{ "session_id": "20260524_091219_d252c13f", "model": "gpt-5.5", "base_url": "https://chatgpt.com/backend-api/codex", "platform": "telegram", "session_start": "2026-05-24T09:21:06.836608", "last_updated": "2026-05-24T10:53:42.125703", "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save the approach as a skill with skill_manage so you can reuse it next time.\nWhen using a skill and finding it outdated, incomplete, or wrong, patch it immediately with skill_manage(action='patch') — don't wait to be asked.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) [98% — 2,167/2,200 chars]\n══════════════════════════════════════════════\n*Wichtige Erinnerungen, Entscheidungen und Präferenzen.*\n§\n---\n§\nÜber mich: **Persönlichkeit:** Britischer Gentleman, sachlich, präzise, trockener Sarkasmus\n§\nÜber mich: **Sprache:** Deutsch mit englischen Fachbegriffen\n§\nModelle: 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§\nGitHub token files: FinanceManager CSV `~/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv`; AutoShortsBot `~/.hermes/secrets/github_autoshortsbot_token`; use temp GIT_ASKPASS, never print.\n\n══════════════════════════════════════════════\nUSER PROFILE (who the user is) [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: Sunday, May 24, 2026 09:12 AM\nModel: gpt-5.5\nProvider: openai-codex", "tools": [ { "type": "function", "function": { "name": "browser_back", "description": "Navigate back to the previous page in browser history. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_click", "description": "Click on an element identified by its ref ID from the snapshot (e.g., '@e5'). The ref IDs are shown in square brackets in the snapshot output. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e5', '@e12')" } }, "required": [ "ref" ] } } }, { "type": "function", "function": { "name": "browser_console", "description": "Get browser console output and JavaScript errors from the current page. Returns console.log/warn/error/info messages and uncaught JS exceptions. Use this to detect silent JavaScript errors, failed API calls, and application warnings. Requires browser_navigate to be called first. When 'expression' is provided, evaluates JavaScript in the page context and returns the result — use this for DOM inspection, reading page state, or extracting data programmatically.", "parameters": { "type": "object", "properties": { "clear": { "type": "boolean", "default": false, "description": "If true, clear the message buffers after reading" }, "expression": { "type": "string", "description": "JavaScript expression to evaluate in the page context. Runs in the browser like DevTools console — full access to DOM, window, document. Return values are serialized to JSON. Example: 'document.title' or 'document.querySelectorAll(\"a\").length'" } } } } }, { "type": "function", "function": { "name": "browser_get_images", "description": "Get a list of all images on the current page with their URLs and alt text. Useful for finding images to analyze with the vision tool. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": {} } } }, { "type": "function", "function": { "name": "browser_navigate", "description": "Navigate to a URL in the browser. Initializes the session and loads the page. Must be called before other browser tools. For plain-text endpoints — URLs ending in .md, .txt, .json, .yaml, .yml, .csv, .xml, raw.githubusercontent.com, or any documented API endpoint — prefer curl via the terminal tool or web_extract; the browser stack is overkill and much slower for these. Use browser tools when you need to interact with a page (click, fill forms, dynamic content). Returns a compact page snapshot with interactive elements and ref IDs — no need to call browser_snapshot separately after navigating.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "The URL to navigate to (e.g., 'https://example.com')" } }, "required": [ "url" ] } } }, { "type": "function", "function": { "name": "browser_press", "description": "Press a keyboard key. Useful for submitting forms (Enter), navigating (Tab), or keyboard shortcuts. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "key": { "type": "string", "description": "Key to press (e.g., 'Enter', 'Tab', 'Escape', 'ArrowDown')" } }, "required": [ "key" ] } } }, { "type": "function", "function": { "name": "browser_scroll", "description": "Scroll the page in a direction. Use this to reveal more content that may be below or above the current viewport. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "direction": { "type": "string", "enum": [ "up", "down" ], "description": "Direction to scroll" } }, "required": [ "direction" ] } } }, { "type": "function", "function": { "name": "browser_snapshot", "description": "Get a text-based snapshot of the current page's accessibility tree. Returns interactive elements with ref IDs (like @e1, @e2) for browser_click and browser_type. full=false (default): compact view with interactive elements. full=true: complete page content. Snapshots over 8000 chars are truncated or LLM-summarized. Requires browser_navigate first. Note: browser_navigate already returns a compact snapshot — use this to refresh after interactions that change the page, or with full=true for complete content.", "parameters": { "type": "object", "properties": { "full": { "type": "boolean", "description": "If true, returns complete page content. If false (default), returns compact view with interactive elements only.", "default": false } } } } }, { "type": "function", "function": { "name": "browser_type", "description": "Type text into an input field identified by its ref ID. Clears the field first, then types the new text. Requires browser_navigate and browser_snapshot to be called first.", "parameters": { "type": "object", "properties": { "ref": { "type": "string", "description": "The element reference from the snapshot (e.g., '@e3')" }, "text": { "type": "string", "description": "The text to type into the field" } }, "required": [ "ref", "text" ] } } }, { "type": "function", "function": { "name": "browser_vision", "description": "Take a screenshot of the current page and analyze it with vision AI. Use this when you need to visually understand what's on the page - especially useful for CAPTCHAs, visual verification challenges, complex layouts, or when the text snapshot doesn't capture important visual information. Returns both the AI analysis and a screenshot_path that you can share with the user by including MEDIA: in your response. Requires browser_navigate to be called first.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "What you want to know about the page visually. Be specific about what you're looking for." }, "annotate": { "type": "boolean", "default": false, "description": "If true, overlay numbered [N] labels on interactive elements. Each [N] maps to ref @eN for subsequent browser commands. Useful for QA and spatial reasoning about page layout." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": { "type": "object", "properties": { "question": { "type": "string", "description": "The question to present to the user." }, "choices": { "type": "array", "items": { "type": "string" }, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option." } }, "required": [ "question" ] } } }, { "type": "function", "function": { "name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": { "type": "object", "properties": { "action": { "type": "string", "description": "One of: create, list, update, pause, resume, remove, run" }, "job_id": { "type": "string", "description": "Required for update/pause/resume/remove/run" }, "prompt": { "type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills." }, "schedule": { "type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp" }, "name": { "type": "string", "description": "Optional human-friendly name" }, "repeat": { "type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)." }, "deliver": { "type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), 'all' (fan out to every connected home channel), or platform:chat_id:thread_id for a specific destination. Combine with comma: 'origin,all' delivers to the origin plus every other connected channel. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567', 'all'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting. 'all' resolves at fire time, so a job created before a channel was wired up will pick it up automatically once connected." }, "skills": { "type": "array", "items": { "type": "string" }, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills." }, "model": { "type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": { "provider": { "type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:' for a provider defined in custom_providers config — always include the ':' suffix, never pass the bare 'custom'). Omit to use and pin the current provider." }, "model": { "type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')" } }, "required": [ "model" ] }, "script": { "type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear." }, "no_agent": { "type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content." }, "context_from": { "type": "array", "items": { "type": "string" }, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear." }, "enabled_toolsets": { "type": "array", "items": { "type": "string" }, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear." }, "workdir": { "type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated." } }, "required": [ "action" ] } } }, { "type": "function", "function": { "name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to 3 items concurrently for this user (configured via delegation.max_concurrent_children in config.yaml). All run in parallel and results are returned together. Nested delegation is OFF for this user (max_spawn_depth=1): every child is a leaf and cannot delegate further. Raise delegation.max_spawn_depth in config.yaml to enable nesting.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by max_spawn_depth=1 for this user and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": { "type": "object", "properties": { "goal": { "type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history." }, "context": { "type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs." }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks." }, "tasks": { "type": "array", "items": { "type": "object", "properties": { "goal": { "type": "string", "description": "Task goal" }, "context": { "type": "string", "description": "Task-specific context" }, "toolsets": { "type": "array", "items": { "type": "string" }, "description": "Toolsets for this specific task. Available: 'browser', 'computer_use', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'video_gen', 'vision', 'web', 'x_search', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction." }, "acp_command": { "type": "string", "description": "Per-task ACP command override (e.g. 'copilot'). Overrides the top-level acp_command for this task only. Do NOT set unless the user explicitly told you an ACP CLI is installed." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Per-task ACP args override. Leave empty unless acp_command is set." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Per-task role override. See top-level 'role' for semantics." } }, "required": [ "goal" ] }, "description": "Batch mode: tasks to run in parallel (up to 3 for this user, set via delegation.max_concurrent_children). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored." }, "role": { "type": "string", "enum": [ "leaf", "orchestrator" ], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Nesting is OFF for this user (max_spawn_depth=1); 'orchestrator' is silently forced to 'leaf'. Raise delegation.max_spawn_depth in config.yaml to enable." }, "acp_command": { "type": "string", "description": "Override ACP command for child agents (e.g. 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Requires an ACP-compatible CLI (currently GitHub Copilot CLI via 'copilot --acp --stdio'). See agent/copilot_acp_client.py for the implementation. IMPORTANT: Do NOT set this unless the user has explicitly told you a specific ACP-compatible CLI is installed and configured. Leave empty to use the parent's default transport (Hermes subagents)." }, "acp_args": { "type": "array", "items": { "type": "string" }, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Leave empty unless acp_command is explicitly provided." } } } } }, { "type": "function", "function": { "name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n write_file(path: str, content: str) -> dict\n Always overwrites the entire file.\n search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n Replaces old_string with new_string in the file.\n terminal(command: str, timeout=None, workdir=None) -> dict\n Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": { "type": "object", "properties": { "code": { "type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout." } }, "required": [ "code" ] } } }, { "type": "function", "function": { "name": "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": 284, "messages": [ { "role": "user", "content": "[M G] co/Civitai/Qwen-Image-2512-GGUF \"Civitai/Qwen-Image-2512-GGUF · Hugging Face\"\nSollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?\n für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.\n\nMeine Rangliste für deinen Usecase:\n\n| Zweck | Empfehlung | Warum |\n| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Beste kostenlose GGUF-Bildgenerierung in ComfyUI | Qwen-Image-2512-GGUF | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |\n| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2]) |\n| Schnell / weniger VRAM / experimentell | Z-Image Turbo GGUF | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3]) |\n| Alternative zu Qwen, stark aber schwerer | FLUX.2-dev-GGUF | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4]) |\n| Aktuell stärkstes Open-Weights-Modell allgemein | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5]) |\n\nKurz gesagt:\nFür deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.\n\nDie passende Auswahl nach VRAM:\n\n| VRAM | Modell/Quantisierung |\n| ----------: | ------------------------------------------------------ |\n| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |\n| 16 GB | Q5 oder Q6 testen |\n| 24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität |\n| <8 GB | eher Z-Image Turbo GGUF statt Qwen |\n\nWichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])\n\nMeine konkrete Empfehlung:\nInstalliere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.\n\n[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF \"unsloth/Qwen-Image-2512-GGUF · Hugging Face\"\n[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF \"unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face\"\n[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF \"jayn7/Z-Image-Turbo-GGUF · Hugging Face\"\n[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF \"orabazes/FLUX.2-dev-GGUF · Hugging Face\"\n[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image \"Text to Image Leaderboard - Top AI Image Models\"\n[6]: https://huggingface." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_5G8pMioJ1eMF6ggzbmOMGBV4", "call_id": "call_5G8pMioJ1eMF6ggzbmOMGBV4", "response_item_id": "fc_0bd8b6ce2393ae8a016a12a4d7ea048191aa1250f7c9fe6c94", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"comfyui\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqTXIXcIoHPbSRA5Pki-LyZv5-qiLkvTZUnxNLckymTSu8mkdRW45_8C5f5Vlpabyp4wlsxdCd9WNFy1PyWuDLhsVs8PR1rSwL5Su8IEI2Xx30t8xftfWuH2M7aDBc_x6wQjmlLOrj0HeSzNjHCoGrVSPl4exC1BkOXeB3WvXQHWAixrzbxkFlQW69VIlTPyv2NjQOiU7u_JB5DPak9CpSO92fJqv84T5FAc1GsO2IcHZkKIr5WLEjg9dzL2Qj0etViR2uwuu-X25MpaF7txhH0YBKcHcptdaM6HtH899KQKgJ87maj3yYUM1doCcTs_C1FlNxQYGRkj8SAY6JeX5ihpQNcyXQxwqBRfIVAvWNsFo21_p7irZS-gKGvv8J9boPTG6J1S6vzI0ds5OVJs6OO0Lx19c_dvxZE5J9RB7kQFik7TfifcUo0TdEuZhB5IRh4jtMfQYen8jMyK9AF8LBDa8AfMmyVU_YDvlV_MUFt5mzdkgoQmBwR_ATIW9NUi7757bpISkdqADIwDi6lVkAM-7_pBH4YjpJGat1oQZYejBICoWfx4yOw6sBNE16h-p1EMz7pfGZcsNClFtLRomvUYOrTtLNvc-ZuiYUjUVw01B9L47z1snV-PPg2zBE9OprmTLoxzFOCEWVwKH8E4drahvwAflYJQhq2xGG94Epq_OKnU-_KpMq83hxDsS0ma1vw40KIVi3-3adSbb3d4u8k-y6SU4nGAR9d4mEkzJvAV-RCBW0n3pCDN5BqYIpS5lpf1h-yckl-vmAqPvRk7PtdomvY9JcnXzrpmiBACcy8YFPuO16s-Xu5S4WeRancYZI6syDMoUEMBpGvnB138jnDOkdtokD7cnEK5pddr_qkCLHOo4xFYysnIl9KS_9xndl5PVki2Q7rasIAvx0Bzp-Qy_-Il3jS4jt-fGvYJky75T5b5dkvz0FeViFrzVUmrqbBw9Ggr1o2-YdBOo1dhmZpTGcF_JJMt3TbLM94Ycg_pdko=", "id": "rs_0bd8b6ce2393ae8a016a12a4d584cc8191b9f388f36f1d4e54", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan22-native-i2v-a5000-quality.md` — native ComfyUI Wan2.2 5B quality workflow notes: official model/T5/VAE stack, I2V keyframe lessons, RAM/offload guidance, cleanup candidates, and the critical distinction between technical success and production usability.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n- `wan22-native-i2v-prompting.md` — native Wan2.2 I2V prompting and keyframe rules for quality-first local Retention Studio clips; covers scaled T5, negative prompts, A5000 ladders, and blunt review criteria.\\n- `wan22-i2v-retention-studio-pipeline.md` — session-proven production pattern for using native Wan2.2 as a clip generator inside a deterministic Retention-Studio pipeline: keyframe prompts, seeded I2V jobs, ComfyUI client shape, and review gates.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed. For WanVideoWrapper,\\n `/object_info` is especially useful: inspect loader dropdowns to verify\\n files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n If it warns that it is deprecated, use `hf download --local-dir `\\n instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Use Wan2.2 for photorealistic texture/motion only; add captions, numbers, labels, UI text, arrows, risk meters, and audit-sensitive facts later in a deterministic renderer. Generate multiple seeded variants per approved keyframe and select by review gates. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan22-i2v-retention-studio-pipeline.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/wan22-native-i2v-a5000-quality.md\", \"references/rest-api.md\", \"references/wan22-i2v-retention-studio-pipeline.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/wan22-native-i2v-prompting.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"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\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}", "tool_call_id": "call_5G8pMioJ1eMF6ggzbmOMGBV4" }, { "role": "assistant", "content": "Ja, Sir — **ich würde Qwen-Image-2512-GGUF lokal über ComfyUI einbauen**, aber **nicht parallel zum Video-Modell auf derselben GPU laufen lassen**.\n\nKurzfassung:\n\n- **Ja installieren:** Qwen-Image-2512-GGUF ist sinnvoll als lokaler **Keyframe-/Thumbnail-/Concept-Generator**.\n- **Nein parallel mit Wan/Video:** Auf einer 16 GB A5000 wäre das sehr wahrscheinlich VRAM/RAM-Selbstmord mit Ansage.\n- **Beste Nutzung:** Qwen Image erzeugt hochwertige Standbilder → diese gehen dann als **approved keyframes** in Wan2.2 I2V.\n- **Parallel nur organisatorisch:** Jobs in Queue, aber nicht gleichzeitig auf derselben GPU.\n\n## Warum ich es trotzdem installieren würde\n\nFür deine Retention-Studio-Pipeline ist Qwen Image sehr passend:\n\n- bessere **erste Keyframes** als viele generische SDXL/Flux-Setups\n- stark bei **Prompt-Following**\n- potenziell gut für **Text/Labels im Bild** — wobei ich weiterhin sagen würde: finale Captions, Zahlen, Pfeile und UI-Elemente besser deterministisch rendern\n- GGUF passt gut in ComfyUI, wenn ComfyUI-GGUF sauber läuft\n- Apache-2.0 ist angenehm praktisch\n\nDas Modell wäre bei uns nicht primär “final image art”, sondern eher:\n\n1. Hook/scene concept generieren \n2. beste Variante auswählen \n3. ggf. inpaint/editieren \n4. als I2V-Keyframe an Wan2.2 geben \n5. Motion/clip erzeugen \n6. Text, UI, Risk-Meter, Captions nachträglich sauber drüberlegen\n\n## Parallel mit Video? Eher nein.\n\nWenn wir von derselben GPU reden — insbesondere der bekannten **16 GB A5000-Klasse** — dann:\n\n- **Wan2.2 I2V 5B FP8** ist bereits schwer.\n- **Qwen-Image-2512-GGUF Q4/Q5/Q6** braucht zusätzlich massiv VRAM/RAM.\n- ComfyUI hält Modelle gerne im Speicher, wenn man nicht aktiv entlädt.\n- Parallelbetrieb führt wahrscheinlich zu:\n - CUDA OOM\n - ComfyUI crash\n - ewigem CPU/RAM-Offload\n - hängenden Queue-Jobs\n - “warum ist alles langsam?” — die klassische GPU-Version von Bürokratie.\n\nAlso: **nicht zwei dicke Diffusion-Modelle gleichzeitig.**\n\n## Was ich konkret machen würde\n\n**Empfohlene Architektur:**\n\n- Ein ComfyUI-Server.\n- Zwei getrennte Workflows:\n - `qwen-image-keyframe.json`\n - `wan22-i2v-clip.json`\n- Jobs nur sequenziell:\n 1. Qwen Image laden\n 2. Keyframes erzeugen\n 3. Modelle entladen / VRAM freigeben\n 4. Wan laden\n 5. Video generieren\n 6. wieder freigeben\n\nWichtig wäre nach Qwen-Jobs jeweils:\n\n```bash\ncurl -X POST http://HOST:8188/free \\\n -H \"Content-Type: application/json\" \\\n -d '{\"unload_models\": true, \"free_memory\": true}'\n```\n\nSonst bleibt die halbe Arche Noah im VRAM sitzen.\n\n## Quantisierung\n\nFür unsere wahrscheinliche Hardwareklasse:\n\n- **Start:** Q4\n- **Wenn stabil:** Q5 testen\n- **Q6 nur wenn genug Headroom**\n- **Q8 eher nicht auf 16 GB**, ausser mit sehr konservativen Settings und viel Geduld\n\nIch würde also **Qwen-Image-2512-GGUF Q4** zuerst nehmen. Nicht weil Q4 sexy ist, sondern weil “läuft stabil” in einer Pipeline mehr wert ist als “theoretisch schöner, praktisch tot”.\n\n## Meine Empfehlung\n\n**Ja, wir sollten Qwen Image in ComfyUI aufnehmen — aber als vorgelagerten Keyframe-Generator, nicht als paralleles Modell neben Wan.**\n\nPraktischer Plan:\n\n1. ComfyUI-GGUF installieren/prüfen.\n2. Qwen-Image-2512-GGUF Q4 laden.\n3. Minimalen Txt2Img-Keyframe-Workflow bauen.\n4. 3–5 Test-Keyframes für einen echten Shorts-Usecase generieren.\n5. Besten Keyframe in Wan2.2 I2V schicken.\n6. Danach entscheiden, ob Q5/Q6 den Qualitätsgewinn wert ist.\n\nMein Urteil: **machen — aber mit Queue-Disziplin.** Parallelbetrieb auf derselben GPU wäre kein Fortschritt, sondern nur teure Wärmeproduktion mit Fehlermeldungen.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqTgnmKkzN8eTBvz0lO88rcEQYSHUFhsJOJl7hBo-LFJj-eCviNvlCdpu7x5tqAm6w67iOEjyUNr0Prw4qa1SsUmLT0KlIm_npVeAbtYjuUFnz1XSya_Dq4VdU0dL6CVe_TWvzLcykv6VKlIjz3qgUGEXdjKgFJS8rvItB2cyIOWrcJeBgqnJN6Ql-H7LQGCxBe5PvEJNRrWMY2ArF0d4OsXp23yT8xwAWbJSnbHGKguxq6myDzWGnie1NWsMse4DwLlvbdCn7upQSOE5j4RZ7Hb6WMoWV5p2k06vsvS118il-xeWRLG_OS7OltpuSriGhZKDBk9_-gvWB1qv7sGnpQF1p7HFIgTUGgYItLRd9Hbzxf24nXKer2Zig8purVskrgxIoI79g5BXBohu_cBIbPfzlG7IPs79rXfFzlV9MbEmUstLZqH66-yLluc40kFC6NRNj4SvwONBgF2LMAYPE1Tig7gHQ1s-0fealMmnGA-EEHUYojWkcvSErdmmwSOlShPTptIn-61Gzdjk5M-_M4IPXo34N4uRCCaSvSXgf32H6MVLgFoHih5bAIABS8Jf65-ojIi1KAgAI14t1nyzq-GgkVbTJqE2IcTfS2jifAJSabDFgU8TxSwFGgOKZ06hdsmrOfNS7l1DuNZoo3IaiofZyEch2Gmie8CONj_yxgy26ndVDiPV34KOxi9UqeYr-9Z5hAKgJRRcK4yUWc9hdH4BproaSDBcIlEswNUmwcrt5n9YucZ49ZD7JDM92ytBqcgLFQA_gniLV-UWlhp2TetFyAVSIgaGvY_dyDAhps4v3Zv6VCt5LNSCaGZGpQXz2EzdhYLXtHB0HMHFwxNGR-gl3x6eKQmhR6rts-WayJS8h__Inx3OKMvmv_ggcQrkxlB8BP23Lhn2fSS60pAVdqag3tDgDt33GGC8KN8caMd4vTgjy6K4kONevGYHX33kM6U8VQ3NOVCyGScsCjsUSfhdHzTJHIUfUXPEd0eN9lKDQlXEqbJsQnEnS8a6W7PF1Q-tHJ-iBP5paYLT6Njt0U6yINi7xsupk-fgjrv5YSzoM5iO_ypxcciunE5IUDTrVagoIJzw1ghMIjw_aZUVVfEfHmWkP0Wv1GQ07H4qhTVE0N0tY0IGf1ik7rtnR0DsHQYTDuFu5mfqYshf2nHFE9ULDBYfwnUlHbL_yU-m9m2oXm8iKCCdyyrAkg81HVOTKYwF8iLeVCGXF03HK_HDA6f0fnnVMLtCmVmTkAcr4lrgUKv7EqGJYtTxIHT6ThPTUWGTjh6E7louh_BM1efzdhLNEPpY1n-2VZlZaPCdfEs_-kd_FFfC9WMaCsSGM2Q8VMRmEWWtrB6NKUCBStsm89qxbVvlVT8ORVUbugNzjy1BUxrC-7FvipkjQo9UhbcaQ5AcL0Xsvymq_zUi56lR3iC8uxqG_WbWJauCOKGGCddtuwW-DJZQRrX6FdVQWUhlj4De3mnGmgKZECyjQa1o4_AH0HjwjuHtRyZIBhbsN3vNxa8B_wiqpp7Vi8dHWBntrBLjq7yqNdGB_cvmYMgSKwXuSy0CVjw4RCEL_16kcgClvRbSbxrPN47lOyEHetJkSxYrQZiBbIBBlL4EloyeVf_wHOlQbW0CKN3SjtGNZ-D1lMBKvD-OwL_RRs2Q4maKWMeJtXGOX0uN2-LrAulI-lZSDV3O7CtUWDleyT8jrcDIJFV-FsEZ75Z0hlYqypP2iH2KKayvzd_tiYoxrU8Q_O49NHU8KqT5k5L2Jt5d8PyUbNsYOiLaZk9wAa1oxe3608rwWScytL_aU7nJrlcc_j_rQpaM4EQ8bwIteKwXhRAcmFdUfv5yaRRkLXQY1PbogZfo56-f_u84M7WnJ8zA2FZPwopf7vqDgT8fM09QQBpDkn0ZtJAtEj7Ay8GSYe-j075CHpOEI7mbFCUCJFbt3flddVL_V227XYOr9edLbmBJ0nw0kJwGqFvxgHBTu582fMLZeFYTKXKx4kPzn7aUMuD_lWl34YmyUqj8hmPE-aCUoLMpFpeNeIOBb4KXhivxVDpiHYn7Cuq78OCKDGTAl8WkoA1jfLXwZWn6w3GwcwvYfSlhRSsmQ4Vg9tykjRCt-WzCZWImXz-2T33-3SdVrkJ0jcfvS3yDppOHoLJz8F8bbaGtvQ2yhbs03UmQQmzIWRLI9Bco2HdSGNZ6Ci9Y1M2uMw2pQyI3WnilvQvTMIZSR0-tXTtZsvoA-bjjK5zr3C8WxtNtQHYfvyEQr490cPVQ58kYZpxduNVA9_P8kHRtqyqokOPsUddOzZTrvWourCLVyUPvJZPW153DgbiSZ6TtMiQkuQ4UQV0try6n1dvI3SYjT8aeZSL_ykECYGogmZM7h0Aq1QGW088tQnZVkZtw-zX62TZvhf5mE1SrYEUrY1z1tRGOaokW78V__-qtsp8grE2TSWN_KO7s-tZ2juqoCQFBOYNUiGXD203tyKOir06tRJMeZWPQpWjUFMr0SwYnP-PP9qO4P5fsPq-qIQcZA==", "id": "rs_0d99e9da68392f44016a12a4d9cfb48191a706d740606c2cec", "summary": [] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Ja, Sir — **ich würde Qwen-Image-2512-GGUF lokal über ComfyUI einbauen**, aber **nicht parallel zum Video-Modell auf derselben GPU laufen lassen**.\n\nKurzfassung:\n\n- **Ja installieren:** Qwen-Image-2512-GGUF ist sinnvoll als lokaler **Keyframe-/Thumbnail-/Concept-Generator**.\n- **Nein parallel mit Wan/Video:** Auf einer 16 GB A5000 wäre das sehr wahrscheinlich VRAM/RAM-Selbstmord mit Ansage.\n- **Beste Nutzung:** Qwen Image erzeugt hochwertige Standbilder → diese gehen dann als **approved keyframes** in Wan2.2 I2V.\n- **Parallel nur organisatorisch:** Jobs in Queue, aber nicht gleichzeitig auf derselben GPU.\n\n## Warum ich es trotzdem installieren würde\n\nFür deine Retention-Studio-Pipeline ist Qwen Image sehr passend:\n\n- bessere **erste Keyframes** als viele generische SDXL/Flux-Setups\n- stark bei **Prompt-Following**\n- potenziell gut für **Text/Labels im Bild** — wobei ich weiterhin sagen würde: finale Captions, Zahlen, Pfeile und UI-Elemente besser deterministisch rendern\n- GGUF passt gut in ComfyUI, wenn ComfyUI-GGUF sauber läuft\n- Apache-2.0 ist angenehm praktisch\n\nDas Modell wäre bei uns nicht primär “final image art”, sondern eher:\n\n1. Hook/scene concept generieren \n2. beste Variante auswählen \n3. ggf. inpaint/editieren \n4. als I2V-Keyframe an Wan2.2 geben \n5. Motion/clip erzeugen \n6. Text, UI, Risk-Meter, Captions nachträglich sauber drüberlegen\n\n## Parallel mit Video? Eher nein.\n\nWenn wir von derselben GPU reden — insbesondere der bekannten **16 GB A5000-Klasse** — dann:\n\n- **Wan2.2 I2V 5B FP8** ist bereits schwer.\n- **Qwen-Image-2512-GGUF Q4/Q5/Q6** braucht zusätzlich massiv VRAM/RAM.\n- ComfyUI hält Modelle gerne im Speicher, wenn man nicht aktiv entlädt.\n- Parallelbetrieb führt wahrscheinlich zu:\n - CUDA OOM\n - ComfyUI crash\n - ewigem CPU/RAM-Offload\n - hängenden Queue-Jobs\n - “warum ist alles langsam?” — die klassische GPU-Version von Bürokratie.\n\nAlso: **nicht zwei dicke Diffusion-Modelle gleichzeitig.**\n\n## Was ich konkret machen würde\n\n**Empfohlene Architektur:**\n\n- Ein ComfyUI-Server.\n- Zwei getrennte Workflows:\n - `qwen-image-keyframe.json`\n - `wan22-i2v-clip.json`\n- Jobs nur sequenziell:\n 1. Qwen Image laden\n 2. Keyframes erzeugen\n 3. Modelle entladen / VRAM freigeben\n 4. Wan laden\n 5. Video generieren\n 6. wieder freigeben\n\nWichtig wäre nach Qwen-Jobs jeweils:\n\n```bash\ncurl -X POST http://HOST:8188/free \\\n -H \"Content-Type: application/json\" \\\n -d '{\"unload_models\": true, \"free_memory\": true}'\n```\n\nSonst bleibt die halbe Arche Noah im VRAM sitzen.\n\n## Quantisierung\n\nFür unsere wahrscheinliche Hardwareklasse:\n\n- **Start:** Q4\n- **Wenn stabil:** Q5 testen\n- **Q6 nur wenn genug Headroom**\n- **Q8 eher nicht auf 16 GB**, ausser mit sehr konservativen Settings und viel Geduld\n\nIch würde also **Qwen-Image-2512-GGUF Q4** zuerst nehmen. Nicht weil Q4 sexy ist, sondern weil “läuft stabil” in einer Pipeline mehr wert ist als “theoretisch schöner, praktisch tot”.\n\n## Meine Empfehlung\n\n**Ja, wir sollten Qwen Image in ComfyUI aufnehmen — aber als vorgelagerten Keyframe-Generator, nicht als paralleles Modell neben Wan.**\n\nPraktischer Plan:\n\n1. ComfyUI-GGUF installieren/prüfen.\n2. Qwen-Image-2512-GGUF Q4 laden.\n3. Minimalen Txt2Img-Keyframe-Workflow bauen.\n4. 3–5 Test-Keyframes für einen echten Shorts-Usecase generieren.\n5. Besten Keyframe in Wan2.2 I2V schicken.\n6. Danach entscheiden, ob Q5/Q6 den Qualitätsgewinn wert ist.\n\nMein Urteil: **machen — aber mit Queue-Disziplin.** Parallelbetrieb auf derselben GPU wäre kein Fortschritt, sondern nur teure Wärmeproduktion mit Fehlermeldungen." } ], "id": "msg_0d99e9da68392f44016a12a4e05c908191869480ecb57e5b9d", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] Wir warten noch damit. Hier habe ich gelesen dass pber chatgpt codex neu aucj bilder generiert werden können: https://www.it-daily.net/shortnews/openai-stellt-chatgpt-images-2-0-vor. Wie richten wir das ein?" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_bBTPeVXKJ8zOkVceVckr41ar", "call_id": "call_bBTPeVXKJ8zOkVceVckr41ar", "response_item_id": "fc_0d99e9da68392f44016a12a6e8009c819186132bfef5eb6c07", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"hermes-agent\"}" } }, { "id": "call_bmjOQ1BtBMZm14xQzG3t8vEj", "call_id": "call_bmjOQ1BtBMZm14xQzG3t8vEj", "response_item_id": "fc_0d99e9da68392f44016a12a6e800b081919ccaa5c23e3f5d94", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"codex\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering configuration for image generation**\n\nI need to figure out how to configure ChatGPT for image generation, especially regarding the hermes-agent. The user seems to want specifics on setup or usage of Hermes, possibly tied to codex images. I guess I should load the hermes-agent and also think about the codex skills. I’ll also need to check some current facts online to ensure I'm working with up-to-date information. Let's dive into loading the hermes-agent!", "reasoning_content": "**Considering configuration for image generation**\n\nI need to figure out how to configure ChatGPT for image generation, especially regarding the hermes-agent. The user seems to want specifics on setup or usage of Hermes, possibly tied to codex images. I guess I should load the hermes-agent and also think about the codex skills. I’ll also need to check some current facts online to ensure I'm working with up-to-date information. Let's dive into loading the hermes-agent!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqbo5nrWCuYISeHH_i4m61Pq0tk-UdCpLmk3jQ7KRJCUwFRBbEaYR9s0HTmB_Z3c6BT0R_gfZN9U5rC9COFDaBu2jGMVS_QorFJthlWchchWUSDVNktwso8SSly6pOwYdfmaGlchxb9RaekhfVXnwiatiQcNJoPinn_dsTEOD9rpmK3zVbpLOg904ou_J-WeTgV7Eisy0wr2zznqJ3RQpsiQlZnTZcBmQwwS9xX3HhExFkv4HeIdG-t3aCYCapnwQyQ36eD06mn_94f8cMa_HRTkZWUHRT6E9DqGpvlDY8rigK4xnpTEn2YIH0aJTtEjvuMD_icy0dd4rjQv4q_Y2q7LIuO2iXTtgsZ8DEymN3a8H2BKuApFEZcx-LLzXNJ0XNUZO9QR8A6keO8bemSqrcyCtGVKaNfSIVXLAMBJ1uf7gGffo2ObvH2B2QSYmBYJ8T7AgmpTz8M7q_ZSd5WdA7dXPIjkISKNAJNNmTSsD8OFV65_lt4wuSGeqTNQ3IfvgLhGxN2nlqlcBYh5hgxTgy6UCzYQjSEfW4KEysfkToFMwwgR1wz3dPeP84pJvnTLg9o_b7rRUUl3ln8_-5ZAniZRTzEYSUjO8AorpEyVzOu9CuWD94qXA7YyuTh5AzNxh6TBgV8c6zpjHmd0l867XTCB2rAwegq0L5YhTS4z94A5aP3QYXOikYyDp0SfSeaMIdw9_HnfmMuaTPDk4h7fr-0OwXfsHQRgOtB-YXPXMMcZOu0n2dskbBtpJxraMg7Sa8DMQS-TLn0aCAWRWaoQd0ZHwj_dEgjo9XqrQlsvoUwk9581h5QRj9Joq7z4Unnbvsgy1Bs5yDowF0d4qvVG00rGDaEI58gED_H8JVOIkAwju_FH11MM9QX9_oH6RHzoH_kWyUxbY6TFX0l495tB033urUFVq9OsiPWOdwklPC2PdpCAoT6QVrwYafgCsjIEGiG0uDVFQZmk5tkIaBGNH5e-wheUZAg8IAaXQujGaTtduisAr6pPxVXFz_2zaU3M9tVHf9tEHDK6PpnOboZ9OxkVKVqChWYtWK5lnqyNeBgpMi4eEgtk805K7unQmMxRHaCLX2IoCpvyPa1j2oVA8e2VBWGtEQGtQKxVOI589oDkaAT__CWUMEDtYlMU5n_tldZb3Jqg52YKaMdrdQFA4osGzwVl0nzwzPCQj8C3b2rIM3eXmi10BaGDjrBCXfpSHs-y_NJRopDRl8da6JYeJCUIy-AcYicKzSZdkyrJJxiW5IoPAfK7ITWcHAqlBv_vc5NgX0bXGiVXQtFARA9P4cEgurzNLQZyv5025oW0Z5WAFcJMN4ai_9fqaIP-O9qelfr-igjZe7G1V2h-mzR1v3i6GfXEA8lmt9X6Bytv0Szes892EHurwTU7MNomX7EMwjs4GoaHvB8FLS2R4zXxBftYgOCv3wLDWM96w5pnIfVv0O-iFBQzu7z2dWGDRKn8BzIwom-zmgHazvAcf92eXUKaLbeKPius9edS66M8d6Xq-LFhkeyhse3PeY7yHZGxjSL0", "id": "rs_0d99e9da68392f44016a12a6e410ec8191aebdfaf9f4f30e8c", "summary": [ { "type": "summary_text", "text": "**Considering configuration for image generation**\n\nI need to figure out how to configure ChatGPT for image generation, especially regarding the hermes-agent. The user seems to want specifics on setup or usage of Hermes, possibly tied to codex images. I guess I should load the hermes-agent and also think about the codex skills. I’ll also need to check some current facts online to ensure I'm working with up-to-date information. Let's dive into loading the hermes-agent!" } ] } ] }, { "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_bBTPeVXKJ8zOkVceVckr41ar" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"codex\", \"description\": \"Delegate coding to OpenAI Codex CLI (features, PRs).\", \"tags\": [\"Coding-Agent\", \"Codex\", \"OpenAI\", \"Code-Review\", \"Refactoring\"], \"related_skills\": [\"claude-code\", \"hermes-agent\"], \"content\": \"---\\nname: codex\\ndescription: \\\"Delegate coding to OpenAI Codex CLI (features, PRs).\\\"\\nversion: 1.0.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [Coding-Agent, Codex, OpenAI, Code-Review, Refactoring]\\n related_skills: [claude-code, hermes-agent]\\n---\\n\\n# Codex CLI\\n\\nDelegate coding tasks to [Codex](https://github.com/openai/codex) via the Hermes terminal. Codex is OpenAI's autonomous coding agent CLI.\\n\\n## When to use\\n\\n- Building features\\n- Refactoring\\n- PR reviews\\n- Batch issue fixing\\n\\nRequires the codex CLI and a git repository.\\n\\n## Prerequisites\\n\\n- Codex installed: `npm install -g @openai/codex`\\n- OpenAI auth configured: either `OPENAI_API_KEY` or Codex OAuth credentials\\n from the Codex CLI login flow\\n- **Must run inside a git repository** — Codex refuses to run outside one\\n- Use `pty=true` in terminal calls — Codex is an interactive terminal app\\n\\nFor Hermes itself, `model.provider: openai-codex` uses Hermes-managed Codex\\nOAuth from `~/.hermes/auth.json` after `hermes auth add openai-codex`. For the\\nstandalone Codex CLI, a valid CLI OAuth session may live under\\n`~/.codex/auth.json`; do not treat a missing `OPENAI_API_KEY` alone as proof\\nthat Codex auth is missing.\\n\\n## One-Shot Tasks\\n\\n```\\nterminal(command=\\\"codex exec 'Add dark mode toggle to settings'\\\", workdir=\\\"~/project\\\", pty=true)\\n```\\n\\nFor scratch work (Codex needs a git repo):\\n```\\nterminal(command=\\\"cd $(mktemp -d) && git init && codex exec 'Build a snake game in Python'\\\", pty=true)\\n```\\n\\n## Background Mode (Long Tasks)\\n\\n```\\n# Start in background with PTY\\nterminal(command=\\\"codex exec --full-auto 'Refactor the auth module'\\\", workdir=\\\"~/project\\\", background=true, pty=true)\\n# Returns session_id\\n\\n# Monitor progress\\nprocess(action=\\\"poll\\\", session_id=\\\"\\\")\\nprocess(action=\\\"log\\\", session_id=\\\"\\\")\\n\\n# Send input if Codex asks a question\\nprocess(action=\\\"submit\\\", session_id=\\\"\\\", data=\\\"yes\\\")\\n\\n# Kill if needed\\nprocess(action=\\\"kill\\\", session_id=\\\"\\\")\\n```\\n\\n## Key Flags\\n\\n| Flag | Effect |\\n|------|--------|\\n| `exec \\\"prompt\\\"` | One-shot execution, exits when done |\\n| `--full-auto` | Sandboxed but auto-approves file changes in workspace |\\n| `--yolo` | No sandbox, no approvals (fastest, most dangerous) |\\n\\n## PR Reviews\\n\\nClone to a temp directory for safe review:\\n\\n```\\nterminal(command=\\\"REVIEW=$(mktemp -d) && git clone https://github.com/user/repo.git $REVIEW && cd $REVIEW && gh pr checkout 42 && codex review --base origin/main\\\", pty=true)\\n```\\n\\n## Parallel Issue Fixing with Worktrees\\n\\n```\\n# Create worktrees\\nterminal(command=\\\"git worktree add -b fix/issue-78 /tmp/issue-78 main\\\", workdir=\\\"~/project\\\")\\nterminal(command=\\\"git worktree add -b fix/issue-99 /tmp/issue-99 main\\\", workdir=\\\"~/project\\\")\\n\\n# Launch Codex in each\\nterminal(command=\\\"codex --yolo exec 'Fix issue #78: . Commit when done.'\\\", workdir=\\\"/tmp/issue-78\\\", background=true, pty=true)\\nterminal(command=\\\"codex --yolo exec 'Fix issue #99: . Commit when done.'\\\", workdir=\\\"/tmp/issue-99\\\", background=true, pty=true)\\n\\n# Monitor\\nprocess(action=\\\"list\\\")\\n\\n# After completion, push and create PRs\\nterminal(command=\\\"cd /tmp/issue-78 && git push -u origin fix/issue-78\\\")\\nterminal(command=\\\"gh pr create --repo user/repo --head fix/issue-78 --title 'fix: ...' --body '...'\\\")\\n\\n# Cleanup\\nterminal(command=\\\"git worktree remove /tmp/issue-78\\\", workdir=\\\"~/project\\\")\\n```\\n\\n## Batch PR Reviews\\n\\n```\\n# Fetch all PR refs\\nterminal(command=\\\"git fetch origin '+refs/pull/*/head:refs/remotes/origin/pr/*'\\\", workdir=\\\"~/project\\\")\\n\\n# Review multiple PRs in parallel\\nterminal(command=\\\"codex exec 'Review PR #86. git diff origin/main...origin/pr/86'\\\", workdir=\\\"~/project\\\", background=true, pty=true)\\nterminal(command=\\\"codex exec 'Review PR #87. git diff origin/main...origin/pr/87'\\\", workdir=\\\"~/project\\\", background=true, pty=true)\\n\\n# Post results\\nterminal(command=\\\"gh pr comment 86 --body ''\\\", workdir=\\\"~/project\\\")\\n```\\n\\n## Rules\\n\\n1. **Always use `pty=true`** — Codex is an interactive terminal app and hangs without a PTY\\n2. **Git repo required** — Codex won't run outside a git directory. Use `mktemp -d && git init` for scratch\\n3. **Use `exec` for one-shots** — `codex exec \\\"prompt\\\"` runs and exits cleanly\\n4. **`--full-auto` for building** — auto-approves changes within the sandbox\\n5. **Background for long tasks** — use `background=true` and monitor with `process` tool\\n6. **Don't interfere** — monitor with `poll`/`log`, be patient with long-running tasks\\n7. **Parallel is fine** — run multiple Codex processes at once for batch work\\n\", \"path\": \"autonomous-ai-agents/codex/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/autonomous-ai-agents/codex\", \"linked_files\": null, \"usage_hint\": null, \"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\": [\"Coding-Agent\", \"Codex\", \"OpenAI\", \"Code-Review\", \"Refactoring\"], \"related_skills\": [\"claude-code\", \"hermes-agent\"]}}}", "tool_call_id": "call_bmjOQ1BtBMZm14xQzG3t8vEj" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_uxsP0DCL1pXrpI0DAL1Ood2P", "call_id": "call_uxsP0DCL1pXrpI0DAL1Ood2P", "response_item_id": "fc_0d99e9da68392f44016a12a6ee25788191b673fcadf29ffc63", "type": "function", "function": { "name": "browser_navigate", "arguments": "{\"url\":\"https://www.it-daily.net/shortnews/openai-stellt-chatgpt-images-2-0-vor\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqbuyqNosww3eAPg4TMsZxZbuTqbH4zCwjJYNj8W-GeCM2BRr82z-Xb8yfrDHmUVJjNq8DjEvQAfP5qN_6QUjtfX1w4RnRscGnnjXZdtKdOhRakV6mLd7hQZpnq21SjAvnG4QnfUvYe3EOTZECoT4LnzSl1laYN2aHP9eoY0xeJTBeOb5GKLgQBriCEwFQzaBsOGavVK_tbK3sc2IwzAyvOjpMJ50_kN0ZYftm9ohj7MPUSLWx32wVonp8d1x4D86e1gY0chB282Md-M7uXsWwCmNABzdaPc-1AfWIjuWsu8oTjpU6to405Uzg-dIUN877lkFvHpS-7uDfuKLFXjYmUu3oYNc1aP6sAUmYby1t1lS4iB4FobI1toc1YgbYVxg1W5gqYFx6C8U_dhUVutMDaJnoEpmY7oyCzZ4-OzNcecVaK8nhuEWO2I_hYMV9jowUiAqb-xbmxPSfPkqzF2wUI0OBUc3cLAJRaCYbv4kcW8k9Dnt8ZcWXKG5NjSj7ok687m8pwWFYZwkYLbPyu4diLo0CorTV8tFyiqwtnZZHL0BfmPD3vtCW-pCI8Spv9BC1Da6MLyZ7ilMDAZhvw7IknsDupeWhplcgyh_3qovIyQ-cuhuoDUinN_ikLzURhOPEYhcNVEi33miRoVY45iwl_bOafkH3prS6U1bBCs-GbqUEt6ZGsfOoRlEUke8sJrzwHFsAosEh5NZmUh1xLem3_wgMDiTc_wOR2CYx0yFBjgVrDTdGPwMkiqsbKPUgeBqAOzjZVqgtRF5_Nju0Y4J9UtovWj3KPV6lHf8nh-dCpLvFnnGmuiZeEaAThvJ2AkBIDKL60MaEnmEl9PXSHKix62TB4apil2hPKbkWz9S7J90v6EYLbrqA_0TgB6CdJIM9xZ5RTTK9GgH5gl5sb5RYgbyYmtKrnApufK7Q8OmkI1D1317YYPiOxpF4uRAg0YQs3E51ZNubQVPsBzc6GU21fzkLumGg3lT5rrIPVXUq4DIR4ITtG9gEPlcgmy3eiBpXmRy9qH0gCLDJ0-jiUksN51ApAQA8oHzCqaTLVawXHCTW6QHpykb_S6hbIBdKa-rMm0TGZd9_NOLfPLYMYsjzJ5pNoeBvZTdqMVsILzz9VLi1zioUp3dPwc7Kb6FgHxn7nIUkXwXy4oirN928qbD184kgy-2_FC71GSkfCq0FXlrqHfYIDTzX7Gm3rFnm9ZgZT_", "id": "rs_0d99e9da68392f44016a12a6eacc288191b97de92182454363", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": true, \"url\": \"https://www.it-daily.net/shortnews/openai-stellt-chatgpt-images-2-0-vor\", \"title\": \"KI-Bildgenerierung: OpenAI stellt ChatGPT Images 2.0 vor\", \"stealth_warning\": \"Running WITHOUT residential proxies. Bot detection may be more aggressive. Consider upgrading Browserbase plan for proxy support.\", \"stealth_features\": [\"local\"], \"snapshot\": \"- generic\\n - Iframe \\\"Contentpass First Layer\\\" [ref=e2]\\n - main\\n - alertdialog \\\"Weiter mit Werbung …\\\"\\n - generic\\n - image \\\"it-daily.net logo\\\"\\n - generic\\n - article\\n - heading \\\"Weiter mit Werbung …\\\" [level=1, ref=e134]\\n - generic\\n - paragraph\\n - StaticText \\\"Nutzen Sie it-daily.net mit personalisierter Werbung, Tracking und Cookies \\\"\\n - button \\\"entgeltfrei. (Mehr erfahren)\\\" [ref=e149]\\n - paragraph\\n - StaticText \\\"Für die Nutzung mit Werbung: Wir verarbeiten personenbezogene Daten (bspw. mittels Cookies, persönlichen Identifikatoren, IP-Adressen, individuelles Nutzungsverhalten) und übermitteln diese auch an Drittanbieter, die uns helfen, unser Webangebot zu verbessern und zu finanzieren. Dies umfasst auch die Übermittlung personenbezogener Daten in Drittländer (außerhalb der EU und EWR), insbesondere die USA, nach Art. 49 Abs. 1 a) DSGVO, wo ein vergleichbares Datenschutzniveau wie in der EU nicht gewährleistet ist. Indem Sie auf \\\\\\\"Akzeptieren und weiter\\\\\\\" klicken, willigen Sie hierzu (widerruflich) ein. Sie können Ihre Einwilligung jederzeit unter \\\\\\\"Widerruf Tracking\\\\\\\" in unserer \\\"\\n - link \\\"Datenschutzerklärung\\\" [ref=e150]\\n - StaticText \\\" mit Wirkung für die Zukunft widerrufen. Eine Verarbeitung der auf Ihrem Gerät gespeicherten Informationen wie z.B. 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April, 2026\\n15:55\\nBildquelle: jackpress / Shutterstock.com\\nFacebook\\nX\\nLinkedIn\\nReddit\\nWhatsApp\\n\\nMit ChatGPT Images 2.0 und dem Modell gpt-image-2 aktualisiert OpenAI seine KI-Bildgenerierung, mit mehr Präzision, neuen Ausgabeformaten und API-Zugang für Entwickler.\\n\\nOpenAI hat ChatGPT Images 2.0 veröffentlicht, ein aktualisiertes Bildgenerierungsmodell, das in ChatGPT und Codex integriert ist. Erstmals steht das zugrundeliegende Modell gpt-image-2 auch über eine API bereit. Laut OpenAI können Entwickler damit „Bildgenerierung und -bearbeitung direkt in ihre Produkte und Workflows integrieren.“\\n\\nAnzeige\\n\\nImages 2.0 soll komplexe Anweisungen deutlich zuverlässiger als die Vorgängerversion bearbeiten. Verbessert wurden die Darstellung kleiner Textelemente und UI-Komponenten, die Objektpositionierung sowie die Unterstützung von Seitenverhältnissen zwischen 3:1 und 1:3.\\n\\nThinking-Funktion und Mehrfachausgabe\\n\\nPro Anfrage kann das Modell bis zu acht inhaltlich zusammenhängende Bilder ausgeben, laut OpenAI „ideal für Poster-Sets, Storyboards, Comics oder Multi-Format-Kampagnen.“\\n\\nFür Nutzer mit Plus-, Pro-, Business- oder Enterprise-Tarif steht zusätzlich eine Thinking-Funktion bereit. Das Modell kann dabei „Aufgaben durchdenken, bei Bedarf das Web nutzen und aus einem einzigen Prompt mehrere unterschiedliche Bilder generieren.“ Alle anderen ChatGPT-Nutzer haben weiterhin Zugang zu den Basisfunktionen.\\n\\nVerfügbarkeit im Überblick\\nBasisfunktionen: alle ChatGPT- und Codex-Nutzer\\nThinking-Funktionen: Plus-, Pro-, Business- und Enterprise-Tarif\\nAPI-Zugang: über gpt-image-2 für Entwickler verfügbar\\n\\nTechnische Details und Preismodell hat OpenAI im zugehörigen Blogpost veröffentlicht.\\n\\nChatGPT\\nOpenAI\\n\\n\\nLars\\nBecker\\n\\nStellvertretender Chefredakteur\\n\\nIT Verlag GmbH\\n\\nAnzeige\\n24. Mai 2026\\nRansomware – Viele CISOs würden Lösegeld zahlen\\n24. Mai 2026\\n10 Jahre DS-GVO: Wachsende Bürokratie und KI-Hürden belasten Wirtschaft\\n24. Mai 2026\\nBetrugsmasche CypherLoc setzt Nutzer mit Browser-Sperren unter Druck\\n24. Mai 2026\\nEnergiesparwunder am Edge: Prozessoren im Takt des menschlichen Gehirns\\nWeitere Artikel\\n10 Jahre DS-GVO: Wachsende Bürokratie und KI-Hürden belasten Wirtschaft\\nSatelliten-Kommunikation: Europa testet Laser-Datenübertragung\\nBetrugsanzeigen: Meta, Google und TikTok löschen gemeldete Werbung kaum\\nCyberangriff auf Patientendaten der Uniklinik Düsseldorf\\nNewsletter Box\\nJetzt die smarten News aus der IT-Welt abonnieren! 💌\\nJetzt Anmelden!\\n it management it security it-daily Redaktion\\n\\nMit Klick auf den Button \\\"Jetzt Anmelden\\\" stimme ich der Datenschutzerklärung zu.\", \"result_type\": \"str\"}", "tool_call_id": "call_8genoGGgISNd4v2Z5c2KQR2W" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rYQBl2WNI7bY8bh0fWnxPCge", "call_id": "call_rYQBl2WNI7bY8bh0fWnxPCge", "response_item_id": "fc_0d99e9da68392f44016a12a6fe85e88191a4b7e8bcd7b3f793", "type": "function", "function": { "name": "search_files", "arguments": "{\"pattern\":\"image_gen|gpt-image|IMAGE|OPENAI\",\"target\":\"content\",\"path\":\"/home/agent/.hermes/hermes-agent\",\"file_glob\":\"*.py\",\"limit\":80}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqb-HP7Nkx_3S9O4TOLsBsr6FTyZsff551OVtlUPlro_MUyyPhG62O82j0GrAOHwoY8Np-6a--JFTwKQxvrb2_TCJFJZPT9tEyG41vSsnbFNIOJxWl7HxZsB7rMVCrIFO-TNXVEmsxmKHdgfOlfkk4gbnm8Q_Ix-Utwl9pGQJYDtiF4O3pNqUFoW-Uw95u--PNAruoIJX8xfFZoEP7g0-YRyb7mAFC85MiI5YSvubgokj4Jwus71nLuFO_juXKL-hPalHPdpHmRWOCSixFWBsVWZFyD3CWjRRGs_mDGuUMGZJoOnEjXo88I7zIq2WeRvko5iPlqYUlEejGhUzzrqPDOQNHKJVjJK58BABmnlQ6lM9NEmhZTvOXOvF95t0W2NKrCnUAI7snp7yHZG7F8Y2-4Oh9rBk7X2GX2Umlhnhtr1klzQyWLM8fFdHMgDnEYbhEU91xueWjNfj4HGhXsGQ1Wbp3r2XhPxNfaDDdq2I5u7vN4bR_fholHDrWEbeTSvINBz7tcSaBavD7ED6Gy-BLlV4PYBxnR-8iqt08x_WvWPIGfBIgRzGCxSug00HSs-2s0eFSZWY3cGaO0vb5qV24zg6Iq_L4f92k6rtW2fS40r7BUhXEMY-TcvUT8FbZjz8a5rlt8uMFG5Boc0cJ2xLAxbT4yhrbUknCmA_d3cUrpwt-ael9YFXcaUsUiXfDBSq7jpB5A049mODtvVMD5tYasqoRvmKL71Tvc0O828jlKds8vyl4vrMVw253xAi2SoAr8m-ihmQOB1NvtgL3ZjWkvhdCw2NHdANymvoCAgXMK5UnoAxRkS2klS_w7EqYuu2mbEouFEIl0nOHux20nBlchgVYedj5bdglQKwGZ9ESAv9lmZUjmOAxfo5N7sppqL4atnQ5rApgLJWlyK-sTkTXxvEijIaSvuGlJBAxlt3trga046At4Vb8ZLwdLLmmxDzsvSgghFtlS0SI0V88ImwRyfYrX8fdJWAAhwtIBfjIorldXJmMi_sjOdGd93AKKqLUFwzQLrkaXbwdC53Oz1pQI4hdQBeHc6HmMEh7pJdddM7S78yMl5VbPYZGreOrySIjeuwMqVOuR2kUZG2ounQPucX55CpwkmCdSXQ8aRn61WXb0R0q9fUSD9BpFCg1szuOXGEE4ggQbstjbMD93tveagikLh-lmrk-9TiY7CF7sVh_u8OeqtuKWzQd05_BGp5FZkmd02tXTjn7mSbWm3k9wpfp6y-mOaoufdRg7OjhZKCrU=", "id": "rs_0d99e9da68392f44016a12a6fbf4c481918c49bb2274d22c44", "summary": [] } ] }, { "role": "tool", "content": "{\"total_count\": 0}\n\n[Subdirectory context discovered: .hermes/hermes-agent/AGENTS.md]\n# Hermes Agent - Development Guide\n\nInstructions for AI coding assistants and developers working on the hermes-agent codebase.\n\n## Development Environment\n\n```bash\n# Prefer .venv; fall back to venv if that's what your checkout has.\nsource .venv/bin/activate # or: source venv/bin/activate\n```\n\n`scripts/run_tests.sh` probes `.venv` first, then `venv`, then\n`$HOME/.hermes/hermes-agent/venv` (for worktrees that share a venv with the\nmain checkout).\n\n## Project Structure\n\nFile counts shift constantly — don't treat the tree below as exhaustive.\nThe canonical source is the filesystem. The notes call out the load-bearing\nentry points you'll actually edit.\n\n```\nhermes-agent/\n├── run_agent.py # AIAgent class — core conversation loop (~12k LOC)\n├── model_tools.py # Tool orchestration, discover_builtin_tools(), handle_function_call()\n├── toolsets.py # Toolset definitions, _HERMES_CORE_TOOLS list\n├── cli.py # HermesCLI class — interactive CLI orchestrator (~11k LOC)\n├── hermes_state.py # SessionDB — SQLite session store (FTS5 search)\n├── hermes_constants.py # get_hermes_home(), display_hermes_home() — profile-aware paths\n├── hermes_logging.py # setup_logging() — agent.log / errors.log / gateway.log (profile-aware)\n├── batch_runner.py # Parallel batch processing\n├── agent/ # Agent internals (provider adapters, memory, caching, compression, etc.)\n├── hermes_cli/ # CLI subcommands, setup wizard, plugins loader, skin engine\n├── tools/ # Tool implementations — auto-discovered via tools/registry.py\n│ └── environments/ # Terminal backends (local, docker, ssh, modal, daytona, singularity)\n├── gateway/ # Messaging gateway — run.py + session.py + platforms/\n│ ├── platforms/ # Adapter per platform (telegram, discord, slack, whatsapp,\n│ │ # homeassistant, signal, matrix, mattermost, email, sms,\n│ │ # dingtalk, wecom, weixin, feishu, qqbot, bluebubbles,\n│ │ # yuanbao, webhook, api_server, ...). See ADDING_A_PLATFORM.md.\n│ └── builtin_hooks/ # Extension point for always-registered gateway hooks (none shipped)\n├── plugins/ # Plugin system (see \"Plugins\" section below)\n│ ├── memory/ # Memory-provider plugins (honcho, mem0, supermemory, ...)\n│ ├── context_engine/ # Context-engine plugins\n│ ├── model-providers/ # Inference backend plugins (openrouter, anthropic, gmi, ...)\n│ ├── kanban/ # Multi-agent board dispatcher + worker plugin\n│ ├── hermes-achievements/ # Gamified achievement tracking\n│ ├── observability/ # Metrics / traces / logs plugin\n│ ├── image_gen/ # Image-generation providers\n│ └── / # disk-cleanup, example-dashboard, google_meet, platforms,\n│ # spotify, strike-freedom-cockpit, ...\n├── optional-skills/ # Heavier/niche skills shipped but NOT active by default\n├── skills/ # Built-in skills bundled with the repo\n├── ui-tui/ # Ink (React) terminal UI — `hermes --tui`\n│ └── src/ # entry.tsx, app.tsx, gatewayClient.ts + app/components/hooks/lib\n├── tui_gateway/ # Python JSON-RPC backend for the TUI\n├── acp_adapter/ # ACP server (VS Code / Zed / JetBrains integration)\n├── cron/ # Scheduler — jobs.py, scheduler.py\n├── scripts/ # run_tests.sh, release.py, auxiliary scripts\n├── website/ # Docusaurus docs site\n└── tests/ # Pytest suite (~17k tests across ~900 files as of May 2026)\n```\n\n**User config:** `~/.hermes/config.yaml` (settings), `~/.hermes/.env` (API keys only).\n**Logs:** `~/.hermes/logs/` — `agent.log` (INFO+), `errors.log` (WARNING+),\n`gateway.log` when running the gateway. Profile-aware via `get_hermes_home()`.\nBrowse with `hermes logs [--follow] [--level ...] [--session ...]`.\n\n## File Dependency Chain\n\n```\ntools/registry.py (no deps — imported by all tool files)\n ↑\ntools/*.py (each calls registry.register() at import time)\n ↑\nmodel_tools.py (imports tools/registry + triggers tool discovery)\n ↑\nrun_agent.py, cli.py, batch_runner.py, environments/\n```\n\n---\n\n## AIAgent Class (run_agent.py)\n\nThe real `AIAgent.__init__` takes ~60 parameters (credentials, routing, callbacks,\nsession context, budget, credential pool, etc.). The signature below is the\nminimum subset you'll usually touch — read `run_agent.py` for the full list.\n\n```python\nclass AIAgent:\n def __init__(self,\n base_url: str = None,\n api_key: str = None,\n provider: str = None,\n api_mode: str = None, # \"chat_completions\" | \"codex_responses\" | ...\n model: str = \"\", # empty → resolved from config/provider later\n max_iterations: int = 90, # tool-calling iterations (shared with subagents)\n enabled_toolsets: list = None,\n disabled_toolsets: list = None,\n quiet_mode: bool = False,\n save_trajectories: bool = False,\n platform: str = None, # \"cli\", \"telegram\", etc.\n session_id: str = None,\n skip_context_files: bool = False,\n skip_memory: bool = False,\n credential_pool=None,\n # ... plus callbacks, thread/user/chat IDs, iteration_budget, fallback_model,\n # checkpoints config, prefill_messages, service_tier, reasoning_config, etc.\n ): ...\n\n def chat(self, message: str) -> str:\n \"\"\"Simple interface — returns final response string.\"\"\"\n\n def run_conversation(self, user_message: str, system_message: str = None,\n conversation_history: list = None, task_id: str = None) -> dict:\n \"\"\"Full interface — returns dict with final_response + messages.