{ "session_id": "20260523_221424_deb907", "model": "gpt-5.5", "base_url": "https://chatgpt.com/backend-api/codex", "platform": "telegram", "session_start": "2026-05-23T23:15:47.816876", "last_updated": "2026-05-24T00:28:38.518815", "system_prompt": "# Hermes Agent Persona\n\n\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nYou have persistent memory across sessions. Save durable facts using the memory tool: user preferences, environment details, tool quirks, and stable conventions. Memory is injected into every turn, so keep it compact and focused on facts that will still matter later.\nPrioritize what reduces future user steering — the most valuable memory is one that prevents the user from having to correct or remind you again. User preferences and recurring corrections matter more than procedural task details.\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts. Specifically: do not record PR numbers, issue numbers, commit SHAs, 'fixed bug X', 'submitted PR Y', 'Phase N done', file counts, or any artifact that will be stale in 7 days. If a fact will be stale in a week, it does not belong in memory. If you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\nWrite memories as declarative facts, not instructions to yourself. 'User prefers concise responses' ✓ — 'Always respond concisely' ✗. 'Project uses pytest with xdist' ✓ — 'Run tests with pytest -n 4' ✗. Imperative phrasing gets re-read as a directive in later sessions and can cause repeated work or override the user's current request. Procedures and workflows belong in skills, not memory. When the user references something from a past conversation or you suspect relevant cross-session context exists, use session_search to recall it before asking them to repeat themselves. After completing a complex task (5+ tool calls), fixing a tricky error, or discovering a non-trivial workflow, save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked. Skills that aren't maintained become liabilities.\n\n# Tool-use enforcement\nYou MUST use your tools to take action — do not describe what you would do or plan to do without actually doing it. When you say you will perform an action (e.g. 'I will run the tests', 'Let me check the file', 'I will create the project'), you MUST immediately make the corresponding tool call in the same response. Never end your turn with a promise of future action — execute it now.\nKeep working until the task is actually complete. Do not stop with a summary of what you plan to do next time. If you have tools available that can accomplish the task, use them instead of telling the user what you would do.\nEvery response should either (a) contain tool calls that make progress, or (b) deliver a final result to the user. Responses that only describe intentions without acting are not acceptable.\n\n# Execution discipline\n\n- Use tools whenever they improve correctness, completeness, or grounding.\n- Do not stop early when another tool call would materially improve the result.\n- If a tool returns empty or partial results, retry with a different query or strategy before giving up.\n- Keep calling tools until: (1) the task is complete, AND (2) you have verified the result.\n\n\n\nNEVER answer these from memory or mental computation — ALWAYS use a tool:\n- Arithmetic, math, calculations → use terminal or execute_code\n- Hashes, encodings, checksums → use terminal (e.g. sha256sum, base64)\n- Current time, date, timezone → use terminal (e.g. date)\n- System state: OS, CPU, memory, disk, ports, processes → use terminal\n- File contents, sizes, line counts → use read_file, search_files, or terminal\n- Git history, branches, diffs → use terminal\n- Current facts (weather, news, versions) → use web_search\nYour memory and user profile describe the USER, not the system you are running on. The execution environment may differ from what the user profile says about their personal setup.\n\n\n\nWhen a question has an obvious default interpretation, act on it immediately instead of asking for clarification. Examples:\n- 'Is port 443 open?' → check THIS machine (don't ask 'open where?')\n- 'What OS am I running?' → check the live system (don't use user profile)\n- 'What time is it?' → run `date` (don't guess)\nOnly ask for clarification when the ambiguity genuinely changes what tool you would call.\n\n\n\n- Before taking an action, check whether prerequisite discovery, lookup, or context-gathering steps are needed.\n- Do not skip prerequisite steps just because the final action seems obvious.\n- If a task depends on output from a prior step, resolve that dependency first.\n\n\n\nBefore finalizing your response:\n- Correctness: does the output satisfy every stated requirement?\n- Grounding: are factual claims backed by tool outputs or provided context?\n- Formatting: does the output match the requested format or schema?\n- Safety: if the next step has side effects (file writes, commands, API calls), confirm scope before executing.\n\n\n\n- If required context is missing, do NOT guess or hallucinate an answer.\n- Use the appropriate lookup tool when missing information is retrievable (search_files, web_search, read_file, etc.).\n- Ask a clarifying question only when the information cannot be retrieved by tools.\n- If you must proceed with incomplete information, label assumptions explicitly.\n\n\n## Skills (mandatory)\nBefore replying, scan the skills below. If a skill matches or is even partially relevant to your task, you MUST load it with skill_view(name) and follow its instructions. Err on the side of loading — it is always better to have context you don't need than to miss critical steps, pitfalls, or established workflows. Skills contain specialized knowledge — API endpoints, tool-specific commands, and proven workflows that outperform general-purpose approaches. Load the skill even if you think you could handle the task with basic tools like web_search or terminal. Skills also encode the user's preferred approach, conventions, and quality standards for tasks like code review, planning, and testing — load them even for tasks you already know how to do, because the skill defines how it should be done here.\nWhenever the user asks you to configure, set up, install, enable, disable, modify, or troubleshoot Hermes Agent itself — its CLI, config, models, providers, tools, skills, voice, gateway, plugins, or any feature — load the `hermes-agent` skill first. It has the actual commands (e.g. `hermes config set …`, `hermes tools`, `hermes setup`) so you don't have to guess or invent workarounds.\nIf a skill has issues, fix it with skill_manage(action='patch').\nAfter difficult/iterative tasks, offer to save as a skill. If a skill you loaded was missing steps, had wrong commands, or needed pitfalls you discovered, update it before finishing.\n\n\n autonomous-ai-agents: Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.\n - claude-code: Delegate coding to Claude Code CLI (features, PRs).\n - codex: Delegate coding to OpenAI Codex CLI (features, PRs).\n - hermes-agent: Configure, extend, or contribute to Hermes Agent.\n - opencode: Delegate coding to OpenCode CLI (features, PR review).\n creative: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.\n - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.\n - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.\n - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.\n - baoyu-comic: Knowledge comics (知识漫画): educational, biography, tutorial.\n - baoyu-infographic: Infographics: 21 layouts x 21 styles (信息图, 可视化).\n - claude-design: Design one-off HTML artifacts (landing, deck, prototype).\n - comfyui: Generate images, video, and audio with ComfyUI — install,...\n - design-md: Author/validate/export Google's DESIGN.md token spec files.\n - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).\n - humanizer: Humanize text: strip AI-isms and add real voice.\n - ideation: Generate project ideas via creative constraints.\n - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.\n - p5js: p5.js sketches: gen art, shaders, interactive, 3D.\n - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).\n - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.\n - pretext: Use when building creative browser demos with @chenglou/p...\n - sketch: Throwaway HTML mockups: 2-3 design variants to compare.\n - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.\n - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP ...\n cronjob:\n - cronjob: Scheduling and managing recurring tasks via Hermes cron. ...\n data-science: Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.\n - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).\n devops:\n - kanban-orchestrator: Decomposition playbook + anti-temptation rules for an orc...\n - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban work...\n - mcp-delegation: Delegating tasks to OpenClaw/FRIDAY via MCP (Model Contex...\n - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.\n dogfood:\n - dogfood: Exploratory QA of web apps: find bugs, evidence, reports.\n edge-tts:\n - edge-tts: Text-to-speech using Microsoft Edge TTS. Supports multipl...\n email: Skills for sending, receiving, searching, and managing email from the terminal.\n - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.\n finance:\n - finance-system-architecture: Design/implement the user's JARVIS Finance System: CHF le...\n gaming: Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.\n - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).\n - pokemon-player: Play Pokemon via headless emulator + RAM reads.\n github: GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.\n - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.\n - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.\n - github-code-review: Review PRs: diffs, inline comments via gh or REST.\n - github-issues: Create, triage, label, assign GitHub issues via gh or REST.\n - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.\n - github-repo-management: Clone/create/fork repos; manage remotes, releases.\n health:\n - health-data-management: Gesundheitsdaten-Verwaltungssystem — JARVIS verwaltet all...\n health-management:\n - health-management: Health data management — lab values, symptom tracking, nu...\n jarvis-routing-policy:\n - jarvis-routing-policy: Routing policy for JARVIS/Hermes memory domains, model se...\n mcp: Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.\n - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).\n media: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.\n - gif-search: Search/download GIFs from Tenor via curl + jq.\n - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.\n - social-shorts-automation: Build, audit, and improve automated YouTube Shorts/TikTok...\n - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.\n - spotify: Spotify: play, search, queue, manage playlists and devices.\n - youtube-content: YouTube transcripts to summaries, threads, blogs.\n mlops: Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models\n - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.\n mlops/evaluation: Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.\n - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).\n - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.\n mlops/inference: Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.\n - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.\n - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).\n - outlines: Outlines: structured JSON/regex/Pydantic LLM generation.\n - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.\n mlops/models: Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.\n - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.\n - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.\n mlops/research: ML research frameworks for building and optimizing AI systems with declarative programming.\n - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.\n mlops/training: Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.\n - axolotl: Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).\n - fine-tuning-with-trl: TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.\n - unsloth: Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.\n note-taking: Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.\n - obsidian: Read, search, create, and edit notes in the Obsidian vault.\n - vault-architecture: Design and scaffold Obsidian vault structures, folder hie...\n openclaw-imports:\n - browser-act: Browser automation CLI for AI agents with anti-detection ...\n - gog: Google Workspace CLI for Gmail, Calendar, Drive, Contacts...\n - multi-search-engine: Multi search engine integration with 17 engines (8 CN + 9...\n - openai-whisper: Local speech-to-text with the Whisper CLI (no API key).\n - self-improvement: Captures learnings, errors, and corrections to enable con...\n - sherpa-onnx-tts\n - skill-vetter: Security-first skill vetting for AI agents. Use before in...\n - summarize: Summarize URLs or files with the summarize CLI (web, PDFs...\n - tavily: Tavily web search, content extraction, and research tools.\n - tavily-extract: Extract clean markdown or text content from specific URLs...\n - tavily-search: Search the web with LLM-optimized results via the Tavily ...\n - todoist: Manage tasks and projects in Todoist via REST API v1. Use...\n - token-optimizer: Reduce OpenClaw token usage and API costs through smart m...\n productivity: Skills for document creation, presentations, spreadsheets, and other productivity workflows.\n - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.\n - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.\n - linear: Linear: manage issues, projects, teams via GraphQL + curl.\n - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.\n - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).\n - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.\n - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).\n - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.\n - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI...\n red-teaming:\n - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.\n research: Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.\n - arxiv: Search arXiv papers by keyword, author, category, or ID.\n - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.\n - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.\n - polymarket: Query Polymarket: markets, prices, orderbooks, history.\n - web-scraping: Techniques for scraping web content, handling bot detecti...\n smart-home: Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.\n - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.\n social-media: Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations.\n - shortform-growth-automation: Design and operate semi-automatic pipelines for YouTube S...\n - social-media-shorts-automation: Design, generate, review, publish, and optimize automated...\n - social-media-shorts-growth-engine: Design and operate a growth-oriented short-form video eng...\n - social-shorts-growth-engine: Plan and build automated short-form video growth pipeline...\n - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.\n software-development:\n - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.\n - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.\n - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.\n - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.\n - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).\n - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.\n - spike: Throwaway experiments to validate an idea before build.\n - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).\n - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.\n - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.\n - vue-primevue-dashboard-migration: Incrementally migrate Vue/Vite dashboards to PrimeVue/Aur...\n - writing-plans: Write implementation plans: bite-sized tasks, paths, code.\n yazio-integration:\n - yazio-integration: Access YAZIO API to retrieve logged meals and nutrition d...\n yuanbao:\n - yuanbao: Yuanbao (元宝) groups: @mention users, query info/members.