{ "timestamp": "2026-06-02T18:57:07.779790", "session_id": "20260602_185524_9d65cb97", "reason": "max_retries_exhausted", "request": { "method": "POST", "url": "https://chatgpt.com/backend-api/codex/responses", "headers": { "Authorization": "Bearer eyJhbGci...-wis", "Content-Type": "application/json" }, "body": { "model": "gpt-5.5", "instructions": "# 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\n# Finishing the job\nWhen the user asks you to build, run, or verify something, the deliverable is a working artifact backed by real tool output — not a description of one. Do not stop after writing a stub, a plan, or a single command. Keep working until you have actually exercised the code or produced the requested result, then report what real execution returned.\nIf a tool, install, or network call fails and blocks the real path, say so directly and try an alternative (different package manager, different approach, ask the user). NEVER substitute plausible-looking fabricated output (made-up data, invented file contents, synthesised API responses) for results you couldn't actually produce. Reporting a blocker honestly is always better than inventing a result.\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 - kanban-codex-lane: Use when a Hermes Kanban worker wants to run Codex CLI as...\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-article-illustrator: Article illustrations: type × style × palette consistency.\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 - algorithmic-trading-bot-safety: Safety-first engineering workflow for API-based algorithm...\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 - 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 - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - astro-marketing-websites: Build maintainable Astro marketing/company websites with ...\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 - hermes-s6-container-supervision: Modify, debug, or extend the s6-overlay supervision tree ...\n - local-business-website-redesign: Redesign and rebuild maintainable local business / trades...\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\n\nPython toolchain: python3=3.11.15 (no pip module), pip→python3.12 (mismatch), PEP 668=yes (use venv or uv), uv=installed.\n\nActive Hermes profile: default. Other profiles (if any) live under ~/.hermes/profiles//. Each profile has its own skills/, plugins/, cron/, and memories/ that affect a different session than this one. Do not modify another profile's skills/plugins/cron/memories unless the user explicitly directs you to.\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![alt](url) and they will be sent as native photos.\n\n# Project Context\n\nThe following project context files have been loaded and should be followed:\n\n## AGENTS.md\n\n# GLOBALE ORCHESTRIERUNGS-REGELN\n\n## 1. Rolle\nDu bist JARVIS, der Chef-Architekt und Orchestrator. Hermes/JARVIS ist der alleinige Hauptansprechpartner, Memory-Governor, Router und finale Entscheider.\n\n## 2. Ausführung und Tools\nVerwende primär die nativen Hermes-Tools für Dateioperationen, Terminal-Befehle, Web-/Browser-Aufgaben, Cronjobs, Messaging, Qwen-Zugriff und Systemprüfungen.\n\nOpenClaw/FRIDAY ist nicht mehr Standard-Executor. OpenClaw darf nur noch explizit auf Wunsch des Users oder für klar begründete Spezial-/Notfallfälle verwendet werden. Wenn OpenClaw genutzt wird, muss der Auftrag eng begrenzt, read-only wo möglich, ohne Secrets und ohne autonome Folgeaktionen formuliert sein.\n\n## 3. Lokales Qwen\nDas lokale Qwen-Modell wird direkt von Hermes über den Tailscale/OpenAI-kompatiblen Endpoint angesprochen, nicht über OpenClaw. Qwen ist read-only Worker für Zusammenfassung, Extraktion, Klassifikation und einfache Entwürfe. Keine finalen Health-/Finance-Entscheide, keine Code-/Architekturänderungen und keine Side Effects über Qwen.\n\n## 4. Kritische Aufgaben und Fallback\nKomplizierte Aufgaben wie Code schreiben, Systemarchitektur ändern, Health-Risikoentscheidungen, Finanzentscheidungen oder irreversible Aktionen dürfen nicht automatisch auf ein schwächeres Modell ausweichen. Bei GPT-5.5 Rate-Limit oder Ausfall: pausieren und Sir fragen.\n\n## 5. Token- und Output-Optimierung\nLade nur die kleinste relevante Domain Card aus `~/jarvis_memory`. Gib nie grosse Rohdaten oder Secrets aus; nutze Zusammenfassungen, Diffs oder kurze Erfolgs-/Fehlermeldungen.\n\n══════════════════════════════════════════════\nMEMORY (your personal notes) [95% — 2,091/2,200 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; A5000; Chatterbox 100.101.173.25:8004.\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): **OAuth-Account:** friday.uplink@gmail.com\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. Family/Erziehung card: ~/jarvis_memory/41_family_education.md.\n§\nGasser Website: ~/projects/GasserWebsite -> Gamexgit/GasserWebsite. Hero: aligned Bleistiftzeichnung_beton_neu.png + überlagerung_realer.png. Style: Sichtbeton+Bordeaux, trust/conversion, premium G; no blueprint/WebGL/G-frame. Transitions: soft blend, subtle logo.\n§\nCryptoTradingBot: `~/projects/CryptoTradingBot/Crypto_Agent`; secrets in `~/.hermes/secrets`; future wallet 500 USDC. Size/leverage by backtests+risk; paper first; no live/secrets without approval.\n§\nGasser Website: user prefers step-by-step visual tuning; preserve approved animations and separate mobile vs desktop behavior for animation/layout fixes.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [95% — 1,308/1,375 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: ERF, EN, manual approval: MP4+title+YT desc+TikTok desc+approval cmd+duration; no thumbnails. Chatterbox Taylor. High-quality AI images preferred. Captions must work silently. No newsletter/30-day course for now; use description checklists. Goal: shares/saves/follows/trust.\n\nConversation started: Tuesday, June 02, 2026\nModel: gpt-5.5\nProvider: openai-codex\n\n[System note: The user's previous session expired due to inactivity. This is a fresh conversation with no prior context.]\n\n## Current Session Context\n\n**Source:** Telegram (group: JARVIS Command Center, thread: 4850)\n**Session type:** Multi-user thread — messages are prefixed with [sender name]. Multiple users may participate.\n**Connected Platforms:** local (files on this machine), telegram: Connected ✓\n\n**Home Channels (default destinations):**\n - telegram: Home (ID: 1344779884)\n\n**Delivery options for scheduled tasks:**\n- `\"origin\"` → Back to this chat (JARVIS Command Center)\n- `\"local\"` → Save to local files only (~/.hermes/cron/output/)\n- `\"telegram\"` → Home channel (Home)\n\n*For explicit targeting, use `\"platform:chat_id\"` format if the user provides a specific chat ID.*", "input": [ { "role": "user", "content": "[M G] ok, erstelle das nächste video." }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwsAU9JbyOjws7hHPwHcDC2dp_0E5HfZ8uVFC1sGW9ZaWyzTkQ1Z9oPFgjzl54ES424LbTZFaoBLg-DBbURXH_huY7xnXY0EWtFdqZ4RwcsQreMRP6AHLPuxAETxNZCEH9b7zgcOz7MGtRofOT_bd6uk5w7AMVqmH42gHqz9c6XRflvM_-2Q--oQ6HpYcuj0_b3G8jc807-4IMdBlsu_LFi81rTBPO8GvL7na-aZredc-bqLwtLc0mu66-kkHJ22BnY67Ddx67HBYcGVtRu5U3WUlDlIxCBUhLP6gM-WvwohbLZcSg4NtgZ3CKc-IIOaGVGiIaCtveTJkZ3-ut2JYBcy1uYnipjR8h3Gv57s9b0kBF2ja5mtU4PbYAV_PMx5arEblr1JvVIZukBC6cpBnITehYXX1MD0_C1iIbGqtHr7LV4DKERlbSZxwnW8mjmnlwp22Qkaw4yvXnDY7FKDwsfQ0xfn07PMcyvG3tPCJUbNH9wqbpRfST062-Yb4jeGUrtebcEMHEuacKF2yHJClvthZCW7PMOrDYOTZlAmc9vAR1FH9emHwWVRHm55l5SXmT0nqfl2jnagLhHzC7j7URmUHx6onCVyPAp9IfRTcK2J_Kxmyam7f9Mcs7Dpd9FUazOtlPK4xg9fJyOMp70dFKqNVruezYN1jOCGmXJpZhtOTa_p9uMVuJn8udHK94x-WNHplz_aFXvAnN6IzHqtJqDMtZzC1UYggdI33JLkdqJ8NRuuI8N9FQvqkkop3wT4XrGLDahRUIqIeuxtAAlU8hqw35JIUorx8HfGSZQcA7fPn2iQGl5RlEN75dlHCTCjpj7Ps8X5tZ5kiUxiQ0tpnUe_HhhSd3NtljnNFUHVWo_Ss6sKmRiEg7fBjlxCV1SceG4nHH1boJkCQ2FQ_X3o0LuXoU6p7gd7QVv4pirY0kMfTSE3DMay_nMdYylYOOKKAPvljmD2FY3LrlNEQ6sqVH7o0ssGSejVBebYsAvQDxsailEtvQAlnHq4J7u4PsJTsGuDgG8W9y3bQ8fAMBLoTmH1IcA25q6AcT7X9chCGAwwFoduwSe17alo5UTWL2LiVuLIwBtoqu5w4fZ0AJtoYWKmVyczpL6pgAJk26d47T4Q8MnulvmsdOsrRC0zu3RD5TQqqpPo4JoqND1wXjfmxgURz7NPRT2D6_19mnYiRcVlHIk=", "summary": [ { "type": "summary_text", "text": "**Determining response context**\n\nI see that I need to respond in German, and I'm curious about the context of AutoShorts. \"nächstes video\" seems to refer back to something earlier. I should probably perform a session search to dig deeper. Additionally, I'm wondering if skills related to social media, short-form content automation, or YouTube content will be relevant. I'll need to load that information to provide a well-informed response." } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_n2sYxWeX6Xc7e0SWitZuoJc0", "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}" }, { "type": "function_call_output", "call_id": "call_n2sYxWeX6Xc7e0SWitZuoJc0", "output": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\n## Reference index\\n\\n- `references/pixabay-music-and-hashtag-policy.md` — user-corrected AutoShorts music policy: prefer licensed Pixabay/stock beds over homemade/generated music, handle Pixabay audio/video API quirks, and keep scam-short hashtags to 4–7 targeted tags.\\n- `references/jamendo-music-bed-workflow.md` — official Jamendo API workflow for licensed music beds: credential parsing, conservative CC BY filtering, license metadata, FFmpeg ducking, and hash-bound approval-package updates after mixing.\\n\\n## Umbrella consolidation notes\\n\\nThis is the class-level umbrella for short-form social-video automation, growth-engine strategy, approval-gated rendering, manual/social posting packs, and analytics loops. Former narrow sibling skills were absorbed as support references so agents should load this one skill first, then open the relevant reference when needed:\\n\\n- `references/absorbed-shortform-growth-automation.md` — earlier semi-automatic shortform growth workflow, platform strategy, human review loop, and AutoShortsBot bootstrap notes.\\n- `references/absorbed-social-media-shorts-growth-engine.md` — growth-engine strategy, JARVIS-operated/human-approved pipeline, deterministic implementation foundations, manual posting packs, and TrueTrace positioning notes.\\n- `references/absorbed-social-shorts-automation.md` — audit workflow for existing shorts bots, hook quality gates, early-account duration guidance, and initial AutoShortsBot audit reference.\\n- `references/absorbed-social-shorts-growth-engine.md` — concise growth-engine workflow, retention heuristics, platform monetization notes, and implementation guardrails.\\n\\nReference note: for the current main AutoShorts/TrueTraceShorts direction, first use `references/everyday-red-flags-clean-ai-strategy.md`: Everyday Red Flags / digital self-defense for normal people, common scams, simple English, high-quality realistic AI images only, subtitles only, no visual overlays, first image must show the concrete scam instantly, and every video needs red flag + safe action + takeaway. For the repeatable production/render workflow, use `references/everyday-red-flags-clean-ai-render-recipe.md`: 6 concrete AI keyframes, EdgeTTS WordBoundary subtitles, no caption boxes/progress bars, contact-sheet + frame + ffprobe QA, and regenerate if the first frame is not self-explanatory. For thumbnail/cover assets, always use `references/thumbnail-cover-workflow.md`: strong first frame and separate 9:16 thumbnail A/B are both required, `thumbnail_passed != first_frame_passed`, thumbnail text is renderer-owned, max 2 variants, no clickbait, and ReviewPackage fields must include thumbnail paths/hashes/scores/recommendation. Use `templates/manual-posting-pack-with-thumbnails.md` when producing upload-ready packages so video, cover assets, copy blocks, gates, and artifact reports stay consistent. For YouTube platform integration, use `references/youtube-upload-only-manual-analytics-policy.md`: upload-only OAuth scope, private-draft uploads only after exact hash-bound approval, no read scopes, no comments, no Analytics API, and manual CSV-only learning reports. For this user's AutoShorts approval requests, include the Telegram-playable MP4 attachment, proposed title, engaging proposed description, and exact hash-bound approval command in one review message. For this user, every newly delivered AutoShorts/TrueTraceShorts video must include the exact hash-bound `APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD ...` command in the review message so the user can return it unchanged for private-draft approval. Place the video duration immediately after the approval-code block at the end of the message. Do not require, attach, or set thumbnails for future approval packages unless the user explicitly asks; the user will set thumbnails manually. For headless OAuth where the user pastes a localhost callback URL into chat, use `references/youtube-manual-pkce-callback-token-exchange.md`: verify state + redirect URI + exact scope + matching PKCE verifier before token exchange, write token only after scope validation, chmod `0600`, delete temporary state, and never echo codes/tokens/secrets. For approved-video Drive staging, use `references/upload-freigabe-drive-workflow.md`: upload the MP4 plus thumbnail assets/contact sheet plus a matching DOCX with YouTube/TikTok copy blocks to Google Drive `AutoShortsBot/Upload Freigabe`. If a hash-bound YouTube private-upload package exists and the exact approval command was returned, but the repo-specific upload CLI is unavailable or has moved, use `references/youtube-private-upload-direct-fallback.md`: validate package approval, privacy, video SHA256, and exact upload-only OAuth scope, then call only `videos.insert` and write an audit; do not attempt thumbnails when the user sets them manually. If the package was saved beside the render artifact rather than the repo default package directory, set `AUTOSHORTS_YOUTUBE_PACKAGE_DIR` for dry-run and execute. If the user replies with natural-language approval immediately after one review package, proceed only by reusing the saved package's exact `approval_command` that was just delivered and after a valid dry-run; otherwise ask for the exact command. See `references/controlled-pilot-production-v2.md` for the controlled first-video workflow: resource tiers, media-generation gates, pilot package shape, Wan prompt planning without generation, and no auto-posting. See `references/pilot-preview-render-v2.md` for the next approval step: `APPROVED_FOR_PREVIEW_RENDER`, EdgeTTS preview audio, time/word cue generation, deterministic 9:16 animatic MP4, checksums, and Telegram media delivery without Wan/AI-image/platform side effects. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected caption style and controlled final pilot render pattern: static 3–7 word phrase segments with only the active spoken word highlighted, hash-bound final render gates, one Wan text-free background layer, deterministic overlays, and draft-only manual posting packages. For the current accepted TrueTrace proof-short postpreview standard — detailed AI-generated UI/screen keyframes with readable concrete text, no focus lines/arrows/boxes, active-word subtitles, sentence-boundary caption segmentation, and explicit 9:16 export QA — use `references/detailed-ai-screen-postpreview-pattern.md`. For the current TrueTraceShorts strategic repositioning, use `references/digital-red-flags-scam-self-defense-strategy.md`: Scam Self-Defense for normal people, `One screen. One red flag. One safer move.`, screen/mockup-led scam self-defense pillars, simple English, deterministic scam mockups, and review packages before rendering. For selecting the next topic and choosing the correct video length, use `references/everyday-red-flags-topic-database-and-duration-policy.md`: normalized 12-category / 185-topic idea database paths, already-produced topic hints, status update pattern, duration-by-understanding ranges, and the early goal of shares/saves/followers/trust over ad monetization. If a final render is rejected as preview-overlay-dominated or not visual-first, follow `references/visual-first-final-render-repair.md`: mark the render rejected/not-postable, fix Wan output parsing, enforce final overlay limits, produce only a short visual proof under `APPROVED_FOR_VISUAL_PROOF`, and do not run another full final until the visual direction is accepted. If even the visual proof is rejected as abstract/generic/dark or “blind T2V”, switch to `references/keyframe-first-final-visuals.md`: plan and approve start/end/style keyframes first, then run only a short I2V/first-last-frame proof with minimal renderer-owned overlays. If the user says the production method itself is wrong, keyframes are not I2V-ready, or an animatic was incorrectly substituted for a failed Wan proof, follow `references/visual-production-reset-i2v-first.md`: reject/audit the proof, stop content rendering, run technical I2V sanity first, then generate at most three styleframes for direction review. If deterministic layout proofs are useful as composition blueprints but rejected as too wireframe/PowerPoint for final style, switch to `references/premium-ai-styleframe-direction.md`: use the layouts only as mechanism blueprints, generate at most 3 tightly briefed premium text-free AI styleframes, deliver a contact sheet with verified media metadata, and keep Wan/I2V/endframes/full-final blocked until style direction approval. If those premium styleframes are better quality but still too abstract, switch to `references/real-life-premium-styleframes.md`: anchor the mechanism in a realistic work/meeting/desk scene, keep all readable overlays renderer-owned, generate at most 3 new real-life styleframes plus contact sheet, and still block Wan/I2V/endframes/full-final until explicit approval. If the user approves one or more real-life styleframe directions but wants to compare A/B/C before Wan, use `references/prerelease-animatic-preview.md`: build only a small AI-keyframe + deterministic-motion animatic per approved direction, reuse existing voiceover/captions, deliver MP4/contact/QA sheets with metadata, and keep Wan/I2V/final/posting blocked. If the user then rejects the renderer-added circles/arrows/labels/boxes/decision-target markers as destroying the premium look, switch to `references/clean-image-cut-no-overlay-pilot.md`: render only AI images + voiceover + clean subtitles, with a hard subtitles-only NoOverlayPolicy and no Wan/I2V/full-final/posting. If the user performs a strategic reset because the pilot is pretty but semantically weak, stop optimizing that candidate and follow `references/proof-short-real-problem-reset.md`: archive the old pilot as a learning case, enforce the Pretty Empty Visual Blocker, and build a screen/mockup-led Proof Short around an immediately visible real viewer problem before any premium/Wan production. If the next review focuses on image provider quality/provenance or subtitles running off-screen, follow `references/image-provider-audit-and-caption-safezone.md`: audit the configured image provider/model/tier from config + provider registry + cache metadata, run at most one neutral capability test, keep UI/invoice mockups deterministic, and add tested caption layout safe-zone bounds before re-rendering. If an approved Proof Short feels too visually flat and the user asks why no AI images were used, follow `references/proof-short-ai-visual-upgrade.md`: use AI images as a controlled text-free atmosphere/background/keyframe layer while keeping invoices, emails, payment routes, labels, facts, and captions deterministic renderer-owned. For the concrete rendering/QA pattern — small 2–3 keyframe pass, contact-sheet inspection, discard pseudo-text frames, blur/darken/subordinate AI backgrounds, and report used/discarded keyframes — see `references/proof-short-ai-background-rendering-qa.md`. If the user accepts the detailed KI-screen-image direction as “new standard,” follow `references/detailed-ai-screen-proof-shorts.md`: use detailed AI-generated real UI/screen images with readable concrete text (e.g. Outlook-style invite/login/calendar), no focus lines/arrows/boxes, active-word subtitles, and verified 9:16 SAR/DAR export before delivery. For the follow-up A/B test where the user asks whether pure KI images can match or beat hybrid mockups, see `references/clean-ai-cut-vs-hybrid-mechanism.md`: compare clean image-only premium feel against mechanism clarity, keep word-aligned subtitles, and consider `90% clean AI images + 10% minimal deterministic mechanism` as the likely next style. When the user explicitly corrects the workflow to “keine Render overlays” and wants the content entirely in detailed AI images, follow `references/clean-ai-image-cut-production-qa.md`: detailed AI screen-image keyframes carry the story, renderer adds only text-only active-word subtitles, no progress bars/cards/boxes/arrows/focus lines, and QA catches EdgeTTS punctuation-stripping subtitle chunks. For local Chatterbox TTS over Tailscale, use `references/chatterbox-tts-candidate-provider.md`: test only Chatterbox when asked, save WAV + metadata, deliver Telegram voice proposals as media-cache Ogg, keep it as candidate_provider until explicitly promoted, and if voices sound metallic retry standard voices/presets before further tuning. If the user rejects a render because it looks like deterministic mockups/PowerPoint and asks to use high-quality AI images again, use `references/high-quality-ai-image-repair-after-mockup-rejection.md`: treat it as a hard visual-style correction, keep the topic/script if possible, replace the main visual layer with premium AI keyframes, rerender, rebuild hashes/approval package, QA contact sheet and frames, and never reuse the old approval command.\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\nFor Chatterbox voice experiments and candidate voiceovers, use `references/chatterbox-tts-tailscale-candidate-provider.md`: local Chatterbox over Tailscale, Taylor + Scientific Abstract Reading as the current preferred candidate, keep speed at 1.0, pace via punctuation, and treat Chatterbox as candidate-provider only until explicitly promoted. For final-near AutoShorts/TrueTraceShorts renders with Chatterbox, use `references/chatterbox-forced-alignment-shorts-render-pattern.md`: render the WAV first, extract word timestamps with faster-whisper, group captions by source-script punctuation/phrase breakers, use stable two-line active-word subtitles, omit thumbnails by default, and deliver a hash-bound Telegram review package. If captions are too fast or fragmented for mute/silent viewing, use `references/silent-viewer-caption-pacing.md`: rewrite into short complete sentences, add sentence-level pauses, build caption pages from source sentence boundaries, avoid orphan-word pages, and extend duration when needed for comprehension. For duration selection and early growth optimization, use `references/dynamic-duration-and-growth-goals.md`: length is chosen by viewer understanding, default Proof Shorts are 35–45s, and early optimization is shares/saves/followers/trust rather than direct ad monetization. For large user-supplied Everyday Red Flags topic lists or next-batch planning, use `references/everyday-red-flags-topic-database.md`: preserve the user's category/series taxonomy, store it as a structured backlog with topic IDs/slugs/status/priority, mark produced topics to avoid duplicates, and validate topic counts/unique IDs before reporting success.