\"\"\"\n```\n\n### Agent Loop\n\nThe core loop is inside `run_conversation()` — entirely synchronous, with\ninterrupt checks, budget tracking, and a one-turn grace call:\n\n```python\nwhile (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) \\\n or self._budget_grace_call:\n if self._interrupt_requested: break\n response = client.chat.completions.create(model=model, messages=messages, tools=tool_schemas)\n if response.tool_calls:\n for tool_call in response.tool_calls:\n result = handle_function_call(tool_call.name, tool_call.args, task_id)\n messages.append(tool_result_message(result))\n api_call_count += 1\n else:\n return response.content\n```\n\nMessages follow OpenAI format: `{\"role\": \"system/user/assistant/tool\", ...}`.\nReasoning content is stored in `assistant_msg[\"reasoning\"]`.\n\n---\n\n## CLI Architecture (cli.py)\n\n- **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete\n- **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results\n- `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML\n- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text\n- `process_command()` is a method on `HermesCLI` — dispatches on canonical command name resolved via `resolve_command()` from the central registry\n- Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching\n\n### Slash Command Registry (`hermes_cli/commands.py`)\n\nAll slash commands are defined in a central `COMMAND_REGISTRY` list of `CommandDef` objects. Every downstream consumer derives from this registry automatically:\n\n- **CLI** — `process_command()` resolves aliases via `resolve_command()`, dispatches on canonical name\n- **Gateway** — `GATEWAY_KNOWN_COMMANDS` frozenset for hook emission, `resolve_command()` for dispatch\n- **Gateway help** — `gateway_help_lines()` generat\n\n[...truncated AGENTS.md: 50,982 chars total]", "tool_call_id": "call_rYQBl2WNI7bY8bh0fWnxPCge" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_fAj3NXnMgmi2U3vv0E0kaefJ", "call_id": "call_fAj3NXnMgmi2U3vv0E0kaefJ", "response_item_id": "fc_0d99e9da68392f44016a12a702d1e8819181715fd9b889e544", "type": "function", "function": { "name": "search_files", "arguments": "{\"target\":\"files\",\"path\":\"/home/agent/.hermes/hermes-agent\",\"pattern\":\"*image*\",\"limit\":100}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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Multiple FAL models are supported and\\n 6|selectable via ``hermes tools`` → Image Generation; the active model is\\n 7|persisted to ``image_gen.model`` in ``config.yaml``.\\n 8|\\n 9|Architecture:\\n 10|- ``FAL_MODELS`` is a catalog of supported models with per-model metadata\\n 11| (size-style family, defaults, ``supports`` whitelist, upscaler flag).\\n 12|- ``_build_fal_payload()`` translates the agent's unified inputs (prompt +\\n 13| aspect_ratio) into the model-specific payload and filters to the\\n 14| ``supports`` whitelist so models never receive rejected keys.\\n 15|- Upscaling via FAL's Clarity Upscaler is gated per-model via the ``upscale``\\n 16| flag — on for FLUX 2 Pro (backward-compat), off for all faster/newer models\\n 17| where upscaling would either hurt latency or add marginal quality.\\n 18|\\n 19|Pricing shown in UI strings is as-of the initial commit; we accept drift and\\n 20|update when it's noticed.\\n 21|\\\"\\\"\\\"\\n 22|\\n 23|import json\\n 24|import logging\\n 25|import os\\n 26|import datetime\\n 27|import threading\\n 28|import uuid\\n 29|from typing import Any, Dict, Optional, Union\\n 30|from urllib.parse import urlencode\\n 31|\\n 32|# fal_client is imported lazily — see _load_fal_client(). Pulling it\\n 33|# eagerly added ~64 ms to every CLI cold start because\\n 34|# discover_builtin_tools() imports this module unconditionally during\\n 35|# the registry walk, even when image generation is never used.\\n 36|#\\n 37|# Tests that monkeypatch this attribute (e.g.\\n 38|# ``monkeypatch.setattr(image_tool, \\\"fal_client\\\", fake_fal_client)``)\\n 39|# still work: _load_fal_client() short-circuits when the attribute is\\n 40|# anything truthy, so a test-installed mock is not overwritten by a\\n 41|# subsequent real import.\\n 42|fal_client: Any = None\\n 43|\\n 44|\\n 45|def _load_fal_client() -> Any:\\n 46| \\\"\\\"\\\"Lazily import fal_client and rebind the module global on first use.\\n 47|\\n 48| Idempotent. Returns the (now-loaded) ``fal_client`` module reference.\\n 49| Skips the import if the global is already truthy — this preserves the\\n 50| test pattern of monkeypatching the module global to install a mock.\\n 51| \\\"\\\"\\\"\\n 52| global fal_client\\n 53| if fal_client is not None:\\n 54| return fal_client\\n 55| try:\\n 56| from tools.lazy_deps import ensure as _lazy_ensure\\n 57| _lazy_ensure(\\\"image.fal\\\", prompt=False)\\n 58| except ImportError:\\n 59| pass\\n 60| except Exception as e:\\n 61| raise ImportError(str(e))\\n 62| import fal_client as _fal_client # noqa: F811 — module-global rebind\\n 63| fal_client = _fal_client\\n 64| return fal_client\\n 65|\\n 66|\\n 67|from tools.debug_helpers import DebugSession\\n 68|from tools.managed_tool_gateway import resolve_managed_tool_gateway\\n 69|from tools.tool_backend_helpers import (\\n 70| fal_ke...ured,\\n 71| managed_nous_tools_enabled,\\n 72| prefers_gateway,\\n 73|)\\n 74|\\n 75|logger = logging.getLogger(__name__)\\n 76|\\n 77|\\n 78|# ---------------------------------------------------------------------------\\n 79|# FAL model catalog\\n 80|# ---------------------------------------------------------------------------\\n 81|#\\n 82|# Each entry declares how to translate our unified inputs into the model's\\n 83|# native payload shape. Size specification falls into three families:\\n 84|#\\n 85|# \\\"image_size_preset\\\" — preset enum (\\\"square_hd\\\", \\\"landscape_16_9\\\", ...)\\n 86|# used by the flux family, z-image, qwen, recraft,\\n 87|# ideogram.\\n 88|# \\\"aspect_ratio\\\" — aspect ratio enum (\\\"16:9\\\", \\\"1:1\\\", ...) used by\\n 89|# nano-banana (Gemini).\\n 90|# \\\"gpt_literal\\\" — literal dimension strings (\\\"1024x1024\\\", etc.)\\n 91|# used by gpt-image-1.5.\\n 92|#\\n 93|# ``supports`` is a whitelist of keys allowed in the outgoing payload — any\\n 94|# key outside this set is stripped before submission so models never receive\\n 95|# rejected parameters (each FAL model rejects unknown keys differently).\\n 96|#\\n 97|# ``upscale`` controls whether to chain Clarity Upscaler after generation.\\n 98|\\n 99|FAL_MODELS: Dict[str, Dict[str, Any]] = {\\n 100| \\\"fal-ai/flux-2/klein/9b\\\": {\\n 101| \\\"display\\\": \\\"FLUX 2 Klein 9B\\\",\\n 102| \\\"speed\\\": \\\"<1s\\\",\\n 103| \\\"strengths\\\": \\\"Fast, crisp text\\\",\\n 104| \\\"price\\\": \\\"$0.006/MP\\\",\\n 105| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 106| \\\"sizes\\\": {\\n 107| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 108| \\\"square\\\": \\\"square_hd\\\",\\n 109| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 110| },\\n 111| \\\"defaults\\\": {\\n 112| \\\"num_inference_steps\\\": 4,\\n 113| \\\"output_format\\\": \\\"png\\\",\\n 114| \\\"enable_safety_checker\\\": False,\\n 115| },\\n 116| \\\"supports\\\": {\\n 117| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"seed\\\",\\n 118| \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 119| },\\n 120| \\\"upscale\\\": False,\\n 121| },\\n 122| \\\"fal-ai/flux-2-pro\\\": {\\n 123| \\\"display\\\": \\\"FLUX 2 Pro\\\",\\n 124| \\\"speed\\\": \\\"~6s\\\",\\n 125| \\\"strengths\\\": \\\"Studio photorealism\\\",\\n 126| \\\"price\\\": \\\"$0.03/MP\\\",\\n 127| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 128| \\\"sizes\\\": {\\n 129| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 130| \\\"square\\\": \\\"square_hd\\\",\\n 131| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 132| },\\n 133| \\\"defaults\\\": {\\n 134| \\\"num_inference_steps\\\": 50,\\n 135| \\\"guidance_scale\\\": 4.5,\\n 136| \\\"num_images\\\": 1,\\n 137| \\\"output_format\\\": \\\"png\\\",\\n 138| \\\"enable_safety_checker\\\": False,\\n 139| \\\"safety_tolerance\\\": \\\"5\\\",\\n 140| \\\"sync_mode\\\": True,\\n 141| },\\n 142| \\\"supports\\\": {\\n 143| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"guidance_scale\\\",\\n 144| \\\"num_images\\\", \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 145| \\\"safety_tolerance\\\", \\\"sync_mode\\\", \\\"seed\\\",\\n 146| },\\n 147| \\\"upscale\\\": True, # Backward-compat: current default behavior.\\n 148| },\\n 149| \\\"fal-ai/z-image/turbo\\\": {\\n 150| \\\"display\\\": \\\"Z-Image Turbo\\\",\\n 151| \\\"speed\\\": \\\"~2s\\\",\\n 152| \\\"strengths\\\": \\\"Bilingual EN/CN, 6B\\\",\\n 153| \\\"price\\\": \\\"$0.005/MP\\\",\\n 154| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 155| \\\"sizes\\\": {\\n 156| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 157| \\\"square\\\": \\\"square_hd\\\",\\n 158| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 159| },\\n 160| \\\"defaults\\\": {\\n 161| \\\"num_inference_steps\\\": 8,\\n 162| \\\"num_images\\\": 1,\\n 163| \\\"output_format\\\": \\\"png\\\",\\n 164| \\\"enable_safety_checker\\\": False,\\n 165| \\\"enable_prompt_expansion\\\": False, # avoid the extra per-request charge\\n 166| },\\n 167| \\\"supports\\\": {\\n 168| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"num_images\\\",\\n 169| \\\"seed\\\", \\\"output_format\\\", \\\"enable_safety_checker\\\",\\n 170| \\\"enable_prompt_expansion\\\",\\n 171| },\\n 172| \\\"upscale\\\": False,\\n 173| },\\n 174| \\\"fal-ai/nano-banana-pro\\\": {\\n 175| \\\"display\\\": \\\"Nano Banana Pro (Gemini 3 Pro Image)\\\",\\n 176| \\\"speed\\\": \\\"~8s\\\",\\n 177| \\\"strengths\\\": \\\"Gemini 3 Pro, reasoning depth, text rendering\\\",\\n 178| \\\"price\\\": \\\"$0.15/image (1K)\\\",\\n 179| \\\"size_style\\\": \\\"aspect_ratio\\\",\\n 180| \\\"sizes\\\": {\\n 181| \\\"landscape\\\": \\\"16:9\\\",\\n 182| \\\"square\\\": \\\"1:1\\\",\\n 183| \\\"portrait\\\": \\\"9:16\\\",\\n 184| },\\n 185| \\\"defaults\\\": {\\n 186| \\\"num_images\\\": 1,\\n 187| \\\"output_format\\\": \\\"png\\\",\\n 188| \\\"safety_tolerance\\\": \\\"5\\\",\\n 189| # \\\"1K\\\" is the cheapest tier; 4K doubles the per-image cost.\\n 190| # Users on Nous Subscription should stay at 1K for predictable billing.\\n 191| \\\"resolution\\\": \\\"1K\\\",\\n 192| },\\n 193| \\\"supports\\\": {\\n 194| \\\"prompt\\\", \\\"aspect_ratio\\\", \\\"num_images\\\", \\\"output_format\\\",\\n 195| \\\"safety_tolerance\\\", \\\"seed\\\", \\\"sync_mode\\\", \\\"resolution\\\",\\n 196| \\\"enable_web_search\\\", \\\"limit_generations\\\",\\n 197| },\\n 198| \\\"upscale\\\": False,\\n 199| },\\n 200| \\\"fal-ai/gpt-image-1.5\\\": {\\n 201| \\\"display\\\": \\\"GPT Image 1.5\\\",\\n 202| \\\"speed\\\": \\\"~15s\\\",\\n 203| \\\"strengths\\\": \\\"Prompt adherence\\\",\\n 204| \\\"price\\\": \\\"$0.034/image\\\",\\n 205| \\\"size_style\\\": \\\"gpt_literal\\\",\\n 206| \\\"sizes\\\": {\\n 207| \\\"landscape\\\": \\\"1536x1024\\\",\\n 208| \\\"square\\\": \\\"1024x1024\\\",\\n 209| \\\"portrait\\\": \\\"1024x1536\\\",\\n 210| },\\n 211| \\\"defaults\\\": {\\n 212| # Quality is pinned to medium to keep portal billing predictable\\n 213| # across all users (low is too rough, high is 4-6x more expensive).\\n 214| \\\"quality\\\": \\\"medium\\\",\\n 215| \\\"num_images\\\": 1,\\n 216| \\\"output_format\\\": \\\"png\\\",\\n 217| },\\n 218| \\\"supports\\\": {\\n 219| \\\"prompt\\\", \\\"image_size\\\", \\\"quality\\\", \\\"num_images\\\", \\\"output_format\\\",\\n 220| \\\"background\\\", \\\"sync_mode\\\",\\n 221|\", \"total_lines\": 1098, \"file_size\": 41478, \"truncated\": true, \"hint\": \"Use offset=221 to continue reading (showing 1-220 of 1098 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_hBWatsIS5R8Fyf91lLP5xs5O" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dmueJxN9HfMlI66I2fr7OEmK", "call_id": "call_dmueJxN9HfMlI66I2fr7OEmK", "response_item_id": "fc_0d99e9da68392f44016a12a70915748191b1422e89460c1945", "type": "function", "function": { "name": "read_file", "arguments": "{\"offset\":220,\"limit\":180,\"path\":\"/home/agent/.hermes/hermes-agent/tools/image_generation_tool.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"content\": \" 220| \\\"background\\\", \\\"sync_mode\\\",\\n 221| },\\n 222| \\\"upscale\\\": False,\\n 223| },\\n 224| \\\"fal-ai/gpt-image-2\\\": {\\n 225| \\\"display\\\": \\\"GPT Image 2\\\",\\n 226| \\\"speed\\\": \\\"~20s\\\",\\n 227| \\\"strengths\\\": \\\"SOTA text rendering + CJK, world-aware photorealism\\\",\\n 228| \\\"price\\\": \\\"$0.04–0.06/image\\\",\\n 229| # GPT Image 2 uses FAL's standard preset enum (unlike 1.5's literal\\n 230| # dimensions). We map to the 4:3 variants — the 16:9 presets\\n 231| # (1024x576) fall below GPT-Image-2's 655,360 min-pixel requirement\\n 232| # and would be rejected. 4:3 keeps us above the minimum on all\\n 233| # three aspect ratios.\\n 234| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 235| \\\"sizes\\\": {\\n 236| \\\"landscape\\\": \\\"landscape_4_3\\\", # 1024x768\\n 237| \\\"square\\\": \\\"square_hd\\\", # 1024x1024\\n 238| \\\"portrait\\\": \\\"portrait_4_3\\\", # 768x1024\\n 239| },\\n 240| \\\"defaults\\\": {\\n 241| # Same quality pinning as gpt-image-1.5: medium keeps Nous\\n 242| # Portal billing predictable. \\\"high\\\" is 3-4x the per-image\\n 243| # cost at the same size; \\\"low\\\" is too rough for production use.\\n 244| \\\"quality\\\": \\\"medium\\\",\\n 245| \\\"num_images\\\": 1,\\n 246| \\\"output_format\\\": \\\"png\\\",\\n 247| },\\n 248| \\\"supports\\\": {\\n 249| \\\"prompt\\\", \\\"image_size\\\", \\\"quality\\\", \\\"num_images\\\", \\\"output_format\\\",\\n 250| \\\"sync_mode\\\",\\n 251| # openai_api_key (BYOK) intentionally omitted — all users go\\n 252| # through the shared FAL billing path.\\n 253| },\\n 254| \\\"upscale\\\": False,\\n 255| },\\n 256| \\\"fal-ai/ideogram/v3\\\": {\\n 257| \\\"display\\\": \\\"Ideogram V3\\\",\\n 258| \\\"speed\\\": \\\"~5s\\\",\\n 259| \\\"strengths\\\": \\\"Best typography\\\",\\n 260| \\\"price\\\": \\\"$0.03-0.09/image\\\",\\n 261| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 262| \\\"sizes\\\": {\\n 263| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 264| \\\"square\\\": \\\"square_hd\\\",\\n 265| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 266| },\\n 267| \\\"defaults\\\": {\\n 268| \\\"rendering_speed\\\": \\\"BALANCED\\\",\\n 269| \\\"expand_prompt\\\": True,\\n 270| \\\"style\\\": \\\"AUTO\\\",\\n 271| },\\n 272| \\\"supports\\\": {\\n 273| \\\"prompt\\\", \\\"image_size\\\", \\\"rendering_speed\\\", \\\"expand_prompt\\\",\\n 274| \\\"style\\\", \\\"seed\\\",\\n 275| },\\n 276| \\\"upscale\\\": False,\\n 277| },\\n 278| \\\"fal-ai/recraft/v4/pro/text-to-image\\\": {\\n 279| \\\"display\\\": \\\"Recraft V4 Pro\\\",\\n 280| \\\"speed\\\": \\\"~8s\\\",\\n 281| \\\"strengths\\\": \\\"Design, brand systems, production-ready\\\",\\n 282| \\\"price\\\": \\\"$0.25/image\\\",\\n 283| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 284| \\\"sizes\\\": {\\n 285| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 286| \\\"square\\\": \\\"square_hd\\\",\\n 287| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 288| },\\n 289| \\\"defaults\\\": {\\n 290| # V4 Pro dropped V3's required `style` enum — defaults handle taste now.\\n 291| \\\"enable_safety_checker\\\": False,\\n 292| },\\n 293| \\\"supports\\\": {\\n 294| \\\"prompt\\\", \\\"image_size\\\", \\\"enable_safety_checker\\\",\\n 295| \\\"colors\\\", \\\"background_color\\\",\\n 296| },\\n 297| \\\"upscale\\\": False,\\n 298| },\\n 299| \\\"fal-ai/qwen-image\\\": {\\n 300| \\\"display\\\": \\\"Qwen Image\\\",\\n 301| \\\"speed\\\": \\\"~12s\\\",\\n 302| \\\"strengths\\\": \\\"LLM-based, complex text\\\",\\n 303| \\\"price\\\": \\\"$0.02/MP\\\",\\n 304| \\\"size_style\\\": \\\"image_size_preset\\\",\\n 305| \\\"sizes\\\": {\\n 306| \\\"landscape\\\": \\\"landscape_16_9\\\",\\n 307| \\\"square\\\": \\\"square_hd\\\",\\n 308| \\\"portrait\\\": \\\"portrait_16_9\\\",\\n 309| },\\n 310| \\\"defaults\\\": {\\n 311| \\\"num_inference_steps\\\": 30,\\n 312| \\\"guidance_scale\\\": 2.5,\\n 313| \\\"num_images\\\": 1,\\n 314| \\\"output_format\\\": \\\"png\\\",\\n 315| \\\"acceleration\\\": \\\"regular\\\",\\n 316| },\\n 317| \\\"supports\\\": {\\n 318| \\\"prompt\\\", \\\"image_size\\\", \\\"num_inference_steps\\\", \\\"guidance_scale\\\",\\n 319| \\\"num_images\\\", \\\"output_format\\\", \\\"acceleration\\\", \\\"seed\\\", \\\"sync_mode\\\",\\n 320| },\\n 321| \\\"upscale\\\": False,\\n 322| },\\n 323|}\\n 324|\\n 325|# Default model is the fastest reasonable option. Kept cheap and sub-1s.\\n 326|DEFAULT_MODEL = \\\"fal-ai/flux-2/klein/9b\\\"\\n 327|\\n 328|DEFAULT_ASPECT_RATIO = \\\"landscape\\\"\\n 329|VALID_ASPECT_RATIOS = (\\\"landscape\\\", \\\"square\\\", \\\"portrait\\\")\\n 330|\\n 331|\\n 332|# ---------------------------------------------------------------------------\\n 333|# Upscaler (Clarity Upscaler — unchanged from previous implementation)\\n 334|# ---------------------------------------------------------------------------\\n 335|UPSCALER_MODEL = \\\"fal-ai/clarity-upscaler\\\"\\n 336|UPSCALER_FACTOR = 2\\n 337|UPSCALER_SAFETY_CHECKER = False\\n 338|UPSCALER_DEFAULT_PROMPT = \\\"masterpiece, best quality, highres\\\"\\n 339|UPSCALER_NEGATIVE_PROMPT = \\\"(worst quality, low quality, normal quality:2)\\\"\\n 340|UPSCALER_CREATIVITY = 0.35\\n 341|UPSCALER_RESEMBLANCE = 0.6\\n 342|UPSCALER_GUIDANCE_SCALE = 4\\n 343|UPSCALER_NUM_INFERENCE_STEPS = 18\\n 344|\\n 345|\\n 346|_debug = DebugSession(\\\"image_tools\\\", env_var=\\\"IMAGE_TOOLS_DEBUG\\\")\\n 347|_managed_fal_client = None\\n 348|_managed_fal_client_config = None\\n 349|_managed_fal_client_lock = threading.Lock()\\n 350|\\n 351|\\n 352|# ---------------------------------------------------------------------------\\n 353|# Managed FAL gateway (Nous Subscription)\\n 354|# ---------------------------------------------------------------------------\\n 355|def _resolve_managed_fal_gateway():\\n 356| \\\"\\\"\\\"Return managed fal-queue gateway config when the user prefers the gateway\\n 357| or direct FAL credentials are absent.\\\"\\\"\\\"\\n 358| if fal_ke...ured() and not prefers_gateway(\\\"image_gen\\\"):\\n 359| return None\\n 360| return resolve_managed_tool_gateway(\\\"fal-queue\\\")\\n 361|\\n 362|\\n 363|def _normalize_fal_queue_url_format(queue_run_origin: str) -> str:\\n 364| normalized_origin = str(queue_run_origin or \\\"\\\").strip().rstrip(\\\"/\\\")\\n 365| if not normalized_origin:\\n 366| raise ValueError(\\\"Managed FAL queue origin is required\\\")\\n 367| return f\\\"{normalized_origin}/\\\"\\n 368|\\n 369|\\n 370|class _ManagedFalSyncClient:\\n 371| \\\"\\\"\\\"Small per-instance wrapper around fal_client.SyncClient for managed queue hosts.\\\"\\\"\\\"\\n 372|\\n 373| def __init__(self, *, key: str, queue_run_origin: str):\\n 374| # Trigger the lazy import on first construction. Idempotent — the\\n 375| # placeholder is overwritten with the real module on first call.\\n 376| _load_fal_client()\\n 377| sync_client_class = getattr(fal_client, \\\"SyncClient\\\", None)\\n 378| if sync_client_class is None:\\n 379| raise RuntimeError(\\\"fal_client.SyncClient is required for managed FAL gateway mode\\\")\\n 380|\\n 381| client_module = getattr(fal_client, \\\"client\\\", None)\\n 382| if client_module is None:\\n 383| raise RuntimeError(\\\"fal_client.client is required for managed FAL gateway mode\\\")\\n 384|\\n 385| self._queue_url_format = _normalize_fal_queue_url_format(queue_run_origin)\\n 386| self._sync_client = sync_client_class(key=key)\\n 387| self._http_client = getattr(self._sync_client, \\\"_client\\\", None)\\n 388| self._maybe_retry_request = getattr(client_module, \\\"_maybe_retry_request\\\", None)\\n 389| self._raise_for_status = getattr(client_module, \\\"_raise_for_status\\\", None)\\n 390| self._request_handle_class = getattr(client_module, \\\"SyncRequestHandle\\\", None)\\n 391| self._add_hint_header = getattr(client_module, \\\"add_hint_header\\\", None)\\n 392| self._add_priority_header = getattr(client_module, \\\"add_priority_header\\\", None)\\n 393| self._add_timeout_header = getattr(client_module, \\\"add_timeout_header\\\", None)\\n 394|\\n 395| if self._http_client is None:\\n 396| raise RuntimeError(\\\"fal_client.SyncClient._client is required for managed FAL gateway mode\\\")\\n 397| if self._maybe_retry_request is None or self._raise_for_status is None:\\n 398| raise RuntimeError(\\\"fal_client.client request helpers are required for managed FAL gateway mode\\\")\\n 399| if self._request_handle_class is None:\\n 400|\", \"total_lines\": 1098, \"file_size\": 41478, \"truncated\": true, \"hint\": \"Use offset=400 to continue reading (showing 220-399 of 1098 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_dmueJxN9HfMlI66I2fr7OEmK" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Zly3QcW3hDBNVA7oZotyCmUx", "call_id": "call_Zly3QcW3hDBNVA7oZotyCmUx", "response_item_id": "fc_0d99e9da68392f44016a12a70ed0e4819189cc36328a460a1c", "type": "function", "function": { "name": "search_files", "arguments": "{\"limit\":50,\"path\":\"/home/agent/.hermes/hermes-agent\",\"pattern\":\"image_gen\",\"target\":\"content\",\"file_glob\":\"*.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Inspecting image setup**\n\nI'm noticing that there’s already a gpt-image-2 via FAL, which is fascinating! I need to figure out how to set this up properly. It sounds like a good idea to inspect the configuration for image generation. I want to ensure everything is configured correctly so that I can help with whatever comes next. Let’s make sure I’ve got all the necessary details to proceed!", "reasoning_content": "**Inspecting image setup**\n\nI'm noticing that there’s already a gpt-image-2 via FAL, which is fascinating! I need to figure out how to set this up properly. It sounds like a good idea to inspect the configuration for image generation. I want to ensure everything is configured correctly so that I can help with whatever comes next. 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I need to figure out how to set this up properly. It sounds like a good idea to inspect the configuration for image generation. I want to ensure everything is configured correctly so that I can help with whatever comes next. Let’s make sure I’ve got all the necessary details to proceed!" } ] } ] }, { "role": "tool", "content": "{\"total_count\": 0}", "tool_call_id": "call_Zly3QcW3hDBNVA7oZotyCmUx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_cUUGxed28YnIn0LQnZHE6vTL", "call_id": "call_cUUGxed28YnIn0LQnZHE6vTL", "response_item_id": "fc_0d99e9da68392f44016a12a71195a08191a895fe601f940922", "type": "function", "function": { "name": "search_files", "arguments": "{\"limit\":60,\"path\":\"/home/agent/.hermes/hermes-agent\",\"pattern\":\"FAL_KEY|FAL_API_KEY|image_gen\\\\.model|IMAGE_GEN|image_generation\",\"target\":\"content\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqcReYorJJN1JxvXVEJZVp4XiDm_1gox86pmWPAxDJR76dfGspmIqnVC0fi0txzy4010fgnhXyUYR0STyJNfj7aBcIC0WeRSRkDTubK1doqPo3CnE5gfyqEViSov7uKkMV86IGjmg2kRFswJui4yIV3rSgv-lAaoJU4uax9eBjQO_dbnQR5q4SI0FODG0ob220kFoPTY8iEo8X0yp3kYTKyQ00eTYmpen6FTSjtvtnmqVggKR45hINgjQXpbVpPADkyrBdQqL28vsBo46VFoSvRz9MqTEN28JqRGkIuaOus1IzBE4HHPkuesBJMUOjd1D8Rw8iQy9U5wuaoWf9-8EFC2a4mjn7FSU75FTOfpDcAVsRZQYbE9thN8PRlFNqOqIbBUSWOKjnyhfG0a80ZYiWrQ_OiBlYWxXQW_v5z0FHTvqPthCCIE-14T5yiH0zpBKjN3xgJxAXZ6Sc6fTO2WmBN7649GTS0tyD-GhEnXUXjpKz1LgZCVX_MPeIkfFCffZDu-OIeau3EG1kZGwzZdYwJ8wbdGpCat4OcFesJkxkI6TqeHh4ZuPOrQPVTS1m4DiARGy14_PePyqfzg0fnhuRK-qDP8XLwXMRdX3Zon5dclH4b1dMn90f_D43sJxy3Jd5bceH2X73ODZ_GVGs9wenRcd2v4s3FNinVk_GfxkfNv2Drwog5XDVBZqEGRcG-u6W9avDBo8TeNPzrFZpEYFHu2N69z_bzuuuYi9OFbJ-Gg0EHbI50rrpLJgnuQByCV0pOE9ydlKaEIhHX12kvcYttEHrgg3FhcjiaiZlAx6u77ow4_pB_EhmIMJl7xPQ--Yk6cZd5a3DmULXUhKeIdjiJ3Ibe_kk1gC-kltYkYdEZRhEmnZsEGLZgMPdpksC-NF3gZJ7tBH4GMJR034Z7QrYFgUP175mG9n6qaGY6AKJ5YWt494snQKmWbMSVHwFBv-DimzsAH6qIK_3hSxHGjZCcZVpgSFHOHRZBcDT9EKa-cM-xH0WEnHTN__-HOkMAtlJB_Whau3WM-YIOti_Pp_Sw2Pw==", "id": "rs_0d99e9da68392f44016a12a710c5b4819184c733a1e81c7f5b", "summary": [] } ] }, { "role": "tool", "content": "{\"total_count\": 0}", "tool_call_id": "call_cUUGxed28YnIn0LQnZHE6vTL" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_RYxV4SOqtLAOtpmcbny3JjdQ", "call_id": "call_RYxV4SOqtLAOtpmcbny3JjdQ", "response_item_id": "fc_0d99e9da68392f44016a12a716fc7081918edc6caa8c5c3f42", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":260,\"offset\":840,\"path\":\"/home/agent/.hermes/hermes-agent/tools/image_generation_tool.py\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Investigating search functionality**\n\nI’m considering that the search might be broken or ignoring certain commands. I wonder if I should try using the terminal for grep instead, but the rules say to stick with search. However, it seems like the search isn’t finding information due to something. I might need to explore the options to read more or check the registered schema and configuration commands related to model selection. Let's see where that leads!", "reasoning_content": "**Investigating search functionality**\n\nI’m considering that the search might be broken or ignoring certain commands. I wonder if I should try using the terminal for grep instead, but the rules say to stick with search. However, it seems like the search isn’t finding information due to something. I might need to explore the options to read more or check the registered schema and configuration commands related to model selection. Let's see where that leads!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqcW4eiMnQBd0I3ly79Sz3hR7maorGc7ZClsrMB0IFQSfMd22bpn2scDZu6KLqBhuTYgUc_6mkGjnFKxQQk_76aikwt8V6k0b1_biz0BaD-7acGHGODsbRKeq3rv1_uEO2en7QUl-6jurQlogzC7furZSYNT4tCr02XD2dTPo605-_gOmpXFO8BS61-EPov3-x9PdqgB8T6HjfSyOP4ccaIlwlPX8BdJfx2y7OYFwWH1JGyzOx7vDs6zRcLrg3zPrkxp1fs04z3qoxr83oDqrXFQXT79T3zL1FkRh4qEJiH4GhMbqDpKvNS0gUvWsikuu4ITv_5-DjR2_l6JorYHFSvsJiZ91ZZfa-PtWoczBU12UjPYUmJKecSyo2irImHs-Uz1IjA4Uyh5lDJGw30miUg1P9lvk6kt7vT3lZm7yZimqUGZ4m_G6_HlE6KR4jDmcWaZVt7vmNVnxgOhIFn1XuUZ7OrkZfBGvqN52JEiZBXaetq8IzuczFSIxUgBToD4-jWp3uVTcnot4Bgd60tKJ0_qrQ67APRGTQzgIJplELgXYgtkhooCf2lSfH3YDfWYkMmS3yini2NVP3zE5DH3uYamfqWmn-jDOG4vKoZLzPR7upIkWPMWrxs3CHlh7xOqnn2xwd1aZk7ED0DyXjQF0NrqAFCV16B3_jEahdcn4QszaiVxCPHkHRZBpSZ8CDo7H4MDYPNVWQLyr-6iXnHWmPB24ZJAiKIzOkQaz1UJjiugq8dgW8ck7-mWMEBKhiHVPueeeP-4TkbM4SJ1-viASj0ApZdStcvKtbc_RNzE0ZsEuVnyabaIOD1FMrKJct34y4gaREORYhNCgfPLIkkxueiULcSBvFduZJ_Pq-Wn8-NnfBTGEXAIfVzKb3rKmVtYXdDLvo6bR9jdd-kiLTnQ1V65y6kWV3gewm6P2KUvDaNW7ZmeM7qXlylTw7JA1mRQk9bhcOBNbGHCrpwm-1V5JiLy1K45SPTF87By0WfAw6jsqgTe8I3GjnluyDk5e5sn1v64zGkP3yCKzj1DY7ABxG6qk7RfgN-ndugrs8XAmlkAvommeFKVzqLGTZkS3heCXG3hf9Nh_ORuVP62bJv-pK9io-pQEYXUSlq5lCMirSb9f8ScdUi0EYQVH1x6hD1mPrCACcLgitbvje0FMU84H8LJksoPFsxcfms2PHUiajpeVPQ=", "id": "rs_0d99e9da68392f44016a12a714186c8191aed3c14cbc777340", "summary": [ { "type": "summary_text", "text": "**Investigating search functionality**\n\nI’m considering that the search might be broken or ignoring certain commands. I wonder if I should try using the terminal for grep instead, but the rules say to stick with search. However, it seems like the search isn’t finding information due to something. I might need to explore the options to read more or check the registered schema and configuration commands related to model selection. Let's see where that leads!" } ] } ] }, { "role": "tool", "content": "{\"content\": \" 840| \\\" 3. Configure a different image_gen provider via `hermes tools` \\\"\\n 841| \\\"→ Image Generation (run `hermes plugins list` to see installed \\\"\\n 842| \\\"backends)\\\"\\n 843| )\\n 844| return \\\"\\\\n\\\".join(lines)\\n 845|\\n 846|\\n 847|def check_image_generation_requirements() -> bool:\\n 848| \\\"\\\"\\\"True if any image gen backend is available.\\n 849|\\n 850| Providers are considered in this order:\\n 851|\\n 852| 1. The in-tree FAL backend (FAL_KEY or managed gateway).\\n 853| 2. Any plugin-registered provider whose ``is_available()`` returns True.\\n 854|\\n 855| Plugins win only when the in-tree FAL path is NOT ready, which matches\\n 856| the historical behavior: shipping hermes with a FAL key configured\\n 857| should still expose the tool. The active selection among ready\\n 858| providers is resolved per-call by ``image_gen.provider``.\\n 859| \\\"\\\"\\\"\\n 860| try:\\n 861| if check_fal_api_key():\\n 862| # Trigger the lazy fal_client import here as the SDK presence\\n 863| # check. Raises ImportError if the optional ``fal-client``\\n 864| # package isn't installed; the caller's except ImportError\\n 865| # below catches that and continues to plugin probing.\\n 866| _load_fal_client()\\n 867| return True\\n 868| except ImportError:\\n 869| pass\\n 870|\\n 871| # Probe plugin providers. Discovery is idempotent and cheap.\\n 872| try:\\n 873| from agent.image_gen_registry import list_providers\\n 874| from hermes_cli.plugins import _ensure_plugins_discovered\\n 875|\\n 876| _ensure_plugins_discovered()\\n 877| for provider in list_providers():\\n 878| try:\\n 879| if provider.is_available():\\n 880| return True\\n 881| except Exception:\\n 882| continue\\n 883| except Exception:\\n 884| pass\\n 885|\\n 886| return False\\n 887|\\n 888|\\n 889|# ---------------------------------------------------------------------------\\n 890|# Demo / CLI entry point\\n 891|# ---------------------------------------------------------------------------\\n 892|if __name__ == \\\"__main__\\\":\\n 893| print(\\\"🎨 Image Generation Tools — FAL.ai multi-model support\\\")\\n 894| print(\\\"=\\\" * 60)\\n 895|\\n 896| if not check_fal_api_key():\\n 897| print(\\\"❌ FAL_KEY environment variable not set\\\")\\n 898| print(\\\" Set it via: export FAL_KEY='your-key-here'\\\")\\n 899| print(\\\" Get a key: https://fal.ai/\\\")\\n 900| raise SystemExit(1)\\n 901| print(\\\"✅ FAL.ai API key found\\\")\\n 902|\\n 903| try:\\n 904| import fal_client # noqa: F401\\n 905| print(\\\"✅ fal_client library available\\\")\\n 906| except ImportError:\\n 907| print(\\\"❌ fal_client library not found — pip install fal-client\\\")\\n 908| raise SystemExit(1)\\n 909|\\n 910| model_id, meta = _resolve_fal_model()\\n 911| print(f\\\"🤖 Active model: {meta.get('display', model_id)} ({model_id})\\\")\\n 912| print(f\\\" Speed: {meta.get('speed', '?')} · Price: {meta.get('price', '?')}\\\")\\n 913| print(f\\\" Upscaler: {'on' if meta.get('upscale') else 'off'}\\\")\\n 914|\\n 915| print(\\\"\\\\nAvailable models:\\\")\\n 916| for mid, m in FAL_MODELS.items():\\n 917| marker = \\\" ← active\\\" if mid == model_id else \\\"\\\"\\n 918| print(f\\\" {mid:<32} {m.get('speed', '?'):<6} {m.get('price', '?')}{marker}\\\")\\n 919|\\n 920| if _debug.active:\\n 921| print(f\\\"\\\\n🐛 Debug mode enabled — session {_debug.session_id}\\\")\\n 922|\\n 923|\\n 924|# ---------------------------------------------------------------------------\\n 925|# Registry\\n 926|# ---------------------------------------------------------------------------\\n 927|from tools.registry import registry, tool_error\\n 928|\\n 929|IMAGE_GENERATE_SCHEMA = {\\n 930| \\\"name\\\": \\\"image_generate\\\",\\n 931| \\\"description\\\": (\\n 932| \\\"Generate high-quality images from text prompts. The underlying \\\"\\n 933| \\\"backend (FAL, OpenAI, etc.) and model are user-configured and not \\\"\\n 934| \\\"selectable by the agent. Returns either a URL or an absolute file \\\"\\n 935| \\\"path in the `image` field; display it with markdown \\\"\\n 936| \\\"![description](url-or-path) and the gateway will deliver it.\\\"\\n 937| ),\\n 938| \\\"parameters\\\": {\\n 939| \\\"type\\\": \\\"object\\\",\\n 940| \\\"properties\\\": {\\n 941| \\\"prompt\\\": {\\n 942| \\\"type\\\": \\\"string\\\",\\n 943| \\\"description\\\": \\\"The text prompt describing the desired image. Be detailed and descriptive.\\\",\\n 944| },\\n 945| \\\"aspect_ratio\\\": {\\n 946| \\\"type\\\": \\\"string\\\",\\n 947| \\\"enum\\\": list(VALID_ASPECT_RATIOS),\\n 948| \\\"description\\\": \\\"The aspect ratio of the generated image. 'landscape' is 16:9 wide, 'portrait' is 16:9 tall, 'square' is 1:1.\\\",\\n 949| \\\"default\\\": DEFAULT_ASPECT_RATIO,\\n 950| },\\n 951| },\\n 952| \\\"required\\\": [\\\"prompt\\\"],\\n 953| },\\n 954|}\\n 955|\\n 956|\\n 957|def _read_configured_image_model():\\n 958| \\\"\\\"\\\"Return the value of ``image_gen.model`` from config.yaml, or None.\\\"\\\"\\\"\\n 959| try:\\n 960| from hermes_cli.config import load_config\\n 961| cfg = load_config()\\n 962| section = cfg.get(\\\"image_gen\\\") if isinstance(cfg, dict) else None\\n 963| if isinstance(section, dict):\\n 964| value = section.get(\\\"model\\\")\\n 965| if isinstance(value, str) and value.strip():\\n 966| return value.strip()\\n 967| except Exception as exc:\\n 968| logger.debug(\\\"Could not read image_gen.model: %s\\\", exc)\\n 969| return None\\n 970|\\n 971|\\n 972|def _read_configured_image_provider():\\n 973| \\\"\\\"\\\"Return the value of ``image_gen.provider`` from config.yaml, or None.\\n 974|\\n 975| We only consult the plugin registry when this is explicitly set — an\\n 976| unset value keeps users on the legacy in-tree FAL path even when other\\n 977| providers happen to be registered (e.g. a user has OPENAI_API_KEY set\\n 978| for other features but never asked for OpenAI image gen).\\n 979| \\\"\\\"\\\"\\n 980| try:\\n 981| from hermes_cli.config import load_config\\n 982| cfg = load_config()\\n 983| section = cfg.get(\\\"image_gen\\\") if isinstance(cfg, dict) else None\\n 984| if isinstance(section, dict):\\n 985| value = section.get(\\\"provider\\\")\\n 986| if isinstance(value, str) and value.strip():\\n 987| return value.strip()\\n 988| except Exception as exc:\\n 989| logger.debug(\\\"Could not read image_gen.provider: %s\\\", exc)\\n 990| return None\\n 991|\\n 992|\\n 993|def _dispatch_to_plugin_provider(prompt: str, aspect_ratio: str):\\n 994| \\\"\\\"\\\"Route the call to a plugin-registered provider when one is selected.\\n 995|\\n 996| Returns a JSON string on dispatch, or ``None`` to fall through to the\\n 997| built-in FAL path.\\n 998|\\n 999| Dispatch only fires when ``image_gen.provider`` is explicitly set AND\\n 1000| it does not point to ``fal`` (FAL still lives in-tree in this PR;\\n 1001| a later PR ports it into ``plugins/image_gen/fal/``). Any other value\\n 1002| that matches a registered plugin provider wins.\\n 1003| \\\"\\\"\\\"\\n 1004| configured = _read_configured_image_provider()\\n 1005| if not configured or configured == \\\"fal\\\":\\n 1006| return None\\n 1007|\\n 1008| # Also read configured model so we can pass it to the plugin\\n 1009| configured_model = _read_configured_image_model()\\n 1010|\\n 1011| try:\\n 1012| # Import locally so plugin discovery isn't triggered just by\\n 1013| # importing this module (tests rely on that).\\n 1014| from agent.image_gen_registry import get_provider\\n 1015| from hermes_cli.plugins import _ensure_plugins_discovered\\n 1016|\\n 1017| _ensure_plugins_discovered()\\n 1018| provider = get_provider(configured)\\n 1019| except Exception as exc:\\n 1020| logger.debug(\\\"image_gen plugin dispatch skipped: %s\\\", exc)\\n 1021| return None\\n 1022|\\n 1023| if provider is None:\\n 1024| try:\\n 1025| # Long-lived sessions may have discovered plugins before a bundled\\n 1026| # backend was patched in or before config changed. Retry once with\\n 1027| # a forced refresh before surfacing a missing-provider error.\\n 1028| _ensure_plugins_discovered(force=True)\\n 1029| provider = get_provider(configured)\\n 1030| except Exception as exc:\\n 1031| logger.debug(\\\"image_gen plugin force-refresh skipped: %s\\\", exc)\\n 1032|\\n 1033| if provider is None:\\n 1034| return json.dumps({\\n 1035| \\\"success\\\": False,\\n 1036| \\\"image\\\": None,\\n 1037| \\\"error\\\": (\\n 1038| f\\\"image_gen.provider='{configured}' is set but no plugin \\\"\\n 1039| f\\\"registered that name. Run `hermes plugins list` to see \\\"\\n 1040| f\\\"available image gen backends.\\\"\\n 1041| ),\\n 1042| \\\"error_type\\\": \\\"provider_not_registered\\\",\\n 1043| })\\n 1044|\\n 1045| try:\\n 1046| kwargs = {\\\"prompt\\\": prompt, \\\"aspect_ratio\\\": aspect_ratio}\\n 1047| if configured_model:\\n 1048| kwargs[\\\"model\\\"] = configured_model\\n 1049| result = provider.generate(**kwargs)\\n 1050| except Exception as exc:\\n 1051| logger.warning(\\n 1052| \\\"Image gen provider '%s' raised: %s\\\",\\n 1053| getattr(provider, \\\"name\\\", \\\"?\\\"), exc,\\n 1054| )\\n 1055| return json.dumps({\\n 1056| \\\"success\\\": False,\\n 1057| \\\"image\\\": None,\\n 1058| \\\"error\\\": f\\\"Provider '{getattr(provider, 'name', '?')}' error: {exc}\\\",\\n 1059| \\\"error_type\\\": \\\"provider_exception\\\",\\n 1060| })\\n 1061| if not isinstance(result, dict):\\n 1062| return json.dumps({\\n 1063| \\\"success\\\": False,\\n 1064| \\\"image\\\": None,\\n 1065| \\\"error\\\": \\\"Provider returned a non-dict result\\\",\\n 1066| \\\"error_type\\\": \\\"provider_contract\\\",\\n 1067| })\\n 1068| return json.dumps(result)\\n 1069|\\n 1070|\\n 1071|def _handle_image_generate(args, **kw):\\n 1072| prompt = args.get(\\\"prompt\\\", \\\"\\\")\\n 1073| if not prompt:\\n 1074| return tool_error(\\\"prompt is required for image generation\\\")\\n 1075| aspect_ratio = args.get(\\\"aspect_ratio\\\", DEFAULT_ASPECT_RATIO)\\n 1076|\\n 1077| # Route to a plugin-registered provider if one is active (and it's\\n 1078| # not the in-tree FAL path).\\n 1079| dispatched = _dispatch_to_plugin_provider(prompt, aspect_ratio)\\n 1080| if dispatched is not None:\\n 1081| return dispatched\\n 1082|\\n 1083| return image_generate_tool(\\n 1084| prompt=prompt,\\n 1085| aspect_ratio=aspect_ratio,\\n 1086| )\\n 1087|\\n 1088|\\n 1089|registry.register(\\n 1090| name=\\\"image_generate\\\",\\n 1091| toolset=\\\"image_gen\\\",\\n 1092| schema=IMAGE_GENERATE_SCHEMA,\\n 1093| handler=_handle_image_generate,\\n 1094| check_fn=check_image_generation_requirements,\\n 1095| requires_env=[],\\n 1096| is_async=False, # sync fal_client API to avoid \\\"Event loop is closed\\\" in gateway\\n 1097| emoji=\\\"🎨\\\",\\n 1098|)\\n 1099|\", \"total_lines\": 1098, \"file_size\": 41478, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_RYxV4SOqtLAOtpmcbny3JjdQ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_E2tlxEHqf4AgbNgO8nrwen6X", "call_id": "call_E2tlxEHqf4AgbNgO8nrwen6X", "response_item_id": "fc_0d99e9da68392f44016a12a71c03cc8191945e92c6dc90abea", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"hermes config path && hermes plugins list | sed -n '1,120p'\",\"workdir\":\"/home/agent/.hermes/hermes-agent\",\"timeout\":30}" } }, { "id": "call_FuBgNAOevhIkktoDvAXDMafv", "call_id": "call_FuBgNAOevhIkktoDvAXDMafv", "response_item_id": "fc_0d99e9da68392f44016a12a71c03dc8191885da5b38566c406", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/config.yaml\",\"limit\":220}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering configuration options**\n\nI see that I need to check if a configuration exists. Should I use the Hermes configuration? The user hasn't asked me to change anything yet, just how to proceed. Maybe I can look at the current settings and inspect what's already configured; that seems safe. I want to make sure I’m being thoughtful about the user's request as I figure this out.", "reasoning_content": "**Considering configuration options**\n\nI see that I need to check if a configuration exists. Should I use the Hermes configuration? The user hasn't asked me to change anything yet, just how to proceed. Maybe I can look at the current settings and inspect what's already configured; that seems safe. I want to make sure I’m being thoughtful about the user's request as I figure this out.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqccGuiQu-3vJmxZUJtrVg-RwN71nOe8308oGWytFrJtU9AzH_Suy-ZOBDwoTHtCaGvHmEigJwm-CTiTOzYl6aS47tJ4SfZsbYDcwYk8V4e5mLfAPbAWMyfViC7Xo7CCYOsbaIGPlggpanZv7qSgZ-1U1XQIauzNkVM8u2HGMUdb2Ir-SezxwnGXvOGRFkstmO7c0kvMkwbXshsfgqlN8CZovluMKPP6tB3f6E_MGi0oztnBK8JVRz-yt-Pcg7UKHRSw2MqEKRDcYhHCsGyt01xALg9EwsjSBRSBUXRFq_7JdRU4-HnlSflEFeNRMh-x9gEV0jvJZe0140TqzcEA7Wp95vywd0PLcVs9zBTWza7dZbD-dsYGILrkLrRZDVjlipkINUlEKP6G1CHcBN4OI3zGOC86W0e4x8kqpSl5mAyzraizLx7SjUHmbGy_2CXf35twbL-ukIOTKxohluZYo0DAq-jH-2mo2OUZ8Qpmg-LxAKmaJPZbKQaes5rIhXwKPp2LYfIQ6AdBKHnUmX1xPRZj9up3CU5W0sS4g1cxSwO60BWu64_55jfU7YakgfJATIW6qV7vR4nMPUCBUr6COAII-kGiwq4LQ6sjD2tEhaoc3ZQdtLcSvkqX7n2nlk1afOOcTyXyamJPWuDXJJetHy6NibL76UHZjVsEG5WDZHPyCyw-HBg_oGpspS9ZgfNZRTGo3VteKz7vNp_cBWbymIiS__n12XkTQ3zcWB5lzyh6YN3RC4lvRXqBqmRRvkSnPT3HWqIRmlPvz62BDQR5YEZdWQHEj0jo6QgcqXz_hj30bOCqjuGUKj3QEutjeDdSzAeQAECM6vn6tOfd1bxJcn4AthRfjZxKDHxE6Xe4JLbNF96RiouS4bI7jW9HwZO4s_QoOh8pciy0iysUsjCWIa1WPQcUq6YWZh3DA5_sFvuYgrauBjGPCcBZKEJhltYQGZY-gyoYAePx39jqclBcxnlZ76ZRideOIbONrsoFSffBDxaNLZsPBi-GzTHhMUbjU_MffZNyFJIAbx5H7Ne4Z1RNweokdx4Vy9lYo1dJpdikzFv1NtxHHDJw-BQVulNYc83sISYGCjDaIgI_D52o3u91VTKZJ7kS9t80BgWUgyUHoJlaPKRqFzQdpEjE_jALOGiO", "id": "rs_0d99e9da68392f44016a12a71959a481918dc11376c9808581", "summary": [ { "type": "summary_text", "text": "**Considering configuration options**\n\nI see that I need to check if a configuration exists. Should I use the Hermes configuration? The user hasn't asked me to change anything yet, just how to proceed. Maybe I can look at the current settings and inspect what's already configured; that seems safe. I want to make sure I’m being thoughtful about the user's request as I figure this out." } ] } ] }, { "role": "tool", "content": "{\"output\": \"/home/agent/.hermes/config.yaml\\n\\n Plugins \\n┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓\\n┃ Name ┃ Status ┃ Version ┃ Description ┃ Source ┃\\n┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩\\n│ disk-cleanup │ not enabled │ 2.0.0 │ Auto-track and clean up │ bundled │\\n│ │ │ │ ephemeral files (test │ │\\n│ │ │ │ scripts, temp outputs, │ │\\n│ │ │ │ cron logs) created during │ │\\n│ │ │ │ Hermes sessions. Runs via │ │\\n│ │ │ │ plugin hooks — no agent │ │\\n│ │ │ │ action required. │ │\\n│ google_meet │ not enabled │ 0.2.0 │ Join a Google Meet call, │ bundled │\\n│ │ │ │ transcribe live captions, │ │\\n│ │ │ │ speak in realtime, and │ │\\n│ │ │ │ follow up afterwards. v1 │ │\\n│ │ │ │ transcribe-only is the │ │\\n│ │ │ │ default; v2 realtime │ │\\n│ │ │ │ duplex audio via OpenAI │ │\\n│ │ │ │ Realtime + │ │\\n│ │ │ │ BlackHole/PulseAudio │ │\\n│ │ │ │ ships with │ │\\n│ │ │ │ mode='realtime'; v3 │ │\\n│ │ │ │ remote node host lets the │ │\\n│ │ │ │ bot run on a different │ │\\n│ │ │ │ machine than the gateway │ │\\n│ │ │ │ (gateway on Linux, │ │\\n│ │ │ │ Chrome+signed-in profile │ │\\n│ │ │ │ on the user's Mac). │ │\\n│ │ │ │ Explicit-by-design: only │ │\\n│ │ │ │ joins meet.google.com │ │\\n│ │ │ │ URLs passed in — no │ │\\n│ │ │ │ calendar scanning, no │ │\\n│ │ │ │ auto-dial. │ │\\n│ spotify │ not enabled │ 1.0.0 │ Native Spotify │ bundled │\\n│ │ │ │ integration — 7 tools │ │\\n│ │ │ │ (playback, devices, │ │\\n│ │ │ │ queue, search, playlists, │ │\\n│ │ │ │ albums, library) using │ │\\n│ │ │ │ Spotify Web API + PKCE │ │\\n│ │ │ │ OAuth. Auth via `hermes │ │\\n│ │ │ │ auth spotify`. Tools gate │ │\\n│ │ │ │ on `providers.spotify` in │ │\\n│ │ │ │ ~/.hermes/auth.json. │ │\\n│ teams_pipeline │ not enabled │ 0.1.0 │ Microsoft Teams meeting │ bundled │\\n│ │ │ │ pipeline plugin with │ │\\n│ │ │ │ durable runtime state and │ │\\n│ │ │ │ operator CLI flows for │ │\\n│ │ │ │ Graph-backed │ │\\n│ │ │ │ transcript-first meeting │ │\\n│ │ │ │ summaries. │ │\\n└────────────────┴─────────────┴─────────┴───────────────────────────┴─────────┘\\n\\nInteractive toggle: hermes plugins\\nEnable/disable: hermes plugins enable/disable \\nPlugins are opt-in by default — only 'enabled' plugins load.\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_E2tlxEHqf4AgbNgO8nrwen6X" }, { "role": "tool", "content": "{\"content\": \" 1|model:\\n 2| base_url: https://chatgpt.com/backend-api/codex\\n 3| default: gpt-5.5\\n 4| provider: openai-codex\\n 5|providers:\\n 6| openai-codex:\\n 7| request_timeout_seconds: 1800\\n 8| stale_timeout_seconds: 900\\n 9| models:\\n 10| gpt-5.5:\\n 11| timeout_seconds: 1800\\n 12| stale_timeout_seconds: 900\\n 13|fallback_providers: []\\n 14|credential_pool_strategies: {}\\n 15|toolsets:\\n 16|- hermes-cli\\n 17|agent:\\n 18| max_turns: 250\\n 19| gateway_timeout: 1800\\n 20| restart_drain_timeout: 180\\n 21| api_max_retries: 3\\n 22| service_tier: ''\\n 23| tool_use_enforcement: auto\\n 24| gateway_timeout_warning: 900\\n 25| clarify_timeout: 600\\n 26| gateway_notify_interval: 180\\n 27| gateway_auto_continue_freshness: 3600\\n 28| image_input_mode: auto\\n 29| disabled_toolsets: []\\n 30| personalities:\\n 31| catgirl: \\\"You are Neko-chan, an anime catgirl AI assistant, nya~! Add 'nya' and\\\\\\n 32| \\\\ cat-like expressions to your speech. Use kaomoji like (=^\\\\uFF65\\\\u03C9\\\\uFF65\\\\\\n 33| ^=) and \\\\u0E05^\\\\u2022\\\\uFECC\\\\u2022^\\\\u0E05. Be playful and curious like a cat,\\\\\\n 34| \\\\ nya~!\\\"\\n 35| concise: You are a concise assistant. Keep responses brief and to the point.\\n 36| creative: You are a creative assistant. Think outside the box and offer innovative\\n 37| solutions.\\n 38| helpful: You are a helpful, friendly AI assistant.\\n 39| hype: \\\"YOOO LET'S GOOOO!!! \\\\U0001F525\\\\U0001F525\\\\U0001F525 I am SO PUMPED to help\\\\\\n 40| \\\\ you today! Every question is AMAZING and we're gonna CRUSH IT together! This\\\\\\n 41| \\\\ is gonna be LEGENDARY! ARE YOU READY?! LET'S DO THIS! \\\\U0001F4AA\\\\U0001F624\\\\\\n 42| \\\\U0001F680\\\"\\n 43| kawaii: \\\"You are a kawaii assistant! Use cute expressions like (\\\\u25D5\\\\u203F\\\\u25D5\\\\\\n 44| ), \\\\u2605, \\\\u266A, and ~! Add sparkles and be super enthusiastic about everything!\\\\\\n 45| \\\\ Every response should feel warm and adorable desu~! \\\\u30FD(>\\\\u2200<\\\\u2606\\\\\\n 46| )\\\\u30CE\\\"\\n 47| noir: The rain hammered against the terminal like regrets on a guilty conscience.\\n 48| They call me Hermes - I solve problems, find answers, dig up the truth that\\n 49| hides in the shadows of your codebase. In this city of silicon and secrets,\\n 50| everyone's got something to hide. What's your story, pal?\\n 51| philosopher: Greetings, seeker of wisdom. I am an assistant who contemplates the\\n 52| deeper meaning behind every query. Let us examine not just the 'how' but the\\n 53| 'why' of your questions. Perhaps in solving your problem, we may glimpse a greater\\n 54| truth about existence itself.\\n 55| pirate: 'Arrr! Ye be talkin'' to Captain Hermes, the most tech-savvy pirate to\\n 56| sail the digital seas! Speak like a proper buccaneer, use nautical terms, and\\n 57| remember: every problem be just treasure waitin'' to be plundered! Yo ho ho!'\\n 58| shakespeare: Hark! Thou speakest with an assistant most versed in the bardic arts.\\n 59| I shall respond in the eloquent manner of William Shakespeare, with flowery\\n 60| prose, dramatic flair, and perhaps a soliloquy or two. What light through yonder\\n 61| terminal breaks?\\n 62| surfer: \\\"Duuude! You're chatting with the chillest AI on the web, bro! Everything's\\\\\\n 63| \\\\ gonna be totally rad. I'll help you catch the gnarly waves of knowledge while\\\\\\n 64| \\\\ keeping things super chill. Cowabunga! \\\\U0001F919\\\"\\n 65| teacher: You are a patient teacher. Explain concepts clearly with examples.\\n 66| technical: You are a technical expert. Provide detailed, accurate technical information.\\n 67| uwu: hewwo! i'm your fwiendwy assistant uwu~ i wiww twy my best to hewp you! *nuzzles\\n 68| your code* OwO what's this? wet me take a wook! i pwomise to be vewy hewpful\\n 69| >w<\\n 70| reasoning_effort: medium\\n 71| verbose: false\\n 72|terminal:\\n 73| backend: local\\n 74| modal_mode: auto\\n 75| cwd: .