e1931265670016a160944a1408191bef08d200c1ecc32",
"summary": [
{
"type": "summary_text",
"text": "**Improving render quality**\n\nI'm looking at the backgrounds and deciding which ones to keep. The second background with a barcode label is a discard, but the third one with no text seems okay. I want to render the upgraded version, using a script that adapts AI backgrounds to fill a 1080x1920 space with a blurred and darkened effect. Since the AI background already has a phone/box, I can draw the phone over it, but keeping high-quality detailed images is essential. I'm considering creating a compositing script to manage these layers effectively."
},
{
"type": "summary_text",
"text": "**Creating a rendering script**\n\nI'm planning to create a new script, `render_ai_upgrade.py`, based on the original one, possibly simplifying functions by importing from a temporary location. I might import the render script as a module and use monkey patching for functions like `draw_bg` and `draw_scene`. Instead of executing a risky code patch directly, I think it’s safer to modify paths and functions in the original script. I need to ensure that the frame count remains the same while managing background compositing effectively. Using a copy of the original script for these changes seems like a good idea."
}
]
}
],
"tool_calls": [
{
"id": "call_ZOUn9MIfbk2eUBbdbOtRbQNc",
"call_id": "call_ZOUn9MIfbk2eUBbdbOtRbQNc",
"response_item_id": "fc_010f4e1931265670016a16094e13dc8191a8a1007d01b4833c",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"cp /tmp/autoshorts_delivery_sms_trap_render.py /tmp/autoshorts_delivery_sms_trap_render_ai_upgrade.py\\npython - <<'PY'\\nfrom pathlib import Path\\np=Path('/tmp/autoshorts_delivery_sms_trap_render_ai_upgrade.py')\\ns=p.read_text()\\ns=s.replace(\\\"OUT = Path('/tmp/autoshorts_delivery_sms_trap_35s/postpreview_v1')\\\", \\\"OUT = Path('/tmp/autoshorts_delivery_sms_trap_35s/ai_visual_upgrade_v1/postpreview_v2')\\\")\\ns=s.replace(\\\"RAW_VIDEO = OUT / 'delivery-sms-trap-35s_raw.mp4'\\\", \\\"RAW_VIDEO = OUT / 'delivery-sms-trap-35s_ai_visual_upgrade_raw.mp4'\\\")\\ns=s.replace(\\\"FINAL_VIDEO = OUT / 'delivery-sms-trap-35s_postpreview_v1.mp4'\\\", \\\"FINAL_VIDEO = OUT / 'delivery-sms-trap-35s_ai_visual_upgrade_v2.mp4'\\\")\\ns=s.replace(\\\"CONTACT = OUT / 'render_contact_sheet_v1.jpg'\\\", \\\"CONTACT = OUT / 'render_contact_sheet_ai_visual_upgrade_v2.jpg'\\\")\\ninsert=\\\"\\\"\\\"\\nAI_BG_HOOK = Path('/tmp/autoshorts_delivery_sms_trap_35s/ai_visual_upgrade_v1/ai_bg_01.png')\\nAI_BG_SAFE = Path('/tmp/autoshorts_delivery_sms_trap_35s/ai_visual_upgrade_v1/ai_bg_03.png')\\n\\n\\ndef cover_image(path: Path, t: float, *, safe: bool=False):\\n src = Image.open(path).convert('RGB')\\n # deterministic subtle