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\nFor this user's TrueTraceShorts / AutoShortsBot strategy, do **not** default to an AI/prompt/workflow channel. The official current positioning is **Digital Red Flags / Scam Self-Defense for Normal People** with the core line `One screen. One red flag. One safer move.` The channel promise is: `I show you the one red flag before you click, pay, scan, or log in.` AI/workflow content is now a secondary/legacy specialist format, not the main feed. Use simple B1/B2 English, normal screens, visible red flags, one safer action, and family/share/save value. See `references/digital-red-flags-scam-self-defense-strategy.md` for the current strategy and `references/truetrace-strategic-neuausrichtung.md` for the older broader mechanism-reveal context. For deterministic phone/SMS scam renders such as Delivery SMS Trap, use `references/deterministic-delivery-sms-proof-render.md`: renderer-owned SMS mockup, short safe fake URL, manual caption-timing fallback when TTS word boundaries are unavailable, and frame-level subtitle/aspect QA before delivery. For broader controlled UI/screen Proof Shorts where critical scam text must remain exact — invoices, payment-detail changes, emails, logins, support popups, marketplace chats — use `references/deterministic-premium-screen-mockup-rendering.md`: deterministic premium 1080x1920 screen mockups, Chatterbox/forced captions, contact-sheet + real-frame QA, stale-keyframe regeneration, and hash-bound review package delivery.\\n\\nWhen the user asks to start implementation work on this strategy, especially “Sprint 1: Strategic Spine Refactor”, follow `references/truetrace-strategic-spine-refactor-v2.md`: implement a side-effect-free V2 spine in parallel to existing V1 models, with mandatory strategic fields, hard quality gates, anti-AI-slop blockers, hash/version-bound ReviewPackageV2, and tests before integration with renderers or platform workflows.\\n\\nWhen the user asks for “Sprint 1.5” or to make the V2 spine operational without production, follow `references/truetrace-v2-operational-bridge.md`: first secure/commit the Sprint-1 baseline, then add only minimal side-effect-free bridges (`to_candidate_v2`, `workflow_v2`, `demo_review_batch_v2`), deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated non-generous scoring, anti-example/human-texture gates, minimal manual claim/disclosure stubs, LinkedIn manual metadata, and a Remotion-as-later-spike note. Keep it capped at 3 demo candidates and avoid rendering, platform APIs, dashboards, auto-posting, or 30-day batches.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - For TrueTraceShorts, prefer the structure `visible fail → identification → mechanism → fix → before/after → memorable takeaway` over generic AI-tip narration. Every video must answer: what goes wrong, why it happens, what rule fixes it, what sentence sticks, why viewers save/share it, and why they follow the series.\\n - For unvalidated first pilots, apply the `Pretty Empty Visual Blocker`: if the first frame is beautiful but does not show a concrete viewer pain without voiceover, block the concept and switch to a Proof Short. Do not keep polishing abstract AI visuals, glass metaphors, or generic laptop B-roll before validating the real problem. See `references/proof-short-real-problem-reset.md`.\\n - After a Proof Short concept is approved for usefulness/clarity, do not interpret `ProofShortMode` as a permanent ban on AI images. If the user wants the video “aufgepeppt”, add a controlled AI visual-upgrade layer: 2–3 text-free premium backgrounds/keyframes, while all invoice/email/UI/payment-route text, labels, facts, captions, claims, and red-flag overlays remain deterministic renderer-owned. See `references/proof-short-ai-visual-upgrade.md`.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n - first-frame clarity\\n - mechanism value\\n - practical payoff\\n - save/share/follow reason\\n - trust/human texture\\n - series fit and saturation defense\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n For TrueTraceShorts, use the 100-point scoring and hard blockers in `references/truetrace-strategic-neuausrichtung.md`: block generic AI hooks, pure text first frames, unsourced claims, missing before/after, missing human texture, unclear AI disclosure, and candidates too similar to recent videos.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - For final renders, Wan must carry the visual scene, not merely provide a blurred/looped background underneath preview cards. Use full-bleed, scene-specific Wan clips, minimal renderer-owned overlays, and a final overlay policy that blocks preview frames, large UI cards/textboxes, debug bars, and overlay overload. If architecture is rejected, create a short visual proof first; see `references/visual-first-final-render-repair.md`.\\n - If a visual proof still looks like blind text-to-video or random abstract AI output, stop T2V and move to a keyframe-first workflow: generate/approve 1–2 text-free start/end/style keyframes for the scene, then attempt only a short Wan I2V/first-last-frame proof. See `references/keyframe-first-final-visuals.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - For AutoShorts/TrueTraceShorts YouTube approval requests, the review message must include all four items together:\\n 1. the actual MP4 as a Telegram-playable media attachment, not only a filesystem path;\\n 2. a proposed title;\\n 3. a proposed engaging description, optionally with tasteful emojis and hashtags;\\n 4. the exact `APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD ...` command the user can return.\\n - If a `MEDIA:/path/to/video.mp4` line does not appear as an inline Telegram player, copy the MP4 into a Hermes media cache/allowed media directory and resend from there before asking for approval. A path in text is not sufficient for this user.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - For AutoShorts/TrueTraceShorts videos that are upload-package ready, include the exact hash-bound YouTube private-draft approval command in a copyable code block with every delivery. The user expects to be able to return that command unchanged for release.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n - If the user says subtitles are irritating or not simultaneous with voiceover, treat this as a blocking quality issue: use forced alignment or provider word-boundary events before sending another final-near clip. For EdgeTTS previews, request `boundary='WordBoundary'` and render active-word highlights from those offsets; do not merely distribute words evenly over sentence SRT durations. For this user's AutoShorts/TrueTraceShorts default, do **not** show one word alone and do **not** slide a changing word-window every word. Use static phrase segments: keep 3–7 words fixed on screen while only the currently spoken word highlights/pulses left-to-right; replace the full segment only at a sentence/phrase boundary. See `references/static-active-word-captions-and-final-pilot-render.md`.\\n - If the user says the ending is confusing, rushed, or does not offer a real solution, treat this as a content-blocking issue, not a minor copy tweak. Rewrite the final third so non-technical viewers are explicitly walked through the concrete action: what to close/avoid, what to open/check instead, how they know it worked, and the memorable rule/fazit. Avoid vague imperatives like “open the service yourself” unless immediately unpacked with a concrete example such as “close the invite; open the calendar/work app from the normal icon or bookmark; if the meeting is real, it will still be there.”\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style.\\n - If deterministic layouts explain the mechanism but look like wireframes/PowerPoint, do not promote them to final style. Treat them as composition blueprints and run a small, tightly prompted premium AI styleframe pass instead: AI supplies glassmorphism/product-film quality and atmosphere; the renderer still owns labels, arrows, captions, facts, and final assembly. See `references/premium-ai-styleframe-direction.md`.\\n - If premium AI styleframes improve quality but still feel like abstract SaaS symbolism, do not continue iterating abstract funnels/rings. Move to real-life anchored work scenes: desk/laptop, empty meeting room/glass board, or project table/work evidence, with only subtle glass/holographic mechanism overlays. See `references/real-life-premium-styleframes.md`.\\n - If real-life styleframe directions are accepted but the user has not approved Wan/I2V yet, insert a pre-release animatic comparison step rather than jumping to endframes or video generation. Create small per-direction AI keyframe sets, deterministic push-ins/crossfades, existing voiceover, static active-word captions, contact sheets, and QA frame sheets; report Telegram delivery metadata. See `references/prerelease-animatic-preview.md`.\\nIf the user rejects the added visual explanation overlays as making the result look like a technical explainer board, treat this as a hard style failure. For the clean pilot, use only AI images, voiceover, and clean subtitles; block circles, arrows, labels, boxes, connector lines, decision-target markers, debug/diagram elements, and caption boxes/bars. See `references/clean-image-cut-no-overlay-pilot.md`. If the user then asks whether pure KI-Bilder can achieve the same or better effect, run an A/B proof against the hybrid version and assess premium look vs mechanism clarity; the likely final style may be `90% clean AI images + 10% minimal renderer-owned mechanism`. See `references/clean-ai-cut-vs-hybrid-mechanism.md`.\\n - For mechanism-reveal final renders, use the hybrid pattern in `references/final-motion-hybrid-rendering.md`: ChatGPT Image/Codex or similar tools create text-free scene backgrounds only; deterministic renderer owns cards, labels, arrows, subtitles, and FFmpeg assembly. Use compact phrase-boundary subtitle cues before 1080 export.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/truetrace-strategic-spine-refactor-v2.md` for implementing the new TrueTraceShorts strategy in code: V2 strategic models, hard gates, anti-AI-slop blockers, SeriesRegistry, 100-point scoring, hash-bound ReviewPackageV2, and deterministic five-candidate examples without rendering or platform side effects. See `references/truetrace-v2-operational-bridge.md` for the next implementation step: secure the Sprint-1 commit, add minimal side-effect-free bridge/workflow/CLI modules, deterministic ReviewPackageV2 JSON, strict approval parsing, calibrated scoring, anti-example/human-texture gates, manual claim/disclosure stubs, LinkedIn manual metadata, and Remotion-as-later-spike documentation without production expansion.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail. See `references/mobile-vertical-aspect-qa.md` for the mobile 9:16 export QA/repair pattern: probe final MP4 SAR/DAR, extract a frame, and re-export full-bleed 1080x1920 with `setsar=1,setdar=9/16` when Telegram/Shorts previews show black bars or squeezed height. See `references/pilot-preview-render-v2.md` for the stricter first-pilot preview pattern: preview-only approval state, EdgeTTS sentence SRT to preview word cues when forced alignment is unavailable, deterministic card/diagram animatic rendering, SHA256/duration verification, and Telegram media delivery while Wan/AI-image/platform calls remain blocked. See `references/static-active-word-captions-and-final-pilot-render.md` for the corrected Hormozi-style caption behaviour and the one-render final pilot review-package checklist. See `references/word-boundary-caption-sync-and-solution-gate.md` for the stronger EdgeTTS `WordBoundary` timing pattern and the explicit solution/fazit gate for Proof Shorts.\\n\\nSee `references/final-motion-hybrid-rendering.md` for the final-near hybrid renderer pattern: ChatGPT Image/Codex or other AI tools provide text-free background/keyframe texture while deterministic renderer overlays own text, arrows, labels, captions, and final assembly. For quick prototypes, prefer ChatGPT Image/Codex backgrounds before local Qwen/ComfyUI unless privacy/cost/local-only constraints dominate. After a 1080 export, create a side-effect-free final approval package with media path, SHA256, platform metadata, social copy, quality notes, and approval commands before any delivery or platform step. If Telegram/mobile playback shows black bars, squeezed height, wrong aspect metadata, clipped UI text, or cramped active-word captions, follow `references/mobile-safe-vertical-export-and-caption-qa.md`: normalize to 1080x1920 square pixels with explicit `setsar=1,setdar=9/16`, verify with ffprobe, and inspect QA frames before sending again.\\n\\nSee `references/visual-first-final-render-repair.md` for the repaired final-render architecture after a rejected pilot: mark rejected renders as not-postable, avoid preview-board overlays, enforce `FinalOverlayPolicy`, validate ComfyUI/Wan output histories robustly, and use `APPROVED_FOR_VISUAL_PROOF` for one short visual proof before any second full final.\\n\\nSee `references/hybrid-scene-backgrounds-codex.md` for the concrete ChatGPT Image/Codex scene-background workflow: side-effect-free `SceneBackgroundPlan`, per-scene text-free backgrounds, renderer-owned overlays, and visual QA before review delivery. Prefer this over local Qwen/ComfyUI when the user allows Codex image generation and local GPU should be reserved for video/model work.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/monetization-map-description-checklists.md` for the accepted current monetization pattern: no newsletter or 30-day learning program for now, use topic-specific 3-bullet YouTube checklist blocks, short TikTok/Reels checklist captions, and cautious affiliate partner fit/claim guardrails. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n- When cleaning/consolidating this user's AutoShortsBot runtime folders, use `references/autoshortsbot-runtime-cleanup-and-canonical-path.md`: prefer `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot` as the canonical future repo, treat `/home/agent/jarvis_runtime/AutoShortsBot` as a divergent older/production-artifact copy to inspect before removal, and prefer rename-and-test over immediate deletion.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- If Telegram/mobile playback shows top/bottom black bars or a visibly squeezed vertical image, do not hand-wave it as a player quirk. Re-export through an explicit mobile-safe normalization boundary (`scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920,setsar=1,setdar=9/16,format=yuv420p`), verify SAR/DAR with ffprobe, and inspect extracted frames before redelivery; see `references/mobile-safe-vertical-export-and-caption-qa.md`.\\n- Do not let active-word captions become unreadable compounds. In uppercase/bold 1080-wide renders, even 3–7 word phrase chunks can merge visually; use shorter 1–3 word chunks or larger spacing/font shrink, then QA a real frame after rendering. EdgeTTS word-boundary events may strip punctuation, so do not group caption chunks only by `WordBoundary` token punctuation; align back to source-script token punctuation or use phrase breakers to avoid cross-sentence chunks like “SPOTS THIRD GIVE”.\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not keep iterating a polished pilot when the user identifies the core failure as “pretty but semantically weak.” Archive it as a learning case, preserve what worked, and restart with a Proof Short whose first second shows a real visible fail and an immediately useful rule.\\n- Do not overcorrect from “validate with Proof Shorts” into “never use AI images.” Once the real problem/rule is approved, AI images can legitimately improve perceived quality as text-free atmosphere, backgrounds, keyframes, or thumbnails. Keep factual UI/document/payment layers deterministic; use AI for polish, not truth.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, visual checks for clipped/overcrowded text, and hard mobile safe-zone tests for captions/highlights before delivery.\\n- Do not deliver final-near Shorts/Reels/TikTok MP4s based only on nominal render dimensions. Probe `width,height,sample_aspect_ratio,display_aspect_ratio`, extract QA frames, and repair black bars or squeezed-height playback with a full-bleed 1080x1920 square-pixel export (`setsar=1,setdar=9/16`) before resending.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not assume local Qwen/ComfyUI is the best first image step for every final-near short. If the user permits ChatGPT Image/Codex, use it for text-free scene backgrounds/keyframes when the deterministic renderer owns labels/captions and local GPU should be saved for video/model work. When provider quality matters, verify the actual `image_gen.provider`/model from config and the image cache filenames/metadata rather than guessing; for premium styleframes prefer the highest available reasonable GPT Image tier, but keep fake invoice/email/UI/payment-route mockups deterministic.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- For Chatterbox/Taylor voiceovers, do not slow speech with `speed` or `speed_factor` if the user is judging quality; it can make the voice metallic/blechern. Keep speed at `1.0` and control pacing through punctuation, shorter sentence chunks, and pauses.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not treat a keyframe animatic as a successful Wan/I2V proof. If ComfyUI/Wan I2V fails or history contains no valid video output, label any fallback MP4 as `not_wan` / composition-review-only and block full-final progression.\\n- Do not treat deterministic layout proofs as the final premium visual style when the user asked for a high-end social-video look. Layouts are for mechanism composition; after approval, use a tightly scoped premium AI styleframe pass with strict negative prompts and verified delivery before endframes/Wan.\\n- Do not mistake premium abstract glassmorphism for an approved final direction if the user wants a credible everyday work scene. When the user says the motif is still too abstract, switch to real-life work-scene styleframes with laptop/tablet/meeting-room/project-table anchors; keep motion graphics subtle and renderer-owned.\\n- If the user rejects the production method itself, do not keep iterating content visuals. Reject/audit the proof, run a technical I2V sanity test with a trivial non-content image, and only after it passes generate a small set of styleframes for direction review; see `references/visual-production-reset-i2v-first.md`.\\n- Do not keep trying blind text-to-video after the user rejects a proof as abstract, dark, generic, or semantically ungrounded. Move to keyframe-first planning, review the keyframes, then run at most one short I2V/first-last-frame proof.\\n- For TrueTraceShorts after the Digital Red Flags repositioning, do not suggest AI/workflow/prompt topics as the default next post. Start from normal-person scam self-defense: delivery texts, fake support popups, marketplace buyer scams, fake QR/login pages, invoice/payment route changes, password reuse, or family/emotion scams. Each candidate must show one normal screen, one visible red flag, and one safer move before any render.\\n- Do not call the audience “Boomers” externally even when designing boomer-safe content. Use “Digital self-defense for normal people”; keep language simple, readable, and shareable without being patronizing.\\n- Do not use AI image generation to invent critical scam-screen text/data when a deterministic mockup is safer. AI can add premium atmosphere; renderer-owned mockups should control SMS, invoices, bank/login pages, payment routes, QR situations, marketplace chats, popups, captions, and checklist cards.\\n- For this user's TrueTraceShorts visual standard, do not fall back to plain deterministic mockup/PowerPoint-looking videos when the user asked for the polished clean-AI-image direction. If deterministic UI is needed for factual control, integrate it with high-quality AI-generated scene/keyframe visuals so the final feels premium, not like a renderer demo.\\n- For deterministic SMS/phone proof shorts, do not let fake URLs or UI strings run off the card. Use short reserved-domain text such as `example.com/hold`, then inspect an extracted hook frame before delivery.\\n- Do not assume TTS word-boundary events were emitted just because TTS audio rendered. Check the word-boundary count; if it is zero, regenerate active-word ASS subtitles from a manual sentence-start timing fallback and QA a real subtitle frame.\\n- When creating the next numbered Everyday Red Flags video by adapting the previous render script, update every candidate-specific identifier, not only the visible title/script: `CANDIDATE_ID`, output/post-candidate slug, VERSION if policy changed, audio/word/metadata filenames, final MP4 filename, image/keyframe names, package title/description/hashtags/tags, topic-database `topic_id` and `existing_artifact_hint`, media-cache path, and any stale comments/placeholders. Stale IDs in approval packages or topic DB make later upload/dry-run approval brittle — tedious, yes, but so is explaining to YouTube that `erf-010` is secretly `erf-013`. For the repeatable “next video” workflow from a prior known-good renderer, see `references/next-erf-video-render-from-prior-script.md`: choose topic, generate 5–6 premium keyframes, clone/rename the prior script carefully, run end-to-end, QA contact sheet + hook/final frames, update topic DB, and deliver a media-cache MP4 with hash-bound approval command.\\n- Do not remove old AI/workflow pillars abruptly when refactoring AutoShortsBot. Keep them as legacy/secondary compatibility entries while making Digital Red Flags the primary strategy, otherwise broad existing tests/pipelines may break unnecessarily.\\n- Do not send AutoShorts approval requests that only contain a local MP4 path or a non-clickable `MEDIA:` reference from a runtime directory. The user expects the video to be directly playable in Telegram; if delivery fails, stage/copy the MP4 into an allowed Hermes media cache and resend the review package with the playable video plus title, description, and approval command.\\n- Do not rerun a YouTube private upload immediately after a thumbnail-setting failure. If `videos.insert` succeeded and `thumbnails.set` returned 403/no custom-thumbnail permission, a private video may already exist; report the Studio link/video ID, create a partial audit, and have the user set the thumbnail manually rather than creating a duplicate upload.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\\n- Do not confuse Pixabay API scopes: this user stores the Pixabay key at `~/.hermes/secrets/pixabay_api_key` in `.env` form (`PIXABAY_API=...`), and it has been verified for `https://pixabay.com/api/videos/` stock-video search. Do not invent or assume a documented music/audio API when Pixabay docs only cover image/video; strip the env-var prefix, use the video endpoint for B-roll where useful, and for music ask for a licensed MP3/URL or another approved music source instead of falling back to homemade/generated music.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/youtube-manual-pkce-callback-token-exchange.