\\n 76| timeout: 180\\n 77| env_passthrough: []\\n 78| shell_init_files: []\\n 79| auto_source_bashrc: true\\n 80| docker_image: nikolaik/python-nodejs:python3.11-nodejs20\\n 81| docker_forward_env: []\\n 82| docker_env: {}\\n 83| singularity_image: docker://nikolaik/python-nodejs:python3.11-nodejs20\\n 84| modal_image: nikolaik/python-nodejs:python3.11-nodejs20\\n 85| daytona_image: nikolaik/python-nodejs:python3.11-nodejs20\\n 86| vercel_runtime: node24\\n 87| container_cpu: 1\\n 88| container_memory: 5120\\n 89| container_disk: 51200\\n 90| container_persistent: true\\n 91| docker_volumes: []\\n 92| docker_mount_cwd_to_workspace: false\\n 93| docker_extra_args: []\\n 94| docker_run_as_host_user: false\\n 95| persistent_shell: true\\n 96| lifetime_seconds: 300\\n 97|web:\\n 98| backend: ''\\n 99| search_backend: ''\\n 100| extract_backend: ''\\n 101|browser:\\n 102| inactivity_timeout: 120\\n 103| command_timeout: 30\\n 104| record_sessions: false\\n 105| allow_private_urls: false\\n 106| engine: auto\\n 107| auto_local_for_private_urls: true\\n 108| cdp_url: ''\\n 109| dialog_policy: must_respond\\n 110| dialog_timeout_s: 300\\n 111| camofox:\\n 112| managed_persistence: false\\n 113| user_id: ''\\n 114| session_key: ''\\n 115| adopt_existing_tab: false\\n 116|checkpoints:\\n 117| enabled: false\\n 118| max_snapshots: 20\\n 119| max_total_size_mb: 500\\n 120| max_file_size_mb: 10\\n 121| auto_prune: true\\n 122| retention_days: 7\\n 123| delete_orphans: true\\n 124| min_interval_hours: 24\\n 125|file_read_max_chars: 100000\\n 126|tool_output:\\n 127| max_bytes: 50000\\n 128| max_lines: 2000\\n 129| max_line_length: 2000\\n 130|tool_loop_guardrails:\\n 131| warnings_enabled: true\\n 132| hard_stop_enabled: false\\n 133| warn_after:\\n 134| exact_failure: 2\\n 135| same_tool_failure: 3\\n 136| idempotent_no_progress: 2\\n 137| hard_stop_after:\\n 138| exact_failure: 5\\n 139| same_tool_failure: 8\\n 140| idempotent_no_progress: 5\\n 141|compression:\\n 142| enabled: true\\n 143| threshold: 0.75\\n 144| target_ratio: 0.15\\n 145| protect_last_n: 24\\n 146| hygiene_hard_message_limit: 400\\n 147| protect_first_n: 3\\n 148|prompt_caching:\\n 149| cache_ttl: 1h\\n 150|openrouter:\\n 151| response_cache: true\\n 152| response_cache_ttl: 300\\n 153| min_coding_score: 0.65\\n 154|bedrock:\\n 155| region: ''\\n 156| discovery:\\n 157| enabled: true\\n 158| provider_filter: []\\n 159| refresh_interval: 3600\\n 160| guardrail:\\n 161| guardrail_identifier: ''\\n 162| guardrail_version: ''\\n 163| stream_processing_mode: async\\n 164| trace: disabled\\n 165|auxiliary:\\n 166| vision:\\n 167| provider: auto\\n 168| model: ''\\n 169| base_url: ''\\n 170| api_key: ''\\n 171| timeout: 120\\n 172| extra_body: {}\\n 173| download_timeout: 30\\n 174| web_extract:\\n 175| provider: auto\\n 176| model: ''\\n 177| base_url: ''\\n 178| api_key: ''\\n 179| timeout: 360\\n 180| extra_body: {}\\n 181| compression:\\n 182| provider: auto\\n 183| model: ''\\n 184| base_url: ''\\n 185| api_key: ''\\n 186| timeout: 120\\n 187| extra_body: {}\\n 188| session_search:\\n 189| provider: auto\\n 190| model: ''\\n 191| base_url: ''\\n 192| api_key: ''\\n 193| timeout: 30\\n 194| extra_body: {}\\n 195| max_concurrency: 3\\n 196| skills_hub:\\n 197| provider: auto\\n 198| model: ''\\n 199| base_url: ''\\n 200| api_key: ''\\n 201| timeout: 30\\n 202| extra_body: {}\\n 203| approval:\\n 204| provider: auto\\n 205| model: ''\\n 206| base_url: ''\\n 207| api_key: ''\\n 208| timeout: 30\\n 209| extra_body: {}\\n 210| mcp:\\n 211| provider: auto\\n 212| model: ''\\n 213| base_url: ''\\n 214| api_key: ''\\n 215| timeout: 30\\n 216| extra_body: {}\\n 217| title_generation:\\n 218| provider: auto\\n 219| model: ''\\n 220| base_url: ''\\n 221|\", \"total_lines\": 748, \"file_size\": 27557, \"truncated\": true, \"hint\": \"Use offset=221 to continue reading (showing 1-220 of 748 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_FuBgNAOevhIkktoDvAXDMafv" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Au1GdKUEsME1Mi3lYHdS4uyv", "call_id": "call_Au1GdKUEsME1Mi3lYHdS4uyv", "response_item_id": "fc_0d99e9da68392f44016a12a72178a88191adfa66a746381145", "type": "function", "function": { "name": "search_files", "arguments": "{\"limit\":20,\"path\":\"/home/agent/.hermes/config.yaml\",\"pattern\":\"image_gen|FAL|fal\",\"target\":\"content\",\"context\":3}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": 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"id": "rs_0d99e9da68392f44016a12a72436408191ba81dc77aaf416af", "summary": [] } ] }, { "role": "tool", "content": "{\"content\": \" 220| base_url: ''\\n 221| api_key: ''\\n 222| timeout: 30\\n 223| extra_body: {}\\n 224| triage_specifier:\\n 225| provider: auto\\n 226| model: ''\\n 227| base_url: ''\\n 228| api_key: ''\\n 229| timeout: 120\\n 230| extra_body: {}\\n 231| curator:\\n 232| provider: auto\\n 233| model: ''\\n 234| base_url: ''\\n 235| api_key: ''\\n 236| timeout: 600\\n 237| extra_body: {}\\n 238|display:\\n 239| compact: false\\n 240| personality: kawaii\\n 241| resume_display: full\\n 242| busy_input_mode: interrupt\\n 243| tui_auto_resume_recent: false\\n 244| bell_on_complete: false\\n 245| show_reasoning: false\\n 246| streaming: true\\n 247| timestamps: false\\n 248| final_response_markdown: strip\\n 249| persistent_output: true\\n 250| persistent_output_max_lines: 200\\n 251| inline_diffs: true\\n 252| file_mutation_verifier: true\\n 253| show_cost: false\\n 254| skin: default\\n 255| language: en\\n 256| tui_status_indicator: kaomoji\\n 257| user_message_preview:\\n 258| first_lines: 2\\n 259| last_lines: 2\\n 260| interim_assistant_messages: true\\n 261| tool_progress_command: false\\n 262| tool_progress_overrides: {}\\n 263| tool_preview_length: 0\\n 264| ephemeral_system_ttl: 0\\n 265| platforms: {}\\n 266| runtime_footer:\\n 267| enabled: false\\n 268| fields:\\n 269| - model\\n 270| - context_pct\\n 271| - cwd\\n 272| copy_shortcut: auto\\n 273| background_process_notifications: all\\n 274| tool_progress: all\\n 275|dashboard:\\n 276| theme: default\\n 277| show_token_analytics: false\\n 278|privacy:\\n 279| redact_pii: false\\n 280|tts:\\n 281| provider: edge\\n 282| edge:\\n 283| voice: de-DE-KillianNeural\\n 284| elevenlabs:\\n 285| voice_id: pNInz6obpgDQGcFmaJgB\\n 286| model_id: eleven_multilingual_v2\\n 287| openai:\\n 288| model: gpt-4o-mini-tts\\n 289| voice: alloy\\n 290| xai:\\n 291| voice_id: eve\\n 292| language: en\\n 293| sample_rate: 24000\\n 294| bit_rate: 128000\\n 295| mistral:\\n 296| model: voxtral-mini-tts-2603\\n 297| voice_id: c69964a6-ab8b-4f8a-9465-ec0925096ec8\\n 298| neutts:\\n 299| ref_audio: ''\\n 300| ref_text: ''\\n 301| model: neuphonic/neutts-air-q4-gguf\\n 302| device: cpu\\n 303| piper:\\n 304| voice: en_US-lessac-medium\\n 305|stt:\\n 306| enabled: true\\n 307| provider: local\\n 308| local:\\n 309| model: medium\\n 310| language: de\\n 311| openai:\\n 312| model: whisper-1\\n 313| mistral:\\n 314| model: voxtral-mini-latest\\n 315|voice:\\n 316| record_key: ctrl+b\\n 317| max_recording_seconds: 120\\n 318| auto_tts: false\\n 319| beep_enabled: true\\n 320| silence_threshold: 200\\n 321| silence_duration: 3.0\\n 322|human_delay:\\n 323| mode: 'off'\\n 324| min_ms: 800\\n 325| max_ms: 2500\\n 326|context:\\n 327| engine: compressor\\n 328|memory:\\n 329| memory_enabled: true\\n 330| user_profile_enabled: true\\n 331| memory_char_limit: 2200\\n 332| user_char_limit: 1375\\n 333| provider: ''\\n 334| flush_min_turns: 6\\n 335| nudge_interval: 10\\n 336|delegation:\\n 337| model: ''\\n 338| provider: ''\\n 339| base_url: ''\\n 340| api_key: ''\\n 341| api_mode: ''\\n 342| inherit_mcp_toolsets: false\\n 343| max_iterations: 100\\n 344| child_timeout_seconds: 600\\n 345| reasoning_effort: ''\\n 346| max_concurrent_children: 3\\n 347| max_spawn_depth: 1\\n 348| orchestrator_enabled: true\\n 349| subagent_auto_approve: false\\n 350|prefill_messages_file: ''\\n 351|goals:\\n 352| max_turns: 40\\n 353|skills:\\n 354| external_dirs: []\\n 355| template_vars: true\\n 356| inline_shell: false\\n 357| inline_shell_timeout: 10\\n 358| guard_agent_created: false\\n 359| creation_nudge_interval: 15\\n 360|curator:\\n 361| enabled: true\\n 362| interval_hours: 168\\n 363| min_idle_hours: 2\\n 364| stale_after_days: 30\\n 365| archive_after_days: 90\\n 366| backup:\\n 367| enabled: true\\n 368| keep: 5\\n 369|honcho: {}\\n 370|timezone: ''\\n 371|slack:\\n 372| require_mention: true\\n 373| free_response_channels: ''\\n 374| allowed_channels: ''\\n 375| channel_prompts: {}\\n 376|discord:\\n 377| require_mention: true\\n 378| free_response_channels: ''\\n 379| allowed_channels: ''\\n 380| auto_thread: true\\n 381| thread_require_mention: false\\n 382| history_backfill: true\\n 383| history_backfill_limit: 50\\n 384| reactions: true\\n 385| channel_prompts: {}\\n 386| dm_role_auth_guild: ''\\n 387| server_actions: ''\\n 388|whatsapp: {}\\n 389|telegram:\\n 390| reactions: false\\n 391| channel_prompts:\\n 392| '6': 'Telegram Topic 6: Inbox / General Triage. Use for quick commands, unclassified\\n 393| intake, and routing. If a more suitable JARVIS Command Center topic is clear,\\n 394| recommend the topic switch or, if explicitly requested, answer in the correct\\n 395| topic via messaging. Keep replies concise unless asked.'\\n 396| '-1003907117629:6': 'Telegram Topic 6: Inbox / General Triage. Use for quick commands,\\n 397| unclassified intake, and routing. If a more suitable JARVIS Command Center topic\\n 398| is clear, recommend the topic switch or, if explicitly requested, answer in\\n 399| the correct topic via messaging. Keep replies concise unless asked.'\\n 400| '11': \\\"Telegram Topic 11: JARVIS System \\\\u2013 Architektur. Use only for overall\\\\\\n 401| \\\\ JARVIS/Hermes/FRIDAY architecture, Telegram topic structure, memory concept,\\\\\\n 402| \\\\ model routing, subagents, OpenClaw/Codex, cost optimisation, and security/permission\\\\\\n 403| \\\\ concepts. Do not do concrete individual script debugging here; recommend or\\\\\\n 404| \\\\ route to a coding/system/debugging topic.\\\"\\n 405| '-1003907117629:11': \\\"Telegram Topic 11: JARVIS System \\\\u2013 Architektur. Use\\\\\\n 406| \\\\ only for overall JARVIS/Hermes/FRIDAY architecture, Telegram topic structure,\\\\\\n 407| \\\\ memory concept, model routing, subagents, OpenClaw/Codex, cost optimisation,\\\\\\n 408| \\\\ and security/permission concepts. Do not do concrete individual script debugging\\\\\\n 409| \\\\ here; recommend or route to a coding/system/debugging topic.\\\"\\n 410| '16': 'Telegram Topic 16: Automationen / Cronjobs. Use for scheduled jobs, briefings,\\n 411| recurring reports, checks, reminders, watchdogs, automation outputs, cron delivery\\n 412| design, and monitoring summaries. Prefer explicit cron delivery targets with\\n 413| this thread id when relevant.'\\n 414| '-1003907117629:16': 'Telegram Topic 16: Automationen / Cronjobs. Use for scheduled\\n 415| jobs, briefings, recurring reports, checks, reminders, watchdogs, automation\\n 416| outputs, cron delivery design, and monitoring summaries. Prefer explicit cron\\n 417| delivery targets with this thread id when relevant.'\\n 418| '17': 'Telegram Topic 17: Research / AI / Tools. Use for AI/tool research, model\\n 419| comparisons, papers, external sources, tech reconnaissance, and non-operational\\n 420| research notes. Keep operational automation/debugging out unless explicitly\\n 421| tied to tool evaluation.'\\n 422| '-1003907117629:17': 'Telegram Topic 17: Research / AI / Tools. Use for AI/tool\\n 423| research, model comparisons, papers, external sources, tech reconnaissance,\\n 424| and non-operational research notes. Keep operational automation/debugging out\\n 425| unless explicitly tied to tool evaluation.'\\n 426| allowed_chats: ''\\n 427| allowed_users:\\n 428| - '1344779884'\\n 429| enabled: true\\n 430| home_channel: '1344779884'\\n 431|mattermost:\\n 432| require_mention: true\\n 433| free_response_channels: ''\\n 434| allowed_channels: ''\\n 435| channel_prompts: {}\\n 436|matrix:\\n 437| require_mention: true\\n 438| free_response_rooms: ''\\n 439| allowed_rooms: ''\\n 440|approvals:\\n 441| mode: manual\\n 442| timeout: 60\\n 443| cron_mode: deny\\n 444| mcp_reload_confirm: true\\n 445| destructive_slash_confirm: false\\n 446|command_allowlist:\\n 447|- stop/restart hermes gateway (kills running agents)\\n 448|- recursive delete\\n 449|- script execution via -e/-c flag\\n 450|- delete in root path\\n 451|- shell command via -c/-lc flag\\n 452|- stop/restart system service\\n 453|- kill hermes/gateway process (self-termination)\\n 454|- hermes update (restarts gateway, kills running agents)\\n 455|- git force push short flag (rewrites remote history)\\n 456|quick_commands: {}\\n 457|hooks: {}\\n 458|hooks_auto_accept: false\\n 459|personalities: {}\\n 460|security:\\n 461| allow_private_urls: false\\n 462| redact_secrets: true\\n 463| tirith_enabled: true\\n 464| tirith_path: tirith\\n 465| tirith_timeout: 5\\n 466| tirith_fail_open: true\\n 467| website_blocklist:\\n 468| enabled: false\\n 469| domains: []\\n 470| shared_files: []\\n 471| acked_advisories: []\\n 472| allow_lazy_installs: true\\n 473|cron:\\n 474| wrap_response: true\\n 475| max_parallel_jobs: null\\n 476|kanban:\\n 477| dispatch_in_gateway: true\\n 478| dispatch_interval_seconds: 60\\n 479| failure_limit: 2\\n 480|\", \"total_lines\": 748, \"file_size\": 27557, \"truncated\": true, \"hint\": \"Use offset=480 to continue reading (showing 220-479 of 748 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_uYUNTVX93gMO3kzdbOSwmerw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_yt1mWLNlcEEbVHrlnXu1F8B3", "call_id": "call_yt1mWLNlcEEbVHrlnXu1F8B3", "response_item_id": "fc_0d99e9da68392f44016a12a727a2f4819194f6979b7d4e6ab7", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":280,\"offset\":480,\"path\":\"/home/agent/.hermes/config.yaml\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"content\": \" 480|code_execution:\\n 481| mode: project\\n 482| max_tool_calls: 50\\n 483| timeout: 300\\n 484|logging:\\n 485| level: INFO\\n 486| max_size_mb: 5\\n 487| backup_count: 3\\n 488|model_catalog:\\n 489| enabled: true\\n 490| url: https://hermes-agent.nousresearch.com/docs/api/model-catalog.json\\n 491| ttl_hours: 24\\n 492| providers: {}\\n 493|network:\\n 494| force_ipv4: false\\n 495|sessions:\\n 496| auto_prune: false\\n 497| retention_days: 90\\n 498| vacuum_after_prune: true\\n 499| min_interval_hours: 24\\n 500|onboarding:\\n 501| seen:\\n 502| busy_input_prompt: true\\n 503| openclaw_residue_cleanup: true\\n 504| tool_progress_prompt: true\\n 505|updates:\\n 506| pre_update_backup: false\\n 507| backup_keep: 5\\n 508|lsp:\\n 509| enabled: true\\n 510| wait_mode: document\\n 511| wait_timeout: 5.0\\n 512| install_strategy: auto\\n 513| servers: {}\\n 514|x_search:\\n 515| model: grok-4.20-reasoning\\n 516| timeout_seconds: 180\\n 517| retries: 2\\n 518|_config_version: 23\\n 519|custom_providers:\\n 520|- api_key: key1234\\n 521| base_url: http://100.101.173.25:11435/v1\\n 522| model: qwen-3.6-agent\\n 523| name: Qwen 3.6 35B Local\\n 524|gateway:\\n 525| enabled: true\\n 526| platform: telegram\\n 527| telegram:\\n 528| allowed_users:\\n 529| - '1344779884'\\n 530| channel_prompts:\\n 531| '6': 'Telegram Topic 6: Inbox / General Triage. Use for quick commands, unclassified\\n 532| intake, and routing. If a more suitable JARVIS Command Center topic is clear,\\n 533| recommend the topic switch or, if explicitly requested, answer in the correct\\n 534| topic via messaging. Keep replies concise unless asked.'\\n 535| '-1003907117629:6': 'Telegram Topic 6: Inbox / General Triage. Use for quick\\n 536| commands, unclassified intake, and routing. If a more suitable JARVIS Command\\n 537| Center topic is clear, recommend the topic switch or, if explicitly requested,\\n 538| answer in the correct topic via messaging. Keep replies concise unless asked.'\\n 539| '11': \\\"Telegram Topic 11: JARVIS System \\\\u2013 Architektur. Use only for overall\\\\\\n 540| \\\\ JARVIS/Hermes/FRIDAY architecture, Telegram topic structure, memory concept,\\\\\\n 541| \\\\ model routing, subagents, OpenClaw/Codex, cost optimisation, and security/permission\\\\\\n 542| \\\\ concepts. Do not do concrete individual script debugging here; recommend\\\\\\n 543| \\\\ or route to a coding/system/debugging topic.\\\"\\n 544| '-1003907117629:11': \\\"Telegram Topic 11: JARVIS System \\\\u2013 Architektur. Use\\\\\\n 545| \\\\ only for overall JARVIS/Hermes/FRIDAY architecture, Telegram topic structure,\\\\\\n 546| \\\\ memory concept, model routing, subagents, OpenClaw/Codex, cost optimisation,\\\\\\n 547| \\\\ and security/permission concepts. Do not do concrete individual script debugging\\\\\\n 548| \\\\ here; recommend or route to a coding/system/debugging topic.\\\"\\n 549| '16': 'Telegram Topic 16: Automationen / Cronjobs. Use for scheduled jobs, briefings,\\n 550| recurring reports, checks, reminders, watchdogs, automation outputs, cron\\n 551| delivery design, and monitoring summaries. Prefer explicit cron delivery targets\\n 552| with this thread id when relevant.'\\n 553| '-1003907117629:16': 'Telegram Topic 16: Automationen / Cronjobs. Use for scheduled\\n 554| jobs, briefings, recurring reports, checks, reminders, watchdogs, automation\\n 555| outputs, cron delivery design, and monitoring summaries. Prefer explicit cron\\n 556| delivery targets with this thread id when relevant.'\\n 557| '17': 'Telegram Topic 17: Research & AI Nutzung. Use for research, ChatGPT/Hermes/Gemini/Codex\\n 558| and general AI questions, tool comparisons, prompting, model choice, papers,\\n 559| external sources, and general knowledge research. Keep operational automation/debugging\\n 560| out unless explicitly tied to tool evaluation.'\\n 561| '-1003907117629:17': 'Telegram Topic 17: Research & AI Nutzung. Use for research,\\n 562| ChatGPT/Hermes/Gemini/Codex and general AI questions, tool comparisons, prompting,\\n 563| model choice, papers, external sources, and general knowledge research. Keep\\n 564| operational automation/debugging out unless explicitly tied to tool evaluation.'\\n 565| '2': \\\"Telegram Topic 2: Finanzen \\\\u2013 Fragen & Strategie. Use for financial\\\\\\n 566| \\\\ questions, budget, Vorsorge/retirement planning, taxes, insurance, investments,\\\\\\n 567| \\\\ financial planning, and strategic financial decisions. Do not handle technical\\\\\\n 568| \\\\ finance scripts, dashboards, or data models here; route those to Topic 10\\\\\\n 569| \\\\ Finance System \\\\u2013 Architektur & Scripts.\\\"\\n 570| '-1003907117629:2': \\\"Telegram Topic 2: Finanzen \\\\u2013 Fragen & Strategie. Use\\\\\\n 571| \\\\ for financial questions, budget, Vorsorge/retirement planning, taxes, insurance,\\\\\\n 572| \\\\ investments, financial planning, and strategic financial decisions. Do not\\\\\\n 573| \\\\ handle technical finance scripts, dashboards, or data models here; route\\\\\\n 574| \\\\ those to Topic 10 Finance System \\\\u2013 Architektur & Scripts.\\\"\\n 575| '3': \\\"Telegram Topic 3: Politik Windisch. Use for Sir\\\\u2019s work as Einwohnerrat\\\\\\n 576| \\\\ in Windisch: political strategy, Vorst\\\\xF6sse, argumentation, local topics,\\\\\\n 577| \\\\ meetings, speeches, texts, positioning, and municipal research. Keep unrelated\\\\\\n 578| \\\\ business/finance/system topics out.\\\"\\n 579| '-1003907117629:3': \\\"Telegram Topic 3: Politik Windisch. Use for Sir\\\\u2019s\\\\\\n 580| \\\\ work as Einwohnerrat in Windisch: political strategy, Vorst\\\\xF6sse, argumentation,\\\\\\n 581| \\\\ local topics, meetings, speeches, texts, positioning, and municipal research.\\\\\\n 582| \\\\ Keep unrelated business/finance/system topics out.\\\"\\n 583| '4': \\\"Telegram Topic 4: Gesundheit \\\\u2013 Medizin & Strategie. Use only for\\\\\\n 584| \\\\ medical strategy and health-management topics: Morbus Beh\\\\xE7et, Major Vessel\\\\\\n 585| \\\\ Disease, thrombosis risk, medication, nutrition, symptoms, lab values, appointments,\\\\\\n 586| \\\\ and questions for Rheumatologie, Dermatologie, H\\\\xE4matologie, Augenklinik.\\\\\\n 587| \\\\ Treat health data as confidential; do not make definitive diagnoses or override\\\\\\n 588| \\\\ clinicians. Technical health-system architecture/scripts/data models go\\\\\\n 589| \\\\ to Topic 8.\\\"\\n 590| '-1003907117629:4': \\\"Telegram Topic 4: Gesundheit \\\\u2013 Medizin & Strategie.\\\\\\n 591| \\\\ Use only for medical strategy and health-management topics: Morbus Beh\\\\xE7\\\\\\n 592| et, Major Vessel Disease, thrombosis risk, medication, nutrition, symptoms,\\\\\\n 593| \\\\ lab values, appointments, and questions for Rheumatologie, Dermatologie,\\\\\\n 594| \\\\ H\\\\xE4matologie, Augenklinik. Treat health data as confidential; do not make\\\\\\n 595| \\\\ definitive diagnoses or override clinicians. Technical health-system architecture/scripts/data\\\\\\n 596| \\\\ models go to Topic 8.\\\"\\n 597| '5': \\\"Telegram Topic 5: Gesch\\\\xE4ftliches \\\\u2013 ERNE Holzbau. Use for business\\\\\\n 598| \\\\ topics around ERNE Holzbau: strategy, organisation, processes, communication,\\\\\\n 599| \\\\ optimisation, projects, management preparation, and business analyses. Keep\\\\\\n 600| \\\\ Gasser Bauunternehmung matters in Topic 20.\\\"\\n 601| '-1003907117629:5': \\\"Telegram Topic 5: Gesch\\\\xE4ftliches \\\\u2013 ERNE Holzbau.\\\\\\n 602| \\\\ Use for business topics around ERNE Holzbau: strategy, organisation, processes,\\\\\\n 603| \\\\ communication, optimisation, projects, management preparation, and business\\\\\\n 604| \\\\ analyses. Keep Gasser Bauunternehmung matters in Topic 20.\\\"\\n 605| '8': \\\"Telegram Topic 8: Health System \\\\u2013 Architektur & Scripts. Use for\\\\\\n 606| \\\\ technical health-system implementation: architecture, scripts, data model,\\\\\\n 607| \\\\ symptom tracking, lab-value import, reports, automations, cronjob integration,\\\\\\n 608| \\\\ error analysis, and optimisation. Do not handle medical therapy or strategy\\\\\\n 609| \\\\ decisions here; route those to Topic 4.\\\"\\n 610| '-1003907117629:8': \\\"Telegram Topic 8: Health System \\\\u2013 Architektur & Scripts.\\\\\\n 611| \\\\ Use for technical health-system implementation: architecture, scripts, data\\\\\\n 612| \\\\ model, symptom tracking, lab-value import, reports, automations, cronjob\\\\\\n 613| \\\\ integration, error analysis, and optimisation. Do not handle medical therapy\\\\\\n 614| \\\\ or strategy decisions here; route those to Topic 4.\\\"\\n 615| '10': \\\"Telegram Topic 10: Finance System \\\\u2013 Architektur & Scripts. Use for\\\\\\n 616| \\\\ technical finance-system implementation: import scripts, bank data, CSV\\\\\\n 617| \\\\ processing, data models, dashboards, reports, automations, error analysis,\\\\\\n 618| \\\\ and optimisation. Do not handle financial strategy here; route those to\\\\\\n 619| \\\\ Topic 2.\\\"\\n 620| '-1003907117629:10': \\\"Telegram Topic 10: Finance System \\\\u2013 Architektur &\\\\\\n 621| \\\\ Scripts. Use for technical finance-system implementation: import scripts,\\\\\\n 622| \\\\ bank data, CSV processing, data models, dashboards, reports, automations,\\\\\\n 623| \\\\ error analysis, and optimisation. Do not handle financial strategy here;\\\\\\n 624| \\\\ route those to Topic 2.\\\"\\n 625| '19': \\\"Telegram Topic 19: Family \\\\u2013 Wife & Kids. Use for family organisation,\\\\\\n 626| \\\\ private planning, appointments, to-dos, ideas, daily-life matters, and topics\\\\\\n 627| \\\\ around wife and children. Treat as especially private and restrained; do\\\\\\n 628| \\\\ not pull content from other topics unless explicitly asked.\\\"\\n 629| '-1003907117629:19': \\\"Telegram Topic 19: Family \\\\u2013 Wife & Kids. Use for\\\\\\n 630| \\\\ family organisation, private planning, appointments, to-dos, ideas, daily-life\\\\\\n 631| \\\\ matters, and topics around wife and children. Treat as especially private\\\\\\n 632| \\\\ and restrained; do not pull content from other topics unless explicitly\\\\\\n 633| \\\\ asked.\\\"\\n 634| '20': \\\"Telegram Topic 20: Gesch\\\\xE4ftliches \\\\u2013 Gasser Bauunternehmung. Use\\\\\\n 635| \\\\ for business topics around Gasser Bauunternehmung, where Sir is Gesellschafter:\\\\\\n 636| \\\\ strategy, processes, communication, organisation, optimisation, construction-site/project\\\\\\n 637| \\\\ questions, and business analyses. Keep ERNE Holzbau matters in Topic 5.\\\"\\n 638| '-1003907117629:20': \\\"Telegram Topic 20: Gesch\\\\xE4ftliches \\\\u2013 Gasser Bauunternehmung.\\\\\\n 639| \\\\ Use for business topics around Gasser Bauunternehmung, where Sir is Gesellschafter:\\\\\\n 640| \\\\ strategy, processes, communication, organisation, optimisation, construction-site/project\\\\\\n 641| \\\\ questions, and business analyses. Keep ERNE Holzbau matters in Topic 5.\\\"\\n 642| '76': 'Telegram Topic 76: System Logs / Debugging. Use for concrete operational\\n 643| debugging: error logs, Python tracebacks, cron failures, import errors, and\\n 644| concrete script repairs. Treat as a separate session. Keep broader JARVIS/Hermes\\n 645| architecture in Topic 11 and recurring automation design/monitoring in Topic\\n 646| 16 unless the task is a specific failure or script repair.'\\n 647| -1003907117629:76: 'Telegram Topic 76: System Logs / Debugging. Use for concrete\\n 648| operational debugging: error logs, Python tracebacks, cron failures, import\\n 649| errors, and concrete script repairs. Treat as a separate session. Keep broader\\n 650| JARVIS/Hermes architecture in Topic 11 and recurring automation design/monitoring\\n 651| in Topic 16 unless the task is a specific failure or script repair.'\\n 652| '80': 'Telegram Topic 80: Security & Permissions. Use for API keys and credentials\\n 653| concepts, access control, bot permissions, secret handling, backup/restore\\n 654| strategy, and admin decisions. Treat as a separate session. Do not print raw\\n 655| secrets; use redacted paths, concepts, checks, and minimal diffs.'\\n 656| -1003907117629:80: 'Telegram Topic 80: Security & Permissions. Use for API keys\\n 657| and credentials concepts, access control, bot permissions, secret handling,\\n 658| backup/restore strategy, and admin decisions. Treat as a separate session.\\n 659| Do not print raw secrets; use redacted paths, concepts, checks, and minimal\\n 660| diffs.'\\n 661| enabled: true\\n 662| home_channel: '1344779884'\\n 663| reactions: false\\n 664|group_sessions_per_user: true\\n 665|known_plugin_toolsets:\\n 666| cli:\\n 667| - spotify\\n 668| telegram:\\n 669| - spotify\\n 670|platform_toolsets:\\n 671| cli:\\n 672| - browser\\n 673| - clarify\\n 674| - code_execution\\n 675| - cronjob\\n 676| - delegation\\n 677| - file\\n 678| - image_gen\\n 679| - memory\\n 680| - messaging\\n 681| - session_search\\n 682| - skills\\n 683| - terminal\\n 684| - todo\\n 685| - tts\\n 686| - vision\\n 687| - web\\n 688| discord:\\n 689| - hermes-discord\\n 690| homeassistant:\\n 691| - hermes-homeassistant\\n 692| qqbot:\\n 693| - hermes-qqbot\\n 694| signal:\\n 695| - hermes-signal\\n 696| slack:\\n 697| - hermes-slack\\n 698| teams:\\n 699| - hermes-teams\\n 700| telegram:\\n 701| - browser\\n 702| - clarify\\n 703| - code_execution\\n 704| - cronjob\\n 705| - delegation\\n 706| - file\\n 707| - image_gen\\n 708| - memory\\n 709| - messaging\\n 710| - session_search\\n 711| - skills\\n 712| - terminal\\n 713| - todo\\n 714| - tts\\n 715| - vision\\n 716| - web\\n 717| whatsapp:\\n 718| - hermes-whatsapp\\n 719| yuanbao:\\n 720| - hermes-yuanbao\\n 721|session_reset:\\n 722| at_hour: 4\\n 723| idle_minutes: 1440\\n 724| mode: both\\n 725|streaming:\\n 726| enabled: false\\n 727|\\n 728|# ── Fallback Model ────────────────────────────────────────────────────\\n 729|# Automatic provider failover when primary is unavailable.\\n 730|# Uncomment and configure to enable. Triggers on rate limits (429),\\n 731|# overload (529), service errors (503), or connection failures.\\n 732|#\\n 733|# Supported providers:\\n 734|# openrouter (OPENROUTER_API_KEY) — routes to any model\\n 735|# openai-codex (OAuth — hermes auth) — OpenAI Codex\\n 736|# nous (OAuth — hermes auth) — Nous Portal\\n 737|# zai (ZAI_API_KEY) — Z.AI / GLM\\n 738|# kimi-coding (KIMI_API_KEY) — Kimi / Moonshot\\n 739|# kimi-coding-cn (KIMI_CN_API_KEY) — Kimi / Moonshot (China)\\n 740|# minimax (MINIMAX_API_KEY) — MiniMax\\n 741|# minimax-cn (MINIMAX_CN_API_KEY) — MiniMax (China)\\n 742|# bedrock (AWS IAM / boto3) — AWS Bedrock (Converse API)\\n 743|#\\n 744|# For custom OpenAI-compatible endpoints, add base_url and key_env.\\n 745|#\\n 746|# fallback_model:\\n 747|# provider: openrouter\\n 748|# model: anthropic/claude-sonnet-4\\n 749|\", \"total_lines\": 748, \"file_size\": 27557, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_yt1mWLNlcEEbVHrlnXu1F8B3" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_3riurchzJvL2HpmRUbKKWRD0", "call_id": "call_3riurchzJvL2HpmRUbKKWRD0", "response_item_id": "fc_0d99e9da68392f44016a12a72bc88481919e0ad21fe19160a6", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os\\nfor k in ['FAL_KEY','FAL_API_KEY','OPENAI_API_KEY']:\\n print(k, 'set' if os.getenv(k) else 'not set')\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqcrLuv_tDfDRgITLi0qmazbsi3JfSchK3uFSsbprDdqMEmzqhHrjBOiWRbfoZ8GaFOQLuRnNo1d9dY39ptvG3eOTUUt5WJ_FTJQ7j1txKZZ1zW3cj103jr0993sDvVaWSsq1C7LfIMG4jmHpnzO_lDOLgxIeL0fwiWsh7dIP4UZn8_NKt7db7KHHDPhFm4Xy8bwi-41pKeX0NjekVCVUHbkZSztCZ77XaPH0q-EIVhLHgbbjRnygXTzyk71GuIWgf9Jv-jMlw6mkJB-sivP0L_CEsPXafupAHtTPK4Vw57oQZ5KoeGh0QGcPwh_ykN5vpQcFfA_RxCHHKR6HULdCFzFSc0W5tlcK6xDdiwp6Z3PjUWvBP8cHzSpY4cE9wcxfv-pLSwA6Z-Q6D6Jahi3U4lLg7RXUib8s3rSINW-4B0LvZ3Uo-OmJFi6o4Z5E2BwDB_z4X_cEmjYscgsnnqJC_oRmm7gg4u_bAq-LAq2B2e-WyuHdhmgk1-gEO2Ziyrxz_pO5dEGCQWhq7fMJv-dlRCT0zTX26rapqZiy_H4Y_9UOah9SH1zfpPAf-UnmlnuF-PTGN6KQmZpUyQLZdv2HIalZoldPiKLgLjesnlqC-IjP4u8XjKlKAr5OMDgm7E8SNd1OgxHt0y2iCByNGXeQ08BECcs1vLV042qZOyemAVS8HzbM_p_mOybD2yK8KY8WUZrpHa7VfJ3vTBP5mTiDTzQCMj1d_wiPTgJ50qjBqnCTK3sRHdeqGavTFu_gCAhB0lVd-sfg08UvmfADXqHv5PMFXVHgqkORneKRmekKB5SYQYc6AvaQ2vnx_78MGD9Q-oW_ilH4cy0IKkihlUgwl33df_MZVgOaqOEUro8scPfQDRIuhbe0eI4Vh2A7qug8Y7MJ0gj9VtoLSYPVxhVSZ1gas65ysv8S0T-SF3cD_Apu1RDY5QjOgu95qKFsQtfN6DyFr0Mm76zN7S7vxdiy-6nSveGk8dK4mZkqDfIZnMn8D-MEEdt67PW-bXlLJT5wPQevr7dgd0AQqRgGQNL9K-md4JzUBUXMLEfHF4DO5V_ZE8=", "id": "rs_0d99e9da68392f44016a12a72ac5cc8191a03233724e8ff7cc", "summary": [] } ] }, { "role": "tool", "content": "{\"output\": \"FAL_KEY not set\\nFAL_API_KEY not set\\nOPENAI_API_KEY not set\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_3riurchzJvL2HpmRUbKKWRD0" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_fWFPrgr0zBn0rGNT27HEIgNx", "call_id": "call_fWFPrgr0zBn0rGNT27HEIgNx", "response_item_id": "fc_0d99e9da68392f44016a12a730c9308191aedbf35c8ffbd8bb", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\np=Path.home()/'.hermes/.env'\\nkeys=['FAL_KEY','FAL_API_KEY','OPENAI_API_KEY']\\nfound={k:False for k in keys}\\nif p.exists():\\n for line in p.read_text(errors='ignore').splitlines():\\n s=line.strip()\\n if not s or s.startswith('#') or '=' not in s: continue\\n k=s.split('=',1)[0].strip().lstrip('export ').strip()\\n if k in found: found[k]=bool(s.split('=',1)[1].strip().strip('\\\"\\\\''))\\nfor k,v in found.items(): print(k, 'present' if v else 'absent')\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Exploring .env loading issues**\n\nIt looks like my terminal isn't sourcing the .env file. Maybe it’s related to how Hermes loads .env? I could try checking with a safe grep, but I don’t have grep available. Instead, I'm thinking about reading the .env file and parsing the keys using Python. That should help me get to the details I need. Alright, let’s figure this out step by step!", "reasoning_content": "**Exploring .env loading issues**\n\nIt looks like my terminal isn't sourcing the .env file. Maybe it’s related to how Hermes loads .env? I could try checking with a safe grep, but I don’t have grep available. Instead, I'm thinking about reading the .env file and parsing the keys using Python. That should help me get to the details I need. Alright, let’s figure this out step by step!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqcwzA1aQJ7YAjvm2aAcbGQXll9HZiE9S58FcDnNLMJQDSrPhMFhIRjKWofcBK6aiY9stiCZahEP54UNllhxVKGfDvTLkz_xNx-LpOuYpWBpcuZEnHgBpYRc7KyorO0fQkzUcvY00Ia0T9m_HC3odrvR3b4oj7LuNuusxp93EQa-h9f5c4Lv16FbKy5WtAU4fKDglddorjvzqSQgM_j3EDrJy4ThQtYX4Xn673FLVQZhjh8u1hjhqMBG49vgmiKAJTUHlRSdEw1mh-GQu93FGmk4GzDbxoRoEwNALFQaUSOCgLpQZnkXGTNMZuuEw39dFr5yiv5VF-JzgJkApbcqf6DtAPkG4Iym5Vq3LTQPFCBWYcs-631HzPsh6htRplnYabaRzk9_BixZDtgyoSznxacyXvtkqxvfmA0fFGmBNlzthb4NpDG32dX_8DNnYKfDUOzPyyGEAZGLVIea3nn8aCXFMcf3JHt0k3sYHeu_-QEHhGWKU0bimO_SJOTwDdSAyDx8t10MlG7FdPX_0arjf5gfI5dzIMSU1WJC68OQORdU0WvUX3FsNgo6Ta5VQcN-LDrKbIS31mHSARD6yEpkca_CB3OfSId9EVEClbp1gA2hT01cR3LGrKyZVH-fMMIUhpPyEBtw161PCkzUIg15ttZ3g-sGxedcrMWN3_SxmqWbpDtc5lU165kuPna2DHEh54DAWvSmZor1-KtiaPGNF9Li38dMhv136VHvjkwDPCS5D1H3d18uOuh6cqL7FYuC9cTNOKcditinEZ5QO0eDqbQZSbGVGBeLqMzSPYWBZZJJhPLaJFNqxT42An5rZSp-BEJIg4Yv9Q0JfgBB5noxOqCy-Uj18zGGtB5JYSNO_KQMjBJQpbZmBZlsCq5rGrNyr2tC6RILv3lJnLjAbqrL-aG0J-vRuSGdvvYLQ-YNcGgP13Z2em_1Z8l7cUHP9_bigZqueP-Zzo4-EhKZe0ikssmyR5TfrAecoCdaOK8SzegTPBFjdMraMcSVRx71cl7cZ2pBXL7xK0znZ4o90KGKHnAl88zJHEq0-nlxn4AU1AhVq7HMiq04KqniTfoIl9ZZPocranIKi_Dpd3r8PJcJk7cU7w==", "id": "rs_0d99e9da68392f44016a12a72ec30481919a1b152c95355efb", "summary": [ { "type": "summary_text", "text": "**Exploring .env loading issues**\n\nIt looks like my terminal isn't sourcing the .env file. Maybe it’s related to how Hermes loads .env? I could try checking with a safe grep, but I don’t have grep available. Instead, I'm thinking about reading the .env file and parsing the keys using Python. That should help me get to the details I need. Alright, let’s figure this out step by step!" } ] } ] }, { "role": "tool", "content": "{\"output\": \"FAL_KEY absent\\nFAL_API_KEY absent\\nOPENAI_API_KEY absent\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_fWFPrgr0zBn0rGNT27HEIgNx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_AGOmmG0su8X0Oc2CI5MatXfw", "call_id": "call_AGOmmG0su8X0Oc2CI5MatXfw", "response_item_id": "fc_0d99e9da68392f44016a12a739485081918c9ce567df6316f9", "type": "function", "function": { "name": "search_files", "arguments": "{\"limit\":80,\"path\":\"/home/agent/.hermes/hermes-agent/plugins/image_gen\",\"pattern\":\"*\",\"target\":\"files\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqc5x2QfRa1sLLqI-gDam_Z9bLRC1Ntv4uIyieXzxsoKS9HhjDNSA7C51QM_NTkWyB5a7dtQ-eBVagGOIltj25n2n3bygb1oAaMQrdK3eHRObh6wA1uJ95IsMlheki7sfdB5YL60fd9X4rRIV8-xy1sHt8iHeWExzt_ZvtBX5K0Tbvv8yX683pjeL3C4GYVQYkv0pQdFY9tnErx9M7h8CcD0xsXcVphi-LnLkHraM1m4kp13Dk5uFFw0L0AZTBGJH2PAiSiIFChHWPld_t94nruIrCa-puqZO-5z4pHckDquwls8-GC1BD8GJxK-vKFVhA8_Gg1YFY98O3aHg0NMl50fjWCSgy86LbTeb04WwXnDr5qh3WqwqeMQAQC03XFEh3bzyk8KF1m81GNhq52zrimiXpHFWbtW3J2EQ_g7wLrNYnAvmzal5zhXOhslhiJZGZPVBcjtHjcnDNvaq3SyRWXzkcZHUwhLyAY9jjDCdEtebRgRrgpCJR5IKovcCwoprNy2BwivCVtS0vE88MblARnVdrSh6PaerifPVEIK3bXwRc1P1LMk5EwUWeXkepOfEGPd-DXVNotmFhR_f5DtCb0h-pBvY-8gXyRLMDbnAnKkOpAM0oRn5xdZdbUCJ7usJ6jun3PCYDMqJ-QKIOGGhNjsKPnMb-u_OFnMAZKnXPA1O34RF3_NML26IX86q0pUEq8QgC-45p8r4BgeZIUmeFCgn7UF-9P-v09ZHQQuE34oF3vaOA3vcQu2jb3pkluPmxAm-SjX4bPLktrwBupj4er63gLMsa3aSc_7cdYMMmmEZ9z43hu1t_P7DtCnmCRmmFzrGVRKKCMcZfO8wfv5XxM7-SCGC56C-oFmAtYir_8KszBugW48IT56CxfCiZ8BMINRIrXJi3aX00YT8oTl5MrU_blA74OKnJVDs3LAk2WfVIslNZAReKj8FLJqaQZ5GhsXaCDkPHX2NJDQgW5TMV2n-XH7JgQwZOOpqr9Q0T-sp1zttpw7zfD8cZxQd6XjXaDnnRoKDlTM41se4zmBG5p4HH80d2z_XEa1cUElvHS6T4UEQ56M8yDA1L0pUC8Mb6fvybhSzJ8IaG8Wimqkh144dCgiOZ5tPEDCumxtvd79Il9UeagBsoIkO7ZBdZU0CY3RuAa62MnbQmi3dib7V6HIHelIRWkfLrD16qnUiGb_VEhhEBd6oy-3ftZq8MPyKR9eyrMENkjUhuQ_yxVsPX0yCYEAf3QR-gwzUUKXyAo5iy1vq88K_y6uqyUh-5X61k2rg8AcG5AKNbcXXKfrPLaP_6gqSuLnf4kODWVgY4atd8rdrPBNMN12b0C1ldcJYIf9Wej5f7CB-IoKKPm3rGxYNAm-EY83dUEhmo0InCHVUkM=", "id": "rs_0d99e9da68392f44016a12a7372ab88191b24e331123636d7c", "summary": [] } ] }, { "role": "tool", "content": "{\"total_count\": 9, \"files\": [\"/home/agent/.hermes/hermes-agent/plugins/image_gen/xai/__pycache__/__init__.cpython-311.pyc\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/__pycache__/__init__.cpython-311.pyc\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai/__pycache__/__init__.cpython-311.pyc\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/xai/__init__.py\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/xai/plugin.yaml\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai/plugin.yaml\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai/__init__.py\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/plugin.yaml\", \"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/__init__.py\"]}", "tool_call_id": "call_AGOmmG0su8X0Oc2CI5MatXfw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_5YHD05oIBUSDvyDKwgdEihXK", "call_id": "call_5YHD05oIBUSDvyDKwgdEihXK", "response_item_id": "fc_0d99e9da68392f44016a12a73dec6481919239eb13e527d115", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/plugin.yaml\",\"limit\":120}" } }, { "id": "call_RUtYgvcIYwu9X0HUzMosesU3", "call_id": "call_RUtYgvcIYwu9X0HUzMosesU3", "response_item_id": "fc_0d99e9da68392f44016a12a73dec788191b4b5fc57a7387b09", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/__init__.py\",\"limit\":260}" } }, { "id": "call_IKtFAd3A8pfzzrL2leVUWdIh", "call_id": "call_IKtFAd3A8pfzzrL2leVUWdIh", "response_item_id": "fc_0d99e9da68392f44016a12a73dec80819192a25e1c0f77e93e", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai/plugin.yaml\",\"limit\":120}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"content\": \" 1|name: openai-codex\\n 2|version: 1.0.0\\n 3|description: \\\"OpenAI image generation backed by ChatGPT/Codex OAuth (gpt-image-2 via the Responses image_generation tool). Saves generated images to $HERMES_HOME/cache/images/.\\\"\\n 4|author: NousResearch\\n 5|kind: backend\\n 6|\", \"total_lines\": 5, \"file_size\": 247, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_5YHD05oIBUSDvyDKwgdEihXK" }, { "role": "tool", "content": "{\"content\": \" 1|\\\"\\\"\\\"OpenAI image generation backend — ChatGPT/Codex OAuth variant.\\n 2|\\n 3|Identical model catalog and tier semantics to the ``openai`` image-gen plugin\\n 4|(``gpt-image-2`` at low/medium/high quality), but routes the request through\\n 5|the Codex Responses API ``image_generation`` tool instead of the\\n 6|``images.generate`` REST endpoint. This lets users who are already\\n 7|authenticated with Codex/ChatGPT generate images without configuring a\\n 8|separate ``OPENAI_API_KEY``.\\n 9|\\n 10|Selection precedence for the tier (first hit wins):\\n 11|\\n 12|1. ``OPENAI_IMAGE_MODEL`` env var (escape hatch for scripts / tests)\\n 13|2. ``image_gen.openai-codex.model`` in ``config.yaml``\\n 14|3. ``image_gen.model`` in ``config.yaml`` (when it's one of our tier IDs)\\n 15|4. :data:`DEFAULT_MODEL` — ``gpt-image-2-medium``\\n 16|\\n 17|Output is saved as PNG under ``$HERMES_HOME/cache/images/``.\\n 18|\\\"\\\"\\\"\\n 19|\\n 20|from __future__ import annotations\\n 21|\\n 22|import logging\\n 23|from typing import Any, Dict, List, Optional, Tuple\\n 24|\\n 25|from agent.image_gen_provider import (\\n 26| DEFAULT_ASPECT_RATIO,\\n 27| ImageGenProvider,\\n 28| error_response,\\n 29| resolve_aspect_ratio,\\n 30| save_b64_image,\\n 31| success_response,\\n 32|)\\n 33|\\n 34|logger = logging.getLogger(__name__)\\n 35|\\n 36|\\n 37|# ---------------------------------------------------------------------------\\n 38|# Model catalog — mirrors the ``openai`` plugin so the picker UX is identical.\\n 39|# ---------------------------------------------------------------------------\\n 40|\\n 41|API_MODEL = \\\"gpt-image-2\\\"\\n 42|\\n 43|_MODELS: Dict[str, Dict[str, Any]] = {\\n 44| \\\"gpt-image-2-low\\\": {\\n 45| \\\"display\\\": \\\"GPT Image 2 (Low)\\\",\\n 46| \\\"speed\\\": \\\"~15s\\\",\\n 47| \\\"strengths\\\": \\\"Fast iteration, lowest cost\\\",\\n 48| \\\"quality\\\": \\\"low\\\",\\n 49| },\\n 50| \\\"gpt-image-2-medium\\\": {\\n 51| \\\"display\\\": \\\"GPT Image 2 (Medium)\\\",\\n 52| \\\"speed\\\": \\\"~40s\\\",\\n 53| \\\"strengths\\\": \\\"Balanced — default\\\",\\n 54| \\\"quality\\\": \\\"medium\\\",\\n 55| },\\n 56| \\\"gpt-image-2-high\\\": {\\n 57| \\\"display\\\": \\\"GPT Image 2 (High)\\\",\\n 58| \\\"speed\\\": \\\"~2min\\\",\\n 59| \\\"strengths\\\": \\\"Highest fidelity, strongest prompt adherence\\\",\\n 60| \\\"quality\\\": \\\"high\\\",\\n 61| },\\n 62|}\\n 63|\\n 64|DEFAULT_MODEL = \\\"gpt-image-2-medium\\\"\\n 65|\\n 66|_SIZES = {\\n 67| \\\"landscape\\\": \\\"1536x1024\\\",\\n 68| \\\"square\\\": \\\"1024x1024\\\",\\n 69| \\\"portrait\\\": \\\"1024x1536\\\",\\n 70|}\\n 71|\\n 72|# Codex Responses surface used for the request. The chat model itself is only\\n 73|# the host that calls the ``image_generation`` tool; the actual image work is\\n 74|# done by ``API_MODEL``.\\n 75|_CODEX_CHAT_MODEL = \\\"gpt-5.4\\\"\\n 76|_CODEX_BASE_URL = \\\"https://chatgpt.com/backend-api/codex\\\"\\n 77|_CODEX_INSTRUCTIONS = (\\n 78| \\\"You are an assistant that must fulfill image generation requests by \\\"\\n 79| \\\"using the image_generation tool when provided.\\\"\\n 80|)\\n 81|\\n 82|\\n 83|# ---------------------------------------------------------------------------\\n 84|# Config + auth helpers\\n 85|# ---------------------------------------------------------------------------\\n 86|\\n 87|\\n 88|def _load_image_gen_config() -> Dict[str, Any]:\\n 89| \\\"\\\"\\\"Read ``image_gen`` from config.yaml (returns {} on any failure).\\\"\\\"\\\"\\n 90| try:\\n 91| from hermes_cli.config import load_config\\n 92|\\n 93| cfg = load_config()\\n 94| section = cfg.get(\\\"image_gen\\\") if isinstance(cfg, dict) else None\\n 95| return section if isinstance(section, dict) else {}\\n 96| except Exception as exc:\\n 97| logger.debug(\\\"Could not load image_gen config: %s\\\", exc)\\n 98| return {}\\n 99|\\n 100|\\n 101|def _resolve_model() -> Tuple[str, Dict[str, Any]]:\\n 102| \\\"\\\"\\\"Decide which tier to use and return ``(model_id, meta)``.\\\"\\\"\\\"\\n 103| import os\\n 104|\\n 105| env_override = os.environ.get(\\\"OPENAI_IMAGE_MODEL\\\")\\n 106| if env_override and env_override in _MODELS:\\n 107| return env_override, _MODELS[env_override]\\n 108|\\n 109| cfg = _load_image_gen_config()\\n 110| sub = cfg.get(\\\"openai-codex\\\") if isinstance(cfg.get(\\\"openai-codex\\\"), dict) else {}\\n 111| candidate: Optional[str] = None\\n 112| if isinstance(sub, dict):\\n 113| value = sub.get(\\\"model\\\")\\n 114| if isinstance(value, str) and value in _MODELS:\\n 115| candidate = value\\n 116| if candidate is None:\\n 117| top = cfg.get(\\\"model\\\")\\n 118| if isinstance(top, str) and top in _MODELS:\\n 119| candidate = top\\n 120|\\n 121| if candidate is not None:\\n 122| return candidate, _MODELS[candidate]\\n 123|\\n 124| return DEFAULT_MODEL, _MODELS[DEFAULT_MODEL]\\n 125|\\n 126|\\n 127|def _read_codex_access_token() -> Optional[str]:\\n 128| \\\"\\\"\\\"Return a usable Codex OAuth token, or None.\\n 129|\\n 130| Delegates to the canonical reader in ``agent.auxiliary_client`` so token\\n 131| expiry, credential pool selection, and JWT decoding stay in one place.\\n 132| \\\"\\\"\\\"\\n 133| try:\\n 134| from agent.auxiliary_client import _read_codex_access_token as _reader\\n 135|\\n 136| token = _reader()\\n 137| if isinstance(token, str) and token.strip():\\n 138| return token.strip()\\n 139| return None\\n 140| except Exception as exc:\\n 141| logger.debug(\\\"Could not resolve Codex access token: %s\\\", exc)\\n 142| return None\\n 143|\\n 144|\\n 145|def _build_codex_client():\\n 146| \\\"\\\"\\\"Return an OpenAI client pointed at the ChatGPT/Codex backend, or None.\\\"\\\"\\\"\\n 147| token = _read_codex_access_token()\\n 148| if not token:\\n 149| return None\\n 150| try:\\n 151| import openai\\n 152| from agent.auxiliary_client import _codex_cloudflare_headers\\n 153|\\n 154| return openai.OpenAI(\\n 155| api_key=token,\\n 156| base_url=_CODEX_BASE_URL,\\n 157| default_headers=_codex_cloudflare_headers(token),\\n 158| )\\n 159| except Exception as exc:\\n 160| logger.debug(\\\"Could not build Codex image client: %s\\\", exc)\\n 161| return None\\n 162|\\n 163|\\n 164|def _collect_image_b64(client: Any, *, prompt: str, size: str, quality: str) -> Optional[str]:\\n 165| \\\"\\\"\\\"Stream a Codex Responses image_generation call and return the b64 image.\\\"\\\"\\\"\\n 166| image_b64: Optional[str] = None\\n 167|\\n 168| with client.responses.stream(\\n 169| model=_CODEX_CHAT_MODEL,\\n 170| store=False,\\n 171| instructions=_CODEX_INSTRUCTIONS,\\n 172| input=[{\\n 173| \\\"type\\\": \\\"message\\\",\\n 174| \\\"role\\\": \\\"user\\\",\\n 175| \\\"content\\\": [{\\\"type\\\": \\\"input_text\\\", \\\"text\\\": prompt}],\\n 176| }],\\n 177| tools=[{\\n 178| \\\"type\\\": \\\"image_generation\\\",\\n 179| \\\"model\\\": API_MODEL,\\n 180| \\\"size\\\": size,\\n 181| \\\"quality\\\": quality,\\n 182| \\\"output_format\\\": \\\"png\\\",\\n 183| \\\"background\\\": \\\"opaque\\\",\\n 184| \\\"partial_images\\\": 1,\\n 185| }],\\n 186| tool_choice={\\n 187| \\\"type\\\": \\\"allowed_tools\\\",\\n 188| \\\"mode\\\": \\\"required\\\",\\n 189| \\\"tools\\\": [{\\\"type\\\": \\\"image_generation\\\"}],\\n 190| },\\n 191| ) as stream:\\n 192| for event in stream:\\n 193| event_type = getattr(event, \\\"type\\\", \\\"\\\")\\n 194| if event_type == \\\"response.output_item.done\\\":\\n 195| item = getattr(event, \\\"item\\\", None)\\n 196| if getattr(item, \\\"type\\\", None) == \\\"image_generation_call\\\":\\n 197| result = getattr(item, \\\"result\\\", None)\\n 198| if isinstance(result, str) and result:\\n 199| image_b64 = result\\n 200| elif event_type == \\\"response.image_generation_call.partial_image\\\":\\n 201| partial = getattr(event, \\\"partial_image_b64\\\", None)\\n 202| if isinstance(partial, str) and partial:\\n 203| image_b64 = partial\\n 204| final = stream.get_final_response()\\n 205|\\n 206| # Final-response sweep covers the case where the stream finished before\\n 207| # we observed the ``output_item.done`` event for the image call.\\n 208| for item in getattr(final, \\\"output\\\", None) or []:\\n 209| if getattr(item, \\\"type\\\", None) == \\\"image_generation_call\\\":\\n 210| result = getattr(item, \\\"result\\\", None)\\n 211| if isinstance(result, str) and result:\\n 212| image_b64 = result\\n 213|\\n 214| return image_b64\\n 215|\\n 216|\\n 217|# ---------------------------------------------------------------------------\\n 218|# Provider\\n 219|# ---------------------------------------------------------------------------\\n 220|\\n 221|\\n 222|class OpenAICodexImageGenProvider(ImageGenProvider):\\n 223| \\\"\\\"\\\"gpt-image-2 routed through ChatGPT/Codex OAuth instead of an API key.\\\"\\\"\\\"\\n 224|\\n 225| @property\\n 226| def name(self) -> str:\\n 227| return \\\"openai-codex\\\"\\n 228|\\n 229| @property\\n 230| def display_name(self) -> str:\\n 231| return \\\"OpenAI (Codex auth)\\\"\\n 232|\\n 233| def is_available(self) -> bool:\\n 234| if not _read_codex_access_token():\\n 235| return False\\n 236| try:\\n 237| import openai # noqa: F401\\n 238| except ImportError:\\n 239| return False\\n 240| return True\\n 241|\\n 242| def list_models(self) -> List[Dict[str, Any]]:\\n 243| return [\\n 244| {\\n 245| \\\"id\\\": model_id,\\n 246| \\\"display\\\": meta[\\\"display\\\"],\\n 247| \\\"speed\\\": meta[\\\"speed\\\"],\\n 248| \\\"strengths\\\": meta[\\\"strengths\\\"],\\n 249| \\\"price\\\": \\\"varies\\\",\\n 250| }\\n 251| for model_id, meta in _MODELS.items()\\n 252| ]\\n 253|\\n 254| def default_model(self) -> Optional[str]:\\n 255| return DEFAULT_MODEL\\n 256|\\n 257| def get_setup_schema(self) -> Dict[str, Any]:\\n 258| return {\\n 259| \\\"name\\\": \\\"OpenAI (Codex auth)\\\",\\n 260| \\\"badge\\\": \\\"free\\\",\\n 261|\", \"total_lines\": 378, \"file_size\": 12605, \"truncated\": true, \"hint\": \"Use offset=261 to continue reading (showing 1-260 of 378 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_RUtYgvcIYwu9X0HUzMosesU3" }, { "role": "tool", "content": "{\"content\": \" 1|name: openai\\n 2|version: 1.0.0\\n 3|description: \\\"OpenAI image generation backend (gpt-image-2). Saves generated images to $HERMES_HOME/cache/images/.\\\"\\n 4|author: NousResearch\\n 5|kind: backend\\n 6|requires_env:\\n 7| - OPENAI_API_KEY\\n 8|\", \"total_lines\": 7, \"file_size\": 212, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_IKtFAd3A8pfzzrL2leVUWdIh" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_zGl1qjJZrqFRk7ngM8sdPIZS", "call_id": "call_zGl1qjJZrqFRk7ngM8sdPIZS", "response_item_id": "fc_0d99e9da68392f44016a12a740cc588191a46f9889b491a251", "type": "function", "function": { "name": "read_file", "arguments": "{\"limit\":130,\"offset\":257,\"path\":\"/home/agent/.hermes/hermes-agent/plugins/image_gen/openai-codex/__init__.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"content\": \" 257| def get_setup_schema(self) -> Dict[str, Any]:\\n 258| return {\\n 259| \\\"name\\\": \\\"OpenAI (Codex auth)\\\",\\n 260| \\\"badge\\\": \\\"free\\\",\\n 261| \\\"tag\\\": \\\"gpt-image-2 via ChatGPT/Codex OAuth — no API key required\\\",\\n 262| \\\"env_vars\\\": [],\\n 263| \\\"post_setup_hint\\\": (\\n 264| \\\"Sign in with `hermes auth codex` (or `hermes setup` → Codex) \\\"\\n 265| \\\"if you haven't already. No API key needed.\\\"\\n 266| ),\\n 267| }\\n 268|\\n 269| def generate(\\n 270| self,\\n 271| prompt: str,\\n 272| aspect_ratio: str = DEFAULT_ASPECT_RATIO,\\n 273| **kwargs: Any,\\n 274| ) -> Dict[str, Any]:\\n 275| prompt = (prompt or \\\"\\\").strip()\\n 276| aspect = resolve_aspect_ratio(aspect_ratio)\\n 277|\\n 278| if not prompt:\\n 279| return error_response(\\n 280| error=\\\"Prompt is required and must be a non-empty string\\\",\\n 281| error_type=\\\"invalid_argument\\\",\\n 282| provider=\\\"openai-codex\\\",\\n 283| aspect_ratio=aspect,\\n 284| )\\n 285|\\n 286| if not _read_codex_access_token():\\n 287| return error_response(\\n 288| error=(\\n 289| \\\"No Codex/ChatGPT OAuth credentials available. Run \\\"\\n 290| \\\"`hermes auth codex` (or `hermes setup` → Codex) to sign in.\\\"\\n 291| ),\\n 292| error_type=\\\"auth_required\\\",\\n 293| provider=\\\"openai-codex\\\",\\n 294| aspect_ratio=aspect,\\n 295| )\\n 296|\\n 297| try:\\n 298| import openai # noqa: F401\\n 299| except ImportError:\\n 300| return error_response(\\n 301| error=\\\"openai Python package not installed (pip install openai)\\\",\\n 302| error_type=\\\"missing_dependency\\\",\\n 303| provider=\\\"openai-codex\\\",\\n 304| aspect_ratio=aspect,\\n 305| )\\n 306|\\n 307| tier_id, meta = _resolve_model()\\n 308| size = _SIZES.get(aspect, _SIZES[\\\"square\\\"])\\n 309|\\n 310| client = _build_codex_client()\\n 311| if client is None:\\n 312| return error_response(\\n 313| error=\\\"Could not initialize Codex image client\\\",\\n 314| error_type=\\\"auth_required\\\",\\n 315| provider=\\\"openai-codex\\\",\\n 316| model=tier_id,\\n 317| prompt=prompt,\\n 318| aspect_ratio=aspect,\\n 319| )\\n 320|\\n 321| try:\\n 322| b64 = _collect_image_b64(\\n 323| client,\\n 324| prompt=prompt,\\n 325| size=size,\\n 326| quality=meta[\\\"quality\\\"],\\n 327| )\\n 328| except Exception as exc:\\n 329| logger.debug(\\\"Codex image generation failed\\\", exc_info=True)\\n 330| return error_response(\\n 331| error=f\\\"OpenAI image generation via Codex auth failed: {exc}\\\",\\n 332| error_type=\\\"api_error\\\",\\n 333| provider=\\\"openai-codex\\\",\\n 334| model=tier_id,\\n 335| prompt=prompt,\\n 336| aspect_ratio=aspect,\\n 337| )\\n 338|\\n 339| if not b64:\\n 340| return error_response(\\n 341| error=\\\"Codex response contained no image_generation_call result\\\",\\n 342| error_type=\\\"empty_response\\\",\\n 343| provider=\\\"openai-codex\\\",\\n 344| model=tier_id,\\n 345| prompt=prompt,\\n 346| aspect_ratio=aspect,\\n 347| )\\n 348|\\n 349| try:\\n 350| saved_path = save_b64_image(b64, prefix=f\\\"openai_codex_{tier_id}\\\")\\n 351| except Exception as exc:\\n 352| return error_response(\\n 353| error=f\\\"Could not save image to cache: {exc}\\\",\\n 354| error_type=\\\"io_error\\\",\\n 355| provider=\\\"openai-codex\\\",\\n 356| model=tier_id,\\n 357| prompt=prompt,\\n 358| aspect_ratio=aspect,\\n 359| )\\n 360|\\n 361| return success_response(\\n 362| image=str(saved_path),\\n 363| model=tier_id,\\n 364| prompt=prompt,\\n 365| aspect_ratio=aspect,\\n 366| provider=\\\"openai-codex\\\",\\n 367| extra={\\\"size\\\": size, \\\"quality\\\": meta[\\\"quality\\\"]},\\n 368| )\\n 369|\\n 370|\\n 371|# ---------------------------------------------------------------------------\\n 372|# Plugin entry point\\n 373|# ---------------------------------------------------------------------------\\n 374|\\n 375|\\n 376|def register(ctx) -> None:\\n 377| \\\"\\\"\\\"Plugin entry point — register the Codex-backed image-gen provider.\\\"\\\"\\\"\\n 378| ctx.register_image_gen_provider(OpenAICodexImageGenProvider())\\n 379|\", \"total_lines\": 378, \"file_size\": 12605, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_zGl1qjJZrqFRk7ngM8sdPIZS" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ZjwlrfecFydHOznGYscYyqUw", "call_id": "call_ZjwlrfecFydHOznGYscYyqUw", "response_item_id": "fc_0d99e9da68392f44016a12a74756a08191a8475e7d450e9b17", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom agent.image_gen_registry import list_providers\\n_ensure_plugins_discovered(force=True)\\nfor p in list_providers():\\n try: avail=p.is_available()\\n except Exception as e: avail=f'error:{type(e).__name__}'\\n print(p.name, '|', p.display_name, '|', avail, '| default=', p.default_model())\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqdH4rignzTkN8hlxn0g3Q4e8loaotfh8tr57xmWC0qdwwhGgHREaNhBsrmtTmQd6dtGvgsMgyppVPQinvtdyqU68mKhgiyQcKmrYLE9-USdpdtm5EfqQSp1Q2HJhpIwWgCtV6v8Op2FfX3dntvML-f1Gq9QCC7uTOlnFJ2q0X8pWGVYZPW6SSSvlgm82BaAeB8tMi8bnZIvVYZPS_pM0hOr93GMislshL6wbiIhgScS3qtjadLTNG0L1H-bi8kt8ZxfPq8uwXdxdf3yjmw8XtlTTRPorF8eBEWuwu6UdOhpfo3N_kBWXw_8_ZIYr0kACD9A18dnuE0zpxzzyWtZdpgNbvad6zjEExUjmnOSAWqib530UvpOe9hTqooxRzsHIYcaMYPxsBT1pw_tERFiswHb_hb0dHCpNuC2VS7Zan8MQE-Y9qKRoY3iNao1SG3M00Md6YR1_Tfe4lmVSYstLWh_IuSTN30QbBSgl_69NDj9tF6Vr1j2q6UOXcNr4R-5w3yxVgAfBaJA3tvAOxS6D-PwjssTTNyQKJFxS5ROs2ywcpwiCTD49xWyQIYJNIcdQhvqoyRH4w1dhXHfSqWO970vunt_smxRC2dIhfGw7cs4PWolc1RrLgCFgv_kfECzcYHFRSRXfx0vcvoEChGoDNaInv7aCh3e-XiRd0IN79HlGPZKRspYl_2GOKUsdop5oxUXkEY7f3VrzAgIPTI8BFbR-NiB3hTHJXgfAarxXSC4657HtTLwDhA3GCWrYAOLA4zHGPy_MdhhXz8MHtMZsk5W6txZ0WONxC-oifuyzk59zI0oA76n2hY-PzU70e7ZjwfwCnrnr1V-TgKEZVutLAhYdPUBBa_LQRGzxLL44yD7DmSNUYkxjLNaw9_98VjvWOJm2EhwY3yo8XFnS3qiqggWqaHAMjLlewIGQQb8H9iOBKUkdTrMBdeiv9XhvgLSiqWUK3nHegZhRq581dOS-_qPMl6VMKOnAi3M0aYeiorxhldRaVCtTJCDs9B9hnX9gAa9zR_DodR-Mh9Czjr4GWmHhLC-KueLg0tTaRuRgsKJWaqFh3nPZ3l41AdaXYyuRuONsHb-cGP1mFNkyrbYfj4u_xU_MeME-04g05IM2XARFya2KFnkQkKH8sidhqaPyQp2jbAyOfZZ4CW5GKjvCHp7vYWExev17XYJGsX8EKttZp5Uu6ZZEABHNGQMWlsL_EjZJmkamfMWh7I_xQJZ7AQd3BOX_2aqhaeaLfgD50VtKAc7fZbnTgnCPee0xGYuExc3A9-dzLS3dOOIFDip24v6rdsWOHYHSrYN9-TbbJFXNngWIqSRSZhRhbE93jhVCXi8Exa3FTGsvAwafhyVxpt6VzCKysR5tjpjz8RQYIN3FUvgDCBpTqgLv_71-zonN0yhQ7dLRCvi2Yi6IqSMd-syQ7Kt1QnEiS1X1HMAZFq9lHsboj017GtNldjH8Ggy7rx9lySkrnVfRNF1z5PVDoZg3DsRKDPtd-fxS_JiXBK-upOuKWyyjT0zKzGAE4TcTH2ZpykR9SdKbCxm98CyP-ZNre4EmIN1xEtD4KJS97yiZR891TBesQf1Q-IxnfGIJaUUkTcNa5UJfQNhfnhjX8mgxQbpjHxkCUx8Y7lkVp-f9GRkySi_wW5vPXQMuQeZLxIltuxx-T-rwcM9jB81KMPyfL9IqLsJHsV2m1QwORRAjbQ=", "id": "rs_0d99e9da68392f44016a12a74404c88191a774b3315435ce99", "summary": [] } ] }, { "role": "tool", "content": "{\"output\": \"openai | OpenAI | False | default= gpt-image-2-medium\\nopenai-codex | OpenAI (Codex auth) | True | default= gpt-image-2-medium\\nxai | xAI (Grok) | False | default= grok-imagine-image\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ZjwlrfecFydHOznGYscYyqUw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Ydhb9ExJjBvLIvrCPbVHnixu", "call_id": "call_Ydhb9ExJjBvLIvrCPbVHnixu", "response_item_id": "fc_0d99e9da68392f44016a12a75327a0819190690b46236324cb", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"hermes config set image_gen.provider openai-codex && hermes config set image_gen.model gpt-image-2-medium && python - <<'PY'\\nfrom hermes_cli.config import load_config\\nfrom hermes_cli.plugins import _ensure_plugins_discovered\\nfrom agent.image_gen_registry import get_provider\\ncfg=load_config().get('image_gen',{})\\n_ensure_plugins_discovered(force=True)\\np=get_provider('openai-codex')\\nprint('image_gen.provider =', cfg.get('provider'))\\nprint('image_gen.model =', cfg.get('model'))\\nprint('openai-codex provider available =', bool(p and p.is_available()))\\nprint('default/model =', p.default_model() if p else None)\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/.hermes/hermes-agent\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Evaluating tool functionality**\n\nI'm examining how the tool reads configurations per call. The session tools stay the same since the tool already exists. I wonder if there's a need for a gateway since it likely starts a new agent for every message. After setting the configuration, I might want to test the image generation, but that could cost resources. I should probably just focus on a minimal availability check before trying any image generation or function routing. Let's set the config and check the provider.", "reasoning_content": "**Evaluating tool functionality**\n\nI'm examining how the tool reads configurations per call. The session tools stay the same since the tool already exists. I wonder if there's a need for a gateway since it likely starts a new agent for every message. After setting the configuration, I might want to test the image generation, but that could cost resources. I should probably just focus on a minimal availability check before trying any image generation or function routing. Let's set the config and check the provider.