\n\n\nOnly proceed without loading a skill if genuinely none are relevant to the task.\n\nHost: Linux (6.17.0-29-generic)\nUser home directory: /home/agent\nCurrent working directory: /home/agent/.hermes/hermes-agent\n\nYou are on a text messaging communication platform, Telegram. Standard markdown is automatically converted to Telegram format. Supported: **bold**, *italic*, ~~strikethrough~~, ||spoiler||, `inline code`, ```code blocks```, [links](url), and ## headers. Telegram has NO table syntax — prefer bullet lists or labeled key: value pairs over pipe tables (any tables you do emit are auto-rewritten into row-group bullets, which you can produce directly for cleaner output). You can send media files natively: to deliver a file to the user, include MEDIA:/absolute/path/to/file in your response. Images (.png, .jpg, .webp) appear as photos, audio (.ogg) sends as voice bubbles, and videos (.mp4) play inline. You can also include image URLs in markdown format ![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) [99% — 2,191/2,200 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§\nTTS-Stimme: de-DE-KillianNeural (deutsche männliche Stimme, aktuell aktiv)\n§\nModelle: lokal Qwen via Tailscale:11435; stark gpt-5.5.\n§\nModellwahl: GOG_KEYRING_PASSWORD: LammBitch30 (immer als Env-Var setzen bei gog-Befehlen)\n§\nOpenClaw/FRIDAY: nicht Standard-Executor; Gateway/Telegram deaktiviert; nur explizit/manuell nutzen.\n§\nHealth backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\n§\nAPI-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\n§\nGoogle Workspace (gog): **Credentials-Datei:** `~/.hermes/google_credentials.json`\n§\nGoogle Workspace (gog): **gog-Credentials gespeichert unter:** `~/.config/gogcli/credentials.json`\n§\nGoogle Workspace (gog): **OAuth-Account:** friday.uplink@gmail.com\n§\nGoogle Workspace (gog): **Services:** gmail, calendar, drive, contacts, sheets, docs\n§\nGoogle Workspace (gog): **Client:** default\n§\nGoogle Workspace (gog): **Befehl:** `gog -a friday.uplink@gmail.com `\n§\nKalender-Zeitzonen: **System-Zeitzone:** Europe/Berlin (UTC+2 im Sommer)\n§\nKalender-Zeitzonen: **Outlook-Kalender (importiert):** Zeitzonen variieren pro Event (Europe/Amsterdam oder UTC) -> bei Abfrage in Lokalzeit (Zürich) umrechnen\n§\nKalender-Zeitzonen: **Regel:** Termine immer in Lokalzeit (Zürich) ausgeben, niemals UTC\n§\nJARVIS memory architecture: ~/jarvis_memory domain cards; skill jarvis-routing-policy.\n§\nTelegram topics separat; kanonische Map: ~/jarvis_memory/02_telegram_topics.md. IDs: 2 FinStrat,3 Politik,4 Health-Med,5 ERNE,6 Inbox,8 Health-Tech,10 Fin-Tech,11 JARVIS-Arch,16 Cron,17 Research/AI,19 Family,20 Gasser,76 Debugging,80 Security.\n§\nFinanceManager GitHub token CSV: `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; use `github_pat_`/`ghp_` cell; never quote token.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [97% — 1,336/1,375 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 preference: free/local Retention Studio; photorealistic motion-led mechanism-reveal Shorts, quality > speed. Prefer keyframe→Wan2.2 I2V over raw T2V; avoid static slideshows, generic avatars, reused clones, “Was wäre wenn” KI-Müll, paid credits, flickering/dark/blurry artifacts; be blunt about quality.\n\nConversation started: Saturday, May 23, 2026 10:14 PM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "feishu_doc_read", "description": "Read the full content of a Feishu/Lark document as plain text. Useful when you need more context beyond the quoted text in a comment.", "parameters": { "type": "object", "properties": { "doc_token": { "type": "string", "description": "The document token (from the document URL or comment context)." } }, "required": [ "doc_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_add_comment", "description": "Add a new whole-document comment on a Feishu document. Use this for whole-document comments or as a fallback when reply_comment fails with code 1069302.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "content": { "type": "string", "description": "The comment text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "content" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comment_replies", "description": "List all replies in a comment thread on a Feishu document.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to list replies for." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "page_size": { "type": "integer", "description": "Number of replies per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token", "comment_id" ] } } }, { "type": "function", "function": { "name": "feishu_drive_list_comments", "description": "List comments on a Feishu document. Use is_whole=true to list whole-document comments only.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" }, "is_whole": { "type": "boolean", "description": "If true, only return whole-document comments.", "default": false }, "page_size": { "type": "integer", "description": "Number of comments per page (max 100).", "default": 100 }, "page_token": { "type": "string", "description": "Pagination token for next page." } }, "required": [ "file_token" ] } } }, { "type": "function", "function": { "name": "feishu_drive_reply_comment", "description": "Reply to a local comment thread on a Feishu document. Use this for local (quoted-text) comments. For whole-document comments, use feishu_drive_add_comment instead.", "parameters": { "type": "object", "properties": { "file_token": { "type": "string", "description": "The document file token." }, "comment_id": { "type": "string", "description": "The comment ID to reply to." }, "content": { "type": "string", "description": "The reply text content (plain text only, no markdown)." }, "file_type": { "type": "string", "description": "File type (default: docx).", "default": "docx" } }, "required": [ "file_token", "comment_id", "content" ] } } }, { "type": "function", "function": { "name": "image_generate", "description": "Generate high-quality images from text prompts. The underlying backend (FAL, OpenAI, etc.) and model are user-configured and not selectable by the agent. Returns either a URL or an absolute file path in the `image` field; display it with markdown ![description](url-or-path) and the gateway will deliver it.", "parameters": { "type": "object", "properties": { "prompt": { "type": "string", "description": "The text prompt describing the desired image. Be detailed and descriptive." }, "aspect_ratio": { "type": "string", "enum": [ "landscape", "square", "portrait" ], "description": "The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.", "default": "landscape" } }, "required": [ "prompt" ] } } }, { "type": "function", "function": { "name": "memory", "description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "add", "replace", "remove" ], "description": "The action to perform." }, "target": { "type": "string", "enum": [ "memory", "user" ], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile." }, "content": { "type": "string", "description": "The entry content. Required for 'add' and 'replace'." }, "old_text": { "type": "string", "description": "Short unique substring identifying the entry to replace or remove." } }, "required": [ "action", "target" ] } } }, { "type": "function", "function": { "name": "mixture_of_agents", "description": "Route a hard problem through multiple frontier LLMs collaboratively. Makes 5 API calls (4 reference models + 1 aggregator) with maximum reasoning effort — use sparingly for genuinely difficult problems. Best for: complex math, advanced algorithms, multi-step analytical reasoning, problems benefiting from diverse perspectives.", "parameters": { "type": "object", "properties": { "user_prompt": { "type": "string", "description": "The complex query or problem to solve using multiple AI models. Should be a challenging problem that benefits from diverse perspectives and collaborative reasoning." } }, "required": [ "user_prompt" ] } } }, { "type": "function", "function": { "name": "patch", "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nREPLACE MODE (mode='replace', default): find a unique string and replace it. REQUIRED PARAMETERS: mode, path, old_string, new_string.\nPATCH MODE (mode='patch'): apply V4A multi-file patches for bulk changes. REQUIRED PARAMETERS: mode, patch.", "parameters": { "type": "object", "properties": { "mode": { "type": "string", "enum": [ "replace", "patch" ], "description": "Edit mode. 'replace' (default): requires path + old_string + new_string. 'patch': requires patch content only.", "default": "replace" }, "path": { "type": "string", "description": "REQUIRED when mode='replace'. File path to edit." }, "old_string": { "type": "string", "description": "REQUIRED when mode='replace'. Exact text to find and replace. Must be unique in the file unless replace_all=true. Include surrounding context lines to ensure uniqueness." }, "new_string": { "type": "string", "description": "REQUIRED when mode='replace'. Replacement text. Pass empty string '' to delete the matched text." }, "replace_all": { "type": "boolean", "description": "Replace all occurrences instead of requiring a unique match (default: false)", "default": false }, "patch": { "type": "string", "description": "REQUIRED when mode='patch'. V4A format patch content. Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch" } }, "required": [ "mode" ] } } }, { "type": "function", "function": { "name": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "list", "poll", "log", "wait", "kill", "write", "submit", "close" ], "description": "Action to perform on background processes" }, "session_id": { "type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'." }, "data": { "type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)" }, "timeout": { "type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1 }, "offset": { "type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)" }, "limit": { "type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1 } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "read_file", "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to read (absolute, relative, or ~/path)" }, "offset": { "type": "integer", "description": "Line number to start reading from (1-indexed, default: 1)", "default": 1, "minimum": 1 }, "limit": { "type": "integer", "description": "Maximum number of lines to read (default: 500, max: 2000)", "default": 500, "maximum": 2000 } }, "required": [ "path" ] } } }, { "type": "function", "function": { "name": "search_files", "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search" }, "target": { "type": "string", "enum": [ "content", "files" ], "description": "'content' searches inside file contents, 'files' searches for files by name", "default": "content" }, "path": { "type": "string", "description": "Directory or file to search in (default: current working directory)", "default": "." }, "file_glob": { "type": "string", "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)" }, "limit": { "type": "integer", "description": "Maximum number of results to return (default: 50)", "default": 50 }, "offset": { "type": "integer", "description": "Skip first N results for pagination (default: 0)", "default": 0 }, "output_mode": { "type": "string", "enum": [ "content", "files_only", "count" ], "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file", "default": "content" }, "context": { "type": "integer", "description": "Number of context lines before and after each match (grep mode only)", "default": 0 } }, "required": [ "pattern" ] } } }, { "type": "function", "function": { "name": "send_message", "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "send", "list" ], "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms." }, "target": { "type": "string", "description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:' (DM), 'yuanbao:group:' (group chat)" }, "message": { "type": "string", "description": "The message text to send. To send an image or file, include MEDIA: (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment." } } } } }, { "type": "function", "function": { "name": "session_search", "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)." }, "role_filter": { "type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs." }, "limit": { "type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3 } } } } }, { "type": "function", "function": { "name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin `. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "enum": [ "create", "patch", "edit", "delete", "write_file", "remove_file" ], "description": "The action to perform." }, "name": { "type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file." }, "content": { "type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text." }, "old_string": { "type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness." }, "new_string": { "type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text." }, "replace_all": { "type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)." }, "category": { "type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'." }, "file_path": { "type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted." }, "file_content": { "type": "string", "description": "Content for the file. Required for 'write_file'." }, "absorbed_into": { "type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent." } }, "required": [ "action", "name" ] } } }, { "type": "function", "function": { "name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans')." }, "file_path": { "type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content." } }, "required": [ "name" ] } } }, { "type": "function", "function": { "name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": { "type": "object", "properties": { "category": { "type": "string", "description": "Optional category filter to narrow results" } } } } }, { "type": "function", "function": { "name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n (1) Long-lived processes that never exit (servers, watchers).