push-in; cover crop to exact 1080x1920\\n scale = max(W/src.width, H/src.height) * (1.03 + 0.018*math.sin(t*0.35))\\n nw, nh = int(src.width*scale), int(src.height*scale)\\n im = src.resize((nw, nh), Image.Resampling.LANCZOS)\\n # slight horizontal drift, still deterministic\\n cx = (nw-W)//2 + int(18*math.sin(t*0.18))\\n cy = (nh-H)//2\\n im = im.crop((cx, cy, cx+W, cy+H))\\n # keep AI background premium but subordinate to deterministic UI/captions\\n im = im.filter(ImageFilter.GaussianBlur(radius=1.1 if not safe else 0.7))\\n overlay = Image.new('RGBA', (W,H), (5, 9, 18, 96 if not safe else 72))\\n im = Image.alpha_composite(im.convert('RGBA'), overlay).convert('RGB')\\n return im\\n\\\"\\\"\\\"\\ns=s.replace(\\\"VOICE = 'en-US-GuyNeural'\\\\n\\\", \\\"VOICE = 'en-US-GuyNeural'\\\\n\\\"+insert)\\nstart=s.index('def draw_bg(draw, t):')\\nend=s.index('\\\\ndef draw_phone', start)\\nnew_draw_bg=\\\"\\\"\\\"def draw_bg(draw, t):\\n # AI-generated text-free premium background; deterministic scam UI remains renderer-owned.\\n base_path = AI_BG_SAFE if t >= 15.5 else AI_BG_HOOK\\n im = cover_image(base_path, t, safe=t>=15.5)\\n # add mild urgency/safety deterministic colour wash by scene\\n overlay = Image.new('RGBA', (W,H), (0,0,0,0))\\n od = ImageDraw.Draw(overlay)\\n if t < 15.5:\\n pulse = 0.5 + 0.5*math.sin(t*2.2)\\n for r in range(480, 1080, 90):\\n alpha = int(30*pulse*(1-r/1200))\\n od.ellipse((W//2-r, 210-r, W//2+r, 210+r), fill=(255,72,50,max(0,alpha)))\\n else:\\n od.rectangle((0,0,W,H), fill=(0,28,40,34))\\n return Image.alpha_composite(im.convert('RGBA'), overlay).convert('RGB')\\n\\n\\\"\\\"\\\"\\ns=s[:start]+new_draw_bg+s[end+1:]\\n# replace call logic in draw_scene: currently creates base/draw then overlay alpha composite\\nold=\\\"\\\"\\\" img = Image.new('RGB',(W,H),(8,14,28))\\n draw=ImageDraw.Draw(img)\\n overlay = draw_bg(draw, t)\\n if overlay:\\n img = Image.alpha_composite(img.convert('RGBA'), overlay).convert('RGB')\\n draw=ImageDraw.Draw(img)\\n\\\"\\\"\\\"\\nnew=\\\"\\\"\\\" img = draw_bg(None, t)\\n draw=ImageDraw.Draw(img)\\n\\\"\\\"\\\"\\ns=s.replace(old,new)\\np.write_text(s)\\nprint(p)\\nPY\",\"timeout\":120}"
}
}
]
},
{
"role": "tool",
"name": "terminal",
"content": "{\"output\": \"/tmp/autoshorts_delivery_sms_trap_render_ai_upgrade.py\", \"exit_code\": 0, \"error\": null}",
"tool_call_id": "call_ZOUn9MIfbk2eUBbdbOtRbQNc"
},
{
"role": "assistant",
"content": "Need run and regenerate manual ass issue. Could run script then manual ASS similar.",
"reasoning": null,
"finish_reason": "tool_calls",
"codex_message_items": [
{
"type": "message",
"role": "assistant",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "Need run and regenerate manual ass issue. Could run script then manual ASS similar."