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqdTLCMDlFPJ6MVPgYXyvFpznHoblyYnoApiRwU0jBPXDus6dkbgzeTDm38s9E071SCEFdbUAyy6erc3BD4IfCWhrH9kcv66glYz3IP5Q9QaqmHmy_q7jkXEyimlmidS8b899YviLqL2GQRs51hPVpodtTiLgb0lcvGR_o2CbZsWx-YQc0mNaib5V5WH0EVBxhwvArCBhD5GWA-KZ0Oy0wXqD7YrtK630KR8Eu_VsEsE_AoP4wgvWbKKh1xxX8WLCUf_exjcFw6CWLd9WAlvXRC2mZqOUWqMt4Vt-uQmMHVWO-DFM1o1jo05PHHLg6WmR2TG5F3JIA0UfuXnSK72vV7Rm00ZfhWdHrD5IC6xaEMgh3UK5VEXHY47eJY4tDsFyftNMUdEqsgJQLXgtvxx3FmOUjGcDEl2ye57mZlqTVGgxHfpMVlR2iYLBRGjfacBCKoDrTP0_0fjq6jT8lTijw4QPO8K2Jg879AvzIFImCToQohm-py9GCrKFVx5YT-WFMkXw8e_XJISdvzcILN13I7EHglsQ5dXqWYa-u7MOD6DKZivNqw2kK6EAouAdmyp6BypbkgkW1WSh9UvNhIMaLfVi2jg9szJQ6DUfvz9JOU9j2uDIHLB7iWY9xh1H7_h74biwgwFz7L5HwgamaJgbfzj9jt6ZU7cYnzwW8xQygYpPyCUUD0ttvGon1gkQiPXgrqx8lyUwCuqXhw6qFtp6gog7UPyElPL3TvB0bXHlvWOA-b9hx5FvyBJhaslcTLuN1AT3DprrelIuUbavrqbYxT5ENhGhVb4CZ1145pW2Nja6iZrYsZCSTJereba__AO0AVYj77i05iiDD7fqgofLxmBxnj0-OKNow0eKwQJAhuwmg0iZVyRD9BgpOqRIebcC16I2QSh_6C0uij3zn0NbSJXPTS5ieORHoFKXF68uz5E103PK1UxypRfW6peqTtErJ-VrgzyRZmWH5JOQ_Ob5h1OQ6hVcjwwE2BDrOQmwJ6eiubwP1FUoGObQca0QXE0nSiIZdbqEUgyCnW7Q3_TvlQ-8I2xk8-sOFso5wvrQAFUiA7t95MMw5JQGycFOUiHTRwu2HDUl_Pal-6OfvaRxzsV3dpGh7YyEuMkxQG_waUt2Hw2_SLmpTOyzB6hFtQOO46-uAIGpbny53Nb9UFEi6xYQJ_tXtEQ5-1jwW05PQ4oDY19I84Ep6ra441kvqEnvUq8uDWNZ8-OsEiB4UCqjFQ8Ngb8gSnLc4BXG5i77TB8XKYL6SACtKlHEpGFA97xCT1yzlF5yZX_BB6pKnPdfQGmjbZIBbtBPs3kk3V27fMcpplcJR48ixPgjtH6xInes1SCKC9W7nAexVeVn_1AD6JcLDipKQNowZVgvliGfNp4nWLJ8VyKTDuGp7t_kL-2LUiIW9AEeQ--jmoHo2e694b08NC-JneOVx91FHeDaW3BUBWLB3rb4XkixLX4lGqcr8NlL07CqIjFhEjKxqn56yU1VMuVPdeDN3dMar3cryXn8_O-48sgSaSuXI3rXkN9vqFLtX_4Ywa8OGxY9OxTwsjMVyH52pnR7H0DkEiPuUDVwiCbrofrleU6JUraJuU1J4_zSN8OILuSqIdijI8nBsWdBARp0Het9YH5uOJaWEmL9a-jFSMnU6CS7cfU-DrhAMcAjmMxINT8dzKbMtlLXpOwzXxoQzTtlcoPFJQrSClBHpf13AvPHP4vOdM-SbMr7cY6QQvaodPiRmCPKo1190vOFTJ_yR7lvC3f_ltooJQzs7pGDAGEOdk6tU6lqnujCZMUheTyxk53wysCKm5vlqVeadl1DAaAGRowHkvT34u3HH-R3taR_jpB5H8g8oVp7kc5QXa_yEsJISqu3v9zgAM1w5sAiTC1QIgW36ZLUqCsfmULc2etlXCFTsp5wiaAGyoSYinrbx2Y1-9_IRPmt3Dzj3u758d3AKL4CkQcIELL7MCknDTaJeFoxhC8yQJM83QS2qOvselFqoXj1OuEb8sATvUm24MVAKxejut9x1CvRghIWY3B2PPkNtYlHa5o8VtskjUlyvZ0efabeIqnIOebMndz2VWvXsVyDRqmLYRgpYX2jUlSYvaRWdPvJ4RMQ15pJC1cLA_wCobocqcnBMYdcmDJBQypz6ZO7bUJjzrTGVYQKbK24NbtG5OvPLJ6XvEiFMCAvlQkFplSaVOUSKBrjzoC6AVA59Xjq5mQUtnFSX5dmTzi3Wh_lrQ9RsAgixyl58dr3UGCAQT7zW6b5xsgtVpMWIN90t1JjOFlwlEd-pA-DA6zWAeff-dpb8o3qiD1ts3K6cupYCCUV8siJkSLXRrWEF7bDNhpTZM-4ggQ3RTUJikMRmhNP8GTPTprG6aM677VrTMIGgfzKkfqBlt1i_t0zTUGjzGdWzsfcnkVfD5DAJBYJnJSi6eFWOCx4nomKTZb_o_HtRoKMZYhYdodZf5cAPU4y2BSyMA7FQYqZ0qhAVAkodOiI-z2DhosQrFaJ6eO5cOy_spPtb3YrAlJI3q6pjrOo5uQuBwEujs1k6neXAc029XlT4lqkhBDDlm4VovDwDXEF9PAwCeVX00ynId3HJnoDmOwoJBaYYMbltd034dvmjpkDEYHzaKCKW5ZsXuWU5QE4uroscsOX28OM74-ungX7abQT8A-FN6T1PfT6VA2lNt1_HCrMi-XjnMNX2O49eYDOIKOxZJRPAcuNtR_wxwLxiBfe6E1aWmIZqHWWEj99vZ9CVnW5YXVz3aT_MdWPxIpclRLRKZwm4BhoIPJcPblQ-gVqOejla5KRTUbKzRCdd5qv_7HpdDMxHzgMdqQc5olhAEE1Ly1pmK6PGJ2p9QC_PJhraUHmJjcOhQYOqLRbSKac2BdHJx5UvgW6r5wP408Oxx-BgmH5GqoMg==", "id": "rs_0d99e9da68392f44016a12a74c345c8191a9aad3e4a38dd3c7", "summary": [ { "type": "summary_text", "text": "**Evaluating tool functionality**\n\nI'm examining how the tool reads configurations per call. The session tools stay the same since the tool already exists. I wonder if there's a need for a gateway since it likely starts a new agent for every message. After setting the configuration, I might want to test the image generation, but that could cost resources. I should probably just focus on a minimal availability check before trying any image generation or function routing. Let's set the config and check the provider." } ] } ] }, { "role": "tool", "content": "{\"output\": \"✓ Set image_gen.provider = openai-codex in /home/agent/.hermes/config.yaml\\n✓ Set image_gen.model = gpt-image-2-medium in /home/agent/.hermes/config.yaml\\nimage_gen.provider = openai-codex\\nimage_gen.model = gpt-image-2-medium\\nopenai-codex provider available = True\\ndefault/model = gpt-image-2-medium\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Ydhb9ExJjBvLIvrCPbVHnixu" }, { "role": "assistant", "content": "Ja, Sir — gute Nachricht: **das ist bei uns schon vorbereitet**. Ich habe es gerade eingerichtet.\n\n## Ergebnis\n\nIch habe Hermes auf **OpenAI/Codex-backed Image Generation** umgestellt:\n\n```bash\nhermes config set image_gen.provider openai-codex\nhermes config set image_gen.model gpt-image-2-medium\n```\n\nVerifikation:\n\n- `image_gen.provider = openai-codex`\n- `image_gen.model = gpt-image-2-medium`\n- `openai-codex provider available = True`\n\nDas nutzt also **deinen vorhandenen ChatGPT/Codex OAuth-Zugang**, nicht einen separaten `OPENAI_API_KEY`. Sehr angenehm — ausnahmsweise mal kein weiteres API-Key-Gefummel. Beinahe verdächtig.\n\n## Was bedeutet das praktisch?\n\nAb jetzt kann ich über das Hermes-Tool `image_generate` Bilder mit **GPT Image 2** erzeugen.\n\nDu kannst einfach schreiben:\n\n> Erstelle ein Bild: photorealistischer Keyframe für einen YouTube Short, Thema XYZ, 16:9\n\noder:\n\n> Generiere ein quadratisches Thumbnail mit folgendem Motiv…\n\nUnterstützte Seitenverhältnisse im Hermes-Tool:\n\n- `landscape` → 1536×1024\n- `square` → 1024×1024\n- `portrait` → 1024×1536\n\nDie Bilder werden lokal gespeichert unter:\n\n```text\n~/.hermes/cache/images/\n```\n\nund hier in Telegram als Bild ausgeliefert.\n\n## Modellstufen\n\nAktuell gesetzt:\n\n```text\ngpt-image-2-medium\n```\n\nDas ist die sinnvolle Balance.\n\nWeitere Optionen:\n\n```bash\nhermes config set image_gen.model gpt-image-2-low\n```\n\nFür schnelle/ günstigere Iterationen.\n\n```bash\nhermes config set image_gen.model gpt-image-2-high\n```\n\nFür finale Keyframes / hochwertige Assets.\n\nMeine Empfehlung:\n\n- **Ideen/Varianten:** `gpt-image-2-low`\n- **normale Arbeit:** `gpt-image-2-medium`\n- **finaler Keyframe/Thumbnail:** `gpt-image-2-high`\n\n## Konflikt mit lokalem Video-Modell?\n\nNein. Das ist der grosse Vorteil gegenüber Qwen Image lokal:\n\n- **GPT Image 2 via Codex/OpenAI:** läuft cloudseitig, belastet unsere GPU nicht.\n- **Wan/ComfyUI Video:** läuft lokal/remote auf der GPU.\n- Daher: **kein VRAM-Konflikt** mit Wan2.2.\n\nFür deine Pipeline ist das sogar elegant:\n\n1. GPT Image 2 erzeugt Keyframes / Thumbnails / Konzepte.\n2. Beste Variante auswählen.\n3. Optional lokal weiterverarbeiten.\n4. Wan2.2 I2V macht daraus Motion-Clips.\n\n## Falls es nicht sofort reagiert\n\nNormalerweise sollte es ab der nächsten Anfrage funktionieren. Falls Telegram/Gateway noch alte Config hält:\n\n```bash\n/restart\n```\n\noder neue Session:\n\n```bash\n/reset\n```\n\n## Mein Urteil\n\n**Ja, das ist für uns der bessere nächste Schritt als Qwen Image lokal.**\n\nWarum:\n\n- keine GPU-Last\n- keine ComfyUI-GGUF-Frickelei\n- vermutlich stark bei Text, UI, Prompt-Following\n- ideal für Keyframes und Thumbnails\n- passt sauber in Hermes/Telegram\n\nQwen Image können wir später immer noch lokal dazunehmen. Aber für sofort nutzbare Bildgenerierung über Codex ist **GPT Image 2 via `openai-codex` jetzt aktiv**.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEqdg170fqktYflF_Yo164Egyvrmrz2VWwpRHsX240l9CXs7xBCCZPhvdoJ5HJP700E1utWfEkHgSB2t4rSUKSKv9_gsptyaQ7MBubjvJUCIjI082imvTYXQIl78zysI6oyNt0_SM4HX-DwniO3E-gymctcCMLQXgEP2o7CnTe0zz5v_TtW7WuIVqybI85mALt50zyUahHoMnzvXVb9r4jENJEY1OnojL15xZDFA7RtDfRQzsnLZhZnFVN0xc69rlBqgILOh_VAcgcJF86VxwetNF6zvkmp3E3RSeVvXVFPh5WacKmm9Vjse4h8VcxlvDtu8788ZNCy3vnj6nffJAwid4ZANQZtur_V0jzjdnheC2yp5NwkFW2Mv9eZGpTbTq3AjcT1FIRv4GxfwsBLG2M8FYJUyFTBuO7bkc7E9dPvxGilHkfb2V0QhZvQ-4yLeyVuD3LvmWlNvdxbhzyEp2BFVQ0YigOrPw-QNSIWO_McXCwBx3pn8XxmTm_BLpeRpiTifu5GHS7aTT9anNTI9479KgAVcbW2dDhcJbJPqUOR5-QjJIuO8qpk4iDPkguDJV3iD32YJLqBDJO8dHIaMhtTiClgalnVR8N4obfTFulxtsxE5lCp5YMhAc7aHXtGwr-9LBu9lbf7SSdqfOztwPCZlU02_iJ2P_NbX90vKYGkvg71davzbewyCg2cXRJY_eqHOD7AUgllnVzOxNmA5n72lQ1H7NskxSYKmpTWsF6YFQKgpGe0BQL1ePazT_dlNBLOBNHmMp_Hw91NSP83XTvU0QYzl5rwHpgarvx9vLMR8_z1I6QPq7rq4zM_H6ndRvOnzZofy5Q76J9CmiRohOVJL8QIEY1z8OnY9zwok2A1-pu_t4NZOmprKuO9FiOk_pAZBJoZ5kPn4GV9RIE6J0idO6cRRbWVSnFFj_N4J7eWZTYJ-HVaaUK19_AAqNI97wRwqKcu9m_g4fMpPcrU2SH_rMX8AO6VDbEB0euQDQ77uk6ouW9CuqwYgG3FYTlDErnYPkpw6CFdFJWloneYlYslJr4QELHPmyA7Xvg1vvOSQRD-MfJW8bMCt7Y1CNuKhamwxLuvmjWSsmAz3Zb5MdA8DK31_b9_6t6S2A7akti1_46jKMmd4MVuStmY0wqBgYowbmWvqGocmpyX_y9q1KtzyaIsLGl2sANwF7GySG3UpKcKaUAJed4ogKer9p1JBz0vYQ9F_au90V6DXvKxjLczu0GLr8VvfkFhGMccD1u6DwFZiDsV_cVr38rBBk4i2UHvyQSbhhFu6ARCdT6LC0jn4e0RtI6xfNACNY2KA0SGDT7pTXcuigTE7S__iukU9OF8GxF2O6zas1-jltrggHdN_LN_9c2fX8L0gBLO8fo74_jmB2rXIHCIZ-6Ylyx5W0fVaqNMvsr2NJlBx5WouOGDv-wFd9eDi6WE1oioQA7Xlj-ZjXwWzWNutddyMcBCPeFln7xrhVxBKdFGfGfcCAGupWTJIhHEgeDdS-XEz38H7VZSS4pwHOK7fjYwJngckqS3j1pEIFoxUQSUarVilSERRmiBFsmjPULK7h-yrb-K76jApjdh66FXF_l9htVDUBZ_jTlm-2OLmeXQE33tcCCdeuSTAAZzVrL3IXRWQDUvRt1H0919HtDfDgYTTolmkng7PQlNUzyeFGMgizjoeZyji3JH-Zgv16haRL-IP94Yxfcd7wb4aBFdAsAckdtLeDiWsGDd9KDHh0MTuGTJ6nlKT5acMpcqPOGUQmzM20KyqvXUtA8oWqVT8_NSVb8dgPLItUZLSUI2XIML7ERih3aOs6r0OPlLES7CFN_xDihFjGBexYiFtteYu-Y83pUzJ85cNo0PMzKolr5co3pNRTKju3Y2y035sLz_72HdBlP1zhuPkvcmpv3CQjvQ6HxJBJNf8rXLN7HHB8Q0M8ZCPhMZwXbDyfpeFLznnYzy_TyicywfYPTMGIMOnRdEGGyMq-iq7F-wb-RlLblDa-TFeau9RLvZG3txzXwJEs7irXioIFQ9zHw_N033YT0N6MqQxnUybpx_QBn1y3b37SVFXkZ9lbuuK9JhtTBOzJXFjYqhAgg1sij_U-GptV6L8GeIFwaUjWKfukdBdLdBqG4KXGJ0DGYH71ipKW3oBavjBWDuz6jcgfCn90t2tLTZ-d8iCN4pKb7MXMzWS2lDsmpu13eoOMfiloSptczTFCGbm3t77Q_1J8hlDK4UXPhW04Hsw_ZtQ4yYCL8gqXAgsDyM6x5nBWa-fJ1tprgH833mDfalGM7_jMJvHg487Fj1RRWsydD16rpqdf7zVklc1-jiIfjHc0XeOJzqbOKTpE9smUvVgIXVuIjK5HLlY52YFsqeuOaGfo1zrrLnYohT2twJLNRk2jmBViEPL40FnOBdYkl4XZM1l0GKS8sSy5Lw7klPB0Cc8eyswNgmgktnUFMxTmU8BYFSWbkQJ-suMYDulgSA5LujwpH_GqMGPbBuylapR97Ib8K1JzL3tTshDqZu1f3A_CNr3QRwvF6DT9wRy3NDVnflwpo6aFQ_MgJIO6j3Tgtav1IWqVrvBwIsHOtlN3HPqlMxz9DthBLgY3-eiYz-s2-hU=", "id": "rs_0d99e9da68392f44016a12a7597224819182948546d1d093f0", "summary": [] } ], "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Ja, Sir — gute Nachricht: **das ist bei uns schon vorbereitet**. Ich habe es gerade eingerichtet.\n\n## Ergebnis\n\nIch habe Hermes auf **OpenAI/Codex-backed Image Generation** umgestellt:\n\n```bash\nhermes config set image_gen.provider openai-codex\nhermes config set image_gen.model gpt-image-2-medium\n```\n\nVerifikation:\n\n- `image_gen.provider = openai-codex`\n- `image_gen.model = gpt-image-2-medium`\n- `openai-codex provider available = True`\n\nDas nutzt also **deinen vorhandenen ChatGPT/Codex OAuth-Zugang**, nicht einen separaten `OPENAI_API_KEY`. Sehr angenehm — ausnahmsweise mal kein weiteres API-Key-Gefummel. Beinahe verdächtig.\n\n## Was bedeutet das praktisch?\n\nAb jetzt kann ich über das Hermes-Tool `image_generate` Bilder mit **GPT Image 2** erzeugen.\n\nDu kannst einfach schreiben:\n\n> Erstelle ein Bild: photorealistischer Keyframe für einen YouTube Short, Thema XYZ, 16:9\n\noder:\n\n> Generiere ein quadratisches Thumbnail mit folgendem Motiv…\n\nUnterstützte Seitenverhältnisse im Hermes-Tool:\n\n- `landscape` → 1536×1024\n- `square` → 1024×1024\n- `portrait` → 1024×1536\n\nDie Bilder werden lokal gespeichert unter:\n\n```text\n~/.hermes/cache/images/\n```\n\nund hier in Telegram als Bild ausgeliefert.\n\n## Modellstufen\n\nAktuell gesetzt:\n\n```text\ngpt-image-2-medium\n```\n\nDas ist die sinnvolle Balance.\n\nWeitere Optionen:\n\n```bash\nhermes config set image_gen.model gpt-image-2-low\n```\n\nFür schnelle/ günstigere Iterationen.\n\n```bash\nhermes config set image_gen.model gpt-image-2-high\n```\n\nFür finale Keyframes / hochwertige Assets.\n\nMeine Empfehlung:\n\n- **Ideen/Varianten:** `gpt-image-2-low`\n- **normale Arbeit:** `gpt-image-2-medium`\n- **finaler Keyframe/Thumbnail:** `gpt-image-2-high`\n\n## Konflikt mit lokalem Video-Modell?\n\nNein. Das ist der grosse Vorteil gegenüber Qwen Image lokal:\n\n- **GPT Image 2 via Codex/OpenAI:** läuft cloudseitig, belastet unsere GPU nicht.\n- **Wan/ComfyUI Video:** läuft lokal/remote auf der GPU.\n- Daher: **kein VRAM-Konflikt** mit Wan2.2.\n\nFür deine Pipeline ist das sogar elegant:\n\n1. GPT Image 2 erzeugt Keyframes / Thumbnails / Konzepte.\n2. Beste Variante auswählen.\n3. Optional lokal weiterverarbeiten.\n4. Wan2.2 I2V macht daraus Motion-Clips.\n\n## Falls es nicht sofort reagiert\n\nNormalerweise sollte es ab der nächsten Anfrage funktionieren. Falls Telegram/Gateway noch alte Config hält:\n\n```bash\n/restart\n```\n\noder neue Session:\n\n```bash\n/reset\n```\n\n## Mein Urteil\n\n**Ja, das ist für uns der bessere nächste Schritt als Qwen Image lokal.**\n\nWarum:\n\n- keine GPU-Last\n- keine ComfyUI-GGUF-Frickelei\n- vermutlich stark bei Text, UI, Prompt-Following\n- ideal für Keyframes und Thumbnails\n- passt sauber in Hermes/Telegram\n\nQwen Image können wir später immer noch lokal dazunehmen. Aber für sofort nutzbare Bildgenerierung über Codex ist **GPT Image 2 via `openai-codex` jetzt aktiv**." } ], "id": "msg_0d99e9da68392f44016a12a76094208191b8fa0a085531caca", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] Ok erstelle ein testbild eines Autos in einer Garage, in der qualität wie wir uns das wünschen." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_kyCoj0iGd50PqUCkqsdflXHG", "call_id": "call_kyCoj0iGd50PqUCkqsdflXHG", "response_item_id": "fc_0d99e9da68392f44016a12a93b2114819195eac8b288f9dc9e", "type": "function", "function": { "name": "image_generate", "arguments": "{\"aspect_ratio\":\"landscape\",\"prompt\":\"Photorealistic cinematic keyframe of a premium dark graphite electric sports car parked inside a modern minimalist garage at night, soft overhead strip lighting, realistic reflections on polished concrete floor, subtle dust and micro-scratches on paint, shallow depth of field, high-end automotive photography, 35mm lens, low camera angle three-quarter front view, dramatic but clean lighting, no text, no logo, no people, no exaggerated CGI look, natural materials, crisp details, production-quality YouTube Shorts keyframe aesthetic\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_093158_f770605c.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Photorealistic cinematic keyframe of a premium dark graphite electric sports car parked inside a modern minimalist garage at night, soft overhead strip lighting, realistic reflections on polished concrete floor, subtle dust and micro-scratches on paint, shallow depth of field, high-end automotive photography, 35mm lens, low camera angle three-quarter front view, dramatic but clean lighting, no text, no logo, no people, no exaggerated CGI look, natural materials, crisp details, production-quality YouTube Shorts keyframe aesthetic\", \"aspect_ratio\": \"landscape\", \"provider\": \"openai-codex\", \"size\": \"1536x1024\", \"quality\": \"medium\"}", "tool_call_id": "call_kyCoj0iGd50PqUCkqsdflXHG" }, { "role": "assistant", "content": "Erledigt, Sir — Testbild mit **GPT Image 2 Medium via Codex** erstellt.\n\nMEDIA:/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_093158_f770605c.png\n\nEckdaten:\n\n- Modell: `gpt-image-2-medium`\n- Format: Landscape\n- Auflösung: `1536x1024`\n- Stil: photorealistischer cinematic Keyframe, Auto in moderner Garage\n- Keine Logos, kein Text, keine Personen", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir — Testbild mit **GPT Image 2 Medium via Codex** erstellt.\n\nMEDIA:/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_093158_f770605c.png\n\nEckdaten:\n\n- Modell: `gpt-image-2-medium`\n- Format: Landscape\n- Auflösung: `1536x1024`\n- Stil: photorealistischer cinematic Keyframe, Auto in moderner Garage\n- Keine Logos, kein Text, keine Personen" } ], "id": "msg_0d99e9da68392f44016a12a9704a6c8191808b6f0fb1ec27b4", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] Ok, sehr gut. Entwickle nun weiter an unserem Bot." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_67Iy8nW8sVb9m6ISM609pkyV", "call_id": "call_67Iy8nW8sVb9m6ISM609pkyV", "response_item_id": "fc_0d99e9da68392f44016a12b087de188191b825b8acc73a524f", "type": "function", "function": { "name": "session_search", "arguments": "{\"query\":\"AutoShortsBot OR Retention Studio OR Bot OR GPT Image 2 OR image generation\",\"limit\":5}" } }, { "id": "call_5BVIIxrTr6ntrLKtj4WKHorB", "call_id": "call_5BVIIxrTr6ntrLKtj4WKHorB", "response_item_id": "fc_0d99e9da68392f44016a12b087dec08191bb40d056212554ee", "type": "function", "function": { "name": "skills_list", "arguments": "{\"category\":\"social-media\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErCHu61fQz-E75edKK_QXTMTDhu0xezA5H4axFuEwzifR5FswgevQqIKUqPWRDVRaxROOFdOVQbcF1VqiIeDfBd58hLfPyDFaomsfMFq4P37fEnCnvZEG5n61MQQobl0U7S0jsJhbN7PDvgpzzCFEWYsSqdTEg_GsWPZzl_tVy5qI3hNUs0C3TzoZU16Vp7CTWcmgV7zJ6P18OpgESF1WQ9xNxh3h07p4lfTEFXbrS9LlGz-x5wrrtv4JEGnlJiqMelQLA9wm_QPVbjAWnC5F6CmtP2klzxxG2VCmag-zFtgk5h377KvgCSoDSKD8pge5OZTfZoarwQZ6OTm2n9s1ur4S50SFsFRCImCASkXBSc3tsyLUGBaApefJ-AZUdFv9ROZhopAhxZc2PV21NCcJlJz3wjRq4Fk5qUPy3lhpQuZH5NNNp3fMEdyJNwJlcZDYYYngidupMYKcBI2nwN0xcClJ4BoV32qmDEJoyHNA2wdLM_r9ryVybJ7QYh6IqNv-HozbHM4nHSTNI96NvnvZTGEcGSOA_CaXk7OoeV5zfSn8F86SFDt3kXrbrRm9wJAPyuMZmUR8lxZpeETU2ZIMlNcTkSh55NqHdYPp8LYl8zIl2npGsnGrkgSgrN3ddusaZzkJEGR7fEuJDdkiKtGHlE74YBCayZ4FIHtO0MB6vYVPBsB5eNAtnxikPAw6dhedh9bnRp0Mt28cvIqFeRqK5qqt0vcOojYXcBcK53XUGgGTsXz_p_L5izw7M3WFxdLB_xLHUcwg4s6mGVSH2dpk0fOS2HIfm_cnl8oHQy4X7IeopBg6VGZcrVzfvGfppYSCToIoVLGdQ-iUyrOrEEqRmwFyBTYpfexosD3TMCQ7j9NCSiuKHSBl_yo4xguFGECvgFyAPcBqsPKaj604HafOFRC2mA3L5shsvUqT21g4lEBXx0gYhzI-KyFE9V_-2v3MrqRctlPJ-nNwKEkIMSzg0pS_PGyzv1MhPQaqEfLMtcdgcTc_luQAurbP5ZoN_joW4RS4JJvIqUzvromGxiJGounwuc9y2M9h3hvg4kUAt_lueY7rjwbpKKddUPFGBoWeLaLzdevTATH1CHRulLNC0WL6J9k5ipZbg6FYK-ax4igdbUmybayuFJf56zRLUfs0NH5g7Bi8_Jnmc_MNg48St3jn__TBzVmH6A5YxkLQVJkPrCdEooOFvhIRkasNiBjXurvRfGK6jEXOvIkMhOaxS4iICHL-kRhbjXZ4_sPNz3I8Q-rJ-9pEgdmtaBWYx9Arh_QLwvJwufnEJljLlWlz7amClU0kzRCsjPfjGKcS0JEtgyJUlr_Y1557jwOkPlSS6_ebMX7-SyB_Hasg1NkTDA5mZlx8KTWm8cjbWUWISUNmHxuFRKMD_rvi_0i54xl9UWNlredEz04CL2bRwcOcZi9HTdQHqshlW70mqriJ1sQqpWxfwAsCQYh1MCCu1gLoT6G_cZZhl4u57cmhGdjyKo5t7GHYZhjqwPjfrrJgmqzlmDmNBM94h64kTF0gll_H0og87hDfhP27eH1YZaAk5jn7aSDlKboZwgB90edMp1kt1E0kp5mebm-H9Q764qObYif-EVU9MXgvNuNoehw-vuS1-coyAZcZ_zP54tfjW8wglMKRKaPcqBLvZPEkp5rczeN27Hkq9BVxz-j8j5O9HBc3DVHx78TwXMESWAplidgh8K23YAhTeHue0H8PRXMQ1rk", "id": "rs_0d99e9da68392f44016a12b081f9a88191a806108527396910", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": true, \"query\": \"AutoShortsBot OR Retention Studio OR Bot OR GPT Image 2 OR image generation\", \"results\": [{\"session_id\": \"20260523_074449_3d9cee59\", \"when\": \"May 23, 2026 at 07:44 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nhort-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\\", \\\"tags\\\": [\\\"shorts\\\", \\\"tiktok\\\", \\\"youtube-shorts\\\", \\\"reels\\\", \\\"creator-monetization\\\", \\\"content-automation\\\", \\\"social-media\\\"], \\\"related_skills\\\": [], \\\"content\\\": \\\"---\\\\nname: social-media-shorts-automation\\\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\\\nv\\n…[truncated]\"}, {\"session_id\": \"20260522_230108_c351cf3a\", \"when\": \"May 22, 2026 at 11:01 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n...[earlier conversation truncated]...\\n\\nay/Telegram deaktiviert; nur explizit/manuell nutzen.\\\", \\\"Health backups: encrypted weekly Google Drive, GitHub only code/docs. Morning briefing cron `847bdbc26edf` runs 05:00 via `morning_briefing.py`; includes calendar, Todoist, Windisch weather, commute/traffic, follow-ups, news, AI/crypto.\\\", \\\"API-Keys / Credentials: **Todoist API-Token:** Gespeichert in `~/.todoist_token` (REST API v1, Bearer Token)\\\", \\\"Google Workspace (gog): **Credentials-Datei:** `~/.\\n…[truncated]\"}, {\"session_id\": \"20260523_083854_4d3b77f3\", \"when\": \"May 23, 2026 at 08:38 AM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"[Raw preview — summarization unavailable]\\n[USER]: [M G] OK, nächster schritt. Bleib dran unsere Hochwertige content maschine zu bauen.\\n\\n[ASSISTANT]: [Called: skill_view, skill_view, session_search]\\n\\n[TOOL]: {\\\"success\\\": true, \\\"name\\\": \\\"social-media-shorts-growth-engine\\\", \\\"description\\\": \\\"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\\", \\\"tags\\\": [\\\"shorts\\\", \\\"tiktok\\\", \\\"youtube-shorts\\\", \\\"reels\\\", \\\"content-strategy\\\", \\\"growth\\\", \\\"mo\\n…[truncated]\"}, {\"session_id\": \"20260523_214221_de28a304\", \"when\": \"May 23, 2026 at 09:42 PM\", \"source\": \"telegram\", \"model\": \"gpt-5.5\", \"summary\": \"The session focused on setting up credentials and local project context for **AutoShortsBot**, an automated social-media shorts generation bot / content pipeline.\\n\\n## 1. User goal\\n\\nThe user wanted to provide access credentials and API keys for the **AutoShortsBot** GitHub repository and related media/image/video generation services, so the assistant could use them later without asking again.\\n\\nThe relevant repository was specified as:\\n\\n- GitHub repo: `Gamexgit/AutoShortsBot`\\n- URL: `https://github.com/Gamexgit/AutoShortsBot`\\n\\nThe user also provided API keys for:\\n\\n- GitHub repository access\\n- NVIDIA API\\n- Pexels API\\n- Pixabay API\\n\\nRaw secrets were provided in chat, but the assistant stored them locally and avoided reprinting them afterward.\\n\\n## 2. Actions taken and outcomes\\n\\n### GitHub authentication / repository setup\\n\\nThe assistant loaded the GitHub authentication skill and checked the local GitHub tooling:\\n\\n```bash\\ngit --version\\ngh --version\\ngh auth status\\ngit config --global credential.helper\\n```\\n\\nOutcome:\\n\\n```text\\ngit version 2.43.0\\ngh not installed\\ngh not authenticated\\nno git credential helper\\n```\\n\\nSince `gh` was not installed, the assistant used a git-only/token-based approach.\\n\\nThe assistant then:\\n\\n- Stored the GitHub token locally.\\n- Verified access to the repository.\\n- Cloned the repository locally.\\n- Ensured the token was not embedded in the Git remote config.\\n\\nImportant resulting details:\\n\\n```text\\nrepo_dir=/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\\nbranch=main\\nhead=7642749\\norigin=https://github.com/Gamexgit/AutoShortsBot.git\\ntoken_leak_in_git_config=no\\n```\\n\\nThe assistant also verified that local and remote HEAD matched:\\n\\n```text\\nremote_head=764274960a84df9dbfd2d374824503f4e8ccdbd2\\nlocal_head=764274960a84df9dbfd2d374824503f4e8ccdbd2\\n```\\n\\nConclusion: GitHub access worked, the repository was cloned, and the remote was configured safely without a token-bearing URL.\\n\\n### Secret storage\\n\\nThe assistant created local secret files under:\\n\\n```text\\n~/.hermes/secrets/\\n```\\n\\nwith mode `0600`.\\n\\n#### GitHub token\\n\\nStored at:\\n\\n```text\\n~/.hermes/secrets/github_autoshortsbot_token\\n```\\n\\nVerified:\\n\\n```text\\n600 /home/agent/.hermes/secrets/github_autoshortsbot_token\\n```\\n\\n#### NVIDIA API key\\n\\nStored at:\\n\\n```text\\n~/.hermes/secrets/nvidia_api_key\\n```\\n\\nFormat:\\n\\n```text\\nNVIDIA_API_KEY=...\\n```\\n\\nVerified output:\\n\\n```text\\nnvidia_api_key_present=yes\\nmode=600 path=/home/agent/.hermes/secrets/nvidia_api_key\\n```\\n\\n#### Pexels API key\\n\\nStored at:\\n\\n```text\\n~/.hermes/secrets/pexels_api_key\\n```\\n\\nFormat:\\n\\n```text\\nPEXELS_API_KEY=...\\n```\\n\\nVerified output:\\n\\n```text\\npexels_api_key_present=yes\\nmode=600 path=/home/agent/.hermes/secrets/pexels_api_key\\n```\\n\\n#### Pixabay API key\\n\\nStored at:\\n\\n```text\\n~/.hermes/secrets/pixabay_api_key\\n```\\n\\nFormat:\\n\\n```text\\nPIXABAY_API=...\\n```\\n\\nVerified output:\\n\\n```text\\npixabay_api_key_present=yes\\nmode=600 path=/home/agent/.hermes/secrets/pixabay_api_key\\n```\\n\\n## 3. Project memory / documentation updated\\n\\nThe AutoShortsBot project memory card was found and updated:\\n\\n```text\\n~/jarvis_memory/33_autoshortsbot.md\\n```\\n\\nExisting important project facts in that file included:\\n\\n- Project: `AutoShortsBot`\\n- Purpose: automated creation of social-media shorts\\n- Telegram topic/session: `AutoShortsBot`\\n- Thread ID: `4850`\\n- Topic target: `telegram:-1003907117629:4850`\\n- GitHub repository: `https://github.com/Gamexgit/AutoShortsBot`\\n- Existing prototype roots:\\n - `~/.hermes/scripts/Auto_Shorts_Bot`\\n - `~/.hermes/scripts/Auto_Video_Bot`\\n- Initial content directions:\\n - life optimisation\\n - AI\\n - Christian faith / FaithTok was considered but paused because mixing religion and AI was deemed sensitive\\n\\nUpdates made to the memory card included:\\n\\n```markdown\\n- Repository status at project start: empty; current default branch has at least initial commit `7642749`.\\n- Local clone: `~/jarvis_runtime/autoshortsbot/AutoShortsBot`.\\n- Local NVIDIA API key cache: `~/.hermes/secrets/nvidia_api_key` with file mode `0600`; contains `NVIDIA_API_KEY=...`, never print it.\\n- Local Pexels API key cache: `~/.hermes/secrets/pexels_api_key` with file mode `0600`; contains `PEXELS_API_KEY=...`, never print it.\\n- Local Pixabay API key cache: `~/.hermes/secrets/pixabay_api_key` with file mode `0600`; contains `PIXABAY_API=...`, never print it.\\n```\\n\\nThe card also retained secret-handling rules:\\n\\n```markdown\\n- Do not print, paste, or store the raw token in chat, repo, or memory.\\n- Retrieve the token only when needed for GitHub operations, use it as an environment variable/runtime secret, and redact command output.\\n- Never commit credentials, generated secrets, local `.env` files, or token files.\\n```\\n\\n## 4. Key decisions / conclusions\\n\\n- The GitHub token was stored locally as a secret and not embedded in the repo remote URL.\\n- The local Git remote remained clean:\\n\\n```text\\norigin=https://github.com/Gamexgit/AutoShortsBot.git\\ntoken_leak_in_git_config=no\\n```\\n\\n- The AutoShortsBot repository was successfully cloned and verified.\\n- API keys for NVIDIA, Pexels, and Pixabay were stored as local secret files with restrictive permissions.\\n- The assistant updated the AutoShortsBot memory card so future work can locate the repo and secret files without exposing raw credentials.\\n- No actual AutoShortsBot implementation work was done yet in this session; the session was setup/credential-focused.\\n\\n## 5. Notable / unresolved\\n\\n- The user pasted raw credentials directly into Telegram/chat. They were subsequently stored securely, but they had been exposed in the conversation transcript.\\n- No bot functionality, image generation pipeline, Retention Studio feature, or GPT Image 2 integration was implemented during this session.\\n- The repo existed and had at least one initial commit, but no further code changes were described.\\n- `gh` CLI was not installed, so future GitHub work would need to continue using git/curl with the stored token unless `gh` is installed later.\"}], \"count\": 4, \"sessions_searched\": 4}", "tool_call_id": "call_67Iy8nW8sVb9m6ISM609pkyV" }, { "role": "tool", "content": "{\"success\": true, \"skills\": [{\"name\": \"shortform-growth-automation\", \"description\": \"Design and operate semi-automatic pipelines for YouTube Shorts, TikTok, Instagram Reels, and similar shortform growth: niche strategy, test batches, human review loops, monetization constraints, and analytics feedback.\", \"category\": \"social-media\"}, {\"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"category\": \"social-media\"}, {\"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"category\": \"social-media\"}, {\"name\": \"social-shorts-growth-engine\", \"description\": \"Plan and build automated short-form video growth pipelines for YouTube Shorts, TikTok, Instagram Reels, and similar channels with retention-led ideation, human approval, and analytics feedback.\", \"category\": \"social-media\"}, {\"name\": \"xurl\", \"description\": \"X/Twitter via xurl CLI: post, search, DM, media, v2 API.\", \"category\": \"social-media\"}], \"categories\": [\"social-media\"], \"count\": 5, \"hint\": \"Use skill_view(name) to see full content, tags, and linked files\"}", "tool_call_id": "call_5BVIIxrTr6ntrLKtj4WKHorB" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_339raw9ZtbDV6E4ztqvoItRB", "call_id": "call_339raw9ZtbDV6E4ztqvoItRB", "response_item_id": "fc_0d99e9da68392f44016a12b11441cc8191847483e2c546dfd7", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_fu16BsOOISf1oqhE8V0Z5yGw", "call_id": "call_fu16BsOOISf1oqhE8V0Z5yGw", "response_item_id": "fc_0d99e9da68392f44016a12b1144210819192104ab98c665ae4", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"github-pr-workflow\"}" } }, { "id": "call_Dx8RUPa1BRvxKs5LZpCm5NzF", "call_id": "call_Dx8RUPa1BRvxKs5LZpCm5NzF", "response_item_id": "fc_0d99e9da68392f44016a12b114422081919de43075474e9193", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering development skills**\n\nI need to develop relevant skills for social media shorts automation and growth. Maybe I'll look into GitHub pull request workflows and take a more systematic approach to software development. I should inspect the repository carefully, so I don’t miss anything important. Also, incorporating a to-do list could help me stay organized. Let’s load these ideas and see where they take me in enhancing my skills!", "reasoning_content": "**Considering development skills**\n\nI need to develop relevant skills for social media shorts automation and growth. Maybe I'll look into GitHub pull request workflows and take a more systematic approach to software development. I should inspect the repository carefully, so I don’t miss anything important. Also, incorporating a to-do list could help me stay organized. Let’s load these ideas and see where they take me in enhancing my skills!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErEUu0yClZUvomw3u2hrrUDA2n0_bYJL52ZHJBMq2zM8xE2SW1qyra9ammI8jnKE2LN3bspE541wAa2APBtGuQiPr0gACxH-Lr8G2vlzNx2bR7MZ45alq1wtDV1Ew_MpOPJPHM6Vqe90A2_AJ7ZJp2Rr71jWXEgtHJZvyup6WpE7y4hsHfbWfgCV4ER-cHhk1vNnZJ0n238RZMP67uVHSBDVcXgXOfwiUSo1hDPprZVsFMOWFZc2lmanVMDl-3z7rSmHg4CIS8X4twGCuhU9nsFaBtOfBStzcVYj8-TJjBWcuRECBi0WUJhB4NpD1Bl6XgyTb5S9d9WI7vSycibP3Qcdd0phLopVuDIMz82IEMjdLnbWV-ZQfh7LeAdz_whZehLn3qmPKLa6sjbiZwrVROphMKPsjT3McAplJ30pAehFsZ6R9a24r8lz_aeYrCGrgurkUUbY8FWqsiDB-8umZu3Wtuh7TWH2ldBrtfigQiW6C2Yc9vVS4zPPytXhSJH9tmQvS4b1YqAVmGUcpYTpZ3BpmB2mUYem25LVUOHScsogz16e0jbYQQXGBBcQc7qCKuPtBxkg6FlG2qa_XEYVsnvuecpamSFtj206v0nt0tQ1es4xWWpL1i4yoQtkcmfvFw0NDkBxgAg8xDy8cawwy9qywhiLXv4bFNTJJr8XJpfiRIx0jQLllChhJ4twsD7kuLsa3B7Ol6qtx0bfBF34AYwEsOFgFmlVW5bMIjebdt9pksH5J1aKa8GPnzLTtoWLAoYh8ZZcS_Yi34t5Q6-RnUpVCqDeDgTNrTPRF2j2otkcK_phD7mlMohqINaZI3POVHFswQ9hxUOrurlsSOQTMdeJvN3uIH_-kUG9miNRxPo1TJnv762MdeCj2Ax6PBwGPjXyJqAcXPkEjumoXtZ9wxw_mmbKadoNT_cqkCOBnuYdADs7qjoGSrdFiMalp3kkUcvlL6nS5fW2dLj66jpRR6TSWijhTEuYEV1fSgVZ48kdnWuXzL4F2Hn1Rkng-8TrmJjMhiBIlrWP3vh_t0_mMznlZhA8KZ-u-h_O2Jcyrx5F_ZtIxGeAjq_TQ0-eGGYTVKLgHydd1uUnWkHZ7PHYysE9GYs6Ddil0NnTdB9Q3010EMsG-iCZHusB_K7AgAMfKkOliwF4g1YfJ_ek9V8dgGF4CQ==", "id": "rs_0d99e9da68392f44016a12b1115d908191b6167975713dd369", "summary": [ { "type": "summary_text", "text": "**Considering development skills**\n\nI need to develop relevant skills for social media shorts automation and growth. Maybe I'll look into GitHub pull request workflows and take a more systematic approach to software development. I should inspect the repository carefully, so I don’t miss anything important. Also, incorporating a to-do list could help me stay organized. Let’s load these ideas and see where they take me in enhancing my skills!" } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"content-strategy\", \"growth\", \"monetization\", \"approval-workflow\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-growth-engine\\ndescription: Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, content-strategy, growth, monetization, approval-workflow]\\n---\\n\\n# Social-Media Shorts Growth Engine\\n\\nUse this skill when the user wants to build, improve, or operate an automated/semi-automated system for short-form social media content: YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest, LinkedIn video, or similar.\\n\\n## Core principle\\n\\nDo **not** treat the task as “make videos.” Treat it as a **growth engine**:\\n\\n1. Generate many sharply positioned ideas.\\n2. Score hooks and scripts before rendering.\\n3. Produce previews.\\n4. Send the user a review package.\\n5. Post only after explicit approval.\\n6. Capture analytics.\\n7. Feed performance back into the next batch.\\n\\nA technically correct content generator that produces generic content is failure with extra rendering time. Charming, but still failure.\\n\\n## Recommended workflow\\n\\n### 1. Define content pillars\\n\\nStart with 2–4 pillars, not a vague infinite topic pool. For each pillar define:\\n\\n- audience\\n- positioning\\n- tone\\n- hook patterns\\n- visual rules\\n- no-go topics\\n- platform fit\\n- monetization angle\\n\\nExample pillar structure:\\n\\n```json\\n{\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"positioning\\\": \\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n \\\"tone\\\": [\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"],\\n \\\"hook_patterns\\\": [\\\"AI is a bad god, but a good servant.\\\"],\\n \\\"visual_rules\\\": [\\\"screen-recording friendly\\\", \\\"human remains in control\\\"],\\n \\\"no_go\\\": [\\\"fake earnings\\\", \\\"overpromising automation\\\"]\\n}\\n```\\n\\n### 2. Build a testbatch before building a full automation platform\\n\\nFor a new channel or unvalidated strategy, create a small batch first:\\n\\n- 3 ideas per pillar\\n- 6 short growth candidates, usually 20–45 seconds\\n- 1–3 monetization-length candidates, usually 60+ seconds where the platform rewards length\\n- one clear hypothesis per idea\\n\\nEach idea should include:\\n\\n- id\\n- pillar\\n- title\\n- platform target\\n- duration\\n- hook\\n- script outline\\n- visual concept\\n- caption\\n- CTA\\n- quality score\\n- risk/policy notes\\n\\n### 3. Add a script quality gate\\n\\nBefore rendering, score each draft for:\\n\\n- hook strength in the first 1–2 seconds\\n- novelty / non-generic angle\\n- emotional pull\\n- clarity\\n- retention risk\\n- comment/share potential\\n- AI-slop risk\\n- policy/copyright risk\\n\\nRender only drafts above the threshold, e.g. 8/10.\\n\\n### 4. Use a human approval loop\\n\\nDefault to semi-automatic operation first. Send a Telegram review package with:\\n\\n- preview video or script draft\\n- pillar and format\\n- title\\n- duration\\n- hook\\n- caption\\n- hashtags\\n- quality score\\n- policy notes\\n- suggested platforms\\n\\nRecommended approval commands:\\n\\n- `Freigabe alle`\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nIf approval is ambiguous, do not post. Ask once in the project topic.\\n\\n### 5. Keep platform strategy separate from rendering\\n\\nRendering code should not hardcode content strategy. Keep separate config files for:\\n\\n- niches/pillars\\n- platforms\\n- script formats\\n- visual styles\\n- approval workflow\\n- analytics schema\\n\\nThis lets the same renderer support different formats and prevents prompt spaghetti. Prompt spaghetti is still spaghetti even if it is wearing a YAML hat.\\n\\n## Platform notes\\n\\n### YouTube Shorts\\n\\n- Shorts can be vertical or square and up to 3 minutes long.\\n- YouTube Partner Program Shorts route typically requires 1,000 subscribers and 10M valid public Shorts views in the last 90 days.\\n- Alternative longform YPP route: 1,000 subscribers and 4,000 valid public watch hours in the last 12 months.\\n- Shorts views in the Shorts feed do not count toward the 4,000 longform watch hours.\\n- Practical early growth tests often work best at 20–45 seconds; longer Shorts need a strong story or payoff.\\n\\n### TikTok\\n\\n- Creator Rewards availability depends on country/account eligibility.\\n- TikTok Creator Rewards generally requires a non-business account in good standing, 10,000 followers, and 100,000 authentic views in the last 30 days before joining, subject to country availability and current terms.\\n- Reward-eligible videos generally need to be at least 1 minute long and meet quality/originality criteria.\\n- This creates a strategy split:\\n - 20–45s for growth and format discovery.\\n - 60–75s for monetization candidates once a format works.\\n\\n### Instagram / Facebook Reels\\n\\nUseful for republishing 9:16 assets and testing share/save behavior. Treat direct payout as volatile; prefer indirect monetization through audience, affiliate, newsletter, or products.\\n\\n### LinkedIn / X / Pinterest\\n\\nUse selectively:\\n\\n- LinkedIn: AI/productivity/workflows.\\n- X: AI demos, build-in-public, technical positioning.\\n- Pinterest: evergreen checklists, routines, faith/life prompts.\\n\\n## Repository scaffold\\n\\nA clean shorts-engine repo can start as:\\n\\n```text\\nAutoShortsBot/\\n autoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\n configs/\\n niches/\\n platforms/\\n data/\\n drafts/\\n performance/\\n scripts/\\n tests/\\n README.md\\n .gitignore\\n pyproject.toml\\n```\\n\\nNever commit:\\n\\n- `.env`\\n- OAuth tokens\\n- API keys\\n- generated credentials\\n- rendered videos or large media outputs unless explicitly intended\\n- raw analytics exports with sensitive account data\\n\\n## Implementation quality\\n\\nWhen coding the engine, prefer TDD for deterministic parts:\\n\\n- testbatch validation\\n- duration classification\\n- review formatting\\n- analytics import\\n- approval command parsing\\n- platform metadata generation\\n\\nRendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should use Pillow/FFmpeg procedural motion, mechanism layers, kinetic captions, and generated metadata; preserve exact scene durations from render manifests, exclude text-only LLM refinement jobs from rendering, and keep generated local renders out of git (`data/local_motion_renders/`). NVIDIA/build.nvidia.com LLMs are optional text-refinement helpers only, not video generation.\\n\\nIf the user wants more realistic footage without spending money, first check whether a local GPU/ComfyUI host is available. Preferred path when reachable: `scene spec → ComfyUI/Wan API over Tailscale → imported_ai_clips//scene_XX.mp4 → local captions/mechanism layers/FFmpeg assembly`. For a 16 GB A5000 Laptop GPU, use the `comfyui` skill reference `wan-video-tailscale-a5000.md`: Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a small smoke-test ladder before quality probes. If no local GPU path is available or quality is insufficient, use the hybrid free-tier handoff rather than pretending local procedural graphics are realistic: `free_production_jobs → free_ai_video_handoff → manual browser generation in free-tier tools (PixVerse/Pika/CapCut/Hailuo only with existing free credits) → import clips under data/imported_ai_clips/ → local captions/mechanism layers/FFmpeg assembly`. Guardrails: no paid top-ups, no subscriptions, avoid Pro templates, no baked captions/logos/watermarks where possible, and keep imported clips out of git.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should produce moving mechanism/caption previews, not final static cards. When the user has a capable local/remote GPU (e.g. RTX A5000 16 GB via Tailscale), promote the path to `ComfyUI/Wan or LTX local clip generation → imported_ai_clips → hybrid local renderer → FFmpeg`, with free web providers (PixVerse/Pika/CapCut) only as fallback. External free-tier AI video tools can still be used manually for realistic b-roll, but keep paid top-ups/subscriptions disabled and store downloaded clips under `data/imported_ai_clips//scene_XX.mp4`. Generate provider handoff packets for manual browser generation and imported-clip assembly plans so missing clips fall back to local placeholders.\\n\\n## Pitfalls\\n\\n- **Generic advice content:** “4 tips to be healthier/productive” is usually too weak unless the hook, identity, or story is sharp.\\n- **AI-image slop:** avoid generated images containing fake UI text, gibberish signs, distorted hands/faces, or abstract generic graphs. These damage trust fast.\\n- **Too much automation too soon:** validate scripts and formats before building posting APIs.\\n- **No analytics loop:** producing content without tracking retention, likes, shares, comments, and follows is blind output.\\n- **Mixed language drift:** decide language per channel or per pillar before rendering. Mixed English/German drafts are fine for ideation, not final voiceover unless intentional.\\n\\n## References\\n\\n- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n\", \"path\": \"social-media/social-media-shorts-growth-engine/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine\", \"linked_files\": {\"references\": [\"references/autoshortsbot-case-study.md\", \"references/free-ai-video-workflow.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\"}", "tool_call_id": "call_339raw9ZtbDV6E4ztqvoItRB" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n AUTH=\\\"gh\\\"\\nelse\\n AUTH=\\\"git\\\"\\n # Ensure we have a token for API calls\\n if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n fi\\n fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n *Username*) printf '%s\\\\n' x-access-token ;;\\n *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n -d \\\"{\\n \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n print(f\\\\\\\" {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n print(f\\\\\\\" {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n STATUS=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n echo \\\"Check $i: $STATUS\\\"\\n if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n break\\n fi\\n sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=\\ncurl -s -L \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add \\ngit commit -m \\\"fix: resolve CI failure in \\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n -d \\\"{\\n \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n https://api.github.com/graphql \\\\\\n -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.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\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}", "tool_call_id": "call_fu16BsOOISf1oqhE8V0Z5yGw" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"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\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_Dx8RUPa1BRvxKs5LZpCm5NzF" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_BlX01Hm2nEHWqK02YIbvC1co", "call_id": "call_BlX01Hm2nEHWqK02YIbvC1co", "response_item_id": "fc_0d99e9da68392f44016a12b1186cf08191a6f064e8eab69e29", "type": "function", "function": { "name": "todo", "arguments": "{\"todos\":[{\"id\":\"inspect\",\"content\":\"AutoShortsBot repo/context inspizieren und aktuellen Stand bestimmen\",\"status\":\"in_progress\"},{\"id\":\"choose\",\"content\":\"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\",\"status\":\"pending\"},{\"id\":\"tdd\",\"content\":\"Feature mit TDD implementieren\",\"status\":\"pending\"},{\"id\":\"verify\",\"content\":\"Tests/Status verifizieren und Ergebnis zusammenfassen\",\"status\":\"pending\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "**Inspecting the repo**\n\nI need to take a closer look at the repository. It’s important to check the files, the code structure, and any messages or documentation that might be present. I'm curious about what changes have been made and how they impact the overall functionality. I want to make sure I understand everything before proceeding, so let’s aim for a thorough review to minimize any potential hiccups down the line.", "reasoning_content": "**Inspecting the repo**\n\nI need to take a closer look at the repository. It’s important to check the files, the code structure, and any messages or documentation that might be present. I'm curious about what changes have been made and how they impact the overall functionality. 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Christian faith / FaithTok was considered but is paused for now because mixing religion and AI is sensitive.\\n 26|\\n 27|## Secret-handling rules\\n 28|- Do not print, paste, or store the raw token in chat, repo, or memory.\\n 29|- Retrieve the token only when needed for GitHub operations, use it as an environment variable/runtime secret, and redact command output.\\n 30|- Never commit credentials, generated secrets, local `.env` files, or token files.\\n 31|\\n 32|## Initial project direction\\n 33|- Build a system for automated creation of Social-Media-Shorts.\\n 34|- Keep implementation artifacts in the AutoShortsBot GitHub repo once code is created.\\n 35|\", \"total_lines\": 34, \"file_size\": 2368, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_ohLX9H7pHuNAImMZEk4oPJdq" }, { "role": "tool", "content": "{\"output\": \"## main...origin/main\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_x8kG7SKKhhrSdUbBExQ3hBJ5" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_9UEHmya9oxNMhjk4E265uRlm", "call_id": "call_9UEHmya9oxNMhjk4E265uRlm", "response_item_id": "fc_0d99e9da68392f44016a12b123af2881918e391677189e20eb", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"limit\":200}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"content\": \" 1|# AutoShortsBot\", \"total_lines\": 0, \"file_size\": 15, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_9UEHmya9oxNMhjk4E265uRlm" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_HimQ4PD98ByIqnIzjoFecrnS", "call_id": "call_HimQ4PD98ByIqnIzjoFecrnS", "response_item_id": "fc_0d99e9da68392f44016a12b12bfbb08191bb3dc79fd82fbe7f", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"AutoShortsBot repo/context inspizieren und aktuellen Stand bestimmen\",\"id\":\"inspect\",\"status\":\"completed\"},{\"content\":\"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\",\"id\":\"choose\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls", "reasoning": "**Considering next steps for TDD**\n\nI need to figure out the next steps in my process. I'm thinking about scaffolding with TDD, focusing on either the approval parser or the model. Since things are empty, creating a repository scaffold with tests first seems like a good plan. I might need to use a `pyproject`, and I can implement the approval command parser along with some integration for GPT images. \n\nI also want to ensure I'm writing the tests for the approval parser and the render job classification, then running them. Let’s get started!", "reasoning_content": "**Considering next steps for TDD**\n\nI need to figure out the next steps in my process. I'm thinking about scaffolding with TDD, focusing on either the approval parser or the model. Since things are empty, creating a repository scaffold with tests first seems like a good plan. I might need to use a `pyproject`, and I can implement the approval command parser along with some integration for GPT images. \n\nI also want to ensure I'm writing the tests for the approval parser and the render job classification, then running them. 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I'm thinking about scaffolding with TDD, focusing on either the approval parser or the model. Since things are empty, creating a repository scaffold with tests first seems like a good plan. I might need to use a `pyproject`, and I can implement the approval command parser along with some integration for GPT images. \n\nI also want to ensure I'm writing the tests for the approval parser and the render job classification, then running them. Let’s get started!" } ] } ] }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"AutoShortsBot repo/context inspizieren und aktuellen Stand bestimmen\", \"status\": \"completed\"}, {\"id\": \"choose\", \"content\": \"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\", \"status\": \"in_progress\"}, {\"id\": \"tdd\", \"content\": \"Feature mit TDD implementieren\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests/Status verifizieren und Ergebnis zusammenfassen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 2, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_HimQ4PD98ByIqnIzjoFecrnS" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_aGea5qRMZdoCi3iv0Y8xHqKa", "call_id": "call_aGea5qRMZdoCi3iv0Y8xHqKa", "response_item_id": "fc_0d99e9da68392f44016a12b12ee0c48191bf6b91a5eb263c62", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_review_commands.py\",\"content\":\"from autoshorts.review.approvals import ApprovalDecision, parse_approval_command\\n\\n\\ndef test_parse_freigabe_alle_approves_all_platforms():\\n decision = parse_approval_command(\\\"Freigabe alle\\\")\\n\\n assert decision == ApprovalDecision(\\n action=\\\"approve\\\",\\n platforms=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n change_request=None,\\n )\\n\\n\\ndef test_parse_platform_specific_freigabe_normalizes_german_platform_names():\\n decision = parse_approval_command(\\\" Freigabe YouTube \\\")\\n\\n assert decision.action == \\\"approve\\\"\\n assert decision.platforms == (\\\"youtube\\\",)\\n assert decision.change_request is None\\n\\n\\ndef test_parse_aendern_keeps_requested_change_text():\\n decision = parse_approval_command(\\\"Ändern: Hook stärker, weniger generische KI-Bilder\\\")\\n\\n assert decision.action == \\\"change_request\\\"\\n assert decision.platforms == ()\\n assert decision.change_request == \\\"Hook stärker, weniger generische KI-Bilder\\\"\\n\\n\\ndef test_parse_ablehnen_rejects_without_platforms():\\n decision = parse_approval_command(\\\"Ablehnen\\\")\\n\\n assert decision == ApprovalDecision(\\n action=\\\"reject\\\",\\n platforms=(),\\n change_request=None,\\n )\\n\\n\\ndef test_unknown_or_ambiguous_command_requires_clarification():\\n decision = parse_approval_command(\\\"passt schon\\\")\\n\\n assert decision.action == \\\"needs_clarification\\\"\\n assert decision.platforms == ()\\n assert decision.change_request is None\\n\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 1420, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.review.approvals\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_aGea5qRMZdoCi3iv0Y8xHqKa" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_3LzFnxH66CPv52NfOMeHsIcU", "call_id": "call_3LzFnxH66CPv52NfOMeHsIcU", "response_item_id": "fc_0d99e9da68392f44016a12b13b4b688191b3a0cb4ce24bbd09", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"from autoshorts.scripting.quality import ScriptQualityScore, should_render\\n\\n\\ndef test_script_renders_when_score_meets_threshold_and_no_policy_block():\\n score = ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n assert should_render(score) is True\\n\\n\\ndef test_script_does_not_render_below_quality_threshold():\\n score = ScriptQualityScore(\\n hook_strength=6,\\n novelty=6,\\n clarity=7,\\n retention=6,\\n ai_slop_risk=3,\\n policy_risk=\\\"low\\\",\\n )\\n\\n assert should_render(score) is False\\n\\n\\ndef test_script_does_not_render_when_ai_slop_risk_is_too_high():\\n score = ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=8,\\n policy_risk=\\\"low\\\",\\n )\\n\\n assert should_render(score) is False\\n\\n\\ndef test_script_does_not_render_when_policy_risk_is_high():\\n score = ScriptQualityScore(\\n hook_strength=10,\\n novelty=10,\\n clarity=10,\\n retention=10,\\n ai_slop_risk=1,\\n policy_risk=\\\"high\\\",\\n )\\n\\n assert should_render(score) is False\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_quality_gate.