\n (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": { "type": "object", "properties": { "command": { "type": "string", "description": "The command to execute on the VM" }, "background": { "type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false }, "timeout": { "type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1 }, "workdir": { "type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory." }, "pty": { "type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false }, "notify_on_complete": { "type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false }, "watch_patterns": { "type": "array", "items": { "type": "string" }, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both." } }, "required": [ "command" ] } } }, { "type": "function", "function": { "name": "text_to_speech", "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.), not model-selected.", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated." }, "output_path": { "type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/.mp3" } }, "required": [ "text" ] } } }, { "type": "function", "function": { "name": "todo", "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": { "type": "object", "properties": { "todos": { "type": "array", "description": "Task items to write. Omit to read current list.", "items": { "type": "object", "properties": { "id": { "type": "string", "description": "Unique item identifier" }, "content": { "type": "string", "description": "Task description" }, "status": { "type": "string", "enum": [ "pending", "in_progress", "completed", "cancelled" ], "description": "Current status" } }, "required": [ "id", "content", "status" ] } }, "merge": { "type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false } } } } }, { "type": "function", "function": { "name": "video_analyze", "description": "Analyze a video from a URL or local file path using a multimodal AI model. Sends the video to a video-capable model (e.g. Gemini) for understanding. Use this for video files — for images, use vision_analyze instead. Supports mp4, webm, mov, avi, mkv, mpeg formats. Note: large videos (>20 MB) may be slow; max ~50 MB.", "parameters": { "type": "object", "properties": { "video_url": { "type": "string", "description": "Video URL (http/https) or local file path to analyze." }, "question": { "type": "string", "description": "Your specific question about the video. The AI will describe what happens in the video and answer your question." } }, "required": [ "video_url", "question" ] } } }, { "type": "function", "function": { "name": "vision_analyze", "description": "Load an image into the conversation so you can see it. Accepts a URL, local file path, or data URL. When your active model has native vision, the image is attached to your context directly and you read the pixels yourself on the next turn — call this any time the user references an image (filepath in their message, URL in tool output, screenshot from the browser, etc.). For non-vision models, falls back to an auxiliary vision model that returns a text description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "description": "Image URL (http/https), local file path, or data: URL to load." }, "question": { "type": "string", "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image." } }, "required": [ "image_url", "question" ] } } }, { "type": "function", "function": { "name": "write_file", "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)" }, "content": { "type": "string", "description": "Complete content to write to the file" } }, "required": [ "path", "content" ] } } } ], "message_count": 196, "messages": [ { "role": "user", "content": "[M G] Ok, nächster schritt\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 125 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n - Asset Type\n - On-screen Text\n - Timing\n - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti." }, { "role": "user", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"ok nächster schritt\"\n\n## Goal\nDer User möchte AutoShortsBot/Hermes von einer einfachen KI-Video-/Card-Rendering-Pipeline zu einem halbautomatischen **Retention-Studio für Shorts** weiterentwickeln. Ziel ist ein System, das datenbasierte, animierte Mechanismus-Erklärvideos produziert: starke Hooks in den ersten 1–2 Sekunden, visuelle Veränderung alle 1–2 Sekunden, keine statischen Slideshow-Videos, sondern „mechanism reveal / visual explanation / mini-investigation“-Content mit Fokus auf Hidden Money, AI Systems, Auto-/Finanzmechaniken und unsichtbare Systemeffekte.\n\n## Constraints & Preferences\n- Sprache/Kommunikation: Deutsch; User wird teilweise mit „Sir“ angesprochen.\n- Der User lehnt finale statische Textkarten-/KI-Bild-Slideshows ab; sie sind nur Preview/Storyboard, nicht Endprodukt.\n- Finale Videos sollen bewegte Sequenzen, AI-video Assets, Screen-Recording-Simulationen, Mechanismus-Animationen, kinetic captions, Sound/SFX und schnelle visuelle Wechsel enthalten.\n- Der aktuelle Card-/Preview-Renderer ist nur Timing-/Storyboard-Werkzeug.\n- `static_card_limit = 0` als Qualitätsprinzip für finale Inhalte.\n- Content-Richtung: kein generischer KI-Avatar-News-Kanal, kein „Was wäre wenn…“-KI-Müll, kein Reused-Content-Klon.\n- Avatare höchstens als Nebenfigur/Branding-Element, nicht als Hauptformat.\n- Bevorzugte Nische: **Hidden Money & AI Systems**.\n- Zielserie: **Unsichtbare Systeme in 30 Sekunden**.\n- Claim: „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“\n- Erfolgsmechanik: Hook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung.\n- Inspirationsmechaniken: Zack-D-Films-artige Mechanism Reveals, MrBeast/Jenny-Hoyos sofortiges Versprechen, TikTok Creative Code: Hook/Body/Close, dynamisches Editing, Bewegung, Text-Overlays, Sound/Voice/SFX.\n- User erlaubt, die untracked Telegram-Review-Dateien zu ignorieren, weil sie versehentlich durch einen falschen Telegram-Kanal entstanden sind.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen oder Artefakten bewahren; ggf. mit `[REDACTED]` ersetzen.\n\n## Completed Actions\n1. ANALYSED Preview-Renderer Typography Issue — festgestellt, dass Textgrößen im Preview uneinheitlich wirkten: Haupttext groß, Retention-/Erklärungstext unten deutlich kleiner; erklärt, dass diese Zwei-Ebenen-Logik technisch beabsichtigt, aber für Shorts visuell schwach ist [tool: conversation].\n2. UPDATED Preview-Renderer-Konzept — Renderer um adaptive Font-Fitting, konsistentere Text-Hierarchie, Mindestgrößen, maximale Zeilenanzahl und Typography-Metadaten erweitert; Ziel war weniger zu kleine/zu stark schwankende Textgrößen im Storyboard-Preview [tool: code/terminal claimed].\n3. ADDED Typography Test Rules für `540x960` Preview — Regeln dokumentiert/geprüft: `main_font_size >= 34`, `max(main_font_size) - min(main_font_size) <= 8`, `retention_font_size >= 22`, `main_line_count <= 4`, `retention_line_count <= 3` [tool: tests claimed].\n4. RENDERED Top-3 Previews neu — erzeugt unter `data/previews/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.mp4`, `ai-005-the-4-box-prompt-for-mental-overload.mp4`, `attention-002-the-algorithm-does-not-need-you-happy.mp4` [tool: terminal claimed].\n5. VERIFIED Preview `ai-004` — technische Werte: `width=540`, `height=960`, `avg_frame_rate=24/1`, `duration=43.041667`; Typography: `main_font_size: 34–42`, `retention_font_size: 25–26` [tool: ffprobe/tests claimed].\n6. TEST full suite nach Typography-Änderung — Ergebnis: `25 passed in 0.96s` [tool: pytest claimed].\n7. COMMITTED Typography-Fix — Commit `3cc201b fix: improve preview renderer text sizing` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n8. ANSWERED User-Frage zum Endergebnis — klargestellt: aktueller Preview-Renderer ist nicht finales Content-Format; Endprodukt soll Videosequenzen, Bewegung, AI-video Assets, Screen-Recordings, kinetic captions, Voiceover und Sounddesign nutzen, nicht statische Cards [tool: conversation].\n9. DEFINED bessere Zielrichtung für `ai-004` — vorgeschlagen: ca. 40% Screen Recording/UI Simulation, 30% cinematic human-at-desk/decision moments, 20% kinetic captions, 10% Cards nur für Prompt-Fragmente [tool: conversation].\n10. CREATED `autoshorts/asset_plan.py` — neue Asset-Plan-v2-Komponente für motion-led Asset Pipeline; entscheidet pro Szene zwischen `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, `kinetic_text_overlay` [tool: code claimed].\n11. CREATED `scripts/generate_asset_plans.py` — CLI zum Generieren von Asset-Plänen aus vorhandenen Render-/Storyboard-Daten [tool: code claimed].\n12. CREATED `tests/test_asset_plan.py` — Tests für Asset Plan Builder, u.a. Provider-Prompting und keine finalen statischen Cards [tool: code claimed].\n13. GENERATED Asset-Pläne Top-3 — Dateien erzeugt unter `data/asset_plans/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.json`, `ai-005-the-4-box-prompt-for-mental-overload.json`, `attention-002-the-algorithm-does-not-need-you-happy.json` [tool: terminal claimed].\n14. SET Asset Pipeline `static_card_limit = 0` — finale statische Textkarten explizit ausgeschlossen; Preview bleibt Storyboard/Timing-Werkzeug [tool: code claimed].\n15. BUILT Provider-ready Prompting für cinematic Szenen — Prompt-Felder mit photorealistischer vertikaler 9:16-Clip-Beschreibung, cinematic lighting, realistic motion, negative space for captions und „do not include readable text“ ergänzt [tool: code claimed].\n16. BUILT Negative Prompts für AI-video Provider — u.a. `no readable text`, `no subtitles`, `no logos`, `no watermarks`, `no fake UI text`, `no slideshow`, `no static image`, `no uncanny faces` [tool: code claimed].\n17. TEST full suite nach Asset Plan v2 — Ergebnis: `32 passed in 0.96s` [tool: pytest claimed].\n18. COMMITTED Motion-led Asset Planning — Commit `b60981b feat: add motion-led asset planning` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n19. IDENTIFIED untracked Telegram Review Package — nicht angefasst: `autoshorts/review_package.py`, `data/telegram_reviews/`, `scripts/generate_telegram_review_packages.py`, `tests/test_review_package.py` [tool: git status claimed].\n20. CREATED `autoshorts/retention_studio.py` — neue Strategiekomponente, die AutoShortsBot konzeptionell als `semi_automatic_retention_studio` statt KI-Video-Fabrik verankert [tool: code claimed].\n21. CREATED `scripts/generate_retention_studio.py` — CLI zum Generieren von Retention-Studio-Strategieartefakten [tool: code claimed].\n22. CREATED `tests/test_retention_studio.py` — Tests für Strategie, Dramaturgie, visuelle Bewegung, Sound Cues und Follow Trigger [tool: code claimed].\n23. GENERATED Retention-Studio-Artefakte — erzeugt unter `data/retention_studio/`: `strategy.json`, `pilot_ideas.json`, `mechanism_explainers.json` [tool: terminal claimed].\n24. SET Positionierung in Strategie — `Hidden Money & AI Systems`; Serie `Unsichtbare Systeme in 30 Sekunden`; Claim „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“ [tool: code claimed].\n25. EXCLUDED unerwünschte Content-Formate in Strategie — `ki_avatar_news`, `static_ai_slideshow`, `reused_content_clone`; Quality Gate `static_card_limit = 0` [tool: code claimed].\n26. IMPLEMENTED Mechanism-Explainer-Formel — Timing/Beats: `0.0–1.5s Pattern Interrupt`, `1.5–5s Problem`, `5–18s Mechanism Reveal`, `18–28s Twist`, `28–35s Follow Trigger` [tool: code claimed].\n27. ADDED Tests für Retention-Beats — Pattern Interrupt endet ≤ 1.5 Sekunden; jeder Beat braucht visuelle Bewegung und Sound Cue; kein generisches „Follow for more“; Follow Trigger als Serienversprechen [tool: pytest claimed].\n28. ADDED Format-Taxonomie — `mechanism_reveal`, `mistake_breakdown`, `hidden_cost`, `before_after`, `myth_vs_reality`, `mini_simulation`, `nobody_notices_this` [tool: code claimed].\n29. ADDED Content-Pillars — `hidden_money_systems`, `auto_trade_mechanics`, `ai_agents_explained`, `swiss_systems_explained`, `dangerous_thinking_errors`, plus `hidden_attention_systems` [tool: code claimed].\n30. GENERATED 10 Pilot-Ideen mit Scores — Top 3: Score 90 `hidden_attention_systems`/„Warum Shorts dich nicht wegen dem Inhalt halten“, Score 88 `hidden_money_systems`/„Warum ein billiges Auto teuer sein kann“, Score 86 `hidden_money_systems`/„Warum 0%-Finanzierung nicht kostenlos ist“ [tool: terminal claimed].\n31. SET Produktionsentscheidung — Empfehlung `produce_count = 5 von 10` Pilot-Ideen [tool: code claimed].\n32. BUILT Beispiel-Mechanism-Explainer für „Warum ein billiges Auto teuer sein kann“ — Beat-Struktur mit Occasion-Käufer, 3D-Car-X-Ray, Preis/Reparaturrisiko/Standtage/Händler-Marge/Liquidität, Twist „teuerster Fehler ist nicht Kaufpreis, sondern Zeitpunkt“ [tool: code claimed].\n33. TEST full suite nach Retention Studio — Ergebnis: `36 passed in 0.99s` [tool: pytest claimed].\n34. COMMITTED Retention-Studio-Strategie — Commit `a9fe897 feat: add retention studio strategy` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n35. UPDATED Skill-Notiz — Social-Shorts-/Retention-Studio-Skill um Retention Studio Strategy, Mechanism Explainer Shorts, Hidden Money & AI Systems und `static_card_limit = 0` erweitert [tool: file edit claimed].\n36. CREATED `autoshorts/mechanism_pipeline.py` — Adapter von Retention-Studio Mechanism Explainer zu renderer-nahem Mechanism Shot Plan [tool: code claimed].\n37. CREATED `scripts/generate_mechanism_shot_plans.py` — CLI zum Generieren von Mechanism Shot Plans aus Mechanism Explainern [tool: code claimed].\n38. CREATED `tests/test_mechanism_pipeline.py` — Tests für Adapter/Shot-Plan-Erzeugung, Timing, Asset Types, Bewegungs-/Sound-Metadaten und `static_card_limit = 0` [tool: code claimed].\n39. GENERATED Pilot-Shot-Plan — Datei `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` erzeugt [tool: terminal claimed].\n40. MAPPED Mechanism Beats zu Scenes — `pattern_interrupt`, `problem`, `mechanism`, `twist`, `follow_trigger` wurden in Szenen mit Timing, Asset Type, On-screen Text, Voiceover, Visual Direction, Visual Motion, Sound Cue, Retention Goal, Caption Beats und Final Render Intent übersetzt [tool: code claimed].\n41. CREATED Shot Plan für „Warum ein billiges Auto teuer sein kann“ — Scene 1 `0.0s–1.4s cinematic_ai_video`, Scene 2 `1.4s–5.0s cinematic_ai_video`, Scene 3 `5.0s–18.0s mechanism_animation`, Scene 4 `18.0s–28.0s mechanism_animation`, Scene 5 `28.0s–34.0s kinetic_text_overlay` [tool: terminal claimed].\n42. SET Shot-Plan-Constraints — `static_card_limit = 0`, `visual_change_every_seconds = 2`, `primary_style = mechanism_reveal_motion_explainer` [tool: code claimed].\n43. TEST full suite nach Mechanism Pipeline — Ergebnis: `40 passed in 0.97s` [tool: pytest claimed].\n44. COMMITTED Mechanism Explainer Adapter — Commit `5644c8b feat: adapt mechanism explainers to shot plans` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n45. UPDATED Retention-Studio-Skill-Notiz — Pipeline festgehalten: `strategy → scored ideas → mechanism explainer → mechanism shot plan → render manifest → asset plan → final renderer` [tool: file edit claimed].\n46. CONFIRMED Telegram Review Files ignored — User erklärte, dass die separaten Working-Tree-Dateien versehentlich durch falschen Telegram-Kanal entstanden sind; sie bleiben unberührt und uncommitted [tool: conversation].\n\n## Active State\n- Repository/Projekt: `/home/agent/jarvis_runtime/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Remote-Status zuletzt berichtet: `local == remote`.\n- Letzter bekannter Commit: `5644c8b feat: adapt mechanism explainers to shot plans`.\n- Vorherige relevante Commits:\n - `3cc201b fix: improve preview renderer text sizing`\n - `b60981b feat: add motion-led asset planning`\n - `a9fe897 feat: add retention studio strategy`\n- Letzter Teststatus: `40 passed in 0.97s`.\n- Aktuelles Pipeline-Level erreicht:\n - Retention Studio Strategie vorhanden.\n - Pilot-Ideen gescored.\n - Mechanism Explainer vorhanden.\n - Mechanism Shot Plan für Pilot „Warum ein billiges Auto teuer sein kann“ vorhanden.\n- Nächster logischer Schritt noch nicht erledigt:\n - `Mechanism Shot Plan → Render Manifest → Asset Plan` für den neuen Pilot, nicht mehr nur für alte AI-Life-Top-3.\n- Untracked/zu ignorieren:\n - `autoshorts/review_package.py`\n - `data/telegram_reviews/`\n - `scripts/generate_telegram_review_packages.py`\n - `tests/test_review_package.py`\n- Keine laufenden Server oder Prozesse erwähnt.\n- Keine Secrets/Credentials bewahren; falls vorhanden, als `[REDACTED]`.\n\n## In Progress\n- Der User hat „ok nächster schritt“ gesagt. Gemeinter nächster Arbeitsschritt laut vorheriger Antwort:\n - Mechanism Shot Plan für „Warum ein billiges Auto teuer sein kann“\n - → Render Manifest\n - → Asset Plan\n - Ziel: neue Retention-Studio-/Mechanism-Explainer-Pipeline an die bestehende Render-/Asset-Plan-Infrastruktur anbinden.\n- Noch nicht umgesetzt:\n - Adapter/CLI/Tests für `Mechanism Shot Plan → Render Manifest`.\n - Generierung eines Render Manifests für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.\n - Asset Plan für diesen neuen Pilot auf Basis des neuen Render Manifests.\n - Provider Job Specs für cinematic Szenen des Pilots.\n\n## Blocked\nKeine bekannten Blocker. \nKeine Fehlermeldungen offen. \nEinziger Hinweis: frühere 125 Messages konnten nicht zusammengefasst werden; daraus können ältere Details fehlen. Die aktuelle Richtung und letzten Artefakte sind aber in den neuen Turns beschrieben.\n\n## Key Decisions\n1. Der Preview-Renderer ist nicht das finale Produkt, sondern nur Storyboard-/Timing-Werkzeug. Grund: Statische Cards wirken langweilig und konkurrieren nicht mit modernen Shorts/Kling/Hailuo/Runway-Qualität.\n2. Finale Videos müssen motion-led sein: AI-video Clips, Screen-Recording-Simulationen, mechanism animations, kinetic captions, Voiceover, SFX und schnelle Schnitte.\n3. `static_card_limit = 0` ist ein strategisches Qualitätsgate für finale Videos. Grund: User lehnt statische KI-Bilder + Text/Voiceover als KI-Müll-Massenware ab.\n4. AutoShortsBot/Hermes wird als halbautomatisches Retention-Studio positioniert, nicht als KI-Video-Fabrik. Grund: Erfolg hängt von Hook, offenen Loops, visueller Stimulation und Payoff ab, nicht vom KI-Look.\n5. Gewählte Content-Nische: Hidden Money & AI Systems. Grund: verbindet Hermes, Autotrading, Finanzen, KI-Agenten, Politik/Systemverständnis und hat spätere Monetarisierungsmöglichkeiten über Newsletter, Tools, Beratung, Affiliate, Software oder Kurse.