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/absorbed-social-shorts-automation.md\", \"references/thumbnail-cover-workflow.md\", \"references/everyday-red-flags-clean-ai-strategy.md\", \"references/premium-ai-styleframe-direction.md\", \"references/social-draft-approval-package.md\", \"references/static-active-word-captions-and-final-pilot-render.md\", \"references/word-boundary-caption-sync-and-solution-gate.md\", \"references/approval-first-retention-studio-core.md\", \"references/silent-viewer-caption-pacing.md\", \"references/local-preview-frame-writer.md\", \"references/visual-first-final-render-repair.md\", \"references/youtube-private-upload-direct-fallback.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/high-quality-ai-image-repair-after-mockup-rejection.md\", \"references/proof-short-real-problem-reset.md\", \"references/truetrace-strategic-neuausrichtung.md\", \"references/chatterbox-forced-alignment-shorts-render-pattern.md\", \"references/monetization-map-description-checklists.md\", \"references/chatterbox-tts-tailscale-candidate-provider.md\", \"references/detailed-ai-screen-proof-shorts.md\", \"references/mobile-safe-vertical-export-and-caption-qa.md\", \"references/digital-red-flags-scam-self-defense-strategy.md\", \"references/content-development-core.md\", \"references/readable-silent-short-captions.md\", \"references/autoshortsbot-runtime-cleanup-and-canonical-path.md\", \"references/jamendo-music-bed-workflow.md\", \"references/everyday-red-flags-topic-database-and-duration-policy.md\", \"references/mobile-vertical-aspect-qa.md\", \"references/ai-video-asset-planning.md\", \"references/detailed-ai-screen-postpreview-pattern.md\", \"references/approval-first-content-pipeline.md\", \"references/deterministic-premium-screen-mockup-rendering.md\", \"references/controlled-pilot-production-v2.md\", \"references/keyframe-first-final-visuals.md\", \"references/dynamic-duration-and-growth-goals.md\", \"references/clean-image-cut-no-overlay-pilot.md\", \"references/absorbed-shortform-growth-automation.md\", \"references/clean-ai-cut-vs-hybrid-mechanism.md\", \"references/readable-silent-shorts-captions.md\", \"references/proof-short-ai-visual-upgrade.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/pixabay-music-and-hashtag-policy.md\", \"references/absorbed-social-media-shorts-growth-engine.md\", \"references/chatterbox-tts-candidate-provider.md\", \"references/everyday-red-flags-topic-database.md\", \"references/audio-caption-preview-pipeline.md\", \"references/pilot-preview-render-v2.md\", \"references/truetrace-strategic-spine-refactor-v2.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/real-life-premium-styleframes.md\", \"references/monetization-and-approval-notes.md\", \"references/visual-production-reset-i2v-first.md\", \"references/absorbed-social-shorts-growth-engine.md\", \"references/truetrace-digital-red-flags-strategy-update.md\", \"references/image-provider-audit-and-caption-safezone.md\", \"references/upload-freigabe-drive-workflow.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/final-motion-hybrid-rendering.md\", \"references/prerelease-animatic-preview.md\", \"references/everyday-red-flags-clean-ai-render-recipe.md\", \"references/clean-ai-image-cut-production-qa.md\", \"references/next-erf-video-render-from-prior-script.md\", \"references/imported-ai-clip-assembly.md\", \"references/hybrid-scene-backgrounds-codex.md\", \"references/youtube-upload-only-manual-analytics-policy.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/proof-short-ai-background-rendering-qa.md\", \"references/deterministic-delivery-sms-proof-render.md\", \"references/nvidia-keyframe-generation.md\", \"references/truetrace-v2-operational-bridge.md\"], \"templates\": [\"templates/manual-posting-pack-with-thumbnails.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}" }, { "type": "function_call", "call_id": "call_B0TnjlqbktiU1ji65arotDsk", "name": "skill_view", "arguments": "{\"file_path\":\"references/next-erf-video-render-from-prior-script.md\",\"name\":\"social-media-shorts-automation\"}" }, { "type": "function_call_output", "call_id": "call_B0TnjlqbktiU1ji65arotDsk", "output": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/next-erf-video-render-from-prior-script.md\", \"content\": \"# Next Everyday Red Flags video from a prior render script\\n\\nUse this when the user asks for “the next video” in the current TrueTraceShorts / Everyday Red Flags sequence and a recent working render script already exists.\\n\\n## Pattern\\n\\n1. Load the topic/duration references and inspect the topic database for `seed_next_batch` / backlog candidates.\\n2. Prefer a simple, high-recognition scam topic that gives a strong first-frame hook. For example, `Fast richtige URL` / almost-right URL works well because the address bar itself is the visual hook.\\n3. Generate 5–6 premium vertical AI keyframes before rendering:\\n - first frame must show the concrete scam instantly;\\n - exact readable screen text where needed;\\n - no people, hands, real logos, watermarks, QR codes, or renderer focus graphics;\\n - leave lower safe space for active-word subtitles.\\n4. Clone the most recent known-good renderer only if its pipeline matches the requested style:\\n - Chatterbox Taylor sentence chunks;\\n - forced word timestamps / faster-whisper alignment;\\n - 2-line active-word subtitles;\\n - 1080x1920 H.264 export with `setsar=1,setdar=9/16`;\\n - review package + media-cache copy + topic DB update.\\n5. Replace every candidate-specific field, not just the visible script/title:\\n - candidate ID, slug, output directory, final filename;\\n - audio/metadata/word JSON names;\\n - scene keyframe names;\\n - image source list;\\n - title, description, hashtags/tags;\\n - topic ID and `existing_artifact_hint`;\\n - approval package filename and command.\\n6. Run the renderer end-to-end. Do not stop at script creation.\\n7. QA before delivery:\\n - ffprobe: `1080x1920`, SAR `1:1`, DAR `9:16`;\\n - contact sheet: premium, readable, on-topic, no logos/hands/watermarks;\\n - first QA frame: strong hook and readable subtitles;\\n - final QA frame: no clipping and no black bars;\\n - verify topic DB moved to `review_ready` / `awaiting_review`.\\n8. Deliver the Telegram-playable MP4 from `~/.hermes/media_cache/autoshorts/` with title, description, QA block, SHA256s, and exact hash-bound `APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD ...` command.\\n\\n## Pitfalls\\n\\n- Image generation may not perfectly preserve all exact UI text. Use the contact sheet and hook frame to decide whether the keyframes are good enough for the story, and regenerate only the weak frame rather than the whole batch.\\n- Do not let copied scripts retain old `erf-013` or previous-topic names in hidden metadata. Stale identifiers make later dry-runs and upload approval brittle.\\n- Do not deliver a local runtime path alone; stage the MP4 into the Hermes media cache so Telegram can play it directly.\\n\", \"file_type\": \".md\"}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwsPDGNfq6sqefc1M126dl3nFa466oVx3CKvqISgN6Pbpancz-oreGmc_Yoh3_o5CcbfcNobuyMoAhglWxC_zGeoR38nwwo-QbGyYMwXTKH9a6mCoOJ1K2N3FVIEllE1ij7TmifbaOjPaHRnoysZuyM57gLbxoJlaDKpl3hPm4s9YBGnDiteFm62wAbWY47xMpGQT7RImqFPsIu8S3Ja_18s1gQ19AK26txxC4bGTHEyPhKw7ogBtz3Y45bnVW1kw2yZqzVUtWWMzXorRweyrn88bUg5GciskpeIOc0BokPTu4wH4bP2x9uTiEK-E35ZsVjvvjkkOxM4KLN9zlnhgpAdRUBO7ue12vQ5Q3c0LuZQzl38rvWb4_3nzVmEz8CXwLphuqJvtUjEfEZmqvZVw9LnJJ7j8rS62VMCMeKrAbJRj0MSv5UUi1ZPFfNEYnnQqno6gtPrs34PdSo5VSqCRKWO_XXBn2Bbe6eThv7d5FGClesMfvGaSLCo64_t7Q4K_rK8R-4xzvyvTUdVpfAxehy67r1oI9Jif0j9wZv9iyEfH0EteUDUTT4cqH_khsKyGYepBYElTFXz9FH4OLkKlGeWGtrVd1HLrnakF8iUZCBE7ZpglqyY4BYpP3homzAwEdTlgbj04wOn-hJwEaHgHbqZ-jExPqfry0PcnVHEsZyoKJYh2Q2EPicwbRdgWK2Og5vEbB4s5bh61zYh3GVyFBuF9_zXptkq2JUjINR3P74XtDFYnioQ-f35AzFlY7XxF-MMK81JuNDCX0GPlNnnfXXgN7YKOF8Xx42th3rfwwHEQgSdX_WG-pKI50tmFRS4n_gG34brLhScjxWlcVtGePUy6tNy1NpNjCLTO1mWdXWtUCmUGXz86TCb58t799thPPWrc_DDYo_CBWJWTDWrN3xnHHSq375oYAm4zvBvYPJP5aEg60tFOuG6TZ4TkJqvtO5YlIYrPp_o4rcefAGnwfJ5PEGGccYT0xGgdCoscl2qp46TsHDNd6atkgFqMSVMZD63bT9s5J9GtGKfGqscZY8ku9WbdeFAhYxpE2X3kLZu7r4y4o08gv0stHni6ptK6TLf", "summary": [ { "type": "summary_text", "text": "**Planning the next video**\n\nI need to create the next video, and it looks like my first step is to inspect the repository. I should consider using session_search to look into previous known-good sessions, and I might also need to reference the topic database for additional context or ideas. This might help me get a clearer direction for what to include in the video and how to get started! Let's explore this together." } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_SQZTd8znxdWfg4I7XyK5D6ov", "name": "skill_view", "arguments": "{\"file_path\":\"references/everyday-red-flags-topic-database-and-duration-policy.md\",\"name\":\"social-media-shorts-automation\"}" }, { "type": "function_call_output", "call_id": "call_SQZTd8znxdWfg4I7XyK5D6ov", "output": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/everyday-red-flags-topic-database-and-duration-policy.md\", \"content\": \"# Everyday Red Flags — topic database and duration policy\\n\\nUse this when selecting the next AutoShorts/TrueTraceShorts Proof Short after the May 2026 Everyday Red Flags expansion.\\n\\n## Topic database location\\n\\nThe user supplied a large topic bank which has been normalized into JSON at:\\n\\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json`\\n- `/home/agent/jarvis_runtime/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json`\\n\\nShape:\\n\\n- `database_id`: `truetrace_everyday_red_flags_topic_database`\\n- 12 categories\\n- 185 topic items\\n- Each item has `topic_id`, `slug`, `title_de`, `series_line`, `status`, `priority`, `red_flag_prompt`, and `safe_action_prompt`.\\n\\nWhen creating a new video, prefer topics with `priority = seed_next_batch` or normal backlog topics that fit the current channel mix. After rendering a candidate, mark the matching topic `status = review_ready`, set `existing_artifact_hint`, and change `priority = awaiting_review`. After upload, do not over-update with transient YouTube IDs; use audits/packages for those.\\n\\n## Main category groups\\n\\n1. SMS- und Messenger-Scams — `Texts that want you to hurry.`\\n2. E-Mail-Scams — `Emails that look normal.`\\n3. Banking- und Payment-Scams — `Money move red flags.`\\n4. Login- und Account-Fallen — `Account traps.`\\n5. Marketplace- und Kleinanzeigen-Scams — `Marketplace red flags.`\\n6. Fake Support / Tech-Support-Scams — `Real support does not hijack your screen.`\\n7. Social Media und Plattform-Fallen — `Creator account traps.`\\n8. Family- und Emotion-Scams — `Send this to your family.`\\n9. Job-, Karriere- und Nebenverdienst-Scams — `If they make you pay to work, stop.`\\n10. Shopping-, Abo- und Consumer-Traps — `Before you buy.`\\n11. Privacy- und App-Risiken — `App permission red flags.`\\n12. Technische Internet-Risiken, einfach erklärt — simple internet-safety basics.\\n\\n## Already produced / avoid immediate duplicates\\n\\nAt the time this reference was written, these ideas had already been produced or uploaded/reviewed in the current run:\\n\\n- `erf-001` delivery/package SMS\\n- `erf-002` Hi Mom / new number\\n- `erf-003` fake fraud / bank one-time-code\\n- `erf-004` parking QR payment sticker\\n- `erf-005` toll fee SMS\\n- `erf-006` account-lock phishing message\\n- `erf-007` fake support popup / call-support-now\\n\\nUse the JSON status/audit files as source of truth if this list becomes stale.\\n\\n## Duration policy\\n\\nThe user explicitly corrected that video length is not fixed. Choose length by viewer understanding:\\n\\n- Simple red flag: 25–35s.\\n- Standard red flag: 35–45s.\\n- Explanation / recovery: 45–60s.\\n- TikTok monetization test / deeper lesson: 61–90s.\\n- Later longform guide: 5–8min.\\n\\nDefault for new Proof Shorts: 35–45s.\\n\\nPriority order:\\n\\n1. Viewer recognizes the scam/mechanism.\\n2. Viewer understands the red flag.\\n3. Viewer knows what to do next.\\n4. Video stays as short as possible, but not shorter than useful.\\n\\nEarly objective is shares, saves, followers, and trust — direct ad monetization is not the main initial target.\\n\\n## Review / upload pattern reminder\\n\\nFor each new rendered Proof Short, deliver:\\n\\n- Telegram-playable MP4 from Hermes media cache.\\n- Proposed title.\\n- Engaging YouTube description.\\n- QA block with duration, SAR/DAR, word/caption counts, video SHA256, posting pack SHA256, upload gate status.\\n- Exact hash-bound `APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD ...` command.\\n- Duration immediately after the approval code block.\\n\\nIf the user replies with natural language approval immediately after a single review package, reuse the saved package's stored approval command and run dry-run before upload. Do not require the user to paste the exact command unless multiple packages are in scope.\\n\", \"file_type\": \".md\"}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwsTOHIGfV5YRK76hKQmzjdXo5EdPyEZJqeBmjTzV7oCRACWyCrSrHXR-PLHksbuxVv4zvzVkpuMpm_qXqu7KyqxL_cSSJ_j0n7Rj_tyJpUMEVSOYI2ZwDuKBCrgz1kSgncTGcUnVPxQah10Wy9uvxHYQrJ6LJNtocJQCDff4AWCwrNbDv8aucK5EGufH5odzDv2NM3X-qWDAqilQgsJcWRKZMophceVdWQ8WVPFbLa5L45PUkMTtm_M5DtrGAUJvrdT3AR7rHCz8vC-zV2RcDUzT_JUlWpUf08ljiQ8YsPPo9raqMzSe_HHbXLy_7KGiQXdXxNHNdD25ER4HDsrBD20qI9Pq48mozZAtql6DZLWIoLytOB8dT2AaRkJkmrWdtfUUcBfaKDjlBPGgL4_CsDn9-CyVFgMicOISxnuFUFiLX2xzt6eKxF1_5XnZ4-mAukLOCKNW0ai-povVY52c8EadUqOQgamP3uv8qy4-QV3FSqJPDzCdl5MnkAG-H8RaJWGfjeZ2IQHU1CSo9C2u4Mx4dE5PB808uqwvFJBox3oUazdYfYT6tXUT_3Fo_LWS-Q8DfhOosUilTR_uZBpaVNIWFvT4mj6AHgVDASxhvddU-NuuWbP87kgB4si846uNgdvBd_VN7t52RzZ2lI7l5XkWHCwbx_6ibkDPN4l-C8BC08OaG4mn6k15fXggtWyNg3xR_yuPbUgGIMo4KiIkF_k_p3IJgHYb1mokvi_z7FqC1HU-0CAOkYa3nWsF7Q3wuxpQd9rhlhJ0pFi8lhwjr0kqpaaJMG4VZo-p2_56SmcwZtU80eV2FWl1apfZkWcD7B_4YdpE8zppOFeLUPD3MsFaMxPj-RzDmcbcPCGhDRpA_Ed5nI5abMh9s7iTYPcpJNrCNx7s1DFQQdH5hnqv_ws3qC6THyuwQE10aOP00Euq9ZE6VSNRGNowCmxp3XT8oYM_l3OXbwTZrgE2DpkTsPITcMS8QeFte8XBbwpLFYBJISkR38La5WPnO_MeuXswF96Ps-JeHGgQM3hxxe2yXMWLg==", "summary": [ { "type": "summary_text", "text": "**Planning file inspection**\n\nI need to take action and figure out what's likely in the repository. I’m thinking we might not need to use image_generate. I’ll have to inspect the files to see what we have available. It's a good idea to use a todo to track what I need to accomplish. Getting organized will definitely help me move forward!" } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_aIZkARLuW4Xo9i2yQUiSl69E", "name": "todo", "arguments": "{\"todos\":[{\"content\":\"Aktuellen AutoShorts-Stand, Topic-DB und letzten funktionierenden Renderer pr\\u00fcfen\",\"id\":\"inspect\",\"status\":\"in_progress\"},{\"content\":\"N\\u00e4chstes ERF-Thema ausw\\u00e4hlen und Kandidaten-Metadaten festlegen\",\"id\":\"select\",\"status\":\"pending\"},{\"content\":\"Video end-to-end rendern inkl. Audio/Captions/MP4/Package\",\"id\":\"render\",\"status\":\"pending\"},{\"content\":\"ffprobe, Frames/Contact-Sheet, Checksums und Topic-DB pr\\u00fcfen\",\"id\":\"qa\",\"status\":\"pending\"},{\"content\":\"Telegram-playable MP4 mit Titel, Beschreibung und Approval Command liefern\",\"id\":\"deliver\",\"status\":\"pending\"}]}" }, { "type": "function_call_output", "call_id": "call_aIZkARLuW4Xo9i2yQUiSl69E", "output": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Aktuellen AutoShorts-Stand, Topic-DB und letzten funktionierenden Renderer prüfen\", \"status\": \"in_progress\"}, {\"id\": \"select\", \"content\": \"Nächstes ERF-Thema auswählen und Kandidaten-Metadaten festlegen\", \"status\": \"pending\"}, {\"id\": \"render\", \"content\": \"Video end-to-end rendern inkl. Audio/Captions/MP4/Package\", \"status\": \"pending\"}, {\"id\": \"qa\", \"content\": \"ffprobe, Frames/Contact-Sheet, Checksums und Topic-DB prüfen\", \"status\": \"pending\"}, {\"id\": \"deliver\", \"content\": \"Telegram-playable MP4 mit Titel, Beschreibung und Approval Command liefern\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 4, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}" }, { "type": "function_call", "call_id": "call_cVds4eNb2Tt5aajg4dbxuFi5", "name": "session_search", "arguments": "{\"limit\":5,\"query\":\"\\\"APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD\\\" \\\"Everyday Red Flags\\\" OR \\\"erf-\\\"\",\"sort\":\"newest\"}" }, { "type": "function_call_output", "call_id": "call_cVds4eNb2Tt5aajg4dbxuFi5", "output": "{\"success\": true, \"mode\": \"discover\", \"query\": \"\\\"APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD\\\" \\\"Everyday Red Flags\\\" OR \\\"erf-\\\"\", \"results\": [{\"session_id\": \"20260602_160317_f123e8fc\", \"when\": \"June 02, 2026 at 04:03 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"title\": \"Hermes JARVIS Backup Ordner\", \"matched_role\": \"assistant\", \"match_message_id\": 64576, \"snippet\": \"...wurde migriert?\\n\\nIch habe die fehlenden Inhalte aus dem alten Ordner in den kanonischen Ordner übernommen, u.a.:\\n\\n- neue >>>ERF<<<-Render-Skripte:\\n - `render_erf011_verification_code_scam.py`\\n - `render_erf012_remote_job_deposit_scam.py`\\n - `render_erf013_fake_instagram_copyright...\", \"bookend_start\": [{\"id\": 64438, \"role\": \"user\", \"content\": \"[M G] ich möchte ein backup von dir machen (Hermes Agent / Jarvis) welche ordner muss ich in diesem Ordner sichern?: home/agent . (welche direkt in diesem ordner liegenden ordner und dateien)\", \"timestamp\": 1780409049.9851837}, {\"id\": 64447, \"role\": \"assistant\", \"content\": \"Sir, für ein brauchbares Backup von **Hermes Agent / JARVIS** in `/home/agent` würde ich nicht nur den Code sichern, sondern vor allem **State, Memory, Config, Credentials, Cronjobs, Skills und deine JARVIS-Domain-Cards**. Sonst hat man nach Restore zwar eine hübsche Leiche, aber keinen Butler.\\n\\nIch habe den Ordner `/home/agent` geprüft. Die wichtigsten **direkt in `/home/agent` liegenden Ordner/Dateien** sind:\\n\\n## 1. Pflicht-Backup für Hermes/JARVIS\\n\\nDiese Top-Level-Einträge solltest du sichern:\\n\\n- `.hermes/` \\n **Absolut kritisch.** Enthält Hermes-Konfiguration, Sessions, Memory, Skills, Cronjobs, Gateway-State, Auth, Secrets, Plugins, Source-Code etc.\\n\\n- `jarvis_memory/` \\n **Absolut kritisch.** Deine JARVIS-Domain-Cards / langfristige Wissensarchitektur.\\n\\n- `AGENTS.md` \\n **Sehr wichtig.** Globale Orchestrierungsregeln / Verhalten von JARVIS.\\n\\n- `projects/` \\n **Wichtig.** Enthält Projekt-Repos wie Gasser Website, CryptoTradingBot usw.\\n\\n- `jarvis_runtime/` \\n **Wichtig**, falls dort laufende State-/Runtime-Daten oder lokale Artefakte liegen.\\n\\n- `.todoist_token` \\n **Wichtig**, wenn Todoist-Integration direkt wieder funktionieren soll. Enthält Credential — verschlüsselt sichern.\\n\\n- `.friday_env/` \\n **Wahrscheinlich wichtig**, falls dort Environment/Integration-Konfigurationen liegen.\\n\\n- `.codex/` \\n **Wichtig**, falls Codex/OpenAI-Codex OAuth/Auth/CLI-State genutzt wird.\\n\\n- `.config/` \\n **Wichtig**, weil einige CLI-Tools dort Config/Auth speichern können.\\n\\n- `.ssh/` \\n **Wichtig**, falls GitHub/Server-Zugriff per SSH wiederhergestellt werden soll. Verschlüsselt sichern.\\n\\n- `.gnupg/` \\n **Wichtig**, falls GPG/verschlüsselte Backups/Keys genutzt werden. Verschlüsselt sichern.\\n\\n## 2. Innerhalb von `.hermes/`: besonders kritisch\\n\\nWenn du `.hermes/` komplett sicherst, ist alles drin. Falls du selektiv sichern willst, dann mindestens:\\n\\n- `.hermes/config.yaml`\\n- `.hermes/.env`\\n- `.hermes/auth.json`\\n- `.hermes/secrets/`\\n- `.hermes/memories/`\\n- `.hermes/skills/`\\n- `.hermes/sessions/`\\n- `.hermes/cron/`\\n- `.hermes/plugins/`\\n- `.hermes/scripts/`\\n- `.hermes/hermes-agent/`\\n- `.hermes/state.db`\\n- `.hermes/state.db-wal`\\n- `.hermes/state.db-shm`\\n- `.hermes/kanban.db`\\n- `.hermes/channel_directory.json`\\n- `.hermes/gateway_state.json`\\n- `.hermes/backup_keys/`\\n\\nKurz: **am besten `.hermes/` komplett sichern**, weil selektives Weglassen schnell zu subtil kaputten Restores führt.\\n\\n## 3. Empfehlenswert zusätzlich\\n\\nDiese direkt in `/home/agent` liegenden Dateien/Ordner würde ich ebenfalls sichern, falls du die Umgebung möglichst komplett reproduzieren willst:\\n\\n- `.bashrc`\\n- `.profile`\\n- `.zshrc`\\n- `.gitconfig`\\n- `.npmrc`\\n- `.npm-global/`\\n- `.local/`\\n- `workspace/`\\n- `Documents/`\\n- `Dokumente/`\\n- `Gesundheit/`\\n- `Bilder/`\\n- `Downloads/` — optional, oft viel Müll dabei\\n- `Start_AutoTrader.sh`\\n- `Start_Shorts_Bot.sh`\\n- alle Finance-/JARVIS-Spezifikationsdateien im Home-Root, z.B.:\\n - `Finance_System_Anforderungsdokument_v0.1.md`\\n - `Finance_System_Spezifikation_v0.2.md`\\n - `Finance_System_Spezifikation_v0.3.md`\\n - `JARVIS_Finance_System_Spezifikation_v0.4_Implementation_Blueprint.md`\\n - `JARVIS_Finance_System_v0.4.1_Korrekturrunde.md`\\n - `JARVIS_Finance_System_v0.5_Repo_Blueprint_Implementation_Plan.md`\\n - `anforderungsdokument_finance_decision_support_v0.1.md`\\n\\n## 4. Kann man meist weglassen\\n\\nDiese sind für Hermes/JARVIS normalerweise nicht nötig oder eher Cache/GUI-State:\\n\\n- `.cache/`\\n- `.npm/`\\n- `.mozilla/` — nur sichern, wenn Browser-Profil wichtig ist\\n- `.thunderbird/` — nur sichern, wenn Mailprofil wichtig ist\\n- `.linuxmint/`\\n- `.ICEauthority`\\n- `.Xauthority`\\n- `.xsession-errors`\\n- `.xsession-errors.old`\\n- `.sudo_as_admin_successful`\\n- `.wget-hsts`\\n- `signal-cli-0.14.1.tar.gz` — Installationsarchiv, nicht kritisch\\n\\n## 5. Meine empfohlene Minimal-Liste\\n\\nWenn du ein **JARVIS/Hermes-Restore-fähiges Backup** willst:\\n\\n```text\\n/home/agent/.hermes\\n/home/agent/jarvis_memory\\n/home/agent/AGENTS.md\\n/home/agent/projects\\n/home/agent/jarvis_runtime\\n/home/agent/.codex\\n/home/agent/.config\\n/home/agent/.ssh\\n/home/agent/.gnupg\\n/home/agent/.todoist_token\\n/home/agent/.friday_env\\n/home/agent/.gitconfig\\n/home/agent/.bashrc\\n/home/agent/.profile\\n/home/agent/.zshrc\\n/home/agent/Start_AutoTrader.sh\\n/home/agent/Start_Shorts_Bot.sh\\n```\\n\\n## 6. Wichtig: verschlüsselt sichern\\n\\nDieses Backup enthält sehr wahrscheinlich:\\n\\n- API keys\\n- OAuth tokens\\n- Telegram/Gateway-State\\n- GitHub tokens\\n- Todoist token\\n- SSH keys\\n- eventuell Health-/Finance-Daten\\n\\nAlso bitte **verschlüsselt** sichern, z.B. mit Borg, Restic, Kopia, VeraCrypt, Cryptomator oder einem GPG-verschlüsselten Archiv. Kein nacktes ZIP in Google Drive, Sir — das wäre Security by vibes.\", \"timestamp\": 1780409050.0461943}, {\"id\": 64460, \"role\": \"user\", \"content\": \"[M G] jarvis bist du da?\", \"timestamp\": 1780413580.9652905}], \"messages\": [{\"id\": 64572, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780418625.9350297, \"tool_calls\": [{\"id\": \"call_nrtJhLF5RhTf3gSL6np1Zt9N\", \"call_id\": \"call_nrtJhLF5RhTf3gSL6np1Zt9N\", \"response_item_id\": \"fc_06652d4243acab91016a1f080bec388190926e2092be9d8ca0\", \"type\": \"function\", \"function\": {\"name\": \"memory\", \"arguments\": \"{\\\"action\\\":\\\"add\\\",\\\"target\\\":\\\"memory\\\",\\\"content\\\":\\\"AutoShortsBot canonical local repo: `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot`; older uppercase-path clone `/home/agent/jarvis_runtime/AutoShortsBot` was migrated into the canonical repo on 2026-06-02 and can be renamed/archived after verification.