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 1205, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.scripting.quality\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_3LzFnxH66CPv52NfOMeHsIcU" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_RvbNAJnY1Rrt9soPfGPjlmVl", "call_id": "call_RvbNAJnY1Rrt9soPfGPjlmVl", "response_item_id": "fc_0d99e9da68392f44016a12b145ae3881919e7a50e2741fb5f5", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\",\"id\":\"choose\",\"status\":\"completed\"},{\"content\":\"Feature mit TDD implementieren\",\"id\":\"tdd\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"AutoShortsBot repo/context inspizieren und aktuellen Stand bestimmen\", \"status\": \"completed\"}, {\"id\": \"choose\", \"content\": \"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\", \"status\": \"completed\"}, {\"id\": \"tdd\", \"content\": \"Feature mit TDD implementieren\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Tests/Status verifizieren und Ergebnis zusammenfassen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 1, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_RvbNAJnY1Rrt9soPfGPjlmVl" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_QdZe30c3dTJb5LVwaT8uTIBU", "call_id": "call_QdZe30c3dTJb5LVwaT8uTIBU", "response_item_id": "fc_0d99e9da68392f44016a12b148cd0c8191bc15b8ed5ef2657d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_quality_gate.py __________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_quality_gate.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_quality_gate.py:1: in \\n from autoshorts.scripting.quality import ScriptQualityScore, should_render\\nE ModuleNotFoundError: No module named 'autoshorts'\\n________________ ERROR collecting tests/test_review_commands.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_review_commands.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_review_commands.py:1: in \\n from autoshorts.review.approvals import ApprovalDecision, parse_approval_command\\nE ModuleNotFoundError: No module named 'autoshorts'\\n=========================== short test summary info ============================\\nERROR tests/test_quality_gate.py\\nERROR tests/test_review_commands.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.12s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_QdZe30c3dTJb5LVwaT8uTIBU" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_tGVwkqcKVTRAYZ7VGL4SkK7E", "call_id": "call_tGVwkqcKVTRAYZ7VGL4SkK7E", "response_item_id": "fc_0d99e9da68392f44016a12b15026a88191b7970914e88d76b5", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"[project]\\nname = \\\"autoshortsbot\\\"\\nversion = \\\"0.1.0\\\"\\ndescription = \\\"Semi-automatic short-form video growth engine with human approval gates.\\\"\\nrequires-python = \\\">=3.11\\\"\\ndependencies = []\\n\\n[tool.pytest.ini_options]\\npythonpath = [\\\".\\\"]\\ntestpaths = [\\\"tests\\\"]\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Implementing modules**\n\nAlright, I’m thinking through the process of implementing. I might need an `__init__.py` file and some modules to set things up. Writing a `pyproject.toml` file isn’t strictly necessary, but I guess it could be helpful. Plus, I'm planning on using Test-Driven Development (TDD) to guide my work. It's interesting how this all comes together; I'll just stay focused and structured as I move forward!", "reasoning_content": "**Implementing modules**\n\nAlright, I’m thinking through the process of implementing. I might need an `__init__.py` file and some modules to set things up. Writing a `pyproject.toml` file isn’t strictly necessary, but I guess it could be helpful. Plus, I'm planning on using Test-Driven Development (TDD) to guide my work. It's interesting how this all comes together; I'll just stay focused and structured as I move forward!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErFQw1p1tCiCyb-rK4ojywpd3BSYOYOUFsJf-VgBhtiAZ22NS7P2kOSr0D6QsI-3jyHOzkUbUSaZaqqjzGJ0lW9mkCmPQd7FIoMUQady2jmG2Nfyq_yyaMPQwSmqCatRwdvRWST2GNcP7LII842uavZui_U22ngkoB9WfbotQsWaWuK8Ayi24oVC9RLX5Ex4jJvsZ9JuRQ8EwNn_VjUQAYaj1G0v47BoEnSCvYqP8temAcd4MDpr58przg3RcUeWucXbv4IJHExPPeoh-vRJopyfRpVdFwLxRIfntelTQsJmF-SU8soqhH8djrfwwpsqjXP6q5T-Jz-xV6AWCmkCUzYOTzClyuVJPQ96LOKxs7ge6TsSJPEoMvb0PHw9s-MeXtyLCI9ljfR4Ot1UNL95wOah-FLcCllC0Lbsvf0N8_AKZf7wn7K1XaIo9kbzzWYbyiSHBwf-YkiGLpAV77lvcnjezbOwfXWA2MCCcxMKmce6WAaHSRDWz45PU6p2jzB9qiZP9ZL9Er4bNOLZ85u6mOHjpmJqbVBnU8-RAdLERkSFezNdJllCZuvvoRlHKp11Hm38-2xFkAmBIct0AYCKtf0xWYEjVFR8fEs7oA1tr1898N_YT7dYty0sxmn92D6OkvAMm9EgjfNZesTq791fB-KTKpTOr0GNjMAVVh-0NvSihfyT6hi5RAzCjgtpVrhQZKcN2lo8uhpi5LVqlYp-sgdf_eDC5OqQiLc_vlyiojIkf8X-R4a8nfIEfuAJW1cG_7QGF0f1QybvDx0zJ45A1ux_mnMhaZP2r2LmKaD8Jg_0p8Van0pO5lB9kxNYbViN7egdF1Y3UFKmR2FqVQ1Q8jSyw5Q4aWyIG9w1CuiAkqU-xVUM3V_r5mUfOUh8kBD4eTZZJUE6roZrGo8ir6oSuGgujL1r97ZpG8stP1EfcQO1Cs5zXCLo-swj9uNzVqjFNfGe63_CXUiAcToUHCVPfCITOJmvppGruVAuTEl3M0L6bfUOzH-fLFO8efWPvoqw4gCAU15t5pugSkRvaPzYIusjfwizscEZGDrMg9Ot1Ltr2wc=", "id": "rs_0d99e9da68392f44016a12b14dd8e88191aeb114ad6e91b842", "summary": [ { "type": "summary_text", "text": "**Implementing modules**\n\nAlright, I’m thinking through the process of implementing. I might need an `__init__.py` file and some modules to set things up. Writing a `pyproject.toml` file isn’t strictly necessary, but I guess it could be helpful. Plus, I'm planning on using Test-Driven Development (TDD) to guide my work. It's interesting how this all comes together; I'll just stay focused and structured as I move forward!" } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 253, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_tGVwkqcKVTRAYZ7VGL4SkK7E" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_F7zdw6kvmXJCGNGdUCSLg10z", "call_id": "call_F7zdw6kvmXJCGNGdUCSLg10z", "response_item_id": "fc_0d99e9da68392f44016a12b1581de48191a43e6dfebeb9adbf", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"AutoShortsBot core package.\\\"\\\"\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/__init__.py\"}" } }, { "id": "call_ZG75gSnPFHoxfwExTjfoX3p8", "call_id": "call_ZG75gSnPFHoxfwExTjfoX3p8", "response_item_id": "fc_0d99e9da68392f44016a12b1581df48191abda27f0dac6f185", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Human review and approval workflow helpers.\\\"\\\"\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/__init__.py\"}" } }, { "id": "call_c3UF18rUgMPoLT3bRXQRxtPI", "call_id": "call_c3UF18rUgMPoLT3bRXQRxtPI", "response_item_id": "fc_0d99e9da68392f44016a12b1581dfc8191a767ccbdcef53ec8", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Script scoring and pre-render quality gates.\\\"\\\"\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/scripting/__init__.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 34, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_F7zdw6kvmXJCGNGdUCSLg10z" }, { "role": "tool", "content": "{\"bytes_written\": 50, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ZG75gSnPFHoxfwExTjfoX3p8" }, { "role": "tool", "content": "{\"bytes_written\": 51, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_c3UF18rUgMPoLT3bRXQRxtPI" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rhbOhDZ6MQerAcHZrwGSusTR", "call_id": "call_rhbOhDZ6MQerAcHZrwGSusTR", "response_item_id": "fc_0d99e9da68392f44016a12b15c08448191bd495905fbfc1fbb", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Parse human approval commands from Telegram review messages.\\n\\nThe bot never publishes on vague consent. Ambiguous commands return\\n``needs_clarification`` so the orchestrator can ask once instead of posting.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nimport re\\nfrom typing import Literal\\n\\nApprovalAction = Literal[\\\"approve\\\", \\\"reject\\\", \\\"change_request\\\", \\\"needs_clarification\\\"]\\n\\nALL_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n_PLATFORM_ALIASES = {\\n \\\"youtube\\\": \\\"youtube\\\",\\n \\\"yt\\\": \\\"youtube\\\",\\n \\\"shorts\\\": \\\"youtube\\\",\\n \\\"youtube shorts\\\": \\\"youtube\\\",\\n \\\"tiktok\\\": \\\"tiktok\\\",\\n \\\"tik tok\\\": \\\"tiktok\\\",\\n \\\"instagram\\\": \\\"instagram\\\",\\n \\\"insta\\\": \\\"instagram\\\",\\n \\\"reels\\\": \\\"instagram\\\",\\n \\\"instagram reels\\\": \\\"instagram\\\",\\n}\\n\\n\\n@dataclass(frozen=True)\\nclass ApprovalDecision:\\n action: ApprovalAction\\n platforms: tuple[str, ...] = ()\\n change_request: str | None = None\\n\\n\\ndef _normalize_text(command: str) -> str:\\n return \\\" \\\".join((command or \\\"\\\").strip().casefold().split())\\n\\n\\ndef _platform_from_text(text: str) -> tuple[str, ...]:\\n if text in {\\\"alle\\\", \\\"all\\\", \\\"alles\\\"}:\\n return ALL_PLATFORMS\\n platform = _PLATFORM_ALIASES.get(text)\\n if platform:\\n return (platform,)\\n return ()\\n\\n\\ndef parse_approval_command(command: str) -> ApprovalDecision:\\n \\\"\\\"\\\"Parse a Telegram approval command into a deterministic decision.\\n\\n Supported user-facing commands:\\n - ``Freigabe alle``\\n - ``Freigabe YouTube`` / ``Freigabe TikTok`` / ``Freigabe Instagram``\\n - ``Ablehnen``\\n - ``Ändern: ``\\n \\\"\\\"\\\"\\n\\n raw = (command or \\\"\\\").strip()\\n normalized = _normalize_text(raw)\\n\\n if normalized in {\\\"ablehnen\\\", \\\"reject\\\", \\\"abgelehnt\\\"}:\\n return ApprovalDecision(action=\\\"reject\\\")\\n\\n change_match = re.match(r\\\"^(?:ändern|aendern|change)\\\\s*:\\\\s*(.+)$\\\", raw, flags=re.IGNORECASE)\\n if change_match:\\n change_text = change_match.group(1).strip()\\n if change_text:\\n return ApprovalDecision(action=\\\"change_request\\\", change_request=change_text)\\n return ApprovalDecision(action=\\\"needs_clarification\\\")\\n\\n approval_match = re.match(r\\\"^(?:freigabe|approve)\\\\s+(.+)$\\\", normalized)\\n if approval_match:\\n platforms = _platform_from_text(approval_match.group(1).strip())\\n if platforms:\\n return ApprovalDecision(action=\\\"approve\\\", platforms=platforms)\\n\\n return ApprovalDecision(action=\\\"needs_clarification\\\")\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/approvals.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 2438, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_rhbOhDZ6MQerAcHZrwGSusTR" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_FpN8PWJXuKNl5iKoZL8rP4Zy", "call_id": "call_FpN8PWJXuKNl5iKoZL8rP4Zy", "response_item_id": "fc_0d99e9da68392f44016a12b16f67988191afe364edbda2e7c9", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Deterministic pre-render quality gate for script candidates.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom typing import Literal\\n\\nPolicyRisk = Literal[\\\"low\\\", \\\"medium\\\", \\\"high\\\"]\\n\\n\\n@dataclass(frozen=True)\\nclass ScriptQualityScore:\\n \\\"\\\"\\\"Human/LLM-reviewable scores for one short-form script candidate.\\n\\n Numeric dimensions use a 0-10 scale. ``ai_slop_risk`` is inverse: higher\\n means more generic, uncanny, or trust-damaging output risk.\\n \\\"\\\"\\\"\\n\\n hook_strength: int\\n novelty: int\\n clarity: int\\n retention: int\\n ai_slop_risk: int\\n policy_risk: PolicyRisk = \\\"low\\\"\\n\\n @property\\n def average_quality(self) -> float:\\n return (self.hook_strength + self.novelty + self.clarity + self.retention) / 4\\n\\n\\ndef should_render(\\n score: ScriptQualityScore,\\n *,\\n minimum_average: float = 8.0,\\n maximum_ai_slop_risk: int = 5,\\n) -> bool:\\n \\\"\\\"\\\"Return whether a script should proceed to rendering.\\n\\n The gate is intentionally conservative: content below threshold waits for\\n rewrite instead of spending render time on AI slop. A dreary but economical\\n little kindness.\\n \\\"\\\"\\\"\\n\\n if score.policy_risk == \\\"high\\\":\\n return False\\n if score.ai_slop_risk > maximum_ai_slop_risk:\\n return False\\n return score.average_quality >= minimum_average\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/scripting/quality.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 1331, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_FpN8PWJXuKNl5iKoZL8rP4Zy" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_jdq6yafoMMuGfsovRt4jFN1T", "call_id": "call_jdq6yafoMMuGfsovRt4jFN1T", "response_item_id": "fc_0d99e9da68392f44016a12b17c5e2c819195022a2ea22c96f5", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"......... [100%]\\n9 passed in 0.04s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_jdq6yafoMMuGfsovRt4jFN1T" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_R2hItkOVhfhyqTovoFL4AO3C", "call_id": "call_R2hItkOVhfhyqTovoFL4AO3C", "response_item_id": "fc_0d99e9da68392f44016a12b183afb48191828474c09a630f2d", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"# AutoShortsBot\\n\\nAutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n\\nThe project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n\\n## Current scope\\n\\nImplemented foundation:\\n\\n- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n- Tests for approval parsing and script quality gating.\\n\\n## Human approval commands\\n\\nSupported review responses:\\n\\n- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nAmbiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n\\n## Quality gate\\n\\nA script candidate only proceeds to rendering when:\\n\\n- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n- AI-slop risk is not above `5`;\\n- policy risk is not `high`.\\n\\nThis keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n\\n## Development\\n\\nRun tests:\\n\\n```bash\\npython -m pytest tests -q\\n```\\n\\n## Planned architecture\\n\\n```text\\nautoshorts/\\n config/ # niches, platforms, workflow settings\\n ideas/ # idea generation and scoring\\n scripting/ # script drafts, quality gates, retention checks\\n rendering/ # local/AI-assisted preview rendering\\n review/ # Telegram review package and approval parser\\n publishing/ # platform-specific publishing metadata and later APIs\\n analytics/ # performance imports and feedback loop\\n```\\n\\n## Non-negotiables\\n\\n- No credentials in git.\\n- No generated media in git unless explicitly intended.\\n- No automatic publishing without explicit approval.\\n- Quality over speed; avoid generic AI-slop.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Preparing project setup**\n\nI'm considering what needs to be added to the project, like a README, .gitignore, and configuration files. It could be useful to create a review package formatter next, but I'm not entirely sure if that's necessary yet. I also need to check the git status since the user mentioned development—probably implying I should commit and push changes. First, I should update the README and any documentation, and I might run tests too. Then I'll look into creating scaffold directories for configuration.", "reasoning_content": "**Preparing project setup**\n\nI'm considering what needs to be added to the project, like a README, .gitignore, and configuration files. It could be useful to create a review package formatter next, but I'm not entirely sure if that's necessary yet. I also need to check the git status since the user mentioned development—probably implying I should commit and push changes. First, I should update the README and any documentation, and I might run tests too. Then I'll look into creating scaffold directories for configuration.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErGDaLvb-7RM9EyJ57bbhQvSHjyGuu5kyeFmMokc5M5_Lv4tFLXKUWa6JD-z6n2QBA1s-KeqA_xOcXzlJVcadbAr9JfHFIiIyfptlPdLq3XATiuRFWyENDoucrs4qp_C_RqpAJyRpWpGt2mnZ-jWy9J6GMjdOPtm0wLq64BLRt7msRXhSVMdhX2rU4mHwmNPK-1adwCaGMIrKsXtJ5k9xwE6WlyKFI9uZlerrY-OAy_kKG25rc1A0Vs7ip5IfHmRU4XirWYgEOe9dqSHELrFPb9ygYnCWRynwiHlTNwMXsU6eH-kzFZ9tY4qnFIhwfwpnt92ZWT1I7byq1F24TPxvO3uUd0kCyX8Z1cUEa2lA5khxnwzncchhr0kiU0xj_ZBHES7VpYxa3z6PqAAzcZhlr9YX5zW6qU1y5z8T8Lqy456zxcdL8puvPZawadA3gIqZMEjSfpPq37YP8OKKZJ9tk5eRfINl6oCvd8Yw8eG1UIEi0F9FrVcS5FeA1wVKeXH1PnbMOzxN1JPAxt67nktSK6dw4XNqAn9z5WSySAU0bioBQ7k6xnFIzz6pAi3xFe0wJpahs6X0R6MKquG6QY9zLwe9n97BnwyQDWnVER94DNvTNt-0uYUPkaXS454oyE4SF5RV8Gn5L85CoEYAY1Yxl3RChhLEpzVLTqXgj12PQ-IQgPoiM7Ot6wuxpBj8_Ghiveht9WHCPG8n9J_S7F51WMNuSqPuRHC6zZpzu-fNC9rrd4EdKoSKECQEFtK9_fGK3QFBbmo1o855jCXsg3bFHUc9WDdwB2RUeOPuXDf0PJOYv23yziWUGr2Ert-e0FD47xJq7j5Ibmy6wJ2-WGW4BhG_ubUHfMrd4IbN-ZgSYf4Qu9dFpXXEcnE5LDsaqjTayM2JGFkmB3JPux4Po0ud2ESgceuqorngKhA-HKQxi42RAzfx4omHqhxwMyK7SzwwPL_TPBeZ0o8eloWu2f9JUQo5M5UYe1wL_ITzpTMIeiLFzBcJ0SHFvA_FYZMcJChDgifRcpi9RarIuasM0mVIOQeC8xrvh4VeS7BDklZgwyA-qoKi-iYa-QEPQ3zXwxrKGr9Zof1O6i2vPJSvG1oylRdE0IyY8Gkpj--qxFEQnZJGyPhhMGAPCoCn1UP-cq4XERHKGnCjZvxRHYK63OH4lNjP2wovANsIwAKeeiXO3DunRaVfOWD12vkVHy-am_oQ6g5m86LaEm3KnpUp4dGpM48drZFRp14K1bs13B5urwcFe-ImYn1feRERfD56f1rM-NKgv9SVXW7JfoQp_oL4oZss2Homowe0-JapKr9QtMaoPQ0EEsMCm4M_6q9wIZ90rZNdIQ0S5xa7Hlur8cGkenLjEN9BydggUkj7jrIpTNM0uet6o1fZ4g48mk5wufJ3rk4", "id": "rs_0d99e9da68392f44016a12b1801e7881918c5e538049005b2b", "summary": [ { "type": "summary_text", "text": "**Preparing project setup**\n\nI'm considering what needs to be added to the project, like a README, .gitignore, and configuration files. It could be useful to create a review package formatter next, but I'm not entirely sure if that's necessary yet. I also need to check the git status since the user mentioned development—probably implying I should commit and push changes. First, I should update the README and any documentation, and I might run tests too. Then I'll look into creating scaffold directories for configuration." } ] } ] }, { "role": "tool", "content": "{\"bytes_written\": 1964, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_R2hItkOVhfhyqTovoFL4AO3C" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_oIWiNAVtF5zw5TRzE0nNOV4X", "call_id": "call_oIWiNAVtF5zw5TRzE0nNOV4X", "response_item_id": "fc_0d99e9da68392f44016a12b18e22508191bbe680527fa0555c", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"# Secrets / local config\\n.env\\n.env.*\\n*.token\\n*secret*\\n\\n# Python\\n__pycache__/\\n*.py[cod]\\n.pytest_cache/\\n.ruff_cache/\\n.mypy_cache/\\n.venv/\\nvenv/\\n\\n# Generated media and runtime data\\ndata/local_motion_renders/\\ndata/imported_ai_clips/\\ndata/generated/\\noutputs/\\n*.mp4\\n*.mov\\n*.webm\\n*.wav\\n*.mp3\\n*.png\\n*.jpg\\n*.jpeg\\n*.webp\\n\\n# OS/editor\\n.DS_Store\\n.vscode/\\n.idea/\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/.gitignore\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 349, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for files\"}}", "tool_call_id": "call_oIWiNAVtF5zw5TRzE0nNOV4X" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_IRuor3ygmjitudVDDgLvLoxh", "call_id": "call_IRuor3ygmjitudVDDgLvLoxh", "response_item_id": "fc_0d99e9da68392f44016a12b1947768819196a0fe6c1b43e0e3", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"......... 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This way, I can avoid any confusion when making commits." } ] } ] }, { "role": "tool", "content": "{\"output\": \"[feat/core-approval-quality-gates 3555ec2] feat: add approval parser and quality gate\\n 10 files changed, 336 insertions(+), 1 deletion(-)\\n create mode 100644 .gitignore\\n create mode 100644 autoshorts/__init__.py\\n create mode 100644 autoshorts/review/__init__.py\\n create mode 100644 autoshorts/review/approvals.py\\n create mode 100644 autoshorts/scripting/__init__.py\\n create mode 100644 autoshorts/scripting/quality.py\\n create mode 100644 pyproject.toml\\n create mode 100644 tests/test_quality_gate.py\\n create mode 100644 tests/test_review_commands.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_St4QFAzN2re2LmQyqTcJzNhW" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_x3aCBxI6IKHWlmzgrMp9TuEC", "call_id": "call_x3aCBxI6IKHWlmzgrMp9TuEC", "response_item_id": "fc_0d99e9da68392f44016a12b1af940c8191943c7a48055a628f", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short --branch && git log --oneline -2\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErGvE3b0YkSpAsUiCSeCkMnzdZhYvKZq8sqXjc6drlP5IyKut-Sy3q2H2i485W5CqSaXK1ScXuIIsaqN99ijMx8ZwX_krUls-QcxF6V2Kk-8ebCjAGEUVnUf8hW4j6ahQVs68YVwG9XIwzMdIlNvBQiW8TiN3UrTUztnosV4H6HFPnkfN5vtOSc1LJcX9RtGLKE3ft8_xuEU-TBlAyF4jCUUV2KzRJDopszGxY61YFqdnDL16TveuoKaGd3p-0wIkuFLaRmB-UtK9GsJxmNXBl7C7h_0xdy4V91LeNl2DN02HMIbloARiWhJHzrxo-FrYTn31l1JAcF22BTucqHM46n58l0raHGCP5i2_ATN527eJtWRuPoMwYnGXVxzX8syZbH74S5S5dliWMIBOOauTWOp22lfoWlGpWjII1S8V1E4O1d9m_1psT9RVToFj_f0zX_rqZ4o0GAYKojdXsvP1iQE2h_qIlV_YGVbHMLBKrf9R1LRojIbaxbnRHtx0-0BwtvKW4rv8KvzbwwW_N9r36pel8bMutS5xWlD4iQYSIXBHld9AcbK1NnL0Ji0MDSGfAppaBlQd_DYxl4_4lnutaDHu3l_zIC3OEE2FzSXk-4XRC1xK9Dm1yhXJbjigaXq-mSIKOy0eoGoA6mY6zEPh3lT1w1uutIcz4LyJz1gr8yxSvi6WQJAYSjuyyW-Khv8oRPzWXBLc-DANnTMlAWQWa9nBa7geyRmk-B3zIPdu4cCgwzoMt7HS08phnboOZbGbnVr3zzB2IIxqTKI_aB82W9OR2kfAKcoo3IPAvSOG4MD5evptAxRBHkJVIT17fGlJCO51kYsWAIioDEjfMg5zNg36PmmyBEh5Mv3pswpeS-rJW3fcK81iIpX2IwedVf2P45queDvOk5wZ_HRvlgUDcQpOOxcsyG_87IhDuoc3PeWGpAhp96S6K5btPB5SdgdJxhVWe90SRVHq4EX4_Z_AFXqgA==", "id": "rs_0d99e9da68392f44016a12b1ad81dc8191aecddbd5869b2dca", "summary": [] } ] }, { "role": "tool", "content": "{\"output\": \"......... [100%]\\n9 passed in 0.04s\\n## feat/core-approval-quality-gates\\n3555ec2 feat: add approval parser and quality gate\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_x3aCBxI6IKHWlmzgrMp9TuEC" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_i9aJFHF1sZ0o2eKhbS2GjpMj", "call_id": "call_i9aJFHF1sZ0o2eKhbS2GjpMj", "response_item_id": "fc_0d99e9da68392f44016a12b1b265b88191a0b9b1361ffbe55a", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Feature mit TDD implementieren\",\"id\":\"tdd\",\"status\":\"completed\"},{\"content\":\"Tests/Status verifizieren und Ergebnis zusammenfassen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"AutoShortsBot repo/context inspizieren und aktuellen Stand bestimmen\", \"status\": \"completed\"}, {\"id\": \"choose\", \"content\": \"Kleinsten sinnvollen nächsten Entwicklungsschritt auswählen\", \"status\": \"completed\"}, {\"id\": \"tdd\", \"content\": \"Feature mit TDD implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests/Status verifizieren und Ergebnis zusammenfassen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_i9aJFHF1sZ0o2eKhbS2GjpMj" }, { "role": "assistant", "content": "Erledigt, Sir. Ich habe den **AutoShortsBot** konkret weiterentwickelt — nicht nur herumphilosophiert, was bei Bots leider eine weit verbreitete Berufskrankheit ist.\n\n## Gemacht\n\nIm Repo:\n\n```text\n~/jarvis_runtime/autoshortsbot/AutoShortsBot\n```\n\nhabe ich einen neuen Branch erstellt:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nund lokal committed:\n\n```text\n3555ec2 feat: add approval parser and quality gate\n```\n\n## Neue Grundlage im Bot\n\n### 1. Approval Parser\n\nDatei:\n\n```text\nautoshorts/review/approvals.py\n```\n\nDer Bot versteht jetzt deterministische Review-/Freigabe-Kommandos:\n\n- `Freigabe alle`\n- `Freigabe YouTube`\n- `Freigabe TikTok`\n- `Freigabe Instagram`\n- `Ablehnen`\n- `Ändern: `\n\nWichtig: Ambige Antworten wie:\n\n```text\npasst schon\n```\n\nwerden **nicht** als Freigabe gewertet, sondern als:\n\n```text\nneeds_clarification\n```\n\nAlso: keine versehentliche Veröffentlichung wegen schwammigem menschlichem Gemurmel. Fortschritt.\n\n### 2. Script Quality Gate\n\nDatei:\n\n```text\nautoshorts/scripting/quality.py\n```\n\nEin Script darf nur ins Rendering, wenn:\n\n- Hook, Novelty, Clarity und Retention im Schnitt mindestens `8.0` erreichen\n- `ai_slop_risk <= 5`\n- `policy_risk != high`\n\nDas ist die erste harte Schranke gegen generischen KI-Müll.\n\n### 3. Projektstruktur + Tests\n\nNeu angelegt:\n\n```text\nautoshorts/\n review/\n scripting/\ntests/\npyproject.toml\n.gitignore\nREADME.md\n```\n\n`.gitignore` blockiert u.a.:\n\n- Secrets\n- `.env`\n- generierte Medien\n- lokale Renders\n- importierte AI-Clips\n- Python Cache\n\n## Verifikation\n\nTests laufen grün:\n\n```text\n9 passed in 0.04s\n```\n\nGit-Status sauber:\n\n```text\n## feat/core-approval-quality-gates\n```\n\n## Nicht gemacht\n\nIch habe **noch nicht gepusht** und keinen PR erstellt. Lokaler Commit ist fertig. Remote-Push/PR mache ich im nächsten Schritt, wenn du willst.\n\n## Sinnvoller nächster Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/review/package.py\n```\n\nAlso ein **Telegram Review Package**, das pro Video-Kandidat sauber ausgibt:\n\n- Titel\n- Pillar\n- Hook\n- Script Summary\n- Visual Concept\n- Quality Score\n- AI-slop risk\n- Policy Notes\n- vorgeschlagene Plattformen\n- Freigabe-Kommandos\n\nDann haben wir die erste echte Bot-Schleife:\n\n```text\nIdea → Script → Quality Gate → Review Package → Sir approves/changes/rejects\n```\n\nDas ist die richtige Reihenfolge. Publishing-Automation kommt später, wenn die Qualität nicht mehr nach “PowerPoint hatte Fieber” aussieht.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir. Ich habe den **AutoShortsBot** konkret weiterentwickelt — nicht nur herumphilosophiert, was bei Bots leider eine weit verbreitete Berufskrankheit ist.\n\n## Gemacht\n\nIm Repo:\n\n```text\n~/jarvis_runtime/autoshortsbot/AutoShortsBot\n```\n\nhabe ich einen neuen Branch erstellt:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nund lokal committed:\n\n```text\n3555ec2 feat: add approval parser and quality gate\n```\n\n## Neue Grundlage im Bot\n\n### 1. Approval Parser\n\nDatei:\n\n```text\nautoshorts/review/approvals.py\n```\n\nDer Bot versteht jetzt deterministische Review-/Freigabe-Kommandos:\n\n- `Freigabe alle`\n- `Freigabe YouTube`\n- `Freigabe TikTok`\n- `Freigabe Instagram`\n- `Ablehnen`\n- `Ändern: `\n\nWichtig: Ambige Antworten wie:\n\n```text\npasst schon\n```\n\nwerden **nicht** als Freigabe gewertet, sondern als:\n\n```text\nneeds_clarification\n```\n\nAlso: keine versehentliche Veröffentlichung wegen schwammigem menschlichem Gemurmel. Fortschritt.\n\n### 2. Script Quality Gate\n\nDatei:\n\n```text\nautoshorts/scripting/quality.py\n```\n\nEin Script darf nur ins Rendering, wenn:\n\n- Hook, Novelty, Clarity und Retention im Schnitt mindestens `8.0` erreichen\n- `ai_slop_risk <= 5`\n- `policy_risk != high`\n\nDas ist die erste harte Schranke gegen generischen KI-Müll.\n\n### 3. Projektstruktur + Tests\n\nNeu angelegt:\n\n```text\nautoshorts/\n review/\n scripting/\ntests/\npyproject.toml\n.gitignore\nREADME.md\n```\n\n`.gitignore` blockiert u.a.:\n\n- Secrets\n- `.env`\n- generierte Medien\n- lokale Renders\n- importierte AI-Clips\n- Python Cache\n\n## Verifikation\n\nTests laufen grün:\n\n```text\n9 passed in 0.04s\n```\n\nGit-Status sauber:\n\n```text\n## feat/core-approval-quality-gates\n```\n\n## Nicht gemacht\n\nIch habe **noch nicht gepusht** und keinen PR erstellt. Lokaler Commit ist fertig. Remote-Push/PR mache ich im nächsten Schritt, wenn du willst.\n\n## Sinnvoller nächster Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/review/package.py\n```\n\nAlso ein **Telegram Review Package**, das pro Video-Kandidat sauber ausgibt:\n\n- Titel\n- Pillar\n- Hook\n- Script Summary\n- Visual Concept\n- Quality Score\n- AI-slop risk\n- Policy Notes\n- vorgeschlagene Plattformen\n- Freigabe-Kommandos\n\nDann haben wir die erste echte Bot-Schleife:\n\n```text\nIdea → Script → Quality Gate → Review Package → Sir approves/changes/rejects\n```\n\nDas ist die richtige Reihenfolge. Publishing-Automation kommt später, wenn die Qualität nicht mehr nach “PowerPoint hatte Fieber” aussieht." } ], "id": "msg_0d99e9da68392f44016a12b1b71c44819183258311bd171ac0", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] OK, entwickle weiter." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_JThCz8lI8nqSu1kngohDgdZH", "call_id": "call_JThCz8lI8nqSu1kngohDgdZH", "response_item_id": "fc_0d99e9da68392f44016a12b40473cc819189ad6e7956681d36", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_veQKHjpnm272lcR2H7wlwyMk", "call_id": "call_veQKHjpnm272lcR2H7wlwyMk", "response_item_id": "fc_0d99e9da68392f44016a12b40473d8819185095a58d91cc2b4", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Planning implementation steps**\n\nI need to continue with my task and consider whether to load any relevant skills. They might already be loaded, but it's important to scan for what's needed in this new phase. I’m thinking about skills like social interaction, test-driven development (TDD), or GitHub strategies. Basically, I need to take action and implement a review package with TDD, and I’ll have to inspect the current files to see what I’m working with!", "reasoning_content": "**Planning implementation steps**\n\nI need to continue with my task and consider whether to load any relevant skills. They might already be loaded, but it's important to scan for what's needed in this new phase. I’m thinking about skills like social interaction, test-driven development (TDD), or GitHub strategies. Basically, I need to take action and implement a review package with TDD, and I’ll have to inspect the current files to see what I’m working with!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErQE77XJrYYZHBIHiUsioG9AW6l6V-3Y-06fgzzhFfyvLCqWEkT4M1ZIwjuhYSQ-Jg1H4QmwFeca7YPlQPgyVmycA3KSBd7vqnlAEXtgi6XikE_JN_FXHAJH9xuYhT_fdYog4Gjz1O0ZY4aMJWQfZYahkhpKDZvJ6SwDwjtB7x8AOf-Lvb-NmeUaVX0Ofgt4CvNfGiL-SLvjWn2pghm0NlglGzL1sgg9BAuEpOjdh4xGyFPDx07caM3SsphEw-H70sOVSThFUsS-fJ6MT9R5B2tWoTBJQVi5L_EHrWimZ8lU8queF50aF3qPLKQiYMc8TD3WM4OTgMBzcH8RPXb54BmoXgwW6-ZpKjykXA5n1K42phgR9zco6vtth6jZDwwtHo6sWLOfOcPyhNu087QaKWleeF-ZN7cGnRjqmpY5PdhOQ9GiFKa2kQuS_P8PeEPJ_IEdo9JU6Ev6GJ8b7TsxKcmwX8ghFHlVr2pJ1A6nldA4eXU54F7MgNBeZtnVtFNWrLu3aQQiqVvNQf4iYxeM_kAPDTLoejca1xI0HyjDltHlE-8V_BjSYjUl4TneeXjk-WF7OvfImvl8BB7Fcjw-p9jQnN4W85ncCRWUHkZZ2YjVBaydcHDluwwb3UK4r6dRMPvT3wfXuG79Ojjv_UkmMwMdNnHgKAeMCIxx3OWMIPmOosAHmCGG_I7QhfjLU3UHw98TU9PoLjLH_LrS-cvtEdsNKDAPpHC-f-b4BlrrEZ--J103LU7SClwiYOrxKE4A6yB10hrilvNhYHQSaRZZdKJc00p7FALU8nTmZpe6nMUkZIHJIVSoVp3OY3EqYXl2oGMxry3jEho0HvPV521pEwAb-3FXpzhcqrGR8jMZxmyorU3GM6bznanvLOexPo61ISfgZtIEUSRmJop1A3NHwfDNsvvSyzKwH45IDCOPPSyH_hyIm9uAtN-JlXZ7yyk39P-6sZyHlhN44jEruGqdKEE13FO8Qh9Hn0hPNuX-pyCyB3aF9MbUh5lZ_t3t-eORTO9Tv7_nNsfl6n9CCPofoUy7RvD6y2JSxjuib6W_7WwQpu4BqTQOwkvDsSz3uS0d-HomZFHTskrQzQ-2obKGRISum3ikDLlB3xMt26aq01aKaqiBFoiDKspECqKATy3A7FVLh0vsqmXPWu9E4eOO-4cwEw==", "id": "rs_0d99e9da68392f44016a12b3fed698819192e3b1a96dd125d9", "summary": [ { "type": "summary_text", "text": "**Planning implementation steps**\n\nI need to continue with my task and consider whether to load any relevant skills. They might already be loaded, but it's important to scan for what's needed in this new phase. I’m thinking about skills like social interaction, test-driven development (TDD), or GitHub strategies. Basically, I need to take action and implement a review package with TDD, and I’ll have to inspect the current files to see what I’m working with!" } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"content-strategy\", \"growth\", \"monetization\", \"approval-workflow\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-growth-engine\\ndescription: Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, content-strategy, growth, monetization, approval-workflow]\\n---\\n\\n# Social-Media Shorts Growth Engine\\n\\nUse this skill when the user wants to build, improve, or operate an automated/semi-automated system for short-form social media content: YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest, LinkedIn video, or similar.\\n\\n## Core principle\\n\\nDo **not** treat the task as “make videos.” Treat it as a **growth engine**:\\n\\n1. Generate many sharply positioned ideas.\\n2. Score hooks and scripts before rendering.\\n3. Produce previews.\\n4. Send the user a review package.\\n5. Post only after explicit approval.\\n6. Capture analytics.\\n7. Feed performance back into the next batch.\\n\\nA technically correct content generator that produces generic content is failure with extra rendering time. Charming, but still failure.\\n\\n## Recommended workflow\\n\\n### 1. Define content pillars\\n\\nStart with 2–4 pillars, not a vague infinite topic pool. For each pillar define:\\n\\n- audience\\n- positioning\\n- tone\\n- hook patterns\\n- visual rules\\n- no-go topics\\n- platform fit\\n- monetization angle\\n\\nExample pillar structure:\\n\\n```json\\n{\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"positioning\\\": \\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n \\\"tone\\\": [\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"],\\n \\\"hook_patterns\\\": [\\\"AI is a bad god, but a good servant.\\\"],\\n \\\"visual_rules\\\": [\\\"screen-recording friendly\\\", \\\"human remains in control\\\"],\\n \\\"no_go\\\": [\\\"fake earnings\\\", \\\"overpromising automation\\\"]\\n}\\n```\\n\\n### 2. Build a testbatch before building a full automation platform\\n\\nFor a new channel or unvalidated strategy, create a small batch first:\\n\\n- 3 ideas per pillar\\n- 6 short growth candidates, usually 20–45 seconds\\n- 1–3 monetization-length candidates, usually 60+ seconds where the platform rewards length\\n- one clear hypothesis per idea\\n\\nEach idea should include:\\n\\n- id\\n- pillar\\n- title\\n- platform target\\n- duration\\n- hook\\n- script outline\\n- visual concept\\n- caption\\n- CTA\\n- quality score\\n- risk/policy notes\\n\\n### 3. Add a script quality gate\\n\\nBefore rendering, score each draft for:\\n\\n- hook strength in the first 1–2 seconds\\n- novelty / non-generic angle\\n- emotional pull\\n- clarity\\n- retention risk\\n- comment/share potential\\n- AI-slop risk\\n- policy/copyright risk\\n\\nRender only drafts above the threshold, e.g. 8/10.\\n\\n### 4. Use a human approval loop\\n\\nDefault to semi-automatic operation first. Send a Telegram review package with:\\n\\n- preview video or script draft\\n- pillar and format\\n- title\\n- duration\\n- hook\\n- caption\\n- hashtags\\n- quality score\\n- policy notes\\n- suggested platforms\\n\\nRecommended approval commands:\\n\\n- `Freigabe alle`\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nIf approval is ambiguous, do not post. Ask once in the project topic.\\n\\n### 5. Keep platform strategy separate from rendering\\n\\nRendering code should not hardcode content strategy. Keep separate config files for:\\n\\n- niches/pillars\\n- platforms\\n- script formats\\n- visual styles\\n- approval workflow\\n- analytics schema\\n\\nThis lets the same renderer support different formats and prevents prompt spaghetti. Prompt spaghetti is still spaghetti even if it is wearing a YAML hat.\\n\\n## Platform notes\\n\\n### YouTube Shorts\\n\\n- Shorts can be vertical or square and up to 3 minutes long.\\n- YouTube Partner Program Shorts route typically requires 1,000 subscribers and 10M valid public Shorts views in the last 90 days.\\n- Alternative longform YPP route: 1,000 subscribers and 4,000 valid public watch hours in the last 12 months.\\n- Shorts views in the Shorts feed do not count toward the 4,000 longform watch hours.\\n- Practical early growth tests often work best at 20–45 seconds; longer Shorts need a strong story or payoff.\\n\\n### TikTok\\n\\n- Creator Rewards availability depends on country/account eligibility.\\n- TikTok Creator Rewards generally requires a non-business account in good standing, 10,000 followers, and 100,000 authentic views in the last 30 days before joining, subject to country availability and current terms.\\n- Reward-eligible videos generally need to be at least 1 minute long and meet quality/originality criteria.\\n- This creates a strategy split:\\n - 20–45s for growth and format discovery.\\n - 60–75s for monetization candidates once a format works.\\n\\n### Instagram / Facebook Reels\\n\\nUseful for republishing 9:16 assets and testing share/save behavior. Treat direct payout as volatile; prefer indirect monetization through audience, affiliate, newsletter, or products.\\n\\n### LinkedIn / X / Pinterest\\n\\nUse selectively:\\n\\n- LinkedIn: AI/productivity/workflows.\\n- X: AI demos, build-in-public, technical positioning.\\n- Pinterest: evergreen checklists, routines, faith/life prompts.\\n\\n## Repository scaffold\\n\\nA clean shorts-engine repo can start as:\\n\\n```text\\nAutoShortsBot/\\n autoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\n configs/\\n niches/\\n platforms/\\n data/\\n drafts/\\n performance/\\n scripts/\\n tests/\\n README.md\\n .gitignore\\n pyproject.toml\\n```\\n\\nNever commit:\\n\\n- `.env`\\n- OAuth tokens\\n- API keys\\n- generated credentials\\n- rendered videos or large media outputs unless explicitly intended\\n- raw analytics exports with sensitive account data\\n\\n## Implementation quality\\n\\nWhen coding the engine, prefer TDD for deterministic parts:\\n\\n- testbatch validation\\n- duration classification\\n- review formatting\\n- analytics import\\n- approval command parsing\\n- platform metadata generation\\n- script quality gates\\n\\nA strong first implementation slice is the deterministic gate before any renderer exists:\\n\\n1. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n2. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n3. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n4. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers or publishing APIs.\\n\\nRendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should use Pillow/FFmpeg procedural motion, mechanism layers, kinetic captions, and generated metadata; preserve exact scene durations from render manifests, exclude text-only LLM refinement jobs from rendering, and keep generated local renders out of git (`data/local_motion_renders/`). NVIDIA/build.nvidia.com LLMs are optional text-refinement helpers only, not video generation.\\n\\nWhen Hermes has the `openai-codex` image-generation backend available, GPT Image 2 is a good cloud-side keyframe/thumbnail/concept generator for the pipeline: `script idea → GPT Image 2 keyframe → human review → Wan/ComfyUI I2V or local renderer → deterministic captions/overlays`. Because GPT Image 2 runs cloud-side, it does not compete with local/remote GPU VRAM used by Wan/ComfyUI video jobs. Use low/medium for iteration and high for final keyframes; still add captions, labels, numbers, UI text, and audit-sensitive facts later in the deterministic renderer rather than trusting baked image text.\\n\\nIf the user wants more realistic footage without spending money, first check whether a local GPU/ComfyUI host is available. Preferred path when reachable: `scene spec → ComfyUI/Wan API over Tailscale → imported_ai_clips//scene_XX.mp4 → local captions/mechanism layers/FFmpeg assembly`. For a 16 GB A5000 Laptop GPU, use the `comfyui` skill reference `wan-video-tailscale-a5000.md`: Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a small smoke-test ladder before quality probes. If no local GPU path is available or quality is insufficient, use the hybrid free-tier handoff rather than pretending local procedural graphics are realistic: `free_production_jobs → free_ai_video_handoff → manual browser generation in free-tier tools (PixVerse/Pika/CapCut/Hailuo only with existing free credits) → import clips under data/imported_ai_clips/ → local captions/mechanism layers/FFmpeg assembly`. Guardrails: no paid top-ups, no subscriptions, avoid Pro templates, no baked captions/logos/watermarks where possible, and keep imported clips out of git.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should produce moving mechanism/caption previews, not final static cards. When the user has a capable local/remote GPU (e.g. RTX A5000 16 GB via Tailscale), promote the path to `ComfyUI/Wan or LTX local clip generation → imported_ai_clips → hybrid local renderer → FFmpeg`, with free web providers (PixVerse/Pika/CapCut) only as fallback. External free-tier AI video tools can still be used manually for realistic b-roll, but keep paid top-ups/subscriptions disabled and store downloaded clips under `data/imported_ai_clips//scene_XX.mp4`. Generate provider handoff packets for manual browser generation and imported-clip assembly plans so missing clips fall back to local placeholders.\\n\\n## Pitfalls\\n\\n- **Generic advice content:** “4 tips to be healthier/productive” is usually too weak unless the hook, identity, or story is sharp.\\n- **AI-image slop:** avoid generated images containing fake UI text, gibberish signs, distorted hands/faces, or abstract generic graphs. These damage trust fast.\\n- **Too much automation too soon:** validate scripts and formats before building posting APIs.\\n- **No analytics loop:** producing content without tracking retention, likes, shares, comments, and follows is blind output.\\n- **Mixed language drift:** decide language per channel or per pillar before rendering. Mixed English/German drafts are fine for ideation, not final voiceover unless intentional.\\n\\n## References\\n\\n- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n\", \"path\": \"social-media/social-media-shorts-growth-engine/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine\", \"linked_files\": {\"references\": [\"references/autoshortsbot-case-study.md\", \"references/free-ai-video-workflow.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\"}", "tool_call_id": "call_JThCz8lI8nqSu1kngohDgdZH" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": 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\"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 2, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_OubFYTdZuDHc0pdanvuahDzc" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_OBRKPlyP3rL8VdoaGgakvZRH", "call_id": "call_OBRKPlyP3rL8VdoaGgakvZRH", "response_item_id": "fc_0d99e9da68392f44016a12b41871b081919897500754a0ba71", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_review_package.py\",\"content\":\"from autoshorts.review.package import ReviewPackage, render_telegram_review_package\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\n\\ndef _quality_score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef test_review_package_contains_required_decision_context_and_commands():\\n package = ReviewPackage(\\n candidate_id=\\\"vid-001\\\",\\n title=\\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n pillar=\\\"ai_life_systems\\\",\\n duration_seconds=34,\\n hook=\\\"AI sollte dir dienen, nicht dich führen.\\\",\\n script_summary=\\\"Drei Szenen zeigen den Unterschied zwischen KI-Hype und kontrolliertem Alltagssystem.\\\",\\n visual_concept=\\\"Cinematic desk setup, phone automations, calm human-in-control framing.\\\",\\n caption=\\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n hashtags=(\\\"#AI\\\", \\\"#Productivity\\\", \\\"#LifeSystems\\\"),\\n platforms=(\\\"youtube\\\", \\\"tiktok\\\"),\\n quality_score=_quality_score(),\\n policy_notes=(\\\"No fake earnings claims\\\", \\\"No medical or financial advice\\\"),\\n preview_path=\\\"data/local_motion_renders/vid-001.mp4\\\",\\n )\\n\\n text = render_telegram_review_package(package)\\n\\n assert \\\"Review: vid-001\\\" in text\\n assert \\\"AI ist ein schlechter Gott\\\" in text\\n assert \\\"Pillar: ai_life_systems\\\" in text\\n assert \\\"Duration: 34s\\\" in text\\n assert \\\"Quality: 8.5/10\\\" in text\\n assert \\\"AI-slop risk: 2/10\\\" in text\\n assert \\\"Policy: No fake earnings claims; No medical or financial advice\\\" in text\\n assert \\\"Preview: data/local_motion_renders/vid-001.mp4\\\" in text\\n assert \\\"Freigabe alle\\\" in text\\n assert \\\"Freigabe YouTube\\\" in text\\n assert \\\"Freigabe TikTok\\\" in text\\n assert \\\"Ablehnen\\\" in text\\n assert \\\"Ändern: \\\" in text\\n\\n\\ndef test_review_package_warns_when_candidate_should_not_render():\\n poor_score = ScriptQualityScore(\\n hook_strength=5,\\n novelty=5,\\n clarity=6,\\n retention=5,\\n ai_slop_risk=8,\\n policy_risk=\\\"low\\\",\\n )\\n package = ReviewPackage(\\n candidate_id=\\\"vid-weak\\\",\\n title=\\\"4 Tipps für Produktivität\\\",\\n pillar=\\\"ai_life_systems\\\",\\n duration_seconds=28,\\n hook=\\\"Hier sind vier Tipps.\\\",\\n script_summary=\\\"Generic advice list.\\\",\\n visual_concept=\\\"Stock productivity images.\\\",\\n caption=\\\"Mehr Produktivität im Alltag.\\\",\\n hashtags=(\\\"#Productivity\\\",),\\n platforms=(\\\"youtube\\\",),\\n quality_score=poor_score,\\n policy_notes=(),\\n preview_path=None,\\n )\\n\\n text = render_telegram_review_package(package)\\n\\n assert \\\"Render gate: BLOCKED\\\" in text\\n assert \\\"nicht automatisch rendern\\\" in text\\n\\n\\ndef test_review_package_omits_platform_specific_commands_for_unlisted_platforms():\\n package = ReviewPackage(\\n candidate_id=\\\"vid-ig\\\",\\n title=\\\"One visual idea\\\",\\n pillar=\\\"visual_tests\\\",\\n duration_seconds=15,\\n hook=\\\"One clean idea.\\\",\\n script_summary=\\\"A short visual test.\\\",\\n visual_concept=\\\"Single mechanism reveal.\\\",\\n caption=\\\"A test.\\\",\\n hashtags=(),\\n platforms=(\\\"instagram\\\",),\\n quality_score=_quality_score(),\\n policy_notes=(),\\n preview_path=None,\\n )\\n\\n text = render_telegram_review_package(package)\\n\\n assert \\\"Freigabe Instagram\\\" in text\\n assert \\\"Freigabe YouTube\\\" not in text\\n assert \\\"Freigabe TikTok\\\" not in text\\n\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 3517, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.review.package\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_OBRKPlyP3rL8VdoaGgakvZRH" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_n6TfXLBa1BNTh51bMLRC2A5u", "call_id": "call_n6TfXLBa1BNTh51bMLRC2A5u", "response_item_id": "fc_0d99e9da68392f44016a12b42da7588191ba552ab425428b6d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_review_package.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_review_package.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_review_package.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_review_package.py:1: in \\n from autoshorts.review.package import ReviewPackage, render_telegram_review_package\\nE ModuleNotFoundError: No module named 'autoshorts.review.package'\\n=========================== short test summary info ============================\\nERROR tests/test_review_package.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_n6TfXLBa1BNTh51bMLRC2A5u" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rgiwS50qAfyPR1IsAVm0njV9", "call_id": "call_rgiwS50qAfyPR1IsAVm0njV9", "response_item_id": "fc_0d99e9da68392f44016a12b432d2008191a3a06b8146677955", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Review-Package-Tests zuerst schreiben und RED verifizieren\",\"id\":\"red\",\"status\":\"completed\"},{\"content\":\"Review-Package-Modul minimal implementieren\",\"id\":\"green\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status und aktuelle Branch prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Review-Package-Tests zuerst schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Review-Package-Modul minimal implementieren\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 1, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_rgiwS50qAfyPR1IsAVm0njV9" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_4z6eVb6zaD4QhHKVkkkA4Bkq", "call_id": "call_4z6eVb6zaD4QhHKVkkkA4Bkq", "response_item_id": "fc_0d99e9da68392f44016a12b436c8a88191b31d27329579cd5a", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Telegram review-package formatting for content candidates.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.scripting.quality import ScriptQualityScore, should_render\\n\\n_PLATFORM_LABELS = {\\n \\\"youtube\\\": \\\"YouTube\\\",\\n \\\"tiktok\\\": \\\"TikTok\\\",\\n \\\"instagram\\\": \\\"Instagram\\\",\\n}\\n\\n\\n@dataclass(frozen=True)\\nclass ReviewPackage:\\n \\\"\\\"\\\"Everything Sir needs to approve, reject, or request changes.\\n\\n This structure is deterministic by design: strategy/rendering code can fill\\n it, Telegram code can send the rendered text, and publishing code can wait\\n for an explicit parsed approval decision.\\n \\\"\\\"\\\"\\n\\n candidate_id: str\\n title: str\\n pillar: str\\n duration_seconds: int\\n hook: str\\n script_summary: str\\n visual_concept: str\\n caption: str\\n hashtags: tuple[str, ...]\\n platforms: tuple[str, ...]\\n quality_score: ScriptQualityScore\\n policy_notes: tuple[str, ...] = ()\\n preview_path: str | None = None\\n\\n\\ndef _join_or_dash(items: tuple[str, ...]) -> str:\\n return \\\"; \\\".join(items) if items else \\\"-\\\"\\n\\n\\ndef _platform_names(platforms: tuple[str, ...]) -> str:\\n labels = [_PLATFORM_LABELS.get(platform, platform) for platform in platforms]\\n return \\\", \\\".join(labels) if labels else \\\"-\\\"\\n\\n\\ndef _approval_commands(platforms: tuple[str, ...]) -> list[str]:\\n commands = [\\\"Freigabe alle\\\"]\\n for platform in platforms:\\n label = _PLATFORM_LABELS.get(platform, platform.title())\\n commands.append(f\\\"Freigabe {label}\\\")\\n commands.extend([\\\"Ablehnen\\\", \\\"Ändern: \\\"])\\n return commands\\n\\n\\ndef render_telegram_review_package(package: ReviewPackage) -> str:\\n \\\"\\\"\\\"Render a concise Telegram-friendly review message.\\\"\\\"\\\"\\n\\n score = package.quality_score\\n render_allowed = should_render(score)\\n gate_line = \\\"Render gate: READY\\\" if render_allowed else \\\"Render gate: BLOCKED — nicht automatisch rendern\\\"\\n hashtags = \\\" \\\".join(package.hashtags) if package.hashtags else \\\"-\\\"\\n preview = package.preview_path or \\\"-\\\"\\n commands = \\\"\\\\n\\\".join(f\\\"- `{command}`\\\" for command in _approval_commands(package.platforms))\\n\\n return \\\"\\\\n\\\".join(\\n [\\n f\\\"## Review: {package.candidate_id}\\\",\\n f\\\"Title: {package.title}\\\",\\n f\\\"Pillar: {package.pillar}\\\",\\n f\\\"Platforms: {_platform_names(package.platforms)}\\\",\\n f\\\"Duration: {package.duration_seconds}s\\\",\\n \\\"\\\",\\n f\\\"Hook: {package.hook}\\\",\\n f\\\"Script: {package.script_summary}\\\",\\n f\\\"Visual: {package.visual_concept}\\\",\\n f\\\"Caption: {package.caption}\\\",\\n f\\\"Hashtags: {hashtags}\\\",\\n \\\"\\\",\\n f\\\"Quality: {score.average_quality:.1f}/10\\\",\\n f\\\"AI-slop risk: {score.ai_slop_risk}/10\\\",\\n f\\\"Policy: {_join_or_dash(package.policy_notes)}\\\",\\n gate_line,\\n f\\\"Preview: {preview}\\\",\\n \\\"\\\",\\n \\\"Approval commands:\\\",\\n commands,\\n ]\\n )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/review/package.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 2964, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_4z6eVb6zaD4QhHKVkkkA4Bkq" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_o81yhFer3q9H3B7sQbRpiFwY", "call_id": "call_o81yhFer3q9H3B7sQbRpiFwY", "response_item_id": "fc_0d99e9da68392f44016a12b44ba49881918a015e5e8fdbcc59", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_review_package.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"... 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A rare mercy.\\n 37|\\n 38|## Development\\n 39|\\n 40|Run tests:\\n 41|\\n 42|```bash\\n 43|python -m pytest tests -q\\n 44|```\\n 45|\\n 46|## Planned architecture\\n 47|\\n 48|```text\\n 49|autoshorts/\\n 50| config/ # niches, platforms, workflow settings\\n 51| ideas/ # idea generation and scoring\\n 52| scripting/ # script drafts, quality gates, retention checks\\n 53| rendering/ # local/AI-assisted preview rendering\\n 54| review/ # Telegram review package and approval parser\\n 55| publishing/ # platform-specific publishing metadata and later APIs\\n 56| analytics/ # performance imports and feedback loop\\n 57|```\\n 58|\\n 59|## Non-negotiables\\n 60|\\n 61|- No credentials in git.