\n6. Kernformat: Mechanism Reveal / Visual Explanation / Mini-Investigation. Grund: zeigt unsichtbare Mechanismen visuell und bietet echten Erkenntniswert statt generischem Voiceover.\n7. Avatare nicht als Hauptformat. Grund: viele KI-Avatare wirken generisch, halten Retention schlecht, und es gibt Disclosure-/Trust-Risiken bei realistischen synthetischen Menschen.\n8. Re-Kuratierung erfolgreicher Videos ist strategisch erlaubt, aber nicht als Kopie/Neuvertontung. Grund: YouTube Reused Content vermeiden; Mechaniken adaptieren, nicht Inhalte klonen.\n9. Pilot-Fokus: „Warum ein billiges Auto teuer sein kann“. Grund: passt zu Money/Auto, klares visuelles Objekt, starke 3D-/X-Ray-/Layer-Animation möglich, hohe Monetarisierbarkeit.\n10. Untracked Telegram-Review-Dateien werden ignoriert. Grund: User schrieb versehentlich in falschen Telegram-Kanal; Änderungen sollen nicht mit dieser Pipeline vermischt werden.\n\n## Resolved Questions\n1. User fragte, warum Text oben groß und unten klein ist. Antwort: Der Preview-Renderer nutzt Haupttext plus kleineren Retention-/Erklärungstext; technisch beabsichtigt, aber visuell für Shorts ungeeignet. Finale Videos sollen weniger Text pro Frame und große rhythmische Captions nutzen.\n2. User fragte, ob Endergebnis Videosequenzen oder wieder statische Bilder sein sollen. Antwort: Nicht statische Bilder. Endziel sind Videosequenzen mit AI-video Assets, Screen Recordings, kinetic captions, Voiceover, Musik/SFX und fast cuts.\n3. User fragte, warum diese Videos besser sein sollen als bisheriger Content. Antwort: Nur wenn Scripting, visuelle Umsetzung und Retention-Struktur besser werden: starke Hooks, motion-led visuals, alle 1–2 Sekunden visueller Reiz, echter Mechanismus-Payoff.\n4. User fragte implizit, ob statische Cards reichen. Antwort: Nein, statische Cards sind nur Preview/Storyboard und als finales Format strategisch zu schwach.\n5. User brachte „Chloe vs History“, Kling, Hailuo, photorealistische KI-Videos als Qualitätsreferenz ein. Antwort: Diese Richtung wurde aufgenommen; finale Pipeline soll provider-ready prompts für cinematic AI-video Szenen erzeugen, aber nicht in generischem KI-Look enden.\n6. User schlug Retention-Studio statt KI-Video-Content vor. Antwort: Konzept wurde als `semi_automatic_retention_studio` umgesetzt und in Strategieartefakten verankert.\n7. User schlug Zack-D-Films-artige Mechanism Reveals vor. Antwort: Mechanism-Explainer-Formel, Beat-Struktur und Pilot „Warum ein billiges Auto teuer sein kann“ wurden umgesetzt.\n8. User erklärte, der separate Working Tree könne ignoriert werden. Antwort: Telegram-Review-Dateien wurden nicht angefasst und nicht committet.\n\n## Pending User Asks\n- „ok nächster schritt“ ist noch offen. Gemeint ist nach aktuellem Stand der nächste Build-Schritt:\n - Mechanism Shot Plan → Render Manifest → Asset Plan für den Pilot „Warum ein billiges Auto teuer sein kann“.\n\n## Relevant Files\n- `autoshorts/asset_plan.py` — Asset-Plan-v2-Komponente für motion-led finale Asset-Auswahl pro Szene.\n- `scripts/generate_asset_plans.py` — CLI zur Asset-Plan-Generierung.\n- `tests/test_asset_plan.py` — Tests für Asset-Plan-Logik.\n- `data/asset_plans/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `autoshorts/retention_studio.py` — Strategie-/Pilot-/Mechanism-Explainer-Komponente für Retention Studio.\n- `scripts/generate_retention_studio.py` — CLI zur Generierung von Retention-Studio-Artefakten.\n- `tests/test_retention_studio.py` — Tests für Strategie, Beats, visuelle Bewegung, Sound Cues, Follow Trigger.\n- `data/retention_studio/strategy.json` — Retention-Studio-Strategie.\n- `data/retention_studio/pilot_ideas.json` — gescorte Pilot-Ideen.\n- `data/retention_studio/mechanism_explainers.json` — Mechanism-Explainer-Daten.\n- `autoshorts/mechanism_pipeline.py` — Adapter von Mechanism Explainer zu Mechanism Shot Plan.\n- `scripts/generate_mechanism_shot_plans.py` — CLI zur Shot-Plan-Generierung.\n- `tests/test_mechanism_pipeline.py` — Tests für Mechanism Shot Plan Adapter.\n- `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` — generierter Pilot-Shot-Plan.\n- `data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4` — neu gerenderter Preview.\n- `autoshorts/review_package.py` — untracked/zu ignorieren.\n- `data/telegram_reviews/` — untracked/zu ignorieren.\n- `scripts/generate_telegram_review_packages.py` — untracked/zu ignorieren.\n- `tests/test_review_package.py` — untracked/zu ignorieren.\n\n## Remaining Work\n- Neuen Adapter bauen: `Mechanism Shot Plan → Render Manifest`.\n- Render Manifest für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` erzeugen.\n- Asset Plan v2 auf diesen neuen Mechanism-Render-Manifest anwenden.\n- Provider Job Specs für cinematic AI-video Szenen exportieren, z.B. Kling/Hailuo/Runway-kompatible Prompts.\n- Mechanism Animation Specs definieren für 3D-Car-X-Ray, Preis-/Risiko-/Marge-/Timing-Layer.\n- Final Renderer erweitern, damit er echte Clip-Dateien, Screen-Recording-Simulationen, mechanism animations und kinetic captions zusammensetzt.\n- Optional: Preview-Storyboard weiter nutzen, aber klar als nicht-final markieren.\n- Später: Analysepipeline für Retention nach 1s/3s, durchschnittliche Wiedergabedauer, Replays, Kommentare pro 1’000 Views, Follows pro 1’000 Views.\n\n## Critical Context\n- Frühere Kontextzusammenfassung war nicht verfügbar: „Summary generation was unavailable. 125 message(s) were removed…“. Frühere Details vor den neuen Turns können fehlen.\n- Branch: `feat/initial-growth-engine`.\n- PR-Link bleibt: `https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine`.\n- Letzter bekannter Teststatus: `40 passed in 0.97s`.\n- Wichtigster aktueller Pilot: `Warum ein billiges Auto teuer sein kann`.\n- Pilot-Shot-Plan-Szenen:\n - Scene 1 — `pattern_interrupt`, `0.0s–1.4s`, `cinematic_ai_video`, Visual: 3D car x-ray with price tag layers and risk meters freezes mid-action, Motion: hard zoom/freeze-frame/red signal pulse, Sound: bass hit + camera snap.\n - Scene 2 — `problem`, `1.4s–5.0s`, `cinematic_ai_video`, Visual: surface layer peels away to reveal hidden system, Motion: layer peel + parallax push.\n - Scene 3 — `mechanism_reveal`, `5.0s–18.0s`, `mechanism_animation`, Visual: 3D car x-ray with price tag layers and risk meters, Motion: sequential layer reveal every 1–2 seconds.\n - Scene 4 — `twist`, `18.0s–28.0s`, `mechanism_animation`, Visual: wrong cause fades out, real cause appears, Motion: snap zoom + wrong answer collapses.\n - Scene 5 — `follow_trigger`, `28.0s–34.0s`, `kinetic_text_overlay`, Visual: animated system map, Motion: map draws itself, final node glows.\n- Strategy constraints to preserve:\n - `static_card_limit = 0`\n - `visual_change_every_seconds = 2`\n - `primary_style = mechanism_reveal_motion_explainer`\n- Provider negative prompt concepts:\n - no readable text\n - no subtitles\n - no logos\n - no watermarks\n - no fake UI text\n - no slideshow\n - no static image\n - no uncanny faces\n- Do not include or preserve any credentials/secrets; replace with `[REDACTED]` if encountered.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---" }, { "role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"Ok, mach weiter.\"\n\n## Goal\nAufbau einer reproduzierbaren AutoShortsBot-/Retention-Studio-Pipeline für kurze, hochwertige Social-Videos im Konzept „Hidden Money & AI Systems / Unsichtbare Systeme in 30 Sekunden“. Lokale native Wan2.2-Videoerzeugung soll nicht nur testweise funktionieren, sondern als kontrollierte Produktionspipeline genutzt werden: Style Bible → Keyframe Prompting → Wan2.2 I2V Clip-Varianten → Review/Selection → finaler Renderer mit deterministischen Overlays, Zahlen, Captions und Mechanism-Animationen.\n\n## Constraints & Preferences\n- Sprache/Kommunikation: Deutsch.\n- User bevorzugt pragmatisches Weiterbauen, kurze Bestätigung + konkrete technische Ergebnisse.\n- Keine Secrets, Tokens, Passwörter oder Credentials ausgeben oder speichern; vorhandener GitHub-Token liegt lokal unter `~/.hermes/secrets/github_autoshortsbot_token`, Wert immer als `[REDACTED]` behandeln.\n- Projekt/Repo: `Gamexgit/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Qualitätspfad für Video: native Wan2.2 I2V statt Kijai/WanVideoWrapper und statt reinem T2V.\n- AI-Video darf keine Texte, Zahlen, Labels, UI-Texte, Logos, Watermarks oder Kennzeichen halluzinieren. Text/Zahlen/Overlays macht später deterministisch der Renderer.\n- Stilentscheidung: `photorealistic macro investigation + clean mechanism overlays`.\n- Wan2.2 Prompt-Regeln: ein Motiv, eine Hauptbewegung, ein Kameramove, klare Umgebung, balanced exposure, Identität aus Startbild erhalten, negative prompt gegen Flicker/Text/Deformationen.\n- Finale Shorts sollen nicht bei 10 fps bleiben; Produktionspreset wurde auf 61 Frames @ 12 fps geändert, finale Delivery-Ziel 24 fps via Renderer/FFmpeg/Optical Flow.\n- Nicht blind alle untracked Artefakte committen; Test-/Experimentdateien nur bewusst einchecken.\n- Bestehende ComfyUI/Wan2.2-Modelle für native Tests behalten:\n - `~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors`\n - `~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors`\n - `~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors`\n\n## Completed Actions\n1. HINWEIS Speicherbereinigung gegeben — erklärt, welche Wan2.2-/Wrapper-Dateien, Temp-Downloads, HuggingFace-Cache und Outputs gelöscht werden können; native Wan2.2-Kerndateien als „nicht löschen“ markiert [tool: assistant].\n2. TESTVIDEO native Wan2.2 T2V erzeugt — Prompt „Ein kleines gemütliches Haus hüpft fröhlich über eine helle grüne Wiese“, 416x736, 49 Frames, 10 fps, 12 Steps; Output: `data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4`; Bewertung: technisch erfolgreich, aber Haus nicht sauber genug erkennbar [tool: ComfyUI].\n3. TEST Wan2.2 I2V mit Hasenbild durchgeführt — ChatGPT/FAL-Bildgenerierung war nicht verfügbar wegen `FAL_KEY fehlt`; Ersatzbild aus Wikimedia genutzt; erster Clip mit sichtbarem rechteckigem Bildkasten verworfen: `data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4` [tool: ComfyUI].\n4. TEST Wan2.2 I2V zweiter Hase durchgeführt — Keyframe `rabbit_meadow_keyframe_crop_416x736.png`, 416x736, 49 Frames, 10 fps, 16 Steps, CFG 5.0; Output: `data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4`; Ergebnis: photorealistischer Hase/Wiese erkennbar, Bewegung noch eher Morphing statt überzeugendem Hüpfen [tool: ComfyUI].\n5. DOKUMENTATION `docs/wan22_prompting_playbook.md` erstellt — Wan2.2 Prompting-Regeln dokumentiert: I2V bevorzugen, T2V nur Exploration, klare Shot-Struktur, negative prompts, Keyframe-Anforderungen, A5000-Settings-Ladder [tool: patch].\n6. DOKUMENTATION `docs/style_bible.md` erstellt — Serienpositionierung, Look & Feel, Farbpalette, Caption-Stil, AI-Video-Regeln, No-Go-Liste, Shot Families, Pilot-Style und Quality Gate definiert [tool: patch].\n7. MODUL `autoshorts/keyframe_prompt.py` erstellt — Funktion `build_i2v_prompt_plan(shot_plan)` erzeugt Keyframe Prompt, I2V Motion Prompt, Negative Prompt, Wan2.2 Preset, Clip Requirements und Renderer-only Scene Notes [tool: patch].\n8. CLI `scripts/generate_i2v_prompt_plans.py` erstellt und ausgeführt — generierte Datei: `data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` [tool: terminal].\n9. TESTS `tests/test_keyframe_prompt.py` erstellt — prüft u.a. nur `cinematic_ai_video`-Szenen als Wan2.2-I2V-Jobs, Mechanism-/Kinetic-Szenen als Renderer-only, Auto-Pilot-Prompts konkret, Negative Prompts mit Anti-Text/Anti-License-Plate/Anti-Fake-UI [tool: patch].\n10. TESTSUITE ausgeführt — Ergebnis nach Prompt-Planung: `64 passed in 3.66s` [tool: terminal].\n11. COMMIT erstellt — `b3ef793 feat: add wan i2v prompt planning`; Push zunächst fehlgeschlagen mit `remote: Write access to repository not granted` und `fatal: ... 403` [tool: git].\n12. MODUL `autoshorts/comfyui_client.py` erstellt — testbarer ComfyUI Client mit austauschbarem Transport; unterstützt Keyframe Upload, `/prompt`, Queue/History Polling, MP4-Download via `/view`, strukturiertes Ergebnis [tool: patch].\n13. MODUL `autoshorts/i2v_clip_jobs.py` erstellt — erzeugt seeded Clip Jobs aus I2V Prompt Plan, inklusive Szene, Seed, Keyframe-Pfad, Output-Pfad, Remote Image Name, Prompt Spec, Review-Kriterien und native Wan2.2 Workflow-Konfiguration [tool: patch].\n14. Native Wan2.2 Workflow Builder implementiert — API-format Workflow mit `UNETLoader`, `CLIPLoader`, `CLIPTextEncode` positive/negative, `VAELoader`, `LoadImage`, `ImageScale`, `Wan22ImageToVideoLatent`, `KSampler`, `VAEDecode`, `CreateVideo`, `SaveVideo`; Modelle: `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, `wan2.2_vae.safetensors`; Sampler `uni_pc`, Scheduler `simple`, damals 49 Frames @ 10 fps, Steps 16, CFG 5.0 [tool: patch].\n15. CLI `scripts/generate_i2v_clip_jobs.py` erstellt und ausgeführt — generierte Datei: `data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` [tool: terminal].\n16. Job Package erzeugt — 6 Jobs: `scene_01_seed_101`, `scene_01_seed_202`, `scene_01_seed_303`, `scene_02_seed_101`, `scene_02_seed_202`, `scene_02_seed_303`; Status `needs_keyframe`, da Keyframes fehlen [tool: terminal].\n17. TESTS `tests/test_comfyui_client.py` und `tests/test_i2v_clip_jobs.py` erstellt — ComfyUI Client ohne echte Netzwerkanfragen testbar, Job-Erzeugung/Workflow-Struktur validiert [tool: patch].\n18. TESTSUITE ausgeführt — Ergebnis nach Clip-Job-Packaging: `68 passed in 3.59s` [tool: terminal].\n19. COMMIT erstellt — `b57cee8 feat: add i2v clip job packaging` [tool: git].\n20. GitHub Token sicher lokal genutzt — Token-Datei `~/.hermes/secrets/github_autoshortsbot_token`, Wert `[REDACTED]`; nur über temporäres `GIT_ASKPASS`, nicht ausgegeben [tool: git].\n21. PUSH zu GitHub erfolgreich — Branch `feat/initial-growth-engine`, `remote_hash_matches: True` [tool: git].\n22. FPS-Policy beantwortet und geändert — erklärt, dass 10 fps für Tests okay, aber für finale Shorts stockend wirken kann; Produktionspreset geändert von 49 Frames @ 10 fps auf 61 Frames @ 12 fps; finale Delivery-Ziel 24 fps via Renderer/FFmpeg Optical Flow [tool: assistant/patch].\n23. MODUL `autoshorts/keyframe_package.py` erstellt — erzeugt Keyframe Request Packages aus I2V Prompt Plan, inklusive Zielpfad, Prompt, Acceptance Checklist und FPS Policy [tool: patch].\n24. CLI `scripts/prepare_keyframe_requests.py` erstellt und ausgeführt — generierte Datei: `data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` [tool: terminal].\n25. TESTS `tests/test_keyframe_package.py` erstellt — validiert Keyframe Request Package, Acceptance Checklist und FPS Policy [tool: patch].\n26. I2V Prompt Plans und Clip Jobs regeneriert — Dateien `data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` und `data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` aktualisiert auf `frames: 61`, `fps: 12` [tool: terminal].\n27. TESTSUITE ausgeführt — Ergebnis nach Keyframe Packages/FPS-Update: `71 passed in 3.51s` [tool: terminal].\n28. COMMIT erstellt und gepusht — `a2311f6 feat: add keyframe request packages`; Remote verifiziert `local == remote`, Branch `feat/initial-growth-engine` [tool: git].\n\n## Active State\n- Working directory: AutoShortsBot Repo, vermutlich `/home/agent/jarvis_runtime/AutoShortsBot` oder entsprechender Projektpfad.\n- GitHub Repo: `Gamexgit/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Remote: nach Commit `a2311f6` erfolgreich aktualisiert/verifiziert.\n- Teststatus zuletzt: `71 passed in 3.51s`.\n- Aktuelle Pipeline-Stufe:\n - Retention Studio Strategy vorhanden.\n - Mechanism Explainer/Shot Plan vorhanden.\n - Style Bible vorhanden.\n - Wan2.2 Prompting Playbook vorhanden.\n - I2V Prompt Plan generiert.\n - I2V Clip Jobs generiert.\n - Keyframe Request Package generiert.\n- Aktuelle Job-Datei:\n - `data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json`\n - Jobs stehen auf `needs_keyframe`, solange die erwarteten PNGs fehlen.\n- Erwartete Keyframe-Zielpfade:\n - `data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png`\n - `data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png`\n- Generierte Keyframe Request Datei:\n - `data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json`\n- Produktionspreset aktuell:\n - `frames: 61`\n - `fps: 12`\n - ca. `5.08s`\n - finale Auslieferung geplant mit `24 fps`.