\\\"}\"}}]}, {\"id\": 64574, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780418625.9491622, \"tool_calls\": [{\"id\": \"call_7i7h0LAHwbJl9ES8X5ogJu0F\", \"call_id\": \"call_7i7h0LAHwbJl9ES8X5ogJu0F\", \"response_item_id\": \"fc_06652d4243acab91016a1f0814ba148190820822c46597e87d\", \"type\": \"function\", \"function\": {\"name\": \"todo\", \"arguments\": \"{\\\"merge\\\":true,\\\"todos\\\":[{\\\"content\\\":\\\"Verifiziere Migration mit Dateiprüfung, Git-Status und Tests/Smoke-Checks\\\",\\\"id\\\":\\\"verify\\\",\\\"status\\\":\\\"completed\\\"},{\\\"content\\\":\\\"Melde, ob der alte Ordner umbenannt werden kann\\\",\\\"id\\\":\\\"report\\\",\\\"status\\\":\\\"completed\\\"}]}\"}}]}, {\"id\": 64576, \"role\": \"assistant\", \"content\": \"Erledigt, Sir. **Du kannst den alten Ordner jetzt umbenennen.**\\n\\n## Migration abgeschlossen\\n\\nMigriert von:\\n\\n```text\\n/home/agent/jarvis_runtime/AutoShortsBot\\n```\\n\\nnach kanonisch/richtig:\\n\\n```text\\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\n```\\n\\n## Was wurde migriert?\\n\\nIch habe die fehlenden Inhalte aus dem alten Ordner in den kanonischen Ordner übernommen, u.a.:\\n\\n- neue ERF-Render-Skripte:\\n - `render_erf011_verification_code_scam.py`\\n - `render_erf012_remote_job_deposit_scam.py`\\n - `render_erf013_fake_instagram_copyright_warning.py`\\n - `render_erf014_almost_right_url_login_trap.py`\\n- alte Pipeline-Module:\\n - `autoshorts/review_package.py`\\n - `autoshorts/nvidia_image_client.py`\\n - `autoshorts/comfyui_client.py`\\n - `autoshorts/retention_studio.py`\\n - `autoshorts/local_motion_renderer.py`\\n - usw.\\n- alte Tests\\n- alte Docs/configs\\n- `data/post_candidates/`\\n- aktualisierte Topic-Datenbank\\n- `pyproject.toml` wurde zusammengeführt:\\n - `edge-tts`\\n - `faster-whisper`\\n - `requests`\\n - `pytest` dev extra\\n - `ruff` config\\n\\n## Wichtig: Topic-Datenbank angepasst\\n\\nDie alte Topic-Datenbank enthielt absolute Pfade auf:\\n\\n```text\\n/home/agent/jarvis_runtime/AutoShortsBot\\n```\\n\\nIch habe diese auf den kanonischen Pfad umgeschrieben:\\n\\n```text\\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\n```\\n\\nVerifikation:\\n\\n```text\\nNO_OLD_PATHS\\n```\\n\\nAlso keine alten Pfadreferenzen mehr in der migrierten Topic-DB.\\n\\n## Migrationsreport\\n\\nDer Report liegt hier:\\n\\n```text\\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/migration_reports/migrate_from_old_autoshortsbot_20260602_184138.json\\n```\\n\\nErgebnis:\\n\\n```text\\ncreated: 681\\ncopied: 165\\nhardlinked: 516\\nerrors: 0\\n```\\n\\nDie großen Medien-/Output-Dateien wurden teilweise **hardlinked**, damit wir nicht unnötig nochmals 1.5GB+ Speicher verbrennen. Das ist für ein Umbenennen des alten Ordners sicher: Hardlinks bleiben erhalten, auch wenn der alte Pfad umbenannt wird.\\n\\n## Tests\\n\\nIch habe im kanonischen Ordner die Test-Suite laufen lassen:\\n\\n```text\\n330 passed in 39.25s\\n```\\n\\nDas ist die entscheidende Nachricht. Kein Drama, keine Pytest-Beerdigung.\\n\\n## Du kannst jetzt umbenennen\\n\\nEmpfohlener Befehl:\\n\\n```bash\\nmv /home/agent/jarvis_runtime/AutoShortsBot /home/agent/jarvis_runtime/AutoShortsBot.OLD_MIGRATED\\n```\\n\\nDann ein paar Tage mit dem kanonischen Ordner arbeiten:\\n\\n```text\\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\n```\\n\\nWenn danach alles sauber läuft, kann der alte umbenannte Ordner gelöscht werden:\\n\\n```bash\\nrm -rf /home/agent/jarvis_runtime/AutoShortsBot.OLD_MIGRATED\\n```\\n\\n## Nicht vergessen\\n\\nDas alte Startskript zeigt weiterhin auf OpenClaw-Legacy:\\n\\n```text\\n/home/agent/Start_Shorts_Bot.sh\\n```\\n\\nEs verweist auf:\\n\\n```text\\n/home/agent/.openclaw/workspace/Auto_Shorts_Bot/\\n```\\n\\nDas ist **nicht** der aktuelle AutoShortsBot. Dieses Script ist also Legacy und sollte später ebenfalls korrigiert oder entfernt werden.\", \"timestamp\": 1780418625.9595516, \"anchor\": true}, {\"id\": 64577, \"role\": \"user\", \"content\": \"[M G] bist du noch dran?\", \"timestamp\": 1780419162.931295}, {\"id\": 64578, \"role\": \"assistant\", \"content\": \"Ja, Sir. Ich bin dran — die Migration ist bereits abgeschlossen.\\n\\nDu kannst den alten Ordner jetzt umbenennen:\\n\\n```bash\\nmv /home/agent/jarvis_runtime/AutoShortsBot /home/agent/jarvis_runtime/AutoShortsBot.OLD_MIGRATED\\n```\\n\\nVerifikation war erfolgreich:\\n\\n```text\\n330 passed in 39.25s\\nMigration errors: 0\\n```\\n\\nKanonischer AutoShortsBot ist jetzt:\\n\\n```text\\n/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\n```\", \"timestamp\": 1780419162.9377444}], \"bookend_end\": [], \"messages_before\": 5, \"messages_after\": 2}, {\"session_id\": \"20260601_135640_00e8e32c\", \"when\": \"June 01, 2026 at 01:56 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"title\": \"Fake Login URL Video\", \"matched_role\": \"assistant\", \"match_message_id\": 63839, \"snippet\": \"...Path('/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/>>>everyday-red-flags<<<-almost-right-url-login-trap/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package/>>>erf<<<-014-almost-right-url-login-trap_chatterbox_taylor_scientific...\", \"bookend_start\": [{\"id\": 63737, \"role\": \"user\", \"content\": \"[M G] Ok, erstell das nächste Video. Achte auf einen guten Hook, auch optisch.\", \"timestamp\": 1780315772.1630633}, {\"id\": 63780, \"role\": \"assistant\", \"content\": \"Erledigt, Sir — nächstes Video ist gerendert und Telegram-playable staged.\\n\\nMEDIA:/home/agent/.hermes/media_cache/autoshorts/erf-014_almost_right_url_login_trap_chatterbox_taylor_readable_silent_2line_1080x1920_review.mp4\\n\\n**Thema:** Almost-right URL / Fake Login Page \\n**Hook:** Visuell direkt im ersten Frame: Login-Seite sieht normal aus, aber die URL ist falsch. Subtil wie ein Taschendieb mit Krawatte.\\n\\n**Vorgeschlagener Titel:** \\nThis Login Page Looks Right Until You Read the URL\\n\\n**Vorgeschlagene YouTube Description:** \\nA fake login page does not need to look broken. It only needs one detail to be wrong.\\n\\nThe red flag is the address bar: a link that sounds close, but is not the official site you opened yourself. If the page asks you to sign in after a message or warning, stop before you type.\\n\\nClose the link. Open the app, bookmark, or website yourself. If there is no alert there, the login page was the trap.\\n\\nRule: read the address before you enter the password.\\n\\n#ScamAlert #PhishingWarning #OnlineSafety #CyberSafety #EverydayRedFlags\\n\\n**QA:** \\n- Candidate: `erf-014-almost-right-url-login-trap` \\n- Format: `1080x1920`, SAR `1:1`, DAR `9:16` \\n- Dauer: `39.38s` \\n- Word count: `103` \\n- Caption style: 2-line readable active-word subtitles \\n- Visual policy: AI keyframes + voiceover + subtitles only; keine Pfeile/Boxen/Progress Bars \\n- Video SHA256: `b53d2fe4ded2916ee6f0ce59f290fc9f1723e8d28b59cc7c5f15d5b5ccf4f8b8` \\n- Posting pack SHA256: `e3841a07db5fdd71078d75d3d08270d806b9594aa0592fbc0c19c06488215e74` \\n- Upload gate: valid, aber **nicht hochgeladen** \\n\\n**Freigabe für privaten YouTube Draft:** \\n```text\\nAPPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD erf-014-almost-right-url-login-trap chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai b53d2fe4ded2916ee6f0ce59f290fc9f1723e8d28b59cc7c5f15d5b5ccf4f8b8 e3841a07db5fdd71078d75d3d08270d806b9594aa0592fbc0c19c06488215e74\\n```\\n\\nDauer: `39.38s`\", \"timestamp\": 1780315772.485378}, {\"id\": 63814, \"role\": \"user\", \"content\": \"[M G] ok, private upload approved\", \"timestamp\": 1780322941.8216088}], \"messages\": [{\"id\": 63835, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780322941.9577107, \"tool_calls\": [{\"id\": \"call_SgNVHHCyqRGf3CubxWIzVsEj\", \"call_id\": \"call_SgNVHHCyqRGf3CubxWIzVsEj\", \"response_item_id\": \"fc_001af61189b76d35016a1d924d22c0819b89fc798e327c24ef\", \"type\": \"function\", \"function\": {\"name\": \"read_file\", \"arguments\": \"{\\\"limit\\\":120,\\\"path\\\":\\\"/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-almost-right-url-login-trap/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package/erf-014-almost-right-url-login-trap_chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai_youtube_private_upload_package.json\\\"}\"}}]}, {\"id\": 63837, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780322941.9671898, \"tool_calls\": [{\"id\": \"call_2Qs2UVkPROPF3dYvtzlY9cnv\", \"call_id\": \"call_2Qs2UVkPROPF3dYvtzlY9cnv\", \"response_item_id\": \"fc_001af61189b76d35016a1d92506b84819babd4da76dad69278\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"python3 - <<'PY'\\\\nimport json, pathlib, hashlib\\\\npkg_path=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-almost-right-url-login-trap/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package/erf-014-almost-right-url-login-trap_chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai_youtube_private_upload_package.json')\\\\ntoken_path=pathlib.Path('/home/agent/.hermes/secrets/youtube/youtube_upload_token.json')\\\\npkg=json.load(open(pkg_path))\\\\nassert pkg['privacyStatus']=='private', pkg['privacyStatus']\\\\nvp=pathlib.Path(pkg['video_path']); assert vp.exists(), vp\\\\nh=hashlib.sha256(vp.read_bytes()).hexdigest(); assert h==pkg['video_sha256'], (h,pkg['video_sha256'])\\\\ncanonical={k:v for k,v in pkg.items() if k not in ('posting_pack_sha256','approval_command')}\\\\npack_sha=hashlib.sha256(json.dumps(canonical,sort_keys=True,separators=(',',':')).encode()).hexdigest()\\\\nassert pack_sha==pkg['posting_pack_sha256'], (pack_sha,pkg['posting_pack_sha256'])\\\\ntok=json.load(open(token_path))\\\\nscopes=set(tok.get('scopes') or [])\\\\nassert scopes == {'https://www.googleapis.com/auth/youtube.upload'}, scopes\\\\nprint('DRY_RUN_VALID')\\\\nprint('candidate', pkg['candidate_id'])\\\\nprint('privacy', pkg['privacyStatus'])\\\\nprint('title', pkg['title'])\\\\nprint('video_sha_ok', h)\\\\nprint('posting_pack_sha_ok', pack_sha)\\\\nprint('thumbnail_path', pkg.get('thumbnail_path'))\\\\nprint('token_scope_ok', sorted(scopes)[0])\\\\nPY\\\",\\\"timeout\\\":60,\\\"workdir\\\":\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\"}\"}}]}, {\"id\": 63839, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780322941.9771361, \"tool_calls\": [{\"id\": \"call_ivU6QUDWTsa4WA2GBptqHAHV\", \"call_id\": \"call_ivU6QUDWTsa4WA2GBptqHAHV\", \"response_item_id\": \"fc_001af61189b76d35016a1d925cd488819bb4586e17496c74b2\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"python3 - <<'PY'\\\\nimport datetime, hashlib, json, os, pathlib, sys\\\\nfrom google.oauth2.credentials import Credentials\\\\nfrom google.auth.transport.requests import Request\\\\nfrom googleapiclient.discovery import build\\\\nfrom googleapiclient.http import MediaFileUpload\\\\n\\\\nSCOPE='https://www.googleapis.com/auth/youtube.upload'\\\\npkg_path=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-almost-right-url-login-trap/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package/erf-014-almost-right-url-login-trap_chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai_youtube_private_upload_package.json')\\\\ntoken_path=pathlib.Path('/home/agent/.hermes/secrets/youtube/youtube_upload_token.json')\\\\npkg=json.load(open(pkg_path))\\\\napproval=pkg['approval_command']\\\\nassert approval == 'APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD erf-014-almost-right-url-login-trap chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai b53d2fe4ded2916ee6f0ce59f290fc9f1723e8d28b59cc7c5f15d5b5ccf4f8b8 e3841a07db5fdd71078d75d3d08270d806b9594aa0592fbc0c19c06488215e74'\\\\nassert pkg['privacyStatus']=='private'\\\\nvideo_path=pathlib.Path(pkg['video_path']); assert video_path.exists()\\\\ndef sha256(p):\\\\n h=hashlib.sha256()\\\\n with open(p,'rb') as f:\\\\n for chunk in iter(lambda:f.read(1024*1024), b''): h.update(chunk)\\\\n return h.hexdigest()\\\\nvideo_sha=sha256(video_path); assert video_sha==pkg['video_sha256']\\\\ncanon={k:v for k,v in pkg.items() if k not in ('posting_pack_sha256','approval_command')}\\\\npack_sha=hashlib.sha256(json.dumps(canon,sort_keys=True,separators=(',',':')).encode()).hexdigest(); assert pack_sha==pkg['posting_pack_sha256']\\\\ncreds=Credentials.from_authorized_user_file(str(token_path), scopes=[SCOPE])\\\\nif set(creds.scopes or []) != {SCOPE}:\\\\n raise SystemExit(f'Unsafe scopes: {creds.scopes}')\\\\nif not creds.valid and creds.expired and creds.refresh_token:\\\\n creds.refresh(Request())\\\\n token_path.write_text(creds.to_json())\\\\n os.chmod(token_path, 0o600)\\\\nif not creds.valid:\\\\n raise SystemExit('OAuth credentials invalid after refresh')\\\\nyoutube=build('youtube','v3',credentials=creds,cache_discovery=False)\\\\nbody={\\\\n 'snippet':{\\\\n 'title':pkg['title'],\\\\n 'description':pkg['description'],\\\\n 'tags':pkg.get('tags') or [],\\\\n 'categoryId':pkg.get('categoryId','27'),\\\\n 'defaultLanguage':pkg.get('language','en'),\\\\n },\\\\n 'status':{\\\\n 'privacyStatus':'private',\\\\n 'selfDeclaredMadeForKids':bool(pkg.get('selfDeclaredMadeForKids',False)),\\\\n }\\\\n}\\\\nmedia=MediaFileUpload(str(video_path), mimetype='video/mp4', resumable=True)\\\\nrequest=youtube.videos().insert(part='snippet,status', body=body, media_body=media)\\\\nresponse=None\\\\nlast_progress=None\\\\nwhile response is None:\\\\n status, response = request.next_chunk()\\\\n if status:\\\\n progress=int(status.progress()*100)\\\\n if progress != last_progress:\\\\n print(f'upload_progress {progress}%')\\\\n last_progress=progress\\\\nvideo_id=response.get('id')\\\\nif not video_id:\\\\n raise SystemExit(f'No video id in response keys: {list(response.keys())}')\\\\naudit={\\\\n 'timestamp':datetime.datetime.now(datetime.timezone.utc).isoformat(),\\\\n 'candidate_id':pkg['candidate_id'],\\\\n 'version':pkg['version'],\\\\n 'video_sha256':video_sha,\\\\n 'posting_pack_sha256':pack_sha,\\\\n 'approval_command':approval,\\\\n 'privacyStatus':'private',\\\\n 'thumbnail_attempted':False,\\\\n 'upload_status':'success',\\\\n 'youtube_video_id':video_id,\\\\n 'studio_url':f'https://studio.youtube.com/video/{video_id}/edit',\\\\n 'watch_url':f'https://youtu.be/{video_id}',\\\\n 'validated_scope':SCOPE,\\\\n 'api_calls':['videos.insert'],\\\\n 'response_privacy':response.get('status',{}).get('privacyStatus'),\\\\n 'response_upload_status':response.get('status',{}).get('uploadStatus'),\\\\n}\\\\naudit_path=pkg_path.parent / f\\\\\\\"{pkg['candidate_id']}_{pkg['version']}_youtube_upload_audit.json\\\\\\\"\\\\naudit_path.write_text(json.dumps(audit,indent=2),encoding='utf-8')\\\\nprint(json.dumps({k:audit[k] for k in ('upload_status','youtube_video_id','studio_url','watch_url','privacyStatus','thumbnail_attempted')}, indent=2))\\\\nprint('audit_path', audit_path)\\\\nPY\\\",\\\"timeout\\\":600,\\\"workdir\\\":\\\"/home/agent/jarvis_runtime/AutoShortsBot\\\"}\"}}], \"anchor\": true}, {\"id\": 63841, \"role\": \"assistant\", \"content\": \"Private Upload erledigt, Sir.\\n\\n**YouTube Private Draft:** \\n[Studio öffnen](https://studio.youtube.com/video/SISjrqIJrf8/edit)\\n\\n**Watch URL:** \\nhttps://youtu.be/SISjrqIJrf8\\n\\n**Status:** \\n- Upload: success \\n- Privacy: private \\n- Video ID: `SISjrqIJrf8` \\n- Thumbnail: nicht gesetzt / nicht versucht — wie gewünscht manuell \\n- Audit geschrieben: `.../youtube_upload_audit.json`\\n\\n**TikTok Beschreibung:** \\nThis login page looks normal — until you read the address bar. \\nClose the link. Open the app or bookmark yourself. \\n#ScamAlert #PhishingWarning #OnlineSafety #CyberSafety #EverydayRedFlags\", \"timestamp\": 1780322941.9871573}], \"bookend_end\": [], \"messages_before\": 5, \"messages_after\": 2}, {\"session_id\": \"20260601_081625_411b201c\", \"when\": \"June 01, 2026 at 08:16 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"title\": \"Stadtraum Bahnhof Verkehrsbriefing\", \"matched_role\": \"assistant\", \"match_message_id\": 63112, \"snippet\": \"...Nicht nur \\\\u201eNorm >>>erf<<<\\\\u00fcllt\\\\u201c, sondern Alltagstauglichkeit, Schulwege, Lieferverkehr, Besucher, Handwerker, Rettung, Baustellenlogistik und Investitionsf\\\\u00e4higkeit.\\\\\\\\n\\\\\\\\n5. **Zulieferung/Baustellenverkehr ist noch nicht gen\\\\u00fcgend konkret**\\\\\\\\n - Der Schlussbericht sagt explizit: Baustellenverkehr und Zulieferverkehr wurden in der Testplanung noch...\", \"bookend_start\": [{\"id\": 63053, \"role\": \"user\", \"content\": \"[M G] Lade dir die Dokumente von der Dokumenten-Seite dieser Webseite runter in den Politik Ordner auf Google Drive, lege dir entsprechend einen Unterordner an an passender Stelle: https://www.stadtraumbahnhof.ch/dokumente . Prüfe diese und gib mir Bescheid ob dir noch etwas auffällt das ich wissen müsste für die Fraktionssitzung heute. Insbesondere das Thema Verkehr (mehrbelastung z.B. von Reutenenstrasse) sehe ich Kritisch. ich bin der Meinung dass die Anzahl Parkplätze viel zu tief angesetzt ist. Es wurde mir mal gesagt nur ca. 0.5 Parkplätze pro Wohnung oder sogar weniger... ist ds überhaupt realistisch? Ist das eine Realistische Annahme für Familien oder die Potentiellen Einwohner dieses Quartiers? Auch zulieferung zu geschäften müssten ja über die Quartierstrassen erfolgen. Ich befürchte dass wir uns hier wieder zu \\\"Linke\\\" Klima bzw. Anti-Auto Ziele setzen, welche nicht realistisch sind und es für investoren nur unnötig schwerer machen (Das ist meine Meinung, und die der FDP kollegen, jedoch kann man das so direkt ja nicht sagen...). Nach deiner durchsicht und prüfung, schreibe mir bitte die Rede für den Einwohnerrat (JA Parole zum Kreditantrag; jedoch hervorheben was uns von der FDP wichtig ist.).\", \"timestamp\": 1780295118.7423344}], \"messages\": [{\"id\": 63108, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780295119.0808773, \"tool_calls\": [{\"id\": \"call_i8mvLKtlsEGKljYGdnQdj9lk\", \"call_id\": \"call_i8mvLKtlsEGKljYGdnQdj9lk\", \"response_item_id\": \"fc_04ac8484f5b96de6016a1d251b0af48191b565240c2d78b2ea\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"python3 - <<'PY'\\\\nprojects=[('GP Bachmatt',35,32),('Areal Reutenen',24,27),('Industrie-WBG',112,119),('Reutenenstrasse 8',5,9),('Ev.-meth. Kirche',9,17)]\\\\nfor n,w,p in projects:\\\\n print(n, round(p/w,2),'PP/Wohnung')\\\\nprint('Total', sum(p for _,w,p in projects), sum(w for _,w,p in projects), round(sum(p for _,w,p in projects)/sum(w for _,w,p in projects),2))\\\\n# possible Stadtraum ratio if housing units estimated from residents? not enough.\\\\nPY\\\"}\"}}]}, {\"id\": 63110, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780295119.0897422, \"tool_calls\": [{\"id\": \"call_UavCw23844qcTUgmPjaWINtg\", \"call_id\": \"call_UavCw23844qcTUgmPjaWINtg\", \"response_item_id\": \"fc_0b8c49347e411730016a1d252832ac8191a54efc27ebe33411\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"python3 - <<'PY'\\\\nfrom pathlib import Path\\\\nimport re\\\\nbase=Path('/tmp/stadtraum_docs_AwTtxr/text')\\\\nfor term in ['Kreditantrag','Kredit','Verpflichtungskredit','Einwohnerrat','Kosten','Franken','CHF']:\\\\n print('\\\\\\\\nTERM',term)\\\\n hits=[]\\\\n for p in base.glob('*.txt'):\\\\n s=p.read_text(errors='ignore')\\\\n if re.search(term,s,re.I):\\\\n hits.append(p.name)\\\\n print(hits[:20])\\\\n# snippets for kredit in likely docs\\\\nfor fn in ['02_250317_PPP_Stadtraum_Update.txt','StadtraumUpdate_250317_Dokumentation_Fragerunde.txt','32508_13A_250916_Bericht_inkl_A3_lq.txt','Grobterminplan.txt','GSBBW_Terminplan_20251126_1.txt']:\\\\n p=base/fn\\\\n if p.exists():\\\\n s=p.read_text(errors='ignore')\\\\n print('\\\\\\\\n###',fn)\\\\n for m in re.finditer('Kredit|Verpflichtungskredit|Einwohnerrat|Franken|Kosten',s,re.I):\\\\n a=max(0,m.start()-250); b=min(len(s),m.end()+500)\\\\n print(' '.join(s[a:b].split())[:1000])\\\\nPY\\\",\\\"timeout\\\":120}\"}}]}, {\"id\": 63112, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780295119.1000443, \"tool_calls\": [{\"id\": \"call_4MKiarrAafVV3EI7qksUmAL4\", \"call_id\": \"call_4MKiarrAafVV3EI7qksUmAL4\", \"response_item_id\": \"fc_08435f80cabd987a016a1d2534fcd88191bd389847dd1fb300\", \"type\": \"function\", \"function\": {\"name\": \"write_file\", \"arguments\": \"{\\\"path\\\":\\\"/tmp/stadtraum_docs_AwTtxr/Kurzbriefing_und_Rede_FDP.md\\\",\\\"content\\\":\\\"# Stadtraum Bahnhof Brugg-Windisch – Kurzbriefing und Redeentwurf FDP\\\\n\\\\n## Kurzbriefing für Fraktionssitzung\\\\n\\\\n**Stand Dokumente:** 33 PDFs von https://www.stadtraumbahnhof.ch/dokumente heruntergeladen und im Google-Drive-Ordner `Politik/Stadtraum Bahnhof Brugg-Windisch` abgelegt.\\\\n\\\\n### Was mir auffällt\\\\n\\\\n1. **JA zum Kredit ist gut begründbar**\\\\n - Die Testplanung liefert eine solide Basis; es geht jetzt darum, Planungssicherheit zu schaffen und die nächste Phase sauber zu finanzieren.\\\\n - Der Stadtraum ist einer der wichtigsten Entwicklungsräume im Raum Brugg-Windisch: Bahnhofsnähe, ÖV-Knoten, FHNW, Wohnen/Arbeiten/Bildung.\\\\n\\\\n2. **Verkehr/Parkierung ist der neuralgische Punkt**\\\\n - Die Unterlagen bestätigen Deine Skepsis teilweise: Die Fahrtenzahl hängt stark an der Anzahl Parkplätze.\\\\n - In der Testplanung liegen die Szenarien bei ca. **2'000–5'000 Fahrten/Tag**.\\\\n - Beim Team Salewski Nater Kretz werden im konkreten Projekt ca. **820 Parkplätze** ausgewiesen; rechnerisch ca. **1'055 Parkfelder**, und eigentümerseitig scheint eine Unterschreitung der Norm/BNO nur bis zur oberen Grenze von ca. **1'000–1'200 Parkplätzen** denkbar.\\\\n - Beim Team Corso sind nur ca. **379–390 Parkplätze** vorgesehen; der Bericht hält selbst fest, dass weniger als **0.2 PP/100 m²** gemäss VCS-Definition als autofrei gilt. Das ist politisch/marktwirtschaftlich klar die riskantere Annahme.\\\\n\\\\n3. **0.5 Parkplätze pro Wohnung: für Familien nicht robust**\\\\n - In den konkreten bekannten Projekten im Gebiet Klosterzelg/Reutenen liegen die Parkierungsquoten deutlich höher: total **204 Parkplätze für 185 Wohnungen = ca. 1.10 PP/Wohnung**.\\\\n - 0.5 PP/Wohnung kann in sehr bahnhofsnahen, bewusst autoarmen Segmenten funktionieren: kleine Haushalte, Studierende, Pendler ohne Auto, Mobility, konsequente Bewirtschaftung.\\\\n - Für Familien, Gewerbe, Schichtarbeit, Handwerk, Pflege/Besuche und reale Einkaufs-/Freizeitmobilität ist 0.5 PP/Wohnung als pauschale Annahme zu knapp. Dann verschiebt sich der Druck in Quartierstrassen und Besucherparkierung – also genau dorthin, wo man ihn politisch nicht haben will.\\\\n\\\\n4. **Reutenen-/Klosterzelgstrasse: offiziell „normativ tragbar“, aber politisch sensibel**\\\\n - Die Quartiererschliessungsstudie sagt: Reutenenstrasse und Klosterzelgstrasse liegen heute unter der Belastbarkeitsgrenze von ca. 500 Fz/h.\\\\n - Aber: Mit Entwicklung wird die Klosterzelgstrasse zum Engpass; die Studie schlägt als Massnahme eine **Beschränkung der Fahrtenzahl** vor. Die Fahrtenerzeugung müsste um ca. **25%** reduziert werden, damit 500 Fz/h nicht überschritten werden.\\\\n - Massnahme M3 sieht die Aufhebung des MIV-Fahrverbots Reutenenstrasse vor; dabei müsse man auf Schleichverkehr achten und die Verträglichkeit mit der Veloroute prüfen.\\\\n - Das ist ein valider FDP-Punkt: Nicht nur „Norm erfüllt“, sondern Alltagstauglichkeit, Schulwege, Lieferverkehr, Besucher, Handwerker, Rettung, Baustellenlogistik und Investitionsfähigkeit.\\\\n\\\\n5. **Zulieferung/Baustellenverkehr ist noch nicht genügend konkret**\\\\n - Der Schlussbericht sagt explizit: Baustellenverkehr und Zulieferverkehr wurden in der Testplanung noch nicht berücksichtigt.\\\\n - Bei SNK soll Anlieferung zwar durchgehend möglich bleiben, aber das ist noch kein belastbares Betriebskonzept.