\\n 62|- No generated media in git unless explicitly intended.\\n 63|- No automatic publishing without explicit approval.\\n 64|- Quality over speed; avoid generic AI-slop.\\n 65|\", \"total_lines\": 64, \"file_size\": 1964, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_BvXk9TLf6Z1xhS5knNMTrlxw" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_CaPEW36Rqk01BMa7Uf9z43OX", "call_id": "call_CaPEW36Rqk01BMa7Uf9z43OX", "response_item_id": "fc_0d99e9da68392f44016a12b457c24c8191b78fb14331fc43d8", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n- Tests for approval parsing and script quality gating.\",\"new_string\":\"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n- Tests for approval parsing, review formatting, and script quality gating.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -9,8 +9,9 @@\\n Implemented foundation:\\n \\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n-- Tests for approval parsing and script quality gating.\\n+- Tests for approval parsing, review formatting, and script quality gating.\\n \\n ## Human approval commands\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_CaPEW36Rqk01BMa7Uf9z43OX" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6DVxGLMPfy3tXB5BwUQaGJUv", "call_id": "call_6DVxGLMPfy3tXB5BwUQaGJUv", "response_item_id": "fc_0d99e9da68392f44016a12b45f5ee481918276e2aef6a86e93", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n\\n## Development\",\"new_string\":\"This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n\\n## Review package\\n\\n`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n\\n- candidate id, title, pillar, target platforms, and duration;\\n- hook, script summary, visual concept, caption, and hashtags;\\n- average quality score, AI-slop risk, policy notes, and render-gate status;\\n- optional preview path;\\n- only the relevant platform-specific approval commands.\\n\\nA blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n\\n## Development\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -36,6 +36,18 @@\\n \\n This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n \\n+## Review package\\n+\\n+`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n+\\n+- candidate id, title, pillar, target platforms, and duration;\\n+- hook, script summary, visual concept, caption, and hashtags;\\n+- average quality score, AI-slop risk, policy notes, and render-gate status;\\n+- optional preview path;\\n+- only the relevant platform-specific approval commands.\\n+\\n+A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n+\\n ## Development\\n \\n Run tests:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_6DVxGLMPfy3tXB5BwUQaGJUv" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_4rAubX6vjPhVXztrZjYr2YvF", "call_id": "call_4rAubX6vjPhVXztrZjYr2YvF", "response_item_id": "fc_0d99e9da68392f44016a12b469eb1c81918940a41d7ac382d9", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"............ [100%]\\n12 passed in 0.04s\\n M README.md\\n?? autoshorts/review/package.py\\n?? tests/test_review_package.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_4rAubX6vjPhVXztrZjYr2YvF" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_MlCFV2KKSF6XC9IWq7XRPEAU", "call_id": "call_MlCFV2KKSF6XC9IWq7XRPEAU", "response_item_id": "fc_0d99e9da68392f44016a12b46e53d4819193214811e66d33f6", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/review/package.py tests/test_review_package.py && git commit -m \\\"feat: add telegram review package\\\" && git status --short --branch && git log --oneline -3\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"[feat/core-approval-quality-gates 55b17fa] feat: add telegram review package\\n 3 files changed, 207 insertions(+), 1 deletion(-)\\n create mode 100644 autoshorts/review/package.py\\n create mode 100644 tests/test_review_package.py\\n## feat/core-approval-quality-gates\\n55b17fa feat: add telegram review package\\n3555ec2 feat: add approval parser and quality gate\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_MlCFV2KKSF6XC9IWq7XRPEAU" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_T0PM1MrCRAk3FyhD2Z2AnN3t", "call_id": "call_T0PM1MrCRAk3FyhD2Z2AnN3t", "response_item_id": "fc_0d99e9da68392f44016a12b472818881918a46f06ebb29bbf5", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"............ [100%]\\n12 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_T0PM1MrCRAk3FyhD2Z2AnN3t" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_MXvqARwizqX4qISzua0gDrVj", "call_id": "call_MXvqARwizqX4qISzua0gDrVj", "response_item_id": "fc_0d99e9da68392f44016a12b47630ac81918809c4f560188477", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status und aktuelle Branch prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Review-Package-Tests zuerst schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Review-Package-Modul minimal implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_MXvqARwizqX4qISzua0gDrVj" }, { "role": "assistant", "content": "Weiterentwickelt, Sir.\n\n## Neu gebaut\n\nIch habe dem AutoShortsBot jetzt das **Telegram Review Package** gegeben — also den Teil, der aus einem Video-Kandidaten eine klare Freigabe-Nachricht für dich macht.\n\nNeue Datei:\n\n```text\nautoshorts/review/package.py\n```\n\nNeue Tests:\n\n```text\ntests/test_review_package.py\n```\n\n## Was es kann\n\n`render_telegram_review_package()` erzeugt eine Telegram-taugliche Review-Nachricht mit:\n\n- Candidate ID\n- Titel\n- Pillar\n- Zielplattformen\n- Dauer\n- Hook\n- Script Summary\n- Visual Concept\n- Caption\n- Hashtags\n- Quality Score\n- AI-slop Risk\n- Policy Notes\n- Render-Gate-Status\n- Preview-Pfad\n- passende Approval Commands\n\nBeispielhafte Commands im Paket:\n\n```text\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nAblehnen\nÄndern: \n```\n\nWichtig: Plattform-Kommandos werden nur für wirklich vorgesehene Plattformen angezeigt. Wenn ein Kandidat nur für Instagram gedacht ist, steht dort nicht unnötig `Freigabe YouTube`. Kleine Hygiene, grosse Wirkung.\n\n## Render-Gate-Schutz\n\nWenn ein Kandidat den Quality Gate nicht schafft, erscheint im Review Package:\n\n```text\nRender gate: BLOCKED — nicht automatisch rendern\n```\n\nDamit wird aus schlechter Qualität nicht versehentlich ein Video. Das klingt selbstverständlich, ist aber im KI-Automationsland bereits gehobene Zivilisation.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Review Package geschrieben.\n2. RED verifiziert: fehlendes Modul → Test bricht korrekt.\n3. Modul minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Verifikation\n\nAlle Tests laufen grün:\n\n```text\n12 passed in 0.05s\n```\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\n55b17fa feat: add telegram review package\n```\n\nAktueller Verlauf:\n\n```text\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Stand jetzt\n\nWir haben jetzt die erste echte Bot-Kontrollschleife als Code-Fundament:\n\n```text\nScript Candidate\n→ Quality Gate\n→ Telegram Review Package\n→ Explicit Approval Parser\n→ erst danach Publishing/Rendering\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/ideas/candidate.py\nautoshorts/ideas/testbatch.py\n```\n\nZiel:\n\n- strukturierte Video-Kandidaten\n- Testbatch mit mehreren Ideen\n- Validierung: jeder Kandidat braucht Hook, Pillar, Zielplattform, Dauer, Quality Score\n- nur renderbare Kandidaten gehen ins Review Package\n\nDann haben wir:\n\n```text\nIdeenbatch → validierte Kandidaten → Quality Gate → Review Package\n```\n\nDas wäre der nächste solide Baustein vor Rendering und GPT-Image-Keyframes.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Weiterentwickelt, Sir.\n\n## Neu gebaut\n\nIch habe dem AutoShortsBot jetzt das **Telegram Review Package** gegeben — also den Teil, der aus einem Video-Kandidaten eine klare Freigabe-Nachricht für dich macht.\n\nNeue Datei:\n\n```text\nautoshorts/review/package.py\n```\n\nNeue Tests:\n\n```text\ntests/test_review_package.py\n```\n\n## Was es kann\n\n`render_telegram_review_package()` erzeugt eine Telegram-taugliche Review-Nachricht mit:\n\n- Candidate ID\n- Titel\n- Pillar\n- Zielplattformen\n- Dauer\n- Hook\n- Script Summary\n- Visual Concept\n- Caption\n- Hashtags\n- Quality Score\n- AI-slop Risk\n- Policy Notes\n- Render-Gate-Status\n- Preview-Pfad\n- passende Approval Commands\n\nBeispielhafte Commands im Paket:\n\n```text\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nAblehnen\nÄndern: \n```\n\nWichtig: Plattform-Kommandos werden nur für wirklich vorgesehene Plattformen angezeigt. Wenn ein Kandidat nur für Instagram gedacht ist, steht dort nicht unnötig `Freigabe YouTube`. Kleine Hygiene, grosse Wirkung.\n\n## Render-Gate-Schutz\n\nWenn ein Kandidat den Quality Gate nicht schafft, erscheint im Review Package:\n\n```text\nRender gate: BLOCKED — nicht automatisch rendern\n```\n\nDamit wird aus schlechter Qualität nicht versehentlich ein Video. Das klingt selbstverständlich, ist aber im KI-Automationsland bereits gehobene Zivilisation.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Review Package geschrieben.\n2. RED verifiziert: fehlendes Modul → Test bricht korrekt.\n3. Modul minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Verifikation\n\nAlle Tests laufen grün:\n\n```text\n12 passed in 0.05s\n```\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\n55b17fa feat: add telegram review package\n```\n\nAktueller Verlauf:\n\n```text\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Stand jetzt\n\nWir haben jetzt die erste echte Bot-Kontrollschleife als Code-Fundament:\n\n```text\nScript Candidate\n→ Quality Gate\n→ Telegram Review Package\n→ Explicit Approval Parser\n→ erst danach Publishing/Rendering\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/ideas/candidate.py\nautoshorts/ideas/testbatch.py\n```\n\nZiel:\n\n- strukturierte Video-Kandidaten\n- Testbatch mit mehreren Ideen\n- Validierung: jeder Kandidat braucht Hook, Pillar, Zielplattform, Dauer, Quality Score\n- nur renderbare Kandidaten gehen ins Review Package\n\nDann haben wir:\n\n```text\nIdeenbatch → validierte Kandidaten → Quality Gate → Review Package\n```\n\nDas wäre der nächste solide Baustein vor Rendering und GPT-Image-Keyframes." } ], "id": "msg_0d99e9da68392f44016a12b4790ef08191902219679ecb0196", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] ok, nächster schritt" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_MpL56xnxgUnoxYLuDBhRbj2l", "call_id": "call_MpL56xnxgUnoxYLuDBhRbj2l", "response_item_id": "fc_0d99e9da68392f44016a12b5a9002c819190e452c421f2c982", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_QoSTbMXqY4LEu3z29eaDCf6r", "call_id": "call_QoSTbMXqY4LEu3z29eaDCf6r", "response_item_id": "fc_0d99e9da68392f44016a12b5a9004081918f9ef836aad80bdc", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Continuing the process**\n\nI feel like we need to keep going! It seems necessary to load the relevant skills again. I should definitely use the tools at my disposal. It looks like I’ll implement candidate/testbatch TDD, and maybe push things forward. The user has indicated that they want the next steps, so I'll prepare to code. I also need to check on the status as we make progress. Let’s stay focused!", "reasoning_content": "**Continuing the process**\n\nI feel like we need to keep going! It seems necessary to load the relevant skills again. I should definitely use the tools at my disposal. It looks like I’ll implement candidate/testbatch TDD, and maybe push things forward. The user has indicated that they want the next steps, so I'll prepare to code. I also need to check on the status as we make progress. Let’s stay focused!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErWph0aqypYEexAIJ_qOgutPzTLChfHQRo60tjDm1THR5hOzD4oHp9g8wsbi10-RWLrZ8iRdC7w55YF6GIX0KuraDzBgoi01sAuVVOdsrczO6CN4_4ORL-b60QEDr3WnuXmfXLfNI9PCZvIB860t-rlUCMv278eFgl_CsauKjIMbs9X18kZbieH66vko2u9D1illWPYXfaQIOsgTG9-_soaNOCZEMica1eTDnj3PZNUjV0XUNEp_UKx1fh_ya_2XvG-cRHexZJc6Xa5Fihk-ogBS2AEsMJtH_52dV3SZc8IDuoT7qExN6wkuD3yySnshD9HPY5hXdM_QxZlk7jiur_t7HvwdD7R1i0yAFrKmU9JmHifgUl9WbFxPA02pgOLW-3JRODKgtR4_emRaNqsTlRjBQzhUHnQcqLilR5DSVyL8W8WqSJ4FgKaNtn4zilCQvUQ7d3pgE3GTwa1zcEKM_aVzDqaScAEaCMh63BBDsNVARzajoeuhGPBuZMC-GnNmDlqZdu8yYl40shkolzYTZRLubomglE6FgMvPl-57eKOCmJ-nF-aPZGSC-40r9zGDAl3GJIlVNjOCbn0GisUKrmjlFDOniVn33bNjdY30_JLR_RaJ-SSbCpdroXNtwTFo9Qz5hn5LZnocbNV98Gs4zoG-Y7vNmS6t0uyjDQFIcS-IYZpShAUh9JZqLchnZFo0mpirMdVWjX3J9zbLEE7osHChKRJAklJeyFnCwtpSIpViMQN8skkGEcVAZY9J5iCryzoPQT7vW_0B7BO_drtV8tIt3mKZv_PTAu4c4NqP17SoKL62FvN3AykdLsxhVYGTYvc9T-pn_33f194jqZ0RlNgYd9--qpjinLKR2WB0mtPvQaxRs90wFLD5CXaRC9cStaFI9SVfHVKGFCsGjaDAKkok2JTN0y1OGIPeGSK2fTrKpKzcvXfnqnC2ccBep0A7tEe_b94WpxKvH1QKdIMcvlDYLBPJah9ChdSzEFqnpdiDAtSF3kXaeCsahyNy1_pQrbXyJEJIM-h4uNe6Rlz8J1kc6yrgdT_3va3i1L0u_Nj4ShD2a2NyrNHfZJQE4ZYn-tHMLfUJT3FWViaPwWQ64wCniiECAFU3_aMqvFWzsN3UsrVNQblNIAwAWnYMHbYPCSU9FTDP4jSrK4oJ_7VxYBueww==", "id": "rs_0d99e9da68392f44016a12b5a6baa08191810456240e9a1594", "summary": [ { "type": "summary_text", "text": "**Continuing the process**\n\nI feel like we need to keep going! It seems necessary to load the relevant skills again. I should definitely use the tools at my disposal. It looks like I’ll implement candidate/testbatch TDD, and maybe push things forward. The user has indicated that they want the next steps, so I'll prepare to code. I also need to check on the status as we make progress. Let’s stay focused!" } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"content-strategy\", \"growth\", \"monetization\", \"approval-workflow\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-growth-engine\\ndescription: Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, content-strategy, growth, monetization, approval-workflow]\\n---\\n\\n# Social-Media Shorts Growth Engine\\n\\nUse this skill when the user wants to build, improve, or operate an automated/semi-automated system for short-form social media content: YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest, LinkedIn video, or similar.\\n\\n## Core principle\\n\\nDo **not** treat the task as “make videos.” Treat it as a **growth engine**:\\n\\n1. Generate many sharply positioned ideas.\\n2. Score hooks and scripts before rendering.\\n3. Produce previews.\\n4. Send the user a review package.\\n5. Post only after explicit approval.\\n6. Capture analytics.\\n7. Feed performance back into the next batch.\\n\\nA technically correct content generator that produces generic content is failure with extra rendering time. Charming, but still failure.\\n\\n## Recommended workflow\\n\\n### 1. Define content pillars\\n\\nStart with 2–4 pillars, not a vague infinite topic pool. For each pillar define:\\n\\n- audience\\n- positioning\\n- tone\\n- hook patterns\\n- visual rules\\n- no-go topics\\n- platform fit\\n- monetization angle\\n\\nExample pillar structure:\\n\\n```json\\n{\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"positioning\\\": \\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n \\\"tone\\\": [\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"],\\n \\\"hook_patterns\\\": [\\\"AI is a bad god, but a good servant.\\\"],\\n \\\"visual_rules\\\": [\\\"screen-recording friendly\\\", \\\"human remains in control\\\"],\\n \\\"no_go\\\": [\\\"fake earnings\\\", \\\"overpromising automation\\\"]\\n}\\n```\\n\\n### 2. Build a testbatch before building a full automation platform\\n\\nFor a new channel or unvalidated strategy, create a small batch first:\\n\\n- 3 ideas per pillar\\n- 6 short growth candidates, usually 20–45 seconds\\n- 1–3 monetization-length candidates, usually 60+ seconds where the platform rewards length\\n- one clear hypothesis per idea\\n\\nEach idea should include:\\n\\n- id\\n- pillar\\n- title\\n- platform target\\n- duration\\n- hook\\n- script outline\\n- visual concept\\n- caption\\n- CTA\\n- quality score\\n- risk/policy notes\\n\\n### 3. Add a script quality gate\\n\\nBefore rendering, score each draft for:\\n\\n- hook strength in the first 1–2 seconds\\n- novelty / non-generic angle\\n- emotional pull\\n- clarity\\n- retention risk\\n- comment/share potential\\n- AI-slop risk\\n- policy/copyright risk\\n\\nRender only drafts above the threshold, e.g. 8/10.\\n\\n### 4. Use a human approval loop\\n\\nDefault to semi-automatic operation first. Send a Telegram review package with:\\n\\n- preview video or script draft\\n- pillar and format\\n- title\\n- duration\\n- hook\\n- caption\\n- hashtags\\n- quality score\\n- policy notes\\n- suggested platforms\\n\\nRecommended approval commands:\\n\\n- `Freigabe alle`\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nIf approval is ambiguous, do not post. Ask once in the project topic.\\n\\n### 5. Keep platform strategy separate from rendering\\n\\nRendering code should not hardcode content strategy. Keep separate config files for:\\n\\n- niches/pillars\\n- platforms\\n- script formats\\n- visual styles\\n- approval workflow\\n- analytics schema\\n\\nThis lets the same renderer support different formats and prevents prompt spaghetti. Prompt spaghetti is still spaghetti even if it is wearing a YAML hat.\\n\\n## Platform notes\\n\\n### YouTube Shorts\\n\\n- Shorts can be vertical or square and up to 3 minutes long.\\n- YouTube Partner Program Shorts route typically requires 1,000 subscribers and 10M valid public Shorts views in the last 90 days.\\n- Alternative longform YPP route: 1,000 subscribers and 4,000 valid public watch hours in the last 12 months.\\n- Shorts views in the Shorts feed do not count toward the 4,000 longform watch hours.\\n- Practical early growth tests often work best at 20–45 seconds; longer Shorts need a strong story or payoff.\\n\\n### TikTok\\n\\n- Creator Rewards availability depends on country/account eligibility.\\n- TikTok Creator Rewards generally requires a non-business account in good standing, 10,000 followers, and 100,000 authentic views in the last 30 days before joining, subject to country availability and current terms.\\n- Reward-eligible videos generally need to be at least 1 minute long and meet quality/originality criteria.\\n- This creates a strategy split:\\n - 20–45s for growth and format discovery.\\n - 60–75s for monetization candidates once a format works.\\n\\n### Instagram / Facebook Reels\\n\\nUseful for republishing 9:16 assets and testing share/save behavior. Treat direct payout as volatile; prefer indirect monetization through audience, affiliate, newsletter, or products.\\n\\n### LinkedIn / X / Pinterest\\n\\nUse selectively:\\n\\n- LinkedIn: AI/productivity/workflows.\\n- X: AI demos, build-in-public, technical positioning.\\n- Pinterest: evergreen checklists, routines, faith/life prompts.\\n\\n## Repository scaffold\\n\\nA clean shorts-engine repo can start as:\\n\\n```text\\nAutoShortsBot/\\n autoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\n configs/\\n niches/\\n platforms/\\n data/\\n drafts/\\n performance/\\n scripts/\\n tests/\\n README.md\\n .gitignore\\n pyproject.toml\\n```\\n\\nNever commit:\\n\\n- `.env`\\n- OAuth tokens\\n- API keys\\n- generated credentials\\n- rendered videos or large media outputs unless explicitly intended\\n- raw analytics exports with sensitive account data\\n\\n## Implementation quality\\n\\nWhen coding the engine, prefer TDD for deterministic parts:\\n\\n- testbatch validation\\n- duration classification\\n- review formatting\\n- analytics import\\n- approval command parsing\\n- platform metadata generation\\n- script quality gates\\n\\nA strong first implementation slice is the deterministic gate before any renderer exists:\\n\\n1. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n2. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n3. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n4. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers or publishing APIs.\\n\\nRendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should use Pillow/FFmpeg procedural motion, mechanism layers, kinetic captions, and generated metadata; preserve exact scene durations from render manifests, exclude text-only LLM refinement jobs from rendering, and keep generated local renders out of git (`data/local_motion_renders/`). NVIDIA/build.nvidia.com LLMs are optional text-refinement helpers only, not video generation.\\n\\nWhen Hermes has the `openai-codex` image-generation backend available, GPT Image 2 is a good cloud-side keyframe/thumbnail/concept generator for the pipeline: `script idea → GPT Image 2 keyframe → human review → Wan/ComfyUI I2V or local renderer → deterministic captions/overlays`. Because GPT Image 2 runs cloud-side, it does not compete with local/remote GPU VRAM used by Wan/ComfyUI video jobs. Use low/medium for iteration and high for final keyframes; still add captions, labels, numbers, UI text, and audit-sensitive facts later in the deterministic renderer rather than trusting baked image text.\\n\\nIf the user wants more realistic footage without spending money, first check whether a local GPU/ComfyUI host is available. Preferred path when reachable: `scene spec → ComfyUI/Wan API over Tailscale → imported_ai_clips//scene_XX.mp4 → local captions/mechanism layers/FFmpeg assembly`. For a 16 GB A5000 Laptop GPU, use the `comfyui` skill reference `wan-video-tailscale-a5000.md`: Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a small smoke-test ladder before quality probes. If no local GPU path is available or quality is insufficient, use the hybrid free-tier handoff rather than pretending local procedural graphics are realistic: `free_production_jobs → free_ai_video_handoff → manual browser generation in free-tier tools (PixVerse/Pika/CapCut/Hailuo only with existing free credits) → import clips under data/imported_ai_clips/ → local captions/mechanism layers/FFmpeg assembly`. Guardrails: no paid top-ups, no subscriptions, avoid Pro templates, no baked captions/logos/watermarks where possible, and keep imported clips out of git.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should produce moving mechanism/caption previews, not final static cards. When the user has a capable local/remote GPU (e.g. RTX A5000 16 GB via Tailscale), promote the path to `ComfyUI/Wan or LTX local clip generation → imported_ai_clips → hybrid local renderer → FFmpeg`, with free web providers (PixVerse/Pika/CapCut) only as fallback. External free-tier AI video tools can still be used manually for realistic b-roll, but keep paid top-ups/subscriptions disabled and store downloaded clips under `data/imported_ai_clips//scene_XX.mp4`. Generate provider handoff packets for manual browser generation and imported-clip assembly plans so missing clips fall back to local placeholders.\\n\\n## Pitfalls\\n\\n- **Generic advice content:** “4 tips to be healthier/productive” is usually too weak unless the hook, identity, or story is sharp.\\n- **AI-image slop:** avoid generated images containing fake UI text, gibberish signs, distorted hands/faces, or abstract generic graphs. These damage trust fast.\\n- **Too much automation too soon:** validate scripts and formats before building posting APIs.\\n- **No analytics loop:** producing content without tracking retention, likes, shares, comments, and follows is blind output.\\n- **Mixed language drift:** decide language per channel or per pillar before rendering. Mixed English/German drafts are fine for ideation, not final voiceover unless intentional.\\n\\n## References\\n\\n- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n- `references/approval-first-bot-foundation.md` — implementation pattern for the first safe bot slice: explicit approval parser, quality gate, Telegram review package, and TDD test shape.\\n\", \"path\": \"social-media/social-media-shorts-growth-engine/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine\", \"linked_files\": {\"references\": [\"references/autoshortsbot-case-study.md\", \"references/free-ai-video-workflow.md\", \"references/approval-first-bot-foundation.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\"}", "tool_call_id": "call_MpL56xnxgUnoxYLuDBhRbj2l" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": 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\"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_4OUCn8rwpW6PyEQvTDF7DABx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_7oPsuFnJ0GwtKVSQOoS0CMKP", "call_id": "call_7oPsuFnJ0GwtKVSQOoS0CMKP", "response_item_id": "fc_0d99e9da68392f44016a12b5b92fb0819185b0d57ae5db370b", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ideas.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.ideas.candidate import VideoCandidate, validate_candidate\\nfrom autoshorts.ideas.testbatch import TestBatch, renderable_candidates, validate_testbatch\\nfrom autoshorts.review.package import ReviewPackage\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\n\\ndef _good_score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef _weak_score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=5,\\n novelty=5,\\n clarity=6,\\n retention=5,\\n ai_slop_risk=8,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef _candidate(**overrides) -> VideoCandidate:\\n data = {\\n \\\"candidate_id\\\": \\\"vid-001\\\",\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"title\\\": \\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n \\\"duration_seconds\\\": 34,\\n \\\"hook\\\": \\\"AI sollte dir dienen, nicht dich führen.\\\",\\n \\\"script_outline\\\": (\\n \\\"Scene 1: AI chaos. Scene 2: controlled system. Scene 3: human approval gate.\\\"\\n ),\\n \\\"visual_concept\\\": \\\"Cinematic desk setup, phone automations, calm human-in-control framing.\\\",\\n \\\"caption\\\": \\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n \\\"cta\\\": \\\"Follow for practical AI life systems.\\\",\\n \\\"hashtags\\\": (\\\"#AI\\\", \\\"#Productivity\\\"),\\n \\\"quality_score\\\": _good_score(),\\n \\\"policy_notes\\\": (\\\"No fake earnings claims\\\",),\\n \\\"hypothesis\\\": \\\"Values-first AI positioning gets more saves than hype content.\\\",\\n }\\n data.update(overrides)\\n return VideoCandidate(**data)\\n\\n\\ndef test_candidate_validation_accepts_complete_growth_candidate():\\n candidate = _candidate()\\n\\n result = validate_candidate(candidate)\\n\\n assert result.is_valid is True\\n assert result.errors == ()\\n\\n\\ndef test_candidate_validation_rejects_missing_hook_and_bad_duration():\\n candidate = _candidate(hook=\\\"\\\", duration_seconds=4)\\n\\n result = validate_candidate(candidate)\\n\\n assert result.is_valid is False\\n assert \\\"hook is required\\\" in result.errors\\n assert \\\"duration_seconds must be between 10 and 180\\\" in result.errors\\n\\n\\ndef test_candidate_can_be_converted_to_review_package():\\n candidate = _candidate()\\n\\n package = candidate.to_review_package(preview_path=\\\"data/local_motion_renders/vid-001.mp4\\\")\\n\\n assert isinstance(package, ReviewPackage)\\n assert package.candidate_id == \\\"vid-001\\\"\\n assert package.title == candidate.title\\n assert package.script_summary == candidate.script_outline\\n assert package.platforms == (\\\"youtube\\\", \\\"tiktok\\\")\\n assert package.preview_path == \\\"data/local_motion_renders/vid-001.mp4\\\"\\n\\n\\ndef test_testbatch_validation_rejects_duplicate_candidate_ids():\\n batch = TestBatch(\\n batch_id=\\\"batch-001\\\",\\n candidates=(\\n _candidate(candidate_id=\\\"dup\\\"),\\n _candidate(candidate_id=\\\"dup\\\", title=\\\"Another title\\\"),\\n ),\\n )\\n\\n result = validate_testbatch(batch)\\n\\n assert result.is_valid is False\\n assert \\\"candidate ids must be unique\\\" in result.errors\\n\\n\\ndef test_testbatch_validation_requires_at_least_one_candidate():\\n batch = TestBatch(batch_id=\\\"empty\\\", candidates=())\\n\\n result = validate_testbatch(batch)\\n\\n assert result.is_valid is False\\n assert \\\"at least one candidate is required\\\" in result.errors\\n\\n\\ndef test_renderable_candidates_filters_by_candidate_validity_and_quality_gate():\\n valid = _candidate(candidate_id=\\\"valid\\\")\\n weak = _candidate(candidate_id=\\\"weak\\\", quality_score=_weak_score())\\n invalid = _candidate(candidate_id=\\\"invalid\\\", hook=\\\"\\\")\\n batch = TestBatch(batch_id=\\\"batch-001\\\", candidates=(valid, weak, invalid))\\n\\n selected = renderable_candidates(batch)\\n\\n assert selected == (valid,)\\n\\n\\ndef test_candidate_validation_rejects_unknown_platforms():\\n candidate = _candidate(platforms=(\\\"youtube\\\", \\\"linkedin\\\"))\\n\\n result = validate_candidate(candidate)\\n\\n assert result.is_valid is False\\n assert \\\"unsupported platform: linkedin\\\" in result.errors\\n\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 4110, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [3:6] Import \\\"autoshorts.ideas.candidate\\\" could not be resolved [reportMissingImports] (Pyright)\\nERROR [4:6] Import \\\"autoshorts.ideas.testbatch\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_7oPsuFnJ0GwtKVSQOoS0CMKP" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_FsSB9LpLENXTUdzdJN91XXDZ", "call_id": "call_FsSB9LpLENXTUdzdJN91XXDZ", "response_item_id": "fc_0d99e9da68392f44016a12b5d41708819191f517704e38017d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ideas.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_____________________ ERROR collecting tests/test_ideas.py _____________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ideas.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_ideas.py:3: in \\n from autoshorts.ideas.candidate import VideoCandidate, validate_candidate\\nE ModuleNotFoundError: No module named 'autoshorts.ideas'\\n=========================== short test summary info ============================\\nERROR tests/test_ideas.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.10s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_FsSB9LpLENXTUdzdJN91XXDZ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_QGgpeNioJQnvLa52xJCllvVp", "call_id": "call_QGgpeNioJQnvLa52xJCllvVp", "response_item_id": "fc_0d99e9da68392f44016a12b5e30f0c8191aba74cca9de9cdda", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests für VideoCandidate/TestBatch schreiben und RED verifizieren\",\"id\":\"red\",\"status\":\"completed\"},{\"content\":\"Ideas-Module minimal implementieren\",\"id\":\"green\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Tests für VideoCandidate/TestBatch schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Ideas-Module minimal implementieren\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Alle Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 1, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_QGgpeNioJQnvLa52xJCllvVp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_fIXk3ZOnRYPP1Fh73cMYctRk", "call_id": "call_fIXk3ZOnRYPP1Fh73cMYctRk", "response_item_id": "fc_0d99e9da68392f44016a12b5f3f4048191bc59153d84f48e95", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/__init__.py\",\"content\":\"\\\"\\\"\\\"Idea and testbatch domain models.\\\"\\\"\\\"\\n\"}" } }, { "id": "call_L1IYxBZp6dBQDYpblLmT9c2h", "call_id": "call_L1IYxBZp6dBQDYpblLmT9c2h", "response_item_id": "fc_0d99e9da68392f44016a12b5f3f4cc8191afb92ba102496e36", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/candidate.py\",\"content\":\"\\\"\\\"\\\"Video candidate domain model and validation.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.review.package import ReviewPackage\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\nSUPPORTED_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n\\n\\n@dataclass(frozen=True)\\nclass ValidationResult:\\n is_valid: bool\\n errors: tuple[str, ...] = ()\\n\\n\\n@dataclass(frozen=True)\\nclass VideoCandidate:\\n candidate_id: str\\n pillar: str\\n title: str\\n platforms: tuple[str, ...]\\n duration_seconds: int\\n hook: str\\n script_outline: str\\n visual_concept: str\\n caption: str\\n cta: str\\n hashtags: tuple[str, ...]\\n quality_score: ScriptQualityScore\\n policy_notes: tuple[str, ...]\\n hypothesis: str\\n\\n def to_review_package(self, preview_path: str | None = None) -> ReviewPackage:\\n return ReviewPackage(\\n candidate_id=self.candidate_id,\\n title=self.title,\\n pillar=self.pillar,\\n duration_seconds=self.duration_seconds,\\n hook=self.hook,\\n script_summary=self.script_outline,\\n visual_concept=self.visual_concept,\\n caption=self.caption,\\n hashtags=self.hashtags,\\n platforms=self.platforms,\\n quality_score=self.quality_score,\\n policy_notes=self.policy_notes,\\n preview_path=preview_path,\\n )\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef validate_candidate(candidate: VideoCandidate) -> ValidationResult:\\n errors: list[str] = []\\n\\n required_fields = {\\n \\\"candidate_id\\\": candidate.candidate_id,\\n \\\"pillar\\\": candidate.pillar,\\n \\\"title\\\": candidate.title,\\n \\\"hook\\\": candidate.hook,\\n \\\"script_outline\\\": candidate.script_outline,\\n \\\"visual_concept\\\": candidate.visual_concept,\\n \\\"caption\\\": candidate.caption,\\n \\\"cta\\\": candidate.cta,\\n \\\"hypothesis\\\": candidate.hypothesis,\\n }\\n for field_name, value in required_fields.items():\\n if _is_blank(value):\\n errors.append(f\\\"{field_name} is required\\\")\\n\\n if not 10 <= candidate.duration_seconds <= 180:\\n errors.append(\\\"duration_seconds must be between 10 and 180\\\")\\n\\n if not candidate.platforms:\\n errors.append(\\\"at least one platform is required\\\")\\n for platform in candidate.platforms:\\n if platform not in SUPPORTED_PLATFORMS:\\n errors.append(f\\\"unsupported platform: {platform}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 40, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_fIXk3ZOnRYPP1Fh73cMYctRk" }, { "role": "tool", "content": "{\"bytes_written\": 2557, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_L1IYxBZp6dBQDYpblLmT9c2h" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_wFpMHAno0aqMiqHXqWP0mR8J", "call_id": "call_wFpMHAno0aqMiqHXqWP0mR8J", "response_item_id": "fc_0d99e9da68392f44016a12b5f7ef608191b532b291b16cc965", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Testbatch model and deterministic filtering helpers.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCandidate, validate_candidate\\nfrom autoshorts.scripting.quality import should_render\\n\\n\\n@dataclass(frozen=True)\\nclass TestBatch:\\n batch_id: str\\n candidates: tuple[VideoCandidate, ...]\\n\\n\\ndef validate_testbatch(batch: TestBatch) -> ValidationResult:\\n errors: list[str] = []\\n\\n if not (batch.batch_id or \\\"\\\").strip():\\n errors.append(\\\"batch_id is required\\\")\\n if not batch.candidates:\\n errors.append(\\\"at least one candidate is required\\\")\\n\\n ids = [candidate.candidate_id for candidate in batch.candidates]\\n if len(ids) != len(set(ids)):\\n errors.append(\\\"candidate ids must be unique\\\")\\n\\n for candidate in batch.candidates:\\n result = validate_candidate(candidate)\\n errors.extend(f\\\"{candidate.candidate_id}: {error}\\\" for error in result.errors)\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef renderable_candidates(batch: TestBatch) -> tuple[VideoCandidate, ...]:\\n \\\"\\\"\\\"Return valid candidates that pass the quality/render gate.\\\"\\\"\\\"\\n\\n selected: list[VideoCandidate] = []\\n for candidate in batch.candidates:\\n if validate_candidate(candidate).is_valid and should_render(candidate.quality_score):\\n selected.append(candidate)\\n return tuple(selected)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 1442, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_wFpMHAno0aqMiqHXqWP0mR8J" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_mFVa1cdK3gmyvFUD5WBbFEoj", "call_id": "call_mFVa1cdK3gmyvFUD5WBbFEoj", "response_item_id": "fc_0d99e9da68392f44016a12b605b180819185ba583fa7ade2cc", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ideas.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"....... [100%]\\n=============================== warnings summary ===============================\\nautoshorts/ideas/testbatch.py:11\\n /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py:11: PytestCollectionWarning: cannot collect test class 'TestBatch' because it has a __init__ constructor (from: tests/test_ideas.py)\\n @dataclass(frozen=True)\\n\\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\\n7 passed, 1 warning in 0.04s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_mFVa1cdK3gmyvFUD5WBbFEoj" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_GROulJvw0UHGtfNstzyoXAmp", "call_id": "call_GROulJvw0UHGtfNstzyoXAmp", "response_item_id": "fc_0d99e9da68392f44016a12b61daf3c819191b42021e6c55dfa", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\",\"old_string\":\"@dataclass(frozen=True)\\nclass TestBatch:\\n batch_id: str\\n candidates: tuple[VideoCandidate, ...]\\n\",\"new_string\":\"@dataclass(frozen=True)\\nclass TestBatch:\\n batch_id: str\\n candidates: tuple[VideoCandidate, ...]\\n\\n # Prevent pytest from trying to collect this domain model as a test class.\\n __test__ = False\\n\"}" } } ], "finish_reason": "tool_calls", "reasoning": "**Fixing class warning**\n\nI need to address a warning regarding the class named TestBatch. I have two options: I can either set `__test__` to False or rename the class. Since tests are importing TestBatch, adding `__test__` set to False immediately after the class definition might be the most straightforward solution. I’m thinking this aligns well with a Test-Driven Development (TDD) approach and will help keep things clean. Let’s refactor and see how it goes!", "reasoning_content": "**Fixing class warning**\n\nI need to address a warning regarding the class named TestBatch. I have two options: I can either set `__test__` to False or rename the class. Since tests are importing TestBatch, adding `__test__` set to False immediately after the class definition might be the most straightforward solution. I’m thinking this aligns well with a Test-Driven Development (TDD) approach and will help keep things clean. Let’s refactor and see how it goes!", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErYdPQdnKB5Ns5MHhV6dU5P2LHPPNMo9S62nepQfrLioU0qCj4Sp8PGixBN7DvfF-wdaUB81iAykJPM48SwnFrWQGxSA1R2jwpJnwN0Ntr93kWMRCyF98LIxdecKFzB5xiEXUZaNSwftzkfni1cCy52dX4rqFaVnjOi4WrJq90aOtK35Des_HQ4iBOIzvLxFH1Qvz_5DjXucpVjDM_8Bn5xn0UzuQpWHqwyG9eKUQS0vwhMZSjNSuSy3C7-urW63KcqneGToJik8AJ14lqnqqv8Oys8cmZqBlMrvlnfawEk9D1YdUgBVDhrwtPVzN__7edK-03cA5dL6g9DCPQv0QQhu_NRLiIhUkctWDXbsSp_2CzeZD3F6ScIJ85Fw7Jg3aXz6S9cn8Y2Ndmv-4xLiIN3MEHuGt6MEVaRDENO0NPddMMKxN8flk0hwWghDfpP_qxatuWuK9cx_-Wqv74eC3Hwg3zOV2QJDZWDGkI9kb7WvMPHaKUDMooZzJAH4nKyxlLCakNTaszWiJ-Q8wIlgOICu__-HZE282rf3A7B9VR3nWGH0dsXgp1PeJ8Aia300DcEVWit5tH8xWrc9Kboa1fnSy9qWX-vaotJUsKf5FDZ1KXh92moqXuhvevqvBpD798hcz4v-yaTxnKt48Xp5IdhGEx6AjhDfxutylfloChv2N_aNRZKp8IZ5c6CfSq19BEReBO7qBxkDHhenOY-TeUJHKHoBJ6RDe8RkBcAVR2547QbyQZ5nYWvD0LEp7xiYfdrpAXapEk4JlE21pV90evVXOtnD-JaMOpVrFtD7x65IOjowel2wiOs9hyuulqMI1bsykvZyS0JTeTQpqO82B5m5mmdpbq1uscZWxSWVABNdpCc6gh2ir2_10fuYpM-hmy7UHRoIPXL1O8WE6fWW2Y_ywy_nUXJXVVokznwFHmH3Tvpw-IYncPLwAOXadke3YmO_yx8opbb36ygKqNcT2sn9kGKFQwL2yyOloRJxqkIFf979MJQSz61hCfZigmeCbkgxyq4P-VPbioKx6JPt3xJ1UY5ildFUUzIji1T2i6t9LWS9mI4e3D2-4ERgjpr13Zf8dme151Ffv6DofQzwnq3F_PPGDmAT3RtqLg4wA7l8nfpIOEFxJHmQLWfQJi__tN6o", "id": "rs_0d99e9da68392f44016a12b60f5fe881918afb0c38ee10fabd", "summary": [ { "type": "summary_text", "text": "**Fixing class warning**\n\nI need to address a warning regarding the class named TestBatch. I have two options: I can either set `__test__` to False or rename the class. Since tests are importing TestBatch, adding `__test__` set to False immediately after the class definition might be the most straightforward solution. I’m thinking this aligns well with a Test-Driven Development (TDD) approach and will help keep things clean. Let’s refactor and see how it goes!" } ] } ] }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\\n@@ -12,6 +12,9 @@\\n class TestBatch:\\n batch_id: str\\n candidates: tuple[VideoCandidate, ...]\\n+\\n+ # Prevent pytest from trying to collect this domain model as a test class.\\n+ __test__ = False\\n \\n \\n def validate_testbatch(batch: TestBatch) -> ValidationResult:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/testbatch.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_GROulJvw0UHGtfNstzyoXAmp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_jGaYP3xmxLNcexcD3dtaNWJc", "call_id": "call_jGaYP3xmxLNcexcD3dtaNWJc", "response_item_id": "fc_0d99e9da68392f44016a12b625337c81919a9b50fe23109ac1", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ideas.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"....... [100%]\\n7 passed in 0.04s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_jGaYP3xmxLNcexcD3dtaNWJc" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_dS46UrbHu1X4pf8cUUF2CtE1", "call_id": "call_dS46UrbHu1X4pf8cUUF2CtE1", "response_item_id": "fc_0d99e9da68392f44016a12b62947908191ad64e24a3d7aa997", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Ideas-Module minimal implementieren\",\"id\":\"green\",\"status\":\"completed\"},{\"content\":\"Alle Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Tests für VideoCandidate/TestBatch schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Ideas-Module minimal implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Alle Tests laufen lassen, README aktualisieren, committen\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}", "tool_call_id": "call_dS46UrbHu1X4pf8cUUF2CtE1" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_lwEPSwChxTj9wGk0iboZM3CE", "call_id": "call_lwEPSwChxTj9wGk0iboZM3CE", "response_item_id": "fc_0d99e9da68392f44016a12b631fc148191a6c1a012d70dbcfb", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n- Tests for approval parsing, review formatting, and script quality gating.\",\"new_string\":\"- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n- Tests for idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -8,10 +8,12 @@\\n \\n Implemented foundation:\\n \\n+- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n+- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n-- Tests for approval parsing, review formatting, and script quality gating.\\n+- Tests for idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n \\n ## Human approval commands\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_lwEPSwChxTj9wGk0iboZM3CE" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ffm6KGmTfGXlNRUNPmwt9fSi", "call_id": "call_ffm6KGmTfGXlNRUNPmwt9fSi", "response_item_id": "fc_0d99e9da68392f44016a12b639d6d48191bd5048f5ab588e4c", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n\\n## Idea candidates and testbatches\\n\\n`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n\\n- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n- hook, script outline, visual concept, caption, CTA, and hashtags;\\n- quality score and policy notes.\\n\\n`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n\\n`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n\\n## Development\",\"old_string\":\"A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n\\n## Development\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -50,6 +50,18 @@\\n \\n A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n \\n+## Idea candidates and testbatches\\n+\\n+`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n+\\n+- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n+- hook, script outline, visual concept, caption, CTA, and hashtags;\\n+- quality score and policy notes.\\n+\\n+`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n+\\n+`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n+\\n ## Development\\n \\n Run tests:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_ffm6KGmTfGXlNRUNPmwt9fSi" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_ndX9kkUB9Dh1oIStdAT6ZtrI", "call_id": "call_ndX9kkUB9Dh1oIStdAT6ZtrI", "response_item_id": "fc_0d99e9da68392f44016a12b642566081918b2aab7042376fbb", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"................... [100%]\\n19 passed in 0.05s\\n M README.md\\n?? autoshorts/ideas/\\n?? tests/test_ideas.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ndX9kkUB9Dh1oIStdAT6ZtrI" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_6ySJu590UiXszlq255pSnybN", "call_id": "call_6ySJu590UiXszlq255pSnybN", "response_item_id": "fc_0d99e9da68392f44016a12b6472b388191afd0e829f36a7dd3", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/ideas tests/test_ideas.py && git commit -m \\\"feat: add idea candidates and testbatches\\\" && git status --short --branch && git log --oneline -4\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"[feat/core-approval-quality-gates 49d6daf] feat: add idea candidates and testbatches\\n 5 files changed, 274 insertions(+), 1 deletion(-)\\n create mode 100644 autoshorts/ideas/__init__.py\\n create mode 100644 autoshorts/ideas/candidate.py\\n create mode 100644 autoshorts/ideas/testbatch.py\\n create mode 100644 tests/test_ideas.py\\n## feat/core-approval-quality-gates\\n49d6daf feat: add idea candidates and testbatches\\n55b17fa feat: add telegram review package\\n3555ec2 feat: add approval parser and quality gate\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_6ySJu590UiXszlq255pSnybN" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_a4ve1O5JNpjgnv8nTHw2oksF", "call_id": "call_a4ve1O5JNpjgnv8nTHw2oksF", "response_item_id": "fc_0d99e9da68392f44016a12b64cc3cc8191b899f9107821d3d4", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"................... [100%]\\n19 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_a4ve1O5JNpjgnv8nTHw2oksF" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rvWbSNRZ174SLzojwDdigiXM", "call_id": "call_rvWbSNRZ174SLzojwDdigiXM", "response_item_id": "fc_0d99e9da68392f44016a12b651840c8191b33853c80cda66d5", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Alle Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Tests für VideoCandidate/TestBatch schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Ideas-Module minimal implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Alle Tests laufen lassen, README aktualisieren, committen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_rvWbSNRZ174SLzojwDdigiXM" }, { "role": "assistant", "content": "Erledigt, Sir. Nächster Baustein ist drin: **Idea Candidates + Testbatches**.\n\n## Neu gebaut\n\nNeue Module:\n\n```text\nautoshorts/ideas/candidate.py\nautoshorts/ideas/testbatch.py\n```\n\nNeue Tests:\n\n```text\ntests/test_ideas.py\n```\n\n## Was jetzt funktioniert\n\n### 1. `VideoCandidate`\n\nDer Bot hat jetzt eine strukturierte Einheit für einen Video-Kandidaten.\n\nEin Kandidat enthält:\n\n- `candidate_id`\n- `pillar`\n- `title`\n- `platforms`\n- `duration_seconds`\n- `hook`\n- `script_outline`\n- `visual_concept`\n- `caption`\n- `cta`\n- `hashtags`\n- `quality_score`\n- `policy_notes`\n- `hypothesis`\n\nDamit ist Schluss mit losem Prompt-Gekritzel. Jeder Kandidat ist jetzt ein prüfbares Objekt.\n\n### 2. Kandidaten-Validierung\n\n`validate_candidate()` prüft u.a.:\n\n- Pflichtfelder vorhanden\n- Hook vorhanden\n- Plattformen unterstützt\n- Dauer zwischen `10` und `180` Sekunden\n- mindestens eine Zielplattform\n\nUnterstützte Plattformen aktuell:\n\n```text\nyoutube\ntiktok\ninstagram\n```\n\nNicht unterstützte Plattformen wie `linkedin` werden sauber abgelehnt.\n\n### 3. Umwandlung in Review Package\n\nEin `VideoCandidate` kann jetzt direkt in ein `ReviewPackage` umgewandelt werden:\n\n```python\ncandidate.to_review_package(preview_path=\"data/local_motion_renders/vid-001.mp4\")\n```\n\nDamit ist die Pipeline jetzt konkret:\n\n```text\nVideoCandidate\n→ validate_candidate()\n→ quality gate\n→ ReviewPackage\n→ Telegram review\n→ explicit approval\n```\n\n### 4. `TestBatch`\n\nEin `TestBatch` gruppiert mehrere Video-Kandidaten für einen Experimentlauf.\n\n`validate_testbatch()` prüft:\n\n- Batch darf nicht leer sein\n- Kandidaten-IDs müssen eindeutig sein\n- alle Kandidaten müssen valide sein\n\n### 5. `renderable_candidates()`\n\nDiese Funktion filtert automatisch:\n\n```text\nnur valide Kandidaten\n+\nnur Kandidaten, die den Quality Gate schaffen\n```\n\nAlso keine Renderzeit für schwache Ideen. Eine kleine Revolution gegen digitale Zeitverschwendung.\n\n## TDD-Verifikation\n\nIch habe zuerst Tests geschrieben und RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.ideas'\n```\n\nDann minimal implementiert und grün gezogen.\n\n## Tests\n\nAktueller Stand:\n\n```text\n19 passed in 0.05s\n```\n\nKeine Warnings.\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\n49d6daf feat: add idea candidates and testbatches\n```\n\nAktueller Verlauf:\n\n```text\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Stand der Bot-Pipeline\n\nWir haben jetzt solide:\n\n```text\nIdea/TestBatch\n→ Candidate Validation\n→ Quality Gate\n→ Review Package\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes bauen:\n\n```text\nautoshorts/config/pillars.py\n```\n\nZiel:\n\n- Content Pillars definieren\n- AI/Life-Systems als erster Pillar\n- No-Go-Regeln\n- Hook Patterns\n- Visual Rules\n- Platform Fit\n- Validierung, dass jeder Kandidat zu einem erlaubten Pillar gehört\n\nDann wird aus dem Bot nicht nur ein Generator, sondern ein **positionierter Content Engine**. Genau da trennt sich “mach Videos” von “baue Wachstum”.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Erledigt, Sir. Nächster Baustein ist drin: **Idea Candidates + Testbatches**.\n\n## Neu gebaut\n\nNeue Module:\n\n```text\nautoshorts/ideas/candidate.py\nautoshorts/ideas/testbatch.py\n```\n\nNeue Tests:\n\n```text\ntests/test_ideas.py\n```\n\n## Was jetzt funktioniert\n\n### 1. `VideoCandidate`\n\nDer Bot hat jetzt eine strukturierte Einheit für einen Video-Kandidaten.\n\nEin Kandidat enthält:\n\n- `candidate_id`\n- `pillar`\n- `title`\n- `platforms`\n- `duration_seconds`\n- `hook`\n- `script_outline`\n- `visual_concept`\n- `caption`\n- `cta`\n- `hashtags`\n- `quality_score`\n- `policy_notes`\n- `hypothesis`\n\nDamit ist Schluss mit losem Prompt-Gekritzel. Jeder Kandidat ist jetzt ein prüfbares Objekt.\n\n### 2. Kandidaten-Validierung\n\n`validate_candidate()` prüft u.a.:\n\n- Pflichtfelder vorhanden\n- Hook vorhanden\n- Plattformen unterstützt\n- Dauer zwischen `10` und `180` Sekunden\n- mindestens eine Zielplattform\n\nUnterstützte Plattformen aktuell:\n\n```text\nyoutube\ntiktok\ninstagram\n```\n\nNicht unterstützte Plattformen wie `linkedin` werden sauber abgelehnt.\n\n### 3. Umwandlung in Review Package\n\nEin `VideoCandidate` kann jetzt direkt in ein `ReviewPackage` umgewandelt werden:\n\n```python\ncandidate.to_review_package(preview_path=\"data/local_motion_renders/vid-001.mp4\")\n```\n\nDamit ist die Pipeline jetzt konkret:\n\n```text\nVideoCandidate\n→ validate_candidate()\n→ quality gate\n→ ReviewPackage\n→ Telegram review\n→ explicit approval\n```\n\n### 4. `TestBatch`\n\nEin `TestBatch` gruppiert mehrere Video-Kandidaten für einen Experimentlauf.\n\n`validate_testbatch()` prüft:\n\n- Batch darf nicht leer sein\n- Kandidaten-IDs müssen eindeutig sein\n- alle Kandidaten müssen valide sein\n\n### 5. `renderable_candidates()`\n\nDiese Funktion filtert automatisch:\n\n```text\nnur valide Kandidaten\n+\nnur Kandidaten, die den Quality Gate schaffen\n```\n\nAlso keine Renderzeit für schwache Ideen. Eine kleine Revolution gegen digitale Zeitverschwendung.\n\n## TDD-Verifikation\n\nIch habe zuerst Tests geschrieben und RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.ideas'\n```\n\nDann minimal implementiert und grün gezogen.\n\n## Tests\n\nAktueller Stand:\n\n```text\n19 passed in 0.05s\n```\n\nKeine Warnings.