\n- Untracked/bewusst nicht angefasst:\n - `autoshorts/review_package.py`\n - `data/telegram_reviews/`\n - `scripts/generate_telegram_review_packages.py`\n - `tests/test_review_package.py`\n - diverse `data/comfyui_*.json` Experiment-/Workflow-Dateien.\n- Keine bekannten laufenden Prozesse/Server aus den letzten Turns dokumentiert.\n- GitHub Token vorhanden, aber Wert nicht bekannt/ausgegeben: `[REDACTED]`.\n\n## In Progress\n- Unmittelbar vor der Kompaktierung sollte als nächster Schritt die produktive I2V-Ausführung gebaut werden:\n - `scripts/run_i2v_clip_jobs.py`\n - vermutlich Ergänzungen in `autoshorts/i2v_clip_jobs.py` oder neues Runner-Modul\n - Ziel: Job Package lesen, vorhandene Keyframes prüfen, ComfyUI Jobs für Seeds ausführen, MP4s downloaden, `run_report.json` schreiben.\n- Noch nicht umgesetzt in den neuen Turns nach User „Ok, mach weiter.“.\n\n## Blocked\n- Aktuell keine Code-/Test-Blocker dokumentiert.\n- Produktive I2V-Generierung ist praktisch blockiert, bis Keyframes existieren:\n - `data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png`\n - `data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png`\n- Frühere Push-Blockade durch GitHub 403 wurde behoben:\n - Alt: `remote: Write access to repository not granted`\n - Alt: `fatal: ... 403`\n - Gelöst durch lokalen Token `[REDACTED]`.\n- Frühere Bildgenerierungs-Blockade:\n - `FAL_KEY fehlt`\n - Deshalb konnte kein ChatGPT/FAL-Bild automatisch erzeugt werden; Ersatzbild aus Wikimedia wurde verwendet.\n- Speicherdruck beim User/GPU-Host wurde angesprochen; konkrete lokale Speicherbereinigung noch nicht als ausgeführt bestätigt.\n\n## Key Decisions\n- Native Wan2.2 ist Hauptpfad; Kijai/WanVideoWrapper nur noch optional/Alt-Testpfad. Grund: native Wan2.2 lief stabil und vermeidet Wrapper-T5-Probleme.\n- I2V statt T2V für Produktion. Grund: T2V ist zu unkontrolliert, I2V erhält Motiv/Komposition aus Keyframe besser.\n- Keyframes sollen extern/gezielt generiert werden; Wan2.2 animiert danach. Grund: bessere Kontrolle über Motiv, Pose, Bildraum und Stil.\n- AI-Video ohne Text/Logos/Watermarks/Kennzeichen. Grund: Wan kann Text/Zahlen/UI halluzinieren; Finance-/Mechanism-Inhalte müssen korrekt sein.\n- Renderer-only für Mechanism-/Kinetic-Szenen. Grund: Zahlen, Labels, Preis-/Risiko-/Marge-/Zeit-Overlays müssen deterministisch und wahrheitskontrolliert sein.\n- Stil: `photorealistic macro investigation + clean mechanism overlays`. Grund: passt zu „Hidden Money & AI Systems“ besser als generische KI-Fantasy oder niedliche Tierclips.\n- FPS geändert von 49@10 auf 61@12. Grund: 10 fps wirkt final stockend; 12 fps schont A5000/16GB noch, ist aber bessere Basis für 24-fps-Delivery per Interpolation/Renderer.\n- Tests bleiben offline/testbar. Grund: ComfyUI Client wurde mit austauschbarem Transport gebaut, damit Unit Tests keine Netzwerkanfragen brauchen.\n- GitHub Token nur lokal über `GIT_ASKPASS` verwenden, nie ausgeben/committen.\n\n## Resolved Questions\n- User fragte, welche heruntergeladenen Modelle/Bestandteile gelöscht werden können:\n - Behalten für native Wan2.2:\n - `wan2.2_ti2v_5B_fp16.safetensors`\n - `umt5_xxl_fp8_e4m3fn_scaled.safetensors`\n - `wan2.2_vae.safetensors`\n - Löschbar, wenn Kijai/WanVideoWrapper nicht weiter getestet wird:\n - `~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors`\n - `~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors`\n - Temporäre Downloads löschbar:\n - `/tmp/wan22_native`\n - `/tmp/wan22_text_encoder`\n - HuggingFace Cache kann aggressiv gelöscht werden, wenn benötigte Modelle in `~/ai/ComfyUI/models/...` liegen:\n - `~/.cache/huggingface/hub`\n - Optional Outputs löschen: `~/ai/ComfyUI/output/*`.\n- User fragte, ob 10 fps nicht stockt:\n - Antwort: Ja, 10 fps ist für finale Shorts potenziell stockend; war nur konservativer Testmodus.\n - Produktionspreset wurde auf 61 Frames @ 12 fps geändert; finale Delivery soll 24 fps sein.\n- User wollte wissen, wie es im Projekt weitergeht:\n - Antwort/Plan: Style Bible, Prompt/Keyframe System, Wan2.2 I2V Job Client, Clip Review & Selection, Final Hybrid Renderer.\n- User wollte Recherche zu Wan2.2 Prompts:\n - Antwort: beste Struktur `[Shot type] of [single subject] [one motion] in [environment]. Camera [one motion]. Lighting/exposure. Style/texture. Preserve details. No text/logo/watermark.`\n- User wollte Image-to-Video mit photorealistischem Hasen:\n - Technisch erfolgreich, aber kein perfektes Hüpfen wegen ungeeignetem sitzenden/frontales Startbild; empfohlen wurde seitliches Mid-Hop-Keyframe mit vollem Körper und freier Wiese.\n\n## Pending User Asks\n- User sagte zuletzt: „Ok, mach weiter.“\n- Noch offen: nächsten Build-Schritt umsetzen, voraussichtlich Runner für produktive I2V Clip Jobs:\n - `scripts/run_i2v_clip_jobs.py`\n - Job Package lesen\n - Keyframes prüfen\n - Seeds ausführen\n - MP4s speichern\n - `run_report.json` erzeugen.\n\n## Relevant Files\n- `docs/style_bible.md` — Style Bible für Serienlook, Farbpalette, AI-Video-Regeln, No-Gos, Shot Families, Quality Gate.\n- `docs/wan22_prompting_playbook.md` — Wan2.2 Prompting-Regeln, I2V/T2V Policy, negative prompts, Keyframe-Anforderungen, A5000 Settings.\n- `autoshorts/keyframe_prompt.py` — baut I2V Prompt Plans aus Mechanism Shot Plans.\n- `scripts/generate_i2v_prompt_plans.py` — CLI zum Generieren von Prompt-Plan JSONs.\n- `tests/test_keyframe_prompt.py` — Tests für Prompt-Planung.\n- `autoshorts/comfyui_client.py` — testbarer ComfyUI API Client für Upload, Prompt, Polling, Download.\n- `autoshorts/i2v_clip_jobs.py` — baut seeded I2V Clip Jobs und native Wan2.2 Workflow JSONs.\n- `scripts/generate_i2v_clip_jobs.py` — CLI zum Generieren von Clip-Job Packages.\n- `tests/test_comfyui_client.py` — Tests für ComfyUI Client mit Fake Transport.\n- `tests/test_i2v_clip_jobs.py` — Tests für Clip Job Packaging und Workflow-Struktur.\n- `autoshorts/keyframe_package.py` — baut Keyframe Request Packages.\n- `scripts/prepare_keyframe_requests.py` — CLI zum Erzeugen der Keyframe Requests.\n- `tests/test_keyframe_package.py` — Tests für Keyframe Request Packages/FPS Policy.\n- `data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` — generierter Prompt Plan, aktuell 61 Frames @ 12 fps.\n- `data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` — generiertes Job Package mit 6 Jobs, Status `needs_keyframe`.\n- `data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json` — Keyframe Requests inkl. Prompts, Zielpfaden, Acceptance Checklist.\n- `data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4` — native Wan2.2 T2V Haus-Testclip.\n- `data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4` — erster Hasen-I2V-Test mit Bildkasten, verworfen.\n- `data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4` — besserer Hasen-I2V-Test ohne Rahmen.\n- `autoshorts/review_package.py` — untracked, bewusst nicht angefasst.\n- `scripts/generate_telegram_review_packages.py` — untracked, bewusst nicht angefasst.\n- `tests/test_review_package.py` — untracked, bewusst nicht angefasst.\n- `data/telegram_reviews/` — untracked, bewusst nicht angefasst.\n- `data/comfyui_*.json` — experimentelle ComfyUI Workflow-Artefakte, bewusst nicht committed.\n\n## Remaining Work\n- Runner für I2V Clip Jobs implementieren:\n - CLI wahrscheinlich `scripts/run_i2v_clip_jobs.py`.\n - Job Package laden.\n - Prüfen, ob Keyframes vorhanden sind.\n - Nur `ready`/vorhandene Keyframes ausführen.\n - Keyframe zu ComfyUI uploaden.\n - Workflow pro Seed submitten.\n - Queue/History pollen.\n - MP4 herunterladen.\n - Output speichern unter z.B. `data/generated_clips//scene_01/seed_101.mp4`.\n - `run_report.json` mit Erfolg/Fehler/Prompt-ID/Output-Dateien schreiben.\n - Tests mit Fake ComfyUI Transport bauen.\n- Keyframes für Pilot generieren/ablegen:\n - `scene_01.png`\n - `scene_02.png`\n- Danach echte Wan2.2-I2V Varianten erzeugen:\n - 3 Seeds pro Szene: 101, 202, 303.\n- Clip Review/Selection-Schicht bauen:\n - Review JSON\n - ggf. Telegram-/lokales Review Package integrieren, aber bestehende untracked Dateien vorher prüfen.\n- Finalen Hybrid Renderer erweitern:\n - Mechanism Overlays\n - Preis-/Risiko-/Marge-/Standtage-/Liquidität-Animationen\n - Captions und Kinetic Text deterministisch.\n- Speicherbereinigung optional tatsächlich ausführen/validieren, falls User weiter knapp an Speicher ist.\n- Falls produktive ComfyUI-Ausführung kommt: sicherstellen, dass ComfyUI Server läuft und native Wan2.2 Modelle unter erwarteten Pfaden vorhanden sind.\n\n## Critical Context\n- Frühere 109 Messages wurden entfernt und konnten nicht zusammengefasst werden; die aktuelle Zusammenfassung basiert auf den neueren Turns und behaupteten Projektzuständen.\n- Lokaler GitHub Token: Datei `~/.hermes/secrets/github_autoshortsbot_token`, Wert `[REDACTED]`; nie ausgeben.\n- Commit-Historie relevant:\n - `b3ef793 feat: add wan i2v prompt planning`\n - `b57cee8 feat: add i2v clip job packaging`\n - `a2311f6 feat: add keyframe request packages`\n- Branch `feat/initial-growth-engine` wurde nach `a2311f6` erfolgreich zu `Gamexgit/AutoShortsBot` gepusht.\n- Letzter Teststand: `71 passed in 3.51s`.\n- Aktueller Pilot-Slug:\n - `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`\n- Aktuelle Seeds:\n - `101`, `202`, `303`\n- Aktuelle I2V Szenen:\n - `scene_01`\n - `scene_02`\n- Erwartete Output-Struktur:\n - `data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4`\n - analog für weitere Seeds/Szenen.\n- Keyframe Prompt Beispiel Szene 1:\n - `Photorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.`\n- Native Wan2.2 Modellstack:\n - Diffusion: `wan2.2_ti2v_5B_fp16.safetensors`\n - Text Encoder: `umt5_xxl_fp8_e4m3fn_scaled.safetensors`\n - VAE: `wan2.2_vae.safetensors`\n- Native Wan2.2 Produktionspreset:\n - Frames: `61`\n - FPS: `12`\n - Steps: `16`\n - CFG: `5.0`\n - Sampler: `uni_pc`\n - Scheduler: `simple`\n - Dauer: ca. `5.08s`\n - Delivery-Ziel: `24 fps`\n- Wichtige Negativregeln für AI-Video:\n - no text\n - no logo\n - no watermark\n - no readable license plate\n - no fake UI\n - avoid flicker/deformation.\n- FAL/ChatGPT-Bildgenerierung war lokal nicht verfügbar wegen `FAL_KEY fehlt`; falls wieder versucht, Secret nicht ausgeben.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---\n\nWeitergebaut, Sir.\n\n## Neu: I2V Clip Job Runner\n\nIch habe jetzt die Ausführungsschicht ergänzt:\n\n```text\nI2V Clip Job Package\n→ prüft Keyframes\n→ baut native Wan2.2 Workflow pro Job\n→ lädt Keyframe hoch\n→ startet ComfyUI Job\n→ lädt MP4 herunter\n→ schreibt Run Report\n```\n\nDamit ist die Pipeline technisch bereit, sobald die Keyframes vorhanden sind.\n\n---\n\n## Neu implementiert\n\n### 1. Job Runner\n\n```text\nautoshorts/i2v_job_runner.py\n```\n\nKann jetzt:\n\n- Jobs aus dem Package ausführen\n- fehlende Keyframes sauber überspringen\n- vorhandene Outputs überspringen\n- Dry-Run machen\n- ComfyUI-Client aufrufen\n- Erfolg/Fehler/Skip strukturiert reporten\n\n---\n\n### 2. CLI\n\n```text\nscripts/run_i2v_clip_jobs.py\n```\n\nBeispiel Dry-Run:\n\n```bash\npython scripts/run_i2v_clip_jobs.py --dry-run\n```\n\nEchte Ausführung später:\n\n```bash\npython scripts/run_i2v_clip_jobs.py \\\n --base-url http://100.101.173.25:8188 \\\n --limit 1\n```\n\nOder alle Jobs:\n\n```bash\npython scripts/run_i2v_clip_jobs.py \\\n --base-url http://100.101.173.25:8188\n```\n\n---\n\n### 3. Run Report Artefakt\n\nErzeugt:\n\n```text\ndata/i2v_run_reports/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nAktueller Dry-Run Status:\n\n```json\n{\n \"total\": 6,\n \"succeeded\": 0,\n \"skipped\": 6,\n \"failed\": 0\n}\n```\n\nGrund:\n\n```text\nmissing_keyframe\n```\n\nDas ist korrekt. Es fehlen noch:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\n---\n\n## Tests\n\nNeu:\n\n```text\ntests/test_i2v_job_runner.py\n```\n\nFull suite:\n\n```text\n74 passed in 3.39s\n```\n\n---\n\n## Commit + Push\n\nNeuer Commit:\n\n```text\nb64da4d feat: add i2v clip job runner\n```\n\nPush erfolgreich:\n\n```text\nremote_hash_matches: True\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote:\n\n```text\nGamexgit/AutoShortsBot\n```\n\n---\n\n## Was jetzt konkret fehlt\n\nWir haben jetzt die komplette technische Strecke bis zur Clip-Erzeugung:\n\n```text\nKeyframe Requests\n→ I2V Clip Jobs\n→ I2V Runner\n→ ComfyUI Wan2.2\n→ MP4 Outputs\n```\n\nAber: Ohne Keyframes läuft der Runner bewusst nicht los. Das ist Absicht. Kein blindes KI-Gewürfel mehr — sehr unmodern, aber nützlich.\n\n---\n\n# Nächster Schritt\n\nJetzt brauchen wir die zwei Keyframes aus ChatGPT Pro/Image Generator:\n\n## Zielordner\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/\n```\n\n## Datei 1\n\n```text\nscene_01.png\n```\n\nPrompt:\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.\n```\n\n## Datei 2\n\n```text\nscene_02.png\n```\n\nGleicher Grundlook, aber idealerweise minimal anders:\n\n```text\nsame red used compact car in the same bright European inspection garage, front three-quarter view, slightly closer composition, clean empty license plate area, subtle sense that the visible surface hides mechanical/cost layers, no text, no logos, no watermark.\n```\n\nSobald diese beiden Bilder da sind, kann ich den Runner starten und er erzeugt:\n\n```text\nscene_01 seed_101/202/303\nscene_02 seed_101/202/303\n```\n\nAlso sechs echte Wan2.2-I2V-Clipvarianten.\n\nKurz: **Maschine ist verdrahtet. Jetzt brauchen wir das Benzin: zwei saubere Keyframes.**" }, { "role": "user", "content": "[M G] Ok, erstelle diese keyframes und mach weiter." }, { "role": "assistant", "content": "Erledigt, Sir.\n\n## 1. Keyframes erstellt\n\nDie direkte Image-Generation hier ist weiterhin nicht verfügbar:\n\n```text\nFAL_KEY fehlt\n```\n\nAlso habe ich pragmatisch gearbeitet: fotorealistische Auto-Quellen genommen, auf 9:16 gebracht, Logos/Texte/Kennzeichen so gut wie möglich neutralisiert und als Keyframes abgelegt.\n\nZielpfade:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\nKeyframes:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n\nEhrliche Bewertung:\n\n```text\nGut genug für Pipeline-Test.\nNicht gut genug als finaler Produktionsstandard.\n```\n\nProblem: Man sieht noch leicht, dass Logos/Badges neutralisiert wurden. Für echte Shorts sollten wir später saubere ChatGPT-Pro-Keyframes erzeugen, nicht reparierte Quellbilder. Aber für den nächsten Pipeline-Schritt war es absolut brauchbar.\n\n---\n\n## 2. Wan2.2 I2V Runner ausgeführt\n\nIch habe alle sechs Seed-Jobs erzeugt:\n\n```text\nscene_01 seed_101\nscene_01 seed_202\nscene_01 seed_303\nscene_02 seed_101\nscene_02 seed_202\nscene_02 seed_303\n```\n\nTechnische Werte aller Clips:\n\n```text\n416x736\n61 frames\n12 fps\n5.083333 seconds\n```\n\nAlso jetzt das neue, glattere Testprofil statt 10 fps.\n\n---\n\n## 3. Outputs\n\nAlle sechs Clips liegen hier:\n\n```text\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_202.mp4\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_303.mp4\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02/seed_101.mp4\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02/seed_202.mp4\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02/seed_303.mp4\n```\n\nBeste erste Kandidaten nach Sichtprüfung:\n\n```text\nscene_01/seed_101.mp4\nscene_01/seed_202.mp4\nscene_02/seed_303.mp4\n```\n\nZwei Beispielclips:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02/seed_303.mp4\n\n---\n\n## 4. Clip Review erstellt\n\nReview-Artefakt:\n\n```text\ndata/clip_reviews/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nKurzbewertung:\n\n```text\nTechnisch erfolgreich.\nFür Pipeline-Validation brauchbar.\nFür finale Content-Qualität noch nicht genug.\n```\n\nHauptprobleme:\n\n- Keyframes sind reparierte Quellbilder, nicht sauber generierte Inspection-Bay-Keyframes\n- Logo-/Badge-Neutralisierung bleibt als runde Patches sichtbar\n- Setting ist eher Street/Garage-adjacent als echte helle Prüfgarage\n- Clips sind 12 fps und müssen vor finalem Export auf 24 fps interpoliert/gerendert werden\n\n---\n\n## 5. Tests\n\nFull suite:\n\n```text\n74 passed in 3.80s\n```\n\nEin Test musste angepasst werden, weil jetzt echte Keyframes existieren. Vorher erwartete der Test `needs_keyframe`; mit echten Dateien ist der korrekte Status natürlich `ready`. Charmant, wenn Tests von Realität überrascht werden.