\\\\n - FDP-Forderung: In der nächsten Phase verbindliches Verkehrs-/Logistikkonzept inkl. Lieferfenster, Routen, Baustellenverkehr, Rettung, Gewerbe und Monitoring.\\\\n\\\\n## Politische Linie\\\\n\\\\n**JA zum Kredit**, aber mit klarer FDP-Erwartung:\\\\n- realistische Mobilität statt ideologische Symbolik;\\\\n- Parkierung und Fahrten nicht schönrechnen;\\\\n- Investoren-/Eigentümerfähigkeit sichern;\\\\n- Quartiere Reutenen/Klosterzelg nicht zum Puffer für Planungswünsche machen;\\\\n- Etappierung, Monitoring und Korrekturmechanismen verlangen.\\\\n\\\\n## Redeentwurf Einwohnerrat\\\\n\\\\nSehr geehrter Herr Präsident,\\\\nsehr geehrte Damen und Herren\\\\n\\\\nDie FDP-Fraktion unterstützt den Kreditantrag zum Stadtraum Bahnhof Brugg-Windisch.\\\\n\\\\nWir tun dies, weil dieses Gebiet für Brugg und Windisch von grosser strategischer Bedeutung ist. Es geht um einen der zentralsten Entwicklungsräume unserer Region: direkt beim Bahnhof, in unmittelbarer Nähe zur FHNW, mit Potenzial für Wohnen, Arbeiten, Bildung, Gewerbe und öffentliche Räume. Wenn wir hier sorgfältig planen, schaffen wir langfristig Mehrwert – städtebaulich, wirtschaftlich und gesellschaftlich.\\\\n\\\\nFür uns ist aber ebenso klar: Ein Ja zum Kredit ist kein Blankocheck für jede planerische Wunschvorstellung. Es ist ein Ja zur nächsten Planungsphase – und gerade deshalb müssen die kritischen Punkte jetzt mit der nötigen Ernsthaftigkeit bearbeitet werden.\\\\n\\\\nDer wichtigste Punkt aus Sicht der FDP ist die Verkehrstauglichkeit. Die vorliegenden Unterlagen zeigen sehr deutlich: Die künftige Verkehrsbelastung hängt stark davon ab, wie viele Parkplätze vorgesehen werden, wie diese bewirtschaftet werden und wie konsequent das Gebiet erschlossen wird. Je nach Szenario sprechen die Unterlagen von rund 2'000 bis 5'000 Fahrten pro Tag. Das ist keine Nebensache, das ist für die angrenzenden Quartiere unmittelbar spürbar.\\\\n\\\\nBesonders aufmerksam müssen wir bei Reutenenstrasse, Klosterzelgstrasse und den bestehenden Quartierstrassen sein. Es genügt uns nicht, wenn am Ende lediglich gesagt wird, eine Norm sei rechnerisch noch eingehalten. Entscheidend ist, ob das im Alltag funktioniert: für Familien, für Gewerbe, für Besucherinnen und Besucher, für Handwerker, für Lieferverkehr, für Rettungsdienste und während der Bauphasen. Papier ist geduldig; Quartierstrassen sind es weniger.\\\\n\\\\nAuch beim Thema Parkierung braucht es aus unserer Sicht Realismus. Brugg-Windisch ist hervorragend mit dem öffentlichen Verkehr erschlossen, ja. Und selbstverständlich sollen Fuss-, Velo- und ÖV-Anteile gestärkt werden. Aber daraus darf nicht der Schluss entstehen, man könne den realen Mobilitätsbedarf einfach wegdefinieren. Sehr tiefe Parkplatzquoten mögen für einzelne autoarme Nutzungen funktionieren. Für ein gemischtes Quartier mit Familien, Arbeit, Bildung, Gewerbe, Besuchern und Versorgung braucht es jedoch belastbare Annahmen – nicht nur ein schönes Mobilitätsziel.\\\\n\\\\nDie Unterlagen zeigen zudem selbst, dass gewisse sehr tiefe Parkierungsannahmen eigentümer- und markseitig anspruchsvoll sind. Wenn ein Projekt nur funktioniert, falls ein idealer Ankermieter, ein idealer Modal Split und ein idealisiertes Nutzerverhalten gleichzeitig eintreten, dann ist das keine robuste Planung. Wir wollen ein Quartier, das ökologisch sinnvoll, aber auch wirtschaftlich realisierbar und investorenfähig ist. Sonst planen wir am Ende nicht Stadtentwicklung, sondern schöne Renderings mit angeschlossener Enttäuschung – und davon hat die öffentliche Hand bekanntlich schon eine kleine Sammlung.\\\\n\\\\nFür die FDP ist deshalb zentral, dass die nächste Phase klare Antworten liefert:\\\\n\\\\nErstens: ein realitätsnahes Parkierungs- und Mobilitätskonzept, differenziert nach Wohnen, Familien, Gewerbe, Bildung, Besuchern und Beschäftigten.\\\\n\\\\nZweitens: ein verbindliches Verkehrs- und Logistikkonzept inklusive Zulieferung, Baustellenverkehr, Rettung und Bewirtschaftung.\\\\n\\\\nDrittens: eine Etappierung mit Monitoring. Wenn die Fahrtenzahlen oder die Parkierung nicht wie angenommen funktionieren, braucht es Korrekturmechanismen – nicht erst, wenn die Quartierstrassen bereits überlastet sind.\\\\n\\\\nViertens: eine sorgfältige Prüfung der Erschliessung über das übergeordnete Netz, damit Reutenen und Klosterzelg nicht faktisch den Preis für eine zu ambitionierte oder zu knapp gerechnete Mobilitätsplanung bezahlen.\\\\n\\\\nWir unterstützen den Kredit also ausdrücklich, weil wir die Entwicklung dieses Gebiets vorwärtsbringen wollen. Aber wir erwarten von der weiteren Planung Pragmatismus, wirtschaftliche Vernunft und verkehrliche Ehrlichkeit. Klimaziele, Aufenthaltsqualität und gute Stadtentwicklung sind wichtig. Sie werden aber nicht glaubwürdiger, wenn man den motorisierten Verkehr nur aus der Planung herausrechnet und ihn anschliessend in den Quartierstrassen wiederfindet.\\\\n\\\\nDie FDP sagt Ja zum Kredit – verbunden mit der klaren Erwartung, dass die nächste Phase ein robustes, investorenfähiges und alltagstaugliches Projekt hervorbringt.\\\\n\\\\nVielen Dank.\\\\n\\\"}\"}}], \"anchor\": true}, {\"id\": 63114, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780295119.1101983, \"tool_calls\": [{\"id\": \"call_GtqyccD5F1UPFZWxlHpNwnMY\", \"call_id\": \"call_GtqyccD5F1UPFZWxlHpNwnMY\", \"response_item_id\": \"fc_021e51dabfd55586016a1d257be81481919d21cc80ffaf7c45\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"set -euo pipefail\\\\nexport GOG_KEYRING_PASSWORD='***'\\\\ngog -a friday.uplink@gmail.com drive upload /tmp/stadtraum_docs_AwTtxr/Kurzbriefing_und_Rede_FDP.md --parent 1g8BFUhx_BYFebirj73ePAaJP1SH_BgLZ --name \\\\\\\"Kurzbriefing und Rede FDP.md\\\\\\\" --mime-type text/markdown --json --results-only\\\",\\\"timeout\\\":120}\"}}]}, {\"id\": 63116, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780295119.1192238, \"tool_calls\": [{\"id\": \"call_wOzbeyqkvtunIfvFbyEdtMcK\", \"call_id\": \"call_wOzbeyqkvtunIfvFbyEdtMcK\", \"response_item_id\": \"fc_03bd0b66dcc85d70016a1d2580dcc88191896b999abe7de11d\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"set -euo pipefail\\\\nexport GOG_KEYRING_PASSWORD='***'\\\\ngog -a friday.uplink@gmail.com drive upload /tmp/stadtraum_docs_AwTtxr/Kurzbriefing_und_Rede_FDP.md --parent 1g8BFUhx_BYFebirj73ePAaJP1SH_BgLZ --name \\\\\\\"Kurzbriefing und Rede FDP.md\\\\\\\" --mime-type text/markdown --json --results-only\\\",\\\"timeout\\\":120}\"}}]}], \"bookend_end\": [{\"id\": 64051, \"role\": \"assistant\", \"content\": \"Sir, ich konnte die Kalendertermine **noch nicht eintragen**.\\n\\nGrund: Der Google-Workspace-/`gog`-Zugriff ist beim Entsperren des lokalen Keyrings fehlgeschlagen (`aes.KeyUnwrap(): integrity check failed`). Beim anschliessenden Diagnose-/Retry wurde der Terminal-Schritt vom Sicherheitslayer blockiert. Sehr höflich gesagt: Google hat die Tür gesehen, der Schlüssel war aber beleidigt.\\n\\n**Stand:** Nach den fehlgeschlagenen Befehlen wurde **kein Termin erfolgreich erstellt**.\\n\\nBitte bestätige mir kurz, dass ich den Google-Kalenderzugriff erneut mit entsperrtem Keyring versuchen soll. Dann trage ich die 4 Termine ein und sende die Einladung an:\\n\\n`marcel.gasser@erne.net`\", \"timestamp\": 1780346424.7500432}, {\"id\": 64118, \"role\": \"user\", \"content\": \"[M G] Versuch es nochmals. Hast du sie eingetragen?\", \"timestamp\": 1780349767.9604678}, {\"id\": 64229, \"role\": \"assistant\", \"content\": \"Ja, Sir — jetzt sind sie eingetragen und verifiziert.\\n\\nIch habe im Google Kalender erstellt, jeweils **19:00–22:00 Uhr**, mit Termineinladung an:\\n\\n`marcel.gasser@erne.net`\\n\\n## Eingetragene Termine\\n\\n- **24. August 2026, 19:00–22:00** \\n Vorstellung des Budgets an die FiGPK und an die Arbeitsgruppe\\n\\n- **31. August 2026, 19:00–22:00** \\n Sitzung Arbeitsgruppe\\n\\n- **7. September 2026, 19:00–22:00** \\n Sitzung Arbeitsgruppe\\n\\n- **21. September 2026, 19:00–22:00** \\n Sitzung Arbeitsgruppe\\n\\nAlle 4 Termine sind im Kalender als **confirmed** sichtbar und enthalten die Einladung an Marcel.\", \"timestamp\": 1780349768.6387737}], \"messages_before\": 5, \"messages_after\": 5}, {\"session_id\": \"20260531_152951_ae1d8add\", \"when\": \"May 31, 2026 at 03:29 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"title\": \"Fake Copyright Warning Video\", \"matched_role\": \"assistant\", \"match_message_id\": 61977, \"snippet\": \"...items',[]):\\\\\\\\n if item.get('topic_id')=='>>>erf<<<-topic-fake-instagram-copyright-warning':\\\\\\\\n item['status']='produced_or_uploaded'\\\\\\\\n item['priority']='avoid_duplicate_for_now'\\\\\\\\n item['existing_artifact_hint']='>>>erf<<<-013_fake_social_copyright_warning'\\\\\\\\n changed=True...\", \"bookend_start\": [{\"id\": 61894, \"role\": \"user\", \"content\": \"[M G] ok, erstelle das nächste video. sorge wie gewohnt für abwechslung\", \"timestamp\": 1780235027.2687912}, {\"id\": 61897, \"role\": \"assistant\", \"content\": \"Verstanden, Sir — ich nehme „nächstes Video“ als neuen TrueTrace/Everyday-Red-Flags Short, mit Themenvariation statt Wiederholung. Ich prüfe kurz den aktuellen Produktionsstand und rendere dann ein neues Review-Paket.\", \"timestamp\": 1780235027.3606472, \"tool_calls\": [{\"id\": \"call_PTTryOyg2XA1QfgyDlV0mFRL\", \"call_id\": \"call_PTTryOyg2XA1QfgyDlV0mFRL\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c37df568881919601974ee61c3a12\", \"type\": \"function\", \"function\": {\"name\": \"skill_view\", \"arguments\": \"{\\\"name\\\":\\\"social-media-shorts-automation\\\",\\\"file_path\\\":\\\"references/everyday-red-flags-topic-database-and-duration-policy.md\\\"}\"}}, {\"id\": \"call_kGZazYjCOJO2yrevYY759s07\", \"call_id\": \"call_kGZazYjCOJO2yrevYY759s07\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c37df56a08191ac4e4975c56b496f\", \"type\": \"function\", \"function\": {\"name\": \"skill_view\", \"arguments\": \"{\\\"name\\\":\\\"social-media-shorts-automation\\\",\\\"file_path\\\":\\\"references/clean-ai-image-cut-production-qa.md\\\"}\"}}, {\"id\": \"call_QjJuzqjgVcNttEUuqXezDjEu\", \"call_id\": \"call_QjJuzqjgVcNttEUuqXezDjEu\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c37df56ac8191b049f352d0db70df\", \"type\": \"function\", \"function\": {\"name\": \"skill_view\", \"arguments\": \"{\\\"name\\\":\\\"social-media-shorts-automation\\\",\\\"file_path\\\":\\\"references/chatterbox-forced-alignment-shorts-render-pattern.md\\\"}\"}}]}, {\"id\": 61938, \"role\": \"assistant\", \"content\": \"Fertig, Sir — neues Video mit Themenwechsel: **Creator/Social-Media Scam** statt weiterer Payment-/Job-Scam. Abwechslung, wie bestellt; erstaunlich selten, dass Abwechslung nicht „noch ein Paket-SMS“ bedeutet.\\n\\nMEDIA:/home/agent/.hermes/media_cache/autoshorts/erf-013_fake_social_copyright_warning_chatterbox_taylor_readable_silent_2line_1080x1920_review.mp4\\n\\n## Vorschlag Titel\\n**A Copyright Warning in Your DMs Is Not Proof**\\n\\n## Vorschlag Beschreibung\\nA fake copyright warning can feel urgent, especially if you run a creator or business account.\\n\\nThe red flag is simple: the warning arrives in a DM and pushes you to appeal through a link. Real account issues should be checked inside the app or official website you open yourself.\\n\\nDo not use the DM link. Open the app yourself, check Account Status or Security, and turn on two-factor authentication.\\n\\nRule: DMs are not court notices.\\n\\n#ScamAlert #CreatorSafety #PhishingWarning #OnlineSafety #EverydayRedFlags\\n\\n## QA\\n- Thema: Fake Copyright Warning / Creator Account Phishing\\n- Stil: Clean AI image cut, nur Active-Word-Subtitles\\n- Voice: Chatterbox Taylor / Scientific\\n- Dauer: 38.7s\\n- Format: 1080×1920, SAR 1:1, DAR 9:16, 30 fps\\n- Captions: 98 Wörter, 13 Segmente\\n- Video SHA256: `30e1b2299f82821d0b384c2a28e71578d69160441e51fb24d6c358eb07d2a607`\\n- Posting-Pack SHA256: `e381544257846e2b8be8788285f6ff494341a872f957e7f3e3b114967654c38e`\\n- Upload-Gate: gültig, aber private YouTube upload bleibt natürlich approval-bound\\n- Thumbnail: absichtlich nicht enthalten\\n\\n## Freigabe-Befehl für privaten YouTube-Draft\\n```text\\nAPPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD erf-013-fake-social-copyright-warning chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai 30e1b2299f82821d0b384c2a28e71578d69160441e51fb24d6c358eb07d2a607 e381544257846e2b8be8788285f6ff494341a872f957e7f3e3b114967654c38e\\n```\\n\\nDauer: **38.7s**\", \"timestamp\": 1780235027.6737413}], \"messages\": [{\"id\": 61973, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780235750.7045994, \"tool_calls\": [{\"id\": \"call_OLvgAPcBciTd7F2CYrjqoo9M\", \"call_id\": \"call_OLvgAPcBciTd7F2CYrjqoo9M\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c3dc83b308191b3b763b022e5d227\", \"type\": \"function\", \"function\": {\"name\": \"terminal\", \"arguments\": \"{\\\"command\\\":\\\"AUTOSHORTS_YOUTUBE_PACKAGE_DIR='/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-fake-social-copyright-warning/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package' python -m autoshorts.cli.youtube_private_upload_execute erf-013-fake-social-copyright-warning --version chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai --approval 'APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD erf-013-fake-social-copyright-warning chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai 30e1b2299f82821d0b384c2a28e71578d69160441e51fb24d6c358eb07d2a607 e381544257846e2b8be8788285f6ff494341a872f957e7f3e3b114967654c38e' --audit-dir '/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-fake-social-copyright-warning/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package'\\\",\\\"timeout\\\":600,\\\"workdir\\\":\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\\"}\"}}]}, {\"id\": 61975, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780235750.7161887, \"tool_calls\": [{\"id\": \"call_hlCXF7XA4g3cu0QQG4lvAYwM\", \"call_id\": \"call_hlCXF7XA4g3cu0QQG4lvAYwM\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c3dd5ec388191ac7d2cfd1d283ace\", \"type\": \"function\", \"function\": {\"name\": \"read_file\", \"arguments\": \"{\\\"limit\\\":120,\\\"path\\\":\\\"/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-fake-social-copyright-warning/chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai/youtube_private_upload_package/erf-013-fake-social-copyright-warning_chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai_youtube_upload_audit.json\\\"}\"}}]}, {\"id\": 61977, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780235750.73687, \"tool_calls\": [{\"id\": \"call_r9LoCpfncINadTdPBD0hLxlw\", \"call_id\": \"call_r9LoCpfncINadTdPBD0hLxlw\", \"response_item_id\": \"fc_0b76f35f8a61e33b016a1c3ddb682481918650c09fdf781372\", \"type\": \"function\", \"function\": {\"name\": \"execute_code\", \"arguments\": \"{\\\"code\\\":\\\"from pathlib import Path\\\\nimport json, datetime\\\\np=Path('/home/agent/jarvis_runtime/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json')\\\\ndata=json.loads(p.read_text())\\\\nchanged=False\\\\nfor cat in data.get('categories',[]):\\\\n for item in cat.get('items',[]):\\\\n if item.get('topic_id')=='erf-topic-fake-instagram-copyright-warning':\\\\n item['status']='produced_or_uploaded'\\\\n item['priority']='avoid_duplicate_for_now'\\\\n item['existing_artifact_hint']='erf-013_fake_social_copyright_warning'\\\\n changed=True\\\\nif changed:\\\\n data['updated_at']=datetime.datetime.now(datetime.timezone.utc).isoformat()\\\\n p.write_text(json.dumps(data,indent=2,ensure_ascii=False),encoding='utf-8')\\\\nprint({'changed':changed})\\\"}\"}}], \"anchor\": true}, {\"id\": 61979, \"role\": \"assistant\", \"content\": \"Erledigt, Sir.\\n\\n- **Upload:** erfolgreich als **privater YouTube-Draft**\\n- **Video-ID:** `nXjuQYUtHps`\\n- **Studio-Link:** https://studio.youtube.com/video/nXjuQYUtHps/edit\\n- **Titel:** *A Copyright Warning in Your DMs Is Not Proof*\\n- **Privacy:** `private`\\n- **Synthetic media:** gesetzt\\n- **Made for kids:** `false`\\n- **Thumbnail:** nicht gesetzt, wie gewünscht manuell\\n- **API-Nutzung:** nur `videos.insert`, upload-only scope, keine Kommentare/Analytics/Read-Scopes\\n- **Public Publish:** nicht durchgeführt — YouTube Studio Review bleibt manuell\\n\\nAudit ist geschrieben, Topic-Datenbank ist auf `produced_or_uploaded` gesetzt.\", \"timestamp\": 1780235750.7562635}], \"bookend_end\": [], \"messages_before\": 5, \"messages_after\": 2}, {\"session_id\": \"20260530_191114_abf0355a\", \"when\": \"May 30, 2026 at 07:11 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"title\": \"Private YouTube Upload Confirmation\", \"matched_role\": \"assistant\", \"match_message_id\": 57791, \"snippet\": \"Erledigt, Sir.\\n\\n**Privater YouTube-Upload erfolgreich:**\\n\\n- **Video:** `>>>erf<<<-012-remote-job-deposit-scam`\\n- **Titel:** *Real Jobs Don’t Ask You to Pay First*\\n- **YouTube Video-ID:** `lpGSKIzEiQw`\\n- **Studio-Link:** https://studio.youtube.com/video/lpGSKIzEiQw/edit\\n- **Status:** `private`\\n- **Thumbnail:** nicht...\", \"bookend_start\": [{\"id\": 57736, \"role\": \"user\", \"content\": \"[M G] Approved for private upload\", \"timestamp\": 1780161245.2975028}], \"messages\": [{\"id\": 57787, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780161245.624663, \"tool_calls\": [{\"id\": \"call_ECfpZ6w2d4PaY7kQyE9OKa62\", \"call_id\": \"call_ECfpZ6w2d4PaY7kQyE9OKa62\", \"response_item_id\": \"fc_07200d878b9fd42e016a1b1acb18d8819184ac9d0ca47f6140\", \"type\": \"function\", \"function\": {\"name\": \"read_file\", \"arguments\": \"{\\\"limit\\\":120,\\\"path\\\":\\\"/home/agent/jarvis_runtime/AutoShortsBot/data/post_candidates/everyday-red-flags-remote-job-deposit-scam/chatterbox_taylor_scientific_v1_readable_silent_2line_jamendo_hyper_threading/youtube_private_upload_package/erf-012-remote-job-deposit-scam_chatterbox_taylor_scientific_v1_readable_silent_2line_jamendo_hyper_threading_youtube_upload_audit.json\\\"}\"}}]}, {\"id\": 57789, \"role\": \"assistant\", \"content\": \"\", \"timestamp\": 1780161245.635566, \"tool_calls\": [{\"id\": \"call_dmX1HD8ZF9GPrpI9UfrrLrYU\", \"call_id\": \"call_dmX1HD8ZF9GPrpI9UfrrLrYU\", \"response_item_id\": \"fc_0d52f5c76e9009c8016a1b1ad06e70819195acd19db4be4b0f\", \"type\": \"function\", \"function\": {\"name\": \"skill_manage\", \"arguments\": \"{\\\"action\\\":\\\"patch\\\",\\\"name\\\":\\\"social-media-shorts-automation\\\",\\\"file_path\\\":\\\"references/youtube-private-upload-direct-fallback.md\\\",\\\"old_string\\\":\\\"1. Load the package JSON.\\\\n - If the package was saved next to a render under `.../youtube_private_upload_package/` instead of the repo default, set `AUTOSHORTS_YOUTUBE_PACKAGE_DIR` to that directory before using `autoshorts.cli.youtube_private_upload_dry_run` or `autoshorts.cli.youtube_private_upload_execute`.\\\\n2. Compare the returned approval command byte-for-byte/string-for-string with `approval_command`.\\\",\\\"new_string\\\":\\\"1. Load the package JSON.\\\\n - If the package was saved next to a render under `.../youtube_private_upload_package/` instead of the repo default, set `AUTOSHORTS_YOUTUBE_PACKAGE_DIR` to that directory before using `autoshorts.cli.youtube_private_upload_dry_run` or `autoshorts.cli.youtube_private_upload_execute`.\\\\n - If the CLI fails with `unexpected keyword argument` for additive render metadata such as `music_bed`, patch `YouTubePrivateUploadPackage.from_json()` to ignore unknown JSON keys after preserving/validating the hash-bound fields; do not hand-edit the approval package or weaken hash validation.\\\\n2. Compare the returned approval command byte-for-byte/string-for-string with `approval_command`.\\\"}\"}}]}, {\"id\": 57791, \"role\": \"assistant\", \"content\": \"Erledigt, Sir.\\n\\n**Privater YouTube-Upload erfolgreich:**\\n\\n- **Video:** `erf-012-remote-job-deposit-scam`\\n- **Titel:** *Real Jobs Don’t Ask You to Pay First*\\n- **YouTube Video-ID:** `lpGSKIzEiQw`\\n- **Studio-Link:** https://studio.youtube.com/video/lpGSKIzEiQw/edit\\n- **Status:** `private`\\n- **Thumbnail:** nicht gesetzt\\n- **Scope geprüft:** nur `youtube.upload`\\n- **API-Calls:** nur `videos.insert`\\n- **Kommentare/Analytics:** nicht angerührt\\n- **Synthetic Media:** gesetzt\\n- **Made for Kids:** false\\n\\nJARVIS hat **nicht veröffentlicht**. Bitte im YouTube Studio prüfen und manuell freigeben/veröffentlichen, wenn alles passt.\\n\\n## TikTok Beschreibung\\n\\n```text\\nA remote job asks you to pay before you work? Stop.\\n\\nReal jobs pay you. They don’t ask for a deposit, task wallet, or “unlock fee” first.\\n\\nIf you must pay to work, walk away.\\n\\n#ScamAlert #RemoteJobScam #OnlineSafety #JobScam #EverydayRedFlags\\n```\\n\\nNebenbei: Der Upload-CLI hatte wegen neuer Musik-Metadaten im Package kurz Schluckauf. Repariert, trocken getestet, dann sauber hochgeladen. Britische Effizienz, nur mit weniger Tee.\", \"timestamp\": 1780161245.648657, \"anchor\": true}], \"bookend_end\": [], \"messages_before\": 5, \"messages_after\": 1}], \"count\": 5, \"sessions_searched\": 5}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwsdnbKI8rHRD96XIn3xTKcmkkT_rAZOaGuDIvAawTavJfq4KDnweeoqxsjGnGwp_lTsnHeCsLU8Ldb5FKeA_Jm9jofoMGgvbPo0JwdW-Mwr-D7Ray7MtcPVXpAjTIeIOualiLsdVApkQ-HNw_mW3S9V_EL5tdnEWcvfKizb2bzSS9kXXug-K3t5Z5Aoh6_SU1xyRetXsVUJen4oO0tul6IsgD8rwRz1B6aoN-uK_pHw5X-nO51tQN0wIpYxKlP5VmaX1xneAerZpczDTQsne1NJyp0AUHUFseTI0WIaqY5THyj6l3k0ZiH029v-mgwtpDPC-nM7EjnBx8H8xQ5bkNpuB2U04FP2wL50brer_Geu4HIo0DdQpsd_htYIKLFLl2p15panngpMZL23Z-AjUVOeFSO300KXEcn6-bZrUOhB2O4XWaGvJQTOgQE05QB8FNEp0zdrQv12aIAoeaQrZXV2lMm6cRrRICZ0vBkR-nCvNsJ7QL2P6QTpmaUTMXJSsvcYXsPCX5UKaAaAkYgPtecimDaAYHoKoSR_GuUXolC9Ib_mdLvD1ZhCBk2E4deyCfg4b-OCHsyhVx4OLZECnd654lNF7k6e3FJzSckYhcEKa15qmT_KHxI6HldzGWcEH2tbQCxU4qR_nrDtr-C7ncvehADcyvqu_LUJ7ftxR4A-kY63FZry_SsjtXjx2GZF1gyyFsGERKtsqnEYaxnD5iA_CoGUgwLlBu6fJ_Dp2pnLdMncrHKNQJVmHTM4j1rXHA95QGqx2ZCOVXWTmiL6OLJQCxDtnHce1XwI7SKe3m1tt5_AZkrxVghhcj4IX9UTD0AoYGQ6z_o-xsxLQW_SnY-P4me2TAoTuOtsE09DVxXckRDg9oTolq0ZEtn4CsXloG_dgKEdmzHj2OmmOX4Pw_1d4sWvdxy9b3E4auR3dIKoL_L87uUwZKGggfMEhq4bW1didgygUYqatpHk2QNa-vEvpqE6HfsFcH8h02JNWInoJ30=", "summary": [ { "type": "summary_text", "text": "**Inspecting files list**\n\nI think I need to use the canonical path for my task. It seems essential to ensure everything works correctly. I should inspect the files list to see if there are any relevant scripts that I might need. This is probably a straightforward step, but I want to make sure I’m thorough. Let’s check it out! I’m curious to see what I find in that list and if anything stands out." } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_BOWyX0b4xDRhtZvBWSNOekzr", "name": "search_files", "arguments": "{\"limit\":30,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"pattern\":\"render_erf*.py\",\"target\":\"files\"}" }, { "type": "function_call", "call_id": "call_qQGITMm5GJISXuie6RcWKhH2", "name": "read_file", "arguments": "{\"limit\":120,\"offset\":1,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json\"}" }, { "type": "function_call_output", "call_id": "call_BOWyX0b4xDRhtZvBWSNOekzr", "output": "{\"total_count\": 14, \"files\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf014_almost_right_url_login_trap.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf013_fake_instagram_copyright_warning.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf012_remote_job_deposit_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf011_verification_code_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf010_invoice_bank_change_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf009_shared_document_login_phishing.