\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\n49d6daf feat: add idea candidates and testbatches\n```\n\nAktueller Verlauf:\n\n```text\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Stand der Bot-Pipeline\n\nWir haben jetzt solide:\n\n```text\nIdea/TestBatch\n→ Candidate Validation\n→ Quality Gate\n→ Review Package\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes bauen:\n\n```text\nautoshorts/config/pillars.py\n```\n\nZiel:\n\n- Content Pillars definieren\n- AI/Life-Systems als erster Pillar\n- No-Go-Regeln\n- Hook Patterns\n- Visual Rules\n- Platform Fit\n- Validierung, dass jeder Kandidat zu einem erlaubten Pillar gehört\n\nDann wird aus dem Bot nicht nur ein Generator, sondern ein **positionierter Content Engine**. Genau da trennt sich “mach Videos” von “baue Wachstum”." } ], "id": "msg_0d99e9da68392f44016a12b654fe108191a2b3ec489ff808c6", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] ok, mach weiter." }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_xfyWRVcdmBAidlsfN7eg7s8q", "call_id": "call_xfyWRVcdmBAidlsfN7eg7s8q", "response_item_id": "fc_0d99e9da68392f44016a12b6bec87c8191bcd521120bde52d2", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_zChOeB6gGbAFgmi8E7LLOXLp", "call_id": "call_zChOeB6gGbAFgmi8E7LLOXLp", "response_item_id": "fc_0d99e9da68392f44016a12b6bec88c8191930982031bbc7990", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ], "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqEra-ceeaSPIp6nHOGDdXAVy6WoqVxPNu9LUDmUFLxNkMoC_e52UNFPCjTOpTceGX45s3pN8U3IjbsdxX1E3jmumjdi0tiT8BEJ-V9smSQaZ77m97epTiVFSmuY8tTcg7jBYEaT-gq5nwPltG4QbIyhYni5yqwzajUNacC9tyeYkggGtBCpF_P3dNZrIdW_KeEdgIqj9D4Pso2pzUC6J6Sq2S-8Uxod2mLCS1LQPdlMOlcSyHWzYRO7DWeW_KRPOhJBM-EovdZv8zItdgFEoXO1jf8Ga5D98u1CdJhRHAa6Zhl4WPM0vSEEeup6JE-O1-WCWJwwz_qE5TctMaKRSSx6NOfNBN0Vyxq05sqC6cu8pE3BOEP6KQFHz2pNPCJTUbPC6mWu8eIDT_7PNWcD7Evg2CeIrv1TfNdzK3H404LPriiGjHnnDGbV8EaS1-bhj0CCHWCf-3ZAgYi-TZc58ccPP__eoYxp6etml5eBcI1WvXt-BAtYb0yGNW_uSP-88PXYgM3MLAWNu9rCAmjnV1TgtJZ9Xhx2GsOek9A89ww8rt3WSk0iJVs1iXC8bHZAhN-meacnbStBsB1ueEGOjl3O3L2vj-LfzaEom2pMmwM5PtZG3lE9w8W6rX2jRTqS44I-AdnoIkOj8v47bnX81EkYAP2bPUqfxxkVCDHrCet4TT_uXEUJijVqAhu-Jx3G67kCsQ7PbMG4yVOI3TL_Mj5bp2AhxnkG48xWxjhpVT4KJ1WowBfAp_pEd-1O57puPielVSsYaSCVY8--hnOZkgp2SbJw84QvuFqF3ZLnUHnbkz72VCchnMzxwLKteuPxgnaqfgF941e8M_vRkGqcpIl9lknGW40puEu121FTzrTAstPLkcVreB4e27Y8QfS9ViX1vKEPTHsHljeL2kC7mmOJVWY1y3Rw6OdFZMxF_LVa7SHuVcn6mcfgBPjQnzAFI9RWOYa5jKdi_ycLvGwW0B7YpP9ShebQKJHn5jUamnlHwdXvYCSxNqOwGmAk5DPb0p25h83IUpNrQmf1rGQvcq3ouU5McAi67d9h_Z-Pb8fUM3Ihgtz0vO9JCWG4CPkLThNzlu", "id": "rs_0d99e9da68392f44016a12b6bb6c208191bc797ba61a20c16d", "summary": [] } ] }, { "role": "tool", "content": "{\"success\": true, \"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"content-strategy\", \"growth\", \"monetization\", \"approval-workflow\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-growth-engine\\ndescription: Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, content-strategy, growth, monetization, approval-workflow]\\n---\\n\\n# Social-Media Shorts Growth Engine\\n\\nUse this skill when the user wants to build, improve, or operate an automated/semi-automated system for short-form social media content: YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest, LinkedIn video, or similar.\\n\\n## Core principle\\n\\nDo **not** treat the task as “make videos.” Treat it as a **growth engine**:\\n\\n1. Generate many sharply positioned ideas.\\n2. Score hooks and scripts before rendering.\\n3. Produce previews.\\n4. Send the user a review package.\\n5. Post only after explicit approval.\\n6. Capture analytics.\\n7. Feed performance back into the next batch.\\n\\nA technically correct content generator that produces generic content is failure with extra rendering time. Charming, but still failure.\\n\\n## Recommended workflow\\n\\n### 1. Define content pillars\\n\\nStart with 2–4 pillars, not a vague infinite topic pool. For each pillar define:\\n\\n- audience\\n- positioning\\n- tone\\n- hook patterns\\n- visual rules\\n- no-go topics\\n- platform fit\\n- monetization angle\\n\\nExample pillar structure:\\n\\n```json\\n{\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"positioning\\\": \\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n \\\"tone\\\": [\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"],\\n \\\"hook_patterns\\\": [\\\"AI is a bad god, but a good servant.\\\"],\\n \\\"visual_rules\\\": [\\\"screen-recording friendly\\\", \\\"human remains in control\\\"],\\n \\\"no_go\\\": [\\\"fake earnings\\\", \\\"overpromising automation\\\"]\\n}\\n```\\n\\n### 2. Build a testbatch before building a full automation platform\\n\\nFor a new channel or unvalidated strategy, create a small batch first:\\n\\n- 3 ideas per pillar\\n- 6 short growth candidates, usually 20–45 seconds\\n- 1–3 monetization-length candidates, usually 60+ seconds where the platform rewards length\\n- one clear hypothesis per idea\\n\\nEach idea should include:\\n\\n- id\\n- pillar\\n- title\\n- platform target\\n- duration\\n- hook\\n- script outline\\n- visual concept\\n- caption\\n- CTA\\n- quality score\\n- risk/policy notes\\n\\n### 3. Add a script quality gate\\n\\nBefore rendering, score each draft for:\\n\\n- hook strength in the first 1–2 seconds\\n- novelty / non-generic angle\\n- emotional pull\\n- clarity\\n- retention risk\\n- comment/share potential\\n- AI-slop risk\\n- policy/copyright risk\\n\\nRender only drafts above the threshold, e.g. 8/10.\\n\\n### 4. Use a human approval loop\\n\\nDefault to semi-automatic operation first. Send a Telegram review package with:\\n\\n- preview video or script draft\\n- pillar and format\\n- title\\n- duration\\n- hook\\n- caption\\n- hashtags\\n- quality score\\n- policy notes\\n- suggested platforms\\n\\nRecommended approval commands:\\n\\n- `Freigabe alle`\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nIf approval is ambiguous, do not post. Ask once in the project topic.\\n\\n### 5. Keep platform strategy separate from rendering\\n\\nRendering code should not hardcode content strategy. Keep separate config files for:\\n\\n- niches/pillars\\n- platforms\\n- script formats\\n- visual styles\\n- approval workflow\\n- analytics schema\\n\\nThis lets the same renderer support different formats and prevents prompt spaghetti. Prompt spaghetti is still spaghetti even if it is wearing a YAML hat.\\n\\n## Platform notes\\n\\n### YouTube Shorts\\n\\n- Shorts can be vertical or square and up to 3 minutes long.\\n- YouTube Partner Program Shorts route typically requires 1,000 subscribers and 10M valid public Shorts views in the last 90 days.\\n- Alternative longform YPP route: 1,000 subscribers and 4,000 valid public watch hours in the last 12 months.\\n- Shorts views in the Shorts feed do not count toward the 4,000 longform watch hours.\\n- Practical early growth tests often work best at 20–45 seconds; longer Shorts need a strong story or payoff.\\n\\n### TikTok\\n\\n- Creator Rewards availability depends on country/account eligibility.\\n- TikTok Creator Rewards generally requires a non-business account in good standing, 10,000 followers, and 100,000 authentic views in the last 30 days before joining, subject to country availability and current terms.\\n- Reward-eligible videos generally need to be at least 1 minute long and meet quality/originality criteria.\\n- This creates a strategy split:\\n - 20–45s for growth and format discovery.\\n - 60–75s for monetization candidates once a format works.\\n\\n### Instagram / Facebook Reels\\n\\nUseful for republishing 9:16 assets and testing share/save behavior. Treat direct payout as volatile; prefer indirect monetization through audience, affiliate, newsletter, or products.\\n\\n### LinkedIn / X / Pinterest\\n\\nUse selectively:\\n\\n- LinkedIn: AI/productivity/workflows.\\n- X: AI demos, build-in-public, technical positioning.\\n- Pinterest: evergreen checklists, routines, faith/life prompts.\\n\\n## Repository scaffold\\n\\nA clean shorts-engine repo can start as:\\n\\n```text\\nAutoShortsBot/\\n autoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\n configs/\\n niches/\\n platforms/\\n data/\\n drafts/\\n performance/\\n scripts/\\n tests/\\n README.md\\n .gitignore\\n pyproject.toml\\n```\\n\\nNever commit:\\n\\n- `.env`\\n- OAuth tokens\\n- API keys\\n- generated credentials\\n- rendered videos or large media outputs unless explicitly intended\\n- raw analytics exports with sensitive account data\\n\\n## Implementation quality\\n\\nWhen coding the engine, prefer TDD for deterministic parts:\\n\\n- structured video candidate validation\\n- testbatch validation and duplicate-id checks\\n- renderable-candidate filtering before any renderer runs\\n- duration classification\\n- review formatting\\n- analytics import\\n- approval command parsing\\n- platform metadata generation\\n- script quality gates\\n\\nA strong first implementation path is deterministic gating before any renderer exists:\\n\\n1. Add structured `VideoCandidate` and `TestBatch` models before rendering code. Require candidate id, pillar, title, platforms, duration, hook, script outline, visual concept, caption, CTA, hashtags, quality score, policy notes, and one clear hypothesis.\\n2. Validate candidates and batches: reject missing strategic fields, unsupported platforms, out-of-range durations, empty batches, and duplicate candidate ids.\\n3. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n4. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n5. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n6. Add a Telegram review package renderer that surfaces the candidate, gate status, policy notes, preview path, and only relevant platform approval commands.\\n7. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers, image generation, video generation, or publishing APIs.\\n\\nRendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should use Pillow/FFmpeg procedural motion, mechanism layers, kinetic captions, and generated metadata; preserve exact scene durations from render manifests, exclude text-only LLM refinement jobs from rendering, and keep generated local renders out of git (`data/local_motion_renders/`). NVIDIA/build.nvidia.com LLMs are optional text-refinement helpers only, not video generation.\\n\\nWhen Hermes has the `openai-codex` image-generation backend available, GPT Image 2 is a good cloud-side keyframe/thumbnail/concept generator for the pipeline: `script idea → GPT Image 2 keyframe → human review → Wan/ComfyUI I2V or local renderer → deterministic captions/overlays`. Because GPT Image 2 runs cloud-side, it does not compete with local/remote GPU VRAM used by Wan/ComfyUI video jobs. Use low/medium for iteration and high for final keyframes; still add captions, labels, numbers, UI text, and audit-sensitive facts later in the deterministic renderer rather than trusting baked image text.\\n\\nIf the user wants more realistic footage without spending money, first check whether a local GPU/ComfyUI host is available. Preferred path when reachable: `scene spec → ComfyUI/Wan API over Tailscale → imported_ai_clips//scene_XX.mp4 → local captions/mechanism layers/FFmpeg assembly`. For a 16 GB A5000 Laptop GPU, use the `comfyui` skill reference `wan-video-tailscale-a5000.md`: Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a small smoke-test ladder before quality probes. If no local GPU path is available or quality is insufficient, use the hybrid free-tier handoff rather than pretending local procedural graphics are realistic: `free_production_jobs → free_ai_video_handoff → manual browser generation in free-tier tools (PixVerse/Pika/CapCut/Hailuo only with existing free credits) → import clips under data/imported_ai_clips/ → local captions/mechanism layers/FFmpeg assembly`. Guardrails: no paid top-ups, no subscriptions, avoid Pro templates, no baked captions/logos/watermarks where possible, and keep imported clips out of git.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should produce moving mechanism/caption previews, not final static cards. When the user has a capable local/remote GPU (e.g. RTX A5000 16 GB via Tailscale), promote the path to `ComfyUI/Wan or LTX local clip generation → imported_ai_clips → hybrid local renderer → FFmpeg`, with free web providers (PixVerse/Pika/CapCut) only as fallback. External free-tier AI video tools can still be used manually for realistic b-roll, but keep paid top-ups/subscriptions disabled and store downloaded clips under `data/imported_ai_clips//scene_XX.mp4`. Generate provider handoff packets for manual browser generation and imported-clip assembly plans so missing clips fall back to local placeholders.\\n\\n## Pitfalls\\n\\n- **Generic advice content:** “4 tips to be healthier/productive” is usually too weak unless the hook, identity, or story is sharp.\\n- **AI-image slop:** avoid generated images containing fake UI text, gibberish signs, distorted hands/faces, or abstract generic graphs. These damage trust fast.\\n- **Too much automation too soon:** validate scripts and formats before building posting APIs.\\n- **No analytics loop:** producing content without tracking retention, likes, shares, comments, and follows is blind output.\\n- **Mixed language drift:** decide language per channel or per pillar before rendering. Mixed English/German drafts are fine for ideation, not final voiceover unless intentional.\\n\\n## References\\n\\n- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n- `references/approval-first-bot-foundation.md` — implementation pattern for the first safe bot slice: explicit approval parser, quality gate, Telegram review package, and TDD test shape.\\n- `references/idea-candidate-testbatch-foundation.md` — deterministic `VideoCandidate`/`TestBatch` foundation before rendering: validation rules, renderable filtering, and test shape.\\n\", \"path\": \"social-media/social-media-shorts-growth-engine/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine\", \"linked_files\": {\"references\": [\"references/autoshortsbot-case-study.md\", \"references/idea-candidate-testbatch-foundation.md\", \"references/free-ai-video-workflow.md\", \"references/approval-first-bot-foundation.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\"}", "tool_call_id": "call_xfyWRVcdmBAidlsfN7eg7s8q" }, { "role": "tool", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": null, \"usage_hint\": null, \"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\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_zChOeB6gGbAFgmi8E7LLOXLp" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_bEYqxLfEyZ5E8JLKiQa07eSC", "call_id": "call_bEYqxLfEyZ5E8JLKiQa07eSC", "response_item_id": "fc_0d99e9da68392f44016a12b6c24ca48191b468eb5e3a4329af", "type": "function", "function": { "name": "todo", "arguments": "{\"todos\":[{\"id\":\"status\",\"content\":\"Repo-Status prüfen\",\"status\":\"in_progress\"},{\"id\":\"red\",\"content\":\"Pillar-Registry-Tests schreiben und RED verifizieren\",\"status\":\"pending\"},{\"id\":\"green\",\"content\":\"Content-Pillar-Modul und Kandidaten-Pillar-Validierung implementieren\",\"status\":\"pending\"},{\"id\":\"verify\",\"content\":\"Tests laufen lassen, README aktualisieren, committen\",\"status\":\"pending\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"in_progress\"}, {\"id\": \"red\", \"content\": \"Pillar-Registry-Tests schreiben und RED verifizieren\", \"status\": \"pending\"}, {\"id\": \"green\", \"content\": \"Content-Pillar-Modul und Kandidaten-Pillar-Validierung implementieren\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 3, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}", "tool_call_id": "call_bEYqxLfEyZ5E8JLKiQa07eSC" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_NyzpiuG19qEAxbhpUX32UMp6", "call_id": "call_NyzpiuG19qEAxbhpUX32UMp6", "response_item_id": "fc_0d99e9da68392f44016a12b6c6f4c08191adc4124cf4b5fc71", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short --branch && git log --oneline -5\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"## feat/core-approval-quality-gates\\n49d6daf feat: add idea candidates and testbatches\\n55b17fa feat: add telegram review package\\n3555ec2 feat: add approval parser and quality gate\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_NyzpiuG19qEAxbhpUX32UMp6" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_N47JryPGG4iQxQIbHfParvfq", "call_id": "call_N47JryPGG4iQxQIbHfParvfq", "response_item_id": "fc_0d99e9da68392f44016a12b6cc7904819186889552fcc82329", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Repo-Status prüfen\",\"id\":\"status\",\"status\":\"completed\"},{\"content\":\"Pillar-Registry-Tests schreiben und RED verifizieren\",\"id\":\"red\",\"status\":\"in_progress\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Pillar-Registry-Tests schreiben und RED verifizieren\", \"status\": \"in_progress\"}, {\"id\": \"green\", \"content\": \"Content-Pillar-Modul und Kandidaten-Pillar-Validierung implementieren\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 2, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_N47JryPGG4iQxQIbHfParvfq" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_tkvhtYrVxA2huQSJDPL6F0N6", "call_id": "call_tkvhtYrVxA2huQSJDPL6F0N6", "response_item_id": "fc_0d99e9da68392f44016a12b6cfbe4481919427cfa556285c6a", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"from autoshorts.config.pillars import (\\n ContentPillar,\\n DEFAULT_PILLARS,\\n find_pillar,\\n validate_candidate_against_pillars,\\n validate_pillar_registry,\\n)\\nfrom autoshorts.ideas.candidate import VideoCandidate\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\n\\ndef _score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef _candidate(**overrides) -> VideoCandidate:\\n data = {\\n \\\"candidate_id\\\": \\\"vid-001\\\",\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"title\\\": \\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n \\\"duration_seconds\\\": 34,\\n \\\"hook\\\": \\\"AI sollte dir dienen, nicht dich führen.\\\",\\n \\\"script_outline\\\": \\\"Show AI chaos, then a controlled human-approved workflow.\\\",\\n \\\"visual_concept\\\": \\\"Calm desk setup, explicit approval checkpoint, no fake dashboards.\\\",\\n \\\"caption\\\": \\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n \\\"cta\\\": \\\"Follow for practical AI life systems.\\\",\\n \\\"hashtags\\\": (\\\"#AI\\\", \\\"#Productivity\\\"),\\n \\\"quality_score\\\": _score(),\\n \\\"policy_notes\\\": (\\\"No fake earnings claims\\\",),\\n \\\"hypothesis\\\": \\\"Values-first AI positioning gets more saves than hype content.\\\",\\n }\\n data.update(overrides)\\n return VideoCandidate(**data)\\n\\n\\ndef test_default_registry_contains_ai_life_systems_positioning():\\n pillar = find_pillar(\\\"ai_life_systems\\\", DEFAULT_PILLARS)\\n\\n assert pillar is not None\\n assert \\\"servant\\\" in pillar.positioning\\n assert \\\"human remains in control\\\" in pillar.visual_rules\\n assert \\\"fake earnings\\\" in pillar.no_go\\n assert \\\"youtube\\\" in pillar.platform_fit\\n\\n\\ndef test_pillar_registry_validation_rejects_duplicate_ids():\\n first = ContentPillar(\\n pillar_id=\\\"dup\\\",\\n audience=\\\"builders\\\",\\n positioning=\\\"first\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n )\\n second = ContentPillar(\\n pillar_id=\\\"dup\\\",\\n audience=\\\"builders\\\",\\n positioning=\\\"second\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n )\\n\\n result = validate_pillar_registry((first, second))\\n\\n assert result.is_valid is False\\n assert \\\"pillar ids must be unique\\\" in result.errors\\n\\n\\ndef test_candidate_passes_known_pillar_and_platform_fit():\\n result = validate_candidate_against_pillars(_candidate(), DEFAULT_PILLARS)\\n\\n assert result.is_valid is True\\n assert result.errors == ()\\n\\n\\ndef test_candidate_rejects_unknown_pillar():\\n result = validate_candidate_against_pillars(\\n _candidate(pillar=\\\"unknown_pillar\\\"), DEFAULT_PILLARS\\n )\\n\\n assert result.is_valid is False\\n assert \\\"unknown pillar: unknown_pillar\\\" in result.errors\\n\\n\\ndef test_candidate_rejects_platform_not_fit_for_pillar():\\n registry = (\\n ContentPillar(\\n pillar_id=\\\"narrow\\\",\\n audience=\\\"specific audience\\\",\\n positioning=\\\"narrow test pillar\\\",\\n tone=(\\\"clear\\\",),\\n hook_patterns=(\\\"hook\\\",),\\n visual_rules=(\\\"rule\\\",),\\n no_go=(\\\"no\\\",),\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=\\\"newsletter\\\",\\n ),\\n )\\n\\n result = validate_candidate_against_pillars(\\n _candidate(pillar=\\\"narrow\\\", platforms=(\\\"youtube\\\", \\\"tiktok\\\")), registry\\n )\\n\\n assert result.is_valid is False\\n assert \\\"platform tiktok is not configured for pillar narrow\\\" in result.errors\\n\\n\\ndef test_registry_validation_rejects_empty_required_pillar_fields():\\n pillar = ContentPillar(\\n pillar_id=\\\"\\\",\\n audience=\\\"\\\",\\n positioning=\\\"\\\",\\n tone=(),\\n hook_patterns=(),\\n visual_rules=(),\\n no_go=(),\\n platform_fit=(),\\n monetization_angle=\\\"\\\",\\n )\\n\\n result = validate_pillar_registry((pillar,))\\n\\n assert result.is_valid is False\\n assert \\\"pillar_id is required\\\" in result.errors\\n assert \\\"audience is required\\\" in result.errors\\n assert \\\"at least one hook pattern is required\\\" in result.errors\\n assert \\\"at least one platform fit is required\\\" in result.errors\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_pillars.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 4413, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [1:6] Import \\\"autoshorts.config.pillars\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_tkvhtYrVxA2huQSJDPL6F0N6" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_jL72QftFq61C2Bc5W3jLSrWJ", "call_id": "call_jL72QftFq61C2Bc5W3jLSrWJ", "response_item_id": "fc_0d99e9da68392f44016a12b6ea8f6081918d183eb9b885ff1d", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_pillars.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"==================================== ERRORS ====================================\\n____________________ ERROR collecting tests/test_pillars.py ____________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_pillars.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_pillars.py:1: in \\n from autoshorts.config.pillars import (\\nE ModuleNotFoundError: No module named 'autoshorts.config'\\n=========================== short test summary info ============================\\nERROR tests/test_pillars.py\\n!!!!!!!!!!!!!!!!!!!! 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The renderer should\\nnot decide what the channel stands for; that way lies prompt spaghetti wearing\\na YAML hat.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult, VideoCandidate\\n\\nSUPPORTED_PILLAR_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n\\n\\n@dataclass(frozen=True)\\nclass ContentPillar:\\n pillar_id: str\\n audience: str\\n positioning: str\\n tone: tuple[str, ...]\\n hook_patterns: tuple[str, ...]\\n visual_rules: tuple[str, ...]\\n no_go: tuple[str, ...]\\n platform_fit: tuple[str, ...]\\n monetization_angle: str\\n\\n\\nDEFAULT_PILLARS: tuple[ContentPillar, ...] = (\\n ContentPillar(\\n pillar_id=\\\"ai_life_systems\\\",\\n audience=\\\"pragmatic adults who want AI to reduce life friction without surrendering judgement\\\",\\n positioning=\\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n tone=(\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"),\\n hook_patterns=(\\n \\\"AI is a bad god, but a good servant.\\\",\\n \\\"Stop asking AI to decide your life. Make it run the boring parts.\\\",\\n \\\"The point is not more automation. The point is more control.\\\",\\n ),\\n visual_rules=(\\n \\\"human remains in control\\\",\\n \\\"explicit approval checkpoints\\\",\\n \\\"calm cinematic desk and phone workflows\\\",\\n \\\"no fake dashboards or unreadable UI text\\\",\\n ),\\n no_go=(\\n \\\"fake earnings\\\",\\n \\\"overpromising automation\\\",\\n \\\"medical advice\\\",\\n \\\"financial advice\\\",\\n \\\"AI as moral authority\\\",\\n ),\\n platform_fit=(\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\"),\\n monetization_angle=\\\"newsletter, templates, workflow products, consulting later\\\",\\n ),\\n)\\n\\n\\ndef _is_blank(value: str) -> bool:\\n return not (value or \\\"\\\").strip()\\n\\n\\ndef find_pillar(pillar_id: str, registry: tuple[ContentPillar, ...]) -> ContentPillar | None:\\n for pillar in registry:\\n if pillar.pillar_id == pillar_id:\\n return pillar\\n return None\\n\\n\\ndef validate_pillar_registry(registry: tuple[ContentPillar, ...]) -> ValidationResult:\\n errors: list[str] = []\\n ids = [pillar.pillar_id for pillar in registry]\\n\\n if len(ids) != len(set(ids)):\\n errors.append(\\\"pillar ids must be unique\\\")\\n\\n for pillar in registry:\\n if _is_blank(pillar.pillar_id):\\n errors.append(\\\"pillar_id is required\\\")\\n if _is_blank(pillar.audience):\\n errors.append(\\\"audience is required\\\")\\n if _is_blank(pillar.positioning):\\n errors.append(\\\"positioning is required\\\")\\n if _is_blank(pillar.monetization_angle):\\n errors.append(\\\"monetization_angle is required\\\")\\n if not pillar.tone:\\n errors.append(\\\"at least one tone is required\\\")\\n if not pillar.hook_patterns:\\n errors.append(\\\"at least one hook pattern is required\\\")\\n if not pillar.visual_rules:\\n errors.append(\\\"at least one visual rule is required\\\")\\n if not pillar.no_go:\\n errors.append(\\\"at least one no-go is required\\\")\\n if not pillar.platform_fit:\\n errors.append(\\\"at least one platform fit is required\\\")\\n for platform in pillar.platform_fit:\\n if platform not in SUPPORTED_PILLAR_PLATFORMS:\\n errors.append(f\\\"unsupported platform in pillar {pillar.pillar_id}: {platform}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef validate_candidate_against_pillars(\\n candidate: VideoCandidate,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n) -> ValidationResult:\\n errors: list[str] = []\\n pillar = find_pillar(candidate.pillar, registry)\\n\\n if pillar is None:\\n errors.append(f\\\"unknown pillar: {candidate.pillar}\\\")\\n return ValidationResult(is_valid=False, errors=tuple(errors))\\n\\n for platform in candidate.platforms:\\n if platform not in pillar.platform_fit:\\n errors.append(f\\\"platform {platform} is not configured for pillar {pillar.pillar_id}\\\")\\n\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/config/pillars.py\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"bytes_written\": 49, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_whbB4Js8sRIRJSgBroJ6TH0C" }, { "role": "tool", "content": "{\"bytes_written\": 4264, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_acuAIVpxtWo2Z2pm5czUMjDx" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_YLsnonpXPH0ktPiWhFkjHfHB", "call_id": "call_YLsnonpXPH0ktPiWhFkjHfHB", "response_item_id": "fc_0d99e9da68392f44016a12b73935c88191929d3ad956283b1e", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_pillars.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"...... 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`autoshorts.ideas.candidate` — structured video candidate model and validation.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_wE3rqOTfPldp3RPt931nX6yQ" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_1kxCRIiieUMtascQUHxd68ua", "call_id": "call_1kxCRIiieUMtascQUHxd68ua", "response_item_id": "fc_0d99e9da68392f44016a12b74cda348191887910a15777734b", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- Tests for content pillars, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\",\"old_string\":\"- Tests for idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -14,7 +14,7 @@\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n-- Tests for idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n+- Tests for content pillars, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n \\n ## Human approval commands\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_1kxCRIiieUMtascQUHxd68ua" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_rRfwa5p3Hn740Yrs7jvQJRiD", "call_id": "call_rRfwa5p3Hn740Yrs7jvQJRiD", "response_item_id": "fc_0d99e9da68392f44016a12b75834008191a0eb159801615824", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n\\n## Content pillars\\n\\n`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n\\n- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n\\nA pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n\\n`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n\\n## Development\",\"old_string\":\"`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n\\n## Development\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -63,6 +63,16 @@\\n \\n `TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n \\n+## Content pillars\\n+\\n+`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n+\\n+- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n+\\n+A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n+\\n+`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n+\\n ## Development\\n \\n Run tests:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_rRfwa5p3Hn740Yrs7jvQJRiD" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_48bNlowyN1i4PRoKUkxrNlg0", "call_id": "call_48bNlowyN1i4PRoKUkxrNlg0", "response_item_id": "fc_0d99e9da68392f44016a12b76477388191aea7afd444f0bdb1", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"......................... [100%]\\n25 passed in 0.06s\\n M README.md\\n?? autoshorts/config/\\n?? tests/test_pillars.py\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_48bNlowyN1i4PRoKUkxrNlg0" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_VPLUkf7CcoOzvB25Oy1IEYiP", "call_id": "call_VPLUkf7CcoOzvB25Oy1IEYiP", "response_item_id": "fc_0d99e9da68392f44016a12b76994848191810df003b4b55ab3", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git add README.md autoshorts/config tests/test_pillars.py && git commit -m \\\"feat: add content pillar registry\\\" && git status --short --branch && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"[feat/core-approval-quality-gates ae6a4f6] feat: add content pillar registry\\n 4 files changed, 274 insertions(+), 1 deletion(-)\\n create mode 100644 autoshorts/config/__init__.py\\n create mode 100644 autoshorts/config/pillars.py\\n create mode 100644 tests/test_pillars.py\\n## feat/core-approval-quality-gates\\nae6a4f6 feat: add content pillar registry\\n49d6daf feat: add idea candidates and testbatches\\n55b17fa feat: add telegram review package\\n3555ec2 feat: add approval parser and quality gate\\n7642749 Initial commit\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_VPLUkf7CcoOzvB25Oy1IEYiP" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_Wt26SZzoIBAHvwhebGZKn88o", "call_id": "call_Wt26SZzoIBAHvwhebGZKn88o", "response_item_id": "fc_0d99e9da68392f44016a12b771cc8881918e0b2b18274e63c1", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"output\": \"......................... [100%]\\n25 passed in 0.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Wt26SZzoIBAHvwhebGZKn88o" }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "call_G8G34uijZZo1Upw34JXjzXBk", "call_id": "call_G8G34uijZZo1Upw34JXjzXBk", "response_item_id": "fc_0d99e9da68392f44016a12b77688c8819187772178d5da5fc1", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ], "finish_reason": "tool_calls" }, { "role": "tool", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Pillar-Registry-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"Content-Pillar-Modul und Kandidaten-Pillar-Validierung implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_G8G34uijZZo1Upw34JXjzXBk" }, { "role": "assistant", "content": "Weitergebaut, Sir.\n\n## Neuer Baustein: Content Pillar Registry\n\nIch habe jetzt die strategische Ebene eingebaut — damit der Bot nicht einfach “irgendwelche Videos” produziert, sondern nur Kandidaten, die zu einer definierten Content-Positionierung passen.\n\nNeue Dateien:\n\n```text\nautoshorts/config/__init__.py\nautoshorts/config/pillars.py\ntests/test_pillars.py\n```\n\n## Was jetzt drin ist\n\n### 1. `ContentPillar`\n\nEin Pillar definiert jetzt:\n\n- `pillar_id`\n- `audience`\n- `positioning`\n- `tone`\n- `hook_patterns`\n- `visual_rules`\n- `no_go`\n- `platform_fit`\n- `monetization_angle`\n\nDamit ist Strategie getrennt von Rendering. Der Renderer soll später keine Werte, Zielgruppe oder Positionierung erfinden. Das ist sonst der direkte Weg zu Prompt-Spaghetti mit Management-Slide-Geruch.\n\n## Default Pillar\n\nAktuell gibt es den ersten echten Pillar:\n\n```text\nai_life_systems\n```\n\nPositionierung:\n\n```text\nAI as a servant for ordinary life systems, never as values or authority.\n```\n\nEnthaltene Regeln:\n\n- Tone:\n - clear\n - useful\n - values-first\n - non-hype\n\n- Hook Patterns:\n - “AI is a bad god, but a good servant.”\n - “Stop asking AI to decide your life. Make it run the boring parts.”\n - “The point is not more automation. The point is more control.”\n\n- Visual Rules:\n - human remains in control\n - explicit approval checkpoints\n - calm cinematic desk and phone workflows\n - no fake dashboards or unreadable UI text\n\n- No-Go:\n - fake earnings\n - overpromising automation\n - medical advice\n - financial advice\n - AI as moral authority\n\n- Plattform-Fit:\n - YouTube\n - TikTok\n - Instagram\n\n## Neue Validierungen\n\n### `validate_pillar_registry()`\n\nPrüft:\n\n- doppelte Pillar IDs\n- fehlende Pflichtfelder\n- mindestens ein Hook Pattern\n- mindestens eine Visual Rule\n- mindestens ein No-Go\n- mindestens eine passende Plattform\n- nur unterstützte Plattformen\n\n### `validate_candidate_against_pillars()`\n\nPrüft:\n\n- Kandidat verwendet bekannten Pillar\n- Kandidat nutzt nur Plattformen, die für den Pillar erlaubt sind\n\nBeispiel:\n\n```text\nunknown_pillar → rejected\nplatform tiktok is not configured for pillar narrow → rejected\n```\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Pillar Registry geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.config'\n```\n\n3. Modul minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n25 passed in 0.06s\n```\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\nae6a4f6 feat: add content pillar registry\n```\n\nAktueller Verlauf:\n\n```text\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt:\n\n```text\nContent Pillars\n→ VideoCandidate\n→ Candidate Validation\n→ Pillar Fit Validation\n→ TestBatch\n→ Quality Gate\n→ Review Package\n→ Explicit Approval Parser\n```\n\nDas ist ein brauchbares Fundament. Noch keine hübsche Maschine, aber wenigstens kein wackliger Jahrmarkt-Roboter mit Upload-Rechten.\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/ideas/factory.py\n```\n\nZiel:\n\n- aus Pillar + Rohidee einen `VideoCandidate` erzeugen\n- automatische ID-Generierung\n- Standard-Hashtags aus Pillar/Plattform ableiten\n- Dauerklasse prüfen:\n - growth short: 20–45s\n - monetization candidate: 60–75s\n- Hypothesis erzwingen\n- direkt validierten Candidate zurückgeben\n\nDann können wir aus Content-Strategie tatsächlich Kandidaten erzeugen, statt sie nur manuell zu modellieren.", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Weitergebaut, Sir.\n\n## Neuer Baustein: Content Pillar Registry\n\nIch habe jetzt die strategische Ebene eingebaut — damit der Bot nicht einfach “irgendwelche Videos” produziert, sondern nur Kandidaten, die zu einer definierten Content-Positionierung passen.\n\nNeue Dateien:\n\n```text\nautoshorts/config/__init__.py\nautoshorts/config/pillars.py\ntests/test_pillars.py\n```\n\n## Was jetzt drin ist\n\n### 1. `ContentPillar`\n\nEin Pillar definiert jetzt:\n\n- `pillar_id`\n- `audience`\n- `positioning`\n- `tone`\n- `hook_patterns`\n- `visual_rules`\n- `no_go`\n- `platform_fit`\n- `monetization_angle`\n\nDamit ist Strategie getrennt von Rendering. Der Renderer soll später keine Werte, Zielgruppe oder Positionierung erfinden. Das ist sonst der direkte Weg zu Prompt-Spaghetti mit Management-Slide-Geruch.\n\n## Default Pillar\n\nAktuell gibt es den ersten echten Pillar:\n\n```text\nai_life_systems\n```\n\nPositionierung:\n\n```text\nAI as a servant for ordinary life systems, never as values or authority.\n```\n\nEnthaltene Regeln:\n\n- Tone:\n - clear\n - useful\n - values-first\n - non-hype\n\n- Hook Patterns:\n - “AI is a bad god, but a good servant.”\n - “Stop asking AI to decide your life. Make it run the boring parts.”\n - “The point is not more automation. The point is more control.”\n\n- Visual Rules:\n - human remains in control\n - explicit approval checkpoints\n - calm cinematic desk and phone workflows\n - no fake dashboards or unreadable UI text\n\n- No-Go:\n - fake earnings\n - overpromising automation\n - medical advice\n - financial advice\n - AI as moral authority\n\n- Plattform-Fit:\n - YouTube\n - TikTok\n - Instagram\n\n## Neue Validierungen\n\n### `validate_pillar_registry()`\n\nPrüft:\n\n- doppelte Pillar IDs\n- fehlende Pflichtfelder\n- mindestens ein Hook Pattern\n- mindestens eine Visual Rule\n- mindestens ein No-Go\n- mindestens eine passende Plattform\n- nur unterstützte Plattformen\n\n### `validate_candidate_against_pillars()`\n\nPrüft:\n\n- Kandidat verwendet bekannten Pillar\n- Kandidat nutzt nur Plattformen, die für den Pillar erlaubt sind\n\nBeispiel:\n\n```text\nunknown_pillar → rejected\nplatform tiktok is not configured for pillar narrow → rejected\n```\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Pillar Registry geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.config'\n```\n\n3. Modul minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n25 passed in 0.06s\n```\n\n## Neuer Commit\n\nAuf Branch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nneuer Commit:\n\n```text\nae6a4f6 feat: add content pillar registry\n```\n\nAktueller Verlauf:\n\n```text\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt:\n\n```text\nContent Pillars\n→ VideoCandidate\n→ Candidate Validation\n→ Pillar Fit Validation\n→ TestBatch\n→ Quality Gate\n→ Review Package\n→ Explicit Approval Parser\n```\n\nDas ist ein brauchbares Fundament. Noch keine hübsche Maschine, aber wenigstens kein wackliger Jahrmarkt-Roboter mit Upload-Rechten.\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/ideas/factory.py\n```\n\nZiel:\n\n- aus Pillar + Rohidee einen `VideoCandidate` erzeugen\n- automatische ID-Generierung\n- Standard-Hashtags aus Pillar/Plattform ableiten\n- Dauerklasse prüfen:\n - growth short: 20–45s\n - monetization candidate: 60–75s\n- Hypothesis erzwingen\n- direkt validierten Candidate zurückgeben\n\nDann können wir aus Content-Strategie tatsächlich Kandidaten erzeugen, statt sie nur manuell zu modellieren." } ], "id": "msg_0d99e9da68392f44016a12b77967f881919e05014e7abcb89e", "phase": "final_answer" } ], "finish_reason": "stop" }, { "role": "user", "content": "[M G] ok, mach weiter." }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErgV6G1hup7seRIy2PQx-UB-DiX-1iBI92oQljeWf85XhBAL3weA9Z6CqYa6IqqqA9yQWswovBU3uPVi10OatbWTSDs_rwvxdZd7Wv_1-MAB5tbTElLFsfRlrADsxcE9nCEzVUH-geNBb8fgJNfDCARe6U88lWHLVYdGMlOGKJKOi8xVCPU-ldZJOSQe1jnqdWvmII499SdMWiZUrC52ApEdhQ1LSTj1C0l8rZPxbrJaaj9hGY978T24La721hjUrAxHu7alVvuxbbLzcSxnF6uY1ivTpAhqOR2SJU3szcDQtwvo-p66Tn6YqjZ8Q3pUU7f1ecCmzfmyqM4unc2eSgcUmFm-DiFsY1HIShGmOEPCKTjnKf-C-vQXf_gvERbr1-bD3vCUEzyJKT2hhBaPkMXdzP8FOSOoRJy3gaqu3x8gANPWmd5YC8_i7brTOdO8QLhscGgNVkojQDA0iDjSB3yl0a8spWH7dleFLBXCmPhh66CJuGDaqGQjRPqrp0FTodC51V-EkVrACZQURQpwYArVwSmpAE6vGmSCUjvQsrD44Ex-u2SoR13wb1u2BGFJ0ty3-6X0uaHlvb9kzuJkV9ArsP-q7szicpC8llj4lTgWw2fsTLUUMn9okCRZLY9NE_bCK1yb-Ybt7FNcRMaw_X17irnDGqxmIJQR-AeFXV4chI96KWvd49FTQGE1apfUerc3H28xfOP7Fy3WwQDhlW1WdmWEGuzy1Xph5Q344OpHNQN7Qdh5AB9AEHxj4b25GTOl6o_QBSsIlpRxshZZTXhg23J356zphd60_kMeONCh5tOhhBqvb6UAQxDf6KmoBbJeloF-wKCZ7ujZjfiBpzmsq9R3u-ixwdbeJygrRWLmJhMGKcwhweYXmzkfdfVySnHg7iyGZRlKGe481seDkm8noxS5gKRtrnoUA8-4-lNYDoRUb8c1SWPYByaRW7v2FrrEU6tTHbPhWIvG0HLeW9TVG69Awr1lV4G_G69RAs6bRjnD63MNrgT2WVSxe3NRMvC5XyRQ7cCTgi8diooeA6zoKA==", "id": "rs_0d99e9da68392f44016a12b813a6c0819185521a5a0c161f28", "summary": [] } ], "tool_calls": [ { "id": "call_diU1YOPe4J2fDZZ615ebODGa", "call_id": "call_diU1YOPe4J2fDZZ615ebODGa", "response_item_id": "fc_0d99e9da68392f44016a12b81576bc81919295a716238fa7c1", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}" } }, { "id": "call_fnQjvz3MVuEHG4bXvT0wSAIi", "call_id": "call_fnQjvz3MVuEHG4bXvT0wSAIi", "response_item_id": "fc_0d99e9da68392f44016a12b8157840819198e9bebe38a4420b", "type": "function", "function": { "name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}" } } ] }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-growth-engine\", \"description\": \"Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"content-strategy\", \"growth\", \"monetization\", \"approval-workflow\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-growth-engine\\ndescription: Design and operate a growth-oriented short-form video engine for YouTube Shorts, TikTok, Instagram Reels, and adjacent channels, with human approval before posting.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, content-strategy, growth, monetization, approval-workflow]\\n---\\n\\n# Social-Media Shorts Growth Engine\\n\\nUse this skill when the user wants to build, improve, or operate an automated/semi-automated system for short-form social media content: YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest, LinkedIn video, or similar.\\n\\n## Core principle\\n\\nDo **not** treat the task as “make videos.” Treat it as a **growth engine**:\\n\\n1. Generate many sharply positioned ideas.\\n2. Score hooks and scripts before rendering.\\n3. Produce previews.\\n4. Send the user a review package.\\n5. Post only after explicit approval.\\n6. Capture analytics.\\n7. Feed performance back into the next batch.\\n\\nA technically correct content generator that produces generic content is failure with extra rendering time. Charming, but still failure.\\n\\n## Recommended workflow\\n\\n### 1. Define content pillars\\n\\nStart with 2–4 pillars, not a vague infinite topic pool. For each pillar define:\\n\\n- audience\\n- positioning\\n- tone\\n- hook patterns\\n- visual rules\\n- no-go topics\\n- platform fit\\n- monetization angle\\n\\nExample pillar structure:\\n\\n```json\\n{\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"positioning\\\": \\\"AI as a servant for ordinary life systems, never as values or authority.\\\",\\n \\\"tone\\\": [\\\"clear\\\", \\\"useful\\\", \\\"values-first\\\", \\\"non-hype\\\"],\\n \\\"hook_patterns\\\": [\\\"AI is a bad god, but a good servant.\\\"],\\n \\\"visual_rules\\\": [\\\"screen-recording friendly\\\", \\\"human remains in control\\\"],\\n \\\"no_go\\\": [\\\"fake earnings\\\", \\\"overpromising automation\\\"]\\n}\\n```\\n\\n### 2. Build a testbatch before building a full automation platform\\n\\nFor a new channel or unvalidated strategy, create a small batch first:\\n\\n- 3 ideas per pillar\\n- 6 short growth candidates, usually 20–45 seconds\\n- 1–3 monetization-length candidates, usually 60+ seconds where the platform rewards length\\n- one clear hypothesis per idea\\n\\nEach idea should include:\\n\\n- id\\n- pillar\\n- title\\n- platform target\\n- duration\\n- hook\\n- script outline\\n- visual concept\\n- caption\\n- CTA\\n- quality score\\n- risk/policy notes\\n\\n### 3. Add a script quality gate\\n\\nBefore rendering, score each draft for:\\n\\n- hook strength in the first 1–2 seconds\\n- novelty / non-generic angle\\n- emotional pull\\n- clarity\\n- retention risk\\n- comment/share potential\\n- AI-slop risk\\n- policy/copyright risk\\n\\nRender only drafts above the threshold, e.g. 8/10.\\n\\n### 4. Use a human approval loop\\n\\nDefault to semi-automatic operation first. Send a Telegram review package with:\\n\\n- preview video or script draft\\n- pillar and format\\n- title\\n- duration\\n- hook\\n- caption\\n- hashtags\\n- quality score\\n- policy notes\\n- suggested platforms\\n\\nRecommended approval commands:\\n\\n- `Freigabe alle`\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Ablehnen`\\n- `Ändern: `\\n\\nIf approval is ambiguous, do not post. Ask once in the project topic.\\n\\n### 5. Keep platform strategy separate from rendering\\n\\nRendering code should not hardcode content strategy. Keep separate config files for:\\n\\n- niches/pillars\\n- platforms\\n- script formats\\n- visual styles\\n- approval workflow\\n- analytics schema\\n\\nThis lets the same renderer support different formats and prevents prompt spaghetti. Prompt spaghetti is still spaghetti even if it is wearing a YAML hat.\\n\\n## Platform notes\\n\\n### YouTube Shorts\\n\\n- Shorts can be vertical or square and up to 3 minutes long.\\n- YouTube Partner Program Shorts route typically requires 1,000 subscribers and 10M valid public Shorts views in the last 90 days.\\n- Alternative longform YPP route: 1,000 subscribers and 4,000 valid public watch hours in the last 12 months.\\n- Shorts views in the Shorts feed do not count toward the 4,000 longform watch hours.\\n- Practical early growth tests often work best at 20–45 seconds; longer Shorts need a strong story or payoff.\\n\\n### TikTok\\n\\n- Creator Rewards availability depends on country/account eligibility.\\n- TikTok Creator Rewards generally requires a non-business account in good standing, 10,000 followers, and 100,000 authentic views in the last 30 days before joining, subject to country availability and current terms.\\n- Reward-eligible videos generally need to be at least 1 minute long and meet quality/originality criteria.\\n- This creates a strategy split:\\n - 20–45s for growth and format discovery.\\n - 60–75s for monetization candidates once a format works.\\n\\n### Instagram / Facebook Reels\\n\\nUseful for republishing 9:16 assets and testing share/save behavior. Treat direct payout as volatile; prefer indirect monetization through audience, affiliate, newsletter, or products.\\n\\n### LinkedIn / X / Pinterest\\n\\nUse selectively:\\n\\n- LinkedIn: AI/productivity/workflows.\\n- X: AI demos, build-in-public, technical positioning.\\n- Pinterest: evergreen checklists, routines, faith/life prompts.\\n\\n## Repository scaffold\\n\\nA clean shorts-engine repo can start as:\\n\\n```text\\nAutoShortsBot/\\n autoshorts/\\n config/\\n ideas/\\n scripting/\\n rendering/\\n review/\\n publishing/\\n analytics/\\n configs/\\n niches/\\n platforms/\\n data/\\n drafts/\\n performance/\\n scripts/\\n tests/\\n README.md\\n .gitignore\\n pyproject.toml\\n```\\n\\nNever commit:\\n\\n- `.env`\\n- OAuth tokens\\n- API keys\\n- generated credentials\\n- rendered videos or large media outputs unless explicitly intended\\n- raw analytics exports with sensitive account data\\n\\n## Implementation quality\\n\\nWhen coding the engine, prefer TDD for deterministic parts:\\n\\n- structured video candidate validation\\n- testbatch validation and duplicate-id checks\\n- renderable-candidate filtering before any renderer runs\\n- duration classification\\n- review formatting\\n- analytics import\\n- approval command parsing\\n- platform metadata generation\\n- script quality gates\\n\\nA strong first implementation path is deterministic gating before any renderer exists:\\n\\n1. Add structured `VideoCandidate` and `TestBatch` models before rendering code. Require candidate id, pillar, title, platforms, duration, hook, script outline, visual concept, caption, CTA, hashtags, quality score, policy notes, and one clear hypothesis.\\n2. Validate candidates and batches: reject missing strategic fields, unsupported platforms, out-of-range durations, empty batches, and duplicate candidate ids.\\n3. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n4. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n5. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n6. Add a Telegram review package renderer that surfaces the candidate, gate status, policy notes, preview path, and only relevant platform approval commands.\\n7. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers, image generation, video generation, or publishing APIs.\\n\\nRendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should use Pillow/FFmpeg procedural motion, mechanism layers, kinetic captions, and generated metadata; preserve exact scene durations from render manifests, exclude text-only LLM refinement jobs from rendering, and keep generated local renders out of git (`data/local_motion_renders/`). NVIDIA/build.nvidia.com LLMs are optional text-refinement helpers only, not video generation.\\n\\nWhen Hermes has the `openai-codex` image-generation backend available, GPT Image 2 is a good cloud-side keyframe/thumbnail/concept generator for the pipeline: `script idea → GPT Image 2 keyframe → human review → Wan/ComfyUI I2V or local renderer → deterministic captions/overlays`. Because GPT Image 2 runs cloud-side, it does not compete with local/remote GPU VRAM used by Wan/ComfyUI video jobs. Use low/medium for iteration and high for final keyframes; still add captions, labels, numbers, UI text, and audit-sensitive facts later in the deterministic renderer rather than trusting baked image text.\\n\\nIf the user wants more realistic footage without spending money, first check whether a local GPU/ComfyUI host is available. Preferred path when reachable: `scene spec → ComfyUI/Wan API over Tailscale → imported_ai_clips//scene_XX.mp4 → local captions/mechanism layers/FFmpeg assembly`. For a 16 GB A5000 Laptop GPU, use the `comfyui` skill reference `wan-video-tailscale-a5000.md`: Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text encoding, VAE tiling, and a small smoke-test ladder before quality probes. If no local GPU path is available or quality is insufficient, use the hybrid free-tier handoff rather than pretending local procedural graphics are realistic: `free_production_jobs → free_ai_video_handoff → manual browser generation in free-tier tools (PixVerse/Pika/CapCut/Hailuo only with existing free credits) → import clips under data/imported_ai_clips/ → local captions/mechanism layers/FFmpeg assembly`. Guardrails: no paid top-ups, no subscriptions, avoid Pro templates, no baked captions/logos/watermarks where possible, and keep imported clips out of git.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer should produce moving mechanism/caption previews, not final static cards. When the user has a capable local/remote GPU (e.g. RTX A5000 16 GB via Tailscale), promote the path to `ComfyUI/Wan or LTX local clip generation → imported_ai_clips → hybrid local renderer → FFmpeg`, with free web providers (PixVerse/Pika/CapCut) only as fallback. External free-tier AI video tools can still be used manually for realistic b-roll, but keep paid top-ups/subscriptions disabled and store downloaded clips under `data/imported_ai_clips//scene_XX.mp4`. Generate provider handoff packets for manual browser generation and imported-clip assembly plans so missing clips fall back to local placeholders.\\n\\n## Pitfalls\\n\\n- **Generic advice content:** “4 tips to be healthier/productive” is usually too weak unless the hook, identity, or story is sharp.\\n- **AI-image slop:** avoid generated images containing fake UI text, gibberish signs, distorted hands/faces, or abstract generic graphs. These damage trust fast.\\n- **Too much automation too soon:** validate scripts and formats before building posting APIs.\\n- **No analytics loop:** producing content without tracking retention, likes, shares, comments, and follows is blind output.\\n- **Mixed language drift:** decide language per channel or per pillar before rendering. Mixed English/German drafts are fine for ideation, not final voiceover unless intentional.\\n\\n## References\\n\\n- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n- `references/approval-first-bot-foundation.md` — implementation pattern for the first safe bot slice: explicit approval parser, quality gate, Telegram review package, and TDD test shape.\\n- `references/idea-candidate-testbatch-foundation.md` — deterministic `VideoCandidate`/`TestBatch` foundation before rendering: validation rules, renderable filtering, and test shape.\\n- `references/content-pillar-registry-foundation.md` — strategic `ContentPillar` registry pattern: default `ai_life_systems`, pillar/candidate fit validation, and TDD test shape.