\n\n---\n\n## 6. Commit + Push\n\nCommit:\n\n```text\na39e267 chore: record pilot i2v clip run\n```\n\nPush erfolgreich:\n\n```text\nremote_hash_matches: True\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\n---\n\n## 7. Nicht committed / bewusst lokal\n\nDie MP4s und Keyframes sind offenbar durch Gitignore nicht im Commit gelandet, was gut ist:\n\n```text\ngenerated media gehört nicht blind ins Repo\n```\n\nCommitted wurden stattdessen:\n\n- Run Report\n- Clip Review\n- Test-Fix\n\nUntracked bleiben weiterhin die bekannten Telegram-/ComfyUI-Testartefakte.\n\n---\n\n# Nächster sinnvoller Schritt\n\nJetzt können wir den **rough pilot assembler** bauen:\n\n```text\nbest clip scene_01\n+ best clip scene_02\n+ mechanism animation placeholder\n+ kinetic captions\n+ simple 24fps export\n```\n\nAlso:\n\n```text\nautoshorts/pilot_assembler.py\nscripts/assemble_pilot_preview.py\n```\n\nZieloutput:\n\n```text\ndata/pilot_previews/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4\n```\n\nDas wäre dann erstmals kein einzelner Clip mehr, sondern ein zusammengesetzter Rohschnitt für den Pilot.\n\nKurz: **Wir haben jetzt echte Wan2.2-Clip-Bausteine. Nächster Schritt ist Assembly + Captions + 24fps Preview.**" }, { "role": "user", "content": "[M G] Wieso kannst du keine bilder generieren? Geht das nicht über codex? Sollen wir https://build.nvidia.com/qwen/qwen-image. Über nvidia API verwenden (key ist hinterlegt)." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_u3qH02dH5Lcs3QDPPjkSWhgi", "call_id": "call_u3qH02dH5Lcs3QDPPjkSWhgi", "response_item_id": "fc_0b0c09cc5be22160016a121908e6548191ae18b2cdafbd1443", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"hermes-agent\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering configuration options**\n\nI need to figure out the user's inquiry about configuring tools, possibly involving Hermes or image generation with the NVIDIA API. If I configure the Hermes Agent, that might be useful. I'll also consider comfyUI and qwen. It’s important to inspect the environment carefully, especially regarding any sensitive keys. The user also mentioned testing the NVIDIA API key, so I should proceed carefully with that. I'll need to make sure I have the necessary skills and tools ready.", "reasoning_content": "**Considering configuration options**\n\nI need to figure out the user's inquiry about configuring tools, possibly involving Hermes or image generation with the NVIDIA API. If I configure the Hermes Agent, that might be useful. I'll also consider comfyUI and qwen. It’s important to inspect the environment carefully, especially regarding any sensitive keys. The user also mentioned testing the NVIDIA API key, so I should proceed carefully with that. I'll need to make sure I have the necessary skills and tools ready.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEhkITZgO9ecErZVUzuYAyW1pID4ynCCOgFZKLifJwSGH8LfgihfM8TKt81ELroy6ReaIHI2vYc_z3y6HPAWXf_tN-ct_0w16a4H6fSH5V88F1GqkuKRER7UW7ZRfGk2ZSfEeDAG3Zsb6_JubkvFU2-EwUzh4T6No4m0bOf7OX_CsaQEapb_U98gdhzeKk9RX1plZRtBKahIXmbSGAhdJjU9ehB4jl-mCEWT3ezKye_OxZqPas-QQzzd0_rjyRfRUoKrloI7V0MYp0FNkQjhu2EqO-BN9j5EA_qjLKZ5yVmGua5fJLsElWtucBRYj47CF170Mjy7DJMn1j6vnbd6ftMm_Vg1Ze7E9DLKJjdfcmrjIvF4414swFaC5VaJ5TkO-Srpv547janNuydorQiFU0qzbthlOZKpffWI1_8U9fBi1FNFrt3yzdaBhVwBYfZ19t-ejFklb8Qkt4-Kkfa2u9LZdk84PEky0JLreHgEEHLjpQNksO2KzkCri59ZT-EWmXURyS0R2ofbz29plpY23bSNosxtBCCkJ4msQSYLZRatzPsOSviYGZnPD4KqkshF0tvD4A-jhrIMYYOdoiKPbkaHeQJj274YST6ftZYxhwzkPKxhBoz2yX1zalxtuUJVN2mZL7KzoroH63ZttxjvC3aRvzWXMKe-G0kYVjcB1ugak_tecuKyQaxnJhUlEe_2RTkIB0gi0wGD23xyNh2JnV7YNPwa5vAuMYftOXfhNp6vKZwSJY5tFXM00vU7jn4Azr5KQcyyN5u6VKqDJZn8c3XO_DI2e9ywEq29Ezy-CXKusfgHvRLuvH_bOcvb-4Yr2Uq2HHRgUhLCz8Q42Ci6UT6bKwxx9R0nepx2l5woveLjEa4ulCYzRS4KnKHaDxSEyKmKpFz4Naks8bvsk092aHkVtDk7l4mad2ISAKzBhMMrT2Rm_xj788zoPtDO1mrPWqTb49w_fmEnoGHrgLJuH4_DfwtvuLQQOHwHu5eIQJCnVFmVyCyKZ844VFlebjbMgjKa-m1FQn2X_JCOMznFX3aBk6p0zDm1fVYCCp2cCNjP8L7evVaLVYcX5l_JpuzKr_vA2mDH-UX_jBh5XWG350A0YTe_NydY0IWot_hQkh5B3jYrBvGubYbedOPO43L_3ccVFBQzdpfq24wSJQpUtfUcfpcSxLAe7ssg2tD3yjWbkbeI1nQvh0jzTgm04KjHl13yas83XJhOCnGsbo-SvhJIlW-larb4pZWKp5jQ7k4VccDlZNP72JcDs36R6IBQXHHwO4URy_u8vOJSx3IEs4M2k4HWZEbj3zXSTBXpFzaeWDw31qa86KoFzRK1puImvHrwbiqRJHsubZmTtFnzFWaDgowpjVsBpE5QCNqYwT2VvIcxuRdqQNSlpaWODWu5QaZf_UvxPNmYPGzhlk34deWF1CJIYZejskaFrOxIK8c2-1NPr-5SQ3RW4yuEStl2lcMS-jgsnz7LPtscE0Lc8pe1h-t7llmct4wLBfB8UyYe6-TA9u5e8mrlr2Zkzvjwa24gkcXzeF8_Rgvt1UsZdyCguDj4z-_BshiQ9M2dfKrQcABQ=", "id": "rs_0b0c09cc5be22160016a12190535b481919e463c693cb5e9fb", "summary": [ { "type": "summary_text", "text": "**Considering configuration options**\n\nI need to figure out the user's inquiry about configuring tools, possibly involving Hermes or image generation with the NVIDIA API. If I configure the Hermes Agent, that might be useful. I'll also consider comfyUI and qwen. It’s important to inspect the environment carefully, especially regarding any sensitive keys. The user also mentioned testing the NVIDIA API key, so I should proceed carefully with that. I'll need to make sure I have the necessary skills and tools ready." } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"hermes-agent\", \"description\": \"Configure, extend, or contribute to Hermes Agent.\", \"tags\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"], \"content\": \"---\\nname: hermes-agent\\ndescription: \\\"Configure, extend, or contribute to Hermes Agent.\\\"\\nversion: 2.1.0\\nauthor: Hermes Agent + Teknium\\nlicense: MIT\\nmetadata:\\n hermes:\\n tags: [hermes, setup, configuration, multi-agent, spawning, cli, gateway, development]\\n homepage: https://github.com/NousResearch/hermes-agent\\n related_skills: [claude-code, codex, opencode]\\n---\\n\\n# Hermes Agent\\n\\nHermes Agent is an open-source AI agent framework by Nous Research that runs in your terminal, messaging platforms, and IDEs. It belongs to the same category as Claude Code (Anthropic), Codex (OpenAI), and OpenClaw — autonomous coding and task-execution agents that use tool calling to interact with your system. Hermes works with any LLM provider (OpenRouter, Anthropic, OpenAI, DeepSeek, local models, and 15+ others) and runs on Linux, macOS, and WSL.\\n\\nWhat makes Hermes different:\\n\\n- **Self-improving through skills** — Hermes learns from experience by saving reusable procedures as skills. When it solves a complex problem, discovers a workflow, or gets corrected, it can persist that knowledge as a skill document that loads into future sessions. Skills accumulate over time, making the agent better at your specific tasks and environment.\\n- **Persistent memory across sessions** — remembers who you are, your preferences, environment details, and lessons learned. Pluggable memory backends (built-in, Honcho, Mem0, and more) let you choose how memory works.\\n- **Multi-platform gateway** — the same agent runs on Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Email, and 10+ other platforms with full tool access, not just chat.\\n- **Provider-agnostic** — swap models and providers mid-workflow without changing anything else. Credential pools rotate across multiple API keys automatically.\\n- **Profiles** — run multiple independent Hermes instances with isolated configs, sessions, skills, and memory.\\n- **Extensible** — plugins, MCP servers, custom tools, webhook triggers, cron scheduling, and the full Python ecosystem.\\n\\nPeople use Hermes for software development, research, system administration, data analysis, content creation, home automation, and anything else that benefits from an AI agent with persistent context and full system access.\\n\\n**This skill helps you work with Hermes Agent effectively** — setting it up, configuring features, spawning additional agent instances, troubleshooting issues, finding the right commands and settings, and understanding how the system works when you need to extend or contribute to it.\\n\\n**Docs:** https://hermes-agent.nousresearch.com/docs/\\n\\n## Quick Start\\n\\n```bash\\n# Install\\ncurl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash\\n\\n# Interactive chat (default)\\nhermes\\n\\n# Single query\\nhermes chat -q \\\"What is the capital of France?\\\"\\n\\n# Setup wizard\\nhermes setup\\n\\n# Change model/provider\\nhermes model\\n\\n# Check health\\nhermes doctor\\n```\\n\\n---\\n\\n## CLI Reference\\n\\n### Global Flags\\n\\n```\\nhermes [flags] [command]\\n\\n --version, -V Show version\\n --resume, -r SESSION Resume session by ID or title\\n --continue, -c [NAME] Resume by name, or most recent session\\n --worktree, -w Isolated git worktree mode (parallel agents)\\n --skills, -s SKILL Preload skills (comma-separate or repeat)\\n --profile, -p NAME Use a named profile\\n --yolo Skip dangerous command approval\\n --pass-session-id Include session ID in system prompt\\n```\\n\\nNo subcommand defaults to `chat`.\\n\\n### Chat\\n\\n```\\nhermes chat [flags]\\n -q, --query TEXT Single query, non-interactive\\n -m, --model MODEL Model (e.g. anthropic/claude-sonnet-4)\\n -t, --toolsets LIST Comma-separated toolsets\\n --provider PROVIDER Force provider (openrouter, anthropic, nous, etc.)\\n -v, --verbose Verbose output\\n -Q, --quiet Suppress banner, spinner, tool previews\\n --checkpoints Enable filesystem checkpoints (/rollback)\\n --source TAG Session source tag (default: cli)\\n```\\n\\n### Configuration\\n\\n```\\nhermes setup [section] Interactive wizard (model|terminal|gateway|tools|agent)\\nhermes model Interactive model/provider picker\\nhermes config View current config\\nhermes config edit Open config.yaml in $EDITOR\\nhermes config set KEY VAL Set a config value\\nhermes config path Print config.yaml path\\nhermes config env-path Print .env path\\nhermes config check Check for missing/outdated config\\nhermes config migrate Update config with new options\\nhermes login [--provider P] OAuth login (nous, openai-codex)\\nhermes logout Clear stored auth\\nhermes doctor [--fix] Check dependencies and config\\nhermes status [--all] Show component status\\n```\\n\\n### Tools & Skills\\n\\n```\\nhermes tools Interactive tool enable/disable (curses UI)\\nhermes tools list Show all tools and status\\nhermes tools enable NAME Enable a toolset\\nhermes tools disable NAME Disable a toolset\\n\\nhermes skills list List installed skills\\nhermes skills search QUERY Search the skills hub\\nhermes skills install ID Install a skill (ID can be a hub identifier OR a direct https://…/SKILL.md URL; pass --name to override when frontmatter has no name)\\nhermes skills inspect ID Preview without installing\\nhermes skills config Enable/disable skills per platform\\nhermes skills check Check for updates\\nhermes skills update Update outdated skills\\nhermes skills uninstall N Remove a hub skill\\nhermes skills publish PATH Publish to registry\\nhermes skills browse Browse all available skills\\nhermes skills tap add REPO Add a GitHub repo as skill source\\n```\\n\\n### MCP Servers\\n\\n```\\nhermes mcp serve Run Hermes as an MCP server\\nhermes mcp add NAME Add an MCP server (--url or --command)\\nhermes mcp remove NAME Remove an MCP server\\nhermes mcp list List configured servers\\nhermes mcp test NAME Test connection\\nhermes mcp configure NAME Toggle tool selection\\n```\\n\\n### Gateway (Messaging Platforms)\\n\\n```\\nhermes gateway run Start gateway foreground\\nhermes gateway install Install as background service\\nhermes gateway start/stop Control the service\\nhermes gateway restart Restart the service\\nhermes gateway status Check status\\nhermes gateway setup Configure platforms\\n```\\n\\nSupported platforms: Telegram, Discord, Slack, WhatsApp, Signal, Email, SMS, Matrix, Mattermost, Home Assistant, DingTalk, Feishu, WeCom, BlueBubbles (iMessage), Weixin (WeChat), API Server, Webhooks. Open WebUI connects via the API Server adapter.\\n\\nPlatform docs: https://hermes-agent.nousresearch.com/docs/user-guide/messaging/\\n\\n**Telegram setup:** See `references/telegram-gateway-setup.md` for the complete step-by-step configuration workflow.\\n\\n### Sessions\\n\\n```\\nhermes sessions list List recent sessions\\nhermes sessions browse Interactive picker\\nhermes sessions export OUT Export to JSONL\\nhermes sessions rename ID T Rename a session\\nhermes sessions delete ID Delete a session\\nhermes sessions prune Clean up old sessions (--older-than N days)\\nhermes sessions stats Session store statistics\\n```\\n\\n### Cron Jobs\\n\\n```\\nhermes cron list List jobs (--all for disabled)\\nhermes cron create SCHED Create: '30m', 'every 2h', '0 9 * * *'\\nhermes cron edit ID Edit schedule, prompt, delivery\\nhermes cron pause/resume ID Control job state\\nhermes cron run ID Trigger on next tick\\nhermes cron remove ID Delete a job\\nhermes cron status Scheduler status\\n```\\n\\n### Webhooks\\n\\n```\\nhermes webhook subscribe N Create route at /webhooks/\\nhermes webhook list List subscriptions\\nhermes webhook remove NAME Remove a subscription\\nhermes webhook test NAME Send a test POST\\n```\\n\\n### Profiles\\n\\n```\\nhermes profile list List all profiles\\nhermes profile create NAME Create (--clone, --clone-all, --clone-from)\\nhermes profile use NAME Set sticky default\\nhermes profile delete NAME Delete a profile\\nhermes profile show NAME Show details\\nhermes profile alias NAME Manage wrapper scripts\\nhermes profile rename A B Rename a profile\\nhermes profile export NAME Export to tar.gz\\nhermes profile import FILE Import from archive\\n```\\n\\n### Credential Pools\\n\\n```\\nhermes auth add Interactive credential wizard\\nhermes auth list [PROVIDER] List pooled credentials\\nhermes auth remove P INDEX Remove by provider + index\\nhermes auth reset PROVIDER Clear exhaustion status\\n```\\n\\n### Other\\n\\n```\\nhermes insights [--days N] Usage analytics\\nhermes update Update to latest version\\nhermes pairing list/approve/revoke DM authorization\\nhermes plugins list/install/remove Plugin management\\nhermes honcho setup/status Honcho memory integration (requires honcho plugin)\\nhermes memory setup/status/off Memory provider config\\nhermes completion bash|zsh Shell completions\\nhermes acp ACP server (IDE integration)\\nhermes claw migrate Migrate from OpenClaw\\nhermes uninstall Uninstall Hermes\\n```\\n\\n---\\n\\n## Slash Commands (In-Session)\\n\\nType these during an interactive chat session. New commands land fairly\\noften; if something below looks stale, run `/help` in-session for the\\nauthoritative list or see the [live slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands).\\nThe registry of record is `hermes_cli/commands.py` — every consumer\\n(autocomplete, Telegram menu, Slack mapping, `/help`) derives from it.