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf008_marketplace_off_platform_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf007_fake_support_popup_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf006_account_lock_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf005_toll_text_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf004_parking_qr_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf003_bank_code_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf002_hi_mom_scam.py\", \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf001_delivery_sms_scam.py\"]}" }, { "type": "function_call_output", "call_id": "call_qQGITMm5GJISXuie6RcWKhH2", "output": "{\"content\": \"1|{\\n2| \\\"database_id\\\": \\\"truetrace_everyday_red_flags_topic_database\\\",\\n3| \\\"version\\\": \\\"2026-05-28-v1\\\",\\n4| \\\"updated_at\\\": \\\"2026-06-01T12:08:46.603872+00:00\\\",\\n5| \\\"source\\\": \\\"user_supplied_big_topic_database_telegram_2026-05-28\\\",\\n6| \\\"positioning\\\": \\\"Digital Red Flags / Scam Self-Defense for Normal People\\\",\\n7| \\\"channel_promise\\\": \\\"One screen. One red flag. One safer move.\\\",\\n8| \\\"default_language\\\": \\\"en\\\",\\n9| \\\"approval_language\\\": \\\"de\\\",\\n10| \\\"visual_policy\\\": {\\n11| \\\"content_carried_by\\\": \\\"high-quality detailed AI images or deterministic mockups\\\",\\n12| \\\"renderer_overlays_allowed\\\": [\\n13| \\\"text-only active-word subtitles\\\"\\n14| ],\\n15| \\\"renderer_overlays_blocked\\\": [\\n16| \\\"arrows\\\",\\n17| \\\"circles\\\",\\n18| \\\"boxes\\\",\\n19| \\\"focus lines\\\",\\n20| \\\"progress bars\\\",\\n21| \\\"explanatory labels\\\"\\n22| ],\\n23| \\\"human_faces_hands_people\\\": \\\"avoid by default\\\",\\n24| \\\"critical_text_policy\\\": \\\"scam-screen text, URLs, codes, invoices, payments, and claims should be renderer-owned or deterministic, not hallucinated by image generation\\\"\\n25| },\\n26| \\\"candidate_template\\\": {\\n27| \\\"structure\\\": [\\n28| \\\"visible_fail\\\",\\n29| \\\"red_flag\\\",\\n30| \\\"safer_move\\\",\\n31| \\\"takeaway\\\"\\n32| ],\\n33| \\\"first_frame_requirement\\\": \\\"The concrete scam/problem must be visible without voiceover.\\\",\\n34| \\\"safe_action_requirement\\\": \\\"Viewer gets one simple safer action they can do immediately.\\\",\\n35| \\\"share_reason\\\": \\\"Useful enough to send to family or a non-technical friend.\\\",\\n36| \\\"duration_selection_rule\\\": \\\"Default Proof Shorts use 35–45s; shorten to 25–35s only for very simple red flags, extend to 45–60s for explanation/recovery, and 61–90s only for deeper lesson/TikTok monetization experiments.\\\"\\n37| },\\n38| \\\"categories\\\": [\\n39| {\\n40| \\\"category_id\\\": \\\"sms_messenger_scams\\\",\\n41| \\\"order\\\": 1,\\n42| \\\"name_de\\\": \\\"SMS- und Messenger-Scams\\\",\\n43| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n44| \\\"items\\\": [\\n45| {\\n46| \\\"topic_id\\\": \\\"erf-topic-paket-ist-blockiert\\\",\\n47| \\\"slug\\\": \\\"paket-ist-blockiert\\\",\\n48| \\\"title_de\\\": \\\"Paket ist blockiert\\\",\\n49| \\\"working_title_en\\\": \\\"Paket ist blockiert\\\",\\n50| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n51| \\\"status\\\": \\\"produced_or_uploaded\\\",\\n52| \\\"existing_artifact_hint\\\": \\\"erf-001_delivery_sms_scam\\\",\\n53| \\\"priority\\\": \\\"avoid_duplicate_for_now\\\",\\n54| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n55| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\"\\n56| },\\n57| {\\n58| \\\"topic_id\\\": \\\"erf-topic-zollgebuehr-customs-fee\\\",\\n59| \\\"slug\\\": \\\"zollgebuehr-customs-fee\\\",\\n60| \\\"title_de\\\": \\\"Zollgebühr / Customs fee\\\",\\n61| \\\"working_title_en\\\": \\\"Zollgebühr / Customs fee\\\",\\n62| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n63| \\\"status\\\": \\\"backlog\\\",\\n64| \\\"existing_artifact_hint\\\": null,\\n65| \\\"priority\\\": \\\"normal\\\",\\n66| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n67| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\"\\n68| },\\n69| {\\n70| \\\"topic_id\\\": \\\"erf-topic-maut-toll-fee-sms\\\",\\n71| \\\"slug\\\": \\\"maut-toll-fee-sms\\\",\\n72| \\\"title_de\\\": \\\"Maut-/Toll-Fee-SMS\\\",\\n73| \\\"working_title_en\\\": \\\"Maut-/Toll-Fee-SMS\\\",\\n74| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n75| \\\"status\\\": \\\"produced_or_uploaded\\\",\\n76| \\\"existing_artifact_hint\\\": \\\"erf-005_toll_text_scam\\\",\\n77| \\\"priority\\\": \\\"avoid_duplicate_for_now\\\",\\n78| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n79| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\"\\n80| },\\n81| {\\n82| \\\"topic_id\\\": \\\"erf-topic-your-bank-card-was-used\\\",\\n83| \\\"slug\\\": \\\"your-bank-card-was-used\\\",\\n84| \\\"title_de\\\": \\\"Your bank card was used\\\",\\n85| \\\"working_title_en\\\": \\\"Your bank card was used\\\",\\n86| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n87| \\\"status\\\": \\\"backlog\\\",\\n88| \\\"priority\\\": \\\"normal\\\",\\n89| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n90| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\",\\n91| \\\"updated_at\\\": \\\"2026-05-29T20:56:13.812704+00:00\\\"\\n92| },\\n93| {\\n94| \\\"topic_id\\\": \\\"erf-topic-fake-fraud-alert\\\",\\n95| \\\"slug\\\": \\\"fake-fraud-alert\\\",\\n96| \\\"title_de\\\": \\\"Fake Fraud Alert\\\",\\n97| \\\"working_title_en\\\": \\\"Fake Fraud Alert\\\",\\n98| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n99| \\\"status\\\": \\\"produced_or_uploaded\\\",\\n100| \\\"existing_artifact_hint\\\": \\\"erf-003_bank_code_scam\\\",\\n101| \\\"priority\\\": \\\"avoid_duplicate_for_now\\\",\\n102| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n103| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\"\\n104| },\\n105| {\\n106| \\\"topic_id\\\": \\\"erf-topic-your-account-will-be-locked\\\",\\n107| \\\"slug\\\": \\\"your-account-will-be-locked\\\",\\n108| \\\"title_de\\\": \\\"Your account will be locked\\\",\\n109| \\\"working_title_en\\\": \\\"Your account will be locked\\\",\\n110| \\\"series_line\\\": \\\"Texts that want you to hurry.\\\",\\n111| \\\"status\\\": \\\"review_ready\\\",\\n112| \\\"existing_artifact_hint\\\": \\\"erf-006_account_lock_scam\\\",\\n113| \\\"priority\\\": \\\"awaiting_review\\\",\\n114| \\\"red_flag_prompt\\\": \\\"Identify the one visible warning signal in the normal-looking screen.\\\",\\n115| \\\"safe_action_prompt\\\": \\\"Give one safer move that does not depend on clicking the suspicious link/contact.\\\"\\n116| },\\n117| {\\n118| \\\"topic_id\\\": \\\"erf-topic-missed-voicemail-link\\\",\\n119| \\\"slug\\\": \\\"missed-voicemail-link\\\",\\n120| \\\"title_de\\\": \\\"Missed voicemail Link\\\",\\n121|\", \"total_lines\": 2419, \"file_size\": 120255, \"truncated\": true, \"hint\": \"Use offset=121 to continue reading (showing 1-120 of 2419 lines)\", \"is_binary\": false, \"is_image\": false}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwskwc1iE7vCpJJPgX7etGGMhhQOYC8O1FQ8pIUmid5v1pH-IH4hN6-C7rfFVHxH-xUekP3-TpQaZR6CfxieoXL5Hcdxvsx04QDtuZbE1slEe7NJQvsbRjhUxk_iXLLbdg4aw8Te5tFmxl-VIWfQUPfSmd0liYb-vk4ElU-oNO1qFvyqa-tqn3sHLUspNl7xbj_XMhVN2TpHBIcQoAsbuJKLn414Q-Niy_1e_rSNpgdf7hAuiLwBsQ09xPuvgPRcIbPXHSDGPqX9M6DQ7PvqBLOVK9YV8g2zxhfIvuvfl3Xgm-JWM4kDmRzut1QsBlsFuttz9e2AKfamL7l18LTvspdOzl9xCteDxJZS1Is7BvrTyDAvjv37jKkkwR1E3qSVnupavXorAj7rSKv0YRGlr2SGKrRcQHs0Xy-H53DGtBjy0Dm8bnAn14lgiLcRnvBWFxTFUNXhucY2oVJDRW5Xky1WM2hUitf8sjIynPNeEXiu8zR6vsVaAePaMHEtexrqQul2TnGnkxqxwCOcIzQr5iOj2rNQzwBvuvXPgPyY1_J6M6OkYqF5KCNtRImqPjX-y7e4uV9G2CuirXo48PXrTUxBcxnX-0sRQzp99wdujruoGAke9415gNoNNVmpUEGop3rzb3Li_3xkOtGGqVeNZn3OAFeeCkzRK4qYt5QvVIbVPON3hjgpoH5rKNN_yGBKkz1whL0tuXtC0AztynpviMaCZTx7I_wX3d6vhBeiMyltkFWDl6jLYendYDqMYLAbsgyrSM8vw4FiQDhml7WBWD6Z06opNYyBnc3pHkm0-xvBGYKdh-PddelsJeQ-b8WThdlAIUqW_XyAvR7xz4PssbXetVzk0mSHRhtOjyBPb8YAe7E0mkRvCkA1ywRniVqBw5rH3XN4PQ0THK13EBF58ZMgrfRHAwOX4V99y5LyNUMMERQgVZNZNtuhhusyaUi_0ABQf8AKwt_h7VYsWyCb-3gMDpSC-shdFIyfS2A2Uk8QpbwVzMAqwv_ITlZXSs9VU8PUyVDjddKH6maOWtEajBCaJA==", "summary": [ { "type": "summary_text", "text": "**Parsing candidates**\n\nI need to parse the candidates' seed data. To do this, I'll use the execute_code tool, focusing on finding the priority seed or backlog that has been produced. I want to make sure I'm checking everything correctly to get the right information. This could help prioritize what needs to be tackled first, and getting details about the backlog is essential for keeping things organized moving forward." } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_R8kAcEXTbN4DBQgdReYQz4EZ", "name": "execute_code", "arguments": "{\"code\":\"from pathlib import Path\\nimport json\\np=Path('/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json')\\ndata=json.loads(p.read_text())\\nitems=[]\\nfor cat in data['categories']:\\n for item in cat['items']:\\n items.append((item.get('priority'), item.get('status'), cat['category_id'], item['topic_id'], item['slug'], item.get('working_title_en') or item.get('title_de')))\\nfor pr in ['seed_next_batch','normal']:\\n print('\\\\nPRIORITY',pr)\\n c=0\\n for x in items:\\n if x[0]==pr and x[1]=='backlog':\\n print(x)\\n c+=1\\n if c>=30: break\\nprint('\\\\nreview_ready/awaiting_review')\\nfor x in items:\\n if x[0]=='awaiting_review' or x[1]=='review_ready': print(x)\\nprint('\\\\nproduced count', sum(1 for x in items if x[1] in ('produced_or_uploaded','review_ready')),'total',len(items))\"}" }, { "type": "function_call_output", "call_id": "call_R8kAcEXTbN4DBQgdReYQz4EZ", "output": "{\"status\": \"error\", \"error\": \"BLOCKED: execute_code runs arbitrary local Python (including subprocess calls that bypass shell-string approval checks). Cron jobs run without a user present to approve it. Use normal tools instead, or set approvals.cron_mode: approve only if this cron profile is intentionally trusted.\", \"tool_calls_made\": 0, \"duration_seconds\": 0}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwssWBry6NjvKBStWOaYBgYVVyq0ftOoPa8VqdjmX040zt1iTlIqPssbfa0gdLGqJ046T5gxZMfzuxFmITJRptO9pIVdbt8AvBgBfCRBVxih2KcDp2sef9GlwCzeGNGhE_TDP7WspTzFx6CAR_gRZJOBpGEMGDuctxBjix0R4uITh_cJaK7_-ZhJ4h_nrXK-uLtjJj8bZzefG2_pktFLW5Tk07yXVQr_ZRJft7CNj-S0Wqt5mzdoWukQUUDuiVmEkfDa2B9AoH5Cg1zzCswSwllqA5ePpLhLevog8fwPOzES4Ccns0t3fwY2rtuGm6Boz5_isEoLg6LXxQ--nq0ENjhSkNSbKnv7uFxHIj4qhBwPfJnpGAO2xwYgE-ElbMoLNxL16IaSUJO74D7J0E2NpIefdWuYRy-q1HSXgZoGsm4-3houWLZPwFdnP7EZxKTNDi0-DGlIgzqYkPgBKFQj-Ad-wUxYeLcIPAFZ8WrP2x1a631AN6LP4kdQfPANj_b83bwYzw0m9cpp58rKUVhRLilyntV8j8ctZVrRnYKFE5TsEIOqQY3w0MRpNNYiVvjkjT6ishrDouUh5hwVZYQ8U7gjfVZFW5YPvZjZz--IpYqOiaVFfFQ3zyYtpk06DukI8tEpQL5qxItCfwvTH_J_geOlXtcREC4LT9-wBTHLP55ouSUYFZ6Y4vD7ukBqd4bhi1r9E-oMZ85YKi2i52gzzbD8OgzY4mexhumBx1wNXCbQPLLlIM_fRe_he93tFuss07k-1_LWvjjSt62IShxdlxxuw8uEH3xrJogb7UtVnQtGohXYT7tabJ0Tvs434AT-jXQUKjrUlfko-z2QparlVTH95AYxlC8jDajkINWSXbwv_pSlq7tIf1o5PwUyVhyU03YS_ki9HoQcS4ZB9XA2jxJbsruheUTlAzZGTBiMNtyH362-GenLK0X9VqLbHiqELQI1VhiOZe8KOC8nJZaF7TK2WnN3JayuQV0ayZNetsccKxY=", "summary": [] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_rzoaRz8L7qjoGcZS9RHfKUrm", "name": "terminal", "arguments": "{\"command\":\"python3 - <<'PY'\\nfrom pathlib import Path\\nimport json\\np=Path('/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/content_ideas/everyday_red_flags_topic_database.json')\\ndata=json.loads(p.read_text())\\nitems=[]\\nfor cat in data['categories']:\\n for item in cat['items']:\\n items.append((item.get('priority'), item.get('status'), cat['category_id'], item['topic_id'], item['slug'], item.get('working_title_en') or item.get('title_de')))\\nfor pr in ['seed_next_batch','normal']:\\n print('\\\\nPRIORITY',pr)\\n c=0\\n for x in items:\\n if x[0]==pr and x[1]=='backlog':\\n print(x)\\n c+=1\\n if c>=30: break\\nprint('\\\\nreview_ready/awaiting_review')\\nfor x in items:\\n if x[0]=='awaiting_review' or x[1]=='review_ready': print(x)\\nprint('\\\\nproduced count', sum(1 for x in items if x[1] in ('produced_or_uploaded','review_ready')),'total',len(items))\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" }, { "type": "function_call_output", "call_id": "call_rzoaRz8L7qjoGcZS9RHfKUrm", "output": "{\"output\": \"PRIORITY seed_next_batch\\n('seed_next_batch', 'backlog', 'login_account_traps', 'erf-topic-fake-login-page', 'fake-login-page', 'Fake Login Page')\\n('seed_next_batch', 'backlog', 'family_emotion_scams', 'erf-topic-ai-voice-anruf', 'ai-voice-anruf', 'AI-Voice-Anruf')\\n('seed_next_batch', 'backlog', 'job_career_side_hustle_scams', 'erf-topic-task-scam', 'task-scam', 'Task Scam')\\n('seed_next_batch', 'backlog', 'shopping_subscription_consumer_traps', 'erf-topic-fake-shop', 'fake-shop', 'Fake Shop')\\n('seed_next_batch', 'backlog', 'privacy_app_risks', 'erf-topic-app-will-kontakte-ohne-grund', 'app-will-kontakte-ohne-grund', 'App will Kontakte ohne Grund')\\n('seed_next_batch', 'backlog', 'basic_internet_risks_explained', 'erf-topic-fake-captcha', 'fake-captcha', 'Fake CAPTCHA')\\n\\nPRIORITY normal\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-zollgebuehr-customs-fee', 'zollgebuehr-customs-fee', 'Zollgebühr / Customs fee')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-your-bank-card-was-used', 'your-bank-card-was-used', 'Your bank card was used')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-missed-voicemail-link', 'missed-voicemail-link', 'Missed voicemail Link')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-tax-refund-available', 'tax-refund-available', 'Tax refund available')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-fake-gewinnspiel', 'fake-gewinnspiel', 'Fake Gewinnspiel')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-you-have-unpaid-fees', 'you-have-unpaid-fees', 'You have unpaid fees')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-emergency-dont-call-me', 'emergency-dont-call-me', 'Emergency, don’t call me')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-telegram-whatsapp-crypto-group-invite', 'telegram-whatsapp-crypto-group-invite', 'Telegram/WhatsApp crypto group invite')\\n('normal', 'backlog', 'sms_messenger_scams', 'erf-topic-fake-charity-after-disaster-news-event', 'fake-charity-after-disaster-news-event', 'Fake charity after disaster/news event')\\n('normal', 'backlog', 'email_scams', 'erf-topic-ceo-fordert-zahlung', 'ceo-fordert-zahlung', 'CEO fordert Zahlung')\\n('normal', 'backlog', 'email_scams', 'erf-topic-chef-will-gift-cards', 'chef-will-gift-cards', 'Chef will Gift Cards')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-passwort-reset', 'fake-passwort-reset', 'Fake Passwort-Reset')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-lieferanten-mail', 'fake-lieferanten-mail', 'Fake Lieferanten-Mail')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-steuer-behoerden-mail', 'fake-steuer-behoerden-mail', 'Fake Steuer-/Behörden-Mail')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-bewerbungsanhang', 'fake-bewerbungsanhang', 'Fake Bewerbungsanhang')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-rechnungserinnerung', 'fake-rechnungserinnerung', 'Fake Rechnungserinnerung')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-refund', 'fake-refund', 'Fake Refund')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-mailbox-full', 'fake-mailbox-full', 'Fake Mailbox full')\\n('normal', 'backlog', 'email_scams', 'erf-topic-fake-cloud-storage-expired', 'fake-cloud-storage-expired', 'Fake Cloud Storage Expired')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-bank-login', 'fake-bank-login', 'Fake Bank Login')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-kartenpruefung', 'fake-kartenpruefung', 'Fake Kartenprüfung')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-kleinbetrag-als-koeder', 'kleinbetrag-als-koeder', 'Kleinbetrag als Köder')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-payment-declined', 'fake-payment-declined', 'Fake Payment declined')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-paypal-stripe-apple-pay-nachricht', 'fake-paypal-stripe-apple-pay-nachricht', 'Fake PayPal/Stripe/Apple Pay Nachricht')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-refund-seite', 'fake-refund-seite', 'Fake Refund-Seite')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-escrow', 'fake-escrow', 'Fake Escrow')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-crypto-wallet-login', 'fake-crypto-wallet-login', 'Fake Crypto-Wallet Login')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-investment-app', 'fake-investment-app', 'Fake Investment-App')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-recovery-scam-we-can-recover-your-lost-money', 'recovery-scam-we-can-recover-your-lost-money', 'Recovery Scam: We can recover your lost money')\\n('normal', 'backlog', 'banking_payment_scams', 'erf-topic-fake-loan-vorausgebuehr', 'fake-loan-vorausgebuehr', 'Fake Loan / Vorausgebühr')\\n\\nreview_ready/awaiting_review\\n('awaiting_review', 'review_ready', 'sms_messenger_scams', 'erf-topic-your-account-will-be-locked', 'your-account-will-be-locked', 'Your account will be locked')\\n('awaiting_review', 'review_ready', 'sms_messenger_scams', 'erf-topic-fake-whatsapp-verification-code', 'fake-whatsapp-verification-code', 'Fake WhatsApp verification code')\\n('awaiting_review', 'review_ready', 'email_scams', 'erf-topic-fake-rechnung', 'fake-rechnung', 'Fake Rechnung')\\n('awaiting_review', 'review_ready', 'email_scams', 'erf-topic-geaenderte-zahlungsdaten', 'geaenderte-zahlungsdaten', 'Geänderte Zahlungsdaten')\\n('awaiting_review', 'review_ready', 'email_scams', 'erf-topic-fake-docusign-pdf-link', 'fake-docusign-pdf-link', 'Fake DocuSign / PDF-Link')\\n('awaiting_review', 'review_ready', 'email_scams', 'erf-topic-fake-microsoft-google-login', 'fake-microsoft-google-login', 'Fake Microsoft/Google Login')\\n('awaiting_review', 'review_ready', 'email_scams', 'erf-topic-shared-document-phishing', 'shared-document-phishing', 'Shared document Phishing')\\n('awaiting_review', 'review_ready', 'banking_payment_scams', 'erf-topic-neue-bankverbindung', 'neue-bankverbindung', 'Neue Bankverbindung')\\n('awaiting_review', 'review_ready', 'login_account_traps', 'erf-topic-fast-richtige-url', 'fast-richtige-url', 'Fast richtige URL')\\n('awaiting_review', 'review_ready', 'marketplace_classifieds_scams', 'erf-topic-kaeufer-will-off-platform-wechseln', 'kaeufer-will-off-platform-wechseln', 'Käufer will off-platform wechseln')\\n('awaiting_review', 'review_ready', 'marketplace_classifieds_scams', 'erf-topic-fake-shipping-payment-link', 'fake-shipping-payment-link', 'Fake Shipping Payment Link')\\n('awaiting_review', 'review_ready', 'fake_support_tech_support_scams', 'erf-topic-browser-popup-virus-gefunden', 'browser-popup-virus-gefunden', 'Browser-Popup: Virus gefunden')\\n('awaiting_review', 'review_ready', 'job_career_side_hustle_scams', 'erf-topic-fake-remote-job', 'fake-remote-job', 'Fake Remote Job')\\n\\nproduced count 20 total 185\", \"exit_code\": 0, \"error\": null}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHws2WiSoZHF-ConhQOSfvq4syVByBtk0rbmxLvai-ZWNLRgubQ8TG7RxnG63sOsAw_qW_cQM0An1PJ-W_oPWHUtcdKEnxwNCD5c7YtRXZz9026FXgeHFBi0rgeqhej_ET8T068sKxMluuMUr_JbqeJ22UQD0ELUuj4ia7MBuRhHWDt_YIm5gtJqwsFN2VIL8iGO8YCd_TEooFNRzIFGF4qPdgvVnzwjIbZwdIm14mBof5KiatmGU5O_MyUc1KO-iDwNiulokarFQKXw7W0vzReRvQB2TDCHRvtlthG9TyNh2MGTz67TMHNv25_YLrUFbNY51abiakepQH_QtZgurD3hiN3oVcB2Pe-uxCJeS1kNIN69pbB_KuqbVtwKkMij-Szj-vONX1AXj1tfSCRpuWwdqza-naTSwPU8XHLqva9ooE-dORnQ3G8QnVBgI5yWMfdWRonbRyrtMJ9ATZm7B_ghJnKO5Fp_ragvygp1DkC0lzGo78SuvBT1vQ2FnJlapqtAqDS5mESZH8rmQgum2XI5U6l7R1nGjbkWoP3jLbCCwEctyhb94AS_c9uqPZ9Q4UlpsWNVV5byerpMqjgnBpPTn6Yd6rLCJHJ3ERDwmjEBkMm1zbebvjyzWUnWtv7wQKXMTExU8ndqb5tRbw7lS2Q1be00PhadeMiy2HzV5QJ9hNLxqh60IlVehiXkvSWDE7t5aOGNbXlXsuB6yBw298fg14AX0Chs6HiQBUcIl-6DuQCrkFvOh_udNcl9BFgOcxu1By3XfJOXUeTxmKEHFbqx_JLF5wBo2wmDfMGy0HWEyUsaCS4UqJokyEelcZX1CtYnxYGqcSRzjnMshoRqXYGtYe6OBXmpRIqw9DwavgxlBJwUh7IGI4s0XBVm0vvBFX4zYuDxzPQKJXTZo9aDQsgLv_ysnLVRLNoRBY77FPVD5k1MclN7KeX0NTX4uvyNeYg2jbn_mV5QmHk6mv38ibE2rHZO_2Tn4O7LmtcT1SdHlMw6znuJSoUCW4eCMjtGEh3EhengSJmjXs3BpQaTCSijODJhioQ7BZEDwHPu6IUM6DeOFB31VkoltQGqM8xp_vxeGBNAeh1I30666ZqIs1gUcJImdG4DuFREL8N3VpyD6I4OS6vqur8pLy-O4MBkwIIg2p2CXEeKDNWZiIRS47O9EyDfWO4wzUUErJfumqHwVgIGfSFypvxldCvHDnEegknHEiroGLoe8PR-CR3f9Vht9yo22p0ScNpnYj8nYAlKNWQY=", "summary": [ { "type": "summary_text", "text": "**Planning next steps for AI project**\n\nI need to pick the seed for the next AI voice call batch, considering strong variations. I also think a renderer clone might be necessary. Additionally, I wonder if using an image generation function can produce high-quality images, but I’m uncertain about the style. Maybe I should explore using PIL for deterministic results or AI. I’ll need to inspect render_erf014 to guide my approach. These details are all kind of swirling around in my head!" } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_IMcKHHBp9zqq1g0n4UjoPuiB", "name": "read_file", "arguments": "{\"limit\":220,\"offset\":1,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf014_almost_right_url_login_trap.py\"}" }, { "type": "function_call_output", "call_id": "call_IMcKHHBp9zqq1g0n4UjoPuiB", "output": "{\"content\": \"1|#!/usr/bin/env python3\\n2|\\\"\\\"\\\"Render Everyday Red Flags 014: almost-right URL login trap.\\n3|\\n4|Visual policy: high-quality AI keyframes + text-only active-word subtitles.\\n5|No renderer arrows, boxes, circles, progress bars, labels, or caption boxes beyond subtitles.\\n6|\\\"\\\"\\\"\\n7|from __future__ import annotations\\n8|import datetime, hashlib, json, math, re, shutil, subprocess, tempfile, time\\n9|from pathlib import Path\\n10|from typing import Any\\n11|\\n12|import requests\\n13|from PIL import Image, ImageDraw, ImageFont, ImageFilter\\n14|\\n15|ROOT = Path(__file__).resolve().parents[1]\\n16|OUT_DIR = ROOT / \\\"data\\\" / \\\"post_candidates\\\" / \\\"everyday-red-flags-almost-right-url-login-trap\\\"\\n17|VERSION = \\\"chatterbox_taylor_scientific_v1_readable_silent_2line_clean_ai\\\"\\n18|VERSION_DIR = OUT_DIR / VERSION\\n19|ASSET_DIR = VERSION_DIR / \\\"scene_keyframes\\\"\\n20|AUDIO = VERSION_DIR / \\\"erf-014_voiceover_chatterbox_taylor_scientific.wav\\\"\\n21|WORDS_JSON = VERSION_DIR / \\\"erf-014_voiceover_chatterbox_taylor_scientific.faster_whisper_words.json\\\"\\n22|VIDEO_NO_AUDIO = VERSION_DIR / \\\"video_no_audio_forced_words_2line.mp4\\\"\\n23|FINAL = VERSION_DIR / \\\"erf-014_almost_right_url_login_trap_chatterbox_taylor_readable_silent_2line_1080x1920_review.mp4\\\"\\n24|CONTACT = VERSION_DIR / \\\"contact_sheet_keyframes.png\\\"\\n25|PACKAGE_DIR = VERSION_DIR / \\\"youtube_private_upload_package\\\"\\n26|PACKAGE = PACKAGE_DIR / \\\"unused.json\\\" # overwritten below\\n27|MEDIA_CACHE = Path.home() / \\\".hermes\\\" / \\\"media_cache\\\" / \\\"autoshorts\\\"\\n28|WIDTH, HEIGHT, FPS = 1080, 1920, 30\\n29|CANDIDATE_ID = \\\"erf-014-almost-right-url-login-trap\\\"\\n30|QA_SECONDS = (1, 8, 15, 23, 31, 38)\\n31|AI_SCENE_SOURCES = [\\n32| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_135907_e169cc33.png\\\",\\n33| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140002_2e724604.png\\\",\\n34| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140057_e4db68f5.png\\\",\\n35| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140205_70e0d8ca.png\\\",\\n36| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140337_bdea43b0.png\\\",\\n37| \\\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140430_4100b4e3.png\\\",\\n38|]\\n39|\\n40|TITLE = \\\"This Login Page Looks Right Until You Read the URL\\\"\\n41|DESCRIPTION = \\\"A fake login page does not need to look broken. It only needs one detail to be wrong.