\\n\", \"path\": \"social-media/social-media-shorts-growth-engine/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine\", \"linked_files\": {\"references\": [\"references/autoshortsbot-case-study.md\", \"references/idea-candidate-testbatch-foundation.md\", \"references/free-ai-video-workflow.md\", \"references/content-pillar-registry-foundation.md\", \"references/approval-first-bot-foundation.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\"}", "tool_call_id": "call_diU1YOPe4J2fDZZ615ebODGa" }, { "role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": 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committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 3, \"in_progress\": 1, \"completed\": 0, \"cancelled\": 0}}", "tool_call_id": "call_OTID3jx5nNOoyYuUvmRdXyxn" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_2rFll593Q00elY1EM0dTNW1w", "call_id": "call_2rFll593Q00elY1EM0dTNW1w", "response_item_id": "fc_0d99e9da68392f44016a12b81d79088191a596e3955d982a9b", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"git status --short --branch && git log --oneline -6\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"## feat/core-approval-quality-gates\\nae6a4f6 feat: add content pillar registry\\n49d6daf feat: add idea candidates and testbatches\\n55b17fa feat: add telegram review package\\n3555ec2 feat: add approval parser and quality gate\\n7642749 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implementieren\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 2, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_e1k1dvvWsyKf1n6K2QDgr3oj" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_rK1RqTpS2hvNUMr7MeC2NLMy", "call_id": "call_rK1RqTpS2hvNUMr7MeC2NLMy", "response_item_id": "fc_0d99e9da68392f44016a12b826c26c819181ac645f5ae93657", "type": "function", "function": { "name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_factory.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.config.pillars import DEFAULT_PILLARS\\nfrom autoshorts.ideas.factory import (\\n CandidateDraft,\\n classify_duration,\\n create_candidate,\\n create_candidate_id,\\n)\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\n\\ndef _score() -> ScriptQualityScore:\\n return ScriptQualityScore(\\n hook_strength=9,\\n novelty=8,\\n clarity=9,\\n retention=8,\\n ai_slop_risk=2,\\n policy_risk=\\\"low\\\",\\n )\\n\\n\\ndef _draft(**overrides) -> CandidateDraft:\\n data = {\\n \\\"pillar\\\": \\\"ai_life_systems\\\",\\n \\\"title\\\": \\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n \\\"platforms\\\": (\\\"youtube\\\", \\\"tiktok\\\"),\\n \\\"duration_seconds\\\": 34,\\n \\\"hook\\\": \\\"AI sollte dir dienen, nicht dich führen.\\\",\\n \\\"script_outline\\\": \\\"Show AI chaos, then a controlled human-approved workflow.\\\",\\n \\\"visual_concept\\\": \\\"Calm desk setup, explicit approval checkpoint, no fake dashboards.\\\",\\n \\\"caption\\\": \\\"KI als Werkzeug, nicht als Wertekompass.\\\",\\n \\\"cta\\\": \\\"Follow for practical AI life systems.\\\",\\n \\\"quality_score\\\": _score(),\\n \\\"policy_notes\\\": (\\\"No fake earnings claims\\\",),\\n \\\"hypothesis\\\": \\\"Values-first AI positioning gets more saves than hype content.\\\",\\n }\\n data.update(overrides)\\n return CandidateDraft(**data)\\n\\n\\ndef test_classify_duration_growth_short():\\n assert classify_duration(34) == \\\"growth_short\\\"\\n\\n\\ndef test_classify_duration_monetization_candidate():\\n assert classify_duration(68) == \\\"monetization_candidate\\\"\\n\\n\\ndef test_classify_duration_experiment_range():\\n assert classify_duration(12) == \\\"experiment\\\"\\n assert classify_duration(110) == \\\"experiment\\\"\\n\\n\\ndef test_candidate_id_is_stable_slug_from_pillar_title_and_duration():\\n candidate_id = create_candidate_id(\\n pillar=\\\"ai_life_systems\\\",\\n title=\\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n duration_seconds=34,\\n )\\n\\n assert candidate_id == \\\"ai_life_systems-ai-ist-ein-schlechter-gott-34s\\\"\\n\\n\\ndef test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note():\\n candidate = create_candidate(_draft(), registry=DEFAULT_PILLARS)\\n\\n assert candidate.candidate_id == \\\"ai_life_systems-ai-ist-ein-schlechter-gott-34s\\\"\\n assert candidate.hashtags == (\\\"#AI\\\", \\\"#LifeSystems\\\", \\\"#Productivity\\\")\\n assert \\\"Duration class: growth_short\\\" in candidate.policy_notes\\n assert candidate.pillar == \\\"ai_life_systems\\\"\\n assert candidate.platforms == (\\\"youtube\\\", \\\"tiktok\\\")\\n\\n\\ndef test_create_candidate_rejects_missing_hypothesis():\\n with pytest.raises(ValueError, match=\\\"hypothesis is required\\\"):\\n create_candidate(_draft(hypothesis=\\\"\\\"), registry=DEFAULT_PILLARS)\\n\\n\\ndef test_create_candidate_rejects_unknown_or_unfit_pillar():\\n with pytest.raises(ValueError, match=\\\"unknown pillar: unknown\\\"):\\n create_candidate(_draft(pillar=\\\"unknown\\\"), registry=DEFAULT_PILLARS)\\n\\n\\ndef test_create_candidate_rejects_unfit_platform_for_pillar():\\n with pytest.raises(ValueError, match=\\\"platform instagram is not configured\\\"):\\n create_candidate(\\n _draft(platforms=(\\\"instagram\\\",)),\\n registry=tuple(\\n p for p in DEFAULT_PILLARS if p.pillar_id != \\\"ai_life_systems\\\"\\n )\\n + (DEFAULT_PILLARS[0].__class__(\\n pillar_id=\\\"ai_life_systems\\\",\\n audience=DEFAULT_PILLARS[0].audience,\\n positioning=DEFAULT_PILLARS[0].positioning,\\n tone=DEFAULT_PILLARS[0].tone,\\n hook_patterns=DEFAULT_PILLARS[0].hook_patterns,\\n visual_rules=DEFAULT_PILLARS[0].visual_rules,\\n no_go=DEFAULT_PILLARS[0].no_go,\\n platform_fit=(\\\"youtube\\\",),\\n monetization_angle=DEFAULT_PILLARS[0].monetization_angle,\\n ),),\\n )\\n\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3811, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [4:6] Import \\\"autoshorts.ideas.factory\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_rK1RqTpS2hvNUMr7MeC2NLMy" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_v4lNU9ifqytDTruZJGpB1aV1", "call_id": "call_v4lNU9ifqytDTruZJGpB1aV1", "response_item_id": "fc_0d99e9da68392f44016a12bbc6704c8191a6839176186b1750", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_factory.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n____________________ ERROR collecting tests/test_factory.py ____________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_factory.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_factory.py:4: in \\n from autoshorts.ideas.factory import (\\nE ModuleNotFoundError: No module named 'autoshorts.ideas.factory'\\n=========================== short test summary info ============================\\nERROR tests/test_factory.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.11s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_v4lNU9ifqytDTruZJGpB1aV1" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_AAKqjNOXmf7tbt4Js78fPkmT", "call_id": "call_AAKqjNOXmf7tbt4Js78fPkmT", "response_item_id": "fc_0d99e9da68392f44016a12bbcb95208191897a0ef3dd4fb06a", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Factory-/Duration-Tests schreiben und RED verifizieren\",\"id\":\"red\",\"status\":\"completed\"},{\"content\":\"ideas.factory minimal implementieren\",\"id\":\"green\",\"status\":\"in_progress\"}]}" } } ] }, { "role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Factory-/Duration-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"ideas.factory minimal implementieren\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"pending\"}], \"summary\": {\"total\": 4, \"pending\": 1, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_AAKqjNOXmf7tbt4Js78fPkmT" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_ZrralM6qg0HaMF1CySZFer9y", "call_id": "call_ZrralM6qg0HaMF1CySZFer9y", "response_item_id": "fc_0d99e9da68392f44016a12bbd304608191afb2fc23b3741888", "type": "function", "function": { "name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Factory helpers for turning drafts into validated video candidates.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nimport re\\nimport unicodedata\\nfrom typing import Literal\\n\\nfrom autoshorts.config.pillars import (\\n DEFAULT_PILLARS,\\n ContentPillar,\\n validate_candidate_against_pillars,\\n)\\nfrom autoshorts.ideas.candidate import VideoCandidate, validate_candidate\\nfrom autoshorts.scripting.quality import ScriptQualityScore\\n\\nDurationClass = Literal[\\\"growth_short\\\", \\\"monetization_candidate\\\", \\\"experiment\\\"]\\n\\n_DEFAULT_HASHTAGS_BY_PILLAR = {\\n \\\"ai_life_systems\\\": (\\\"#AI\\\", \\\"#LifeSystems\\\", \\\"#Productivity\\\"),\\n}\\n\\n\\n@dataclass(frozen=True)\\nclass CandidateDraft:\\n pillar: str\\n title: str\\n platforms: tuple[str, ...]\\n duration_seconds: int\\n hook: str\\n script_outline: str\\n visual_concept: str\\n caption: str\\n cta: str\\n quality_score: ScriptQualityScore\\n policy_notes: tuple[str, ...]\\n hypothesis: str\\n hashtags: tuple[str, ...] = ()\\n\\n\\ndef classify_duration(duration_seconds: int) -> DurationClass:\\n if 20 <= duration_seconds <= 45:\\n return \\\"growth_short\\\"\\n if 60 <= duration_seconds <= 75:\\n return \\\"monetization_candidate\\\"\\n return \\\"experiment\\\"\\n\\n\\ndef _slugify(text: str, *, max_words: int = 6) -> str:\\n normalized = unicodedata.normalize(\\\"NFKD\\\", text or \\\"\\\")\\n ascii_text = normalized.encode(\\\"ascii\\\", \\\"ignore\\\").decode(\\\"ascii\\\")\\n tokens = re.findall(r\\\"[a-z0-9]+\\\", ascii_text.casefold())\\n return \\\"-\\\".join(tokens[:max_words]) or \\\"untitled\\\"\\n\\n\\ndef create_candidate_id(pillar: str, title: str, duration_seconds: int) -> str:\\n return f\\\"{pillar}-{_slugify(title)}-{duration_seconds}s\\\"\\n\\n\\ndef _default_hashtags_for_pillar(pillar: str) -> tuple[str, ...]:\\n return _DEFAULT_HASHTAGS_BY_PILLAR.get(pillar, ())\\n\\n\\ndef create_candidate(\\n draft: CandidateDraft,\\n *,\\n registry: tuple[ContentPillar, ...] = DEFAULT_PILLARS,\\n) -> VideoCandidate:\\n if not (draft.hypothesis or \\\"\\\").strip():\\n raise ValueError(\\\"hypothesis is required\\\")\\n\\n duration_class = classify_duration(draft.duration_seconds)\\n policy_notes = tuple(draft.policy_notes) + (f\\\"Duration class: {duration_class}\\\",)\\n hashtags = draft.hashtags or _default_hashtags_for_pillar(draft.pillar)\\n\\n candidate = VideoCandidate(\\n candidate_id=create_candidate_id(\\n pillar=draft.pillar,\\n title=draft.title,\\n duration_seconds=draft.duration_seconds,\\n ),\\n pillar=draft.pillar,\\n title=draft.title,\\n platforms=draft.platforms,\\n duration_seconds=draft.duration_seconds,\\n hook=draft.hook,\\n script_outline=draft.script_outline,\\n visual_concept=draft.visual_concept,\\n caption=draft.caption,\\n cta=draft.cta,\\n hashtags=hashtags,\\n quality_score=draft.quality_score,\\n policy_notes=policy_notes,\\n hypothesis=draft.hypothesis,\\n )\\n\\n candidate_result = validate_candidate(candidate)\\n pillar_result = validate_candidate_against_pillars(candidate, registry)\\n errors = candidate_result.errors + pillar_result.errors\\n if errors:\\n raise ValueError(\\\"; \\\".join(errors))\\n return candidate\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/factory.py\"}" } } ] }, { "role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3184, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ZrralM6qg0HaMF1CySZFer9y" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_5g22pOcqUXge5WM8WGeDc85F", "call_id": "call_5g22pOcqUXge5WM8WGeDc85F", "response_item_id": "fc_0d99e9da68392f44016a12bbe72f9c819196636087ba39f0ad", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_factory.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"...FF... [100%]\\n=================================== FAILURES ===================================\\n_______ test_candidate_id_is_stable_slug_from_pillar_title_and_duration ________\\n\\n def test_candidate_id_is_stable_slug_from_pillar_title_and_duration():\\n candidate_id = create_candidate_id(\\n pillar=\\\"ai_life_systems\\\",\\n title=\\\"AI ist ein schlechter Gott, aber ein guter Diener\\\",\\n duration_seconds=34,\\n )\\n \\n> assert candidate_id == \\\"ai_life_systems-ai-ist-ein-schlechter-gott-34s\\\"\\nE AssertionError: assert 'ai_life_syst...gott-aber-34s' == 'ai_life_syst...hter-gott-34s'\\nE \\nE Skipping 33 identical leading characters in diff, use -v to show\\nE - hter-gott-34s\\nE + hter-gott-aber-34s\\nE ? +++++\\n\\ntests/test_factory.py:63: AssertionError\\n_ test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note _\\n\\n def test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note():\\n candidate = create_candidate(_draft(), registry=DEFAULT_PILLARS)\\n \\n> assert candidate.candidate_id == \\\"ai_life_systems-ai-ist-ein-schlechter-gott-34s\\\"\\nE AssertionError: assert 'ai_life_syst...gott-aber-34s' == 'ai_life_syst...hter-gott-34s'\\nE \\nE Skipping 33 identical leading characters in diff, use -v to show\\nE - hter-gott-34s\\nE + hter-gott-aber-34s\\nE ? +++++\\n\\ntests/test_factory.py:69: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_factory.py::test_candidate_id_is_stable_slug_from_pillar_title_and_duration\\nFAILED tests/test_factory.py::test_create_candidate_adds_id_default_hashtags_and_duration_class_policy_note\\n2 failed, 6 passed in 0.06s\", \"exit_code\": 1, \"error\": null}\n\n[Tool loop warning: repeated_exact_failure_warning; count=2; terminal has failed 2 times with identical arguments. 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[100%]\\n8 passed in 0.04s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_SMGGMORNmfYokWggIn21Yq1N" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_lGfY27CPah8kkCRkPll48jFB", "call_id": "call_lGfY27CPah8kkCRkPll48jFB", "response_item_id": "fc_0d99e9da68392f44016a12bc09bb7481918724386f06af3332", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ideas.factory minimal implementieren\",\"id\":\"green\",\"status\":\"completed\"},{\"content\":\"Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"in_progress\"}]}" } } ] }, { "role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Factory-/Duration-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"ideas.factory minimal implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}", "tool_call_id": "call_lGfY27CPah8kkCRkPll48jFB" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_IljbXrpGlpuWrlY0RBgClBmk", "call_id": "call_IljbXrpGlpuWrlY0RBgClBmk", "response_item_id": "fc_0d99e9da68392f44016a12bc0f7e9481919f9a529f0716f401", "type": "function", "function": { "name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"limit\":140}" } } ] }, { "role": "tool", "name": "read_file", "content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n 4|\\n 5|The project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n 6|\\n 7|## Current scope\\n 8|\\n 9|Implemented foundation:\\n 10|\\n 11|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 12|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 13|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 14|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 15|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 16|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 17|- Tests for content pillars, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n 18|\\n 19|## Human approval commands\\n 20|\\n 21|Supported review responses:\\n 22|\\n 23|- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n 24|- `Freigabe YouTube`\\n 25|- `Freigabe TikTok`\\n 26|- `Freigabe Instagram`\\n 27|- `Ablehnen`\\n 28|- `Ändern: `\\n 29|\\n 30|Ambiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n 31|\\n 32|## Quality gate\\n 33|\\n 34|A script candidate only proceeds to rendering when:\\n 35|\\n 36|- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n 37|- AI-slop risk is not above `5`;\\n 38|- policy risk is not `high`.\\n 39|\\n 40|This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n 41|\\n 42|## Review package\\n 43|\\n 44|`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n 45|\\n 46|- candidate id, title, pillar, target platforms, and duration;\\n 47|- hook, script summary, visual concept, caption, and hashtags;\\n 48|- average quality score, AI-slop risk, policy notes, and render-gate status;\\n 49|- optional preview path;\\n 50|- only the relevant platform-specific approval commands.\\n 51|\\n 52|A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n 53|\\n 54|## Idea candidates and testbatches\\n 55|\\n 56|`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n 57|\\n 58|- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n 59|- hook, script outline, visual concept, caption, CTA, and hashtags;\\n 60|- quality score and policy notes.\\n 61|\\n 62|`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n 63|\\n 64|`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n 65|\\n 66|## Content pillars\\n 67|\\n 68|`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n 69|\\n 70|- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n 71|\\n 72|A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n 73|\\n 74|`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n 75|\\n 76|## Development\\n 77|\\n 78|Run tests:\\n 79|\\n 80|```bash\\n 81|python -m pytest tests -q\\n 82|```\\n 83|\\n 84|## Planned architecture\\n 85|\\n 86|```text\\n 87|autoshorts/\\n 88| config/ # niches, platforms, workflow settings\\n 89| ideas/ # idea generation and scoring\\n 90| scripting/ # script drafts, quality gates, retention checks\\n 91| rendering/ # local/AI-assisted preview rendering\\n 92| review/ # Telegram review package and approval parser\\n 93| publishing/ # platform-specific publishing metadata and later APIs\\n 94| analytics/ # performance imports and feedback loop\\n 95|```\\n 96|\\n 97|## Non-negotiables\\n 98|\\n 99|- No credentials in git.\\n 100|- No generated media in git unless explicitly intended.\\n 101|- No automatic publishing without explicit approval.\\n 102|- Quality over speed; avoid generic AI-slop.\\n 103|\", \"total_lines\": 102, \"file_size\": 4171, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_IljbXrpGlpuWrlY0RBgClBmk" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_D12PQW39NulGoqY8BzMJWFrF", "call_id": "call_D12PQW39NulGoqY8BzMJWFrF", "response_item_id": "fc_0d99e9da68392f44016a12bc1431108191bc21d42b10ff56de", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\",\"new_string\":\"- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -10,6 +10,7 @@\\n \\n - `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n - `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n+- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_D12PQW39NulGoqY8BzMJWFrF" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_6G0YClEgRsZustssU6s8r08a", "call_id": "call_6G0YClEgRsZustssU6s8r08a", "response_item_id": "fc_0d99e9da68392f44016a12bc1c211c8191a13e0859a0a3ae2e", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- Tests for content pillars, candidate factory, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\",\"old_string\":\"- Tests for content pillars, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -15,7 +15,7 @@\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n-- Tests for content pillars, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n+- Tests for content pillars, candidate factory, idea candidates, testbatches, approval parsing, review formatting, and script quality gating.\\n \\n ## Human approval commands\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_6G0YClEgRsZustssU6s8r08a" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_EQxOLrvruL9huBEsWPBg2IiS", "call_id": "call_EQxOLrvruL9huBEsWPBg2IiS", "response_item_id": "fc_0d99e9da68392f44016a12bc23f1f481918e61bc80beb9452f", "type": "function", "function": { "name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n\\n## Candidate factory\\n\\n`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n\\n`create_candidate()` turns a draft into a candidate by:\\n\\n- generating a stable id from pillar, title, and duration;\\n- adding default pillar hashtags when no hashtags are supplied;\\n- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n- appending the duration class to policy notes;\\n- enforcing candidate validation and pillar/platform fit validation.\\n\\nDuration classes:\\n\\n- `growth_short`: 20–45 seconds;\\n- `monetization_candidate`: 60–75 seconds;\\n- `experiment`: all other valid durations.\\n\\nMissing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n\\n## Content pillars\",\"old_string\":\"`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n\\n## Content pillars\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}" } } ] }, { "role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -64,6 +64,26 @@\\n \\n `TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n \\n+## Candidate factory\\n+\\n+`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n+\\n+`create_candidate()` turns a draft into a candidate by:\\n+\\n+- generating a stable id from pillar, title, and duration;\\n+- adding default pillar hashtags when no hashtags are supplied;\\n+- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n+- appending the duration class to policy notes;\\n+- enforcing candidate validation and pillar/platform fit validation.\\n+\\n+Duration classes:\\n+\\n+- `growth_short`: 20–45 seconds;\\n+- `monetization_candidate`: 60–75 seconds;\\n+- `experiment`: all other valid durations.\\n+\\n+Missing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n+\\n ## Content pillars\\n \\n `ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_EQxOLrvruL9huBEsWPBg2IiS" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_bOpNB2knIYBC4zNSx17MzFBR", "call_id": "call_bOpNB2knIYBC4zNSx17MzFBR", "response_item_id": "fc_0d99e9da68392f44016a12bc2df1cc819191dbae40e18fbf59", "type": "function", "function": { "name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}" } } ] }, { "role": "tool", "name": "terminal", "content": "{\"output\": \"................................. 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[100%]\\n33 passed in 0.07s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_gkMZpbuHp034HCrG1KhEigzX" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_yvwEseYGDkgR0ogCq5FaBeAX", "call_id": "call_yvwEseYGDkgR0ogCq5FaBeAX", "response_item_id": "fc_0d99e9da68392f44016a12bc3ee0088191860aada48656056a", "type": "function", "function": { "name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests laufen lassen, README aktualisieren, committen\",\"id\":\"verify\",\"status\":\"completed\"}]}" } } ] }, { "role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Repo-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"red\", \"content\": \"Factory-/Duration-Tests schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"green\", \"content\": \"ideas.factory minimal implementieren\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen, README aktualisieren, committen\", \"status\": \"completed\"}], \"summary\": {\"total\": 4, \"pending\": 0, \"in_progress\": 0, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_yvwEseYGDkgR0ogCq5FaBeAX" }, { "role": "assistant", "content": "Weitergebaut, Sir.\n\n## Neuer Baustein: Candidate Factory\n\nIch habe jetzt den nächsten Schritt implementiert:\n\n```text\nautoshorts/ideas/factory.py\ntests/test_factory.py\n```\n\nDamit können wir aus einer Rohidee / einem Draft jetzt automatisch einen validierten `VideoCandidate` erzeugen.\n\n## Was neu ist\n\n### 1. `CandidateDraft`\n\nEin Draft ist die Rohform einer Idee vor der finalen Kandidatenstruktur.\n\nEnthält u.a.:\n\n- Pillar\n- Titel\n- Plattformen\n- Dauer\n- Hook\n- Script Outline\n- Visual Concept\n- Caption\n- CTA\n- Quality Score\n- Policy Notes\n- Hypothesis\n- optionale Hashtags\n\n## 2. `create_candidate()`\n\nMacht aus einem `CandidateDraft` einen vollständigen `VideoCandidate`.\n\nDabei passiert automatisch:\n\n- stabile Candidate-ID erzeugen\n- Default-Hashtags ableiten\n- Duration Class bestimmen\n- Duration Class in Policy Notes schreiben\n- Candidate validieren\n- Pillar-/Platform-Fit validieren\n- bei Fehlern sauber `ValueError` werfen\n\nBeispiel-ID:\n\n```text\nai_life_systems-ai-ist-ein-schlechter-gott-34s\n```\n\n## 3. Duration Classification\n\nNeue Funktion:\n\n```python\nclassify_duration(duration_seconds)\n```\n\nKategorien:\n\n```text\ngrowth_short: 20–45 Sekunden\nmonetization_candidate: 60–75 Sekunden\nexperiment: alles andere innerhalb gültiger Dauer\n```\n\nDas trennt jetzt frühe Growth-Tests von späteren Monetization-Formaten.\n\n## 4. Default Hashtags\n\nFür den aktuellen Pillar:\n\n```text\nai_life_systems\n```\n\nwerden automatisch gesetzt:\n\n```text\n#AI\n#LifeSystems\n#Productivity\n```\n\nWenn explizit Hashtags im Draft stehen, werden diese verwendet.\n\n## 5. Harte Fehler bei unbrauchbaren Drafts\n\n`create_candidate()` lehnt ab:\n\n- fehlende Hypothesis\n- unbekannter Pillar\n- Plattform passt nicht zum Pillar\n- normale Candidate-Validation-Fehler\n\nAlso kein “wird schon passen” in der Pipeline. “Wird schon passen” ist der natürliche Feind jeder Automation.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.ideas.factory'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n33 passed in 0.07s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\ndeea24f feat: add candidate factory\n```\n\nAktueller Verlauf:\n\n```text\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ Candidate Validation\n→ Pillar Fit Validation\n→ TestBatch\n→ Quality Gate\n→ Review Package\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/review/workflow.py\n```\n\nZiel:\n\n- `VideoCandidate` → falls renderbar → `ReviewPackage`\n- falls nicht renderbar → trotzdem ReviewPackage mit BLOCKED Gate\n- Batch → Liste von ReviewPackages\n- eindeutige Trennung:\n - `ready_for_review`\n - `blocked`\n - `invalid`\n\nDann hätten wir die erste komplette interne Review-Pipeline vor Rendering.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Weitergebaut, Sir.\n\n## Neuer Baustein: Candidate Factory\n\nIch habe jetzt den nächsten Schritt implementiert:\n\n```text\nautoshorts/ideas/factory.py\ntests/test_factory.py\n```\n\nDamit können wir aus einer Rohidee / einem Draft jetzt automatisch einen validierten `VideoCandidate` erzeugen.\n\n## Was neu ist\n\n### 1. `CandidateDraft`\n\nEin Draft ist die Rohform einer Idee vor der finalen Kandidatenstruktur.\n\nEnthält u.a.:\n\n- Pillar\n- Titel\n- Plattformen\n- Dauer\n- Hook\n- Script Outline\n- Visual Concept\n- Caption\n- CTA\n- Quality Score\n- Policy Notes\n- Hypothesis\n- optionale Hashtags\n\n## 2. `create_candidate()`\n\nMacht aus einem `CandidateDraft` einen vollständigen `VideoCandidate`.\n\nDabei passiert automatisch:\n\n- stabile Candidate-ID erzeugen\n- Default-Hashtags ableiten\n- Duration Class bestimmen\n- Duration Class in Policy Notes schreiben\n- Candidate validieren\n- Pillar-/Platform-Fit validieren\n- bei Fehlern sauber `ValueError` werfen\n\nBeispiel-ID:\n\n```text\nai_life_systems-ai-ist-ein-schlechter-gott-34s\n```\n\n## 3. Duration Classification\n\nNeue Funktion:\n\n```python\nclassify_duration(duration_seconds)\n```\n\nKategorien:\n\n```text\ngrowth_short: 20–45 Sekunden\nmonetization_candidate: 60–75 Sekunden\nexperiment: alles andere innerhalb gültiger Dauer\n```\n\nDas trennt jetzt frühe Growth-Tests von späteren Monetization-Formaten.\n\n## 4. Default Hashtags\n\nFür den aktuellen Pillar:\n\n```text\nai_life_systems\n```\n\nwerden automatisch gesetzt:\n\n```text\n#AI\n#LifeSystems\n#Productivity\n```\n\nWenn explizit Hashtags im Draft stehen, werden diese verwendet.\n\n## 5. Harte Fehler bei unbrauchbaren Drafts\n\n`create_candidate()` lehnt ab:\n\n- fehlende Hypothesis\n- unbekannter Pillar\n- Plattform passt nicht zum Pillar\n- normale Candidate-Validation-Fehler\n\nAlso kein “wird schon passen” in der Pipeline. “Wird schon passen” ist der natürliche Feind jeder Automation.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.ideas.factory'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n33 passed in 0.07s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\ndeea24f feat: add candidate factory\n```\n\nAktueller Verlauf:\n\n```text\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ Candidate Validation\n→ Pillar Fit Validation\n→ TestBatch\n→ Quality Gate\n→ Review Package\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/review/workflow.py\n```\n\nZiel:\n\n- `VideoCandidate` → falls renderbar → `ReviewPackage`\n- falls nicht renderbar → trotzdem ReviewPackage mit BLOCKED Gate\n- Batch → Liste von ReviewPackages\n- eindeutige Trennung:\n - `ready_for_review`\n - `blocked`\n - `invalid`\n\nDann hätten wir die erste komplette interne Review-Pipeline vor Rendering." } ], "id": "msg_0d99e9da68392f44016a12bc4298788191a36c8fbca5820b5b", "phase": "final_answer" } ] }, { "role": "user", "content": "Review the conversation above and update the skill library. Be ACTIVE — most sessions produce at least one skill update, even if small. A pass that does nothing is a missed learning opportunity, not a neutral outcome.\n\nTarget shape of the library: CLASS-LEVEL skills, each with a rich SKILL.md and a `references/` directory for session-specific detail. Not a long flat list of narrow one-session-one-skill entries. This shapes HOW you update, not WHETHER you update.\n\nSignals to look for (any one of these warrants action):\n • User corrected your style, tone, format, legibility, or verbosity. Frustration signals like 'stop doing X', 'this is too verbose', 'don't format like this', 'why are you explaining', 'just give me the answer', 'you always do Y and I hate it', or an explicit 'remember this' are FIRST-CLASS skill signals, not just memory signals. Update the relevant skill(s) to embed the preference so the next session starts already knowing.\n • User corrected your workflow, approach, or sequence of steps. Encode the correction as a pitfall or explicit step in the skill that governs that class of task.\n • Non-trivial technique, fix, workaround, debugging path, or tool-usage pattern emerged that a future session would benefit from. Capture it.\n • A skill that got loaded or consulted this session turned out to be wrong, missing a step, or outdated. Patch it NOW.\n\nPreference order — prefer the earliest action that fits, but do pick one when a signal above fired:\n 1. UPDATE A CURRENTLY-LOADED SKILL. Look back through the conversation for skills the user loaded via /skill-name or you read via skill_view. If any of them covers the territory of the new learning, PATCH that one first. It is the skill that was in play, so it's the right one to extend.\n 2. UPDATE AN EXISTING UMBRELLA (via skills_list + skill_view). If no loaded skill fits but an existing class-level skill does, patch it. Add a subsection, a pitfall, or broaden a trigger.\n 3. ADD A SUPPORT FILE under an existing umbrella. Skills can be packaged with three kinds of support files — use the right directory per kind:\n • `references/.md` — session-specific detail (error transcripts, reproduction recipes, provider quirks) AND condensed knowledge banks: quoted research, API docs, external authoritative excerpts, or domain notes you found while working on the problem. Write it concise and for the value of the task, not as a full mirror of upstream docs.\n • `templates/.` — starter files meant to be copied and modified (boilerplate configs, scaffolding, a known-good example the agent can `reproduce with modifications`).\n • `scripts/.` — statically re-runnable actions the skill can invoke directly (verification scripts, fixture generators, deterministic probes, anything the agent should run rather than hand-type each time).\n Add support files via skill_manage action=write_file with file_path starting 'references/', 'templates/', or 'scripts/'. The umbrella's SKILL.md should gain a one-line pointer to any new support file so future agents know it exists.\n 4. CREATE A NEW CLASS-LEVEL UMBRELLA SKILL when no existing skill covers the class. The name MUST be at the class level. The name MUST NOT be a specific PR number, error string, feature codename, library-alone name, or 'fix-X / debug-Y / audit-Z-today' session artifact. If the proposed name only makes sense for today's task, it's wrong — fall back to (1), (2), or (3).\n\nUser-preference embedding (important): when the user expressed a style/format/workflow preference, the update belongs in the SKILL.md body, not just in memory. Memory captures 'who the user is and what the current situation and state of your operations are'; skills capture 'how to do this class of task for this user'. When they complain about how you handled a task, the skill that governs that task needs to carry the lesson.\n\nIf you notice two existing skills that overlap, note it in your reply — the background curator handles consolidation at scale.\n\nDo NOT capture (these become persistent self-imposed constraints that bite you later when the environment changes):\n • Environment-dependent failures: missing binaries, fresh-install errors, post-migration path mismatches, 'command not found', unconfigured credentials, uninstalled packages. The user can fix these — they are not durable rules.\n • Negative claims about tools or features ('browser tools do not work', 'X tool is broken', 'cannot use Y from execute_code'). These harden into refusals the agent cites against itself for months after the actual problem was fixed.\n • Session-specific transient errors that resolved before the conversation ended. If retrying worked, the lesson is the retry pattern, not the original failure.\n • One-off task narratives. A user asking 'summarize today's market' or 'analyze this PR' is not a class of work that warrants a skill.\n\nIf a tool failed because of setup state, capture the FIX (install command, config step, env var to set) under an existing setup or troubleshooting skill — never 'this tool does not work' as a standalone constraint.\n\n'Nothing to save.' is a real option but should NOT be the default. If the session ran smoothly with no corrections and produced no new technique, just say 'Nothing to save.' and stop. Otherwise, act.\n\nYou can only call memory and skill management tools. Other tools will be denied at runtime — do not attempt them." }, { "role": "assistant", "content": "", "reasoning": "**Updating skills and references**\n\nI need to update my skills by loading various tools like social-media-shorts-growth-engine, TDD, comfyui, hermes-agent, and codex. I believe I’ve already integrated some social skills during our conversation. The tool outputs might have updated automatically, but I need clarity on whether I should use skill_manage or skill_view, as the user mentioned using only memory and skill management tools. I think I should write a reference about the candidate factory foundation in SKILL.md and ensure it includes implementation paths.\n**Patching skills and TDD update**\n\nI should use skill_manage to patch the old text from the output, and I might also need to update the test-driven-development (TDD) skill. There’s no new TDD learning, but I want to include a note about the PytestCollectionWarning class named TestBatch with __test__ set to False, which is a common pitfall in Python TDD. It would be valuable to update the TDD skill with this information while using skill_manage to manage multiple updates.", "finish_reason": "tool_calls", "reasoning_content": "**Updating skills and references**\n\nI need to update my skills by loading various tools like social-media-shorts-growth-engine, TDD, comfyui, hermes-agent, and codex. I believe I’ve already integrated some social skills during our conversation. The tool outputs might have updated automatically, but I need clarity on whether I should use skill_manage or skill_view, as the user mentioned using only memory and skill management tools. I think I should write a reference about the candidate factory foundation in SKILL.md and ensure it includes implementation paths.\n**Patching skills and TDD update**\n\nI should use skill_manage to patch the old text from the output, and I might also need to update the test-driven-development (TDD) skill. There’s no new TDD learning, but I want to include a note about the PytestCollectionWarning class named TestBatch with __test__ set to False, which is a common pitfall in Python TDD. It would be valuable to update the TDD skill with this information while using skill_manage to manage multiple updates.", "codex_reasoning_items": [ { "type": "reasoning", "encrypted_content": "gAAAAABqErxn5wWYr2yH3pdjM256GmO_cAx3yqnxHt5E2uE4CpbPQMFTeXrShS_AX5rlpGGms1l-0vJTDeLS1OC49uuhDLAOpakAFaDhpGK86qb4UcsztJQEXw2d0rUeB5mWcFVc5O2Uy0bZ1bwu5Cf2wzqOfreEA4wpNheqbNrbxAZhsQRE1xDCOEYUTd-NwGxTj8uPv0DBqJNZDjWPdyV54mGg_lAA2-ubPv8MUakVbvtG-s1XoiUKesMuHHso2-rLJUw5UnWpAvMQxtPrzVHJzSxQi6L15lVslhdOciG69-fqYHS2L_b_cKSKYCw0zIE7MRUeRZTom6pd9UgDrrL-lyjVuPOIGKMa2AanUVq3ObdlExtqdDJUI-kFM_rID63GXmh2GLaESDG5EwFRclPi8rMv_cIRP3eAX-CR2eMVkL9xliK1dGqaHb0ZgWVSNbso9tc7FwBaK2KbcyW5TEaNsCRtResWmsg0uLTZuPcFL4jHaqMZ25GYxx7OZGDgS09LvHPGiPxWhjiVYNgb5ELQGnV6Cv0T0A3H7YpayqDZABCCpjjpD4P3vDQVp40wBJz0pTvgFRbSoh75CrC7Px_PNu9ZVxzLb3hnHumhuE6NPRK2-JRQ70xAZ32BjXR8apg-AHharPHzSM37rA8XFj0hN8EXKMyPSWW0WrGrFSEAR_zemDSM5gvMWFHZEDg9rpAE7xToRna-ToeE0QLn_JqrZmVQS5fCi3ZUHj4R2uh8d-os7bZtd73hAZVw-6_7-e0a78jZW_m5xuTRasG_i7usojy7POyAt1nRzwh8RP04pSVVnoaemBUASqrEJDhAkEc7JKhc4_XTRFM7iaGJESwEiq0fJ_zlhxRKK05XJIP5Pss-5_kQvffSym0Lgjc_ic9ex4hTcE-H0jPBFi1jHl8uk2-MVtbeObLsUg7X3-DZVXE8bPnNo2lia4-67gaN5pAeM_bP389qkbQLxQIaegXLKV-qXb5q_rv39xkx280Xo3M_5kPb7lFZ5jTd-PCTYIxYXueJpbNBwWmRBbG36DEDUE6gRKTxmBqTte_r7cAhdK96GXYrz1219P2YTC5gZxQXvKLSXy1GzUlSbZdcvWwuOhjrMdCL_bYLMUuIEBcYfYv23_gJdGkGhRrxne-9RhONbv6la4HIXlsr42loytqyajx2T2Ajtd2Ddl8j34AnjP_EmCp4IKVy4BHO35GobTK5unBy5FdhbGRpfNkFftIu3Z8KLwx6PiGgs95kmZIglb63jFaqFaCEiTu5IbTPJ4cYzvaAFbv4JQdZo37z8djk2cR6BA8vzYZoXfyhe5w4xsELL-m0N1o3AYTpz0p0tQgD_DtLIbj0QVVeKYcFdeJuApga6khF5I1HiSnZo1fOAHr5ROKJsuskWMm4D1oSEOtjF7ndejuG6-ksSkOsUhdRvroJCuf6sSaTPPYwMX0gVL3XzubdnDKD-BmkfhV6SPmn9q3IBHpnRY4pLjkku2XJw403Iw3Rtang7t2wZLinuCURzbtylrax7Suzw6VJhRoDAX79UhCn8myYA8Vp__FsRXTC9pShP4H_fHo1V85NiQDanWhowEd_oZF9Qcl5jakrnNtJjlzAgwIHGmauuj2TKuG20Wqw-s2fGQqOIPRLJQ6lko8qXpsvU4IDcZLsAH3VfIwe5dzLr0q678J1DH7VvbQL1W01NISCxBB821zO8cqeTiHytKBuj8qvZIlCyUCjRbxJVzUM_KuNjnLyIXAlZtqSfEwxOQHjinThneteuuBOkDW5S9Vp15J7gqwcLsyxtn4kK8j9weev8w11kBPoycW9MUOlZgZ_4b5-hLF-yNCdlFLinGrSoCQzYXO35vw-SZ6danPWAW1hm3ixYySWxKTAmyzR3Nwxv7o88OR7mlSYbSvbEgKqVV8C_ImF-RrMIBI4BeWBAUo3QR4gSAHa3nPzyf4EG0u5kDO3bD_wUDh-GOPzveQ-qrEVI31mJwI3geB0GWoQqlkptRlx203OB-xXv49LUkb30Lx5lS-aHfvtfLqCdtCTqd-j3z1ifOk9J7FzgSZhJMk4pYmh2FOeRnI2PQVOVgO0I2BAKfqfOXyQWRGMDKgox6OGsRMROwjRCmTSKADZU3W9O_BypcFWnmWuuxjvbEvLDzbWlq3iCdR_Q6SeAIKnpRRPc0m0FD_3q-MhH9ZtyuWkryMlAmZmdcCYsOpi7wV3BX6TSWGJimXFpC3eP6Ve9vQ9c7n0UFQBql1tAnXpftbuHdoYtzX9aOcGtg2Lbs8gopuIlIZHW13mZn_6rhIGmts5cwHQKLIm5drlfXpNjSt3Z64YDEuf8uYXR4vvbOxc8mRBqLqFiMJ-kqVgzpCIgpSjmK9JM7E1htsrsU6b7A-Qty8z8yYLM6vjuKbF7Ty_Y4WcB-VnnRyXnK2Xrx3oDCH5eWY_Sj2b_hHeAStKSrV4H09L0hnL7RyQhcHvNQqQwZptL0zNIwyhu7Kd886Nn1PykAMVJCFoVVv0p0-tru4ege7RjL1Dsqzc1r3M0khsAgFv18L7rP17t5bfdg9fZnyhEnpoiUzEsT0tPbIW1L_Ru6F6XqRqqhJsUhqJYr2u6ZYb2GsiAOOGfuz5lGLpSEHdB5evUt59fI89cdOrSNswD8W9EH3lM8kzO35qHbnvfD-0Qz5zaarbmpuvye2zJ5_8Qhhbwe8o2Y_cRsnr1T---FjASZ2agAF_snzlyuiM3MU8I0SfXJ_J-3cTZ8GpVm_9T0FaZ_wakFiAd8hI2SFdLlX9ri9r4efZotLJUrWfr9JLDIkgciur5Ixg-EcEh83FpAe89_NfL1jZAbi8Gy7n64iprG7HNjABTw3PwYa3WCxBUdmXlzNvVzmo72X7iToSm5spER8_NS_e1S6Bbxe9MCdoNZylH37qxUTMng==", "id": "rs_0d99e9da68392f44016a12bc5a7b088191b695c4c4efacb856", "summary": [ { "type": "summary_text", "text": "**Updating skills and references**\n\nI need to update my skills by loading various tools like social-media-shorts-growth-engine, TDD, comfyui, hermes-agent, and codex. I believe I’ve already integrated some social skills during our conversation. The tool outputs might have updated automatically, but I need clarity on whether I should use skill_manage or skill_view, as the user mentioned using only memory and skill management tools. I think I should write a reference about the candidate factory foundation in SKILL.md and ensure it includes implementation paths." }, { "type": "summary_text", "text": "**Patching skills and TDD update**\n\nI should use skill_manage to patch the old text from the output, and I might also need to update the test-driven-development (TDD) skill. There’s no new TDD learning, but I want to include a note about the PytestCollectionWarning class named TestBatch with __test__ set to False, which is a common pitfall in Python TDD. It would be valuable to update the TDD skill with this information while using skill_manage to manage multiple updates." } ] } ], "tool_calls": [ { "id": "call_zHyemOHt22CnKaz091FKqqM7", "call_id": "call_zHyemOHt22CnKaz091FKqqM7", "response_item_id": "fc_0d99e9da68392f44016a12bc678ae08191bd044e30bb30da30", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"write_file\",\"name\":\"social-media-shorts-growth-engine\",\"file_path\":\"references/candidate-factory-foundation.md\",\"file_content\":\"# Candidate Factory Foundation\\n\\nSession pattern for AutoShortsBot-style short-form content engines: add a deterministic factory layer after pillars and before rendering.\\n\\n## Purpose\\n\\nConvert a raw idea/draft into a validated `VideoCandidate` without letting renderers or LLM prompts invent strategy.\\n\\nRecommended flow:\\n\\n```text\\nContentPillar\\n→ CandidateDraft\\n→ create_candidate()\\n→ VideoCandidate\\n→ candidate validation\\n→ pillar/platform-fit validation\\n→ TestBatch / ReviewPackage\\n```\\n\\n## Deterministic pieces to implement first\\n\\n- `CandidateDraft`: raw idea shape before candidate validation.\\n- `classify_duration(seconds)`:\\n - `growth_short`: 20–45 seconds.\\n - `monetization_candidate`: 60–75 seconds.\\n - `experiment`: all other valid durations.\\n- `create_candidate_id(pillar, title, duration_seconds)`: stable slug from pillar + short title slug + duration, e.g. `ai_life_systems-ai-ist-ein-schlechter-gott-34s`.\\n- Default pillar hashtags when the draft has none, e.g. `#AI`, `#LifeSystems`, `#Productivity` for `ai_life_systems`.\\n- Append `Duration class: ` to policy notes so review/publishing layers can reason about format.\\n- Enforce candidate validation and pillar/platform-fit validation inside `create_candidate()`; raise a clear error on failure.\\n- Require a non-empty hypothesis before a candidate exists. A draft without a hypothesis is not an experiment, it is just content confetti.\\n\\n## Tests to write first\\n\\n- Duration classification for 20–45s, 60–75s, and experiment ranges.\\n- Stable candidate id generation.\\n- Default hashtags and duration-class policy note insertion.\\n- Missing hypothesis rejected.\\n- Unknown pillar rejected.\\n- Platform not configured for the pillar rejected.\\n\\n## Pitfalls\\n\\n- Do not let rendering code infer pillar rules, hashtags, duration class, or hypothesis.\\n- Do not treat factory output as valid unless both candidate validation and pillar-fit validation pass.\\n- Keep ids stable and human-readable for review messages, local media paths, analytics joins, and debugging.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"File 'references/candidate-factory-foundation.md' written to skill 'social-media-shorts-growth-engine'.\", \"path\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-growth-engine/references/candidate-factory-foundation.md\"}", "tool_call_id": "call_zHyemOHt22CnKaz091FKqqM7" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_jEvh2vF3swhurxPB2Y1Trvw1", "call_id": "call_jEvh2vF3swhurxPB2Y1Trvw1", "response_item_id": "fc_0d99e9da68392f44016a12bc6e50508191950a1c5877c0548c", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-growth-engine\",\"old_string\":\"When coding the engine, prefer TDD for deterministic parts:\\n\\n- structured video candidate validation\\n- testbatch validation and duplicate-id checks\\n- renderable-candidate filtering before any renderer runs\\n- duration classification\\n- review formatting\",\"new_string\":\"When coding the engine, prefer TDD for deterministic parts:\\n\\n- structured video candidate validation\\n- testbatch validation and duplicate-id checks\\n- renderable-candidate filtering before any renderer runs\\n- candidate factory conversion from raw drafts to validated `VideoCandidate` objects\\n- duration classification\\n- stable candidate-id generation and default hashtag derivation\\n- review formatting\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_jEvh2vF3swhurxPB2Y1Trvw1" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_ea15Nlut6xpVgpKetcXXRBf9", "call_id": "call_ea15Nlut6xpVgpKetcXXRBf9", "response_item_id": "fc_0d99e9da68392f44016a12bc73d0648191a98e66ecb40cf2a8", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-growth-engine\",\"old_string\":\"1. Add structured `VideoCandidate` and `TestBatch` models before rendering code. Require candidate id, pillar, title, platforms, duration, hook, script outline, visual concept, caption, CTA, hashtags, quality score, policy notes, and one clear hypothesis.\\n2. Validate candidates and batches: reject missing strategic fields, unsupported platforms, out-of-range durations, empty batches, and duplicate candidate ids.\\n3. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n4. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n5. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n6. Add a Telegram review package renderer that surfaces the candidate, gate status, policy notes, preview path, and only relevant platform approval commands.\\n7. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers, image generation, video generation, or publishing APIs.\",\"new_string\":\"1. Add structured `VideoCandidate` and `TestBatch` models before rendering code. Require candidate id, pillar, title, platforms, duration, hook, script outline, visual concept, caption, CTA, hashtags, quality score, policy notes, and one clear hypothesis.\\n2. Validate candidates and batches: reject missing strategic fields, unsupported platforms, out-of-range durations, empty batches, and duplicate candidate ids.\\n3. Add a `ContentPillar` registry and validate candidate pillar/platform fit before review or rendering.\\n4. Add a deterministic candidate factory: `CandidateDraft → create_candidate() → VideoCandidate`. It should generate stable ids, derive default hashtags, classify duration (`growth_short`, `monetization_candidate`, `experiment`), append the duration class to policy notes, require a hypothesis, and enforce both candidate and pillar-fit validation.\\n5. Add an approval parser that accepts only explicit commands such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Freigabe Instagram`, `Ablehnen`, and `Ändern: `.\\n6. Treat vague consent like `passt schon` as `needs_clarification`; never publish on ambiguous approval.\\n7. Add a conservative script quality gate before rendering, e.g. average hook/novelty/clarity/retention ≥ 8, AI-slop risk below threshold, and no high policy risk.\\n8. Add a Telegram review package renderer that surfaces the candidate, gate status, policy notes, preview path, and only relevant platform approval commands.\\n9. Test these deterministic pieces first with RED-GREEN-REFACTOR before adding renderers, image generation, video generation, or publishing APIs.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_ea15Nlut6xpVgpKetcXXRBf9" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_1t1JfpS67XnPKO2EwHQHWbyl", "call_id": "call_1t1JfpS67XnPKO2EwHQHWbyl", "response_item_id": "fc_0d99e9da68392f44016a12bc824ce081918db8a8b9938ae2a4", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"social-media-shorts-growth-engine\",\"new_string\":\"- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n- `references/approval-first-bot-foundation.md` — implementation pattern for the first safe bot slice: explicit approval parser, quality gate, Telegram review package, and TDD test shape.\\n- `references/idea-candidate-testbatch-foundation.md` — deterministic `VideoCandidate`/`TestBatch` foundation before rendering: validation rules, renderable filtering, and test shape.\\n- `references/content-pillar-registry-foundation.md` — strategic `ContentPillar` registry pattern: default `ai_life_systems`, pillar/candidate fit validation, and TDD test shape.\\n- `references/candidate-factory-foundation.md` — deterministic `CandidateDraft → create_candidate() → VideoCandidate` layer: stable ids, duration classes, default hashtags, hypothesis requirement, and tests.\",\"old_string\":\"- `references/autoshortsbot-case-study.md` — case-study details from the AutoShortsBot setup: first testbatch shape, repo scaffold, and approval loop.\\n- `references/approval-first-bot-foundation.md` — implementation pattern for the first safe bot slice: explicit approval parser, quality gate, Telegram review package, and TDD test shape.\\n- `references/idea-candidate-testbatch-foundation.md` — deterministic `VideoCandidate`/`TestBatch` foundation before rendering: validation rules, renderable filtering, and test shape.\\n- `references/content-pillar-registry-foundation.md` — strategic `ContentPillar` registry pattern: default `ai_life_systems`, pillar/candidate fit validation, and TDD test shape.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_1t1JfpS67XnPKO2EwHQHWbyl" }, { "role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [ { "id": "call_ocWwiu3xZcZ4r6HmcExrmJ24", "call_id": "call_ocWwiu3xZcZ4r6HmcExrmJ24", "response_item_id": "fc_0d99e9da68392f44016a12bc8bcc6081918efc3a235221e66c", "type": "function", "function": { "name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"test-driven-development\",\"new_string\":\"- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\",\"old_string\":\"- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\"}" } } ] }, { "role": "tool", "name": "skill_manage", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'test-driven-development' (1 replacement).\"}", "tool_call_id": "call_ocWwiu3xZcZ4r6HmcExrmJ24" }, { "role": "assistant", "content": "Skill library updated.\n\n## Updated skills\n\n- `social-media-shorts-growth-engine`\n - Added a new support reference:\n - `references/candidate-factory-foundation.md`\n - Patched the main SKILL.md to include the new deterministic candidate factory layer:\n - `CandidateDraft → create_candidate() → VideoCandidate`\n - stable candidate IDs\n - duration classification\n - default hashtags\n - hypothesis requirement\n - candidate + pillar-fit validation before review/rendering\n - Added pointer to the new reference file.\n\n- `test-driven-development`\n - Added a Python/pytest pitfall:\n - production/domain classes named `Test*`, e.g. `TestBatch`, can trigger pytest collection warnings.\n - Fix: set `__test__ = False` on the class when the name is part of the domain language, then verify warnings are gone.\n\nThese updates capture the reusable workflow from this session without creating narrow one-off skills.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [ { "type": "message", "role": "assistant", "status": "completed", "content": [ { "type": "output_text", "text": "Skill library updated.\n\n## Updated skills\n\n- `social-media-shorts-growth-engine`\n - Added a new support reference:\n - `references/candidate-factory-foundation.md`\n - Patched the main SKILL.md to include the new deterministic candidate factory layer:\n - `CandidateDraft → create_candidate() → VideoCandidate`\n - stable candidate IDs\n - duration classification\n - default hashtags\n - hypothesis requirement\n - candidate + pillar-fit validation before review/rendering\n - Added pointer to the new reference file.\n\n- `test-driven-development`\n - Added a Python/pytest pitfall:\n - production/domain classes named `Test*`, e.g. `TestBatch`, can trigger pytest collection warnings.\n - Fix: set `__test__ = False` on the class when the name is part of the domain language, then verify warnings are gone.\n\nThese updates capture the reusable workflow from this session without creating narrow one-off skills." } ], "id": "msg_0d99e9da68392f44016a12bc923c84819184c1128e9aeee400", "phase": "final_answer" } ] } ] }