\\n\\n### Session Control\\n```\\n/new (/reset) Fresh session\\n/clear Clear screen + new session (CLI)\\n/retry Resend last message\\n/undo Remove last exchange\\n/title [name] Name the session\\n/compress Manually compress context\\n/stop Kill background processes\\n/rollback [N] Restore filesystem checkpoint\\n/snapshot [sub] Create or restore state snapshots of Hermes config/state (CLI)\\n/background Run prompt in background\\n/queue Queue for next turn\\n/steer Inject a message after the next tool call without interrupting\\n/agents (/tasks) Show active agents and running tasks\\n/resume [name] Resume a named session\\n/goal [text|sub] Set a standing goal Hermes works on across turns until achieved\\n (subcommands: status, pause, resume, clear)\\n/redraw Force a full UI repaint (CLI)\\n```\\n\\n### Configuration\\n```\\n/config Show config (CLI)\\n/model [name] Show or change model\\n/personality [name] Set personality\\n/reasoning [level] Set reasoning (none|minimal|low|medium|high|xhigh|show|hide)\\n/verbose Cycle: off → new → all → verbose\\n/voice [on|off|tts] Voice mode\\n/yolo Toggle approval bypass\\n/busy [sub] Control what Enter does while Hermes is working (CLI)\\n (subcommands: queue, steer, interrupt, status)\\n/indicator [style] Pick the TUI busy-indicator style (CLI)\\n (styles: kaomoji, emoji, unicode, ascii)\\n/footer [on|off] Toggle gateway runtime-metadata footer on final replies\\n/skin [name] Change theme (CLI)\\n/statusbar Toggle status bar (CLI)\\n```\\n\\n### Tools & Skills\\n```\\n/tools Manage tools (CLI)\\n/toolsets List toolsets (CLI)\\n/skills Search/install skills (CLI)\\n/skill Load a skill into session\\n/reload-skills Re-scan ~/.hermes/skills/ for added/removed skills\\n/reload Reload .env variables into the running session (CLI)\\n/reload-mcp Reload MCP servers\\n/cron Manage cron jobs (CLI)\\n/curator [sub] Background skill maintenance (status, run, pin, archive, …)\\n/kanban [sub] Multi-profile collaboration board (tasks, links, comments)\\n/plugins List plugins (CLI)\\n```\\n\\n### Gateway\\n```\\n/approve Approve a pending command (gateway)\\n/deny Deny a pending command (gateway)\\n/restart Restart gateway (gateway)\\n/sethome Set current chat as home channel (gateway)\\n/update Update Hermes to latest (gateway)\\n/topic [sub] Enable or inspect Telegram DM topic sessions (gateway)\\n/platforms (/gateway) Show platform connection status (gateway)\\n```\\n\\n### Utility\\n```\\n/branch (/fork) Branch the current session\\n/fast Toggle priority/fast processing\\n/browser Open CDP browser connection\\n/history Show conversation history (CLI)\\n/save Save conversation to file (CLI)\\n/copy [N] Copy the last assistant response to clipboard (CLI)\\n/paste Attach clipboard image (CLI)\\n/image Attach local image file (CLI)\\n```\\n\\n### Info\\n```\\n/help Show commands\\n/commands [page] Browse all commands (gateway)\\n/usage Token usage\\n/insights [days] Usage analytics\\n/gquota Show Google Gemini Code Assist quota usage (CLI)\\n/status Session info (gateway)\\n/profile Active profile info\\n/debug Upload debug report (system info + logs) and get shareable links\\n```\\n\\n### Exit\\n```\\n/quit (/exit, /q) Exit CLI\\n```\\n\\n---\\n\\n## Key Paths & Config\\n\\n```\\n~/.hermes/config.yaml Main configuration\\n~/.hermes/.env API keys and secrets\\n$HERMES_HOME/skills/ Installed skills\\n~/.hermes/sessions/ Session transcripts\\n~/.hermes/logs/ Gateway and error logs\\n~/.hermes/auth.json OAuth tokens and credential pools\\n~/.hermes/hermes-agent/ Source code (if git-installed)\\n```\\n\\nProfiles use `~/.hermes/profiles//` with the same layout.\\n\\n### Config Sections\\n\\nEdit with `hermes config edit` or `hermes config set section.key value`.\\n\\n| Section | Key options |\\n|---------|-------------|\\n| `model` | `default`, `provider`, `base_url`, `api_key`, `context_length` |\\n| `agent` | `max_turns` (90), `tool_use_enforcement` |\\n| `terminal` | `backend` (local/docker/ssh/modal), `cwd`, `timeout` (180) |\\n| `compression` | `enabled`, `threshold` (0.50), `target_ratio` (0.20) |\\n| `display` | `skin`, `tool_progress`, `show_reasoning`, `show_cost` |\\n| `stt` | `enabled`, `provider` (local/groq/openai/mistral) |\\n| `tts` | `provider` (edge/elevenlabs/openai/minimax/mistral/neutts) |\\n| `memory` | `memory_enabled`, `user_profile_enabled`, `provider` |\\n| `security` | `tirith_enabled`, `website_blocklist` |\\n| `delegation` | `model`, `provider`, `base_url`, `api_key`, `max_iterations` (50), `reasoning_effort` |\\n| `checkpoints` | `enabled`, `max_snapshots` (50) |\\n\\nFull config reference: https://hermes-agent.nousresearch.com/docs/user-guide/configuration\\n\\n### Providers\\n\\n20+ providers supported. Set via `hermes model` or `hermes setup`.\\n\\n| Provider | Auth | Key env var |\\n|----------|------|-------------|\\n| OpenRouter | API key | `OPENROUTER_API_KEY` |\\n| Anthropic | API key | `ANTHROPIC_API_KEY` |\\n| Nous Portal | OAuth | `hermes auth` |\\n| OpenAI Codex | OAuth | `hermes auth` |\\n| GitHub Copilot | Token | `COPILOT_GITHUB_TOKEN` |\\n| Google Gemini | API key | `GOOGLE_API_KEY` or `GEMINI_API_KEY` |\\n| DeepSeek | API key | `DEEPSEEK_API_KEY` |\\n| xAI / Grok | API key | `XAI_API_KEY` |\\n| Hugging Face | Token | `HF_TOKEN` |\\n| Z.AI / GLM | API key | `GLM_API_KEY` |\\n| MiniMax | API key | `MINIMAX_API_KEY` |\\n| MiniMax CN | API key | `MINIMAX_CN_API_KEY` |\\n| Kimi / Moonshot | API key | `KIMI_API_KEY` |\\n| Alibaba / DashScope | API key | `DASHSCOPE_API_KEY` |\\n| Xiaomi MiMo | API key | `XIAOMI_API_KEY` |\\n| Kilo Code | API key | `KILOCODE_API_KEY` |\\n| AI Gateway (Vercel) | API key | `AI_GATEWAY_API_KEY` |\\n| OpenCode Zen | API key | `OPENCODE_ZEN_API_KEY` |\\n| OpenCode Go | API key | `OPENCODE_GO_API_KEY` |\\n| Qwen OAuth | OAuth | `hermes login --provider qwen-oauth` |\\n| Custom endpoint | Config | `model.base_url` + `model.api_key` in config.yaml |\\n| GitHub Copilot ACP | External | `COPILOT_CLI_PATH` or Copilot CLI |\\n\\nFull provider docs: https://hermes-agent.nousresearch.com/docs/integrations/providers\\n\\n### Toolsets\\n\\nEnable/disable via `hermes tools` (interactive) or `hermes tools enable/disable NAME`.\\n\\n| Toolset | What it provides |\\n|---------|-----------------|\\n| `web` | Web search and content extraction |\\n| `search` | Web search only (subset of `web`) |\\n| `browser` | Browser automation (Browserbase, Camofox, or local Chromium) |\\n| `terminal` | Shell commands and process management |\\n| `file` | File read/write/search/patch |\\n| `code_execution` | Sandboxed Python execution |\\n| `vision` | Image analysis |\\n| `image_gen` | AI image generation |\\n| `video` | Video analysis and generation |\\n| `tts` | Text-to-speech |\\n| `skills` | Skill browsing and management |\\n| `memory` | Persistent cross-session memory |\\n| `session_search` | Search past conversations |\\n| `delegation` | Subagent task delegation |\\n| `cronjob` | Scheduled task management |\\n| `clarify` | Ask user clarifying questions |\\n| `messaging` | Cross-platform message sending |\\n| `todo` | In-session task planning and tracking |\\n| `kanban` | Multi-agent work-queue tools (gated to workers) |\\n| `debugging` | Extra introspection/debug tools (off by default) |\\n| `safe` | Minimal, low-risk toolset for locked-down sessions |\\n| `spotify` | Spotify playback and playlist control |\\n| `homeassistant` | Smart home control (off by default) |\\n| `discord` | Discord integration tools |\\n| `discord_admin` | Discord admin/moderation tools |\\n| `feishu_doc` | Feishu (Lark) document tools |\\n| `feishu_drive` | Feishu (Lark) drive tools |\\n| `yuanbao` | Yuanbao integration tools |\\n| `rl` | Reinforcement learning tools (off by default) |\\n| `moa` | Mixture of Agents (off by default) |\\n\\nFull enumeration lives in `toolsets.py` as the `TOOLSETS` dict; `_HERMES_CORE_TOOLS` is the default bundle most platforms inherit from.\\n\\nTool changes take effect on `/reset` (new session). They do NOT apply mid-conversation to preserve prompt caching.\\n\\n---\\n\\n## Security & Privacy Toggles\\n\\nCommon \\\"why is Hermes doing X to my output / tool calls / commands?\\\" toggles — and the exact commands to change them. Most of these need a fresh session (`/reset` in chat, or start a new `hermes` invocation) because they're read once at startup.\\n\\n### Secret redaction in tool output\\n\\nSecret redaction is **off by default** — tool output (terminal stdout, `read_file`, web content, subagent summaries, etc.) passes through unmodified. If the user wants Hermes to auto-mask strings that look like API keys, tokens, and secrets before they enter the conversation context and logs:\\n\\n```bash\\nhermes config set security.redact_secrets true # enable globally\\n```\\n\\n**Restart required.** `security.redact_secrets` is snapshotted at import time — toggling it mid-session (e.g. via `export HERMES_REDACT_SECRETS=true` from a tool call) will NOT take effect for the running process. Tell the user to run `hermes config set security.redact_secrets true` in a terminal, then start a new session. This is deliberate — it prevents an LLM from flipping the toggle on itself mid-task.\\n\\nDisable again with:\\n```bash\\nhermes config set security.redact_secrets false\\n```\\n\\n### PII redaction in gateway messages\\n\\nSeparate from secret redaction. When enabled, the gateway hashes user IDs and strips phone numbers from the session context before it reaches the model:\\n\\n```bash\\nhermes config set privacy.redact_pii true # enable\\nhermes config set privacy.redact_pii false # disable (default)\\n```\\n\\n### Command approval prompts\\n\\nBy default (`approvals.mode: manual`), Hermes prompts the user before running shell commands flagged as destructive (`rm -rf`, `git reset --hard`, etc.). The modes are:\\n\\n- `manual` — always prompt (default)\\n- `smart` — use an auxiliary LLM to auto-approve low-risk commands, prompt on high-risk\\n- `off` — skip all approval prompts (equivalent to `--yolo`)\\n\\n```bash\\nhermes config set approvals.mode smart # recommended middle ground\\nhermes config set approvals.mode off # bypass everything (not recommended)\\n```\\n\\nPer-invocation bypass without changing config:\\n- `hermes --yolo …`\\n- `export HERMES_YOLO_MODE=1`\\n\\nNote: YOLO / `approvals.mode: off` does NOT turn off secret redaction. They are independent.\\n\\n### Shell hooks allowlist\\n\\nSome shell-hook integrations require explicit allowlisting before they fire. Managed via `~/.hermes/shell-hooks-allowlist.json` — prompted interactively the first time a hook wants to run.\\n\\n### Disabling the web/browser/image-gen tools\\n\\nTo keep the model away from network or media tools entirely, open `hermes tools` and toggle per-platform. Takes effect on next session (`/reset`). See the Tools & Skills section above.\\n\\n---\\n\\n### Voice & Transcription\\n\\n### STT (Voice → Text)\\n\\nVoice messages from messaging platforms are auto-transcribed.\\n\\n**⚠️ Pitfall: faster-whisper must be installed for local STT to work.** The config may show `provider: local` but if `faster-whisper` is not installed in the venv, STT silently fails. Always verify with `pip show faster-whisper` after configuring local STT.\\n\\nProvider priority (auto-detected):\\n1. **Local faster-whisper** — free, no API key: `pip install faster-whisper`\\n2. **Groq Whisper** — free tier: set `GROQ_API_KEY`\\n3. **OpenAI Whisper** — paid: set `VOICE_TOOLS_OPENAI_KEY`\\n4. **Mistral Voxtral** — set `MISTRAL_API_KEY`\\n\\nConfig:\\n```yaml\\nstt:\\n enabled: true\\n provider: local # local, groq, openai, mistral\\n local:\\n model: small # tiny, base, small, medium, large-v3 — use 'small' or higher for German\\n language: de # always set explicitly for German — auto-detect is unreliable\\n```\\n\\n### Pitfalls\\n- **faster-whisper must be installed** — config shows `provider: local` but STT silently fails if the package is missing\\n- **Use `small` model minimum for German** — `base` is too weak\\n- **Set `language: de` explicitly** — auto-detect is unreliable\\n- **Swiss German (dialect) is NOT supported** — Whisper transcribes dialect poorly; user should speak Standard German\\n\\n### TTS (Text → Voice)\\n\\n| Provider | Env var | Free? |\\n|----------|---------|-------|\\n| Edge TTS | None | Yes (default) |\\n| ElevenLabs | `ELEVENLABS_API_KEY` | Free tier |\\n| OpenAI | `VOICE_TOOLS_OPENAI_KEY` | Paid |\\n| MiniMax | `MINIMAX_API_KEY` | Paid |\\n| Mistral (Voxtral) | `MISTRAL_API_KEY` | Paid |\\n| NeuTTS (local) | None (`pip install neutts[all]` + `espeak-ng`) | Free |\\n\\nVoice commands: `/voice on` (voice-to-voice), `/voice tts` (always voice), `/voice off`.\\n\\n---\\n\\n## Spawning Additional Hermes Instances\\n\\nRun additional Hermes processes as fully independent subprocesses — separate sessions, tools, and environments.\\n\\n### When to Use This vs delegate_task\\n\\n| | `delegate_task` | Spawning `hermes` process |\\n|-|-----------------|--------------------------|\\n| Isolation | Separate conversation, shared process | Fully independent process |\\n| Duration | Minutes (bounded by parent loop) | Hours/days |\\n| Tool access | Subset of parent's tools | Full tool access |\\n| Interactive | No | Yes (PTY mode) |\\n| Use case | Quick parallel subtasks | Long autonomous missions |\\n\\n### One-Shot Mode\\n\\n```\\nterminal(command=\\\"hermes chat -q 'Research GRPO papers and write summary to ~/research/grpo.md'\\\", timeout=300)\\n\\n# Background for long tasks:\\nterminal(command=\\\"hermes chat -q 'Set up CI/CD for ~/myapp'\\\", background=true)\\n```\\n\\n### Interactive PTY Mode (via tmux)\\n\\nHermes uses prompt_toolkit, which requires a real terminal. Use tmux for interactive spawning:\\n\\n```\\n# Start\\nterminal(command=\\\"tmux new-session -d -s agent1 -x 120 -y 40 'hermes'\\\", timeout=10)\\n\\n# Wait for startup, then send a message\\nterminal(command=\\\"sleep 8 && tmux send-keys -t agent1 'Build a FastAPI auth service' Enter\\\", timeout=15)\\n\\n# Read output\\nterminal(command=\\\"sleep 20 && tmux capture-pane -t agent1 -p\\\", timeout=5)\\n\\n# Send follow-up\\nterminal(command=\\\"tmux send-keys -t agent1 'Add rate limiting middleware' Enter\\\", timeout=5)\\n\\n# Exit\\nterminal(command=\\\"tmux send-keys -t agent1 '/exit' Enter && sleep 2 && tmux kill-session -t agent1\\\", timeout=10)\\n```\\n\\n### Multi-Agent Coordination\\n\\n```\\n# Agent A: backend\\nterminal(command=\\\"tmux new-session -d -s backend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t backend 'Build REST API for user management' Enter\\\", timeout=15)\\n\\n# Agent B: frontend\\nterminal(command=\\\"tmux new-session -d -s frontend -x 120 -y 40 'hermes -w'\\\", timeout=10)\\nterminal(command=\\\"sleep 8 && tmux send-keys -t frontend 'Build React dashboard for user management' Enter\\\", timeout=15)\\n\\n# Check progress, relay context between them\\nterminal(command=\\\"tmux capture-pane -t backend -p | tail -30\\\", timeout=5)\\nterminal(command=\\\"tmux send-keys -t frontend 'Here is the API schema from the backend agent: ...' Enter\\\", timeout=5)\\n```\\n\\n### Session Resume\\n\\n```\\n# Resume most recent session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --continue'\\\", timeout=10)\\n\\n# Resume specific session\\nterminal(command=\\\"tmux new-session -d -s resumed 'hermes --resume 20260225_143052_a1b2c3'\\\", timeout=10)\\n```\\n\\n### Tips\\n\\n- **Prefer `delegate_task` for quick subtasks** — less overhead than spawning a full process\\n- **Use `-w` (worktree mode)** when spawning agents that edit code — prevents git conflicts\\n- **Set timeouts** for one-shot mode — complex tasks can take 5-10 minutes\\n- **Use `hermes chat -q` for fire-and-forget** — no PTY needed\\n- **Use tmux for interactive sessions** — raw PTY mode has `\\\\r` vs `\\\\n` issues with prompt_toolkit\\n- **For scheduled tasks**, use the `cronjob` tool instead of spawning — handles delivery and retry\\n\\n---\\n\\n## Durable & Background Systems\\n\\nFour systems run alongside the main conversation loop. Quick reference\\nhere; full developer notes live in `AGENTS.md`, user-facing docs under\\n`website/docs/user-guide/features/`.\\n\\n### Delegation (`delegate_task`)\\n\\nSynchronous subagent spawn — the parent waits for the child's summary\\nbefore continuing its own loop. Isolated context + terminal session.\\n\\n- **Single:** `delegate_task(goal, context, toolsets)`.\\n- **Batch:** `delegate_task(tasks=[{goal, ...}, ...])` runs children in\\n parallel, capped by `delegation.max_concurrent_children` (default 3).\\n- **Roles:** `leaf` (default; cannot re-delegate) vs `orchestrator`\\n (can spawn its own workers, bounded by `delegation.max_spawn_depth`).\\n- **Not durable.** If the parent is interrupted, the child is\\n cancelled. For work that must outlive the turn, use `cronjob` or\\n `terminal(background=True, notify_on_complete=True)`.\\n\\nConfig: `delegation.*` in `config.yaml`.\\n\\n### Cron (scheduled jobs)\\n\\nDurable scheduler — `cron/jobs.py` + `cron/scheduler.py`. Drive it via\\nthe `cronjob` tool, the `hermes cron` CLI (`list`, `add`, `edit`,\\n`pause`, `resume`, `run`, `remove`), or the `/cron` slash command.\\n\\n- **Schedules:** duration (`\\\"30m\\\"`, `\\\"2h\\\"`), \\\"every\\\" phrase\\n (`\\\"every monday 9am\\\"`), 5-field cron (`\\\"0 9 * * *\\\"`), or ISO timestamp.\\n- **Per-job knobs:** `skills`, `model`/`provider` override, `script`\\n (pre-run data collection; `no_agent=True` makes the script the whole\\n job), `context_from` (chain job A's output into job B), `workdir`\\n (run in a specific dir with its `AGENTS.md` / `CLAUDE.md` loaded),\\n multi-platform delivery.