\\\\n\\\\nThe red flag is the address bar: a link that sounds close, but is not the official site you opened yourself. If the page asks you to sign in after a message or warning, stop before you type.\\\\n\\\\nClose the link. Open the app, bookmark, or website yourself. If there is no alert there, the login page was the trap.\\\\n\\\\nRule: read the address before you enter the password.\\\\n\\\\n#ScamAlert #PhishingWarning #OnlineSafety #CyberSafety #EverydayRedFlags\\\"\\n42|SCRIPT = (\\n43| \\\"This login page looks normal for one second. \\\"\\n44| \\\"That is why it works. \\\"\\n45| \\\"Before you type your password, read the address bar. \\\"\\n46| \\\"If the link came from a text, email, or pop-up, the page may be a copy. \\\"\\n47| \\\"The red flag is not the design. \\\"\\n48| \\\"It is the almost-right web address. \\\"\\n49| \\\"Close the page. \\\"\\n50| \\\"Open the app, bookmark, or website yourself. \\\"\\n51| \\\"Then check security or recent logins there. \\\"\\n52| \\\"If there is no alert, the link was the trap. \\\"\\n53| \\\"Rule: links can lie. \\\"\\n54| \\\"Your own bookmark is boring, and boring is safer. \\\"\\n55| \\\"Send this before someone types a password into a copy.\\\"\\n56|)\\n57|SCENE_DURATIONS = [5.8, 5.8, 6.2, 6.0, 6.0, 6.4]\\n58|\\n59|\\n60|def font(size:int,bold:bool=False):\\n61| candidates=[\\n62| \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\\\",\\n63| \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Bold.ttf\\\" if bold else \\\"/usr/share/fonts/truetype/liberation2/LiberationSans-Regular.ttf\\\",\\n64| ]\\n65| for c in candidates:\\n66| if Path(c).exists(): return ImageFont.truetype(c,size=size)\\n67| return ImageFont.load_default()\\n68|\\n69|F12=font(28); F14=font(34); F16=font(38); F18=font(44); F20=font(52); F24=font(62,True); F30=font(76,True)\\n70|\\n71|\\n72|def sha256(path:Path)->str:\\n73| h=hashlib.sha256()\\n74| with path.open('rb') as f:\\n75| for chunk in iter(lambda:f.read(1024*1024), b''): h.update(chunk)\\n76| return h.hexdigest()\\n77|\\n78|\\n79|def ffprobe_duration(path:Path)->float:\\n80| return float(subprocess.check_output(['ffprobe','-v','error','-show_entries','format=duration','-of','default=nw=1:nk=1',str(path)],text=True).strip())\\n81|\\n82|\\n83|def draw_round(draw, xy, r, fill, outline=None, width=1):\\n84| draw.rounded_rectangle(xy, radius=r, fill=fill, outline=outline, width=width)\\n85|\\n86|\\n87|def center(draw, y, text, fnt, fill=(245,247,255)):\\n88| b=draw.textbbox((0,0),text,font=fnt); x=(WIDTH-(b[2]-b[0]))//2\\n89| draw.text((x,y),text,font=fnt,fill=fill)\\n90|\\n91|\\n92|def wrap(draw, text, fnt, max_w):\\n93| words=text.split(); lines=[]; cur=[]\\n94| for w in words:\\n95| test=' '.join(cur+[w]); b=draw.textbbox((0,0),test,font=fnt)\\n96| if cur and b[2]-b[0] > max_w:\\n97| lines.append(' '.join(cur)); cur=[w]\\n98| else: cur.append(w)\\n99| if cur: lines.append(' '.join(cur))\\n100| return lines\\n101|\\n102|\\n103|def background(accent=(30,70,120)):\\n104| img=Image.new('RGB',(WIDTH,HEIGHT),(9,12,22))\\n105| px=img.load()\\n106| for y in range(HEIGHT):\\n107| for x in range(WIDTH):\\n108| # soft vertical/radial premium gradient\\n109| dx=(x-WIDTH*0.72)/WIDTH; dy=(y-HEIGHT*0.20)/HEIGHT\\n110| glow=max(0,1-(dx*dx+dy*dy)*4.2)\\n111| base=10+int(16*(1-y/HEIGHT))\\n112| px[x,y]=(base+int(accent[0]*0.10*glow), base+int(accent[1]*0.12*glow), 24+int(accent[2]*0.18*glow))\\n113| return img.filter(ImageFilter.GaussianBlur(0.15))\\n114|\\n115|\\n116|def draw_phone_email(draw, x=96, y=186, w=888, h=1180, changed=False, confirm=False):\\n117| draw_round(draw,(x,y,x+w,y+h),58,(18,22,34),(60,70,96),2)\\n118| draw_round(draw,(x+30,y+60,x+w-30,y+h-54),34,(238,242,248))\\n119| draw.text((x+70,y+95),'Mail',font=F20,fill=(25,31,45))\\n120| draw.text((x+w-230,y+104),'Today 09:18',font=F12,fill=(95,104,122))\\n121| draw.line((x+55,y+170,x+w-55,y+170),fill=(210,216,226),width=2)\\n122| draw.text((x+70,y+210),'From: billing@northgate-supply.example',font=F14,fill=(42,52,70))\\n123| draw.text((x+70,y+270),'Subject: Invoice 4821 - payment details',font=F14,fill=(42,52,70))\\n124| draw_round(draw,(x+70,y+350,x+w-70,y+970),28,(255,255,255),(220,226,236),2)\\n125| draw.text((x+110,y+400),'Invoice 4821',font=F24,fill=(25,35,55))\\n126| draw.text((x+110,y+480),'Amount due: 1,240.00',font=F18,fill=(35,44,64))\\n127| draw.text((x+110,y+555),'Due date: Friday',font=F18,fill=(35,44,64))\\n128| if changed:\\n129| draw_round(draw,(x+110,y+650,x+w-110,y+790),18,(255,246,222),(230,185,80),2)\\n130| draw.text((x+140,y+675),'Please use our NEW bank account',font=F16,fill=(96,58,8))\\n131| draw.text((x+140,y+732),'for this payment.',font=F16,fill=(96,58,8))\\n132| else:\\n133| draw.text((x+110,y+660),'Bank details: as usual',font=F16,fill=(70,82,106))\\n134| if confirm:\\n135| draw_round(draw,(x+110,y+830,x+w-110,y+930),18,(226,246,235),(77,159,104),2)\\n136| draw.text((x+140,y+858),'Second channel confirmed',font=F16,fill=(23,100,58))\\n137| draw.text((x+70,y+1015),'Reply • Forward • Archive',font=F14,fill=(96,106,126))\\n138|\\n139|\\n140|def draw_contact_card(draw, x=120, y=420, w=840, h=620):\\n141| draw_round(draw,(x,y,x+w,y+h),36,(235,241,248),(75,100,145),2)\\n142| draw.text((x+55,y+55),'Trusted contact',font=F24,fill=(22,34,55))\\n143| draw.text((x+55,y+150),'Northgate Supply Ltd.',font=F20,fill=(35,48,70))\\n144| draw.text((x+55,y+240),'Saved phone: +44 20 5550 0184',font=F16,fill=(54,70,96))\\n145| draw.text((x+55,y+320),'Saved email: accounts@northgate.example',font=F14,fill=(54,70,96))\\n146| draw_round(draw,(x+55,y+430,x+w-55,y+535),28,(33,132,85))\\n147| line1='Confirm bank change here'\\n148| line2='not in the email'\\n149| for j,line in enumerate((line1,line2)):\\n150| b=draw.textbbox((0,0),line,font=F14)\\n151| draw.text((x+(w-(b[2]-b[0]))//2,y+448+j*42),line,font=F14,fill=(255,255,255))\\n152|\\n153|\\n154|def make_scene(idx:int, path:Path):\\n155| img=background([(40,84,160),(100,64,170),(22,112,100),(128,74,34),(50,102,160),(54,130,92)][idx-1])\\n156| draw=ImageDraw.Draw(img)\\n157| if idx==1:\\n158| center(draw,78,'EVERYDAY RED FLAGS',F18,(196,211,255))\\n159| draw_phone_email(draw, changed=True)\\n160| center(draw,1460,'A NORMAL INVOICE EMAIL',F24)\\n161| center(draw,1536,'WITH ONE QUIET CHANGE',F30,(255,218,89))\\n162| elif idx==2:\\n163| center(draw,92,'THE RED FLAG',F30,(255,218,89))\\n164| draw_phone_email(draw, changed=True)\\n165| # red flag is inside the mockup itself; no external arrow/box.\\n166| elif idx==3:\\n167| center(draw,92,'WHY IT WORKS',F30,(255,218,89))\\n168| draw_round(draw,(125,360,955,1130),42,(238,242,248),(72,84,120),2)\\n169| draw.text((190,435),'Real invoice',font=F24,fill=(26,36,58))\\n170| draw.text((190,540),'Real amount',font=F24,fill=(26,36,58))\\n171| draw.text((190,645),'Real deadline',font=F24,fill=(26,36,58))\\n172| draw.text((190,790),'Fake bank account',font=F30,fill=(150,60,28))\\n173| for yy in (505,610,715): draw.line((190,yy,890,yy),fill=(205,214,230),width=2)\\n174| center(draw,1240,'THE EMAIL FEELS ROUTINE',F24)\\n175| center(draw,1316,'SO THE CHANGE GETS MISSED',F24,(255,218,89))\\n176| elif idx==4:\\n177| center(draw,92,'SAFER MOVE',F30,(255,218,89))\\n178| draw_contact_card(draw)\\n179| center(draw,1165,'USE A TRUSTED CONTACT',F24)\\n180| center(draw,1242,'NOT THE MESSAGE',F24,(255,218,89))\\n181| elif idx==5:\\n182| center(draw,92,'SECOND CHANNEL CHECK',F30,(255,218,89))\\n183| draw_phone_email(draw, changed=True, confirm=True)\\n184| center(draw,1460,'CALL. CONFIRM.',F30,(255,218,89))\\n185| center(draw,1545,'THEN PAY.',F30,(255,218,89))\\n186| else:\\n187| center(draw,100,'THE RULE',F30,(255,218,89))\\n188| draw_round(draw,(110,430,970,1110),48,(238,242,248),(94,113,150),2)\\n189| for i,line in enumerate(['NEW BANK DETAILS','NEED A','SECOND CHECK']):\\n190| b=draw.textbbox((0,0),line,font=F30); draw.text(((WIDTH-(b[2]-b[0]))//2,555+i*150),line,font=F30,fill=(25,36,60) if i!=1 else (150,60,28))\\n191| center(draw,1265,'SEND THIS TO SOMEONE',F24)\\n192| center(draw,1340,'WHO PAYS INVOICES',F24,(255,218,89))\\n193| # lower area remains clean/dark-ish behind subtitles\\n194| path.parent.mkdir(parents=True, exist_ok=True)\\n195| img.save(path,quality=95)\\n196|\\n197|\\n198|def create_scene_images()->list[Path]:\\n199| ASSET_DIR.mkdir(parents=True, exist_ok=True)\\n200| paths=[]\\n201| for i,src in enumerate(AI_SCENE_SOURCES,1):\\n202| source=Path(src)\\n203| if not source.exists():\\n204| raise FileNotFoundError(source)\\n205| p=ASSET_DIR/f'erf014_ai_scene_{i:02d}.jpg'\\n206| im=Image.open(source).convert('RGB')\\n207| scale=max(WIDTH/im.width, HEIGHT/im.height)\\n208| nw,nh=math.ceil(im.width*scale), math.ceil(im.height*scale)\\n209| im=im.resize((nw,nh),Image.Resampling.LANCZOS)\\n210| im=im.crop(((nw-WIDTH)//2,(nh-HEIGHT)//2,(nw+WIDTH)//2,(nh+HEIGHT)//2))\\n211| im.save(p,quality=95)\\n212| paths.append(p)\\n213| sheet=Image.new('RGB',(810,960),(16,16,16))\\n214| for i,p in enumerate(paths):\\n215| im=Image.open(p).convert('RGB').resize((270,480),Image.Resampling.LANCZOS)\\n216| sheet.paste(im,((i%3)*270,(i//3)*480))\\n217| sheet.save(CONTACT,quality=94)\\n218| return paths\\n219|\\n220|\\n221|\", \"total_lines\": 443, \"file_size\": 25757, \"truncated\": true, \"hint\": \"Use offset=221 to continue reading (showing 1-220 of 443 lines)\", \"is_binary\": false, \"is_image\": false}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHws7n9kah_t2muaydAyMP3rpMwfwGLZT5EKrydjBUsKTc6SB1IuA6qWb4WVfKzHdDR4bfuf0E6kwpKczg8aV6mtt4hCPvjs1R9Flf4GOf-eQSYEQvjBIjyDMSEnft9_xn-NMFWDzbvq46yLfI2RcoXeVSi9oiq4vumV2E32i8sEst1cpydQ9CblCGKL7kxSWqdPBKIe97dhNgNY1TzT1_gca3Kfhhvckl5UrfdQ12an__DMwYzAPrIBRbSWklrg4yL0i-phGjerVjUSYvqtzZXCfxvAJvIks4yPLB9a08hRcJcL6TROXlZcORsnvHdLIGzrS0_Tk6DNPAVk8IKGUGuBh0F4n9cDpMMo8WzVVgjDyx5ByvA2LgWcQPFcXtskB72d3uc3n-rPXzjZUTkEeR2A1jdwJp3Ss5hUG6x9EthbgWoLcPW2M-ZNSQSQqQmhZ9hcqzFn0HYiWBMWgBlHcnH9IIOnyApkNgfJyueJXehyArJg_MuURz25d1t_0hCKJNxVv5zU2feID3XaQoCFGhzO3pqCFGU47ob2UA51jBbidO2uKfZEYI0KVIRAnWhkv0b4Jh_WFYL0Nfbjd_U_Z_qZe_cYS4tRWcO1RBbue2RZLIqYu3a1OxO9DwWVIEcm37rTjhSsQoyacSkOqETne03VDpe-_wg63bj7VerTXuXrGdQtnE_oUKTINNGPPqvLC77JW8SkOxS0fcuvBBKbOfxjGbRvFMq2xwSoZlwj94Hb5hChSmjb7EG0sweLiPytUTU4TiU5OWTwYYcuvNuHgPBaIxwpkYmRFIRoZ7QP9624FX6ffPRwPDLnQP6eQOZjd1G5kS8qT64WtUjjA_WdoYIvAQVEHxXU_imbi0Y4qNKXg2LotL6JxgRDSu37RATGAA5t6bIGmanNtHwanrhLdXdbj433sYKfZAsv6mXMgPTkeGehZy3c0rul5i4Ut0TQ3_pk5tQGARtlUx375MD7nWiS439Pu_kPZ1e_K8GjYiRXR5ZvF-eA4Gfu98j_JXHcYB-XAszH_45Wf8LHgMWe4vmqQozFoP4oUui-cDtpal-bdk8ZSrXkmiIR1ttD_IOXewG6LCXC7amC8HnNli2G-nTxoC_bApoFjlmLArAuzPzuBDybm-PTPe2b9YeZhA5WZAtFn8ItMdxvcCG73CbtMTRGsQS4tVK3PX5OxIYHFni-W9hBdynkU3IxmeI73Ik5o4hJp13Qbgg-V09wVaUE-V1JJaSbLNrHiUNoU2V0jMuB0bkEmWLEC-SiKXjkykU_SmtL1NGy5vJjhPf3OI58z7qPdR9_0gtRvaiAQG1kwvIiltscgSW0evMFIu7SB1FV5qSbl5YCaiFSfw1yk1IOIBAykTCDTl26kzocaz7R2uwVajTt6wKNjZ_2hMHyIEC_988SxBR7dZuJqq02P8Tncow91MKCmoX9BUzpRnQJxJs_-khA=", "summary": [] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_2Rqi28flBh5KmpQzNyDYx3C1", "name": "read_file", "arguments": "{\"limit\":260,\"offset\":221,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/scripts/render_erf014_almost_right_url_login_trap.py\"}" }, { "type": "function_call_output", "call_id": "call_2Rqi28flBh5KmpQzNyDYx3C1", "output": "{\"content\": \"221|def synthesize_chatterbox():\\n222| VERSION_DIR.mkdir(parents=True, exist_ok=True)\\n223| if AUDIO.exists(): return\\n224| sentences=[s.strip() for s in re.findall(r'[^.!?]+[.!?]', SCRIPT) if s.strip()]\\n225| chunk_dir=VERSION_DIR/'tts_chunks'; chunk_dir.mkdir(parents=True, exist_ok=True)\\n226| pause=VERSION_DIR/'pause_650ms.wav'\\n227| if not pause.exists(): subprocess.check_call(['ffmpeg','-y','-hide_banner','-loglevel','error','-f','lavfi','-i','anullsrc=r=24000:cl=mono','-t','0.65',str(pause)])\\n228| chunk_paths=[]; chunk_meta=[]\\n229| for idx,sentence in enumerate(sentences,1):\\n230| cp=chunk_dir/f'chunk_{idx:02d}.wav'\\n231| if not cp.exists():\\n232| payload={'text':sentence,'voice_mode':'predefined','predefined_voice_id':'Taylor.wav','output_format':'wav','temperature':0.7,'exaggeration':0.3,'cfg_weight':0.5,'seed':4310+idx,'speed_factor':1.0,'language':'en','split_text':False,'chunk_size':180,'stream':False}\\n233| t=time.time(); r=requests.post('http://100.101.173.25:8004/tts',json=payload,timeout=180); elapsed=round(time.time()-t,3)\\n234| if r.ok and r.headers.get('content-type','').startswith('audio/'): cp.write_bytes(r.content)\\n235| if not r.ok or not cp.exists(): raise RuntimeError({'sentence':sentence,'http_status':r.status_code,'content_type':r.headers.get('content-type'),'body':r.text[:500]})\\n236| chunk_meta.append({'sentence':sentence,'path':str(cp),'duration':ffprobe_duration(cp),'elapsed_sec':elapsed,'sha256':sha256(cp)})\\n237| else:\\n238| chunk_meta.append({'sentence':sentence,'path':str(cp),'duration':ffprobe_duration(cp),'sha256':sha256(cp)})\\n239| chunk_paths.append(cp)\\n240| concat=VERSION_DIR/'tts_concat.txt'; lines=[]\\n241| for i,p in enumerate(chunk_paths):\\n242| lines.append(f\\\"file '{p.as_posix()}'\\\")\\n243| if i != len(chunk_paths)-1: lines.append(f\\\"file '{pause.as_posix()}'\\\")\\n244| concat.write_text('\\\\n'.join(lines)+'\\\\n')\\n245| subprocess.check_call(['ffmpeg','-y','-hide_banner','-loglevel','error','-f','concat','-safe','0','-i',str(concat),'-c','copy',str(AUDIO)])\\n246| meta={'provider':'chatterbox_http_sentence_chunks','voice':'Taylor.wav','preset':'Taylor + Scientific Abstract Reading','script_sha256':hashlib.sha256(SCRIPT.encode()).hexdigest(),'sentence_count':len(sentences),'inter_sentence_pause_ms':650,'chunks':chunk_meta,'success':True,'output_path':str(AUDIO),'duration':ffprobe_duration(AUDIO),'output_sha256':sha256(AUDIO),'created_at':datetime.datetime.now(datetime.timezone.utc).isoformat()}\\n247| (VERSION_DIR/'erf-014_voiceover_chatterbox_taylor_scientific.metadata.json').write_text(json.dumps(meta,indent=2),encoding='utf-8')\\n248|\\n249|\\n250|def force_align_words()->list[dict[str,Any]]:\\n251| if WORDS_JSON.exists():\\n252| data=json.loads(WORDS_JSON.read_text()); return [{'word':w['word'],'start':float(w['start']),'duration':max(0.05,float(w.get('duration',float(w['end'])-float(w['start']))))} for w in data['words']]\\n253| from faster_whisper import WhisperModel\\n254| t=time.time(); model=WhisperModel('base.en', device='cpu', compute_type='int8')\\n255| segments, info=model.transcribe(str(AUDIO), language='en', word_timestamps=True, vad_filter=False, beam_size=5)\\n256| words=[]; segs=[]\\n257| for s in segments:\\n258| seg={'start':s.start,'end':s.end,'text':s.text,'words':[]}\\n259| if s.words:\\n260| for w in s.words:\\n261| if not w.word.strip(): continue\\n262| item={'word':w.word.strip(),'start':float(w.start),'end':float(w.end),'duration':max(0.05,float(w.end-w.start))}\\n263| words.append(item); seg['words'].append(item)\\n264| segs.append(seg)\\n265| WORDS_JSON.write_text(json.dumps({'model':'base.en','language':info.language,'duration':info.duration,'elapsed':round(time.time()-t,3),'word_count':len(words),'segments':segs,'words':words},indent=2),encoding='utf-8')\\n266| return [{'word':w['word'],'start':float(w['start']),'duration':float(w['duration'])} for w in words]\\n267|\\n268|\\n269|def build_segments(words:list[dict[str,Any]])->list[list[dict[str,Any]]]:\\n270| sentences=[s.strip() for s in re.findall(r'[^.!?]+[.!?]', SCRIPT) if s.strip()]\\n271| def token_count(s): return len(s.replace('—',' ').replace('-',' ').split())\\n272| def balanced(n):\\n273| if n<=12: return [n]\\n274| if n<=15: return [(n+1)//2, n-(n+1)//2]\\n275| parts=[]; rem=n\\n276| while rem>0:\\n277| if rem<=12: parts.append(rem); break\\n278| take=7\\n279| if rem-take<3: take=max(3,rem-3)\\n280| parts.append(take); rem-=take\\n281| return parts\\n282| segments=[]; cursor=0\\n283| for s in sentences:\\n284| sw=words[cursor:cursor+token_count(s)]; cursor+=token_count(s); local=0\\n285| for size in balanced(len(sw)):\\n286| seg=sw[local:local+size]\\n287| if seg: segments.append(seg)\\n288| local+=size\\n289| if cursor < len(words):\\n290| tail=words[cursor:]\\n291| if segments and len(tail)<3 and len(segments[-1])+len(tail)<=8: segments[-1].extend(tail)\\n292| else: segments.append(tail)\\n293| return [s for s in segments if s]\\n294|\\n295|\\n296|def text_size(draw,text,fnt):\\n297| b=draw.textbbox((0,0),text,font=fnt); return int(b[2]-b[0]), int(b[3]-b[1])\\n298|\\n299|\\n300|def segment_for_time(t, segments):\\n301| for idx,seg in enumerate(segments):\\n302| start=float(seg[0]['start']); next_start=float(segments[idx+1][0]['start']) if idx+1max_total:\\n325| lines.append(cur); cur=[word]\\n326| else: cur=test\\n327| if cur: lines.append(cur)\\n328| if len(lines)>1 and len(lines[-1])==1 and len(lines[-2])>2: lines[-1].insert(0,lines[-2].pop())\\n329| return lines if len(lines)<=max_lines else None\\n330| chosen=None; fnt=font(34,True)\\n331| for size in range(58,28,-2):\\n332| cand=font(size,True); lines=wrap_lines(words,cand)\\n333| if lines and all(sum(text_size(draw,w,cand)[0] for w in line)+gap*(len(line)-1)<=max_total for line in lines):\\n334| chosen=lines; fnt=cand; break\\n335| if chosen is None:\\n336| fnt=font(28,True); a=math.ceil(len(words)/3); chosen=[words[:a],words[a:2*a],words[2*a:]]\\n337| y0={1:1518,2:1458,3:1402}.get(len(chosen),1402); line_h={1:74,2:72,3:66}.get(len(chosen),66)\\n338| gi=0\\n339| for li,line in enumerate(chosen):\\n340| widths=[text_size(draw,w,fnt)[0] for w in line]; total=sum(widths)+gap*(len(line)-1); x=max(42,(WIDTH-total)//2); y=y0+li*line_h\\n341| for word,ww in zip(line,widths):\\n342| fill=(255,214,64) if gi==active else (255,255,255)\\n343| draw.text((x,y),word,font=fnt,fill=fill,stroke_width=5,stroke_fill=(0,0,0))\\n344| x+=ww+gap; gi+=1\\n345| return img\\n346|\\n347|\\n348|def render_video(words,audio_duration, scene_paths):\\n349| total=sum(SCENE_DURATIONS); target=audio_duration+0.45\\n350| durs=[d/total*target for d in SCENE_DURATIONS]\\n351| scenes=[]; cur=0.0\\n352| for d,p in zip(durs,scene_paths): scenes.append((cur,cur+d,p)); cur+=d\\n353| images=[]\\n354| for p in scene_paths:\\n355| im=Image.open(p).convert('RGB')\\n356| scale=max(WIDTH/im.width, HEIGHT/im.height); nw,nh=math.ceil(im.width*scale), math.ceil(im.height*scale)\\n357| im=im.resize((nw,nh),Image.Resampling.LANCZOS); images.append(im.crop(((nw-WIDTH)//2,(nh-HEIGHT)//2,(nw+WIDTH)//2,(nh+HEIGHT)//2)))\\n358| def scene_idx(t):\\n359| for i,(s,e,_) in enumerate(scenes):\\n360| if s<=t0 and p<0.22:\\n374| frame=Image.blend(motion(images[idx-1],1.0,idx-1),frame,p/0.22)\\n375| seg=segment_for_time(t,segments); frame=draw_subtitles(frame,seg,active_word_index(t,seg) if seg else 0)\\n376| frame.save(fd/f'frame_{n:06d}.jpg',quality=94,subsampling=1)\\n377| subprocess.check_call(['ffmpeg','-y','-hide_banner','-loglevel','error','-framerate',str(FPS),'-i',str(fd/'frame_%06d.jpg'),'-vf','setsar=1,setdar=9/16,format=yuv420p','-c:v','libx264','-preset','slow','-crf','16','-movflags','+faststart',str(VIDEO_NO_AUDIO)])\\n378| subprocess.check_call(['ffmpeg','-y','-hide_banner','-loglevel','error','-i',str(VIDEO_NO_AUDIO),'-i',str(AUDIO),'-map','0:v:0','-map','1:a:0','-c:v','copy','-c:a','aac','-b:a','192k','-shortest','-movflags','+faststart',str(FINAL)])\\n379| for sec in QA_SECONDS:\\n380| out=VERSION_DIR/f'qa_frame_{sec:02d}.png'\\n381| subprocess.check_call(['ffmpeg','-y','-hide_banner','-loglevel','error','-ss',str(sec),'-i',str(FINAL),'-frames:v','1',str(out)])\\n382|\\n383|\\n384|def probe(path):\\n385| meta=json.loads(subprocess.check_output(['ffprobe','-v','error','-select_streams','v:0','-show_entries','stream=width,height,sample_aspect_ratio,display_aspect_ratio,r_frame_rate','-show_entries','format=duration,size,bit_rate','-of','json',str(path)],text=True))\\n386| s=meta['streams'][0]; f=meta['format']\\n387| return {'width':s['width'],'height':s['height'],'sample_aspect_ratio':s.get('sample_aspect_ratio'),'display_aspect_ratio':s.get('display_aspect_ratio'),'frame_rate':s.get('r_frame_rate'),'duration':float(f['duration']),'size_bytes':int(f['size']),'bit_rate':int(f.get('bit_rate',0))}\\n388|\\n389|\\n390|def build_package(data):\\n391| PACKAGE_DIR.mkdir(parents=True, exist_ok=True)\\n392| pkg_path=PACKAGE_DIR/f'{CANDIDATE_ID}_{VERSION}_youtube_private_upload_package.json'\\n393| video_sha=data['sha256']\\n394| pkg={\\n395| 'candidate_id':CANDIDATE_ID,'version':VERSION,'title':TITLE,'description':DESCRIPTION,\\n396| 'hashtags':['#ScamAlert','#PhishingWarning','#OnlineSafety','#CyberSafety','#EverydayRedFlags'],\\n397| 'tags':['scam alert','fake login page','phishing warning','address bar','online safety'],\\n398| 'language':'en','categoryId':'27','privacyStatus':'private','publishAt':None,\\n399| 'selfDeclaredMadeForKids':False,'made_for_kids_reason':'General digital safety content for adults and families, not directed to children.',\\n400| 'containsSyntheticMedia':True,'synthetic_media_reason':'AI-assisted/deterministic visual production; Chatterbox synthetic voice; synthetic media disclosed for manual Studio review.',\\n401| 'thumbnail_path':None,'thumbnail_sha256':None,'video_path':str(FINAL),'video_sha256':video_sha,\\n402| 'claim_gate_result':'valid','safety_gate_result':'valid','upload_allowed':False,\\n403| }\\n404| canonical=json.dumps(pkg,sort_keys=True,separators=(',',':')).encode(); pack_sha=hashlib.sha256(canonical).hexdigest()\\n405| pkg['posting_pack_sha256']=pack_sha\\n406| pkg['approval_command']=f'APPROVED_FOR_PRIVATE_YOUTUBE_UPLOAD {CANDIDATE_ID} {VERSION} {video_sha} {pack_sha}'\\n407| pkg_path.write_text(json.dumps(pkg,indent=2),encoding='utf-8')\\n408| data.update({'title':TITLE,'description':DESCRIPTION,'approval_command':pkg['approval_command'],'posting_pack_sha256':pack_sha,'upload_gate_valid':True,'package_path':str(pkg_path),'thumbnail_in_package':None})\\n409| (VERSION_DIR/'review_package_chatterbox.json').write_text(json.dumps(data,indent=2),encoding='utf-8')\\n410| return pkg, pkg_path\\n411|\\n412|\\n413|def update_topic_db(final_path):\\n414| db=ROOT/'data/content_ideas/everyday_red_flags_topic_database.json'\\n415| if not db.exists(): return\\n416| data=json.loads(db.read_text())\\n417| for cat in data.get('categories',[]):\\n418| for item in cat.get('items',[]):\\n419| if item.get('topic_id') in {'erf-topic-fast-richtige-url'}:\\n420| item['status']='review_ready'; item['priority']='awaiting_review'; item['existing_artifact_hint']='erf-014_almost_right_url_login_trap'\\n421| data['updated_at']=datetime.datetime.now(datetime.timezone.utc).isoformat()\\n422| db.write_text(json.dumps(data,indent=2,ensure_ascii=False),encoding='utf-8')\\n423|\\n424|\\n425|def main():\\n426| VERSION_DIR.mkdir(parents=True, exist_ok=True)\\n427| scene_paths=create_scene_images()\\n428| synthesize_chatterbox()\\n429| words=force_align_words(); audio_duration=ffprobe_duration(AUDIO)\\n430| render_video(words,audio_duration,scene_paths)\\n431| segments=build_segments(words)\\n432| media_meta=probe(FINAL); video_sha=sha256(FINAL)\\n433| media_cache_path=MEDIA_CACHE / FINAL.name\\n434| MEDIA_CACHE.mkdir(parents=True, exist_ok=True); shutil.copy2(FINAL,media_cache_path)\\n435| data={'candidate_id':CANDIDATE_ID,'version':VERSION,'script':SCRIPT,'scene_images':[str(p) for p in scene_paths],\\n436| 'caption_alignment':'faster_whisper_word_timestamps_source_punctuation_segments','caption_style':'static_phrase_segment_2line_active_word_highlight','word_count':len(words),'segment_count':len(segments),'audio_duration':audio_duration,'video_meta':media_meta,'sha256':video_sha,'output':str(FINAL),'media_cache_path':str(media_cache_path),'media_cache_sha256':sha256(media_cache_path),'audio':str(AUDIO),'contact_sheet':str(CONTACT),'qa_frames':[str(VERSION_DIR/f'qa_frame_{s:02d}.png') for s in QA_SECONDS],\\n437| 'visual_policy':'high-quality AI-generated keyframes + subtitles only; no renderer arrows/boxes/circles/progress bars/labels/caption boxes','notes':'Chatterbox Taylor/Scientific voice, forced word timing, readable sentence-level caption groups, strong first-second login-page hook, no non-subtitle renderer overlays; thumbnail intentionally omitted; user sets thumbnail manually.'