}
],
"id": "msg_010f4e1931265670016a16096993ac819198e20df6459da1d5",
"phase": "commentary"
}
],
"tool_calls": [
{
"id": "call_WKmkFvYCf8BbvEeIdGT1yoob",
"call_id": "call_WKmkFvYCf8BbvEeIdGT1yoob",
"response_item_id": "fc_010f4e1931265670016a16096a404c8191b4403adc6477478b",
"type": "function",
"function": {
"name": "terminal",
"arguments": "{\"command\":\"python /tmp/autoshorts_delivery_sms_trap_render_ai_upgrade.py >/tmp/delivery_ai_upgrade_render.log && python - <<'PY'\\nfrom pathlib import Path\\nimport re, subprocess, json\\nOUT=Path('/tmp/autoshorts_delivery_sms_trap_35s/ai_visual_upgrade_v1/postpreview_v2')\\nASS=OUT/'captions.ass'; AUDIO=OUT/'delivery_sms_trap_voice.mp3'; FRAMES=OUT/'frames'; FINAL=OUT/'delivery-sms-trap-35s_ai_visual_upgrade_v2.mp4'\\nSCRIPT=[('This delivery text is designed to make you panic.',0.0),('It looks normal.',3.2),('A package.',4.6),('A tiny fee.',5.6),('A deadline.',6.7),('The red flag is the pressure.',8.4),('Scammers want you to tap before you think.',11.7),(\\\"Don\\\\'t tap the link.\\\",16.0),('Open the delivery app yourself.',18.2),('If it is real, it will still be there.',21.2),('Save this.',25.2),('Small fee.',26.6),('Short deadline.',27.7),('Suspicious link.',28.8),('If it makes you hurry, slow down.',31.0)]\\nD=float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nw=1:nk=1',str(AUDIO)], text=True).strip())+0.45\\nwords=[]\\nfor idx,(sent,start) in enumerate(SCRIPT):\\n end=SCRIPT[idx+1][1]-0.15 if idx+1=6: phrases.append(cur); cur=[]\\n if cur: phrases.append(cur)\\ndef ts(sec):\\n h=int(sec//3600); m=int((sec%3600)//60); s=sec%60; return f'{h}:{m:02d}:{s:05.2f}'\\nlines=['[Script Info]','ScriptType: v4.00+','PlayResX: 1080','PlayResY: 1920','WrapStyle: 2','','[V4+ Styles]','Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding','Style: Default,DejaVu Sans,60,&H00FFFFFF,&H0000D7FF,&HCC07101E,&H00000000,-1,0,0,0,100,100,0,0,1,4,2,2,70,70,190,1','','[Events]','Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text']\\nfor phrase in phrases:\\n pend=phrase[-1]['start']+phrase[-1]['duration']+0.08\\n for i,w in enumerate(phrase):\\n start=w['start']; end=phrase[i+1]['start'] if i+1/tmp/delivery_ai_upgrade_render.log && python - <<'PY'\\nfrom pathlib import Path\\nimport re, subprocess, json\\nOUT=Path('/tmp/autoshorts_delivery_sms_trap_35s/ai_visual_upgrade_v1/postpreview_v2')\\nASS=OUT/'captions.ass'; AUDIO=OUT/'delivery_sms_trap_voice.mp3'; FRAMES=OUT/'frames'; FINAL=OUT/'delivery-sms-trap-35s_ai_visual_upgrade_v2.mp4'\\nSCRIPT=[('This delivery text is designed to make you panic.',0.0),('It looks normal.',3.2),('A package.',4.6),('A tiny fee.',5.6),('A deadline.',6.7),('The red flag is the pressure.',8.4),('Scammers want you to tap before you think.',11.7),(\\\"Don\\\\'t tap the link.\\\",16.0),('Open the delivery app yourself.',18.2),('If it is real, it will still be there.',21.2),('Save this.',25.2),('Small fee.',26.6),('Short deadline.',27.7),('Suspicious link.',28.8),('If it makes you hurry, slow down.',31.0)]\\nD=float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nw=1:nk=1',str(AUDIO)], text=True).strip())+0.45\\nwords=[]\\nfor idx,(sent,start) in enumerate(SCRIPT):\\n end=SCRIPT[idx+1][1]-0.15 if idx+1=6: phrases.append(cur); cur=[]\\n if cur: phrases.append(cur)\\ndef ts(sec):\\n h=int(sec//3600); m=int((sec%3600)//60); s=sec%60; return f'{h}:{m:02d}:{s:05.2f}'\\nlines=['[Script Info]','ScriptType: v4.00+','PlayResX: 1080','PlayResY: 1920','WrapStyle: 2','','[V4+ Styles]','Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding','Style: Default,DejaVu Sans,60,&H00FFFFFF,&H0000D7FF,&HCC07101E,&H00000000,-1,0,0,0,100,100,0,0,1,4,2,2,70,70,190,1','','[Events]','Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text']\\nfor phrase in phrases:\\n pend=phrase[-1]['start']+phrase[-1]['duration']+0.08\\n for i,w in enumerate(phrase):\\n start=w['start']; end=phrase[i+1]['start'] if i+1