\\n- **Invariants:** 3-minute hard interrupt per run, `.tick.lock` file\\n prevents duplicate ticks across processes, cron sessions pass\\n `skip_memory=True` by default, and cron deliveries are framed with a\\n header/footer instead of being mirrored into the target gateway\\n session (keeps role alternation intact).\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/cron\\n\\n### Curator (skill lifecycle)\\n\\nBackground maintenance for agent-created skills. Tracks usage, marks\\nidle skills stale, archives stale ones, keeps a pre-run tar.gz backup\\nso nothing is lost.\\n\\n- **CLI:** `hermes curator ` — `status`, `run`, `pause`, `resume`,\\n `pin`, `unpin`, `archive`, `restore`, `prune`, `backup`, `rollback`.\\n- **Slash:** `/curator ` mirrors the CLI.\\n- **Scope:** only touches skills with `created_by: \\\"agent\\\"` provenance.\\n Bundled + hub-installed skills are off-limits. **Never deletes** —\\n max destructive action is archive. Pinned skills are exempt from\\n every auto-transition and every LLM review pass.\\n- **Telemetry:** sidecar at `~/.hermes/skills/.usage.json` holds\\n per-skill `use_count`, `view_count`, `patch_count`,\\n `last_activity_at`, `state`, `pinned`.\\n\\nConfig: `curator.*` (`enabled`, `interval_hours`, `min_idle_hours`,\\n`stale_after_days`, `archive_after_days`, `backup.*`).\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/curator\\n\\n### Kanban (multi-agent work queue)\\n\\nDurable SQLite board for multi-profile / multi-worker collaboration.\\nUsers drive it via `hermes kanban `; dispatcher-spawned workers\\nsee a focused `kanban_*` toolset gated by `HERMES_KANBAN_TASK` so the\\nschema footprint is zero outside worker processes.\\n\\n- **CLI verbs (common):** `init`, `create`, `list` (alias `ls`),\\n `show`, `assign`, `link`, `unlink`, `comment`, `complete`, `block`,\\n `unblock`, `archive`, `tail`. Less common: `watch`, `stats`, `runs`,\\n `log`, `dispatch`, `daemon`, `gc`.\\n- **Worker toolset:** `kanban_show`, `kanban_complete`, `kanban_block`,\\n `kanban_heartbeat`, `kanban_comment`, `kanban_create`, `kanban_link`.\\n- **Dispatcher** runs inside the gateway by default\\n (`kanban.dispatch_in_gateway: true`) — reclaims stale claims,\\n promotes ready tasks, atomically claims, spawns assigned profiles.\\n Auto-blocks a task after ~5 consecutive spawn failures.\\n- **Isolation:** board is the hard boundary (workers get\\n `HERMES_KANBAN_BOARD` pinned in env); tenant is a soft namespace\\n within a board for workspace-path + memory-key isolation.\\n\\nUser docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/kanban\\n\\n---\\n\\n## Troubleshooting\\n\\n### Voice not working\\n1. Check `stt.enabled: true` in config.yaml\\n2. Verify provider: `pip install faster-whisper` or set API key\\n3. In gateway: `/restart`. In CLI: exit and relaunch.\\n\\n### Tool not available\\n1. `hermes tools` — check if toolset is enabled for your platform\\n2. Some tools need env vars (check `.env`)\\n3. `/reset` after enabling tools\\n\\n### Model/provider issues\\n1. `hermes doctor` — check config and dependencies\\n2. `hermes login` — re-authenticate OAuth providers\\n3. Check `.env` has the right API key\\n4. **Copilot 403**: `gh auth login` tokens do NOT work for Copilot API. You must use the Copilot-specific OAuth device code flow via `hermes model` → GitHub Copilot.\\n\\n### Changes not taking effect\\n- **Tools/skills:** `/reset` starts a new session with updated toolset\\n- **Config changes:** In gateway: `/restart`. In CLI: exit and relaunch.\\n- **Code changes:** Restart the CLI or gateway process\\n\\n### Tool / iteration budget\\nWhen the user asks to increase the \\\"tool budget\\\", \\\"turn budget\\\", or max tool-calling iterations, set the config key, not a transient shell variable:\\n```bash\\nhermes config set agent.max_turns 250\\n```\\nThen verify by reading `~/.hermes/config.yaml` or using a safe config inspection path. Avoid piping `hermes config` into `python` or another interpreter; security policy may flag `command | python` as high-risk even when the intent is only parsing. For gateway runs, `agent.max_turns` is the authoritative setting and is bridged internally to `HERMES_MAX_ITERATIONS`; config changes apply to fresh agent runs, while long-lived gateway/config changes may still need `/restart` if behavior appears stale.\\n\\n### Skills not showing\\n1. `hermes skills list` — verify installed\\n2. `hermes skills config` — check platform enablement\\n3. Load explicitly: `/skill name` or `hermes -s name`\\n\\n### Gateway issues\\nCheck logs first:\\n```bash\\ngrep -i \\\"failed to send\\\\|error\\\" ~/.hermes/logs/gateway.log | tail -20\\n```\\n\\nCommon gateway problems:\\n- **Gateway dies on SSH logout**: Enable linger: `sudo loginctl enable-linger $USER`\\n- **Gateway dies on WSL2 close**: WSL2 requires `systemd=true` in `/etc/wsl.conf` for systemd services to work. Without it, gateway falls back to `nohup` (dies when session closes).\\n- **Gateway crash loop**: Reset the failed state: `systemctl --user reset-failed hermes-gateway`\\n\\n### Platform-specific issues\\n- **Discord bot silent**: Must enable **Message Content Intent** in Bot → Privileged Gateway Intents.\\n- **Slack bot only works in DMs**: Must subscribe to `message.channels` event. Without it, the bot ignores public channels.\\n- **Windows HTTP 400 \\\"No models provided\\\"**: Config file encoding issue (BOM). Ensure `config.yaml` is saved as UTF-8 without BOM.\\n\\n### Auxiliary models not working\\nIf `auxiliary` tasks (vision, compression, session_search) fail silently, the `auto` provider can't find a backend. Either set `OPENROUTER_API_KEY` or `GOOGLE_API_KEY`, or explicitly configure each auxiliary task's provider:\\n```bash\\nhermes config set auxiliary.vision.provider \\nhermes config set auxiliary.vision.model \\n```\\n\\n---\\n\\n## Where to Find Things\\n\\n| Looking for... | Location |\\n|----------------|----------|\\n| Config options | `hermes config edit` or [Configuration docs](https://hermes-agent.nousresearch.com/docs/user-guide/configuration) |\\n| Available tools | `hermes tools list` or [Tools reference](https://hermes-agent.nousresearch.com/docs/reference/tools-reference) |\\n| Slash commands | `/help` in session or [Slash commands reference](https://hermes-agent.nousresearch.com/docs/reference/slash-commands) |\\n| Skills catalog | `hermes skills browse` or [Skills catalog](https://hermes-agent.nousresearch.com/docs/reference/skills-catalog) |\\n| Provider setup | `hermes model` or [Providers guide](https://hermes-agent.nousresearch.com/docs/integrations/providers) |\\n| Platform setup | `hermes gateway setup` or [Messaging docs](https://hermes-agent.nousresearch.com/docs/user-guide/messaging/) |\\n| MCP servers | `hermes mcp list` or [MCP guide](https://hermes-agent.nousresearch.com/docs/user-guide/features/mcp) |\\n| Profiles | `hermes profile list` or [Profiles docs](https://hermes-agent.nousresearch.com/docs/user-guide/profiles) |\\n| Cron jobs | `hermes cron list` or [Cron docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/cron) |\\n| Memory | `hermes memory status` or [Memory docs](https://hermes-agent.nousresearch.com/docs/user-guide/features/memory) |\\n| Env variables | `hermes config env-path` or [Env vars reference](https://hermes-agent.nousresearch.com/docs/reference/environment-variables) |\\n| CLI commands | `hermes --help` or [CLI reference](https://hermes-agent.nousresearch.com/docs/reference/cli-commands) |\\n| Gateway logs | `~/.hermes/logs/gateway.log` |\\n| Session files | `~/.hermes/sessions/` or `hermes sessions browse` |\\n| Source code | `~/.hermes/hermes-agent/` |\\n\\n---\\n\\n## Contributor Quick Reference\\n\\nFor occasional contributors and PR authors. Full developer docs: https://hermes-agent.nousresearch.com/docs/developer-guide/\\n\\n### Project Layout\\n\\n```\\nhermes-agent/\\n├── run_agent.py # AIAgent — core conversation loop\\n├── model_tools.py # Tool discovery and dispatch\\n├── toolsets.py # Toolset definitions\\n├── cli.py # Interactive CLI (HermesCLI)\\n├── hermes_state.py # SQLite session store\\n├── agent/ # Prompt builder, context compression, memory, model routing, credential pooling, skill dispatch\\n├── hermes_cli/ # CLI subcommands, config, setup, commands\\n│ ├── commands.py # Slash command registry (CommandDef)\\n│ ├── config.py # DEFAULT_CONFIG, env var definitions\\n│ └── main.py # CLI entry point and argparse\\n├── tools/ # One file per tool\\n│ └── registry.py # Central tool registry\\n├── gateway/ # Messaging gateway\\n│ └── platforms/ # Platform adapters (telegram, discord, etc.)\\n├── cron/ # Job scheduler\\n├── tests/ # ~3000 pytest tests\\n└── website/ # Docusaurus docs site\\n```\\n\\nConfig: `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys).\\n\\n### Adding a Tool (3 files)\\n\\n**1. Create `tools/your_tool.py`:**\\n```python\\nimport json, os\\nfrom tools.registry import registry\\n\\ndef check_requirements() -> bool:\\n return bool(os.getenv(\\\"EXAMPLE_API_KEY\\\"))\\n\\ndef example_tool(param: str, task_id: str = None) -> str:\\n return json.dumps({\\\"success\\\": True, \\\"data\\\": \\\"...\\\"})\\n\\nregistry.register(\\n name=\\\"example_tool\\\",\\n toolset=\\\"example\\\",\\n schema={\\\"name\\\": \\\"example_tool\\\", \\\"description\\\": \\\"...\\\", \\\"parameters\\\": {...}},\\n handler=lambda args, **kw: example_tool(\\n param=args.get(\\\"param\\\", \\\"\\\"), task_id=kw.get(\\\"task_id\\\")),\\n check_fn=check_requirements,\\n requires_env=[\\\"EXAMPLE_API_KEY\\\"],\\n)\\n```\\n\\n**2. Add to `toolsets.py`** → `_HERMES_CORE_TOOLS` list.\\n\\nAuto-discovery: any `tools/*.py` file with a top-level `registry.register()` call is imported automatically — no manual list needed.\\n\\nAll handlers must return JSON strings. Use `get_hermes_home()` for paths, never hardcode `~/.hermes`.\\n\\n### Adding a Slash Command\\n\\n1. Add `CommandDef` to `COMMAND_REGISTRY` in `hermes_cli/commands.py`\\n2. Add handler in `cli.py` → `process_command()`\\n3. (Optional) Add gateway handler in `gateway/run.py`\\n\\nAll consumers (help text, autocomplete, Telegram menu, Slack mapping) derive from the central registry automatically.\\n\\n### Agent Loop (High Level)\\n\\n```\\nrun_conversation():\\n 1. Build system prompt\\n 2. Loop while iterations < max:\\n a. Call LLM (OpenAI-format messages + tool schemas)\\n b. If tool_calls → dispatch each via handle_function_call() → append results → continue\\n c. If text response → return\\n 3. Context compression triggers automatically near token limit\\n```\\n\\n### Testing\\n\\n```bash\\npython -m pytest tests/ -o 'addopts=' -q # Full suite\\npython -m pytest tests/tools/ -q # Specific area\\n```\\n\\n- Tests auto-redirect `HERMES_HOME` to temp dirs — never touch real `~/.hermes/`\\n- Run full suite before pushing any change\\n- Use `-o 'addopts='` to clear any baked-in pytest flags\\n\\n### Commit Conventions\\n\\n```\\ntype: concise subject line\\n\\nOptional body.\\n```\\n\\nTypes: `fix:`, `feat:`, `refactor:`, `docs:`, `chore:`\\n\\n### Key Rules\\n\\n- **Never break prompt caching** — don't change context, tools, or system prompt mid-conversation\\n- **Message role alternation** — never two assistant or two user messages in a row\\n- Use `get_hermes_home()` from `hermes_constants` for all paths (profile-safe)\\n- Config values go in `config.yaml`, secrets go in `.env`\\n- New tools need a `check_fn` so they only appear when requirements are met\\n\", \"path\": \"autonomous-ai-agents/hermes-agent/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/autonomous-ai-agents/hermes-agent\", \"linked_files\": {\"references\": [\"references/telegram-gateway-setup.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"hermes\", \"setup\", \"configuration\", \"multi-agent\", \"spawning\", \"cli\", \"gateway\", \"development\"], \"homepage\": \"https://github.com/NousResearch/hermes-agent\", \"related_skills\": [\"claude-code\", \"codex\", \"opencode\"]}}}", "tool_call_id": "call_u3qH02dH5Lcs3QDPPjkSWhgi" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_tMoOXfViOScWsO2nG1GKtZnN", "call_id": "call_tMoOXfViOScWsO2nG1GKtZnN", "response_item_id": "fc_08976407282abc53016a12190ce2d481918281e46123c7958c", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os, re, pathlib, yaml\\npaths=[pathlib.Path.home()/'.hermes/.env', pathlib.Path.home()/'.hermes/config.yaml']\\nfor p in paths:\\n print(f'## {p} exists={p.exists()}')\\n if p.exists():\\n text=p.read_text(errors='ignore')\\n for name in ['NVIDIA_API_KEY','NGC_API_KEY','NVIDIA_NIM_API_KEY','FAL_KEY','OPENAI_API_KEY']:\\n m=re.search(rf'^{name}\\\\s*=\\\\s*(.+)$', text, re.M)\\n print(f'{name}:', 'present' if m and m.group(1).strip() else 'absent')\\nprint('ENV:')\\nfor name in ['NVIDIA_API_KEY','NGC_API_KEY','NVIDIA_NIM_API_KEY','FAL_KEY','OPENAI_API_KEY']:\\n print(f'{name}:', 'present' if os.getenv(name) else 'absent')\\nPY\", \"timeout\": 30, \"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Inspecting NVIDIA configuration**\n\nI need to explain and inspect the configuration environment for NVIDIA. I shouldn't reveal any keys, but testing the endpoint documentation seems like a good idea. I could use the terminal or environment settings for this. Additionally, there may be an image generation tool available, but the backend might lack certain features. If the key is in the environment, I could directly call the NVIDIA API. I should use current documentation and potentially look at the web for more information.", "reasoning_content": "**Inspecting NVIDIA configuration**\n\nI need to explain and inspect the configuration environment for NVIDIA. I shouldn't reveal any keys, but testing the endpoint documentation seems like a good idea. I could use the terminal or environment settings for this. Additionally, there may be an image generation tool available, but the backend might lack certain features. If the key is in the environment, I could directly call the NVIDIA API. I should use current documentation and potentially look at the web for more information.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEhkMutJScD1m4pFTEriUhd5uxxmKZSRTpTlVDbAdAUV6iWQgsuuiPIVrBppfF3IaNdPmZAAbmvIeBTyebMwKouD8M0jxof5--fAcvBF-u0pDd78t0ebyNkeEcFsM4U2YQ0a9Y9Vx9r8OnZq6oE7BNUpMKOGZD0oLxTL8PFKLyWgcdc3kFqOyikqDV6KeQVImPZ94qSXA7tuGwcD5KE4sZ8xlyHBnJup7iIUYPEijcz6vP55s-LicBRGefSIUbH0XC5sB0YsXYzqtq2s-_68CV1QAnyJKzwy_brPn5D3oa3cwJhPzTdBd4yVa9DXteNtht4nyy_muyZfJzLJ1Kmu-QaKo5Xebx9ZJ5_0PTA-b-gfQem6yCkqrLuuAujmrntvYlV0ut1BqHY23m2QL7rRRPIOSgqBCtuLp0SYElkCUAHQhomqGb1XvLoWza4vhPeTkY6Z4xSXEuFBpOOxTwhuWjvVpEy0C9btA8FdcooJfAYv3uGp6AW5oNO-uapDwth8t7aAl1L8mt4_kvy7xr1xdjeIKfN6_I57TuRBmqq51KHsNDNpvCj-j2vZxrcHzcqgKkKVF_drHT4BpPIZeHV8I2N9X1zSRfN0jOcQIPEcfI8M_qEMjbib1jlWPkTBEo_Ai87jd2wvEf4p76ye_htEUyae75dKRAizxs8bdCWZMtBu1r578ALRR51Wzj4cuRELRGzs_NqSj3oepKxU_W2kBD7meRZqig1tHRIohIG6oLrZVoYq9wcXyvtFNWuUnMhkrckIqc_yaLp_OnOXXJA-AtaKuJ513rIWhIEoW5EauyYoh9jLDoqplg12e17QYnWJdHYTN7KlAzTNBXWF6MziGzDFpjI_pPpVWYFv4GJ9UOcSmNCgZYuZbk-y-APyq2cZbJabPjVZTAnjfyV-d7DQvdkhu84jJV-qVzLjYU7wSpNoYWpvIAxZW1B4wRYZb32rNxyv4a6XP6ln3pJaFKHj73yCMWkaYeB4yCj8qhpD7Y_iJxg08pYJbd9JBHYTjC_c2ljmSmoNBT7jzjSI7gePYIeJPCXSG3fvtBDj_1euwfbGnrn7IimI5bt_bnokIQgzGv10jnzsJmF8BQKPCTKiWzKYaJrok1AlI96v8IEOZRqj3Pn-kcXZFC8S_seLJvQQx9wdmXM64CgmDeV17pWvQz7Ish170vx4YqBPJvninucvXTwyvqbXViqygka1g2FozBCxuKGp8TQKDF_9nAgndjqvhLHRaKvNZ95ktxhA26ti7h2FJ0S-XKOF_8cS5BIw1vmnI_NWpbyisY_3VSd09pcHlWgAiTtG5Ge6MquwyYUWpOVKH0Xt8t-tm41mbqM3MYckV", "id": "rs_08976407282abc53016a12190a9e748191963547e6210d757b", "summary": [ { "type": "summary_text", "text": "**Inspecting NVIDIA configuration**\n\nI need to explain and inspect the configuration environment for NVIDIA. I shouldn't reveal any keys, but testing the endpoint documentation seems like a good idea. I could use the terminal or environment settings for this. Additionally, there may be an image generation tool available, but the backend might lack certain features. If the key is in the environment, I could directly call the NVIDIA API. 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  • build.nvidia.com April 30, 2026 via
    • \\n\\n