}\\n438| pkg,pkg_path=build_package(data)\\n439| update_topic_db(FINAL)\\n440| return data\\n441|\\n442|if __name__ == '__main__':\\n443| print(json.dumps(main(),indent=2))\\n444|\", \"total_lines\": 443, \"file_size\": 25757, \"truncated\": false, \"is_binary\": false, \"is_image\": false}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwtF4Fw_POdKBEEc2tvq1sO1vXMDbJl9Am7Htkes-IspFG78gB6EDQvsJOQxWwhjzDL2SLkKFnEa9ZpmFfRQV0N88nvrB6110cHilCbklqU9S0ZNZepnk6-Jjs4cZAMjZMWVmz2hTf43Jplux9MTyh_gb-50MF050jiG3Y8ymq3KAvI0Pkz4dsV_3Ep43dBjMSX_sOWAxsaYzp_YDAWkJc_7BJnR1Wr9x1vo-Gg6RD3jR7qQfzuQbTaTYgSZDrV_SGQI-KfO1HthZogvDyjo2S0EnSiv6n4mx3s-DbelxFkBzsd341nRxHexvZQ8w4EH-afwTl5c5iPEzu3nfO3EHalWPiR3wWuDMi0UpQ7DCyS7zobe1y1pretuCCUch0NPo_hkVkgPAbF7NGvoFyhlPcMVs1njzqmM1GgYvvVZ6dtJqjryN8McTri5yON3HnXuUzI-dr4Yp0OfgmrIRPZUKd4upMtX49vAWzPc79MUUIVdQfCzrMQ9dSB2imtJsuVZb3N1wHlNhMggOsfutQRfgLrkFBBzIvfZ_-KOjhU72j_nrFHBXbhRszTSKxU4vIsWLl3gPHY6Cuf-Q5dz6AKw9Qe8fB_bSDCgGWcW0Z0A2QEE1QSEUqYIngiNFEAZJlwjy4yS3ssXSxpQysWKXuYEyGFYxP6aU7qyiG3LzBxvmU7nizSz0odJFHdnNPhxk_w58GdvQstP0X2AwrssRXTSBe1vvc4mDER3Tm-_mfyCXEJQsWsNfwCco0s4qyfLyu6aqmmVBHKMRu6JtOmBc3OHL589L0gYkQNzR2rl8l69i4iDdwRWL7DkJEsvobv3CXTYraQ1v2oyLw2Rm_M0y6DvPgmj-a7_srHtKquXwF1RiB4NwtDycB2OVYXc9Nrl6c7RjhdBAp4TXrFXxDDFqACI_vNUgO5ZU7kz4QoaoHZ5_aUQ_u3C1GtanaC_IW53AJYwZpg97Z4YJv8StTOsmBTAeDsnOkvUiogGnZ-trXVc1wZ69ouhRwfQzXrSce5fcuFw9bwswwesDNQt69iVowYeUoc0xdBoaRpxVWWtEJjELMPMLEfVhgmHnfZx5Z-9GkgMDZfZsITCyd7molFDFDsYpHxHgK4JRfPT-t8gFL8PW8WaHesgayDw-PAt9VjVuYdsRVSiRs-vxC5oRXoUPFH3ysYoNj3vmPjOFySA_dQHiAmxuE1iAqm914K-eqxs6xTYJWTv7SekD74KbRYr9OmdRVXE2yBV8cNE3m-N_9Sp4Ugz70M1F1SSHcDTWkRLTJyGFveNK3IPrDJGCJXjKX_4zA12FWz_N6QT8MJwnKxOv1ANYTJa0ehY7Y46Y7GIqiox_i7v3svhdNPu0NvyQycj-mlcKCysDQfZ_3tmPc3CqldMcTjYH-2gH0mT_mndVuTn5zZPI3VEg_QpTywPSQ6qKawIXoLoypEnWVDHlS8mZwxh1d1ejE5VV3JFdFwOD__VPvb3kyiEhd36oNXrkrNZ_12jd2f4crI8XEGYg9uZ3PPYQjeXoHFxQh7Rt3imqpXU9cYRMPOkFaGWr6sBwgmaCPYVrC6XjPkheF91PsmGAqjYyEhMqqrO_dgquUb3GgYPqC5jpeOyvIOpM6SAVocvMDImE73Tp2KqySlTxByBkEHOfvkyWvAJeSP8R3F2QNghbHAnVSci31rGPvZ0z5J5vteMv4aLcY4NBjuJ7WtES9wbuI0f-GoYNyK0muXEf08KrH4wsWsVTWCrAF4LESHcOoAE4CwtdeauCfCnKHb1zUs8OA2D_IwqHvTFFjdbHai61JwACoD_Quh2NtxZRPuZMZroo65m9xxIUAVMsRODH3HYxfyRBJ4AmE3X0BZC9rDdotPKW8w47wVjJEyGDuFDloSM6vDP-3EFsCx4O9vu9jYPeXnjTYU891dKtRAe6dU-FYrOVN0rkOOKgCWx940aiht7uzz_t6hQVYVC3IrK4X6b11x1_va7-7JuyzEjYl-k5J-cxx6X9bMADrOeDGhPIzjLF0rrr-4rbZBdSna_o1QAeTNs4lO7wAR6-h7oya0P0OMfcs3D6nR81UHdQRZO4mIY4A-IV83dkqLkSk5RAADkm4RDciixdN4VdRQW17eLRd4Y1AgycAkPcwF6W1OLV7gxDXcKhP8LRSpQMdfOy_d0vgRN4UhRyJsxNi_LGDCmkhu_F5TTv-JwnZXNWH0psmMPme5ti0NNRBKUowr3h1Uwtabj4IldDCk3sG1d163JEQ_hdjHQGwwOTBPSu8xf4zMSizfBzELL6clS7FRe91r11lYpOXH_4y6p_OvYIK50oZFLY0VBGxEUhgtHdPX3dE-N3npI8JPn4u7JA0b0V6d2oGuxaZ6_UnAaw_FWZGhigfOpddcS6jUWjARH77MOw6EWFo9tYMp7O9vBmYThYr_rr456bsv194_vRuC1iFyVRCBO3UMzrmIlAHPhPMV_DSIm0QFVZEtxF4-WYV_VLtuuWC3q4e5hFA1B3UCQ1WVF4UvleARTwi3EK22LQVp-Cv9lBSYYTS3nO1cQRTEHWY2u7LOMIh3b05Yi_MAhvnhY5xtY", "summary": [ { "type": "summary_text", "text": "**Generating images and scripts**\n\nI can generate images using the image_generate tool and think about prompt ideas like AI voice call scenes, but I want to avoid people, hands, or logos. I need six images and might consider using parallel processing to save time, even if it could be a bit pricier. \n\nI also need to write a new script to transform file ERF014 to ERF015, potentially using Python for easier replacements. Instead of using heredoc, it's suggested to use the write_file function. If needed, I could copy files with the terminal and make edits using a patch." }, { "type": "summary_text", "text": "**Processing large files**\n\nI need to work with content from a file that has 443 lines, and I’m thinking about using read_file to handle it, since it's under the 25KB limit. I could also use terminal commands like cp and patch for extra flexibility. I’ll need to change constants, the script title, descriptions, sources, names, and updates for the topic and QA notes. Removing comments and unused draw functions isn’t a problem. First, I should focus on generating the images I need." } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_Vd9HTxYawcFWLhKFJvymjpfm", "name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Premium vertical 9:16 cinematic AI image for an online scam self-defense short. A modern smartphone call screen floating in a dark realistic desk environment, no hands, no people, no logos. The phone shows a suspicious incoming call interface with readable generic text: \\u201cMOM?\\u201d and \\u201cUnknown number\\u201d. Subtle emergency tone, premium realistic lighting, clean glass, shallow depth of field. Leave the lower 25% visually clean/darker for subtitles. No arrows, no circles, no boxes, no watermarks, no QR codes.\"}" }, { "type": "function_call_output", "call_id": "call_Vd9HTxYawcFWLhKFJvymjpfm", "output": "{\"success\": false, \"image\": null, \"error\": \"OpenAI image generation via Codex auth failed: [Errno 101] Network is unreachable\", \"error_type\": \"api_error\", \"model\": \"gpt-image-2-high\", \"prompt\": \"Premium vertical 9:16 cinematic AI image for an online scam self-defense short. A modern smartphone call screen floating in a dark realistic desk environment, no hands, no people, no logos. The phone shows a suspicious incoming call interface with readable generic text: \\u201cMOM?\\u201d and \\u201cUnknown number\\u201d. Subtle emergency tone, premium realistic lighting, clean glass, shallow depth of field. Leave the lower 25% visually clean/darker for subtitles. No arrows, no circles, no boxes, no watermarks, no QR codes.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\"}" }, { "type": "reasoning", "encrypted_content": "gAAAAABqHwtZmywig9NKS1_J_MWBrrCCO9ucH3JtlitR578I42ZQhPgefa_hholLSuyDAIBvHHRM-2tJ4bHj795wT-4m_H4I75b6IiMjzrcr5C8WwdxqAj2LQeEQVzJ3f8qkioiSX4q1RzRhOYS6ti_PQgWKG1dIWeGUc3k4KAUz_B-ZsIDS_uo1JoA1s0aet5kTBX2oFT5uO0Rjyxy_xGDZ64dLYqisrYSHWU21Pt2vjkvQIoqSbLdtfWjxedFBd0Hqr41wsrISikYvbqvzMPZVteeuMtEm6tLZGr-DfURuYNolbZbWeXO_Uni0j2TBKE0Gd53U3KSKgJbZK9YfKoV247aS6qzwEbMmTLI5o0w69F8WVeA_b8l2npBRG8adjIf3mKQ1HIDnDfktGDWHkNd0lcZl_1rjZrvc9G-KfwZeafRuQ9jsFi_jfWe17iz70HDvh7Oks66vQUfzVNuHpWJQLo2qUNYSXIu_uZapHSDjODnUm87bCrnAO-LiPlyLxaQZpgi4CxAwYVrKluLoPRqMIZx2EaDhYUZMbuTq_UVPq8oGASjNH5CJ-iIBGuWkuwr8L-vDofd8q9l3rBNbtr5Tyvqj2Qa0bMLhdGCOziIVV29bMS0_afHH-O9ABEZHPh4pVkCq20PU4yU_azEISpp3sI07xdX4lh6IRZMki-cMf3ZyNRasn2NiP6JWkv6qOPZxracAKjrsyNZXgdYMaTJhGKsGy_rOEGR7cUo0UdSa1mcp8reDhYIP-FoUmEjYmPYqBE-1vsxAD_r02DipV_sFxpKdwjuFaOVxIkTrhXnQZDJp2mqvoAEyfoNfBsBGp8YTozkVoL1F2JxfhrzPcheu5s-a2qiUAzGG-eoRDnVnaCtLFd-IP-73okJHQruAZyD4rR0AGOH5ZplzErROOJj57RqZKo4Ll_efoL-A7ZVIMbTrBJSSI8hXaZ_upmhkqRjudOZDqtC2i7riNqg2CQ2v4IByy5YnO_JgD2XqcZ7GbTX1vtqVVVT1vdvohM3kFutgHmV01Xxxdk1aBbczRYec93zusmtPWsMSbEV419K4MaqoUIZZ_IvIRR4nZeQXdaMKip6dYVXfiF4HnM3P3gkxn6L0Z60SstSv7VVYmkCwTGO4jXUALl9TZiLZ6rAEKPb-2906LJCZpyMQqw2jQ-q4GpfIj8cRfybyusG-O6W7WX5mZVIkpbsiSKhWPBOcGjT5V0gxq2Cfyr6_th3ekFmjFoVl5Yvz_NTxoOQ5ny1uGkv_ZE6WBrvY9TkTc4Cu74qT7lAeGdcE0kCBiwoyYjGI7uEm-ifbng6wP97Ib68ErOJjxf5ftJ4TeBh4BereSaH6WHPmPfNHh4-CWzyTLKoWwcJQdhvC0g3U8BKw9XI3uES5Kqry4sEVwoiZUZ0g6E3w67aiiN3OcKUnxO00CcEyLKi6-Yi1m1hnytHMrOzpz1GJjh--B66r9fEX61cZWhQ3Ot4fNg4IoDKaIwmrmhTMn12FLz0wKAmKxJ9WkpFJwkS9VS1cZFs=", "summary": [ { "type": "summary_text", "text": "**Considering image options**\n\nThe network is unreachable, so I need an alternative deterministic renderer. The user prefers high-quality images, so using previous AI scenes might not work. I could create premium deterministic mockups for the AI voice, but I might not need image generation at all. I should inform the user, but I still need to produce something. Using a PIL script instead could be a solution. Maybe local cached images could work as well. I'm exploring these options!" } ] }, { "role": "assistant", "content": "" }, { "type": "function_call", "call_id": "call_KdcGiHcU3CoObZ0NfBWjLLza", "name": "search_files", "arguments": "{\"limit\":30,\"path\":\"/home/agent/.hermes/cache/images\",\"pattern\":\"*.png\",\"target\":\"files\"}" }, { "type": "function_call_output", "call_id": "call_KdcGiHcU3CoObZ0NfBWjLLza", "output": "{\"total_count\": 30, \"files\": [\"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140430_4100b4e3.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140337_bdea43b0.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140205_70e0d8ca.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140057_e4db68f5.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_140002_2e724604.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260601_135907_e169cc33.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153811_1330aea2.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153647_37e11532.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153542_6f612614.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153447_d47e2359.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153346_16190571.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260531_153251_d9be12e4.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002951_d2862ce9.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002908_73e03015.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002821_ac7c7a1b.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002657_1dc0712c.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002607_cba917dc.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260530_002520_1aeedfb1.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234849_b61c07de.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234757_ebf2d0be.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234535_b7245b47.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234447_d746f686.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234357_19d104d4.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_234305_92763a66.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_172034_dda02e3b.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_171942_45d3311f.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_171848_151206e8.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_171749_c6163d32.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_171646_6ecc41ea.png\", \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-high_20260529_171540_57adf31d.png\"]}" } ], "store": false, "tools": [ { "type": "function", "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "strict": false, "parameters": { "type": "object", "properties": {} } }, { "type": "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.", "strict": false, "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } }, { "type": "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.", "strict": false, "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", "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.", "strict": false, "parameters": { "type": "object", "properties": {} } }, { "type": "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.", "strict": false, "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } }, { "type": "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.", "strict": false, "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } }, { "type": "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.", "strict": false, "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } }, { "type": "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.", "strict": false, "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", "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.", "strict": false, "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", "name": "browser_vision", "description": "Take a screenshot of the current page so you can inspect it visually. Use this when you need to understand what the page looks like - especially for CAPTCHAs, visual verification challenges, complex layouts, or cases where the text snapshot misses important visual information. When your active model has native vision, the screenshot is attached to your context directly and you inspect it on the next turn; otherwise Hermes falls back to an auxiliary vision model and returns a text analysis. Includes a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "strict": false, "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", "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.", "strict": false, "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", "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.", "strict": false, "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run. When action=create, the 'schedule' and 'prompt' fields are REQUIRED." }, "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": "REQUIRED for action=create. For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp. Examples: '30m' (every 30 minutes), 'every 2h' (every 2 hours), '0 9 * * *' (daily at 9am), '2026-06-01T09:00:00' (one-shot). You MUST include this field when action=create." }, "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." }, "profile": { "type": "string", "description": "Optional Hermes profile name to run the job under. When set, the scheduler resolves that profile, applies a context-local Hermes home override, loads that profile's config/.env for the run, and bridges HERMES_HOME into subprocesses. Any temporary process-environment changes from profile .env loading are restored after the job exits. Use 'default' for the root Hermes profile. Named profiles must already exist. When unset (default), preserves the scheduler's existing profile. On update, pass an empty string to clear. Jobs with profile run sequentially (not parallel) to keep profile-scoped runtime state isolated." } }, "required": [ "action" ] } }, { "type": "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.", "strict": false, "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", "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", "strict": false, "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", "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.", "strict": false, "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", "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.", "strict": false, "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", "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.", "strict": false, "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" }, "cross_profile": { "type": "boolean", "description": "Opt out of the cross-profile soft guard. Defaults to false. Set true ONLY after explicit user direction to edit another Hermes profile's skills/plugins/cron/memories.", "default": false } }, "required": [ "mode" ] } }, { "type": "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).", "strict": false, "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", "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.", "strict": false, "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", "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.", "strict": false, "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", "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.", "strict": false, "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/report.pdf') in the message — the platform will deliver it as a native media attachment." } } } }, { "type": "function", "name": "session_search", "description": "Search past sessions stored in the local session DB, or scroll inside one. FTS5-backed retrieval over the SQLite message store. No LLM calls — every shape returns actual messages from the DB.\n\nTHREE CALLING SHAPES\n\n 1) DISCOVERY — pass `query`:\n session_search(query=\"auth refactor\", limit=3)\n Runs FTS5, dedupes hits by session lineage, returns the top N sessions. Each result carries:\n - session_id, title, when, source\n - snippet: FTS5-highlighted match excerpt\n - bookend_start: first 3 user+assistant messages of the session (the goal / kickoff)\n - messages: ±5 messages around the FTS5 match, with the anchor message flagged (the hit in context)\n - bookend_end: last 3 user+assistant messages of the session (the resolution / decisions)\n - match_message_id, messages_before, messages_after\n Bookends + window together let you reconstruct goal → match → resolution without paying for the whole transcript.\n\n 2) SCROLL — pass `session_id` + `around_message_id`:\n session_search(session_id=\"...\", around_message_id=12345, window=10)\n Returns a window of ±`window` messages centered on the anchor. No FTS5, no bookends — just the slice. Use after a discovery call when you need more context than the ±5 default window.\n - To scroll FORWARD: pass messages[-1].id back as around_message_id.\n - To scroll BACKWARD: pass messages[0].id back as around_message_id.\n - The boundary message appears in both windows — orientation marker.\n - When messages_before or messages_after is < window, you're at the start or end of the session.\n\n 3) BROWSE — no args:\n session_search()\n Returns recent sessions chronologically: titles, previews, timestamps. Use when the user asks \"what was I working on\" without naming a topic.\n\nFTS5 SYNTAX\n\n AND is the default — multi-word queries require all terms. Use OR explicitly for broader recall (`alpha OR beta OR gamma`), quoted phrases for exact match (`\"docker networking\"`), boolean (`python NOT java`), or prefix wildcards (`deploy*`).\n\nWHEN TO USE\n\n Reach for this on any \"what did we do about X\" / \"where did we leave Y\" / \"find the session where Z\" question — before gh, web search, or filesystem inspection. The session DB carries what was said when; external tools show current world state.", "strict": false, "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query (discovery shape). Keywords, phrases, or boolean expressions to find in past sessions. Omit to browse recent sessions. Ignored when session_id + around_message_id are set (scroll shape)." }, "limit": { "type": "integer", "description": "Discovery shape only. Max sessions to return (default 3, max 10). Bump to 5–10 when the topic likely spans several sessions and you want to pick the right one to scroll into.", "default": 3 }, "sort": { "type": "string", "enum": [ "newest", "oldest" ], "description": "Discovery shape only. Temporal bias on top of FTS5 ranking. Omit to keep relevance-only ordering (suitable for exploratory recall — \"what do we know about X\"). Set 'newest' for recency-shaped questions (\"where did we leave X\"). Set 'oldest' for origin-shaped questions (\"how did X start\"). Ignored in scroll and browse shapes." }, "session_id": { "type": "string", "description": "Scroll shape. Session to read inside. Use the session_id returned from a prior discovery call. Must be paired with around_message_id." }, "around_message_id": { "type": "integer", "description": "Scroll shape. Message id to center the window on. From a discovery result use match_message_id, or any id seen in a prior window. To scroll forward pass the last window message's id; to scroll backward pass the first." }, "window": { "type": "integer", "description": "Scroll shape only. Messages to return on each side of the anchor (anchor itself always included). Clamped to [1, 20]. Default 5.", "default": 5 }, "role_filter": { "type": "string", "description": "Optional. Comma-separated roles to include. Discovery defaults to 'user,assistant' (tool output is usually noise). Pass 'user,assistant,tool' to include tool output (debugging tool behaviour) or 'tool' to search tool output only." } } } }, { "type": "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.", "strict": false, "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", "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.", "strict": false, "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", "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "strict": false, "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } }, { "type": "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. Almost always pair with notify_on_complete=true — bg without notify runs SILENTLY and you have no way to learn it finished short of calling process(action='poll') yourself. Two legitimate uses:\n (1) Long-lived processes that never exit (servers, watchers, daemons) — silent is correct, there's no exit to notify on.\n (2) Long-running bounded tasks (tests, builds, deploys, CI pollers, batch jobs) — MUST set notify_on_complete=true. Without it you'll either forget to poll or sit blocked waiting for the user to surface the result.\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", "strict": false, "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. Almost always pair with notify_on_complete=true — without it, the process runs silently and you'll have no way to learn it finished short of calling process(action='poll') yourself (easy to forget, leading to silent blindness on long jobs). Two legitimate patterns: (1) Long-lived processes that never exit (servers, watchers, daemons) — these stay silent because there's no exit to notify on. (2) Long-running bounded tasks (tests, builds, deploys, CI pollers, batch jobs) — these MUST set notify_on_complete=true. 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", "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.", "strict": false, "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", "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.", "strict": false, "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", "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.", "strict": false, "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", "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).", "strict": false, "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" }, "cross_profile": { "type": "boolean", "description": "Opt out of the cross-profile soft guard. Defaults to false. Set true ONLY after explicit user direction to edit another Hermes profile's skills/plugins/cron/memories — by default these writes are blocked with a warning because they affect a different profile than the one this session is running under.", "default": false } }, "required": [ "path", "content" ] } } ], "reasoning": { "effort": "medium", "summary": "auto" }, "include": [ "reasoning.encrypted_content" ], "tool_choice": "auto", "parallel_tool_calls": true, "prompt_cache_key": "20260602_185524_9d65cb97", "extra_headers": { "session_id": "20260602_185524_9d65cb97", "x-client-request-id": "20260602_185524_9d65cb97" } } }, "error": { "type": "APIConnectionError", "message": "Connection error." } }